All metadata for: English

https://washdata.org/report/jmp-methodology-2017-update

JMP Reports: https://washdata.org/reports

JMP Methods: https://washdata.org/monitoring/methods

JMP Methodology: 2017 update and SDG baselines

JMP Core questions on water, sanitation and hygiene for household surveys: https://washdata.org/report/jmp-2018-core-questions-household-surveys

UNDP 2016 Technical Notes Calculating the Human Development Indices, https://hdr.undp.org/sites/default/files/2021-22_HDR/hdr2021-22_technical_notes.pdf
The World Bank Group, ESMAP, 2015 Beyond Connections Energy Access Redefined http://www.worldbank.org/en/topic/energy/publication/energy-access-redefined
ITU, 2015 ICT Indicators for the SDG Monitoring Framework , http://www.itu.int/en/ITU-D/Statistics/Documents/intlcoop/sdgs/ITU-ICT-technical-information-sheets-for-the-SDG-indicators.pdf
Wilson et al - Wasteaware ISWM indicators - doi10.1016j.wasman.2014.10.006 - January 2015, https://eprints.whiterose.ac.uk/85319/9/Wilson_et_al_Supplementary_information_Wasteaware_ISWM_Benchmark_Indicators_User_Manual_FINAL.pdf

Gender and Transport Resource Guide. https://www.ssatp.org/sites/ssatp/files/publications/HTML/Gender-RG/index.html

Transport brief: World Bank. https://www.worldbank.org/en/topic/transport

Table 1. Links to methodologies for Indicator 1.4.1 components.

Component	Measured by:	Link to methodology
Basic drinking water services	Proportion of population with access to an improved source with collection time of not more than 30 minutes for a roundtrip including queuing (Part of SDG 6.1.1)	https://washdata.org/monitoring/drinking-water https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf
Basic sanitation services	Proportion of population using improved facilities which are not shared with other households (Part of SDG 6.2.1a)	https://washdata.org/monitoring/sanitation https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01a.docx
Basic hygiene services	Proportion of population with a handwashing facility with soap and water available at home (SDG 6.2.1b)	https://washdata.org/monitoring/hygiene https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01b.docx
Waste collection	11.6.1 Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities	https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-01.pdf
Mobility and transport	9.1.1 Proportion of the rural population who live within 2 km of an all-season road 11.2.1 Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities	https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-01.pdf https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf
Modern energy	7.1.2 Percentage of population with primary reliance on clean fuels and technology	https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-02.pdf
ICT	9.c.1 Proportion of population covered by a mobile network, by technology	https://unstats.un.org/sdgs/metadata/files/Metadata-09-0C-01.pdf
Education	4.1.1 Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex	https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-01A.pdf
Health	3.8.1 Coverage of essential health services	https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf

1.4.2

0.a. Goal

Goal 1: End poverty in all its forms everywhere

0.b. Target

0.c. Indicator

Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenure

0.d. Series

Metadata applies to all series under this indicator

0.e. Metadata update

2021-08-01

0.f. Related indicators

This indicator is Goal 1, and is also particularly related to Goal 5, 5.a.1 (access to agricultural land) and 5.a.2 (legal framework for land governance). Tenure security also matters for Goal 2, Target 2.3 (2.3.1 and 2.3.2 addressing smallholder farmers; Target 2.4 (2.4.1 on agricultural area), to Goal 11, to target 11.1 (access to affordable housing/upgrading slums) and target 11.3 (sustainable urbanization/settlement planning). Land tenure also influences land use and is thus key to achieving Goal 14 (b) to provide access to small-scale fishers and marine resources, and to Goal 15 on the sustainable use of land and natural resources. Similarly, land is a significant source of conflict, and thus also matters for Goal 16 for promoting peace and inclusive societies and institutions.

0.g. International organisations(s) responsible for global monitoring

UN-Habitat and World Bank

1.a. Organisation

UN-Habitat and World Bank

2.a. Definition and concepts

Definition:

Indicator 1.4.2 measures the relevant part of Target 1.4 (ensure men and women have equal rights to economic resources, as well as access to …, ownership of and control over land and other forms of property, inheritance, natural resources). It measures the results of policies that aim to strengthen tenure security for all, including women and other vulnerable groups.

Indicator 1.4.2 covers (a) all types of land use (such as residential, commercial, agricultural, forestry, grazing, wetlands based on standard land-use classification) in both rural and urban areas; and (b) all land tenure types as recognized at the country level, such as freehold, leasehold, public land, customary land. An individual can hold land in his/her own name, jointly with other individuals, as a member of a household, or collectively as member of group^[1], cooperative or other type of association.

Secure tenure rights: comprised of two sub-components: (i) legally recognized documentation and (ii) perception of the security of tenure, which are both necessary to provide a full measurement of tenure security.

Legally recognized documentation: Legal documentation of rights refers to the recording and publication of information on the nature and location of land, rights and right holders in a form that is recognized by government, and is therefore official. For purposes of computing SDG Indicator 1.4.2, the country specific metadata will define what documentation on land rights will be counted as legally recognized (see next section for rationale).

Perceived security of tenure: Perception of tenure security refers to an individual’s perception of the likelihood of involuntary loss of land, such as disagreement of the ownership rights over land or ability to use it, regardless of the formal status and can be more optimistic or pessimistic. Although those without land rights’ documentation may frequently be perceived to be under threat, and those with documentation perceived as protected, there may be situations where documented land rights alone are insufficient to guarantee tenure security. Conversely, even without legally recognized documentation, individuals may feel themselves to be protected against eviction or dispossession. Therefore, capturing and analysing these diverse ranges of situations will enable a more comprehensive understanding of land tenure security, based on a country specific context.

For purposes of constructing the indicator (see next section for rationale), we define perceptions of tenure to be secure if:

The landholder does not report a fear of involuntary loss of the land within the next five years due to, for example, intra-family, community or external threats and
The landholder reports having the right to bequeath the land.

Total adult population: A country’s adult population^[2] is measured by census data or through surveys using an adequate sampling frame.

Interpretation:

One motivation that makes the indicator actionable is that, in many developing countries, the gap between data on the availability of documentation and on perception of tenure security can be large. For example, tenure may be perceived as secure, even though rights are not formally documented, as in the case of customary systems and trusted local land governance arrangements. Or, the opposite, tenure may be perceived as insecure even when there is a high level of formal documentation of rights. The latter situation can be caused by various factors, including limited trust in land administration services, possible duplicated documents, high cost of having state institutions protecting such rights.

Reporting on perceived security will provide important information on people’s satisfaction with the institutional quality of service, transparency, appropriateness, accessibility and affordability of land administration services and justice systems.

Concepts:

The concepts below are based on the “Voluntary Guidelines for the Responsible Governance of Tenure of Land, Forests and Fisheries in the Context of National Food Security” (shorthand VGGT), which were endorsed by the United Nations World Committee on World Food Security in 2012 and therefore considered an internationally accepted standard. Other international frameworks using these concepts are the African Union Agenda on Land as laid out in the 2009 Framework and Guidelines on Land Policy in Africa and the 2014 Nairobi Action Plan on Large-Scale Land-Based Investments.

Tenure: How people, communities and others gain access to land and natural resources (including fisheries and forests) is defined and regulated by societies through systems of tenure. These tenure systems determine who can use which resources, for how long, and under what conditions. Tenure systems may be based on written policies and laws, as well as on unwritten customs and practices. No tenure right, including private ownership, is absolute. All tenure rights are limited by the rights of others and by the measures taken by states for public purposes (VGGT, 2012).

Tenure typology: A tenure typology is country specific and refers to categories of tenure rights, for example customary, leasehold, public and freehold. Rights can be held collectively, jointly or individually and may cover one or more elements of the bundle of rights (the right of possession, of control, of exclusion, of enjoyment and of disposition).

Land governance: Rules, processes and structures through which decisions are made regarding access to and the use (and transfer) of land, how those decisions are implemented and the way that conflicting interests in land are managed. States provide legal recognition for tenure rights through policies, law and land administration services, and define the categories of rights that are considered official.

Group rights include shared or collective rights, and examples include the ejido in Mexico, indigenous territories in Honduras, perpetual DUAT for rural communities in Mozambique. Collective rights occur in a situation where holders of rights to land and natural resources are clearly defined as a collective group and have the right to exclude third parties from the enjoyment of those rights. ↑

Country specific legal definition of an ‘adult’ will be applied. ↑

2.b. Unit of measure

Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)

Proportion of people who perceive their rights to land as secure, out of total adult population by sex (%)

2.c. Classifications

Not applicable.

3.a. Data sources

The data sources used are census, multi-topic household surveys conducted by national statistical Organizations and, depending on availability, administrative data on land tenure reported by national land institutions (in most cases land registries and cadastres).

Household surveys and census

Household surveys and census that have been implemented by national statistical agencies, are a key source of information for computing the indicator.

Censuses: These provide a complete enumeration of all the populations of the country at a specific time. In many recent censuses, questions on household characteristics, including short modules on security of tenure, are collected. So far, 41 countries have carried out a census in which questions on land tenure were included. Options for expanding land-related questions in the upcoming agricultural census are being discussed together with FAO (custodians of 5.a.1).

Household-level consumption/expenditure surveys: To provide aggregate information on levels of consumption, prices and, often, estimates of GDP, many countries conduct this type of survey. As one of the key assets, this often includes questions on how residential land is accessed but rarely goes beyond this in terms of the type of documents held or the gender of rights holders. Elaborated housing modules are often included, and which already contain some questions on tenure status of the dwelling and documentation held. In consultation with the NSO, these modules will be fine-tuned to fully cover the essential land questions identified for 1.4.2.

Multi-topic household surveys: Building on the need to generate reliable poverty estimates and understand the factors that lead households to fall into poverty or escape from it in developing countries, these surveys include a roster of household members and, where agriculture is a main source of livelihood, a detailed agricultural module that in many cases obtains information on tenure status, ownership, and production at plot level. The essential questions for 1.4.2 as well as 5.a.1 have been included in the Living Standard Measurement Surveys approach, which includes individual surveys and puts much emphasis on measuring intra household dynamics through direct reporting.

Demographic and Health Surveys (DHS): Responding to a need for more frequent and reliable information on population and health, especially in developing countries, these types of surveys provide nationally representative data on a wide range of areas including fertility, family planning, maternal and child health, gender, HIV/AIDS, malaria, and nutrition. A standard questionnaire, regularly revised to incorporate newly emerging issues, is administrated at the household and individual level. It is a nationally representative survey. In a majority of DHS surveys, people eligible for individual interviews include women of reproductive age (15-49) and men age 15-49, 15-54, or 15-59. The individual questionnaires in the latest version (round 7) includes questions on whether respondents own land, if they have formal ownership documents, and if their name is included on these documents.

Multiple Indicator Cluster Surveys (MICS): Surveys implemented by NSOs under the program developed by the United Nations Children's Fund (UNICEF) to provide internationally comparable, statistically rigorous data on the situation of children and women. They cover topics such as health, education, child protection, and water and sanitation. The survey design follows closely that of DHS questions and modules. This facilitates cross-country comparisons of estimates obtained using DHS data with those obtained using MICS data. In addition to the household questionnaire, there are questionnaires for women of reproductive ages (15-49), men aged between 15 and 49 and children (aged 0-5 and aged 5-17). The household questionnaire includes questions on ownership of land that can be used for agriculture by any member of the household, and on the size of the agricultural land owned by the household members. Also, there are questions about ownership/rental of dwelling where the household lives.

Discussions are ongoing with the teams in charge of DHS and MICS, specifically on expanding questions on land in their standardized and nationally representative surveys, in order to cover all data requirements for 1.4.2.

Urban Inequity Surveys (UIS): These specialized surveys were designed by UN-Habitat as household surveys to monitor and assess water and sanitation service coverage and other topics on urban inequities, including tenure. More recently, these surveys have been expanded to cover both rural and urban areas. The upcoming UIS surveys will be reviewed to ensure that the data requirements for SDG 1.4.2 are covered.

Administrative data

Production of land records and maps is a core function of public land registries, with legally recognized documentation being the output. Reporting on the information contained in these land records ((i) names of people holding rights, (ii) type of rights and (iii) location) is not difficult in principle if records are kept in a computerized format. Using household surveys, this land information can be cross-checked against survey information with respect to quality and coverage. In the case of registered communal or group rights, identifying the group members who gain tenure security through its registration is equally possible.

The country specific metadata will include a description of the structure of the land information data base, available information and approach for routine SDG reporting.

3.b. Data collection method

The custodians of 1.4.2 together with FAO and UN Women, custodians of 5. a.1^[3], developed a standardized, consolidated and succinct survey instrument with essential questions as data collection requirements are partly similar (https://gltn.net/download/measuring-individuals-rights-to-land-an-integrated-approach-to-data-collection-for-sdg-indicators-1-4-2-and-5-a-1-english/?wpdmdl=16316&refresh=5efb342458df61593521188). The standardization of indicator definitions improves data comparability across countries. The scope and capacity for standardized data collection, analysis and reporting across NSOs is expected to rise with progressive data collection and implementation of the methodology.

The module is made available to NSOs for integration in survey instruments already in place, and will be used by other international household survey programs working with NSOs (such as LSMS and UIS). The module can be used by any other complementary survey instrument implemented by other actors, using a data collection protocol that meets SDG 1.4.2 requirements, while the data produced are approved and reported by NSO to the custodians. In addition, both the USAID and the Millennium Challenge Cooperation (MCC), have agreed to incorporate the essential questions from 5.a.1 and 1.4.2 into future land impact evaluations and has already done so for upcoming ones. The Property Rights Index initiative has integrated the SDG questions into its data collection tools on perceptions of tenure security. This range of efforts will further expand data availability and leverage efforts by NSOs to report on this indicator.

Country-specific metadata will be elaborated that provides an inventory of the tenure types and type of documents in use, identifies which documents are legally recognized as evidence of land rights with images of each document, and elaborates on the correspondence between the two types of data sets (survey data and administrative data). This instrument will ensure consistency of definitions across countries. These country specific metadata will also be used for customizing surveys.

Indicator title 5.a.1: (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) Share of women among owners or rights-bearers of agricultural land, by type of tenure. ↑

3.c. Data collection calendar

Data collection will be the responsibility of national agencies. DHS, MICS and LSMS-type surveys are conducted in a cycle of about three years, while census data is available every 10 years. Administrative data can be reported on an annual basis where land information systems are fully electronic, with the accompanying population data made available from censuses or inter-censual projections.

Via the EGMs conducted, the custodians have been able to put together a network of NSOs and land administration institutions to link to NSOs and their regional representations, and to provide administrative data. The World Bank, UN-Habitat, the GDWGL, GLTN/GLII and other partners will support capacity strengthening at regional and country level for data providers and reporting mechanisms, and promote understanding of this indicator at all levels. Concerted investments are ongoing to expand data availability by integrating the consolidated land data module with essential questions in upcoming surveys, as already indicated above.

A capacity assessment^[4] on the preparedness and ability of NSOs to report on indicator 1.4.2 indicator was conducted by the custodians, with support of GLTN/GLII. The findings show NSOs agree to build on existing national survey systems and are ready to coordinate with land agencies to generate data and report on this indicator. Capacity needs were also identified and being used to develop a country capacity development strategy for NSOs, jointly with FAO and UN Women. The custodians of 1.4.2 and 5.a.1 have agreed to work closely with country and regional statistical agencies and global partners to support for country data collection, analysis and reporting. Similar capacity building support will be developed for land agencies to set up gender disaggregated electronic reporting systems.

⁴

Reports received from 17 countries: Bhutan, Bangladesh, Cameroon, Tunisia, Tanzania, Senegal, Uganda, Mauritius, Colombia, Japan, Slovenia, Sweden, Jamaica, Singapore, Madagascar, Niger and India. ↑

3.d. Data release calendar

No fixed releases; depends on release of relevant survey data.

3.e. Data providers

National data providers:

Statistical agencies – surveys
Government administrative sources /registries, cadastres

Compilation & reporting at the global level:

UN-Habitat - United Nations Human Settlements Programme
World Bank

Development of methodology and data collection tools was done with support of NSOs (Colombia, India, Jamaica, Tanzania, Uganda, Cameroon, the United States, the Africa Centre for Statistics/UNECA) and land agencies (Belgium, Brazil, Colombia, Republic of Korea, Mexico, Netherlands, Romania, Spain, United Arab Emirates and Uganda) and regional organizations of land agencies (registries, cadastres, ministries responsible for land) through international Expert Group Meetings.

The data collection tool was developed in coordination with FAO and UN Women/EDGE to harmonize instruments for 1.4.2 and 5.a.1.

The development of this SDG indicator is supported by the Global Donor Working Group on Land (GDWGL). This is a network of 24 bi- and multilateral donors and international organizations committed to improving land governance worldwide and which collectively represents virtually all global donor assistance in the land sector: the Global Land Tool Network (GLTN) and the Global Land Indicator Initiative (GLII), a network of over 70 CSOs, NGOs, professional organizations, research and training organizations; the International Land Coalition (ILC), an alliance of more than 200 intergovernmental and civil society organizations working on land; and the African Union/UNECA/AfDB Land Policy Initiative.

3.f. Data compilers

UN-Habitat - United Nations Human Settlements Programme
World Bank

3.g. Institutional mandate

No set of rules or instructions available.

4.a. Rationale

Tenure systems increasingly face stress as the world’s growing population requires food security, and as urbanization, environmental degradation and climate affect land use and productivity. Many tenure problems also arise because of weak land governance, disputes due to land acquisition or large-scale land-based investments, and attempts to address tenure problems associated with dualisms to tenure regimes. Responsible governance of tenure of land is inextricably linked with access to and management of other natural resources, such as forests, water, fisheries and mineral resources. The governance of tenure is a crucial element in determining if and how people, communities and others acquire rights, and their associated obligations, to use and control land and natural resources. Legal recognition to group tenure or adopting a ‘fit for purpose’ land administration and using these to recognize outer boundaries of land held under communal or customary arrangements have increasingly received government attention in the recent past.

Increasing demand for pro-poor land reforms has created the need for a core set of land indicators that have national application and global comparability, and culminated in SDG 1.4.2^[5]. Regular reporting on indicator 1.4.2 will provide an impetus to improve the availability of data from surveys as well as regularity of reporting on land administration service delivery to people by registries and other line agencies. Indicator 1.4.2 thus measures gender disaggregated progress in tenure security.

All forms of tenure should provide people with a degree of tenure security, with states protecting legitimate tenure rights, ensuring that people are not arbitrarily evicted and that their legitimate tenure rights are not otherwise extinguished or infringed. Perceptions of tenure security matter because they influence the way that land is used. Sources of perceived insecurity may include contestation from within households, families, communities or as a result of the actions of governments or private land claimants. Secure tenure rights for women require particular attention and could be affected by a number of factors, including intra-household power relations, community level inequalities, or different tenure regimes, and which can be cross tabulated against other factors of difference to ensure that women are no left behind. If measured at the individual level, the right to bequeath is another proxy of perception of tenure security. Women’s ability to influence intergenerational land transfers is an important aspect of female empowerment (and one way in which this indicator links with indicator 5.a.1).

“Legally recognized documentation” and “perception of tenure security” are two complementary parts of this indicator and which reflects several insights, namely (i) land is a key asset that is essential for poverty reduction, human rights and equality of opportunity including by gender; (ii) secure land tenure creates incentives for investment in land, allows land to be transferred, and creates the institutional precondition for use of land as collateral to access finance for economic activity; (iii) there is a need to complement formal measures of tenure security with perception-based measures.

This indicator will inform policy and allow for the assessment of specific outcomes and practical priorities for further improvements of tenure security at the country level. Regular reporting on the two components of Indicator 1.4.2 will:

provide incentives for governments to improve performance on progress with responsible land governance
inform governments and non-state actors to what extent countries’ legal and institutional frameworks recognize and support different land-tenure categories
provide information on implementation capacity to protect such rights in practice, as well as progress
identify the scope for additional action required at the country level as well as at a subnational level or for certain categories, geographic entities or ecosystems, and
provide for equity between men and women in land rights.

⁵

This need for data led to a collaboration between UN-Habitat, the Millennium Challenge Corporation and the World Bank in 2012, facilitated by the Global Land Tool Network, to develop a set of core land indicators to measure tenure security globally and at country level; the process saw the start of the Global Land Indicators Initiative (GLII), a platform used by the global land community to underscore the need for tenure security through evidence-based policymaking through more and better data. ↑

4.b. Comment and limitations

In 2016, a total of 116 countries reported having electronic land information systems in place. Countries with paper-based systems will have more difficulties with reporting on administrative data and household surveys will be the main source of data for this indicator in these countries. The expansion of digitization of records and land data management is one way to facilitate the ease of reporting administrative data for this indicator. Coverage may, however, be geographically skewed, for example towards urban or specific rural regions where cadastral coverage is concentrated, and therefore sub-national dimensions should be properly considered and conveyed in narrative reporting by specific countries to accompany the headline data.

In federal countries with decentralized land registry systems and no centralized reporting yet, data reporting systems for aggregation will be put in place. For countries where the land administration system does not yet collect information on gender, and gender disaggregation cannot be computed using other core data (social security numbers, ID etc), land agencies are encouraged to start expanding this by recording also the gender of owners/users of newly registered land.

Most of the national household surveys’ target samples are sufficiently large to provide the statistical power for disaggregation by sex and tenure type at rural /urban and sub-national levels. Inferring the extent to which the adult population is tenure secure based on the existing web of surveys, will require the use of a standardized set of questions so that surveys can be combined. However, even nationally representative surveys tend to cover certain segments of the population (those living in agricultural areas, families in which there are women of reproductive age, official urban areas etc.). Even when all the existing surveys are aggregated, there may be pockets of the population that are not captured by the surveys and for which there is thus no data on tenure security. This may include families living in areas that are too far or costly to reach, like forest areas.

Household surveys generally collect household-level data from proxy respondents. Family members who are not the head or the most knowledgeable person in their households are not interviewed, as is also noted in the methodological note for the IAEG-SDG Secretariat for Indicator 5.a.1. This approach is problematic for measuring tenure rights and security due to the introduction of non-random measurement errors^[6]. For instance, proxy reporting by one member of the household tends to incorrectly assign rights and misjudge and underestimate both women’s and men’s rights and use of land. Indicator 1.4.2 should therefore be based on self-reported rather than proxy data. If not all household members are surveyed, only those surveyed should be reported, estimating the global adult population based on the smaller sample enumerated. This lack of information affects only the numerators of the indicator; it has no bearing on the denominator which should always be the total adult population. In other words, the indicator reports and tracks the proportion of the population for which there is self-reported data stating that they are tenure secure. People for whom there is no information cannot be assumed to be tenure secure and therefore are not counted in the numerator. NSOs should report the data collected from household surveys as individual level data that corresponds to the respondent and is not extrapolated to the rest of his/her household. Any limitations in the representativeness of this data should be clearly noted in the country specific metadata submitted with the reporting, including who was included in the enumeration.

Data will still be used for countries that do not yet have survey instruments in place that survey individuals, while capacity for expanding sampling and individual self-reporting by NSOs is expanded progressively through DHS, MICS, LSMS and other type of surveys in coordination with FAO and UN-Women. Addressing this challenge will require combined efforts. Custodians of the land rights indicators1.4.2 and 5.a.1, and relevant stakeholders from the land sector, will work with custodians from other SDG indicators also require surveying of individuals, and in particular the NSOs, to identify effective approaches to start filling the void on self-reported data. NSOs need to be supported to collect data by interviewing individual adult household member. The custodians will leverage the work of the UN - Evidence and Data for Gender Equality EDGE project^[7], in particular, which is the most advanced in using and testing gender sensitive methodologies and approaches. They have found the approach feasible and have developed training materials and data collection instruments suitable for this effort.

⁶

Findings from the Methodological Experiment on Measuring Asset Ownership from A Gender Perspective (MEXA) experiment revealed that data from proxy respondents yield different estimates than self-reported data, with variations by asset, by type of ownership and by the sex of the owner. For instance, the study found that self-reported data increase both women’s and men’s reported ownership of agricultural land in Uganda. Such increase is greater for men (15 percentage points) than for women (10 percentage points), and is less pronounced when we consider documented ownership (+7 percentage points for men and +2 percentage points for women) (Kilic and Moylan, 20160. ↑

⁷

https://unstats.un.org/edge/ ↑

4.c. Method of computation

Indicator 1.4.2 is composed of two parts: (A) measures the incidence of adults with legally recognized documentation over land among the total adult population; while (B) focuses on the incidence of adults who report having perceived secure rights to land among the adult population. Part (A) and part (B) provide two complementary data sets on security of tenure rights, needed for measuring the indicator.

Part (A): $\frac{P e o p l e (A d u l t) w i t h l e g a l l y r e c o g n i z e d d o c u m e n t a t i o n o v e r l a n d}{T o t a l a d u l t p o p u l a t i o n }$ X 100

Part (B): $\frac{P e o p l e (a d u l t) w h o p e r c e i v e t h e i r r i g h t s a s s e c u r e}{T o t a l a d u l t p o p u l a t i o n }$ x 100

Part A will be computed using national census data or household survey data generated by the national statistical system and/or administrative data generated by land agency (depending on data availability)^[8].

Part B will be computed using national census data or household survey data that feature the perception questions globally agreed through the EGMs and standardized in the module with the list of essential questions.

⁸

The decision on data source will be taken at the specific country level. ↑

4.d. Validation

Computing of indicator by custodians based on survey data released by NSO and/ or administrative data submitted by government agency.

4.e. Adjustments

Not applicable.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

4.g. Regional aggregations

4.h. Methods and guidance available to countries for the compilation of the data at the national level

4.i. Quality management

Only use of raw (but cleaned and quality check applied) data released by NSO or government agency; computed by statistical staff world bank.

4.j. Quality assurance

4.k. Quality assessment

Standard quality criteria are met.

5. Data availability and disaggregation

Data availability:

This indicator was reclassified from Tier III to Tier II during the 6^th Meeting of IAEG-SDG. An internationally established methodology exists but data is not regularly produced by countries. Administrative data are routinely produced by land administration institutions. The 116 countries reporting having electronic land information systems, can generate the required data at a low cost on a routine basis, and at high levels of disaggregation, once the queries for the SDG dashboard are put in place.

Nationally representative multi-topic household surveys have collected land related data in many countries. These provide information, separately for residential and non-residential land, on (i) the share of individuals with legally documented rights; and (ii) the share of individuals who perceive their rights to be secure. Nationally representative household surveys will also provide data on two other key elements, namely (i) reported type of documentation and (ii) perception of tenure security by tenure type and other disaggregations discussed above.

Time series:

Disaggregation:

This indicator will be disaggregated by sex and type of tenure, using the standards developed by the working group on data disaggregation, which is a subgroup of the Inter-Agency Expert Group on SDGs^[9].

⁹

https://unstats.un.org/sdgs/files/meetings/iaeg-sdgs-meeting-05/12_14.%20Data%20disaggregation_plenary.pdf ↑

6. Comparability/deviation from international standards

Sources of discrepancies:

7. References and Documentation

Kilic, T., and Moylan, H. (2016). “Methodological experiment on measuring asset ownership from a gender perspective (MEXA): technical report.” Washington, DC: World Bank

Selected Land policy normative documents

Africa Union, African Development bank and United Nations Economic Commission for Africa (1999). Land Policy in Africa: A Framework to Strengthen Land Rights, Enhance Productivity and Secure Livelihoods. Available at: https://www.uneca.org/publications/framework-and-guidelines-landpolicy-africa

Africa Union, African Development bank and United Nations Economic Commission for Africa (2014). Guiding Principles on Large-Scale Land-Based Investment in Africa. Nairobi. Available at: https://www.uneca.org/sites/default/files/PublicationFiles/guiding_principles_eng_rev_era_size.pdf

Food and Agriculture Organization of the United Nations (2012). Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security. Available at: http://www.fao.org/docrep/016/i2801e/i2801e.pdf

Proceedings EGMs for SDG 1.4.2

Expert Group Meetings on methodology development using survey data: https://gltn.net/home/download/international-expert-group-meeting-on-land-tenure-security-to-develop-a-set-of-household-survey-questions-for-monitoring-sdg-indicator-1-4-2/?wpdmdl=111

Expert Group Meetings on methodology development using administrative data (http://documents.worldbank.org/curated/en/482991505367111149/pdf/119691-WP-P095390-PUBLIC-SDGEGMproceedingsuseofadministrativedatalandagencies.pdf)

Consolidated essential questions land module for 1.4.2 and 5.a.1 (FAO, UN-Habitat, UN Women, World Bank). Module for individual interviewing under preparation; Version for household surveys with proxy respondents; available at: http://documents.worldbank.org/curated/en/812621505371556739/Land-tenure-module-essential-questions-for-data-collection-for-1-4-2-and-5-a-1).

ANNEX:

Full methodology development narrative (including list of pilot countries, data and other results from pilot studies)

METHODOLOGICAL DEVELOPMENT

Global consultations on the methodological developments of this indicator were conducted with a diverse range of participants and partners. The custodian agencies, working directly with NSOs and land agencies, developed tools and capacity development packs, followed by computation of data points for relevant variables for this indicator for several countries on a pilot basis, using existing data sources from nationally representative surveys and census and, in exceptional cases, rigorous impact evaluations without national coverage.

Methodology development and piloting results

Formulas and combining different elements

The process used for methodology development are presented above. As discussed in detail there, indicator 1.4.2 comprises two parts: (A) measures the incidence of adults with legally recognized documentation over land among the total adult population; while (B) focuses on the incidence of adults who report having perceived secure rights to land among the adult population. Part (A) and part (B) provide two complementary data sets on security of tenure rights, needed for measuring the indicator.

Part (A):

$\frac{P e o p l e (A d u l t) w i t h l e g a l l y r e c o g n i z e d d o c u m e n t a t i o n o v e r l a n d}{T o t a l a d u l t p o p u l a t i o n }$ X 100

Part (B):

$\frac{P e o p l e (a d u l t) w h o p e r c e i v e t h e i r r i g h t s a s s e c u r e}{T o t a l a d u l t p o p u l a t i o n }$ X 100

The computation formula has built in system for computing the individual components of this indicator.

Where survey data are collected separately for agricultural and residential land, double counting is avoided by adjusting for households that access both types of land simultaneously.
Strata title: cases where a residence is in an apartment building, the rights to the residency are counted as rights to the land.
For purposes of retrospective data collection, parcels that are already affected by a dispute are also included in the reporting below on fear for involuntary loss of land.

As required by the indicator definition, any component can be disaggregated by gender and tenure type.

The national censuses or household surveys by the national statistical system were used to assess the number of people to access any land either through individual or joint ownership or via rental. Gender was calculated from surveys or calculated by land agencies using administrative data.

More detailed technical issues, e.g. ways to deal with proxy reporting by one member of the household on and when and how administrative data can be used are explained in the draft meta data for SDG 1.4.2.

Piloting results

Results from applying the methodology to select data are summarized in Table 1. They demonstrate not only the viability of the methodology, including the scope for how survey and administrative data to complement each other in a useful way, as well as the ability to derive a meaningful and actionable indicator. Rather than discussing substantive implications and actionability at country level, we focus on cross-cutting and data issues, illustrating in particular how different data sources can usefully complement each other.

Table 1: Selected countries with data on indicator 1.4.2

Country/Region	Data Source(s)	Year	Land access via		Formal	Perceived	Index	Gender
			Ownership	Rental	Document	Security
Africa
Benin	INSAE, MCC & Admin	2011	0.809	0.047	0.113	0.903	0.51	0.123
Lesotho	MCC	2013	0.914	0.029	0.611	0.929	0.77
Mozambique	INE	2011	0.882	0.033	0.498	0.811	0.65	0.112
Malawi	NBS	2015	0.868	0.023	0.019	0.697	0.36	0.226
Nigeria	NBS	2013	0.741	0.025	0.021	0.741	0.38	0.162
Rwanda	LSMS-ISA & admin data	2015	0.886	0.002	0.858	0.969	0.91	0.864
Tanzania	LSMS-ISA	2013	0.839	0.123	0.250	0.960	0.61	0.339
Uganda	LSMS-ISA	2014	0.902	0.080	0.080	0.919	0.50	0.525
Asia
Korea, Rep.	Census & Admin	2016	0.723	0.237	1.000	0.960	0.98
Mongolia	MCC-SHPS	2012	0.809	0.163	0.654	0.966	0.81	0.268
Americas
Costa Rica	Census & Admin	2011	0.699	0.279	1.000	0.978	0.99
Europe
Belgium	Census & Admin	2011	0.628	0.362	0.948	0.939	0.94	0.543
Netherlands	Census & Admin	2011	0.539	0.429	1.000	0.968	0.98	0.640
Oceania
New Zealand	Census & Admin	2013	0.607	0.327	0.990	0.925	0.96

Source: SDG Indicator 1.4.2 Global Database

Selected data comments

The data on formal documentation of land rights of the indicator are self-reported from household surveys for most low-income countries or from land agencies’ records. Ranging from less about 2% in Malawi and Nigeria to full coverage in Costa Rica, the Netherlands, and Korea, there is enormous variation in this part of the indicator across countries.

Data on perceived tenure security, which is from survey data and self-reported for all except the European countries and Costa Rica. In the latter case, we used the share of the population who, according to the population census or household surveys, either report owning or renting land or their residence to represent the share of the population who enjoy legally recognized documentation and by implication whose tenure is legally secure.

Benin: Administrative data indicate that the population have received individual documents based on the Plan Foncier Rural, but also suggest that, with about 12% of documents registered in their name, women have not benefited to the extent that may be expected.

Costa Rica: Administrative records suggest that in Costa Rica, all land is covered with land records. But census data indicate that some 2.5% of the population still suffers from precarious tenure, highlighting that even in cases where administrative data are available, they need to be linked to population-based evidence to give a fuller picture.

Malawi: Although the Government is engaged in an ambitious effort to digitize available records that would provide a basis for better land administration and reporting, only information from household survey data is available. The survey data point to high levels of tenure insecurity that are mostly gender related.

Nigeria: As a federal country, several states in Nigeria have administrative data that are of sufficient quality for reporting for the pilot. Data suggest that insecurity is high and, in many cases, is caused by the state due to expropriation.

Rwanda: A representative survey is available and points towards high levels of tenure security. However, information on the gender distribution of legally recognized documentation can be more reliably obtained from administrative data that show that more than 86% of women have land registered in their name either individually or jointly and perception of tenure security is high.

The Netherlands: This case shows that administrative data can be gender-disaggregated, and that in an advanced economy the share of individuals accessing land through various forms of institutions is high.

The Questionnaire Module with essential question for reporting on SDG 1.4.2

The module with essential questions for reporting on 1.4.2 is discussed in detail below. The module is developed by the UN Habitat and the World Bank, together with FAO and UN Women, with inputs from other stakeholders through GDWGL and GLII, and supported by the Living Standard Measurement Survey (LSMS) team.

The results of the EDGE project and other recent evidence suggest that individual level data collection is preferred to potential proxy respondents (where feasible).

Because of the scalability benefits of collecting data for both indicators simultaneously, the module is designed to provide the data required to compute indicators 1.4.2 as well as 5.a.1. Only the essential questions for indicator computation are included, however, the module may be expanded upon as needed by NSOs to address a wider range of land tenure issues relevant at the country level.

The module example, appended to this note is designed as a household level questionnaire in which a full roster of parcels is collected at the household level and the module is then implemented for each parcel, where the respondent is the most knowledgeable household member for the given parcel.

The module incorporates lessons learnt from methodological experiments1, as well as from implementation at national scale by the national Statistical Office of Malawi in its 2016/17 Integrated Household Survey (IHS4). The IHS4 interviewed 12,480 cross-sectional households across 780 EAs, and in parallel, revisited a national sub-sample of 2,516 households that had been previously interviewed in 2010 and 2013. As part of the IHS4 panel component, the survey administered up to 4 adult individual interviews per household. The modules asked separately questions regarding (i) reported ownership, (ii) economic ownership, (iii) documented ownership, and rights to (iv) sell, (v) bequeath, (vi) use as collateral, (vii) rent out, and (viii) make improvements/ invest.

Indicator 1.4.2 considers two aspects of tenure security: documentation and perception. Only documentation that is official, and therefore provides legally protected tenure rights, is considered under indicator 1.4.2. That is combined with perception of tenure security, which is captured through the respondent-estimated probability of involuntary loss of land rights in the next five-year period and the reported right to bequeath.

While the module has been carefully designed to be as universal as possible to maintain comparability of the computed indicator across time and space, certain questions, marked in the questionnaire, will require customization at the country level. Customization cannot be avoided in full due to the varying legal systems and land tenure arrangements across countries. Collection of metadata, including the identification of legally recognized documentation in the particular country context must take place prior to implementation of the module.

The Questionnaire Module

The questionnaire module assumes a survey that has households as the unit of enumeration and analysis, and where a household roster is used to identify household members and collected basic information on their demographics including age and gender. In this process, each household member is assigned a unique identifier (HHID). In the Annex, the questions are color-coded to identify those required for indicator 1.4.2 only, for indicator 5.a.1 only, for both indicators, and those included for disaggregation or other analytical purposes. In what follows, practical issues for implementation are discussed, and explanatory notes on each individual question presented.

Scalability & Up-Take

While it is to be expected that the module will be usually implemented in conjunction with a larger survey operation, nothing prevents users to implement it independently. Implementing the module in the context of multi-topic surveys will increase its analytical value as, beyond generating an indicator for the SDG monitoring process, countries would be in a position to explore how land tenure issues relate to other development outcomes, including other SDG goals. The custodians foresee implementing the module as part of Living Standard Measurement Study (LSMS) surveys and the Urban Inequality Survey (UIS), and will be discussed with the USAID-funded Demographic and Health Surveys (DHS) programme, and UNICEF Multiple Indicator Cluster Surveys (MICS). Any nationally representative sample survey can of course become a vehicle for implementing the module. The custodians envisage working with National Statistical Offices to engage in dissemination and capacity development, as integrating the module in national statistical programs is the only viable way to ensure sustainability of the data collection process and ownership of the results by countries.

Implementation Method

The questionnaire module has been designed for paper assisted personal interviewing (PAPI) implementation to have the widest reach. However, an electronic version of the questionnaire will be created by the custodians for use in computer assisted personal interviewing. The application will be created using the World Bank’s open access CAPI platform, Survey Solutions (solutions.worldbank. org), and will be made publicly available. The CAPI application can be customized from the base module as necessary. Implementation of the module via CAPI is recommended, as this can minimize data entry errors, allow for more immediate data review and analysis, and enable quick use of photo aids (which can improve data quality).

Before Going to the Field: Collecting Metadata

In this context, metadata refers to the classification of land documentation into legally recognized and unrecognized types as defined for indicator 1.4.2. The metadata will vary by country and will therefore, need to be released along with the computation of the indicator for transparency, and update in the case of changes in the regulatory frameworks. The metadata will identify which types of documentation are legally recognized, and therefore, what constitutes secure tenure. Questions on unrecognized and/or informal documentation can be asked separately, but is not considered in the computation of Indicator 1.4.2.

Question-by-Question Guidance

The implementation of the questionnaire included in the Annex is fairly intuitive, yet it is recommended that prior to its implementation, adequate training is provided and an enumerator manual is produced to guide data collection, including with images of the range of tenure related documentation in use by land holders. Detailed explanatory notes on each question are found below, which can be used to develop such manuals. Where customization is necessary, this is indicated. Annex I also indicates skip patterns (indicated by the arrow sign ‘>>’).

Guidance for sample questionnaire annex 1 (household survey with parcel roster)

RESPONDENT ID:	The respondent ID is the ID of the person responding for the respective parcel, recorded from the household roster. The respondent should be the most knowledgeable household member for each parcel. Therefore, the respondent may differ for each parcel. The optimal respondent should be identified through a discussion amongst the enumerator and all adult members of the household (or as many as possible) prior to beginning the module. During this meeting, the full roster of parcels should be recorded and the optimal respondent identified for each.
Q1:	The roster of parcels should contain all parcels used by, owned by, or occupied by any household member(s) at the time of the interview. Alternatively, a single set date could be identified for a given survey. This option is especially applicable in when fieldwork is conducted over an extended period of time (such as a 12-month rolling fieldwork design). The first parcel listed should be the parcel on which the household resides. The parcel name must be unique to each parcel, as it will be used to refer to the specific parcel throughout the remainder of the module. In the case of panel surveys, or surveys with multiple visits, parcel names referring to a crop grown, for example, should be avoided as that may change over time.
Q2:	Parcel area has been included in the module to allow for disaggregation of the indicator (for example, for smallholder farmers only). Farmer estimation of parcel area should be collected for all parcels. Additionally, GPS measurement of parcels is strongly advised, wherever feasible. Recent evidence points to systematic bias in farmer estimates of land area1. Land area units must be customized for the country context.
Q3:	Parcel acquisition type is used as a filter question for the following questions, allowing for maximum efficiency in skipping questions where possible. Response code to be reviewed in light of the country context.
Q4:	The tenure system of the parcel is used to disaggregate indicator 1.4.2. Response codes to be reviewed in light of the country context.
Q5:	The primary use of the current parcel is used to disaggregate indicator 1.4.2, and to identify land subject to indicator 5.a.1, which pertains to agricultural land. In some cases, such as when land is rented out, the actual use may not be known, hence the inclusion of the “Don’t Know” response. However, wherever possible, the actual use of the land, rather than current ownership or use arrangements, should be recorded.
Q6:	Question 6 identifies the owner(s) or use right holder(s) of the parcel, as reported by the respondent. Multiple household members may be listed, as joint ownership/use right holding is common.
Q7:	This module only seeks to identify the possession of documents that are pre-determined to be legally recognized in the given context. Question 7, therefore, asks about the possession of documents from a specific government agency(ies). Examples of relevant documents are embedded in the question to provide context to the respondent and to clarify that documents other than title deeds are relevant. The government agency(ies) and example documents embedded in the question must be customized for the country context. Refer to the section above on metadata for guidance on determining what is to be classified as legally recognized.
Q8:	If the response to Question 7 is “yes”, question 8 is answered to record the specific type of documents held by the household, and which members are named on each. Codes must be customized at country level to include all legally recognized documents (as determined through the pre-survey preparation of metadata). Rental contracts of some form should be included, as long as rights are legally protected. To minimize errors in naming and classifying documents, a photo aid containing an image of all legally recognized documents should be constructed and shown to the respondent. The integration of visual aids (e.g. a photo of an actual document of the reproduction of a facsimile) is most easily done in a CAPI application, but can also be integrated in traditional PAPI interviews.
Q9:	The right to sell the parcel is captured in questions 9 and 10. Question 9 is a filter question, asking if any household member has the right to sell the parcel, either alone or jointly. That is, if any household member has the right to sell (or believes they have the right to sell) whether that be alone or with the approval/signature/etc. of another person either within or outside the household, the respond should be “yes”. This question is skipped for parcels acquired through short-term rentals (<3 years) and sharecropping-in. Questions on the right to sell are used for computation of indicator 5.a.1 only.
Q10:	List the ID codes of the household members that have the right to sell the parcel. If there are any external members that have the right to sell, enter the code accordingly. This question is skipped for parcels acquired through short-term rentals (<3 years) and sharecropping-in.
Q11:	The right to bequeath the parcel is captured in questions 10 and 11. Question 10 is a filter question, asking if any household member has the right to bequeath the parcel, either alone or jointly. That is, if any household member has the right to bequeath (or believes they have the right to bequeath) whether that be alone or with the approval/signature/etc. of another person either within or outside the household, the respond should be “yes”. This question is skipped for parcels acquired through short-term rentals (<3 years) and sharecropping-in. Here, bequeath is defined as the ability to transfer rights to the parcel either in life or in death.
Q12:	List the ID codes of the household members that have the right to bequeath the parcel. If there are any external members that have the right to bequeath, enter the code accordingly. This question is skipped for parcels acquired through short-term rentals (<3 years) and sharecropping-in.
Q13:	Question 13 identifies the likelihood of involuntarily losing ownership/use rights to the parcel in the next five years. Responses are made on a scale from 1 to 7, with 1 being not at all likely and 7 being extremely likely. This question is asked about each owner/use right holder separately that was identified in Question 6 (but asked all to the same parcel-level respondent). This formulation of the question allows for the observance of intra-household insecurity, for example involuntary transfer of rights from female to male household members. For parcels acquired through short-term rental (<3 years), the question will be asked for likelihood of involuntary loss in the remaining duration of the contract.

G. Carletto, S. Gourlay, S. Murray, and A. Zezza (2016), Land Area Measurement in Household Surveys, Washington DC, The World Bank.

1.5.1

0.a. Goal

Goal 13: Take urgent action to combat climate change and its impacts

0.b. Target

Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries

0.c. Indicator

Indicator 13.1.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population

0.e. Metadata update

2018-03-01

0.g. International organisations(s) responsible for global monitoring

United Nations Office for Disaster Reduction (UNISDR)

1.a. Organisation

United Nations Office for Disaster Reduction (UNISDR)

2.a. Definition and concepts

Definition:

This indicator measures the number of people who died, went missing or were directly affected by disasters per 100,000 population.

Concepts:

Death: The number of people who died during the disaster, or directly after, as a direct result of the hazardous event.

Missing: The number of people whose whereabouts is unknown since the hazardous event. It includes people who are presumed dead, for whom there is no physical evidence such as a body, and for which an official/legal report has been filed with competent authorities.

Directly affected: The number of people who have suffered injury, illness or other health effects; who were evacuated, displaced, relocated or have suffered direct damage to their livelihoods, economic, physical, social, cultural and environmental assets. Indirectly affected are people who have suffered consequences, other than or in addition to direct effects, over time, due to disruption or changes in economy, critical infrastructure, basic services, commerce or work, or social, health and psychological consequences.

3.a. Data sources

Data sources and collection method:

Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.

4.a. Rationale

The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, “Target A: Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality between 2020-2030 compared with 2005-2015” and “Target B: Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared with 2005-2015” will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.

The open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OIEWG) established by the General Assembly (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the UNGA (OIEWG report A/71/644). The relevant global indicators for the Sendai Framework will be used to report for this indicator.

Disaster loss data is greatly influenced by large-scale catastrophic events, which represent important outliers. UNISDR recommends countries report the data by event, so that complementary analysis can be undertaken to obtain trends and patterns in which such catastrophic events (that can represent outliers) can be included or excluded.

4.b. Comment and limitations

The Sendai Framework Monitoring System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States will be able to report through the System from March 2018. The data for SDG indicators will be compiled and reported by UNISDR.

Proxy, alternative and additional indicators:

In most cases international data sources only record events that surpass some threshold of impact and use secondary data sources which usually have non uniform or even inconsistent methodologies, producing heterogeneous datasets.

4.c. Method of computation

Related indicators as of February 2020

$X_{} = \frac{(A_{2} + A_{3} + B_{1})}{G l o b a l P o p u l a t i o n} \times 100, 000$

Where:

A₂ Number of deaths attributed to disasters;

A₃ Number of missing persons attributed to disasters; and

B₁ Number of directly affected people attributed to disasters.

* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

• At regional and global levels

5. Data availability and disaggregation

Data availability:

Time series:

Disaggregation:

Number of deaths attributed to disasters;

Number of missing persons attributed to disasters; and

Number of directly affected people attributed to disasters.

[Desirable Disaggregation]:

Hazard

Geography (Administrative Unit)

Sex

Age (3 categories)

Disability

Income

6. Comparability/deviation from international standards

Sources of discrepancies:

7. References and Documentation

Official SDG Metadata URL: https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf

Internationally agreed methodology and guideline URL:

Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNISDR 2017)

https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf

Other references:

Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2nd February 2017. Available at: https://www.preventionweb.net/publications/view/51748

1.5.2

0.a. Goal

Goal 1: End poverty in all its forms everywhere

0.b. Target

Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters

0.c. Indicator

Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)

0.e. Metadata update

2018-03-01

0.g. International organisations(s) responsible for global monitoring

United Nations Office for Disaster Reduction (UNISDR)

1.a. Organisation

United Nations Office for Disaster Reduction (UNISDR)

2.a. Definition and concepts

Definition:

This indicator measures the ratio of direct economic loss attributed to disasters in relation to GDP.

Concepts:

Economic Loss: Total economic impact that consists of direct economic loss and indirect economic loss.

Direct economic loss: the monetary value of total or partial destruction of physical assets existing in the affected area. Direct economic loss is nearly equivalent to physical damage.

Indirect economic loss: a decline in economic value added as a consequence of direct economic loss and/or human and environmental impacts.

Annotations:

Examples of physical assets that are the basis for calculating direct economic loss include homes, schools, hospitals, commercial and governmental buildings, transport, energy, telecommunications infrastructures and other infrastructure; business assets and industrial plants; production such as crops, livestock and production infrastructure. They may also encompass environmental assets and cultural heritage. Direct economic losses usually happen during the event or within the first few hours after the event and are often assessed soon after the event to estimate recovery cost and claim insurance payments. These are tangible and relatively easy to measure.

3.a. Data sources

4.a. Rationale

The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, “Target C: Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030” will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.

4.b. Comment and limitations

4.c. Method of computation

Related indicators as of February 2020

$X_{} = \frac{(C_{2} + C_{3} + C_{4} + C_{5} + C_{6})}{G l o b a l G D P}$

Where:

C₂ Direct agricultural loss attributed to disasters;

C₃ Direct economic loss to all other damaged or destroyed productive assets attributed to disasters;

C₄ Direct economic loss in the housing sector attributed to disasters;

C₅ Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters;

C₆ Direct economic loss to cultural heritage damaged or destroyed attributed to disasters.

* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)

5. Data availability and disaggregation

Disaggregation:

Direct agricultural loss attributed to disasters

Direct economic loss to all other damaged or destroyed productive assets attributed to disasters.

Direct economic loss in the housing sector attributed to disasters.

Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters.

Direct economic loss to cultural heritage damaged or destroyed attributed to disasters

[Desirable Disaggregation]:

Hazard

Geography (Administrative Unit)

7. References and Documentation

Official SDG Metadata URL: https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf

Internationally agreed methodology and guideline URL:

Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNISDR 2017)

https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf

Other references:

Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2nd February 2017. Available at: https://www.preventionweb.net/publications/view/51748

Country examples:

Proxy, alternative and additional indicators:

1.5.3

0.a. Goal

Goal 13: Take urgent action to combat climate change and its impacts

0.b. Target

Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries

0.c. Indicator

Indicator 13.1.2: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030

0.e. Metadata update

2017-07-07

0.f. Related indicators

1.5; 11.5; 11.b; 13.1; 2.4; 3.6; 3.9; 3.d; 4.a; 6.6; 9.1; 9.a; 11.1; 11.3; 11.c; 13.2; 13.3; 13.a; 13.b; 14.2; 15.1; 15.2; 15.3; 15.9.

0.g. International organisations(s) responsible for global monitoring

United Nations Office for Disaster Reduction (UNISDR)

1.a. Organisation

United Nations Office for Disaster Reduction (UNISDR)

2.a. Definition and concepts

Definition:

[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.

Concepts:

3.a. Data sources

National Progress Report of the Sendai Monitor, reported to UNISDR

3.b. Data collection method

The official counterpart(s) at the country level will provide National Progress Report of the Sendai Monitor.

3.c. Data collection calendar

2017-2018

3.d. Data release calendar

Initial datasets in 2017, a first fairly complete dataset by 2019

3.e. Data providers

The coordinating lead institution chairing the National DRR platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.

3.f. Data compilers

UNISDR

4.a. Rationale

The indicator will build bridge between the SDGs and the Sendai Framework for DRR. Increasing number of national governments that adopt and implement national and local DRR strategies, which the Sendai Framework calls for, will contribute to sustainable development from economic, environmental and social perspectives.

4.b. Comment and limitations

The HFA Monitor started in 2007 and over time, the number of countries reporting to UNISDR increased from 60 in 2007 to 140+ countries now undertaking voluntary self-assessment of progress in implementing the HFA. During the four reporting cycles to 2015 the HFA Monitor has generated the world’s largest repository of information on national DRR policy inter alia. Its successor, provisionally named the Sendai Monitor, is under development and will be informed by the recommendations of the OEIWG. A baseline as of 2015 is expected to be created in 2016-2017 that will facilitate reporting on progress in achieving the relevant targets of both the Sendai Framework and the SDGs.

Members of both the OEIWG and the IAEG-SDGs have addressed that indicators that simply count the number of countries are not recommended, instead that, indicators to measure progress over time have been promoted. Further to the deliberations of the OEIWG as well as the IAEG, UNISDR has proposed computation methodologies that allow the monitoring of improvement in national and local DRR strategies over time. These methodologies range from a simple quantitative assessment of the number of these strategies to a qualitative measure of alignment with the Sendai Framework, as well as population coverage for local strategies.

4.c. Method of computation

Note: Computation methodology for several indicators is very comprehensive, very long (about 180 pages) and probably out of the scope of this Metadata. UNISDR prefers to refer to the outcome of the Open Ended Intergovernmental Working Group, which provides a full detailed methodology for each indicator and sub-indicator.

The latest version of these methodologies can be obtained at:

http://www.preventionweb.net/documents/oiewg/Technical%20Collection%20of%20Concept%20Notes%20on%20Indicators.pdf

A short summary:

Summation of data from National Progress Reports of the Sendai Monitor

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

In the Sendai Monitor, which will be undertaken as a voluntary self-assessment like the HFA Monitor, missing values and 0 or null will be considered equivalent.

• At regional and global levels

4.g. Regional aggregations

See under Computation Method.

It will be calculated, at the discretion of the OEIWG, as either a linear average of the index described in Computation Method, or as a weighted average of the index times the population of the country, divided by global population.

5. Data availability and disaggregation

Data availability:

Around 100 countries

The HFA Monitor started in 2007 and over time, the number of countries reporting to UNISDR increased from 60 in 2007 to 140+ countries now undertaking voluntary self-assessment of progress in implementing the HFA. Given the requirements for disaster risk reduction strategies enshrined in reporting on the SDGs and the targets of the Sendai Framework, it is expected that by 2020, all member states will report their DRR strategies according to the recommendations and guidelines by the OEIWG.

Time series:

2013 and 2015: HFA monitor

Disaggregation:

By country

By city (applying sub-national administrative units)

6. Comparability/deviation from international standards

Sources of discrepancies:

There is no global database collecting DRR policy information besides the HFA Monitor and the succeeding Sendai Monitor.

7. References and Documentation

URL:

http://www.preventionweb.net/documents/oiewg/Technical%20Collection%20of%20Concept%20Notes%20on%20Indicators.pdf

References:

The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction (OEIWG) was given the responsibility by the UNGA for the development of a set of indicators to measure global progress in the implementation of the Sendai Framework, against the seven global targets. The work of the OEIWG shall be completed by December 2016 and its report submitted to the General Assembly for consideration. The IAEG-SDGs and the UN Statistical Commission formally recognizes the role of the OEIWG, and has deferred the responsibility for the further refinement and development of the methodology for disaster-related SDGs indicators to this working group.

http://www.preventionweb.net/drr-framework/open-ended-working-group/

The latest version of documents are located at:

http://www.preventionweb.net/drr-framework/open-ended-working-group/sessional-intersessional-documents

1.5.4

0.a. Goal

Goal 13: Take urgent action to combat climate change and its impacts

0.b. Target

Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries

0.c. Indicator

Indicator 13.1.3: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies

0.e. Metadata update

2018-02-01

0.f. Related indicators

1.5; 11.5; 11.b; 13.1; 2.4; 3.6; 3.9; 3.d; 4.a; 6.6; 9.1; 9.a; 11.1; 11.3; 11.c; 13.2; 13.3; 13.a; 13.b; 14.2; 15.1; 15.2; 15.3; 15.9.

0.g. International organisations(s) responsible for global monitoring

United Nations Office for Disaster Reduction (UNISDR)

1.a. Organisation

United Nations Office for Disaster Reduction (UNISDR)

2.a. Definition and concepts

Definition:

The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. One of the targets is: “Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020”.

In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, disaster risk reduction strategies and policies should mainstream and integrate disaster risk reduction within and across all sectors, across different timescales and with targets, indicators and time frames. These strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.

The open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OIEWG) established by the General Assembly (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the UNGA (OIEWG report A/71/644). The relevant SDG indicators reflect the Sendai Framework indicators.

Concepts:

3.a. Data sources

Sendai Framework Monitor, reported to UNISDR

3.b. Data collection method

The national Sendai Framework Focal Points will compile all inputs from their line ministries, NSO, and other entities, if appropriate, and report through the Sendai Framework Monitoring System.

3.c. Data collection calendar

2015 –

3.d. Data release calendar

Every year from Q2 2018

3.e. Data providers

National Sendai Framework Focal Points usually represent the coordinating lead institution chairing the National DRR platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.

3.f. Data compilers

UNISDR

4.a. Rationale

Increasing the proportion of local governments that adopt and implement local disaster risk reduction strategies, which the Sendai Framework calls for, will contribute to sustainable development and strengthen economic, social, health and environmental resilience. Their economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.

4.b. Comment and limitations

The Hyogo Framework for Action Monitor (HFA Monitor) started in 2007 and over time, the number of countries reporting to UNISDR increased from 60 in 2007 to approximately 100 countries in 2015 undertaking voluntary self-assessment of progress in implementing the HFA. During the four reporting cycles the HFA Monitor has generated the world’s largest repository of information on national disaster risk reduction policy inter alia. In 2018 the Sendai Framework Monitor system will launch and all Member States are expected to report data of the previous year(s).

4.c. Method of computation

Member States count the number of local governments that adopt and implement local DRR strategies in line with the national strategy and express it as a percentage of the total number of local governments in the country.

Local governments are determined by the reporting country for this indicator, considering sub-national public administrations with responsibility to develop local disaster risk reduction strategies. It is recommended that countries report on progress made by the lowest level of government accorded the mandate for disaster risk reduction, as the Sendai Framework promotes the adoption and implementation of local disaster risk reduction strategies in every local authority.

Each Member State will calculate the ratio of the number of local governments with local DRR strategies in line with national strategies and the total number of local governments.

Global Average will then be calculated as below through arithmetic average of the data from each Member State.

Further information of the methodology can be obtained in the Technical Guidance (see reference).

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

If a country does not report (missing Value), it will be considered to be 0 or null as same as the HFA Monitor.

• At regional and global levels

4.g. Regional aggregations

It could be calculated as an arithmetic average of reports by Member States.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction

http://www.preventionweb.net/events/view/55594

(The latest version will be uploaded on this site in early November)

4.j. Quality assurance

• Description of practices and guidelines for quality assurance followed at your agency.

• UNISDR Regional Office will have a regular contact with National Sendai Framework Focal Points (data providers).

5. Data availability and disaggregation

Data availability:

UNISDR conducted the Sendai Framework Data Readiness Review which 87 Member States responded between February and April in 2017.

In Q1 2018 all Member States will be invited to start reporting. Since in the previous monitoring approximately 100 countries reported their National HFA Monitor in each cycle, we expect the similar number of reporting.

Time series:

from 2015

Disaggregation:

By country

By local government (applying sub-national administrative unit)

6. Comparability/deviation from international standards

Sources of discrepancies:

N/A (There is no global database collecting DRR policy information besides the HFA Monitor and the succeeding Sendai Framework Monitor.)

7. References and Documentation

URL:

1) http://www.preventionweb.net/files/50683_oiewgreportenglish.pdf

2) http://www.preventionweb.net/english/hyogo/progress/

3) http://www.preventionweb.net/events/view/55594 <uploaded soon>

References:

1) Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction [A/71/644]

The IAEG-SDGs and the UN Statistical Commission deferred the responsibility for the further refinement and development of the methodology for disaster-related SDGs indicators to the OIEWG and formally adopted the OIEWG Report.

2) Hyogo Framework for Action Progress Reports

During the four reporting cycles the HFA Monitor has generated the world’s largest repository of information on national DRR policy inter alia.

3) Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (Draft)

The latest version will be available on-line in early November

2.a.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countries

0.c. Indicator

Indicator 2.a.1: The agriculture orientation index for government expenditures

0.d. Series

Primary series: Agriculture orientation index for government expenditures (AG_PRD_ORTIND)

Complementary series: Agriculture value added share of GDP (%) (AG_PRD_AGVAS)

Complementary series: Agriculture share of Government Expenditure (%) (AG_XPD_AGSGB)

0.e. Metadata update

2023-05-15

0.f. Related indicators

Indicators 17.1.1 and 17.1.2 also apply IMF GFS methodology.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The Agriculture Orientation Index (AOI) for Government Expenditures is defined as the Agriculture share of Government Expenditure, divided by the Agriculture value added share of GDP, where Agriculture refers to the agriculture, forestry, fishing and hunting sector. The measure is a currency-free index, calculated as the ratio of these two shares. National governments are requested to compile Government Expenditures according to the Government Finance Statistics (GFS) and the Classification of the Functions of Government (COFOG), and Agriculture value added share of GDP according to the System of National Accounts (SNA).

Concepts:

Agriculture refers to the agriculture, forestry, fishing and hunting sector, or Division A of ISIC Rev 4 (equal to Division A+B of ISIC Rev 3.2).

Government Expenditure comprise all expense and acquisition of non-financial assets associated with supporting a particular sector, as defined in the Government Finance Statistics Manual (GFSM) 2014 developed by the International Monetary Fund (IMF). NOTE: Transactions in assets and liabilities, such as loans by general government units (disbursement and repayment), are excluded when compiling COFOG data for GFS reporting purposes.

Government Expenditure are classified according to the Classification of the Functions of Government (COFOG), a classification developed by the Organisation for Economic Co-operation and Development (OECD) and published by the United Nations Statistical Division (UNSD).

Agriculture value-added and GDP are based on the System of National Accounts (SNA).

2.b. Unit of measure

Index

See 4.c. Method of computation, below.

2.c. Classifications

The Classification of the Functions of Government (COFOG) is a detailed classification of the functions, or socioeconomic objectives, that general government units aim to achieve through various kinds of expenditure. Functions are classified using a three-level scheme, consistent with the International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4. In particular, the scheme includes:

10 first-level, or two digit, categories, referred to as divisions, including Economic Affairs (04) and Environmental Protection (05);
within each division, 2 or more 3-digit three-digit categories, referred to as groups, such as Agriculture, Forestry, Fishing, and Hunting (042) and Protection of Biodiversity and Landscapes (054); and
within each group, one or more four-digit categories, referred to as classes, such as Agriculture (0421), Forestry (0422) and Fishing and hunting (0423), as well as related Research and Development (0482), covering the administration and operation of government agencies engaged in applied research and experimental development related to the sector, including that undertaken by nongovernment bodies, such as research institutes and universities funded by government grants and subsidies.

The International Monetary Fund (IMF) questionnaire on Government Finance Statistics (GFS) collects annual data on the first two levels (divisions and groups). The FAO questionnaire aims at collecting information on classes, as well as a breakdown of the related expenditure in recurrent and capital expenditures. The three classification levels and the contents of each class are described in the GFSM 2014, accessible at https://www.imf.org/external/np/sta/gfsm/.

FAOSTAT geographic classification is used to aggregate indicators across country groups (http://www.fao.org/faostat/en/#definitions).

3.a. Data sources

Data on government expenditures is collected from countries through an annual questionnaire administered by FAO. These data are not affected by sampling error, given that countries typically compile the questionnaires administered by FAO on the basis of their financial and accounting systems, using administrative information on government expenditures based on the availability and comprehensiveness of source data. For some countries that do not report directly data to FAO, key expenditure aggregates needed to calculate Indicator 2.a.1 are obtained either from the IMF GFS database, from other regional organizations, or from official national governmental websites.

Data on agriculture value-added and GDP are retrieved from the UN Statistics Division, which provides national accounts estimates for 220 countries and territories.

3.b. Data collection method

Data for the denominator are annually collected from countries using the FAO questionnaire on Government Expenditure on Agriculture (GEA), developed in collaboration with the IMF., For countries with missing information, data is supplemented with data collected by the IMF, regional organizations or published on official national governmental websites. The official counterpart(s) at country level are, depending on the country, from the national statistics office, the ministry of finance (or other central planning agency), or the ministry of agriculture. Validation and consultation were conducted through various FAO commissions and committees, including its two agricultural statistics commissions in Africa and the Asia and Pacific, its Committee on Agriculture and Livestock Statistics in Latin America and the Caribbean, and its Committee on Agriculture.

3.c. Data collection calendar

The t-1 reference year data collection cycle for Government Expenditure on Agriculture (GEA) will start in March/April of year t. Due to the time required to collect, compile and publish national data, countries may experience delays in reporting timely data.

3.d. Data release calendar

As the COFOG data is largely compiled annually, this indicator is released every year in March, covering data up to reference year t-2 (for the countries for which data collection, compilation, release is more timely).

3.e. Data providers

Ministry of Finance, Central Planning Agency, Central Banks, National Statistics Office, and/or Ministry of Agriculture.

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

Article I of the FAO Constitution requires the Organization to "collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture." (http://www.fao.org/docrep/x5584e/x5584e00.htm). Member countries reaffirmed this mandate in 2000. Within the FAO's statistical program of work, member countries endorsed the development of an investment statistics domain, including ongoing work on government expenditure on agriculture, during meetings of three statutory bodies: the Asia and Pacific Commission on Agricultural Statistics (APCAS) held in Vietnam in February 2014; the African Commission on Agricultural Statistics (AFCAS) held in Morocco in December 2013; and the IICA working group on agricultural and livestock statistics for Latin America and the Caribbean, held in Trinidad and Tobago in June 2013.

4.a. Rationale

An Agriculture Orientation Index (AOI) greater than 1 reflects a higher orientation towards the agriculture sector, which receives a higher share of government spending relative to its contribution to economic value-added. An AOI less than 1 reflects a lower orientation to agriculture, while an AOI equal to 1 reflects neutrality in a government’s orientation to the agriculture sector.

Government spending in agriculture includes spending on sector policies and programs; soil improvement and soil degradation control; irrigation and reservoirs for agricultural use; animal health management, livestock research and training in animal husbandry; marine/freshwater biological research; afforestation and other forestry projects; etc.

Spending in these agricultural activities helps to increase sector efficiency, productivity and income growth by increasing physical or human capital and/or reducing inter-temporal budget constraints.

However, the private sector typically under-invests in these activities due to the presence of market failure (e.g. the public good nature of research and development; the positive externalities from improved soil and water conditions; lack of access to competitive credit due to asymmetric information between producers and financial institutions, etc.). Similarly, the high risk faced by agricultural producers, particular smallholders unable to hedge against risk, often requires government intervention in terms of income redistribution to support smallholders in distress following crop failures and livestock loss from pests, droughts, floods, infrastructure failure, or severe price changes.

Government spending in agriculture is essential to address these market failures and the periodic need for income redistribution. This leads to several potential indicators for the SDGs, which include: a) the level of Government Expenditure on Agriculture (GEA); b) the Agriculture share of Government Expenditure, and c) the AOI for Government Expenditures.

An indicator that measures GEA levels fails to take into account the size of an economy. If two countries, A and B, have the same level of GEA, and the same agriculture contribution to GDP, but country A’s economy is 10 times that of country B, setting the same target levels for GEA fails to take economic size into account.

An indicator that measures the Agriculture share of Government Expenditure fails to take into account the relative contributions of the agricultural sector to a country’s GDP. Consider two countries with the same economic size, C and D, where agriculture contributes 2 percent to C’s GDP, and 10 per cent to country D’s GDP. If total Government Expenditures were equal in both countries, C would experience greater relative investment in Agriculture than D. If total Government Expenditures differed, the result could be magnified or diluted.

The AOI index takes into account a country’s economic size, Agriculture’s contribution to GDP, and the total amount of Government Expenditure. While the indicator does not allow setting of a universal and achievable target, it is useful to interpret the AOI in combination with its numerator and denominator separately: the Agriculture share of Government Expenditure and the Agriculture value-added Share of GDP.

4.b. Comment and limitations

Since the numerator of this data is based on financial and accounting systems and administrative sources, there is no confidence interval or standard error associated with government expenditure data. For the denominator, national accounts data typically do not provide any standard error or confidence interval information.

The key limitation with this indicator is that Consolidated General Government expenditure – the best measure for cross-country comparisons – is not available for all reporting countries. While most advanced economies – and many emerging market economies – do report these data, many smaller and/or low-income economies either do not have significant fiscal interventions in agriculture at the state/provincial and local/municipal levels; or do not have adequate source data to compile meaningful general government estimates for each subsector, as relevant. Given that in several countries, significant intervention in agriculture is implemented by sub-national governments, the Indicator 2.a.1 is calculated using the highest level of government available for the reporting country. For some countries, such as India, where the general government sector is defined for fiscal policy purposes as budgetary central government plus state government, the Indicator will take this into account.

Annex I lists the reporting countries, their M49 code, the latest year for which data are available and the level of government for which data has been reported. The level of government notation used is as follows: GG: Consolidated General Government; CG Consolidated Central Government (excluding Social Security Funds): CGI: Consolidated Central Government (including Social Security Funds); BA: Budgetary Central Government.

4.c. Method of computation

$A O I = \frac{A g r i c u l t u r e S h a r e o f G o v e r n m e n t E x p e n d i t u r e s}{A g r i c u l t u r e v a l u e a d d e d S h a r e o f G D P}$

where:

$A g r i c u l t u r e S h a r e o f G o v e r n m e n t E x p e n d i t u r e s$

$= \frac{G o v e r n m e n t E x p e n d i t u r e s o n A g r i c u l t u r e}{T o t a l G o v e r n m e n t E x p e n d i t u r e s} \times 100$

Agriculture refers to COFOG category 042 (agriculture, forestry, fishing and hunting); and

$A g r i c u l t u r e v a l u e a d d e d S h a r e o f G D P$

$= \frac{A g r i c u l t u r e v a l u e a d d e d}{G D P} \times 100$

Agriculture refers to the Division A of ISIC Rev 4 (agriculture, forestry, fishing and hunting), equal to Division A+B of ISIC Rev 3.2.

4.d. Validation

Countries are asked to validate and update historical questionnaire data that pre-populates their questionnaire. FAO validates data against the historical series, as well as data submitted to IMF, regional organizations and from country's websites.

4.e. Adjustments

FAO revises data only when historical revisions or missing historical data are provided by countries, the IMF or regional organizations or when they become available through the national authorities’ websites. For example, prefilled questionnaires are sent out with reported data for t-2 through t-5, which countries are asked to review, revise where needed, and - to the extent possible – fill-in missing information. Conversion of values into millions is done as well.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Missing values of government expenditure in agriculture were forecasted using trends in GDP and 3 to 5 year moving averages of the share of agriculture in total expenditure. Forecasted values are employed to compute regional and global aggregates, but not presented at the national level.

At regional and global levels

Regional and global aggregates of were based on a mixture of data directly reported by countries (to FAO or IMF) and forecasts of missing values. For time series period, regional and global aggregates are computed on the basis of based on data as reported by countries and interpolations of missing values.

4.g. Regional aggregations

Global and regional estimates are compiled by first separately summing across countries the four individual components of the index: government expenditure on agriculture, total government expenditure, agriculture value-added, and GDP. These are added only for those countries in a region (or globally) for which all components are available, and the index is then calculated for this larger region.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries are requested to reference the IMF's Government Finance Statistics Manual (GFSM 2014), particularly Chapter 6 - Annex: Classification of the Functions of Government and Chapter 2 – Institutional Units and Sectors, available at https://www.imf.org/external/np/sta/gfsm.

4.i. Quality management

Comparisons of key aggregates reported in both the FAO GEA and IMF GFS questionnaires are periodically conducted in order to ensure consistency.

4.j. Quality assurance

The FAO Statistics Quality Assurance Framework is available at: http://www.fao.org/docrep/019/i3664e/i3664e.pdf

4.k. Quality assessment

The quality of the data may vary considerably among countries, as not all of them apply the COFOG classification. In such cases, FAO seeks to validate reported aggregates against fiscal data published by national authorities' websites. Since 2012, the FAO Statistics Division also fields a detailed annual questionnaire on Government Expenditure on Agriculture that is pre-populated with key major aggregates reported to the IMF or identified by FAO. Where reported details diverge significantly from the pre-populated aggregates, queries are sent to national counterparts, to ensure the methodological quality, objectivity and reliability of the data submitted by countries.

5. Data availability and disaggregation

Data availability:

Data are reported for the highest level of government available (Consolidated general government, consolidated central government or budgetary central government) and are available for about 100 countries on a regular basis. In some cases (for example, India and Pakistan), data may reflect the general government sector as per national norm. That is, budgetary central government combined with state government.

Time series:

From 2001 forward

Disaggregation:

Since this indicator is based on national accounts data and total government expenditures, it does not allow for disaggregation by demographic characteristics or geographic location. However, where countries report expenditure data for the consolidated general government and it subsectors, disaggregation by level of government is possible.

6. Comparability/deviation from international standards

Sources of discrepancies:

When in-country compilation errors are identified and FAO has modified government expenditure data reported by countries, or where errors are found in comparison with the IMF GFS COFOG data or fiscal data published on national authorities' websites after querying to national respondents, there may be some difference between data reported by FAO and unrevised national figures.

7. References and Documentation

URL:

www.fao.org

References:

FAOSTAT domain of Government Expenditure on Agriculture http://www.fao.org/faostat/en/#data/IG;
IMF Government Finance Statistics Manual 2014
https://www.imf.org/external/np/sta/gfsm/.

2.a.1 metadata ANNEX I: Highest Level of Government Available – last updated 01 March 2022

Latest year	M49 code	Area	Level of government	Latest year	M49 code	Area	Level of government
2017	4	Afghanistan	GG	2020	214	Dominican Republic	BA
2020	8	Albania	GG	2020	218	Ecuador	BA
2018	12	Algeria	BA	2020	818	Egypt	GG
2020	24	Angola	GG	2020	222	El Salvador	GG
2020	28	Antigua and Barbuda	GG	2020	226	Equatorial Guinea	BA
2020	32	Argentina	CG	2019	233	Estonia	GG
2020	51	Armenia	GG	2018	748	Eswatini	BA
2020	36	Australia	GG	2019	231	Ethiopia	BA
2019	40	Austria	GG	2020	242	Fiji	BA
2020	31	Azerbaijan	GG	2019	246	Finland	GG
2020	44	Bahamas	BA	2019	250	France	GG
2019	48	Bahrain	BA	2020	270	Gambia	BA
2016	50	Bangladesh	BA	2020	268	Georgia	GG
2005	52	Barbados	BA	2020	276	Germany	GG
2019	112	Belarus	GG	2019	288	Ghana	BA
2019	56	Belgium	GG	2019	300	Greece	GG
2020	84	Belize	CG	2020	308	Grenada	GG
2020	204	Benin	BA	2020	320	Guatemala	GG
2020	64	Bhutan	BA	2019	324	Guinea	BA
2014	68	Bolivia (Plurinational State of)	GG	2017	624	Guinea-Bissau	BA
2020	72	Botswana	GG	2020	328	Guyana	BA
2020	76	Brazil	GG	2020	340	Honduras	BA
2020	100	Bulgaria	GG	2019	348	Hungary	GG
2019	854	Burkina Faso	BA	2019	352	Iceland	GG
2019	108	Burundi	BA	2019	356	India	GG
2020	132	Cabo Verde	CG	2020	360	Indonesia	GG
2019	124	Canada	GG	2009	364	Iran (Islamic Republic of)	CG
2020	140	Central African Republic	BA	2019	372	Ireland	GG
2020	152	Chile	GG	2020	376	Israel	GG
2019	156	China	GG	2019	380	Italy	GG
2019	344	China, Hong Kong SAR	GG	2020	388	Jamaica	CG
2019	170	Colombia	GG	2019	392	Japan	GG
2018	178	Congo	BA	2019	400	Jordan	BA
2019	184	Cook Islands	GG	2019	398	Kazakhstan	GG
2020	188	Costa Rica	GG	2020	404	Kenya	BA
2019	384	Côte d'Ivoire	BA	2020	412	Kosovo (Serbia)	GG
2019	191	Croatia	GG	2020	414	Kuwait	GG
2019	192	Cuba	CG	2020	417	Kyrgyzstan	GG
2019	196	Cyprus	GG	2019	418	Lao PDR	GG
2020	203	Czechia	GG	2019	428	Latvia	GG
2020	180	Dem. Rep. of the Congo	BA	2020	422	Lebanon	BA
2020	208	Denmark	GG	2020	426	Lesotho	BA
2019	212	Dominica	CG	2020	430	Liberia	BA

Latest year	M49 code	Area	Level of government	Latest year	M49 code	Area	Level of government
2019	440	Lithuania	GG	2020	662	Saint Lucia	BA
2020	442	Luxembourg	GG	2020	670	Saint Vincent and the Grenadines	BA
2019	450	Madagascar	BA	2020	882	Samoa	BA
2019	454	Malawi	BA	2019	678	Sao Tome and Principe	BA
2020	458	Malaysia	BA	2019	682	Saudi Arabia	BA
2018	462	Maldives	CG	2020	686	Senegal	BA
2019	466	Mali	BA	2020	688	Serbia	GG
2019	470	Malta	GG	2020	690	Seychelles	GG
2018	584	Marshall Islands	BA	2020	694	Sierra Leone	BA
2019	478	Mauritania	BA	2020	702	Singapore	GG
2020	480	Mauritius	GG	2019	703	Slovakia	GG
2020	484	Mexico	CG	2019	705	Slovenia	GG
2019	583	Micronesia (Federated States of)	BA	2020	90	Solomon Islands	BA
2020	496	Mongolia	GG	2019	706	Somalia	CG
2015	499	Montenegro	BA	2019	710	South Africa	GG
2020	504	Morocco	BA	2020	728	South Sudan	GG
2020	508	Mozambique	BA	2020	724	Spain	GG
2019	104	Myanmar	GG	2019	144	Sri Lanka	BA
2020	516	Namibia	BA	2019	275	State of Palestine	CG
2020	524	Nepal	BA	2018	729	Sudan	CG
2020	528	Netherlands	GG	2020	740	Suriname	BA
2020	554	New Zealand	GG	2019	752	Sweden	GG
2020	558	Nicaragua	CG	2019	756	Switzerland	GG
2019	562	Niger	BA	2019	762	Tajikistan	GG
2019	566	Nigeria	BA	2019	764	Thailand	GG
2020	807	North Macedonia	GG	2019	626	Timor-Leste	BA
2020	578	Norway	GG	2018	768	Togo	GG
2019	512	Oman	GG	2020	780	Trinidad and Tobago	CG
2020	586	Pakistan	GG	2017	788	Tunisia	BA
2018	585	Palau	BA	2020	792	Turkey	GG
2018	591	Panama	BA	2019	800	Uganda	GG
2019	598	Papua New Guinea	BA	2020	804	Ukraine	GG
2020	600	Paraguay	GG	2020	784	United Arab Emirates	BA
2020	604	Peru	GG	2019	826	UK of Great Britain and Northern Ireland	GG
2020	608	Philippines	BA	2020	834	United Republic of Tanzania	BA
2019	616	Poland	GG	2020	840	United States of America	GG
2019	620	Portugal	GG	2020	858	Uruguay	CG
2005	634	Qatar	BA	2019	860	Uzbekistan	GG
2019	410	Republic of Korea	CG	2019	548	Vanuatu	BA
2020	498	Republic of Moldova	GG	2014	862	Venezuela (Bolivarian Republic of)	CG
2019	642	Romania	GG	2020	704	Viet Nam	GG
2020	643	Russian Federation	GG	2014	887	Yemen	GG
2020	646	Rwanda	GG	2020	894	Zambia	BA
2019	659	Saint Kitts and Nevis	CG	2020	716	Zimbabwe	BA

2.a.2

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

0.c. Indicator

Indicator 2.a.2: Total official flows (official development assistance plus other official flows) to the agriculture sector

0.e. Metadata update

2017-07-09

0.f. Related indicators

Other ODA indicators

0.g. International organisations(s) responsible for global monitoring

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definition:

Gross disbursements of total ODA and other official flows from all donors to the agriculture sector.

Concepts:

ODA: The DAC defines ODA as “those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are

provided by official agencies, including state and local governments, or by their executive agencies; and
each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and

is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)

Other official flows (OOF): Other official flows (excluding officially supported export credits) are defined as transactions by the official sector which do not meet the conditions for eligibility as ODA, either because they are not primarily aimed at development, or because they are not sufficiently concessional.

(See http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf, Para 24).

The agriculture sector is as defined by the DAC and comprises all CRS sector codes in the 311 series (see here: http://www.oecd.org/dac/stats/purposecodessectorclassification.htm)

3.a. Data sources

The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.b. Data collection method

3.c. Data collection calendar

Data are published on an annual basis in December for flows in the previous year.

Detailed 2015 flows will be published in December 2016.

3.d. Data release calendar

December 2016.

3.e. Data providers

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.f. Data compilers

OECD

4.a. Rationale

Total ODA and OOF flows to developing countries quantify the public effort (excluding export credits) that donors provide to developing countries for agriculture.

4.b. Comment and limitations

Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements.

4.c. Method of computation

The sum of ODA and OOF flows from all donors to developing countries in the agriculture sector.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Due to high quality of reporting, no estimates are produced for missing data.

At regional and global levels

Not applicable

4.g. Regional aggregations

Global and regional figures are based on the sum of ODA and OOF flows to the agriculture sector.

5. Data availability and disaggregation

Data availability:

On a recipient basis for all developing countries eligible for ODA.

Time series:

Data available since 1973 on an annual (calendar) basis

Disaggregation:

This indicator can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid (project agriculture sub-sector) etc.

6. Comparability/deviation from international standards

Sources of discrepancies:

DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.

7. References and Documentation

URL:

www.oecd.org/dac/stats

References:

See all links here: http://www.oecd.org/dac/stats/methodology.htm

2.b.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.b: Correct and prevent trade restrictions and distortions in world agricultural markets, including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in accordance with the mandate of the Doha Development Round

0.c. Indicator

Indicator 2.b.1: Agricultural export subsidies

0.e. Metadata update

2020-03-01

0.g. International organisations(s) responsible for global monitoring

The World Trade Organization (WTO)

1.a. Organisation

The World Trade Organization (WTO)

2.a. Definition and concepts

Definition:

Agricultural export subsidies are defined as export subsidies budgetary outlays and quantities as notified by WTO Members in Tables ES:1 and supporting Tables ES:2 (following templates in document G/AG/2 dated 30 June 1995).

Data cover:

• Notifications by WTO Members with export subsidy reduction commitments included in part IV of their Schedules;

• Notifications of export subsidies by developing country Members pursuant to the provisions of article 9.4 of the Agreement on Agriculture.

Other WTO Members are not entitled to use export subsidies and their notifications are therefore not recorded in the indicator series.

Budgetary outlays and quantities are expressed in a currency (national or other) and in quantity units as per Member's notification practices. For Members with export subsidy reduction commitments included in part IV of their Schedules, the currency used in the notifications is similar to the one used in the Schedules.

Data are available by country and by products or groups of products, according to Members' schedules for Members with export subsidy reduction commitments included in part IV of their Schedules and according to Member's notification practices in the case of developing country Members using export subsidies under the provisions of article 9.4 of the Agreement on Agriculture."

3.a. Data sources

The sources of data are WTO Members' notifications in their Table ES:1 and supporting table ES:2 notifications, pursuant to the notification requirements and formats adopted by the WTO Committee on Agriculture and contained in document G/AG/2.

3.b. Data collection method

Not relevant. Cf. previous replies

3.c. Data collection calendar

Data are collected on a regular basis, following the timing of WTO Members' notification submissions.

3.d. Data release calendar

Cf. above

3.e. Data providers

WTO Members

The WTO is receiving WTO Members notifications and compiling the information contained in these notifications to report on this indicator.

4.a. Rationale

The purpose of this indicator is to give detailed information on the level of export subsidies applied annually per product or group of products, as notified by WTO Members.

4.b. Comment and limitations

The quality of the indicator depends on WTO Members' timeliness and accuracy of their notifications.

4.c. Method of computation

The country level data come directly from Members' notifications to the WTO and are not subject to any computation by the WTO. Each WTO Member collects data following his own national practice to prepare his notification.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Values are missing when a WTO Member has not submitted their notification. Missing values cannot be estimated.

At regional and global levels

Not relevant.

4.g. Regional aggregations

The WTO does not calculate regional aggregates.

An overall global indicator measuring the total annual applied export subsidies budgetary outlays is calculated by summing all the available data after having converted them into a single currency (US$).

5. Data availability and disaggregation

Data availability:

Cf. latest revision of WTO document series G/AG/GEN/86 (table under section 2.4 – Members with shaded cells) for a detailed description of data availability for export subsidies notified by Members with export subsidy reduction commitments.

In addition, 10 developing country Members notified since 1995 the use of export subsidies, pursuant to the provisions of article 9.4 of the Agreement on Agriculture.

Contrary to the information for developed country Members with export subsidy reduction commitments that is available for all notified years, information for developing country Members using export subsidies, pursuant to the provisions of article 9.4 of the Agreement on Agriculture is available only for the years during which these export subsidies were used.

Time series:

Since 1995

Disaggregation:

The indicator gives country and product based information on the level of applied export subsidies, both in terms of budgetary outlays and quantities.

6. Comparability/deviation from international standards

Sources of discrepancies:

The WTO does not estimate data. Only data contained in WTO Members' notifications are used. Therefore, there is no difference between country produced data and data available at the WTO.

7. References and Documentation

URL:

www.wto.org

References:

http://agims.wto.org/Pages/ES/ESSearchAnalyse.aspx?ReportId=1403&Reset=True

https://www.wto.org/english/tratop_e/agric_e/transparency_toolkit_e.htm

2.c.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatility

0.c. Indicator

Indicator 2.c.1: Indicator of food price anomalies

0.d. Series

Indicator of Food Price Anomalies (IFPA), by type of product (AG_FPA_COMM)

Indicator of Food Price Anomalies (IFPA), by Consumer Food Price Index (AG_FPA_CFPI)

Proportion of countries recording abnormally high or moderately high food prices, according to the Indicator of Food Price Anomalies (%) (AG_FPA_HMFP)

0.e. Metadata update

2023-05-15

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The indicator of food price anomalies (IFPA) identifies market prices that are abnormally high. The IFPA relies on a weighted compound growth rate that accounts for both within year and across year price growth. The indicator directly evaluates growth in prices over a particular month over many years, taking into account seasonality in agricultural markets and inflation, allowing to answer the question of whether or not a change in price is abnormal for any particular period.

Concepts:

The indicator of price anomalies (IFPA) relies on two compound growth rates (CGR’s), a quarterly compound growth rate (CQGR) and an annual compound growth rate (CAGR). A CGR is a geometric mean^[1] that assumes that a random variable grows at a steady rate, compounded over a specific period of time. Because it assumes a steady rate of growth the CGR smoothes the effect of volatility of price changes. The CGR is the growth in any random variable from time period $t_{A}$ to $t_{B}$ , raised to the power of one over the length of the period of time being considered.

${C X G R}_{t} = {(\frac{P_{t_{B}}}{P_{t_{A}}})}^{\frac{1}{t_{B} - t_{A}}} - 1$ (1)

where:

${C X G R}_{t}$ is the quarterly or annual compound growth rate in month t

$P_{t_{A}}$ is the price at the beginning of the period

$P_{t_{B}}$ is the price at the end of the period,

$t_{B} - t_{A}$ is the time in months between periods $A$ and $B$ .

A geometric mean is a type of average, which indicates the typical value of a set of numbers by using the product of their values as opposed to the arithmetic mean which relies on their sum (Wikipedia, 2017) ↑

2.b. Unit of measure

Index and Percent.

2.c. Classifications

Not applicable

3.a. Data sources

FAO relies on official domestic price data that it compiles in the Food Price Monitoring and Analysis (FPMA) tool to calculate and monitor the indicator. Five cereal products will be monitored: maize & maize products, wheat & wheat flour, rice, sorghum and millet. While diets across the world have become more diversified with increasing incomes, cereals still account for 45 percent of a person’s daily caloric intake, making this commodity group the most important in terms of its contribution to caloric intake, particularly for low-income populations (FAOSTAT, 2017). For the purpose of a more comprehensive coverage at the global level, FAO also calculates IFPA on countries’ officially reported food price indices as reported in FAOSTAT, which facilitates cross country comparisons as it uses a national level food basket covering all the most important commodities consumed. While the basket differs from country to country, this approach is more reflective of national and global trends as countries have predefined the commodities that have the most impact on local consumers. This approach also facilitates the implementation of the indicator as countries will not be asked to create a new index or modify existing methodologies.

For the Food CPI, the FAOSTAT monthly CPI & Food CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its subcomponents originates from the International Monetary Fund (IMF), and the UN Statistics Division (UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), the European statistics (EUROSTAT), the Latin America and the Caribbean statistics (CEPALSTAT), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI. The FAO CPI dataset for all items (or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. It further contains regional and global food CPIs compiled by FAO using population weights to aggregate across countries.

3.b. Data collection method

Food commodity prices are collected from webpages, newsletters or emails from national agencies responsible for collecting and disseminating food prices. Food Price Indices are collected from FAOSTAT (please refer to the 3.a. Data sources).

3.c. Data collection calendar

Food commodity prices in the Food Price Monitoring and Analysis (FPMA) tool are updated monthly. Food Price Indices in FAOSTAT are updated quarterly.

3.d. Data release calendar

During the second quarter of each year

3.e. Data providers

The sources of the price information are numerous and are listed for each price series in the FPMA tool at https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home.

For the Food Price Indices, the source is FAOSTAT http://www.fao.org/faostat/en/#data/CP.

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture http://www.fao.org/3/K8024E/K8024E.pdf.

4.a. Rationale

The thresholds for the ${I F P A}_{y}$ are expressed as the normalized difference of the compound growth rate of prices from their historical mean for the predefined period of time. And three ranges are established: 1) a less than half a standard deviation difference from the mean is considered normal; 2) a difference that is half but less than one standard deviation is considered moderately high; 3) a difference from the historical mean that is at least one standard deviation greater than the mean is considered abnormally high.

$\begin{matrix} 0.5 \leq {I F P A}_{y} < 1 M o d e r a t e l y H i g h \\ {I F P A}_{y} \geq 1 A b n o r m a l l y H i g h \\ - 0.5 \leq {I F P A}_{y} < 0.5 N o r m a l \end{matrix}$

We use one standard deviation as the relevant threshold since we want to minimize the probability of missing a significant market event. Events that deviate by more than one standard deviation from their historical distribution have a low probability of occurring and thus are easier to identify as abnormally high prices.

4.b. Comment and limitations

It is appropriate to caution the reader that the indicator is just a guide to understanding market dynamics. As such, one cannot rely on it as the sole element to determine whether a food price in a particular market at a given time is abnormally high or low due to the direct effects of local policies. Results must be weighed with other available information on market fundamentals, macroeconomic context and external shocks. The main challenge in implementing the indicator is data availability and data quality. The calculation of the indicator requires an uninterrupted monthly price series (i.e. if more than 3 consecutive months of data are missing the series may be dropped) of at least 5 years, which include the year being analysed and the 4 preceding years to generate averages and standard deviations. Finally, the indicator is calculated on real price terms to net out the effects of inflation and compare prices in constant money terms over time. However, if food items’ contribution to CPI is high, it induces downward bias in food real price – i.e., it underestimates the extent of the price increase (nominal prices or a non-food CPI could be used).

4.c. Method of computation

Mathematically the IFPA for a particular year $y$ in month $t$ is calculated as the weighted sum of the quarterly indicator of food price anomalies ${(Q I F P A}_{y t}^{}$ ), and the annual indicator of food price anomalies ${(A I F P A}_{y t}^{})$ .

$(\frac{{C X G R}_{y t} - {\bar{W_C X G R}}_{t}}{{\hat{σ}}_{{W_C X G R}_{t}}}) = {X I F P A}_{y t}^{}$ (2)

Where:

${C X G R}_{y t}$ is either the quarterly or annual compound growth rate in month t for year y

${\bar{W_C X G R}}_{t}$ is the weighted average of either the quarterly or annual compound growth rate for month t across years y

${\hat{σ}}_{{W_C X G R}_{t}}$ is the weighted standard deviation of either the quarterly or annual compound growth rate for month t over years y,

${X I F P A}_{y t}^{}$ is either the quarterly or annual indicator of a price anomaly in month t for year y.

Then IFPA is defined as:

${I F P A}_{y t} = γ {Q I F P A}_{y t}^{} + (1 - γ) {A I F P A}_{y t}^{}$ (3)

Where:

${I F P A}_{y t}$ is the indicator of food price anomalies in year $y$ and month $t$

${Q I F P A}_{y t}^{}$ is the quarterly indicator of food price anomalies in year $y$ and month $t$

${A I F P A}_{y t}^{}$ is the annual indicator of food price anomalies in year $y$ and month $t$

$γ$ is a weight with a value of 0.4.

The weight $γ$ establishes the relative importance of quarterly ( ${Q I F P A}_{y t}^{}$ ) anomalies to the year-on-year price variations ( ${A I F P A}_{y t}^{})$ . The weight $γ$ is set to 0.4, giving a weight of 0.6-- $(1 - γ)$ -- to abnormal price growth from year-to-year. This is done to better capture the price level relative to its seasonal trends, which is measured to the price level a year earlier. SDG indicator 2.c.1 is then calculated as the arithmetic mean over $t$ months of the ${I F P A}_{y t}$ as follows:

${I F P A}_{y} = \frac{1}{t} \sum_{i = 1}^{t} {I F P A}_{y t}$ (4)

Where:

${I F P A}_{y}$ is the annual indicator of food price anomalies in year $y$

${I F P A}_{y t}$ is the indicator of food price anomalies in year $y$ and month $t$

$t$ is the number of months in a year

4.d. Validation

Not applicable

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For the domestic food commodity prices, the data is republished data harvested from national governmental organizations without imputation of missing values. For the purpose of the indicator, if more than 3 consecutive months of data are missing or if less than 5 years are available the series may be dropped from monitoring.

For the food price index in FAOSTAT, the data is republished data harvested from other international organizations without imputation of missing values. For the purpose of the indicator, if more than 3 consecutive months of data are missing or if less than 5 years are available the series may be dropped from monitoring.

At regional and global levels

Not Applicable

4.g. Regional aggregations

Consumer Food Price Index: Results are organized on a regional basis but IFPA values are not aggregated as such. The unit of the indicator provided for each region represents instead the proportion of countries recording abnormally high or moderately high food prices in each region.

Five key commodities (maize, rice, wheat, sorghum, millet): Results are not organized on a regional basis but at country level. This is because the commodities and food baskets monitored across countries are not sufficiently homogenous to aggregate into one price index. However, if a majority of countries within a region presents abnormally high prices, either for a particular commodity or the food price index, this region is qualified as a region suffering from high prices.

Sources of discrepancies:

FAO relies on the Food Price Indices as reported in FAOSTAT as well as on available official domestic food price data that it compiles in the Food Price Monitoring and Analysis (FPMA) tool to calculate the indicator. The FPMA database brings together price series for main food commodities (mainly cereal products) in selected markets in countries around the world. As a result, the indicator estimated by FAO can differ from the indicator estimated at country level, as it may be calculated on prices for a different market or commodity.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

An interactive e-learning course is available on SDG Indicator 2.c.1 – Food price anomalies to complement countries’ efforts in monitoring the 2030 Agenda and broaden the subject’s understanding. The course covers basic concepts related to market functioning, prices determination and price volatility and explains how to calculate the indicator and use the online Food Price Monitoring and Analysis (FPMA) tool to interpret indicator results, at national and international level. Besides in English, the online version of this course is also available in Russian, French and Spanish.

4.i. Quality management

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: http://www.fao.org/docrep/019/i3664e/i3664e.pdf, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO’s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).

4.j. Quality assurance

The indicator is calculated on food price data, which is gathered from official sources, same as for the food price index published in FAOSTAT. To ensure the correct calculation of the indicator, the process for the calculation of the indicator relies on an automated system.
On request, countries are supported by FAO to implement the indicator and interpret the results. In addition, training is provided in the country upon request.

4.k. Quality assessment

The responsible officer conducts a self-assessment of the calculation process and its outputs on the basis of the FAO Statistics Quality Assurance Framework (SQAF). The SQAF considers the following principles: relevance, accuracy and reliability, timelessness and punctuality, coherence and comparability, and accessibility and clarity.

5. Data availability and disaggregation

Data availability:

IFPA on commodity prices is available for about two fifths of countries, while IFPA on Food CPI is available for almost all countries.

Time series:

IFPA on commodity prices is available annually from 2015, while IFPA on Food CPI is available annually since 2010.

Disaggregation:

Type of product, level of price anomaly.

6. Comparability/deviation from international standards

7. References and Documentation

URL:

http://www.fao.org/giews/food-prices/research/en/

References:

https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home

2.1.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round

0.c. Indicator

Indicator 2.1.1: Prevalence of undernourishment

0.d. Series

Primary series: Prevalence of undernourishment (SN_ITK_DEFC)

Complementary series: Number of undernourished people (SN_ITK_DEFCN)

0.e. Metadata update

2023-05-15

0.f. Related indicators

2.1.2, 2.2.1, 2.2.2, 2.2.3

Comments:

Links with Target 2.2, to the extent that hunger may lead to malnutrition, and Target 2.2 may not be achieved if Target 2.1 is not achieved.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The prevalence of undernourishment (PoU) (French: pourcentage de sous-alimentation; Spanish: porcentaje de sub-alimentación; Italian: prevalenza di sotto-alimentazione) is an estimate of the proportion of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. It is expressed as a percentage.

Concepts:

Undernourishment is defined as the condition by which a person has access, on a regular basis, to the amount of food that are insufficient to provide the energy required for conducting a normal, healthy and active life, given his or her own dietary energy requirements.

Though strictly related, “undernourishment” as defined here is different from the physical conditions of “malnutrition” and “undernutrition” as it refers to the condition of insufficient intake of food, rather than to the outcome in terms of nutritional status. In French, Spanish and Italian the difference is marked by the use of the terms alimentation, alimentación, or alimentazione, instead of nutrition, nutrición or nutrizione, in the name of the indicator. A more appropriate expression in English that would render the precise meaning of the indicator might have been “prevalence of under-feeding” but by now the term “undernourishment” has long been associated with the indicator.

While the undernourishment condition applies to individuals, due to conceptual and data-related considerations, the indicator can only be referred to a population, or group of individuals. The prevalence of undernourishment is thus an estimate of the percentage of individuals in a group that are in that condition, but it does not allow for the identification of which individuals in the group are, in fact, undernourished.

2.b. Unit of measure

Prevalence of undernourishment: Percent (%) Number of undernourished people: Millions (of people)

2.c. Classifications

The construction of the regional and global estimates, as well as estimates for specific groups, such as Least Developed Countries, Land Locked Developing countries, Small Island Developing States, Developed Regions, and Developing Regions, of this indicator follows the UN M49 Standard.

3.a. Data sources

The ideal source of data to estimate the PoU would be a carefully designed and skillfully conducted individual dietary intake survey, in which actual daily food consumption, together with heights and weights for each surveyed individual, are repeatedly measured on a sample that is representative of the target population. Due to their cost, however, such surveys are rare.

In principle, a well-designed household survey that collects information on food acquisitions might be sufficient to inform a reliable estimate of the Prevalence of Undernourishment (PoU) in a population, at a reasonable cost and with the necessary periodicity to inform the SDG monitoring process, provided that:

All sources of food consumption for all members of the households are properly accounted for, including, in particular, food that is consumed away from home;
Sufficient information is available to convert the data on food consumption or on food expenditures into their contribution to dietary energy intake;
The proper methods to compute the PoU are used, to control for excess variability in the estimated levels of habitual food consumption across households, allowing for the presence on normal variability in the distribution of food consumption across individuals, induced by the differences in energy requirements of the members of the population.

Examples of surveys that could be considered for this purpose include surveys conducted to compute economic statistics and conduct poverty assessments, such as Household Income and Expenditure Surveys, Household Budget Surveys and Living Standard Measurement Surveys.

In practice, however, it is often impossible, and not advisable, to rely only on data collected through a household survey, as the information needed to estimate the four parameters of the PoU model is either missing or imprecise.

Household Survey food consumption data often must be integrated by

a) Data on the demographic structure of the population of interest by sex and age;

b) Data or information on the median height of individuals in each sex and age class;

c) Data on the distribution of physical activity levels in the population;

d) Alternative data on the total amounts of food available for human consumption, to correct for biases in the estimate of the national average daily dietary energy consumption in the population.

Data for a), b) and c) could be available through the same multipurpose survey that provides food consumption data, but are more likely available from other sources, such as National Demographic and Health Surveys (for a) and b)) and Time Use Surveys (for c)).

Correcting for bias in the estimated average daily dietary energy consumption might need to be based on alternative sources on food consumption, such as aggregate food supply and utilization accounts and food balance sheets.

To inform its estimate of PoU at national, regional and global level, in addition to all household surveys for which it is possible to obtain micro data on food consumption, FAO relies on:

a) UN Population Division’s World Population Prospects (https://esa.un.org/unpd/wpp/Download/Standard/Population/), which provide updated estimates of the structures of the national population by sex and age every two years for most countries in the world;

b) FAO Food Balance Sheets (http://www.fao.org/faostat/en/#data), which provides updated estimates of the national availability of food every year for most countries in the world.

Micro data from household surveys that collect food consumption data are sourced by FAO directly through the National Statistical Agencies’ websites, or through specific bilateral agreements.

3.b. Data collection method

Official information on food commodity production, trade and utilization used by FAO to compile Food Balance Sheets is provided mainly by Statistical Units of the Ministry of Agriculture. FAO sends out a data collection questionnaire every year to an identified focal point.

Microdata of household surveys are generally owned and provided by National Statistical Agencies. When available, data is sourced by FAO directly through the NSA’ website. In several cases, when microdata is not available in the public domain, bilateral agreements have been signed, usually in the contexts of technical assistance and capacity development programs.

Data on the population size and structure for all monitored countries is obtained from the UN Population Division’s World Population Prospects.

3.c. Data collection calendar

Continuing

3.d. Data release calendar

Data are released each year alongside the State of Food Security and Nutrition in the World report, usually in mid-July.

3.e. Data providers

Given the various data sources, national data providers vary. Official information on food commodity production, trade and utilization used by FAO to compile Food Balance Sheets is provided mainly by Statistical Units of the Ministry of Agriculture. Microdata of household surveys are generally owned and provided by National Statistical Agencies.

3.f. Data compilers

Food and Agriculture Organization of the United Nations, Statistics Division, Food Security and Nutrition Statistics Team

3.g. Institutional mandate

The Office of the Chief Statistician of FAO manages the Interdepartmental Working Group on SDG indicators under the FAO custodianship and identifies a focal point for each of them. The team leader of the Food Security and Nutrition Statistics Team of the Statistics Division is formally appointed as the focal person for the collection, processing, and dissemination of statistics for this indicator.

4.a. Rationale

The indicator has been used by FAO to monitor the World Food Summit Target and the MDG Target 1C, at national, regional and global level, since 1999. It allows monitoring trends in the extent of dietary energy inadequacy in a population over time, generated as a result of the combination of changes in the overall availability of food, in the households’ ability to access it, and in the socio-demographic characteristics of the population, as well as differences across countries and regions in any given moment in time.

The parametric approach adopted by FAO allows obtaining reliable estimated for relatively large population groups. As it reflects a severe condition of lack of food, it is fully consistent with the spirit of a Goal that aims at reducing hunger.

4.b. Comment and limitations

Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements.

Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual.

The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger.

Further specific consideration

1. Feasibility

Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country’s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country’s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division’s World Population Prospects data.

2. Reliability

Reliability mostly depends on the quality of the data used to inform the estimation of the model’s parameters.

DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted.

DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero & Del Grossi, forthcoming.)

DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year.

To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years.

Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake.

3. Comparability

If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data.

4. Limitations

Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger.

If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level."

4.c. Method of computation

To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).

The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population’s representative average individual) as in the formula below:

$P o U = \int_{x < M D E R}^{ } f (x | θ) d x$

where θ is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV).

A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER.

Different data sources can be used to estimate the different parameters of the model.

DEC

Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO’s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (https://www.fao.org/faostat/en/#data/FBS).

When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.

When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.

Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.

In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category ‘Active or moderately active lifestyle’.

The total CV is then obtained as the geometric mean of the CV|y and the CV|r:

$C V = \sqrt{({C V | y}^{2}) + ({C V | r}^{2})}$

Challenges and limitations: While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.

Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.

MDER

Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a range of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.

Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.

Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.

4.d. Validation

There are no formal country consultations. Data validation is internal to FAO. This indicator has been in existence since 1999. FAO has produced it to inform the World Food Summit target and the MDG target 1.C without country consultations. Upon request, FAO has provided countries with details on the data used in their specific case.

4.e. Adjustments

None

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

When no data on food consumption is available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of DEC from Food Balance Sheets, an indirect estimate of CV based on information on the country’s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country’s Under 5 Mortality Rate, and an estimate of the MDER based on the UN Population Division’s World Population Prospects data.

See the section on method of computation for details.

• At regional and global levels

Missing values for individual countries are implicitly imputed to be equal to the population weighted average of the estimated values of the countries present in the same subregion or region.

4.g. Regional aggregations

Regional and global aggregates of the PoU are computed as:

${P o U}_{R E G} = \frac{\sum_{i} {P o U}_{i} \times N_{i}}{\sum_{i} N_{i}}$

where PoU_i are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N_i the corresponding population size.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The main three sources of data at national level are:

a) Official reports on the production, trade and utilization of the major food crop and livestock productions.

b) Household survey data on food consumption

c) Demographic characteristics of the national population

Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions.

The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to ‘mirror’ trade statistics to cross-check quantities and values).

Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) – to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption.

These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated).

The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool.

FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps.

The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based ‘shiny’ application).

Detail on FBS methodology: http://www.fao.org/economic/ess/fbs/ess-fbs02/en/.

The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level.

Some FBS background text also available on FAOSTAT: http://www.fao.org/faostat/en/#data/FBS.

4.i. Quality management

ESS conducts trend analysis of the newly updated indicator with other relevant indicators. Meanwhile, preliminary estimates of each round of the update are circulated among regional offices for review. Because of their knowledge of their regions and countries, they often provide invaluable inputs to the revisions and finalization of the update.

4.j. Quality assurance

FBS capacity development programme in cooperation with the Global Strategy (more details may be provided if required); capacity development in cooperation with the ESS Food Security team as a PoU/FBS package (financed by projects); and direct FBS capacity development based on specific direct country requests.

4.k. Quality assessment

High

5. Data availability and disaggregation

Data availability:

Since 2017 FAO has reported separate estimates of PoU for 160 countries.

While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates.

Time series:

2000 - current

Disaggregation:

Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only.

In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model’s parameters for that specific group, that is, if data on the group’s food consumption levels, age/gender structure and – possibly – physical activity levels, exist.

The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).

6. Comparability/deviation from international standards

Sources of discrepancies:

Many countries have produced and reported on estimates of the Prevalence of Undernourishment, including in their national MDG Reports, but almost invariably using a different methodology than the one developed by FAO, which makes national figures not comparable to those reported by FAO for global monitoring.

The most common approach used in preparing national reports has been to calculate the percentage of households for which the average per capita daily dietary energy consumption is found to be below thresholds based on daily Recommended Dietary Intake, usually set at 2,100 kcal, based on household survey data. In some cases, also lower thresholds of around 1,400 kcal have been used, probably as a reaction to the fact that percentages of households reporting average daily consumption of less than 2,100 kcal per capita were implausibly high estimates of the prevalence of undernourishment.

Almost without exception, no consideration related to the presence of excess variability in the dietary energy consumption data is made, and the reports reveal limited or no progress in the reduction of PoU over time.

As discussed in the section on the method of computation, the results obtained through these alternative methods are highly unreliable and almost certainly biased toward overestimation. It is therefore advisable that a concerted effort is made to advocate for use of the FAO methods also in preparation of national reports. FAO stands ready to provide all necessary technical support.

7. References and Documentation

URL:

https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/

References:

http://www.fao.org/docrep/012/w0931e/w0931e16.pdf

http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06

http://www.fao.org/3/a-i4060e.pdf

http://www.fao.org/3/a-i4046e.pdf

2.1.2

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round

0.c. Indicator

Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)

0.d. Series

Prevalence of moderate or severe food insecurity in the adult population (%) (AG_PRD_FIESMS)

Prevalence of severe food insecurity in the adult population (%) (AG_PRD_FIESS)

Total population in moderate or severe food insecurity (thousands of people) (AG_PRD_FIESMSN)

Total population in severe food insecurity (thousands of people) (AG_PRD_FIESSN)

0.e. Metadata update

2023-05-15

0.f. Related indicators

2.1.1, 2.2.1, 2.2.2, 2.2.3

Comments:

Links with Target 2.2, to the extent that hunger may lead to malnutrition, and Target 2.2 may not be achieved if Target 2.1 is not achieved.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organisation of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organisation of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The indicator measures the percentage of individuals in the population who have experienced food insecurity at moderate or severe levels during the reference period. The severity of food insecurity, defined as a latent trait, is measured on the Food Insecurity Experience Scale global reference scale, a measurement standard established by FAO through the application of the Food Insecurity Experience Scale in more than 140 countries worldwide, starting in 2014.

Concepts:

Extensive research over more than 25 years has demonstrated that the inability to access food results in a series of experiences and conditions that are fairly common across cultures and socio-economic contexts and that range from being concerned about the ability to obtain enough food, to the need to compromise on the quality or the diversity of food consumed, to being forced to reduce the intake of food by cutting portion sizes or skipping meals, up to the extreme condition of feeling hungry and not having means to access any food for a whole day. Typical conditions like these form the basis of an experience-based food insecurity measurement scale. When analysed through sound statistical methods rooted in Item Response Theory, data collected through such scales provide the basis to compute theoretically consistent, cross country comparable measures of the prevalence of food insecurity. The severity of the food insecurity condition as measured by this indicator thus directly reflects the extent of households’ or individuals’ inability to regularly access the food they need.

2.b. Unit of measure

Prevalence of food insecurity: Percent (%)

Number of food insecure people: Millions (of people)

2.c. Classifications

3.a. Data sources

Data can be collected using the Food Insecurity Experience Scale survey module (FIES-SM) developed by FAO, or any other experience-based food security scale questionnaires, including:

the Household Food Security Survey Module (HFSSM) developed by the Economic Research Service of the US Department of Agriculture, and used in the US and Canada,
the Latin American and Caribbean Food Security Scale (or Escala Latinoamericana y Caribeña de Seguridad Alimentaria – ELCSA), used in Guatemala and tested in several other Spanish speaking countries in Latin America,
the Mexican Food Security Scale (or Escala Mexicana de Seguridad Alimentaria, - EMSA), an adaptation of the ELCSA used in Mexico,
the Brazilian Food Insecurity Scale (Escala Brasileira de medida de la Insegurança Alimentar – EBIA) used in Brazil, or
the Household Food Insecurity Access Scale (HFIAS),

or any adaptation of the above that can be calibrated against the global FIES.

Two versions of the FIES-SM are available for use in surveys of individuals or households respectively, and the difference stands in whether respondents are asked to report only on their individual experiences, or also on that of other member of the household.

The current FIES-SM module include eight questions as in the table below.

GLOBAL FOOD INSECURITY EXPERIENCE SCALE
Now I would like to ask you some questions about food.
Q1. During the last 12 MONTHS, was there a time when you (or any other adult in the household) were worried you would not have enough food to eat because of a lack of money or other resources?	0 No 1 Yes 98 Don’t Know 99 Refused
Q2. Still thinking about the last 12 MONTHS, was there a time when you (or any other adult in the household) were unable to eat healthy and nutritious food because of a lack of money or other resources?	0 No 1 Yes 98 Don’t Know 99 Refused
Q3. And was there a time when you (or any other adult in the household) ate only a few kinds of foods because of a lack of money or other resources?	0 No 1 Yes 98 Don’t Know 99 Refused
Q4. Was there a time when you (or any other adult in the household) had to skip a meal because there was not enough money or other resources to get food?	0 No 1 Yes 98 Don’t Know 99 Refused
Q5. Still thinking about the last 12 MONTHS, was there a time when you (or any other adult in the household) ate less than you thought you should because of a lack of money or other resources?	0 No 1 Yes 98 Don’t Know 99 Refused
Q6. And was there a time when your household ran out of food because of a lack of money or other resources?	0 No 1 Yes 98 Don’t Know 99 Refused
Q7. Was there a time when you (or any other adult in the household) were hungry but did not eat because there was not enough money or other resources for food?	0 No 1 Yes 98 Don’t Know 99 Refused
Q8. Finally, was there a time when you (or any other adult in the household) went without eating for a whole day because of a lack of money or other resources?	0 No 1 Yes 98 Don’t Know 99 Refused

The questions should be adapted and administered in the respondents’ preferred language and enumerators instructed to make sure that respondents recognize the reference period and the qualifier according to which experiences should be reported only when due to “lack of money or other resources” and not, for example, for reasons related to health or other cultural habits (such as fasting for religious credos).

The FIES-SM can be included in virtually any telephone-based or personal interview-based survey of the population, though face to face interview is preferred.

Since 2014, the individual referenced FIES-SM is applied to nationally representative samples of the population aged 15 or more in all countries covered by the Gallup World Poll (more than 140 countries every year, covering 90% of the world population). In most countries, samples include about 1000 individuals (with larger samples of 3000 individuals in India and 5000 in mainland China).

Additionally to the GWP, in 2020 FAO collected data in 20 countries through Geopoll® with the specific objective of assessing food insecurity during the COVID-19 pandemic. The countries covered were: Afghanistan, Burkina Faso, Cameroon, Central African Republic, Chad, Democratic Republic of the Congo, El Salvador, Ethiopia, Guatemala, Haiti, Iraq, Liberia, Mozambique, Myanmar, Niger, Nigeria, Sierra Leone, Somalia, South Africa and Zimbabwe. For all these countries, the 2020 assessment was based on Geopoll data.

Other national surveys exist that already collect FIES compatible data.

For Afghanistan, Angola, Armenia, Botswana, Burkina Faso, Cabo Verde, Canada, Chile, Costa Rica, Ecuador, Fiji, Ghana, Greece, Grenada, Honduras, Indonesia, Israel, Kazakhstan, Kenya, Kiribati, Kyrgyzstan, Lesotho, Malawi, Mauritania, Mexico, Morocco, Namibia, Niger, Nigeria, Palestine, Philippines, Republic of Korea, Russian Federation, Saint Lucia, Samoa, Senegal, Seychelles, Sierra Leone, South Sudan, Sudan, Tonga, Uganda, United Republic of Tanzania, United States of America, Vanuatu, Viet Nam and Zambia, national government survey data were used to calculate the prevalence estimates of food insecurity by applying FAO’s statistical methods to adjust national results to the same global reference standard, covering approximately a quarter of the world population. Countries are considered for the year/years when national data are available, informing the regional and subregional aggregates assuming a constant trend in the period 2014–2020, or integrating the remaining years with GWP or Geopoll data in case they were compatible. Exceptions to this rule are: Armenia, Botswana, Burkina Faso, Chile, Costa Rica, Ecuador, Ghana, Honduras, Indonesia, Israel, Malawi, Namibia, Niger, Nigeria, Sierra Leone, Uganda and Zambia. In these cases, the following procedure was followed:

Use national data collected in one year to inform the corresponding year.
For the remaining years, apply the smoothed trend coming from the data collected by FAO through the Gallup© World Poll to the national data to describe evolution over time. Smoothed trend is computed by taking the mean of the rates of change between consecutive three-year averages.

The motivation behind this procedure was the strong evidence found in support of the trend suggested by data collected by FAO (for instance, evolution of poverty, extreme poverty, employment, food inflation, among others), allowing to provide a more updated description of the trend in the period 2014–2020.

In Indonesia, Kazakhstan, Kyrgyzstan, Mauritania, Nicaragua, Paraguay, Rwanda, Seychelles, Sudan and United Republic of Tanzania, due to lack of data in 2020, the corresponding subregional trend between 2019 and 2020 was used to inform 2020.

Obtaining internationally comparable data for global monitoring:

To ensure comparability of the FImod+sev and FIsev indicators computed for different populations, universal thresholds are defined on the FIES global reference scale and converted into corresponding values on the “local” scales obtained as a result of application of the Rasch model on any specific population, through a process of “equating”.

Equating is a form of standardization of the metric based on identification of the subset of items that can be considered common to the global FIES and the specific scale used for measurement in each context. The severity levels associated with the common items are used as anchoring points to adjust the global FIES thresholds to the local scales. The standardization process ensures that the mean and standard deviation of the set of common items is the same when measured on the global FIES or on the national scale. Compatibility with the global FIES and the possibility to compile this indicator requires that at least four of the eight FIES items are identified as common.

The Statistics Division at FAO has developed the RM.weights package under R, which provides routines for estimating the parameters of the Rasch model using conditional maximum likelihood, with the possibility to allow for the complex survey design.

3.b. Data collection method

Face-to-face and telephone interviews within national surveys.

3.c. Data collection calendar

Continuing

3.d. Data release calendar

Data are released each year alongside the State of Food Security and Nutrition in the World report, usually in mid-July.

3.e. Data providers

National data providers will be the National Statistical Authorities that are responsible for the survey in which the FIES or similar scale is included. FAO will provide data for countries where the FIES or compatible module is not included in any national survey.

3.f. Data compilers

Organization(s) responsible for compilation and reporting on this indicator at the global level: Food and Agriculture Organization of the United Nations, Statistics Division, Food Security and Nutrition Statistics Team.

3.g. Institutional mandate

The Office of the Chief Statistician of FAO manages the Interdepartmental Working Group on SDG indicators under the FAO custodianship, and identifies a focal point for each of them. The team leader of the Food Security and Nutrition Statistics Team of the Statistics Division is formally appointed as the focal person for the collection, processing, and dissemination of statistics for this indicator.

4.a. Rationale

Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger.

Short questionnaires like the FIES are very easy to administer at limited cost, which is one of the main advantages of their use. The ability to precisely determine the food insecurity status of specific individuals or households, however, is limited by the small number of questions, a reason why assignment of individual respondents to food insecurity classes is best done in probability terms, thus ensuring that estimates of prevalence rates in a population are sufficiently reliable even when based on relatively small sample sizes.

As with any statistical assessment, reliability and precision crucially depend on the quality of the survey design and implementation. One major advantage of the analytic treatment of the data through the Rasch model-based methods is that it permits testing the quality of the data collected and evaluating the likely margin of uncertainty around estimated prevalence rates, which should always be reported.

4.b. Comment and limitations

An average of less than three minutes of survey time is estimated to collect FIES data in a well-conducted face-to-face survey, which should make it possible to include the FIES-SM in a nationally representative survey in every country in the world, at a very reasonable cost. FAO provides versions of the FIES-SM adapted and translated in each of the more than 200 languages and dialects used in the Gallup World Poll.

When used in the Gallup World Poll, with sample sizes of only about 1000 individuals, the width of confidence intervals rarely exceeds 20% of the measured prevalence (that is, prevalence rates of around 50% are estimated with margins of errors of plus or minus 5%). Obviously, confidence intervals are likely to be much smaller when national prevalence rates are estimated using larger samples.

Compared to other proposed non-official indicators of household food insecurity, the FIES based approach has the advantage that food insecurity prevalence rates are directly comparable across population groups and countries. Even if they use similar labels (such as “mild”, “moderate” and “severe” food insecurity) other approaches have yet to demonstrate the formal comparability of the thresholds used for classification, due to lack of the definition of a proper statistical model that links the values of the “indexes” or “scores” used for classification, to the severity of food insecurity. For this reason, care should be taken when comparing the results obtained with the FIES with those obtained with these other indicators, even if, unfortunately, similar labels are used to describe them.

4.c. Method of computation

Data at the individual or household level is collected by applying an experience-based food security scale questionnaire within a survey. The food security survey module collects answers to questions asking respondents to report the occurrence of several typical experiences and conditions associated with food insecurity. The data is analysed using the Rasch model (also known as one-parameter logistic model, 1-PL), which postulates that the probability of observing an affirmative answer by respondent i to question j, is a logistic function of the distance, on an underlying scale of severity, between the position of the respondent, $a_{i}$ , and that of the item, $b_{j}$ .

$P r o b \{X_{i, j} = Y e s\} = \frac{\exp (a_{i} - b_{j})}{1 + \exp (a_{j} - b_{j})}$

Parameters $a_{i}$ and $b_{j}$ can be estimated using maximum likelihood procedures. Parameters $a_{i}$ , in particular, are interpreted as a measure of the severity of the food security condition for each respondent and are used to classify them into classes of food insecurity.

The FIES considers the three classes of (a) food security or mild food insecurity; b) moderate or severe food insecurity, and (c) severe food insecurity, and estimates the probability of being moderately or severely food insecure ( $p_{m o d + s e v}$ ) and the probability of being severely food insecure ( $p_{s e v}$ ) for each respondent, with $0 < p_{s e v} < p_{m o d + s e v} < 1$ . The probability of being food secure or mildly food insecure can be obtained as $p_{f s} = 1 - p_{m o d + s e v}$ .

Given a representative sample, the prevalence of food insecurity at moderate or severe levels (FImod+sev), and at severe levels (FIsev) in the population are computed as the weighted sum of the probability of belonging to the moderate or severe food insecurity class, and to the severe food insecurity class, respectively, of all individual or household respondents in a sample:

$(1) F I_{m o d + s e v} = \sum_{i} {p_{i}}_{m o d + s e v} \times w_{i}$

and

$(2) F I_{s e v} = \sum_{i} {p_{i}}_{s e v} \times w_{i}$

where $w_{i}$ are post-stratification weights that indicate the proportion of individual or households in the national population represented by each element in the sample.

It is important to note that if $w_{i}$ are individual sampling weights, then the prevalence of food insecurity refers to the total population of individuals, while if they are household weights, the prevalence refers to the population of households. For the calculation of the indicator 2.1.2, objective is to produce a prevalence of individuals. This implies that:

if a survey is at household level, and provides household sampling weights, they should be transformed to individual sampling weights by multiplying the weights by the household size. This individual weighting system can then be used to calculate the individual prevalence rates in formulas (1) and (2)

If the survey includes only adults, then the adult weights applied to the probabilities in formulas (1) and (2) provide the adult prevalence rates ( $F I^{A d u l t s}$ ). In this case, to calculate the prevalence in the total population, then the proportion of children who live in households where at least one adult is food insecure must also be calculated. This can be done by dividing the adult weights by the number of adults in the household and multiplying those approximate household weights by the number of children in the household. Once the approximate child weights are obtained, the prevalence of food insecurity of children who live in households where at least one adult is food insecure ( $F I^{C h i l d r e n}$ ) can be calculated by applying these weights to the probabilities of food insecurity in formulas (1) and (2). The prevalence of food insecurity in the total population is finally calculated as:

$F I_{m o d + s e v} = \frac{F I_{m o d + s e v}^{A d u l t s} \times N^{A d u l t s} + F I_{m o d + s e v}^{C h i l d r e n} \times N^{C h i l d r e n}}{N^{A d u l t s} + N^{C h i l d r e n}}$

and

$F I_{s e v} = \frac{F I_{s e v}^{A d u l t s} \times N^{A d u l t s} + F I_{s e v}^{C h i l d r e n} \times N^{C h i l d r e n}}{N^{A d u l t s} + N^{C h i l d r e n}}$

Where $N^{A d u l t s}$ and $N^{C h i l d r e n}$ are the adult and children populations in the country.

When applied to the country total population, the prevalence of food insecurity in the total population provides the number of individuals who live in food insecure households (or in households where at least one adult is food insecure) in a country, at different levels of severity ( $N_{m o d + s e v}$ and $N_{s e v}$ ). In the database, the number of food insecure people are expressed in thousands.

4.d. Validation

For data collected by FAO through the Gallup World Poll or other service providers, the country results have been shared with all national statistical offices through an email communication sent by the FAO Chief Statistician, requesting feedback, and published only if they did not refuse to.

4.e. Adjustments

International calibration of food insecurity thresholds is performed to ensure national and sub-national results are comparable.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

The indicator is not computed if no country data are available.

At regional and global levels

Missing values for individual countries are implicitly imputed to be equal to the population weighted average of the estimated values of the countries present in the same region.

4.g. Regional aggregations

Regional and global aggregates of the prevalence of moderate or severe food insecurity (FI) based on FIES are computed as:

$F I_{R E G} = (F I_{i} \times N_{i}) / N_{i}$

where $F I_{i}$ are the values of FI estimated for all countries in the regions for which available data allow to compute a reliable estimate, and $N_{i}$ the corresponding population size.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Experience-based food security scales data are collected through population surveys (either household or individual surveys) using questionnaires/modules that are adapted to the country language and condition.

Examples are provided below:

U.S.A.: Household Food Security Survey Module (https://www.ers.usda.gov/media/8271/hh2012.pdf)

Brazil: Escala Brasileira de Insegurança Alimentar (http://biblioteca.ibge.gov.br/visualizacao/livros/liv91984.pdf, Quadro 5, page 30)

Mexico: Escala Mexicana de Seguridad Alimentaria (https://www.coneval.org.mx/Evaluacion/ECNCH/Documents/CIESAS_alimentacion.pdf)

Guatemala: Escala Latino Americana y Caribena de Seguridad Alimentaria (http://www.ine.gob.gt/sistema/uploads/2015/12/11/DDrIEuLOPuEcXTcLXab1yOkiOV2HQreq.pdf, pagina 3)

FAO – Food Insecurity Experience Scale (http://www.fao.org/3/a-bl404e.pdf)

Inclusion of the FIES survey module in a questionnaire is a simple matter of adapting the questions to the local language by following guidelines provided in the following documents.

http://www.fao.org/3/a-be898e.pdf

http://www.fao.org/3/a-be898f.pdf

http://www.fao.org/3/a-be898s.pdf

http://www.fao.org/3/a-be898r.pdf

http://www.fao.org/3/a-be898a.pdf

http://www.fao.org/3/a-be898c.pdf

4.i. Quality management

4.j. Quality assurance

FIES data are validated through testing of adherence to the Rasch model assumption of equal discrimination of the items and absence of residual correlation and measurement of Rasch reliability indexes. Such a test would reveal whether the data is of sufficient quality to produce reliable estimates of the prevalence of food insecurity according to the FIES standard.

Then, item severity parameters are compared with the FIES global reference standard to verify the possibility of calibrating the measures against such standard and thus produce estimates of the prevalence of food insecurity that can be considered comparable across countries.

Relevant material is available at http://www.fao.org/3/a-i4830e.pdf, http://www.fao.org/3/b-i4830s.pdf, http://www.fao.org/3/c-i4830f.pdf and http://www.fao.org/3/a-i3946e.pdf.

When the estimates are based on official national data, the data used to compile the indicator is obtained directly from the microdata dissemination websites of countries, when available (e.g. USA), or by direct request to the national statistical offices responsible for data collection (e.g. Canada).

4.k. Quality assessment

High. For the vast majority of countries, the quality assurance steps provide indication of high quality and reliable data.

5. Data availability and disaggregation

Data availability:

Data for 2014-2020 are available from FAO for more than 140 countries, areas and territories included in the Gallup World Poll.

Regional and sub regional aggregates are computed for all regions, with the exceptions of the Caribbean and the Middle Africa regions (as less than 50% of the regional population up to 2019 was covered). Both regions can be estimated only for 2020. Data have been subject to a country consultation process and only results validated by national statistical offices are published at country level.

Time series:

Only the 3-year average (2014-2016, 2015-17, 2016-18, 2017-19 and 2018-20) is provided for country level data. Annual values are provided for regional aggregates.

Disaggregation:

As the FIES or any other compatible experience-based food security questionnaire is applied through surveys, the prevalence of food insecurity can be measured in any population group for which the survey used to collect data is representative.

If applied at household level, disaggregation is thus possible based on household characteristics such as location, household income, composition (including for example presence and number of small children, members with disabilities, elderly members, etc.), sex, age and education of the household head, etc. If applied at the individual level, proper disaggregation of the prevalence of food insecurity by sex is possible as the prevalence of food insecurity among male and among female members of the same population group can be measured independently.

When producing disaggregated statistics, attention must be devoted to verifying the validity of the application by estimating the Rasch model with the data from each specific subpopulation group and, if necessary, perform the appropriate equating of the measure before comparing results.

It is good practice to associate a measure of variability (margins of error or upper and lower bound) when disaggregated data are produced.

At the moment, disaggregated statistics by gender of the respondent are provided.

6. Comparability/deviation from international standards

Sources of discrepancies:

In the few cases where indicators of food insecurity based on experience-based food security scales have been reported by countries (U.S., Canada, Mexico, Guatemala and Brazil), these have been based on nationally set thresholds that do not correspond to the international thresholds proposed by the FIES. See Annex I and Table A3 in http://www.fao.org/3/i4830e.pdf for a description of the differences. In the future, it is desirable that country would start reporting prevalence estimates using also the internationally set thresholds for moderate or severe and severe levels, in addition to those based on national thresholds.

FAO is ready to provide assistance on the analytic methods needed to estimate prevalence based on the FIES global reference thresholds.

7. References and Documentation

URL: http://www.fao.org/in-action/Voices-of-the-Hungry/

http://www.fao.org/3/i4830e.pdf

2.2.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture

0.b. Target

Target 2.2: by 2030 end all forms of malnutrition, including achieving by 2025 the internationally agreed targets on stunting and wasting in children under five years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women, and older persons

0.c. Indicator

Indicator 2.2.1: Prevalence of stunting (height for age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age

0.d. Series

Not Applicable

0.e. Metadata update

2023-05-15

0.f. Related indicators

Good nutrition lays the foundation for achieving many of the SDGs with improvements in nutrition directly supporting the achievement of SDG3 (ensuring healthy lives), while also playing a role in ending poverty (SDG1), ensuring quality education (SDG4), achieving gender equality (SDG5), promoting economic growth (SDG8), and reducing inequalities (SDG10). In this way, nutrition is the lifeblood of sustainable development, and drives the changes needed for a more sustainable and prosperous future.

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

World Health Organization (WHO)

World Bank (WB)

1.a. Organisation

United Nations Children's Fund (UNICEF)

World Health Organization (WHO)

World Bank (WB)

2.a. Definition and concepts

Definition:

Prevalence of stunting (height-for-age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age.

(French: pourcentage de retard de croissance (i.e., longueur/taille pour l'âge <-2 écarts types par rapport à la médiane des normes de croissance de l'enfant de l'Organisation Mondiale de la Santé (OMS)) chez les enfants de moins de cinq ans; Spanish: porcentaje de retraso del crecimiento (i.e., longitud/estatura para la edad < -2 desviaciones estándar de la mediana de los estándares de crecimiento infantil de la Organización Mundial de la Salud (OMS)) en los niños y niñas menores de cinco años de edad )

Concepts:

The UNICEF/WHO/World Bank Joint Malnutrition Estimates (JME) working group generates modelled estimates for 205 countries and territories utilizing primary data sources (e.g., household surveys ). The global SDG Indicators Database only contains modelled estimates. Primary data sources can be found at data.unicef.org/nutrition/malnutrition.html, https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, http://datatopics.worldbank.org/child-malnutrition.

2.b. Unit of measure

Proportion

2.c. Classifications

The WHO Multicentre Growth Reference Study (MGRS) (WHO 2006) was undertaken to generate a growth standard for assessing the growth and development of infants and young children around the world. The MGRS collected primary growth data and related information from children from widely different ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman, and the USA). The resulting growth standard can be applied to all children everywhere, regardless of ethnicity, socioeconomic status and type of feeding. The indicator refers to those moderately or severely stunted, that is with a z-score below -2 standard deviations for height-for-age from the median of the growth standard.

3.a. Data sources

For the majority of countries, nationally representative household surveys constitute the primary data source used to generate the JME modelled estimates. For a limited number of countries, data from surveillance systems are also used as a primary data source for generation of the JME modelled estimates if sufficient population coverage is documented (about 80%). For both types of primary data sources, the child’s height/length and date of birth as well as date of measurement (to generate age in days) have to be collected following recommended standard measuring techniques (WHO/UNICEF 2019).

3.b. Data collection method

UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data.

For UNICEF, the cadre of dedicated data and monitoring specialists working at national, regional and international levels in 190 countries routinely provide technical support for the collection and analysis of nutrition data. UNICEF also relies on a data source catalogue that is regularly updated using data sources from catalogues of other international organizations and national statistics offices. This data collection is done in close collaboration with UNICEF regional offices with the purpose of ensuring that UNICEF global databases contain updated and internationally comparable data. The regional office staff work with country offices and local counterparts to ensure that all relevant data are shared.

WHO data gathering strongly relies on the organization’s structure and network established over the past 30 years, since the creation of its global database, the WHO Global Database on Child Growth and Malnutrition, in the late 1980’s (de Onis et al. 2004).

The World Bank Group provides estimates available through the Living Standard Measurement Surveys (LSMS) which usually requires re-analysis of datasets given that the LSMS reports often do not tabulate the stunting data.

3.c. Data collection calendar

Data collection is carried out by the three-agency group throughout the year.

3.d. Data release calendar

The UNICEF-WHO-WB Joint Child Malnutrition (JME) group releases country, regional and worldwide estimates at the end of March every other year so that data are available for the SDG report and database. The JME group also maintain a database of primary data sources (e.g., household surveys) which is updated every six months and used to generate the JME modelled estimates.

3.e. Data providers

The majority of the data sources used are nationally representative household surveys e.g., Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and National Nutrition Surveys (NNS). Some data come from other sources (administrative, sentinel systems). Data providers vary and most commonly are ministries of health, national offices of statistics or national institutes of nutrition.

3.f. Data compilers

UNICEF, WHO and the World Bank group

3.g. Institutional mandate

UNICEF is responsible for global monitoring and reporting on the wellbeing of children. UNICEF actively supports countries in data collection and analysis for reporting on child malnutrition indicators primarily through high-quality MICS surveys, as well as providing technical and financial support to other surveys. UNICEF not only supports household surveys but also works with global partners to define technical standards for the collection and analysis of anthropometric data. UNICEF also compiles statistics on child nutrition with the goal of making internationally comparable estimates and databases publicly available. In-depth analyses of the data on child malnutrition, which are included in relevant data-driven publications, including in its flagship publication, The State of the World’s Children, and the Child Nutrition Report are also conducted by UNICEF.

WHO has an established role in the monitoring of child growth and malnutrition since the late 1980’s and had the mandate to develop the WHO Child Growth Standards, launched in 2006, and adopted by more than 160 countries. WHO has published several peer-reviewed articles with regional and global estimates until 2012, when they joined forces with UNICEF and the World Bank, with the objective of harmonizing child malnutrition estimates. WHO has the mandate to monitor and report progress on the six global nutrition targets, endorsed in 2012 by the World Health Assembly, amongst them, three on child malnutrition, namely stunting, overweight and wasting (SDG 2.2.1, 2.2.2 (1) and 2.2.2 (2)).

4.a. Rationale

Child growth is an internationally accepted outcome reflecting child nutritional status. Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators.

4.b. Comment and limitations

Survey estimates have uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME modelled estimates for stunting take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age data from household surveys supports compilation of a time series that is comparable across countries and over time.

The JME working group carefully utilizes all available national data sources, and documents all the steps taken to infer about country trends based on the national data sources. The estimation method (McClain et al 2018) is based on and closely aligned to country data. The approach smooths and fits a trend line across the national data points. The basis of the estimates are nationally representative household surveys. However, as surveys are conducted infrequently (e.g., less frequently than every 3 years) in some countries, models produce a complete time series with estimates available in the same years for all countries. This allows for comparable assessment of progress; for example, all countries can be assessed using the same baseline year. For any individual country, an increase in the availability of primary data points can result in more robust and accurate modelled estimates.

4.c. Method of computation

National estimates from primary sources (e.g., from household surveys) used to generate the JME country modelled estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (WHO/UNICEF 2019) and WHO Anthro Survey Analyser (WHO, 2019). The JME country modelled estimates are generated using smoothing techniques and covariates (McLain et al. 2018) applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived as the respective country averages weighted by the countries’ under-five population estimates (UNPD-WPP latest available edition) using annual JME country modelled estimates. .

4.d. Validation

UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source used to generate the JME modelled estimates. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted . Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME database, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available.

The methods used to generate the JME country modelled estimates for stunting and overweight were cross-validated to ensure estimates produced by the method are closely aligned to national data points. The methodology used to model these estimates was reviewed through a technical consultation with experts and country representatives of National Statistics Offices as well as IAEG-SDGs Members in 2019 (UNICEF/WHO/World Bank, 2019). Country consultations with SDG 2.2 focal points are also held every two years before finalizing and disseminating each edition of the JME global, regional and country estimates. The purpose of the country consultations is to ensure the estimates include all recent and relevant primary data sources and to engage with and receive feedback from national governments on the estimates.

4.e. Adjustments

Adjustments to reported values are made in cases where raw data are not available for re-analysis and it is known from the report that the estimates were derived based on indicators that do not adhere to the standard definition used for monitoring of the SDGs (e.g., they are based on different growth references, etc.). The three types of adjustments that have been applied to the JME country database include adjustments to standardize for: (i) area of residence, specifically for data sources that were only nationally representative at the rural level; (ii) growth reference, specifically for data sources that used the 1977 NCHS/WHO Growth Reference instead of the 2006 WHO Growth Standards to generate the child malnutrition estimates; and (iii) age, specifically for data sources that did not include the full 0–59-month age group (e.g., data sources reporting on 2–4-year-olds). These three types of adjustments are described further in this section.

i. Adjustment from national rural to national

A number of surveys cover only rural areas, and, while they have been sampled to be nationally representative for the rural parts of the country, they did not sample any urban areas. Given that malnutrition prevalence generally varies between urban and rural areas (i.e., stunting prevalence was reported to be two times higher in rural areas compared to urban areas at the global level (5)), a rural-only survey would not be comparable with a national survey representative of both urban and rural areas. To improve comparability of the rural-only data sources for the specific country, it is necessary to account for urban populations in estimates from these surveys.

The adjustment method used by the JME group is to apply the relative proportions of malnutrition prevalence for each urban and rural area from the closest survey in the country’s JME database includes disaggregated estimates by area of residence, to the survey that covers only rural areas. This is done under the assumption that the urban:rural population ratio remains the same as the survey with the disaggregations available (e.g., the proportion of children living in rural areas in the country is the same in the survey year used for the adjustment as in the survey year being adjusted) and also that relative prevalence of malnutrition across urban-rural areas in the survey with the missing data is the same as in the survey with full information used for the adjustment.

ii. Adjustment to use the 2006 WHO Growth Standard (converted estimates):

The indicators of stunting, wasting and overweight used to track SDG Target 2.2 require a standard deviation (SD) score (z-score) to be calculated for each child who is measured for a data source; and the z-score requires a growth reference against which it can be calculated. Prior to the release of the WHO Child Growth Standards in 2006, the 1977 NCHS/WHO reference was recommended for international comparisons. The WHO Growth Standard results in estimates of stunting and wasting prevalence that are higher as well as estimates of overweight that are lower than estimates generated using the NCHS/WHO growth reference (6). It was therefore necessary to account for these differences and standardize estimates across data sources. As such, data sources published prior to the release of the new growth standard in 2006 had to be re-analysed using the 2006 growth standards to obtain comparable estimates across time and location. When raw data were not available, a standard algorithm was applied to convert estimates from surveys based on the NCHS reference to estimates based on the WHO Growth Standards (7).

iii. Age-adjustment

A limited number of surveys in the JME country database of primary sources that do not have microdata report on age groups that do not cover the entire 0–59-month age range in the standard definition for stunting, wasting and overweight. Adjustment for age is needed as malnutrition prevalence can vary by sub-age group. For example, stunting prevalence among 24–59-month olds in recent surveys with age-disaggregations were more than two times higher than the stunting prevalence among 0–5-month olds (8). Surveys that omit part of the full age range might thus not be comparable with a survey that did cover all 0–59-month olds. Age adjustment can thus help to properly assess the country trend. Similar to the adjustment for rural-only surveys, the proportion of children with malnutrition in the two sub-age groups is assumed to be the same in the survey years in question.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Missing values were derived as part of the methods used to generate the JME country modelled estimates, by closely fitting the estimates from country primary data sources, with due attention to unwarranted variability. Please refer to McLain et al. 2018 for technical details of the methods applied. Based on these methods, the JME country modelled estimates are produced from 2000 until the year before the year of publication (e.g., until 2022 for the JME 2023 edition) and used to generate regional and worldwide aggregates. For countries without any primary input data meeting inclusion criteria, the JME country modelled estimates were produced solely for generation of regional and worldwide aggregates, and were not released to the public

At regional and worldwide levels

There are no missing data for the generation of worldwide and regional estimates as modelled estimates are produced for all countries, those with and those without primary data in the JME country database, even though the country estimates are not released to the public for those countries without primary data.

4.g. Regional aggregations

Regional aggregates are available for the following classifications: UN, SDG, UNICEF, WHO, The World Bank regions and income groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Methods and guidance:

Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019)

Analysis tool: WHO Anthro Survey Analyser (shinyapps.io)

UNICEF-WHO-World Bank 2020. Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight .

4.i. Quality management

The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME modelled estimates. Regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method and discuss potential data quality issues. The JME working group also continuously review methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, a Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.

4.j. Quality assurance

The quality criteria established in the 2019 UNICEF/WHO guidance (WHO/UNICEF, 2019)ewere used to update the JME primary data source review form. The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies (UNICEF, WHO, WB) which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies prior to inclusion in the JME country database of primary sources (e.g., household surveys) that are used to generate the JME country modelled estimates.

4.k. Quality assessment

Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources that are used to generate the JME modelled estimates. Cross-validation exercises are performed for the modelled estimates to ensure the method generates estimates that are aligned to national data points. Country consultations with SDG 2.2 focal points held every other year also provide an opportunity to ensure the estimates include all recent and relevant country data.

5. Data availability and disaggregation

Data availability:

The JME modelled country estimates from 2000 to 2020 for stunting were released for 159 countries that had at least one primary data source (e.g., from household survey) included in the 2023 JME country database.

Time series:

At country level, JME modelled estimates from 2000 to the year before the JME release are presented for countries with at least one data point (e.g., from survey/surveillance) are included in the joint database of primary data sources. Survey years range from 1983 to the year before the JME release. Worldwide and regional levels, annual estimates are available from 2000 to the year before the JME release.

Disaggregation:

Country, regional and worldwide JME modelled estimates refer to the age group of children under 5 years, sexes combined. Disaggregations are currently not available for the JME modelled estimates. However, a disaggregated dataset of national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers' education, residence) is available.

6. Comparability/deviation from international standards

Sources of discrepancies:

For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (WHO/UNICEF 2019) and WHO Anthro Survey Analyser (WHO, 2019) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (WHO, 2019) to undertake survey analysis and harmonize with the global standard analysis methods.

For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. Discrepancies between results from standardised methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (Yang and de Onis, 2008). In addition, when surveys do not cover the age interval 0-59 month, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.

The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (McLain et al. 2018), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).

7. References and Documentation

URL:

data.unicef.org/nutrition/malnutrition.html;

https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;

References:

de Onis M, Blössner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. <https://pubmed.ncbi.nlm.nih.gov/15542535/>

de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health programmes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 <https://www.who.int/childgrowth/publications/Comparison_implications.pdf>

McLain A, Frongillo E, Feng J, Borghi E (2018). Prediction intervals for penalized longitudinal models with multi-source summary measures: an application to childhood malnutrition. Stat Med; 38(6):1002-1012; doi: 10.1002/sim.8024. Epub 2018 Nov 14. <https://pubmed.ncbi.nlm.nih.gov/30430613/>

United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank (2019). Meeting report on Technical Consultation on a Country-level model for SDG2.2. December 2019.

UNICEF-WHO-World Bank (2020). Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight <https://data.unicef.org/resources/jme-2021-country-consultations/>

WHO (2006). WHO Multicentre Growth Reference Study (MGRS) <https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study>

World Health Organization and United Nations Children’s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva:

World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. <https://www.who.int/nutrition/publications/anthropometry-data-quality-report>

WHO. WHO Anthro Survey Analyser (2019). Available at https://www.who.int/tools/child-growth-standards/software.

Yang H and de Onis M (2008). Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards. BMC Pediatrics 2008, 8:19 (05 May 2008) <http://www.biomedcentral.com/1471-2431/8/19>.

2.2.2a

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture

0.b. Target

0.c. Indicator

Indicator 2.2.2: Prevalence of malnutrition (weight for height >+2 or <-2 standard deviation from the median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight)

0.d. Series

Proportion of children moderately or severely overweight (%) SN_STA_OVWGT

Children moderately or severely overweight (thousands) SN_STA_OVWGTN

0.e. Metadata update

2023-05-15

0.f. Related indicators

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

World Health Organization (WHO)

World Bank (WB)

1.a. Organisation

United Nations Children's Fund (UNICEF)

World Health Organization (WHO)

World Bank (WB)

2.a. Definition and concepts

Definition:

Prevalence of overweight (weight for height >+2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age.

(French: pourcentage avec surpoids (i.e., poids pour longueur/taille > +2 écarts-types par rapport à la médiane des normes de croissance de l'enfant de l'Organisation Mondiale de la Santé (OMS)) chez les enfants de moins de cinq ans); Spanish: porcentaje de sobrepeso (i.e., peso para longitud/estatura > +2 desviaciones estándar de la mediana de los estándares de crecimiento infantil de la Organización Mundial de la Salud (OMS)) en niños y niñas menores de cinco años de edad.)

Concepts:

The UNICEF/WHO/World Bank Joint Malnutrition Estimates (JME) working group generates modelled estimates for 205 countries and territories utilizing primary data sources (e.g., household surveys).

The global SDG Indicators Database only contains modelled estimates. Primary data sources can be found at data.unicef.org/nutrition/malnutrition.html, https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, http://datatopics.worldbank.org/child-malnutrition.

The official SDG indicator is overweight as assessed using weight-for-height. Overweight can however also be assessed with other indicators such body mass index (BMI)-for-age. In general BMI-for-age is not used in the joint database of primary sources (e.g., household surveys) but has been considered in absence of any other available estimates.

2.b. Unit of measure

Proportion

2.c. Classifications

The WHO Multicentre Growth Reference Study (MGRS) (WHO 2006) was undertaken to generate a growth standard for assessing the growth and development of infants and young children around the world. The MGRS collected primary growth data and related information from children from widely different ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman, and the USA). The resulting growth standard can be applied to all children everywhere, regardless of ethnicity, socioeconomic status and type of feeding. The indicator refers to those moderately or severely overweight, that is with a z-score above 2 standard deviations from the median weight-for-length/height of the growth standard.

3.a. Data sources

For the majority of countries, nationally representative household surveys constitute the primary data source used to generate the JME modelled estimates. For a limited number of countries data from surveillance systems are also used as a primary data source for generation of the JME modelled estimates if sufficient population coverage is documented (about 80%). For both types of primary data sources, the child’s length/height and weight measurements have to be collected following recommended standard measuring techniques (WHO/UNICEF 2019).

3.b. Data collection method

UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data.

3.c. Data collection calendar

Data collection is carried out by the three-agency group throughout the year.

3.d. Data release calendar

The UNICEF-WHO-WB Joint Child Malnutrition (JME) group releases country, regional and worldwide estimates at the end of March every other year so that data are available for the SDG report and database. The JME group also maintain a database of primary data sources (e.g., household surveys)), which is updated every six months, and used to generate the JME modelled estimates.

3.e. Data providers

The majority of the data sources used are nationally representative household surveys (e.g., Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and National Nutrition Surveys (NNS)). Some data come from other sources (e.g., administrative, sentinel systems, etc). Data providers vary and most commonly are ministries of health, national offices of statistics or national institutes of nutrition.

3.f. Data compilers

UNICEF, WHO and the World Bank group

3.g. Institutional mandate

WHO has an established role in the monitoring of child growth and malnutrition since the late 1980’s and had the mandate to develop the WHO Child Growth Standards, launched in 2006, and adopted by more than 160 countries. WHO has published several per-reviewed articles with regional and global estimates until 2012, when they joined forces with UNICEF and the World Bank, with the objective of harmonizing child malnutrition estimates. WHO has the mandate to monitor and report progress on the six global nutrition targets, endorsed in 2012 by the World Health Assembly, amongst them, three on child malnutrition, namely stunting, overweight and wasting (SDG 2.2.1, 2.2.2 (1) and 2.2.2 (2)).

4.a. Rationale

Child growth is an internationally accepted outcome area reflecting child nutritional status. Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life. Child overweight is one of the World Health Assembly nutrition target indicators.

4.b. Comment and limitations

Survey estimates come have uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME modelled estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age data from household surveys supports compilation of a time series that is comparable across countries and over time.

Of particular concern for overweight is the fact that data for high-income countries are scarce yet the prevalence is generally higher among the high-income countries with data. The JME group are working closely with countries in the European region to increase coverage, as well as to apply age adjustments for data covering only partially the age interval 0 to 59 months.

4.c. Method of computation

National estimates from primary sources (e.g., from household surveys) used to generate the JME modelled estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (WHO/UNICEF 2019) and WHO Anthro Survey Analyser (WHO, 2019). The JME country modelled estimates are generated using smoothing techniques and covariates (McLain et al. 2018) applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived as the respective country averages weighted by the countries’ under-five population estimates (UNPD-WPP latest available edition) using annual JME country modelled estimates.

4.d. Validation

UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source used as to generate the JME modelled estimates. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME database, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available.

The methods used to generate the JME country modelled estimates for stunting and overweight were cross validated to ensure estimates produced by the method are closely aligned to national data points. The methodology used to model these estimates was reviewed through a technical consultation with experts and country representatives of National Statistics Offices as well as IAEG-SDGs Members in 2019 (UNICEF/WHO/World Bank, 2019). Country consultation with SDG 2.2 focal points are also held every two years before finalizing and disseminating each edition of the JME global, regional and country estimates. The purpose of the country consultations is to ensure the estimates include all recent and relevant primary data sources and to engage with and receive feedback from national governments on the estimates.

4.e. Adjustments

i. Adjustment from national rural to national

A number of surveys cover only rural areas, and, while they have been sampled to be nationally representative for the rural parts of the country, they did not sample any urban areas. Given that malnutrition prevalence generally varies between urban and rural areas (i.e., stunting prevalence was reported to be two times higher in rural areas compared to urban areas at the global level (5)), a rural-only survey would not be comparable with a national survey that are representative of both urban and rural areas. To improve comparability of the rural-only data sources for the specific country, it is necessary to account for urban populations in estimates from these surveys.

ii. Adjustment to use the 2006 WHO Growth Standard (converted estimates)

iii. Age-adjustment

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Missing values are derived as part of the methods used to generate the JME country modelled estimates by closely fitting the estimates from country data primary sources, with due attention to unwarranted variability. Please refer to McLain et al. 2018 for technical details of the methods applied. Based on these methods, the JME country modelled estimates are produced from 2000 until the year before the year of publication (e.g., until 2022 for the JME 2023 edition) and used to generate regional and worldwide aggregates. For ~~49 of these~~ countries without any primary input data meeting inclusion criteria, the JME country modelled estimates were produced solely for generation of regional and worldwide aggregates, and were not released to the public

At regional and worldwide levels

4.g. Regional aggregations

Regional aggregates are available for the following classifications: UN, SDG, UNICEF, WHO, The World Bank regions and income groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Methods and guidance:

Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019)

Analysis tool: WHO Anthro Survey Analyser (shinyapps.io)

UNICEF-WHO-World Bank 2020. Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight

4.i. Quality management

The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME country modelled estimates, for which regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method. The JME working group also continuously review methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, a Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.

4.j. Quality assurance

The quality criteria established in the 2019 UNICEF/WHO guidance (WHO/UNICEF, 2019) were used to update the JME primary data source review form . The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies (UNICEF, WHO, WB) which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies prior to inclusion in the JME country database of primary sources (e.g., household surveys) that are used to generate the JME country modelled estimates.

4.k. Quality assessment

Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources that are used to generate the JME modelled estimates. Cross-validation exercises are performed for the modelled estimates to ensure the method generates estimates that are aligned to national data points. Country consultations with SDG 2.2 focal points an held every other year also provide opportunity to ensure the estimates include all recent and relevant country data.

5. Data availability and disaggregation

Data availability:

The JME modelled country estimates from 2000 to 2022 for overweight were released for 161 countries that had at least one primary data source (e.g., from household survey) included in the 2023 JME country database.

Time series:

At country level, JME country modelled estimates from 2000 to the year before the JME release ) are presented for countries with at least one data point (e.g., from survey/surveillance included in the joint database of primary data sources. Survey years range from 1983 to the year before the JME release. Worldwide and regional annual estimates are available from 2000 to the year before the JME release.

Disaggregation:

Country, regional and worldwide JME estimates refer to the age group of children under 5 years, sexes combined. Disaggregations are currently not available for the JME modelled estimates. However, a disaggregated dataset of national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers' education, residence) is available.

6. Comparability/deviation from international standards

Sources of discrepancies:

For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (WHO/UNICEF 2019) and WHO Anthro Survey Analyser (WHO, 2019) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (WHO, 2019) to undertake survey analysis and harmonize with the global standard analysis methods.

For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. Discrepancies between results from standard methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (Yang and de Onis, 2008). In addition, when surveys do not cover the age interval 0-59 months, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.

The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (McLain et al. 2018), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the country modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).

7. References and Documentation

URL:

data.unicef.org/nutrition/malnutrition.html;

https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;

References:

WHO (2006). WHO Multicentre Growth Reference Study (MGRS) <https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study>

World Health Organization and United Nations Children’s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. <https://www.who.int/nutrition/publications/anthropometry-data-quality-report>

WHO. WHO Anthro Survey Analyser (2019). Available at https://www.who.int/tools/child-growth-standards/software.

2.2.2b

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture

0.b. Target

0.c. Indicator

0.d. Series

Proportion of children moderately or severely wasted (%) SH_STA_WAST

Children moderately or severely wasted (thousands) SH_STA_WASTN

0.e. Metadata update

2023-05-15

0.f. Related indicators

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

World Health Organization (WHO)

World Bank (WB)

1.a. Organisation

United Nations Children's Fund (UNICEF)

World Health Organization (WHO)

World Bank (WB)

2.a. Definition and concepts

Definition:

Prevalence of wasting (weight for height <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age.

(French: pourcentage de émaciation (i.e. poids pour longueur/taille < -2 écarts-types par rapport à la médiane des normes de croissance de l'enfant de l'Organisation Mondiale de la Santé (OMS)) chez les enfants de moins de cinq ans; Spanish: porcentaje de emaciación (i.e. peso para longitud/estatura < -2 desviaciones estándar de la mediana de los estándares de crecimiento infantil de la Organización Mundial de la Salud (OMS)) en niños y niñas menores de cinco años de edad.)

Concepts:

The official SDG indicator is wasting as assessed using weight for height. Wasting can however also be assessed with mid upper arm circumference (MUAC). Estimates of wasting based on MUAC are not considered for the JME joint database. In addition, while wasting constitutes the major form of moderate acute malnutrition (MAM), there are acutely malnourished children who would not be picked up with weight-for-height or MUAC, namely those presenting bilateral pitting oedema (characterized by swollen feet, face and limbs). For surveys that report wasting including oedema cases, these are included in the prevalence of low weight-for-height in the JME database unless raw data are available for re-analysis.

2.b. Unit of measure

Proportion

2.c. Classifications

The WHO Multicentre Growth Reference Study (MGRS) (WHO 2006) was undertaken to generate a growth standard for assessing the growth and development of infants and young children around the world. The MGRS collected primary growth data and related information from children from widely different ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman, and the USA). The resulting growth standard can be applied to all children everywhere, regardless of ethnicity, socioeconomic status and type of feeding. The indicator refers to those moderately or severely wasted, that is with a z-score below -2 standard deviations from the median weight-for-length/height of the growth standard.

3.a. Data sources

For the majority of countries, nationally representative household surveys constitute the data source. For a limited number of countries data from surveillance systems is used if sufficient population coverage is documented (about 80%). For both data sources, the child’s length/height and weight measurements have to be collected following recommended standard measuring techniques (WHO/UNICEF 2019).

3.b. Data collection method

UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data.

3.c. Data collection calendar

Data collection is carried out by the three-agency group regularly throughout the year so that data are available for the SDG report and database.

3.d. Data release calendar

The UNICEF-WHO-WB Joint Child Malnutrition (JME) group releases country, regional and worldwide estimates at the end of March every other years so that data are available for the SDG report and database. The JME group also maintain a database of primary data sources (e.g., household surveys), which is updated every six months, and used to generate the JME global and regional estimates.

3.e. Data providers

Data providers vary and most commonly are ministries of health, national offices of statistics or national institutes of nutrition.

3.f. Data compilers

UNICEF, WHO and the World Bank group

3.g. Institutional mandate

WHO has an established role in the monitoring of child growth and malnutrition since the late 1980’s and had the mandate to develop the WHO Child Growth Standards, launched in 2006, and adopted by more than 160 countries. WHO has published several per-reviewed articles with regional and global estimates until 2012, when they joined forces with UNICEF and the World Bank, with the objective of harmonizing child malnutrition estimates. WHO has the mandate to monitor and report progress on the six global nutrition targets, endorsed in 2012 by the World Health Assembly, amongst them, three on child malnutrition, namely stunting, overweight and wasting (SDG 2.2.1, 2.2.2 (1) and 2.2.2 (2)).

4.a. Rationale

Child growth is an internationally accepted outcome reflecting child nutritional status and well-being. Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible. Child wasting is one of the World Health Assembly nutrition target indicators.

4.b. Comment and limitations

Survey estimates have uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age ~~measurements~~ data from household surveys supports compilation of a time series that is comparable across countries and over time. None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.

Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g., rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.

4.c. Method of computation

Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (WHO/UNICEF 2019) and WHO Anthro Survey Analyser (WHO, 2019). Worldwide and regional estimates are based on methodology described in UNICEF-WHO-The World Bank: Joint child malnutrition estimates - Levels and trends (UNICEF/WHO/WB 2012).

4.d. Validation

UNICEF, WHO and the World Bank undertake a joint review for each potential data source (e.g., household survey). The group conducts a review when (at minimum) a final report with full methodological details and results is available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted . Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME database, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available. Country consultations with SDG 2.2 focal points are also held every two years before finalizing and disseminating each edition of the JME estimates. The purpose of the country consultations is to ensure the wasting estimates included all recent and relevant country data and to engage with and receive feedback from national governments on the estimates.

4.e. Adjustments

Adjustments to reported values are made in cases where raw data are not available for re-analysis and it is known from the report that the estimates were derived based on indicators that do not adhere to the standard definition used for monitoring of the SDGs (e.g., they are based on different growth references). The three types of adjustments that have been applied to the JME country dataset include adjustments to standardize for: (i) area of residence, specifically for data sources that were only nationally representative at the rural level; (ii) growth reference, specifically for data sources that used the 1977 NCHS/WHO Growth Reference instead of the 2006 WHO Growth Standards to generate the child malnutrition estimates; and (iii) age, specifically for data sources that did not include the full 0–59-month age group (e.g., data sources reporting on 2–4-year-olds). These three types of adjustments are described further in this section.

i. Adjustment from national rural to national

A number of surveys cover only rural areas, and, while they have been sampled to be nationally representative for the rural parts of the country, they did not sample any urban areas. Given that malnutrition prevalence generally varies between urban and rural areas (i.e., stunting prevalence was reported to be two times higher in rural areas compared to urban areas at the global level (5)), a rural-only survey would not be comparable with a national survey that are representative of both urban and rural areas. To improve comparability of the rural-only data sources for the specific country, it is necessary to account for urban populations in estimates from these surveys.

The adjustment method used by the JME group is to apply the relative proportions of malnutrition prevalence for each urban and rural area from the closest survey in the country’s JME dataset includes disaggregated estimates by area of residence, to the survey that covers only rural areas. This is done under the assumption that the urban:rural population ratio remains the same as the survey with the disaggregations available (e.g., the proportion of children living in rural areas in the country is the same in the survey year used for the adjustment as in the survey year being adjusted) and also that relative prevalence of malnutrition across urban-rural areas in the survey with the missing data is the same as in the survey with full information used for the adjustment.

ii. Adjustment to use the 2006 WHO Growth Standard (converted estimates):

iii. Age-adjustment

Some surveys do not cover the entire age interval 0 to 59 months and thus are not aligned with

the standard definition for the child malnutrition indicators (e.g., 0‐5 or 0‐12 months not

covered). To incorporate these surveys, we ran a linear mixed model on the difference between

the 0‐59‐month prevalence estimate and the estimates at 0‐5‐, 6‐11‐, 12‐23‐, 24‐35‐, 36‐47‐

and 48‐59‐month age‐groups, using data from surveys with both the 0‐59‐month prevalence

and separate age‐group prevalence values. Specifically, this difference was modeled as a

function of the full prevalence, regional grouping, age‐group, and a full prevalence by age‐group interaction.

Model diagnostics showed that the linearity assumption was upheld. With the estimated mixed

model, the data for missing age groups were then imputed using the data from the observed

age groups. The prevalence estimate for the full age range was then aggregated using the

estimated and observed age‐group prevalence rates, for sources with at least one missing age

group.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

No imputation methodology is applied to derive estimates for countries or years where no data are available.

At regional and worldwide levels

Countries and years are treated as missing randomly following a multilevel modelling approach (de Onis et al. 2004).

4.g. Regional aggregations

Regional aggregates are available for the following classifications: UN, SDG, UNICEF, WHO, The World Bank regions and income groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Methods and guidance:

Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019)

UNICEF-WHO-World Bank 2020. Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight

Analysis tool: WHO Anthro Survey Analyser (shinyapps.io)

4.i. Quality management

The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME global estimates, for which regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method. The JME working group also continuously review methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, a Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.

4.j. Quality assurance

The quality criteria established in the 2019 UNICEF/WHO guidance (WHO/UNICEF, 2019) were used to update the JME primary data source review form. The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies which form the JME working group prior to inclusion in the JME country database of primary sources (e.g., household surveys).

4.k. Quality assessment

Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources. Country consultations with SDG 2.2 focal points also provide an overall evaluation of the estimates and help to ensure that all recent and relevant country data are included.

5. Data availability and disaggregation

Data availability:

The JME global estimates from 2000 to 2022 for wasting were released for 1575 countries that had at least one primary data source (e.g., from household survey) included in the 20231 JME country database.

Time series:

At country level, data are provided for the years where primary data sources are included in the JME database. Data source years range from 1983 to the year before the JME release. Worldwide and regional estimates are provided only for the year before the JME release (e.g., wasting estimates released in 2021 were provided only for the year 2020).

Disaggregation:

Worldwide and regional estimates refer to the age group of children under 5 years, sexes combined. A disaggregated dataset of national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers' education, residence) is available.

6. Comparability/deviation from international standards

Sources of discrepancies:

For the survey estimates included in the JME joint database, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (WHO/UNICEF 2019) and WHO Anthro Survey Analyser (WHO, 2019) is applied whenever microdata are available, for enhancing comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (WHO, 2019) to undertake survey analysis and harmonize with the global standard analysis methods.

For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria.

When there is insufficient documentation, the survey is not included until information becomes available. Discrepancies between results from the standard methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (Yang and de Onis, 2008).). In addition, when surveys do not cover the age interval 0-59 months, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.

7. References and Documentation

URL:

data.unicef.org/nutrition/malnutrition.html;

https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;

References:

United Nations Children’s Fund, World Health Organization, The World Bank (2012). UNICEFWHO-World Bank Joint Child Malnutrition Estimates. (UNICEF, New York; WHO, Geneva; The World Bank, Washington, DC; 2012). <https://www.who.int/docs/default-source/child-growth/jme-brochure2012.pdf?sfvrsn=ca20d895_2>

WHO (2006). WHO Multicentre Growth Reference Study (MGRS) <https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study>

WHO. WHO Anthro Survey Analyser (2019). Available at https://www.who.int/tools/child-growth-standards/software.

2.2.3

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older persons

0.c. Indicator

Indicator 2.2.3: Prevalence of anaemia in women aged 15 to 49 years, by pregnancy status (percentage)

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

Anaemia is estimated to contribute to 17% lower productivity in heavy manual labour and 5% lower productivity in other manual labour (Goal 1 End poverty in all its forms everywhere); during pregnancy, it increases the risk of maternal and perinatal mortality and contributes to low birth-weight infants (Goal 3. Good health and well-being); it also limits cognitive development, children who have adequate iron have more energy to participate in classroom exercises, and they are more mentally prepared to master the material (Goal 4. Quality education); anaemia rates in females are much higher than males — while anaemia rates decrease for males by the end of puberty, they remain high for females through reproductive years due to menstruation, thus reducing anaemia contributes to boosting females’ relative academic performance and worker productivity and helps achieve gender equality (Goal 5. Gender equality).

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Percentage of women aged 15−49 years with a haemoglobin concentration less than 120 g/L for non-pregnant women and lactating women, and less than 110 g/L for pregnant women, adjusted for altitude and smoking.

Concepts:

Anaemia: condition in which the concentration of blood haemoglobin concentration falls below established cut-off values.

Iron deficiency: state in which there is insufficient iron to maintain the normal physiological function of blood, brain and muscles (ICD-11, 5B5K.0 iron deficiency)

Iron deficiency anaemia: (ICD-11, 3A00, iron deficiency anaemia)

Blood haemoglobin concentration: concentration of haemoglobin in whole blood

2.b. Unit of measure

Percent (%)

2.c. Classifications

WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, 2011 (WHO/NMH/NHD/MNM/11.1)(http://www.who.int/vmnis/indicators/haemoglobin.pdf, accessed [4 March 2021).

3.a. Data sources

The preferable source of data is population-based surveys. Data from surveillance systems may be used under some conditions, but recorded diagnoses are typically underestimated. Data are from the Micronutrients Database of the WHO Vitamin and Mineral Nutrition Information System (VMNIS) (https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system This database compiles and summarizes data on the micronutrient status of populations from various other sources, including data collected from the scientific literature and through collaborators, including WHO regional and country offices, United Nations organizations, ministries of health, research and academic institutions, and nongovernmental organizations. In addition, anonymized individual-level data are obtained from multi-country surveys, including demographic and health surveys, multiple indicator cluster surveys, reproductive health surveys and malaria indicator surveys.

3.b. Data collection method

The anaemia status of women is assessed using blood haemoglobin concentrations. In surveys, blood haemoglobin concentrations are typically measured using the direct cyanmethemoglobin method in a laboratory or with a portable, battery-operated, haemoglobin photometer in the field that uses the azide-methaemoglobin method.

A PubMed search was carried out for relevant search terms related to anaemia, haemoglobin and iron status, searching for studies published after 1 January 1990. In addition to indexed articles, many reports of national and international agencies were identified and accessed through requests to each corresponding organization. Data are also collected during the country validation process, described below, and from publicly available individual-level survey data.

3.c. Data collection calendar

Data on anaemia are continuously being collected from survey report and manuscripts and entered into the WHO Micronutrients Database.

3.d. Data release calendar

There is no fixed date in which the new round of anaemia estimates will be generated; however, estimates are generally generated every three to five years.

3.e. Data providers

There are two main data sources of survey data for anaemia: 1) reports generated by countries or implementing partners and 2) published manuscripts. Occasionally, Member States, regional offices, the international community or colleagues managing other databases within WHO provide reports directly to staff responsible for maintaining the WHO Micronutrients Database. If data meet the eligibility criteria, they are entered into the database. Reports and publications are primarily requested and collected from:

Ministries of Health through WHO regional and country offices,
National research and academic institutions,
Nongovernmental organizations, and
Organizations of the United Nations system.

3.f. Data compilers

WHO compiles the data fed into the Micronutrients Database of the WHO Vitamin and Mineral Information System (VMNIS).

3.g. Institutional mandate

The Vitamin and Mineral Nutrition Information System (VMNIS), formerly known as the Micronutrient Deficiency Information System (MDIS), was established in 1991 following a request by the World Health Assembly to strengthen surveillance of micronutrient deficiencies at the global level. Part of WHO's mandate is to assess the micronutrient status of populations, monitor and evaluate the impact of strategies for the prevention and control of micronutrient malnutrition, and to track related trends over time.

4.a. Rationale

Anaemia is highly prevalent globally, disproportionately affecting children and women of reproductive age. It negatively affects cognitive and motor development and work capacity, and among pregnant women iron deficiency anaemia is associated with adverse reproductive outcomes, including preterm delivery, low-birth-weight infants, and decreased iron stores for the baby, which may lead to impaired development. Iron deficiency is considered the most common cause of anaemia, but there are other nutritional and non-nutritional causes. Blood haemoglobin concentrations are affected by many factors, including altitude (metres above sea level), smoking, trimester of pregnancy, age and sex. Anaemia can be assessed by measuring blood haemoglobin, and when used in combination with other indicators of iron status, blood haemoglobin provides information about the severity of iron deficiency. The anaemia prevalence for the population is used to classify the public health significance of the problem.

4.b. Comment and limitations

Despite the extensive data search, data for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as child anthropometry (1, 24); this was especially true in the high-income countries of the WHO European Region. As a result, the estimates may not capture the full variation across countries and regions, tending to “shrink” towards global means when data are sparse.

Estimates may differ from those reported by countries.

4.c. Method of computation

Prevalence of anaemia and/or mean haemoglobin in women of reproductive age were obtained from 408 population-representative data sources from 124 countries worldwide. Data collected from 1995 to 2020 were used. Adjustment of data on blood haemoglobin concentrations for altitude and smoking was carried out whenever possible. Biologically implausible haemoglobin values (<25 g/L or >200 g/L) were excluded. A Bayesian hierarchical mixture model was used to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources.

Full details on statistical methods may be found here: Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data (Stevens et al, 2013).

Briefly, the model calculates estimates for each country and year, informed by data from that country and year themselves, if available, and by data from other years in the same country and in other countries with data for similar time periods, especially countries in the same region. The model borrows data, to a greater extent, when data are non-existent or weakly informative, and to a lesser degree for data-rich countries and regions. The resulting estimates are also informed by covariates that help predict blood haemoglobin concentrations (e.g. socio-demographic index, meat supply (kcal/capita), mean BMI for women and log of under-five mortality for children). The uncertainty ranges (credibility intervals) reflect the major sources of uncertainty, including sampling error, non-sampling error due to issues in sample design/measurement, and uncertainty from making estimates for countries and years without data.

4.d. Validation

Once survey data are compiled and the Bayesian hierarchical mixture model is run to generate anaemia estimates, countries are sent a memorandum to provide a background to the estimates and explain the process. Information on the survey data used to generate the estimates for that country, estimates for the years 2000, 2005, 2010, 2015, and 2019, and the resulting plots for each country are provided along with an explanation of the methodology used in generating the estimates. Countries are requested to provide feedback within six weeks.

4.e. Adjustments

Data on mean haemoglobin and anaemia prevalence from high-altitude countries that were not adjusted for altitude when published were adjusted for altitude by WHO, as described in Stevens et al (2013). The Bayesian hierarchical mixture internally adjusts summary statistics computed with non-standard haemoglobin cut-offs to match the standard WHO cut-offs listed above.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

A Bayesian hierarchical mixture model was used to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources. The full description of the methodology for country and region estimates can be found at Supplement to: Stevens GA, Finucane MM, De-Regil LM, et al. Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data. Lancet Glob Health 2013; 1: e16–25. Available at https://www.thelancet.com/cms/10.1016/S2214-109X(13)70001-9/attachment/e073f9da-1330-4a1d-a1a0-67caf08c11bf/mmc1.pdf.

At regional and global levels

Global and regional prevalence estimates were calculated as population-weighted averages of the constituent countries (see treatment of missing values at country level).

4.g. Regional aggregations

Global and regional prevalence estimates were calculated as population-weighted averages of the constituent countries (see methodology for deriving country-level estimates above).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

This indicator is part of the Global Nutrition Monitoring Framework (GNMF), for which operational guidance is offered to countries – the Global nutrition monitoring framework: Operational guidance for tracking progress in meeting targets for 2025 available at https://www.who.int/publications/i/item/9789241513609 in the six UN official languages.

WHO in collaboration with UNICEF, the US Centers for Disease Control and Prevention and Nutrition International updated a Micronutrient Survey Manual, containing details about conducting and national nutrition survey and reporting results.^[1]

Centers for Disease Control and Prevention, World Health Organization, Nutrition International, UNICEF. Micronutrient survey manual. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO. ↑

4.i. Quality management

All surveys included in the database pass through inclusion criteria described below. Data also follows the five WHO Data principles^[2].

WHO data principles. https://www.who.int/data/principles ↑

4.j. Quality assurance

Survey data provided in peer-reviewed publications or survey reports are screened for inclusion in the WHO Micronutrients Database. Eligibility criteria to the Micronutrients database include: details of the sampling method are provided; the sample was representative of at least the 1st administrative level (e.g. state, province, canton, oblast); the sample was population-based, household-based, or facility-based (i.e., for pregnant women, newborns, and preschool and school-age children); the sample was cross-sectional or was the baseline assessment in an intervention programme; and the study used standard, validated data collection techniques and laboratory methodology. If there are particular concerns regarding the reported data, attempts are made to discuss these concerns with a country representative.

4.k. Quality assessment

Data from the Micronutrients database passes an additional screening to be included into the estimates if a facility-based sampling scheme was used in order to exclude data where these would not be representative of the general population. The general threshold for inclusion was 80% affiliation of the target population with the facility. For studies of children sampled from primary care physician rosters or well-child visits, we included the data if national coverage of the third dose of DTP vaccine exceeded 80%. For women sampled from obstetric care providers, data were included if the coverage of at least one ANC care was greater than 80%. For school-based sampling of adolescents, the completion rate of lower secondary school for girls was required to be greater than 80%.

We excluded data if migrants comprised more than 40% of the population in the country, and the data source only covered nationals. Quality checks (e.g. implausible values that are not in according with life, ) are done when data is entered into the database, and when data is compiled for producing the estimates.

5. Data availability and disaggregation

Data availability:

Time series:

Estimates for 2000 to 2019 were derived in the latest exercise.

Disaggregation:

Anaemia prevalence data are generally reported disaggregated by age, sex, income, geographic region (within country) and 1^st administrative level within a country. When producing estimates of anaemia for the purpose of contributing to the monitoring of SDGs, estimates are produced for women of reproductive age (15-49 years) by pregnancy status (pregnant or non-nonpregnant) for each country. Data are then aggregated by WHO or UN region and for the global level.

6. Comparability/deviation from international standards

Sources of discrepancies:

Data conform to the standard WHO definition of anaemia.

7. References and Documentation

• WHO Global Anaemia estimates, 2021 Edition. Global anaemia estimates in women of reproductive age, by pregnancy status, and in children aged 6-59 months. Geneva: World Health Organization; 2021 (Available at https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children)$

• WHO Micronutrients database. Vitamin and Mineral Nutrition Information System (VMNIS). Geneva: World Health Organization; 2021 (Available at https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system)

• WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, 2011 (WHO/NMH/NHD/MNM/11.1) (Available at http://www.who.int/vmnis/indicators/haemoglobin.pdf)

• Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR , Branca F, Peña-Rosas JP, Bhutta ZA, Ezzati M, Nutrition Impact Model Study Group (Anaemia). Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995-2011: a systematic analysis of population-representative data. Lancet Glob Health. 2013 Jul;1(1):e16-25. doi: 10.1016/S2214-109X(13)70001-9. Epub 2013 Jun 25.

• WHO. Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition. Geneva: World Health Organization; 2014. (Available at https://apps.who.int/iris/bitstream/handle/10665/113048/WHO_NMH_NHD_14.1_eng.pdf)

• WHO. Global nutrition targets 2025: anaemia policy brief (WHO/NMH/NHD/14.4). Geneva: World Health (Available at https://www.who.int/publications/i/item/WHO-NMH-NHD-14.4) Organization; 2014.

• Global anaemia reduction efforts among women of reproductive age: impact, achievement of targets and the way forward for optimizing efforts. Geneva: World Health Organization; 2020. Licence: CC BY-NCSA 3.0 IGO. (Available at https://www.who.int/publications/i/item/9789240012202)

• Nutritional anaemias: tools for effective prevention and control. Geneva: World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGO (Available at http://apps.who.int/iris/bitstream/handle/10665/259425/9789241513067-eng.pdf)

• Every Woman Every Child. Global strategy for women's, children's and adolescents' health. New York: United Nations; 2015. (Available at https://www.who.int/life-course/partners/global-strategy/globalstrategyreport2016-2030-lowres.pdf

2.3.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment

0.c. Indicator

Indicator 2.3.1: Volume of production per labour unit by classes of farming/pastoral/forestry enterprise size

0.d. Series

Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2017 international $) (primary series) (PD_AGR_SSFP)

Productivity of large-scale food producers (agricultural output per labour day, PPP) (constant 2017 international $) (complementary series) (PD_AGR_LSFP)

0.e. Metadata update

2023-05-15

0.f. Related indicators

SDG indicator 2.3.2

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization (FAO)

1.a. Organisation

Food and Agriculture Organization (FAO)

2.a. Definition and concepts

Definition:

Volume of agricultural production of small-scale food producer in crop, livestock, fisheries, and forestry activities per number of days worked. The indicator is computed as a ratio of annual output to the number of working days in one year. As the indicator is referred to a set of production units – those of a small scale — the denominator needs to summarize information on the entire production undertaken in each unit. This requires that volumes of production are reported in a common numeraire, given that it is impossible to sum up physical units. The most convenient numeraire for aggregating products in the numerator is a vector of constant prices. When measured at different points in time, as required by the monitoring of the SDG indicators, changes in constant values represent aggregated volume changes.

FAO proposes to define small-scale food producers as producers who:

operate an amount of land falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of land size at national level (measured in hectares); and
operate a number of livestock falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of the number of livestock per production unit at national level (measured in Tropical Livestock Units – TLUs); and
obtain an annual economic revenue from agricultural activities falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of economic revenues from agricultural activities per production unit at national level (measured in Purchasing Power Parity Dollars) not exceeding 34,387 Purchasing Power Parity Dollars.

Concepts:

The following concepts are adopted for the computation of indicators 2.3.1:
Small-scale food producers are defined as those falling in the intersection of the bottom 40 percent of the cumulative distribution of land, livestock and revenues.
Tropical Livestock Units are a conversion scale used for standardization and measurement of the number of livestock heads. One TLU is the metabolic weight equivalent of one cattle in North America. The complete list of conversion factors can be found in the Guidelines for the preparation of livestock sector Reviews
The concept of productivity is standardized by OECD’s Manual for Measuring Productivity. This defines productivity as “a ratio of a volume measure of outputs to a volume measure of input use.” More information on possible definitions can be found in “Productivity and Efficiency Measurement in Agriculture: Literature Review and Gaps Analysis”.

2.b. Unit of measure

Constant PPP USD 2017.

2.c. Classifications

Not applicable

3.a. Data sources

Given that indicator 2.3.1 is measured on a target population of producers – those considered as small-scale – the ideal data source for measuring it is a single survey that collects all the information required with reference to individual production units. The most appropriate data source for collecting information on total value of agricultural production and on labour input adopted on the agricultural holding would be agricultural surveys. Other possibilities to be explored in absence of an agricultural surveys are:

household surveys integrated with an agricultural module,
agricultural censuses,
administrative data.

3.b. Data collection method

The target population of indicator 2.3.1. are small-scale producers for which the best data sources are agricultural surveys. These contain information on agricultural production, economic variables and labour input. However, agricultural surveys are not conducted in a systematic way, so they may be scattered in long time periods. FAO promotes the Agricultural Integrated Surveys (AGRIS) which collects data in a yearly basis for different modules, e.g. agricultural production.

Currently, the indicator is produced mainly using the Living Standards Measurement Study (LSMS) of the World Bank. Some countries contain an Integrated Surveys of Agriculture (LSMS-ISA). These surveys include information such as farm size, disaggregation by geographic areas, type of activities and type of households, values of output, values of production costs and number of work hours in different activities. Such surveys have data relevant to the computation of the indicators.

FAO, along with the World Bank and IFAD are compiling harmonized indicators of rural livelihoods with information on micro-level household data the LSMS surveys and other household surveys publicly available in the initiative called RuLIS (Rural Livelihoods Information System) which includes the indicators disaggregated by gender, rural areas, urban areas, income quintiles and income percentage that comes from agriculture.

Some of the datasets utilized to do the computation of the indicator 2.3.1. can be seen in Annex 1 of the document “Methodology for Computing and Monitoring the Sustainable Development Goal Indicators 2.3.1 and 2.3.2.” available in http://www.fao.org/3/ca3043en/CA3043EN.pdf and Annex 1 of the document “Rural Livelihoods Information System (RuLIS). Technical notes on concepts and definitions used for the indicators derived from household surveys” available in http://www.fao.org/3/ca2813en/CA2813EN.pdf.

3.c. Data collection calendar

The data collection calendar depends on the frequency of surveys required to compute the indicators. FAO is engaging with countries to include the questions needed to measure the indicator into their existing national surveys, i.e., household-based surveys, agricultural surveys and censuses through capacity development activities at national/ regional levels and provision of technical assistance needed to compute the indicator.

3.d. Data release calendar

The data release depends highly on the frequency of surveys required to compute the indicators.

3.e. Data providers

National Statistical Offices or other institutions involved in agricultural surveys, such as dedicated statistics offices of the Ministry of Agriculture.

3.f. Data compilers

Food and Agricultural Organization of the United Nations (FAO)

3.g. Institutional mandate

Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture. http://www.fao.org/3/K8024E/K8024E.pdf.

4.a. Rationale

The 2030 Sustainable Development Agenda has emphasized the importance of enhancing productivity of small-scale food producers, as these producers play an important role in the global production of food. The indicator monitors progress in this area, where the target is to double productivity by year 2030.

The enhancement of labour productivity in small-scale production units also has implications on poverty reduction, as small-scale food producers are often poor, and are frequently found to be close to subsistence conditions.

4.b. Comment and limitations

Given the approved methodology, the computation of the indicator requires survey microdata collected at the farm level on a wide range of variables – including all elements that allow computing revenues and costs of the enterprise, together with labour input and the availability of land and livestock – referred to the same production unit. Such type of surveys are seldom collected at the national level. For this reason, the availability of data for the indicator is altogether limited. In some countries, data can be obtained from household surveys reporting details on agricultural production. These data sources have to be considered as second-best solution, given that their sampling is focused on households and not on food production units. While in many countries there is a considerable degree of overlap between the population of food producers and households, this is still a partial overlap, which can undermine the accuracy of the computation.

4.c. Method of computation

Computation Method:

$S D G 2.3.1 = I_{2.3.1}^{t} = \sum_{j = 1}^{n} (\frac{\sum_{i} V_{i j}^{t} p_{i j}^{t}}{{L d}_{j}^{t}}) / n$

where:

$V_{i j}^{t}$ is the physical volume of agricultural product i sold by the small-scale food producer j during year t;

$p_{i j}^{t}$ is the constant sale price received by the small-scale food producer j for the agricultural product i during same year t;

${L d}_{j}^{t}$ is the number of labour days utilized by the small-scale food producer j during year t;

$n$ is the number of small-scale food producers.

As the indicator is referred to a set of production units – those of a small scale — the denominator needs to summarize information on the entire production undertaken in each unit. This requires that volumes of production are reported in a common numeraire, given that it is impossible to sum up physical units. The most convenient numeraire for aggregating products in the numerator is a vector of constant prices. When measured at different points in time, as required by the monitoring of the SDG indicators, changes in constant values represent aggregated volume changes.

4.d. Validation

FAO is responsible to check the syntaxes used in the computation of the indicator as well as the questions.

4.e. Adjustments

The productivity of small-scale food producers per labour day in the dataset is in local currency units (LCU). For each country and year, the LCU labour value of production has to be converted into PPP 2017 USD. The process first consists on accounting for inflation in the currency, for which the Consumer Price Index (CPI) of each country is used; once deflated, it is converted into PPP 2017 USD, which allows for a homogenous standard of the indicator. SDG 2.3 not only focuses on small-scale farmers, but also on women and people with indigenous status. The indicator (which is at the household level) is then calculated disaggregated by sex of the household head or producer (depending on whether a household or an agricultural survey was used), that is, whether the household head or producer is female or male headed.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Variables employed in the computation are subject to outlier detection, through Median Absolute Deviations and other approaches, on a case by case basis.

At regional and global levels

No imputation of data is made at the regional and global level.

4.g. Regional aggregations

No regional or global aggregates can be computed, given the limited availability of data.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries can rely on the methodology paper available at http://www.fao.org/3/ca3043en/ca3043en.pdf and the eLearning available at https://elearning.fao.org/course/view.php?id=483 .

4.i. Quality management

Logical and arithmetic control of reporting data is carried out.

4.j. Quality assurance

The microdata of surveys utilized in the computation are publicly available, hence their quality rests with the producers. The quality of the calculation was checked with a number of colleagues, and with two independent peer-reviewers of the RuLIS project.

4.k. Quality assessment

Qualitative assessment has been performed on the final estimations of the indicator, which was updated this year and compared with 2019 results. PPP conversion factors are retrieved from the World Bank and are constantly updated, which results in a change of conversion factors and therefore a slight modification in the results on indicator 2.3.1. from 2019 to 2021.

Some countries have data that needs to be assessed further, either checks on the raw data and/or the processing of data by the RuLIS team.

5. Data availability and disaggregation

Data availability:

Data is available for over 40 countries, including all 27 EU countries, 12 countries in Africa and two countries each in Asia and the Americas.

Time series:

A maximum of three data points is available for some countries.

Disaggregation:

Indicator 2.3.1 must be disaggregated by classes of farming/pastoral/forestry enterprise size. The overall SDG Target 2.3 requires specific focus on women, indigenous peoples, family farmers, pastoralists and fishers. For this reason, the indicator must be disaggregated by sex, type of enterprise and by community of reference.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable

7. References and Documentation

Note on the proposed “Methodology for Computing and Monitoring the Sustainable Development Goal Indicator 2.3.1 and 2.3.2”, Office of the Chief Statistician and Statistics Division, FAO, Rome https://www.fao.org/publications/card/en/c/CA3043EN/
Defining Small Scale Food producers to Monitor Target 2.3 of the 2030 Agenda for Sustainable Development. FAO Statistics Division Working Paper available at http://www.fao.org/3/a-i6858e.pdf

2.3.2

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

0.c. Indicator

Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous status

0.d. Series

Income of small-scale food producers (Average income from agriculture, PPP) (constant international $) (primary series) (SI_AGR_SSFP)

Income of large-scale food producers (Average income from agriculture, PPP) (constant international $) (complementary series) (SI_AGR_LSFP)

0.e. Metadata update

2023-05-15

0.f. Related indicators

SDG indicator 2.3.1

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization (FAO)

1.a. Organisation

Food and Agriculture Organization (FAO)

2.a. Definition and concepts

Definition:

SDG indicator 2.3.2 measures income from on-farm production activities, which is related to the production of food and agricultural products. This includes income from crop production, livestock production, fisheries and aquaculture production, and from forestry production.

The indicator is computed as annual income.

FAO proposes to define small-scale food producers as producers who:

operate an amount of land falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of land size at national level (measured in hectares); and
operate a number of livestock falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of the number of livestock per production unit at national level (measured in Tropical Livestock Units – TLUs); and
obtain an annual economic revenue from agricultural activities falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of economic revenues from agricultural activities per production unit at national level (measured in Purchasing Power Parity Dollars) not exceeding 34,387 Purchasing Power Parity Dollars.

Concepts:

The following concepts are adopted for the computation of indicators 2.3.2:

Small-scale food producers are defined as those falling in the intersection of the bottom 40 percent of the cumulative distribution of land, livestock and revenues.
Tropical Livestock Units are a conversion scale used for standardization and measurement of the number of livestock heads. One TLU is the metabolic weight equivalent of one cattle in North America. The complete list of conversion factors can be found in the Guidelines for the preparation of livestock sector Reviews
The computation of income is based on the resolution adopted by the 17th International Conference of Labour Statisticians (ICLS). Income should be computed by deducting from revenues the operating costs and the depreciation of assets.

2.b. Unit of measure

Constant PPP 2017 USD.

2.c. Classifications

Not applicable

3.a. Data sources

Given that indicator 2.3.2 is measured on a target population of producers – those considered as small-scale – the ideal data source for measuring them is a single survey that collects all the information required with reference to individual production units. The most appropriate data source for collecting information on agricultural production and the associated costs are agricultural surveys. Other possibilities to be explored in absence of an agricultural surveys are:

household surveys integrated with an agricultural module,
agricultural censuses,
administrative data.

3.b. Data collection method

The target population of indicator 2.3.2 are small-scale producers for which the best data sources are agricultural surveys. These contain information on agricultural production, economic variables and labour input. However, agricultural surveys are not conducted in a systematic way, so they may be scattered in long time periods. FAO promotes the Agricultural Integrated Surveys (AGRIS) which collects data in a yearly basis for different modules, e.g. agricultural production.

Some of the datasets utilized to do the computation of the indicator 2.3.2. can be seen in Annex 1 of the document “Methodology for Computing and Monitoring the Sustainable Development Goal Indicators 2.3.1 and 2.3.2.” available in http://www.fao.org/3/ca3043en/CA3043EN.pdf and Annex 1 of the document “Rural Livelihoods Information System (RuLIS). Technical notes on concepts and definitions used for the indicators derived from household surveys” available in http://www.fao.org/3/ca2813en/CA2813EN.pdf.

3.c. Data collection calendar

3.d. Data release calendar

The data release depends highly on the frequency of surveys required to compute the indicators.

3.e. Data providers

National Statistical Offices or other institutions involved in agricultural surveys, such as dedicated statistics offices of the Ministry of Agriculture.

3.f. Data compilers

Food and Agricultural Organization of the United Nations (FAO)

3.g. Institutional mandate

4.a. Rationale

The 2030 Sustainable Development Agenda has emphasized the importance of enhancing income of small-scale food producers, as these producers play an important role in the global production of food. The indicator monitors progress in this area, where the target is to double income by year 2030.

The enhancement of income of small-scale production units also has implications on poverty reduction, as small-scale food producers are often poor, and are frequently found to be close to subsistence conditions.

4.b. Comment and limitations

Given the approved methodology, the computation of the indicator requires survey microdata collected at the farm level on a wide range of variables – including all elements allowing to compute revenues and costs of the enterprise together with labour input and the availability of land and livestock – referred to the same production unit. Such type of surveys are seldom collected at the national level. For this reason, the availability of data for the indicator is altogether limited. In some countries, data can be obtained from household surveys reporting details on agricultural production. These data sources have to be considered as second-best solution, given that their sampling is focused on households and not on food production units. While in many countries there is a considerable degree of overlap between the population of food producers and households, this is still a partial overlap, which can undermine the accuracy of the computation.

4.c. Method of computation

Given i agricultural activities, including crops, livestock, fisheries and forestry activities, and j [1,…,n] small scale food producers defined as in the first section as a subset of all N [1,…,k] food producers, the SDG indicator 2.3.2 must be computed using the following formula:

$S D G 2.3.2 = I_{2.3.2}^{t} = \sum_{j = 1}^{n} (\sum_{i} {(V}_{i j}^{t} p_{i j}^{t} - C_{i j}^{t}) / n$

where:

$V_{i j}^{t}$ is the physical volume of agricultural product i sold by the small scale food producer j during year t;
$p_{i j}^{t}$ is the constant sale price received by the small scale food producer j for the agricultural product i during year t;
$C_{i j}^{t}$ is the production cost of agricultural product i supported by the small scale food producer j during year t;
$n$ is the number of small-scale food producer.

In details, physical volumes $V_{i k}^{t}$ are derived, for each k producer, from the following items:

Crop revenues: crop sold, crop for own consumption, crop used as feed, crop saved for seed, crop stored, crop used for by-products, crop given as gift, crop used for paying labour, crop used for paying rent, crop used for paying inputs, crop given out in sharecropping agreement (sharecrop out), crop wasted. Similar criteria apply for the computation of revenues from tree crops and forestry products.
Livestock revenues: livestock sold (alive), livestock gifts given away (component can only be kept if stock variation is possible to construct), livestock by-/products sold, livestock products self-consumed, livestock by-products self-used (also a cost in crop, for example dung used as fertilisers), livestock by-/products pay away, livestock by-/products credit away.
Forestry revenues: products sold, forestry products for own consumption, forestry products stored, forestry products used for paying labour, forestry products used for paying rent, forestry products used for paying inputs, forestry products given out in sharecropping agreement, Forestry products wasted.
Fisheries revenues: captured fresh fish sold, captured processed fish sold, captured fresh fish for own consumption, captured processed fish for own consumption, traded fresh fish sold, traded processed fish sold.

Production costs $C_{i j}^{t}$ are meant to include operating costs. These comprise all variable costs (payments in cash and kind of agricultural inputs as fertiliser, seeds, and occasional labour) and fixed costs (hired labour, land rent and technical assistance costs).

In more details, costs $C_{i j}^{t}$ generally include the following items:

Costs of crop activities: inputs paid in cash, land rent, technical assistance/extension costs, crop saved for seed, crop used for paying labour, crop used for paying rent, crop used for paying inputs, crop given out in sharecropping agreement (sharecrop out), crop wasted, crop used for producing by-products, total value of input purchased, including those reimbursed in kind
Costs of livestock activities: livestock bought, livestock additional expenditures, crop used as feed, technical assistance/extension costs for livestock,
Costs of forestry activities: input costs (seedlings, fertilisers, hired labour, etc.), machine rental costs, land rental costs, other related costs.
Costs of fisheries and aquaculture activities: fishing gear expenditures, hired labour expenditures, trading activities, fresh fish purchases, processed fish purchases, other related costs

To obtain comparable results across countries in the case of income, values must necessarily be expressed in International Dollars at Purchasing Power Parity (PPP $), based on the conversion provided by the World Bank International Comparison Project.

4.d. Validation

FAO is responsible to check the syntaxes used in the computation of the indicator as well as the questions.

4.e. Adjustments

The Average income of small-scale food producers in constant PPP 2011 USD is in the dataset in local currency units (LCU). For each country and year, the LCU labour value of production has to be converted into PPP 2011 USD. The process first consists on accounting for inflation in the currency, for which the Consumer Price Index (CPI) of each country is used; once deflated, it is converted into PPP 2011 USD, which allows for a homogenous standard of the indicator. SDG 2.3 not only focuses on small-scale farmers, but also on women and people with indigenous status. The indicator (which is at the household level) is then calculated disaggregated by sex of the household head or producer (depending on whether a household or an agricultural survey was used), that is, whether the household head or producer is female or male headed.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Variables employed in the computation are subject to outlier detection, through Median Absolute Deviations and other approaches, on a case by case basis.

At regional and global levels

No imputation of data is made at the regional and global level.

4.g. Regional aggregations

No regional or global aggregates can be computed, given the limited availability of data.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries can rely on the methodology paper available at http://www.fao.org/3/ca3043en/ca3043en.pdf and the eLearning available at https://elearning.fao.org/course/view.php?id=483 .

4.i. Quality management

Logical and arithmetic control of reporting data is carried out.

4.j. Quality assurance

4.k. Quality assessment

Qualitative assessment has been performed on the final estimations of the indicator, which was updated this year and compared with previous results. PPP conversion factors are retrieved from the World Bank and are constantly updated, which results in a change of conversion factors and therefore a slight modification in the results on indicator 2.3.2. from 2019 to 2021.

Some countries have data that needs to be assessed further, either checks on the raw data and/or the processing of data by the RuLIS team.

5. Data availability and disaggregation

Data availability:

Data availability is currently limited (though growing) around the world, and most of the available data points derive from suitable surveys in countries in Africa, Asia and Latin America. The limited data availability does not yet allow for producing regional and global aggregates.

Time series:

By 2030

Disaggregation:

Indicator 2.3.2 must be disaggregated by classes of farming/pastoral/forestry enterprise size. The overall SDG Target 2.3 requires specific focus on women, indigenous peoples, family farmers, pastoralists and fishers. For this reason, the indicator must be disaggregated by sex, type of enterprise and by community of reference.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable

7. References and Documentation

Note on the proposed “Methodology for Computing and Monitoring the sustainable Development Goal Indicator 2.3.1 and 2.3.2”, Office of the Chief Statistician and Statistics Division, FAO, Rome https://www.fao.org/3/ca3043en/CA3043EN.pdf

Defining Small Scale Food producers to Monitor Target 2.3 of the 2030 Agenda for Sustainable Development. FAO Statistics Division Working Paper available at http://www.fao.org/3/a-i6858e.pdf

2.4.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality

0.c. Indicator

Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture

0.d. Series

Primary series: Proportion of agricultural area under productive and sustainable agriculture (AG_LND_SUST)

Supplementary series:

Proportion of agricultural land area that has achieved an acceptable or desirable level of farm output value per hectare (AG_LND_FOVH)

Proportion of agricultural land area that has achieved an acceptable or desirable level of net farm income (AG_LND_NFI)

Proportion of agricultural land area that has achieved an acceptable or desirable level of risk mitigation mechanisms (AG_LND_RMM)

Proportion of agricultural land area that has achieved an acceptable or desirable level of soil degradation (AG_LND_SDGRD)

Proportion of agricultural land area that has achieved an acceptable or desirable level of variation in water availability (AG_LND_H2OAVAIL)

Proportion of agricultural land area that has achieved an acceptable or desirable level of management of fertilizers (AG_LND_FERTMG)

Proportion of agricultural land area that has achieved an acceptable or desirable level of management of pesticides (AG_LND_PSTCDSMG)

Proportion of agricultural land area that has achieved an acceptable or desirable level of use of agro-biodiversity supportive practices (AG_LND_AGRBIO)

Proportion of agricultural land area that has achieved an acceptable or desirable level of wage rate in agriculture (AG_LND_AGRWAG)

Proportion of agricultural land area that has achieved an acceptable or desirable level of food security (AG_LND_FIES)

Proportion of agricultural land area that has achieved an acceptable or desirable level of secure tenure rights to agricultural land (AG_LND_LNDSTR)

0.e. Metadata update

2023-05-15

0.f. Related indicators

Direct links to:

2.1.2 Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)

5.a.1 (a) Percentage of people with ownership or secure rights over agricultural land (out of total agricultural population), by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure

Indirect link to:

Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous status

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The scope of indicator 2.4.1 is the agricultural farm holding, and more precisely the agricultural land area of the farm holding, i.e. land used primarily to grow crops and raise livestock. This choice of scope is fully consistent with the intended use of a country’s agricultural land area as the denominator of the aggregate indicator. Specifically, the following are:

Included within scope:

Intensive and extensive crops and livestock production systems.
Subsistence agriculture.
State and common land when used exclusively and managed by the farm holding.
Food and non-food crops and livestock products (e.g. tobacco, cotton, and sheep wool).
Crops grown for fodder or for energy purposes.
Agro-forestry (trees on the agriculture areas of the farm).
Aquaculture, to the extent that it takes place within the agricultural land area. For example, rice-fish farming and similar systems.

Excluded from scope:

State and common land not used exclusively by the farm holding.
Nomadic pastoralism.
Production from gardens and backyards. Production from hobby farms^[1].
Holdings focusing exclusively on aquaculture.
Holdings focusing exclusively on forestry.
Food harvested from the wild.

Concepts:

The literature review (Hayati, 2017) identified a large number of potential sustainability themes across the three dimensions of sustainability and, for each theme, usually a large number of possible sub-indicators. The key considerations in the selection of themes are relevance and measurability. In terms of relevance, the relationship between the associated sub-indicator and sustainable agriculture outcomes at farm level should be strong. Following this approach, only sub-indicators that are responsive to farm level policies aimed at improving sustainable agriculture are considered. In terms of measurability, only a “core” set of themes and sub-indicators for which measurement and reporting is expected in the majority of countries are selected.

A key aspect of all approaches to measuring sustainable agriculture is the recognition that sustainability is a multi-dimensional concept, and that these multiple dimensions need to be reflected in the construction of the indicator. This implies that SDG indicator 2.4.1 must be based on a set of sub-indicators that cover these three dimensions.

Through a consultative process that has lasted over two years, 11 themes and sub-indicators have been identified, which make up SDG 2.4.1.

No.	Themes	Sub-indicators
1	Land productivity	Farm output value per hectare
2	Profitability	Net farm income
3	Resilience	Risk mitigation mechanisms
4	Soil health	Prevalence of soil degradation
5	Water use	Variation in water availability
6	Fertilizer pollution risk	Management of fertilizers
7	Pesticide risk	Management of pesticides
8	Biodiversity	Use of agro-biodiversity-supportive practices
9	Decent employment	Wage rate in agriculture
10	Food security	Food Insecurity Experience Scale (FIES)
11	Land tenure	Secure tenure rights to land

Please see the annex for a detailed description of the sub-indicators.

The countries will define hobby farms as per their national criteria and remove these farms from the population of interest for 2.4.1 until an international definition is available. ↑

2.b. Unit of measure

Percentage (%):

The member countries are required to report the proportion (percentage) of agriculture land area for all 11 sub-indicators separately by sustainability status. Aggregation at the national level is performed for each sub-indicator independently, by adding up the agricultural land area of each agriculture holding (selected through a nationally representative sample) and finally reporting the resulting national total as a percentage of the total nationally representative agriculture land area for the 11 sub-indicators in a dashboard.

2.c. Classifications

The land area classification is that implemented in the FAO Land Use, Irrigation and Agricultural Practices Questionnaire (http://www.fao.org/faostat/en/#data/RL/metadata), which is consistent with the classification of Census of Agriculture and the System of Environmental and Economic Accounts (SEEA).

3.a. Data sources

Different data are collected through different instruments. Often, environmental data are collected through environmental monitoring systems, including remote sensing. Yet many countries do not have the capacity or resources to do so, and therefore these data are sparse or non-existent. In order to propose a manageable and cost-effective solution, a requirement stressed by several countries during the consultations, the methodology offers a single data collection instrument for all sub-indicators: the farm survey.

Several countries have suggested using existing data sources or alternative data sources on the grounds that these instruments can be more cost-effective and sometimes provide more reliable results than farm surveys. These instruments include remote sensing, GIS, models, agricultural surveys, household surveys, administrative data or environmental monitoring systems. The methodology considers the possibility to use such instruments, subject to a series of criteria to ensure data quality and international comparability. Other data sources may also be used to complement and/or validate farm survey results.

The methodology note also recommends that countries complement the farm survey with a monitoring systems that can measure the impact of agriculture on the environment (soil, water, fertilizer and pesticide pollution, biodiversity, etc.) and on health (pesticides residues in food and human bodies). This will provide additional information and help crosscheck the robustness of SDG indicator 2.4.1 with regard to the environmental dimension of sustainability.

3.b. Data collection method

A questionnaire is sent to all countries annually since 2020 (http://www.fao.org/sustainable-development-goals/indicators/241/en/). Furthermore, in order to facilitate data collection by countries, a data module has been designed, which contains the core set of questions necessary to obtain the data for SDG 2.4.1. If farm surveys already exist within a country, these questions can be integrated into existing instruments in order to minimize the burden to National Statistical Offices (NSOs).

All data collection activities will be done through the NSO or the office designated to collect data for this indicator. FAO, together with the Global Strategy to improve Agriculture and Rural Statistics (GSARS), have developed the capacity development material necessary for this indicator, including a methodological guide, an enumerator manual, calculation document, sampling guidance and an e-learning course to train country NSO and other relevant staff on the indictor.

3.c. Data collection calendar

Data collection will depend on currently existing data collection cycles for farm surveys within countries. FAO has integrated the questionnaire module associated with this indicator in in AGRISurvey Programme and 50x2030 initiative.

3.d. Data release calendar

Although new data may not be available annually for each country, all new information is expected to be released annually through FAOSTAT.

3.e. Data providers

National Statistical Offices or designated offices within countries will be responsible for collecting data for this indicator.

3.f. Data compilers

National Statistical Offices or designated offices within countries will be responsible for collecting and compiling data for this indicator. They will in turn report to FAO who will provide capacity development, conduct quality control and disseminate the information through FAOSTAT. FAO will in turn report to the international statistical community and UNSD.

3.g. Institutional mandate

4.a. Rationale

The approaches to framing and defining sustainable agriculture vary in terms of their coverage of the three primary dimensions of sustainability, i.e. economic, environmental and social, and in terms of the scale that is used to assess sustainability, i.e. from field and farm scales, to national and global scales. Some approaches consider different features of sustainability, for example whether current practices are economically feasible, environmentally friendly and socially desirable. Other approaches focus on particular practices such as organic, regenerative or low-input agriculture and can equate these with sustainable agriculture.

The conclusion from a literature review associated with the methodological development of this indicator is that the multi-dimensional approach developed by FAO in 1988 is a meaningful framing of the concept. Thus, sustainable agriculture can be considered as “the management and conservation of the natural resource base, and the orientation of technological and institutional change in such a manner as to ensure the attainment and continued satisfaction of human needs for present and future generation. Such development (in agriculture, forestry and fishing etc.) conserves land, water, plant and animal genetic resources, environmentally non-degrading, technically appropriate, economically viable and socially acceptable.” (FAO, 1988)

4.b. Comment and limitations

During the consultations undertaken, several countries highlighted the difficulties in combining data from different sources and requested that this be avoided to the extent possible. Other, relatively data rich, countries, instead, insisted on the need to allow for the use of existing data sources. The updated methodology addresses both concerns: it offers the farm survey as a single data collection instrument for all sub-indicators, but it also offers the possibility of using a combination of different data sources as an alternative option as long as certain criteria are satisfied.

The decision to use the farm survey as a data collection instrument for this indicator is in line with countries’ efforts, supported by FAO, to develop farm surveys as the most appropriate tool for generating agricultural statistics. It also benefits from the FAO work in developing the Agricultural Integrated Survey (AGRIS) programme, which is implemented as part of a new initiative called 50 X 2030.

The decision to focus on farm survey has implications on the type of information that it is possible to capture in order to cover the different dimensions of sustainability. While farm surveys are well suited to measure the economic dimension of sustainability, they may not be the ideal tool for measuring environmental and social sustainability in terms of impact/outcomes.

Typically, environmental impacts of agriculture are measured through monitoring systems like remote sensing, soil and water sampling, or other tools associated with a specific area, rather than with a single agricultural holding. For several environmental themes, it is unlikely that farmers would be able to assess the environmental impact of their farming practices on issues like fertilizer pollution or pesticide impact. Using a farm survey instrument, instead of environmental monitoring systems, therefore implies moving from measuring outcome/impact to assessing farmers’ practices. Whenever possible, however, the revised methodology continues to focus on measuring outcomes.

Similarly, the sub-themes under the social dimension are usually best captured through household surveys. While in the majority of cases agricultural holdings are closely associated with a given household, this is not always the case, and therefore capturing the social dimension of sustainability through a farm survey, especially if it is not designed to cover social aspects could pose certain challenges.

4.c. Method of computation

The indicator is defined by the formula:

$S D G 2.4.1 = \frac{A r e a u n d e r p r o d u c t i v e a n d s u s t a i n a b l e a g r i c u l t u r e }{A g r i c u l t u r a l l a n d a r e a}$

This implies the need to measure both the extent of land under productive and sustainable agriculture (the numerator), as well as the extent of agriculture land area (the denominator).

The numerator captures the three dimensions of sustainable production: environmental, economic and social. It corresponds to agricultural land area of the farms that satisfy the sustainability criteria of the 11 sub-indicators selected across all three dimensions.
The denominator in turn the sum of agricultural land area (as defined by FAO) utilized by agricultural holdings that are owned (excluding rented-out), rented-in, leased, sharecropped or borrowed. State or communal land used by farm holdings is not included. Please see the methodological document prepared by FAO for a more detailed explanation.

Steps to calculate SDG 2.4.1 include:

Determining the scope of the indicator: The scope of Indicator 2.4.1 is the agricultural farm holding, and more precisely the agricultural land area of the farm holding, i.e., land used primarily to grow crops and raise livestock. Forestry, fisheries and aquaculture activities may be included to the extent that they are secondary activities conducted on the agricultural area of the farm holding, for example rice-fish farming and similar systems.
Determining the dimensions to be covered: Indicator 2.4.1 includes environmental, economic and social dimensions in the sustainability assessment.
Choosing the scale for the sustainability assessment: Indicator 2.4.1 is farm level with aggregation to higher levels.
Selecting the data collection instrument(s): It is recommended that indicator 2.4.1 be collected through a farm survey.
Selecting the themes within each dimension, and choosing a sub-indicator for each theme: The sub-indicators should satisfy a number of criteria (described in annex 1 for each sub-indicator, respectively).
Assessing sustainability performance at farm level for each sub-indicator: Specific sustainability criteria are applied in order to assess the sustainability level of the farm for each theme according to the respective sub-indicators.
Deciding the periodicity of monitoring the indicator: It is recommended to be collected at least every three years.
Modality of reporting the indicator: The set of sub-indicators are presented in the form of a dashboard. The dashboard approach offers a response in terms of measuring sustainability at farm level and aggregating it at national level.

The 2.4.1 methodology proposes reporting of indicator 2.4.1 through a national-level dashboard, presenting the different sub-indicators together but independently. The dashboard approach offers several advantages, including the possibility of combining data from different sources and identification of critical sustainability issues, facilitating the search for a balance between the three sustainability dimensions. As a result, countries can easily visualize their performance in terms of the different sustainability dimensions and themes, and understand where policy efforts can be focused for future improvements.

Computation of results and construction of the dashboard are performed for each sub-indicator separately using the ‘traffic light’ approach already defined for each sub-indicator: aggregation at national level is performed for each sub-indicator independently, by summing the agricultural land area of each agricultural holdings by sustainability category (red, yellow or green), and reporting the resulting national total as percentage of the total national agricultural land area of all agricultural farm holdings in the country. In practice, the reported value of Indicator 2.4.1 is determined by the results of most-limiting sub-indicator in terms of sustainability performance.

4.d. Validation

The data undergo comprehensive validation work that cover: detection of outliers, transmission errors and data consistency checks. Countries asked to examine the disseminated results for their country and either to confirm that they are correct or to provide remarks and/or revise data if they identify errors.

4.e. Adjustments

Adjustments to total national agricultural area may be made to correct for common areas that are out of scope with regards to the indicator methodology.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Partial non-response at individual level (farm holding) will be imputed using appropriate statistical techniques, such as nearest-neighbour algorithms. The decision on whether to impute or not and the choice of the method is a function of the nature of the variable to impute and the amount and type of data available for the imputation, such as the availability of auxiliary data coming from different sources (e.g. surveys, administrative information).

It is important to clearly distinguish missing data from non-applicable events. As specified above and in the sub-indicator methodology sheets, some sub-indicators can be recorded as ‘not applicable’ for a given farm. In this case, the farm will be considered sustainable from the perspective of the given sub-indicators.

At the country level, if and when data are provided using alternative sources for some of the sub-indicators, relevant notes to be provided by the country explaining the type, nature, source and time period of the data reported.

• At regional and global levels

No treatment of missing values will be carried out at the regional and global level. The regional and global estimates will be constructed using data of countries that have reported all 11 sub-indicators and/or those that have reported a sub-set of the 11 only if some of the sub-indicators are not applicable or irrelevant in the context of those country.

4.g. Regional aggregations

These data will be disseminated through FAOSTAT, the largest database of food and agricultural statistics. Therefore, the method of calculation will follow the international standard established by the database. In the case of this indicator, regional and global aggregates will be computed by weighting the national indicators according to the country’s agricultural area.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The methodology note provides a detailed description for the computation of the indicator on the basis of the farm survey.

The values for reporting indicator 2.4.1 can be calculated as follows:

${S D G 241}_{d} = \min_{n : 1 - 11} ({S I}_{d n})$

where:

SDG241_dis proportion of agricultural land area that have achieved the ‘desirable’ level

SI_{d n}is proportion of sub-indicator n that is classified as ‘desirable’

min refers to the minimum level of SI_{d n} at national level across all 11 sub-indicators

SDG241_dis proportion of agricultural area for which all sub-indicators are green.

${S D G 241}_{a + d} = \min_{n : 1 - 11} {{(S I}_{d} + {S I}_{a})}_{n}$

where:

SDG241_a+dis proportion of agricultural land area that have achieved at least the ‘acceptable’ level (estimated by excess, see note below)

SI_{d n}is proportion of sub-indicator n that is classified as ‘desirable’

SI_{a n}is proportion of sub-indicator n that is classified as ‘acceptable’

min refers to the minimum level of (SI_{d n}+ SI_{a n}) at national level across all 11 sub-indicators

SDG241_a+dis proportion of agricultural area for which all indicators are either green or yellow, an acceptable situation, but that could be improved.

${S D G 241}_{u} = 1 - S {D G 241}_{a + d} = \max_{n : 1 - 11} ({S I}_{u n})$

where:

SDG241_uis proportion estimated by default of agricultural area that is ‘unsustainable’ (see note below)

SI_{u n}is proportion of sub-indicator n that is classified as ‘unsustainable’

max refers to the highest value of SI_{u n} across all 11 sub-indicators at national level

SDG241_uis proportion of agricultural area for which at least one sub-indicator is unsustainable, and is therefore classified as unsustainable.

The performances of countries over time can be measured by the change in the value of SDG241_dand SDG241_a+d. An increase over time indicates improvement, while decrease indicates degradation.

4.i. Quality management

Standard quality management of the entire data reporting process and the data itself will be carried out in close coordination with countries to ensure the data reported conforms with the methodology and relevant international standards.

4.j. Quality assurance

FAO will work closely with countries for quality assurance. Not only will data collection for SDG 2.4.1 respect international standards, it will also adhere to FAO’s data quality assurance “Statistics Quality Assurance Framework” (http://www.fao.org/statistics/standards/en/).

4.k. Quality assessment

A qualitative assessment of the overall quality of the statistical outputs is provided in regular reports by summarizing the main strengths and possible quality deficiencies in country data, by sub-indicator.

5. Data availability and disaggregation

Data availability:

The indicator is currently in the Tier II category because few countries are able to report it. Data are expected to be collected either as part of existing farm surveys or through other data sources such as environmental monitoring systems, administrative data or household surveys.

Comprehensive capacity development efforts (using a mix of in person, mass online trainings and bilateral assistance) are underway to build countries capacities. The data will be reported by end of 2022, once the third and final round of the 3 years data collection and reporting cycle is completed

Time series:

SDG Indicator 2.4.1 measures progress towards more sustainable and productive agriculture over a three-year periodicity because for many sub-indicators, it is likely that changes will be relatively limited from a year to another. Furthermore, the 3-year periodicity will enable countries to have three data points on the indicator before 2030.

Disaggregation:

Indicator 2.4.1 is expected to be collected through farm surveys and the result expressed as a national value. However, the methodology is scale independent and can be adopted at any geographical level. In addition, the indicator can be disaggregated according to type of farming system (crop, livestock or mixed) and other characteristics of the farm e.g. household/non-household sector, irrigated/non-irrigated or gender of the farm holder.

6. Comparability/deviation from international standards

Sources of discrepancies:

Given that this is a Tier II indicator, no data currently exists for this indicator. Therefore, there are no discrepancies between national and sub-national data.

7. References and Documentation

FAO. 1988. Report of the FAO Council, 94th Session, 1988. FAO, Rome, Italy.
FAO. 2014. Building a common vision for sustainable food and agriculture: Principles and approaches, FAO, Rome, Italy.
FAO. 2017. Report from the Expert Group Meeting on SDG indicator 2.4.1. April, 2017. FAO, Rome, Italy.
FAO. 2018. Land Use Classification. In: SEEA Agriculture, Forestry and Fisheries, Annex I, pg. 120, 130-135. FAO and UNSD, Rome, Italy..
FAO. 2018. Report of the 26^th Committee on Agriculture, 1-5 October 2018. FAO, Rome, Italy.
Global Strategy for Improving Agricultural and Rural Statistics. 2017. Handbook on the Agricultural Integrated Survey. FAO, Rome, Italy.
FAO. 2020. SDG 2.4,1, methodological note. July, 2020. FAO, Rome, Italy.
Hayati, D. 2017. Literature Review: A Literature Review on Frameworks and Methods for Measuring and Monitoring Sustainable Agriculture. Technical Report n.22. Global Strategy Technical Report. FAO, Rome, Italy..

Annex: description of the sub-indicators

1. Farm output value per hectare

Dimension: Economic

Theme: Land Productivity

Land productivity is a measure of agricultural value of outputs obtained on a given area of land. Maintaining or improving the output over time relative to the area of land used is an important aspect in sustainability for a range of reasons. At farm level, the land productivity reflects technology and production processes for given agro-ecological conditions. In a broader sense, an increase in the level of land productivity enables higher production while reducing pressure on increasingly scarce land resources, commonly linked to deforestation and associated losses of ecosystem services and biodiversity.

Coverage: All farm types

Description:

The sub-indicator is described as farm output value per hectare (holdings that produce crops and livestock or its mix). Information on farm outputs and agricultural area should be standard information available from farm surveys thus providing a good basis for assessment at farm level.

Farm output value: The volume of agricultural output at farm level generally takes into account production of multiple outputs, e.g. crop types and crop and livestock combinations, etc. Since the volume of agricultural outputs is not measured in commensurate units (e.g. not all outputs are measured in tonnes, and tonnes of different output represent different products), it is necessary to establish an appropriate means of aggregation, in this case using a monetary unit. A simple way to enable aggregation is to reflect the multiple outputs produced by a single farm in terms of values (i.e. quantity multiplied by prices).
Farm agricultural land area: defined as the area of land used for agriculture within the farm^[2].

Sustainability criteria:

Distance from the 90^th percentile of the national distribution^[3]:

Green (desirable): Sub-indicator value is ≥ 2/3 of the corresponding 90^th percentile
Yellow (acceptable): Sub-indicator value is ≥ 1/3 and < 2/3 of the corresponding 90^th percentile
Red (unsustainable): Sub-indicator value is < 1/3 of the corresponding 90^th percentile

Data items:

Reference period: last calendar year

1. Quantities and farm gate prices (or value of production) of the 5 main crops and/or livestock products and by-products produced by the farm
2. Quantities and farm gate prices (or value of production) of other agricultural products (agro-forestry or aquaculture products etc.) produced by the farm
3. Agricultural land area of the holding

2. Net Farm Income:

Dimension: Economic

Theme: Profitability

An important part of sustainability in agriculture is the economic viability of the farm, driven to a large extent by its profitability. Profitability is measured using the net income that the farmer is able to gain from farming operations. Availability and use of information on farm economic performance, measured using profitability, will support better decision making both at micro and macro-economic level. Since performance measures drive behaviour, better information on performance can alter behaviour and decision-making by government and producers both in large-scale commercial farming and medium and small-scale subsistence agriculture.

Coverage: All farms types

Description:

The sub-indicator measures if the farm is consistently profitable over a 3-year period. The focus of this sub-indicator is on income from farming operations as distinct from the total income of the farming household, which may include other sources of income such as, for example, employment in local businesses by other family members, tourism activity, etc.

Formula^[4]:

$N F I = C R + Y_{k} - O E - D e p + V I C$

where:

NFI = Total Net Farm Income
CR = Total farm cash receipts including direct program payments
Y_k = Income in kind
OE = Total operating expenses after rebates (including costs of labour)
Dep = Depreciation
VIC = Value of inventory change

Definitions:

Net farm income refers to the return (both monetary and non-monetary) to farm operators for their labor, management and capital, after all production expenses have been paid (that is, gross farm income minus production expenses). It includes net income from farm production, the value of commodities consumed on the farm, depreciation, and inventory changes.
Gross farm income refers to the monetary and non-monetary income received by farm. Its main components include cash receipts from the sale of farm products, direct program payments to producers, other farm income (such as income from custom work), value of food and fuel produced and consumed on the same farm, and change in value of year-end inventories of crops and livestock^[5].
Farm cash receipts include revenues from the sale of agricultural commodities in local currency units that include sales of crops, livestock and its by-products.
Direct program payments to producers included in farm cash receipts represent the amounts paid under various government and private programs to individuals involved in agricultural production. The payments related to current agricultural production include subsidies to encourage production or to compensate producers for low market returns, payments to stabilize incomes and payments to compensate producers for crop or livestock losses caused by extreme climatic conditions, disease or other reasons and insurance payments.
Income-in-kind measures the value of the agricultural goods produced on farms and consumed by farm operator families. It is included to measure total farm production.
Operating expenses represent business costs incurred by farm businesses for goods and services used in the production process. Expenses include both purchase and self-produced items that are: property taxes, custom work, seeds, rent, fertiliser and lime, chemicals, machinery and building repairs, irrigation, fuel for heating and machines, wages, interest and business share of insurance premiums.
Depreciation charges account for the economic depreciation or for the loss in fair market value of the capital assets of the farm business. Calculated on farm buildings, farm machinery, and the farm business share of autos, trucks and the farm home, depreciation is generally considered to be the result of aging, wear and tear, and obsolescence. It represents a decrease in the potential economic benefits that can be generated by the capital asset.
Value of inventory change (VIC) measures the currency value of the physical change in producer-owned inventories. This concept is used to value total agricultural economic production. To calculate VIC, the change in producer-owned inventories (between the end and the beginning of a calendar year) is first derived and then multiplied by the average annual crop prices or value per animal. This calculation is different from the financial or accounting book value approach, which values the beginning and ending stocks, and then derives the change.
The VIC over all the major commodities can vary widely (depending on the size of the change of inventories and prices). The VIC can be either positive (when inventories are larger at the end of the year compared to the beginning levels) or negative (when year- end inventories are smaller than the levels at the beginning of the year). If the inventory levels are the same at the beginning and end of the year, VIC will be zero despite price changes.

Estimating profitability at a farm level will generally require compilation of basic farm financial records, i.e. daily, weekly, monthly or seasonal transactions in an organized way. In general, large commercial farms maintain detailed financial records however, in case of medium farms and small subsistence agriculture, record keeping is seldom practiced and in most of the countries it doesn’t exist at all.

In case when detailed data are not available at farm level, then estimates will be calculated based on farmer declaration of both outputs and inputs quantities and prices. In these cases, depreciation, variation of stocks and taxes may be neglected. This is described below as simplified option (1).

A simplified option (2) is also offered, based on farmer’s declaration of the agricultural holding’s profitability over the last three calendar years. It is recommended to use this simplified option only when other two options are not feasible.

Sustainability criteria:

For a farm to be profitable the net farm income should be above zero.

Green (desirable): above zero for past 3 consecutive years
Yellow (acceptable): above zero for at least 1 of the past 3 consecutive years
Red (unsustainable): below zero for all of the past 3 consecutive years

Data items:

Reference period: last three calendar years

Recommended option:

Data from farm financial records, i.e. daily, weekly, monthly or seasonal transactions are collected in an organized way (in general, large commercial farms maintain detailed financial records on the basis of which the NFI can be calculated as per above equation).

Simplified option (1):

To be used when the detailed data are not available at farm level (better adapted to smallholders and household sector).

1. Quantity produced (i.e. crops and livestock and its products and by-products produced both for market or self-consumption)
2. Farm gate prices of the above quantities produces
3. Operating expenses including inputs quantities and its market prices
4. Quantity/output of other on-farm activities carried out and/or commodities produced on the holding e.g. aquaculture, agroforestry and others
5. Farm gate prices of the other on-farm activities/commodities
6. Input quantities and prices that are used to produce other on-farm outputs

Simplified option (2):

1. Respondent’s declaration on agricultural holding’s profitability over the last 3 calendar years

3. Risk mitigation mechanisms

Dimension: Economic

Theme: Resilience

Resilience encompass absorptive, anticipatory and adaptive capacities and refers to the properties of a system that allows farms to deal with shocks and stresses, to persist and to continue to be well-functioning (in the sense of providing stability, predictable rules, security and other benefits to its members).

Coverage: All farms types

Description:

This sub-indicator measures the incidence of the following mitigation mechanisms:

Access to or availed credit^[6]
Access to or availed insurance
On farm diversification (share of a single agricultural commodity not greater than 66% in the total value of production of the holding)

Access to credit and/or insurance is defined here as when a given service is available and the holder has enough means to obtain the service (required documents, collateral, positive credit history, etc.). Broadly, access to one or more the above 3 factors will allow the farm to prevent, resist, adapt and recover from external shocks such as, floods, droughts, market failure (e.g. price shock), climate shock and pest/animal diseases.

Sustainability criteria:

A farm holding is considered resilient if it has availed or has the means to access the risk mitigation mechanisms as follows:

Green (desirable): Access to or availed at least two of the above-listed mitigation mechanisms.
Yellow (acceptable): Access to or availed at least one of the above-listed mitigation mechanisms.
Red (unsustainable): No access to the listed mitigation mechanisms.

Data items:

Reference period: last calendar year

3.1. Agricultural holding access to or availed of credit, insurance or other financial instruments:

Credit (both formal and informal)
Insurance

3.2 List of other on-farm activities apart from crops and livestock

3.3 Value of output for the listed on-farm activities/commodities

4. Prevalence of soil degradation

Dimension: Environmental

Theme: Soil health

Many of the processes affecting soil health are driven by agricultural practices. FAO and the Intergovernmental Technical Panel on Soils (ITPS) have identified 10 main threats to soil functions: soil erosion; soil organic carbon losses; nutrient imbalance; acidification; contamination; waterlogging; compaction; soil sealing; salinization and loss of soil biodiversity.

Coverage: All farms types

Description:

The sub-indicator measures the extent to which agriculture activities affects soil health and therefore represents a sustainability issue. A review of the 10 threats to soil shows that all except one (soil sealing, which is the loss of natural soil to construction/urbanisation) are potentially and primarily affected by inappropriate agricultural practices. Ideally, therefore, all soils under agricultural land area in a country should be the subject of periodic monitoring in order to assess the impact of agriculture on soils. This requires detailed surveys and sampling campaigns, associated with laboratory testing. In order to propose a manageable solution while capturing the main trends in the country in terms of soil health, the farm survey focuses on the four threats that combine the characteristics more widespread (for national monitoring, countries may choose to add any of the other areas indicated above, depending on relevance), and easier to assess through farm surveys:

Soil erosion
Reduction in soil fertility
Salinization of irrigated land
Waterlogging
Other - Specify

The farm survey captures farmer’s knowledge about the situation of the agricultural holding in terms of soil degradation. Experience has shown that farmers are very much aware of the state of their soils, health and degradation level. Farmers may also be offered the opportunity to mention other threats than the above four.

Other data sources on soil health may either complement the information collected through the farm survey and offer opportunities for cross-checking farmers’ responses; or be used as alternative sources of data. Prior to the farm survey, a desk study could collect all available information on soil health, including using national official statistics or statistics available from international agencies such as FAO. This typically includes maps, models, results from soil sampling, laboratory analysis and field surveys, and all existing report on soil and land degradation at national level. On the basis of this information, maps or tables (by administrative boundaries or other divisions of the country) can be established, showing the threats to soils according to the above 4 categories of threats.

Sustainability criteria:

Proportion of agricultural area of the farm affected by soil degradation.

Green (desirable): The combined area affected by any of the four selected threats to soil health is negligible (less than 10% of the total agriculture area of the farm).
Yellow (acceptable): The combined area affected by any of the four selected threats to soil health is between 10% and 50% of the total agriculture area of the farm.
Red (unsustainable): The combined area affected by any of the four selected threats to soil health is above 50% of the total agriculture area of the farm.

Data items:

Reference period: last three calendar years

4.1 List of soil degradation threats experienced on the holding

- Soil erosion (loss of topsoil through wind or water erosion)
- Reduction in soil fertility^[7]
- Salinization of irrigated land
- Waterlogging
- Other – Specify
- None of the above

4.2 Total area of the holding affected by threats related to soil degradation

5. Variation in water availability

Dimension: Environmental

Theme: Water use

Agriculture, more specifically irrigated agriculture, is by far the main economic sector using freshwater resources. In many places, water withdrawal from rivers and groundwater aquifers is beyond what can be considered environmentally sustainable. This affects both rivers and underground aquifers. Sustainable agriculture therefore requires that that level of use of freshwater for irrigation remains within acceptable boundaries. While there are no internationally agreed standards of water use sustainability, signals associated with unsustainable use of water typically include progressive reduction in the level of groundwater, drying out of springs and rivers, increased conflicts among water users.

Coverage: All farm types

Description:

The sub-indicator captures the extent to which agriculture contributes to unsustainable patterns of water use. Ideally, the level of sustainability in water use is measured at the scale of the river basin or groundwater aquifer, as it is the combined effect of all users sharing the same resource that impact water sustainability. The farm survey captures farmers’ awareness and behaviour in relation with water scarcity, and associates them with three levels of sustainability. These awareness and behaviour are expressed in terms of:

whether the farmer uses water to irrigate crops on at least 10% of the agriculture area of the farm and why, if the answer is negative (does not need, cannot afford);
whether the farmer is aware about issues of water availability in the area of the farm and notices a reduction in water availability over time;
whether there are organizations (water users organisations, others) in charge of allocating water among users and the extent to which these organisations are working effectively.

Other data sources may either complement the farm survey on water use and offer opportunities for cross-checking farmers’ responses; or be used as alternative sources of data. Prior to the farm survey, a desk study should collect all available information on water balance, including national official statistics or statistics available from international agencies such as FAO. Information on water resources and use is usually collected by the entities in charge of water management or monitoring and are organised by hydrological entity (river basin or groundwater aquifer). They typically include hydrological records (river flow, groundwater levels), models and maps showing the extent of water use by hydrological entity.

Sustainability criteria:

Farm sustainability in relation with water use will be assessed as follows:

Green (desirable): Water availability remains stable over the years, for farms irrigating crops on more than 10% of the agriculture area of the farm. Default result for farms irrigating less than 10% of their agricultural area
Yellow (acceptable): uses water to irrigate crops on at least 10% of the agriculture area of the farm, does not know whether water availability remains stable over the years, or experiences reduction on water availability over the years, but there is an organisation that effectively allocates water among users.
Red (unsustainable): in all other cases.

Data items:

Reference period: last three calendar years

5.1 Irrigated agricultural area of the holding

5.2 Reduction in water availability experienced on the holding

5.3 Existence of organizations dealing with water allocation

6. Management of fertilizers

Dimension: Environmental

Theme: Fertilizer pollution risk

Agriculture can affect the quality of the environment through excessive use or inadequate management of fertilizers. Sustainable agriculture implies that the level of chemicals in soil and water bodies remains within acceptable thresholds. Integrated plant nutrient management considers all sources of nutrients (mineral and organic) and their management in order to obtain best nutrient balance. Measuring soil and water quality captures the extent and causes of pollution, but establishing monitoring systems of soil and water is costly and not always feasible in countries.

Note: the management of plant nutrients addresses two sustainability issues: avoiding pollution, and maintaining a good level of soil fertility. This sub-indicator addresses the first issue, while the second one is addressed under sub-indicator 4 ‘Soil health’.

Coverage: All farm types

Description:

The proposed approach is based on questions to farmers about their use of fertilizer, in particular mineral or synthetic fertilizers and animal manure, their awareness about the environmental risks associated with fertilizer and manure applications, and their behaviour in terms of plant nutrient management^[8]. Management measures considered to help reducing risk is as follows:

Follow protocols as per extension service or retail outlet directions or local regulations, not exceeding recommended doses
Use organic source of nutrients (including manure or composting residues) alone, or in combination with synthetic or mineral fertilizers
Use legumes as a cover crop, or component of a multi/crop or pasture system to reduce fertilizer inputs
Distribute synthetic or mineral fertilizer application over the growing period
Consider soil type and climate^[9] in deciding fertilizer application doses and frequencies
Use soil sampling at least every 5 years to perform nutrient budget calculations
Perform site-specific nutrient management or precision farming^[10]
Use buffer strips along water courses.

Sustainability criteria:

Farm sustainability in relation with fertilizer pollution risk will be assessed as follows:

Green (desirable): The farm takes specific measures to mitigate environmental risks (at least four from the list above). Default result for farms not using fertilizers^[11].
Yellow (acceptable): The farm uses fertilizers and takes at least two measures from the above list to mitigate environmental risks
Red (unsustainable): The farm uses fertilizer and takes less than two of the above specific measures to mitigate environmental risks associated with their use.

Data items:

Reference period: last calendar year

6.1 Use of synthetic or mineral fertilizer or animal manure/slurry by the agricultural holding (Y/N)

6.2 Specific measures taken to mitigate the environmental risks associated with the excessive use or misuse use of fertilizers as per list below:

⃝ 1 Follow protocols as per extension service or retail outlet directions or local regulations, not exceeding recommended doses

⃝ 2 Use organic source of nutrients (including manure or composting residues) alone, or in combination with synthetic or mineral fertilizers

⃝ 3 Use legumes as a cover crop, or component of a multi/crop or pasture system to reduce fertilizer inputs

⃝ 4 Distribute synthetic or mineral fertilizer application over the growing period

⃝ 5 Consider soil type and climate in deciding fertilizer application doses and frequencies

⃝ 6 Use soil sampling at least every 5 years to perform nutrient budget calculations

⃝ 7 Perform site-specific nutrient management or precision farming

⃝ 8 Use buffer strips along water courses.

7. Management of pesticides

Dimension: Environmental

Theme: Pesticide risk

Pesticides are important inputs in modern agriculture (crop and livestock), but if not well managed they can cause harm to people’s health or to the environment. Practices associated with integrated pest management (IPM^[12]) exist that contribute to minimise risks associated with the use of pesticides and limit their impact on human health and on the environment. The International Code of Conduct on Pesticide Management defines best practice in pesticide management.

Coverage: All farm types

Description:

The proposed sub-indicator is based on information on the use of pesticides on the farms, the type of pesticide used and the type of measure(s) taken to mitigate the associated risks^[13]. It considers the possibility that the holding uses pesticides in the framework of an Integrated Pest Management (IPM) program, or adopts specific measures to help reducing risks associated with pesticide use. List of possible measures:

Health:

Adherence to label directions for pesticide use (including use of protection equipment while applying pesticides)
Maintenance and cleansing of protection equipment after use
Safe disposal of waste (cartons, bottles and bags)

Environment:

Adherence to label directions for pesticide application
Adopt any of these good practices: adjust planting time, apply crop spacing, crop rotation, mixed cropping or inter-cropping
Perform biological pest control or use biopesticides
Adopt pasture rotation to suppress livestock pest population
Systematic removal of plant parts attacked by pests
Maintenance and cleansing of spray equipment after use
Use one pesticide no more than two times or in mixture in a season to avoid pesticide resistance

Sustainability criteria:

Farm sustainability in relation with pesticides will be assessed as follows:

Green (desirable): The farm uses only moderately or slightly hazardous^[14] pesticides (WHO Class II or III). In this case, it adheres to all three health-related measures and at least four of the environment-related measures. Default result for farms not using pesticides.
Yellow (acceptable): The farm uses only moderately or slightly hazardous pesticides (WHO Class II or III) and takes some measures to mitigate environmental and health risks (at least two from each of the lists above)
Red (unsustainable): The farm uses highly or extremely hazardous pesticides (WHO Class Ia or Ib), illegal pesticides^[15], or uses moderately or slightly hazardous pesticides without taking specific measures to mitigate environmental or health risks associated with their use (fewer than two from each of the lists above).

Data items:

Reference period: last calendar year

7.1 Use of pesticides for crop or livestock by the agricultural holding (Y/N)

7.2 Use of highly or extremely hazardous pesticides by the agricultural holding (Y/N)

7.3 Measures taken to protect people from health-related risks associated with pesticides:

Adherence to label directions for pesticide use, including use of personal protection equipment (Y/N)
Maintenance and cleansing of protection equipment after use (Y/N)
Safe disposal of waste (cartons, bottles and bags) (Y/N)

7.4 Measures taken to avoid environment-related risks associated with pesticides:

Adherence to label directions for pesticide application (Y/N)
Adjustment of planting time (Y/N)
Application of crop spacing (Y/N)
Application of crop rotation (Y/N)
Application of mixed cropping (Y/N)
Application of inter-cropping (Y/N)
Perform biological pest control (Y/N)
Use of biopesticides (Y/N)
Adopting pasture rotation to suppress livestock pest population (Y/N)
Systematic removal of plant parts attacked by pests (Y/N)
Maintenance and cleansing of spray equipment after use (Y/N)
Use one pesticide no more than two times or in mixture in a season to avoid pesticide resistance (Y/N)

8. Use of agro-biodiversity-supportive practices

Dimension: Environmental

Theme: Biodiversity

The Convention on Biological Diversity (CBD) stresses the close relationship between agriculture activities and biodiversity, considering three levels of biodiversity: genetic level diversity; agrobiodiversity at production system level; and ecosystem level (wild) biodiversity. The way agriculture is practiced influences all three levels. Attempts to develop indicators of biodiversity for agriculture systematically consider a large number of sub-indicators, with no universally agreed sustainability criteria. Considering these constraints, and the importance of addressing biodiversity in the construction of Indicator 2.4.1, it is proposed to develop a sub-indicator that captures the efforts towards more sustainable agriculture that better contributes to biodiversity, by identifying a limited list of practices that are conducive to biodiversity conservation.

Coverage: All farm types

Description:

This sub-indicator measures the level of adoption of more sustainable agricultural practices that better contribute to biodiversity by the farm at ecosystem, species and genetic levels. This indicator addresses both crops and livestock. Specifically, in case of this sub-indicator the scope is the entire area of the farm holding as opposed to the agricultural area that is used for rest of the 10 sub-indicators.

In particular, two separate scoring systems depending on the applicability of the organic farming criterion have been proposed.

Depending on whether organic certification system exists, countries will select one of the below two proposed set of criteria and thus will be evaluated/scored differently in terms of their sustainability status. According to this formulation, to secure green status, farms with organic certification, will have to check 3 out of 6 criteria. On the contrary, farms operating with no organic certification, will have to check 2 out of 5 criteria for obtaining the green status.

The detailed formulation of the criteria for the 2 scoring systems is described below:

Criteria for group of holdings with organic certification systems/schemes:
Leaves at least 10% of the holding area for natural or diverse vegetation. This can include natural pasture/grassland, maintaining wildflower strips, stone and wood heaps, trees or hedgerows, natural ponds or wetlands.
Farm produces agricultural products that are organically certified, or its products are undergoing the certification process.
Farm does not use medically important antimicrobials as growth promoters.
At least two of the following contribute to farm production: 1) temporary crops, 2) pasture, 3) permanent crops, 4) trees on farm, 5) livestock or animal products, and 6) aquaculture.
Practices crop or crop/pasture rotation involving at least 2 crops or crops and pastures on at least 80% of the farm agriculture area (excluding permanent crops and permanent pastures) over a period of 3 years. In case of a 2-crop rotation, the 2 crops have to be from different plant genus, e.g. a grass plus a legume, or a grass plus a tuber etc.
Livestock includes locally adapted breeds^[16].

Sustainability status:

- Green (desirable): The agricultural holding meets at least three of the above criteria
- Yellow (acceptable): The agricultural holding meets one or two of the above criteria
- Red (unsustainable): The agricultural holding meets none of the above criteria

Criteria for group of holdings with no organic certification systems/schemes:
Leaves at least 10% of the holding area for natural or diverse vegetation. This can include natural pasture/grassland, maintaining wildflower strips, stone and wood heaps, trees or hedgerows, natural ponds or wetlands.
Farm does not use medically important antimicrobials as growth promoters.
At least two of the following contribute to farm production: 1) temporary crops, 2) pasture, 3) permanent crops, 4) trees on farm, 5) livestock or animal products, and 6) aquaculture
Practices crop or crop/pasture rotation involving at least 2 crops or crops and pastures on at least 80% of the farm cultivated area (excluding permanent crops and permanent pastures) over a period of 3 years. In case of a 2-crop rotation, the 2 crops have to be from different plant genus, e.g. a grass plus a legume, or a grass plus a tuber etc.
Livestock includes locally adapted breeds.

Sustainability status:

- Green (desirable): The agricultural holding meets at least two of the above criteria
- Yellow (acceptable): The agricultural holding meets one of the above criteria
- Red (unsustainable): The agricultural holding meets none of the above criteria

Data items:

Reference period: last calendar year

8.1 Percentage of the holding area covered by natural or diverse vegetation (not cultivated), including natural pasture or grasslands; wildflower strips; stone or wood heaps; trees or hedgerows; natural ponds or wetlands

8.2 Farm produced products (crops and/or livestock) that are organically certified (Y/N)

8.3 Farm produced products (crops and/or livestock) that are undergoing organic certification (Y/N)

8.4 Report the holding organic certification number

8.5 Report the name of organic certifying body

8.6 Area on which certified organic [CROP/LIVESTOCK] was produced

8.7 Use of medically important antimicrobials as growth promoter for livestock (Y/N)

8.8 Value of production of the holding (covered by sub-indicator 1)

⃝ 1 Temporary crops

⃝ 2 Pastures

⃝ 3 Permanent crops

⃝ 4 Trees on farm

⃝ 5 Livestock and animal products

⃝ 6 Aquaculture

8.9 Percentage of the cultivated area on which crop rotation or crop/pasture rotation involving at least two crops (excluding permanent crops and permanent pastures) from different plant genus is practiced over a 3 year period

8.10 Area of the agricultural holding covered by the (up to 5) main crops listed for sub-indicator 1 (excluding pasture)

8.11 List of different breeds and cross-breed and percentage of animals they represent for each animal species

9. Wage rate in agriculture

Dimension: Social

Theme: Decent employment

The theme provides information on the remuneration of employees working for the farm and belonging to the elementary occupation group, as defined by the International Standard Classification of Occupation (ISCO-08 - code 92). It informs about economic risks faced by unskilled workers (those performing simple and routine tasks) in terms of remuneration received, the later benchmarked against the minimum wage set at national level in the agricultural sector. This sub-indicator allows distinguishing between holdings that pay a fair remuneration to its employees under the elementary occupation group, and agricultural holdings paying a remuneration to their employees belonging to the elementary occupation group that is below the minimum wage standard. In the latter case, agricultural holdings are deemed to be non-sustainable since the remuneration paid is not sufficient to ensure a decent living standard.

Coverage: Not applicable to farms that employ only family labour.

Description:

The sub-indicator measures the farm unskilled labour daily wage rate in Local Currency Units (LCU).

$D a i l y w a g e r a t e o f u n s k i l l e d h i r e d l a b o r = \frac{T o t a l a n n u a l c o m p e n s a t i o n }{T o t a l a n n u a l h o u r s w o r k e d } * 8 h o u r$

Where compensation is both monetary and in kind payments expressed in Local Currency Units (LCU)

Sustainability criteria:

Unskilled labour wage rate in relation to national or agriculture sector minimum wage rate. In case there is no national or agriculture sector minimum wage rate, the national poverty line is used instead:

Green (desirable): If the wage rate paid to unskilled labour is above the minimum national wage rate or minimum agricultural sector wage rate (if available). Default result for farms not hiring labour.
Yellow (acceptable): if the wage rate paid to unskilled labour is equals to the minimum national wage rate or minimum agricultural sector wage rate (if available).
Red (unsustainable): if the wage rate paid to unskilled labour is below the minimum national wage rate or minimum agricultural sector wage rate (if available).

Data items:

Reference period: last calendar year

9.1 Unskilled workers hired on the agricultural holding (Y/N)

9.2 Average pay in-cash and/or in-kind paid to the hired unskilled worker per day (of 8 hours)

9.3 Minimum agricultural sector wage rate (if available) or minimum national wage rate

10. Food Insecurity Experience Scale (FIES)

Dimension: Social

Theme: Food security

FIES is a metric of severity of food insecurity at the household level that relies on people’s direct yes/no responses to eight simple questions regarding their access to adequate food. It is a statistical measurement scale similar to other widely-accepted statistical scales designed to measure unobservable traits such as aptitude/intelligence, personality, and a broad range of social, psychological and health-related conditions.

Coverage: Only household farms

Description:

The Food Insecurity Experience Scale (FIES) produces a measure of the severity of food insecurity experienced by individuals or households, based on direct interviews.

The FIES questions refer to the experiences of the individual respondent or of the respondent’s household as a whole. The questions focus on self-reported food-related behaviors and experiences associated with increasing difficulties in accessing food due to resource constraints.

The FIES is derived from two widely-used experience-based food security scales: the U.S. Household Food Security Survey Module and the Latin American and Caribbean Food Security Scale (Spanish acronym ELCSA). It consists of a set of eight short yes/no questions asked directly to people. The questions focus on self-reported, food-related behaviours and experiences associated with increasing difficulties in accessing food due to resource constraints. The FIES is based on a well-grounded construct of the experience of food insecurity composed of three domains: uncertainty/anxiety, changes in food quality, and changes in food quantity.

This sub-indicator is SDG indicator 2.1.2, contextualised for a farm survey.

Sustainability criteria: Level on FIES scale

Green (desirable): Mild food insecurity^[17]
Yellow (acceptable)^[18]: Moderate food insecurity
Red (unsustainable): Severe food insecurity

Data items:

Reference period: last 12 months

10.1 The respondent’s recollection that he/she (or any other adult in the household) would be worried about not having enough food to eat due to lack of money or other resources

10.2 The respondent’s recollection that he/she (or any adult in the household) was unable to eat healthy and nutritious food because of lack of money or other resources

10.3 The respondent’s recollection that he/she (or any adult in the household) only ate a few kinds of food due to lack of money or other resources

10.4 The respondent’s recollection that he/she (or any adult in the household) had to skip a meal because there was no enough money or other resources for food

10.5 The respondent’s recollection that he/she (or any adult in the household) ate less than he/she thought he should due to lack of money or other resources

10.6 The respondent’s recollection that his/her household ran out of food because of a lack of money or other resources

10.7 The respondent’s recollection that he/she (or any adult in the household) was hungry but not eating due to lack of money or other resources for food

10.8 The respondent’s recollection that he/she (or any adult in the household) did not eat for a whole day because of a lack of money or other resources

11. Secure tenure rights to land

Dimension: Social

Theme: Land tenure

The sub-indicator allows assessing sustainability in terms of rights over use of agricultural land areas. Since agricultural land is a key input for agricultural production, having secure rights over land ensures that the agricultural holding controls such a key asset and does not risk losing the land used by the holding for farming.

Evidence shows that farmers tend to be less productive if they have limited access to and control of economic resources and services, particularly land. Long-lasting inequalities of economic and financial resources have positioned certain farmers at a disadvantage relative to others in their ability to participate in, contribute to and benefit from broader processes of development.

As such, adequate distribution of economic resources, particularly land, help ensure equitable economic growth, contributes to economic efficiency and has a positive impact on key development outcomes, including poverty reduction, food security and the welfare of households.

This sub-indicator is SDG indicator 5.a.1, customised for SDG indicator 2.4.1.

Coverage: All farms types

Description:

The sub-indicator measures the ownership or secure rights over use of agricultural land areas using the following criteria:

Formal document issued by the Land Registry/Cadastral Agency
Name of the holder listed as owner/use right holder on legally recognized documents
Rights to sell any of the parcel of the holding
Rights to bequeath any of the parcel of the holding

Sustainability criteria:

Level of security of access to land.

Green (desirable): has a formal document with the name of the holder/holding on it, or has the right to sell any of the parcel of the holding, or has the right to bequeath any of the parcel of the holding
Yellow (acceptable): has a formal document even if the name of the holder/holding is not on it
Red (unsustainable): no positive responses to any of the 4 questions above

Data items:

Reference period: last calendar year

11.1 Type of formal document for any of the agricultural land of the holder/holding that it holds (alternatively ‘possess, use, occupy) issued by the Land Registry/Cadastral Agency

⃝ 1 Title deed

⃝ 2 Certificate of customary tenure

⃝ 3 Certificate of occupancy

⃝ 4 Registered will or registered certificate of hereditary acquisitions

⃝ 5 Registered certificate of perpetual / long term lease

⃝ 6 Registered rental contract

⃝ 7 Other

11.2 Name of any member of the holding listed as an owner or use right holder on any of the legally recognized documents

11.3 The right of the holder/holding to sell any of the parcel of the holding

11.4 The right of the holder/holding to bequeath any of the parcel of the holding

According to the SEEA-AFF classification and the classification of the World Agricultural Census 2020 ↑

The 90^th percentile and respective 1/3 and 2/3 thresholds for productivity are calculated by major production system (crops, livestock, or a mix of crops and livestock or if possible by major agricultural areas of the country). Thereafter the individual farm productivity is estimated and compared with thresholds derived from the productivities of similar farms. ↑

⁴

See Statistics Canada at: http://www.statcan.gc.ca/pub/21-010-x/21-010-x2014001-eng.pdf ↑

⁵

Rental value of farm dwellings is not considered as part of farm income. ↑

⁶

Include cash loans and in-kind loans (e.g., seeds provided by another farmer and repaid with a share of the harvest, seeds, etc.) only for agriculture related investments. ↑

⁷

Reduction in soil fertility will be experienced by farmers as progressive reduction in yield and will be the result of a negative nutrient balance by which the amount of nutrient application (including through mineral and organic fertilizers, legumes, or green manure) is lower than the amount that is lost and exported by crops. ↑

⁸

In order to keep the questionnaire manageable, the module does not consider different type of crop or practice. The method therefore assumes that if a farmer reports best practices, these practices are applied over the entire farm. It may therefore over-estimate the area under good practices. ↑

⁹

Soil type, combined with climate, and in particular the frequency and intensity of rainfall events, are important elements to consider in deciding fertilizer application doses and frequencies. ↑

¹⁰

Precision farming is a farming management concept based on observing, measuring and responding to inter and intra-field variability in crops. ↑

¹¹

Fertilizers to be considered include mineral and synthetic fertilizers as well as animal manure. ↑

¹²

Integrated Pest Management (IPM) is an ecosystem approach to crop production and protection that combines different management strategies and practices to grow healthy crops and minimize the use of pesticides (FAO). ↑

¹³

In order to keep the questionnaire manageable, the module does not consider different types of crop or livestock. The method therefore assumes that if a farmer reports best practices, these practices are applied over the entire farm. It may therefore over-estimate the area under good practices. ↑

¹⁴

WHO Class II or III pesticides as defined by WHO classification (https://www.who.int/publications/i/item/9789240005662or equivalent national classification. ↑

¹⁵

In principle, illegal pesticides refer to any products which do not comply with national regulations on pesticide management, such as un-registered, mislabeled, illegally imported etc. It does not cover "off-label uses," which could be considered as an illegal use action. ↑

¹⁶

Breeds which have been in the country for a sufficient time to be genetically adapted to one or more of traditional production systems or environments in the country. The phrase “sufficient time” refers to time present in one or more of the country’s traditional production systems or environments. Taking cultural, social and genetic aspects into account, a period of 40 years and six generations of the respective species might be considered as a guiding value for “sufficient time”, subject to specific national circumstances (definition of locally adapted breeds adopted by the Fourteenth Regular Session (April 2013) of the FAO Commission on Genetic Resources for Food and Agriculture). ↑

¹⁷

Computation of food insecurity level is described in detail in e-learning course on SDG 2.1.2: http://www.fao.org/elearning/#/elc/en/course/SDG212 ↑

¹⁸

The terminology “Acceptable” must be read within the context of SDG 2.4.1; it should be interpreted as a situation that nevertheless merits attention and actions aimed at improvement. ↑

2.5.1a

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

Target 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreed

0.c. Indicator

Indicator 2.5.1: Number of (a) plant and (b) animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilities

0.d. Series

Plant genetic resources accessions stored ex situ (number) (ER_GRF_PLNTSTOR)

0.e. Metadata update

2023-05-15

0.f. Related indicators

Indicator 2.5.1b on animal genetic resources.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The conservation of plant and animal genetic resources for food and agriculture (GRFA) in medium- or long-term conservation facilities (ex situ, in genebanks) represents the most trusted means of conserving genetic resources worldwide. Plant and animal GRFA conserved in these facilities can be easily used in breeding programmes as well, even directly on-farm.

The measure of trends in ex situ conserved materials provides an overall assessment of the extent to which we are managing to maintain and/or increase the total genetic diversity available for future use and thus protected from any permanent loss of genetic diversity which may occur in the natural habitat, i.e. in situ, or on-farm.

The two components of the indicator 2.5.1, plant (a) and animal (b) GRFA, are separately counted.

The plant component is calculated as the number of accessions of plant genetic resources secured in conservation facilities under medium- or long-term conditions, where an ‘accession’ is defined as a distinct sample of seeds, planting materials or plants which is maintained in a genebank. Genebank Standards for Plant Genetic Resources for Food and Agriculture (accessible at http://www.fao.org/documents/card/en/c/7b79ee93-0f3c-5f58-9adc-5d4ef063f9c7/), set the benchmark for current scientific and technical best practices for conserving plant genetic resources, and support key international policy instruments for the conservation and use of plant genetic resources. These voluntary standards have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture at its Fourteenth Regular Session (http://www.fao.org/docrep/meeting/028/mg538e.pdf).

Concepts:

Plant genetic resources for food and agriculture (PGRFA): Any genetic material of plant origin of actual or potential value for food and agriculture.

Accession: An accession is defined as a sample of seeds, planting materials or plants representing either a wild population, a landrace, a breeding line or an improved cultivar, which is conserved in a genebank. Each accession should be distinct and, in terms of genetic integrity, as close as possible to the sample provided originally.

Base collection: A base collection is defined as a set of unique accessions to be preserved for a medium to long-term period.

Active collection: An active collection is defined as a set of distinct accessions that is used for regeneration, multiplication, distribution, characterization and evaluation. Active collections are maintained in short to medium-term storage and usually duplicated in a base collection.

Medium- or long-term conservation facilities: Biological diversity is often conserved ex situ, outside its natural habitat, in facilities called genebanks. In the case of plant genetic resources, genebanks conserve base collections under medium- or long-term storage conditions, in the form of seeds in cold rooms, plants in the field and tissues in vitro and/or cryoconserved.

2.b. Unit of measure

Number of unique accessions of plant genetic resources secured in medium to long-term conservation facilities, where an ‘accession’ is defined as a distinct sample of seeds, planting materials or plants which is maintained in a genebank.

2.c. Classifications

Genebank Standards for Plant Genetic Resources for Food and Agriculture (accessible at http://www.fao.org/documents/card/en/c/7b79ee93-0f3c-5f58-9adc-5d4ef063f9c7/), set the benchmark for current scientific and technical best practices for conserving plant genetic resources, and support key international policy instruments for the conservation and use of plant genetic resources. These voluntary standards have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture at its Fourteenth Regular Session (http://www.fao.org/docrep/meeting/028/mg538e.pdf).

3.a. Data sources

Data are sourced from officially appointed National Focal Points (NFPs) (see http://www.fao.org/agriculture/crops/thematic-sitemap/theme/seeds-pgr/gpa/national-focal-points/en/) and regional and international agricultural research centres holding PGRFA ex situ collections. Data providers report either (i) directly to FAO by using the spreadsheet contained in document List of descriptors for reporting on the Plant Component of SDG indicator 2.5.1 (see References) accessible from the WIEWS home page (http://www.fao.org/wiews) or (ii) through published information systems which comply with the standard of the FAO/Bioversity Multi-crop Passport Descriptor List (MCPD) v. 2 (see References), e.g. EURISCO (http://eurisco.ipk-gatersleben.de/) and Genesys (https://www.genesys-pgr.org).

Data are stored in the World Information and Early Warning System for plant genetic resources for food and agriculture (WIEWS - http://www.fao.org/wiews), the FAO platform established to facilitate information exchange as well as periodic assessments of the state of the world’s plant genetic resources for food and agriculture.

3.b. Data collection method

The indicator is related to a monitoring framework endorsed by the FAO Commission on Genetic Resources for Food and Agriculture in which the status and trends of plant and animal genetic resources are described through globally agreed indicators and regular country-driven assessments. Officially appointed National Focal Points report directly to FAO, using a format agreed by the FAO Commission on Genetic Resources for Food and Agriculture.

Sessions of the intergovernmental technical working groups on plant genetic resources for food and agriculture allow for formal consultation processes.

3.c. Data collection calendar

Data collection is undertaken on an annual basis in the context of the FAO Commission on Genetic resources for Food and Agriculture.

3.d. Data release calendar

First quarter of the year.

3.e. Data providers

The officially nominated National Focal Points and managers of regional/international genebanks. For information by country see for plant genetic resources http://www.fao.org/agriculture/crops/thematic-sitemap/theme/seeds-pgr/gpa/national-focal-points/en/.

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

The National Focal Points for Plant Genetic Resources are responsible for the provision of national data on the indicator. Their Terms of Reference have been detailed in Circular State Letters asking country to report through their National Focal Points (see http://www.fao.org/agriculture/crops/thematic-sitemap/theme/seeds-pgr/gpa/national-focal-points/en/).

4.a. Rationale

Genetic resources for food and agriculture provide the building blocks of food security and, directly or indirectly, support the livelihoods of every person on earth. As the conservation and accessibility to these resources are of vital importance, medium- or long- term conservation facilities (genebanks) to preserve and make these resources and their associated information accessible for breeding and research have been established at country, regional and global levels. Inventories of genebank holdings provide a dynamic measure of the existing plant and animal diversity and its level of preservation. Data relevant to this indicator facilitate the monitoring of diversity secured and accessible through genebanks and support the development and updating of strategies for the conservation and sustainable use of genetic resources.

The number of materials conserved under medium- or long-term storage conditions provides an indirect measurement of the total genetic diversity, which are managed to secure for future use. Overall, positive variations are therefore approximated to an increase in the agro-biodiversity secured, while negative variations to a loss of it.

Caution needs to be paid in the reporting and interpretation of the indicator. In the case of plant genetic resources, an uncontrolled addition of accessions that are in fact duplicates of samples already conserved and accounted for, or, vice versa, the deletion from the reported collections of redundant duplicates may lead to wrong interpretations. In order to avoid duplicate counting at the national level, primarily base collections should be reported. An active collection could be reported, only when, in the absence of a base collection, it also serves the function of the base collection. Another example that needs to be monitored both while reporting and interpreting the results include the grouping or splitting of accessions, as in both cases the variation in the accounted number does not reflect a variation in the genetic diversity conserved and secured. Therefore, it is crucial that reporting countries and regional/international centres together with the accession level information requested explain also the reason for the decrease or increase in the number of accessions, in particular when this does not reflect a real loss or gain in the genetic diversity conserved and secured.

4.b. Comment and limitations

Broadly, two issues are of concern in using the “number of accessions” as an indicator of diversity in ex situ collections:

Undetected duplicates of accessions may contribute to an increase of the indicator, as each accession is a managed unit, kept and recorded as distinct. The detection of such duplicates will therefore result in a reduction in the number of accessions previously reported. This can occur at different levels, for example within genebank collections and also at international level.

A loss of viability of the material(s) conserved that is not promptly detected may similarly not be reflected in the number of accessions, contributing to an overestimate of the actual number of accessions.

Additional information could be provided by other indicators measuring ex situ conservation, which are part of the monitoring of the implementation of the Global Plan of Action for PGRFA under the FAO Commission on Genetic Resources for Food and Agriculture.

4.c. Method of computation

The plant component of the indicator is calculated as the total number of unique accessions of plant genetic resources secured in medium to long term conservation facilities. This should include all the accessions in base collections, and unique accessions stored in medium term conservation facilities, as active collections, only when these accessions are considered to become part of the national base collections. Base collections may include both seed, field, cryo-preserved or in vitro collections depending on the species conserved and the available facilities in the country.

4.d. Validation

There is no validation process in place.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Missing values are treated as such and not replaced by estimates.

At regional and global levels

Missing values are treated as such and not replaced by estimates.

4.g. Regional aggregations

Aggregates are the sum of country values.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Officially appointed National Focal Points and managers of regional or international genebanks are requested to provide the list of accessions conserved in medium or long term conservation facilities by filling a spreadsheet contained in document List of descriptors for reporting on the Plant Component of SDG indicator 2.5.1 (see References) accessible from the World Information and Early Warning System for plant genetic resources for food and agriculture (WIEWS) home page (http://www.fao.org/wiews). Out of the 13 passport descriptors which can be used to characterize each accession, four are mandatory: (i) the name of the genebank (or holding institute code); (ii) the accession number^[1]; (iii) the scientific name of the accession (name of taxon, including genus, species and lower taxonomic ranking); and (iv) the type of storage.

Reporting on the remaining descriptors is highly recommended, as it allows the analysis of changes in different types of diversity concerned, including changes in the type and origin of the material secured (e.g. biological status; country of origin; locations of safety duplications; etc.) and better describes the composition of the secured materials. The descriptors have been agreed by the FAO Commission on Genetic Resources for Food and Agriculture (see question 6.2 in the Reporting format for monitoring the implementation of the Second global Plan of Action for Plant Genetic Resources for Food and Agriculture http://www.fao.org/3/a-mm294e.pdf). Genebank holdings are counted based on the list of accessions reported. National Focal Points are invited to provide a brief analysis to highlight and explain changes occurred since the previous report.

↑

4.i. Quality management

FAO provides regular training to National Focal Points related to data collection and reporting.

4.j. Quality assurance

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets.

FAO 2014. Genebank Standards for Plant Genetic Resources for Food and Agriculture. Rome. (http://www.fao.org/3/a-i3704e.pdf)

4.k. Quality assessment

Each second year FAO is organizing a global National Coordinators’ Workshops to assess and discuss the collection of data the indicator is based on. The indicators itself is automatically calculated in DAD-IS. Meetings are held as necessary with National Focal Points to assess and discuss data for the indicator and their collection processes.

5. Data availability and disaggregation

Data availability:

The data collected as part of the first monitoring cycle of the implementation of the Second Global Plan of Action for PGRFA serve as baseline (number of accessions as of June 2014).

Data for over 100 countries and 17 international/regional centres are being published. The data collection is carried out annually in January. Continued efforts are made to improve the coverage of countries and international/regional centres, as well as the quality of the information.

Time series:

Data are available in WIEWS since 2014 with either a two-year or one-year periodicity.

Disaggregation:

Not applicable

6. Comparability/deviation from international standards

Sources of discrepancies:

There are no internationally estimated data. Data on this indicator are all produced by countries and regional or international centres.

7. References and Documentation

National Focal Points for the monitoring of the Second Global Plan of Action for Plant Genetic Resources for Food and Agriculture and the preparation of country reports for The Third Report on the State of the World's Plant Genetic Resources for Food and Agriculture. http://www.fao.org/agriculture/crops/thematic-sitemap/theme/seeds-pgr/gpa/national-focal-points/en/

List of descriptors for reporting on the Plant Component of SDG indicator 2.5.1, FAO 2017. http://www.fao.org/fileadmin/user_upload/wiews/docs/SDG_251_data_requirement_sheet_table_EN.docx

Second Global Plan of Action for Plant Genetic Resources for Food and Agriculture. http://www.fao.org/docrep/015/i2624e/i2624e00.htm

Second Report on the State of the World’s Plant Genetic Resources for Food and Agriculture.

http://www.fao.org/docrep/013/i1500e/i1500e00.htm

Genebank Standards for Plant Genetic Resources for Food and Agriculture, FAO, 2014.

http://www.fao.org/documents/card/en/c/7b79ee93-0f3c-5f58-9adc-5d4ef063f9c7/

Targets and Indicators for Plant Genetic Resources for Food and Agriculture, In: Report of the Fourteenth Regular Session of the Commission on Genetic Resources for Food and Agriculture,

CGRFA-14/13/Report, Appendix C. http://www.fao.org/docrep/meeting/028/mg538e.pdf

Reporting Format for Monitoring the Implementation of the Second Global Plan of Action for Plant Genetic Resources for Food and Agriculture, CGRFA-15/15/Inf.9. http://www.fao.org/3/a-mm294e.pdf

FAO/Bioversity Multi-Crop Passport Descriptor (MCPD) v. 2.

http://www.bioversityinternational.org/fileadmin/user_upload/online_library/publications/pdfs/FAO-Bioversity_multi_crop_passport_descriptors_V_2_Final_rev_1526.pdf

2.5.1b

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

0.c. Indicator

Indicator 2.5.1: Number of (a) plant and (b) animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilities

0.d. Series

Primary series:

Number of local breeds for which sufficient genetic resources are stored for reconstitution (ER_GRF_ANIMRCNTN)

Auxiliary series:

Number of local breeds kept in the country (ER_GRF_ANIMKPT)

Number of transboundary breeds for which sufficient genetic resources are stored for reconstitution (ER_GRF_ANIMRCNTN_TRB)

Number of transboundary breeds (including extinct ones) (ER_GRF_ANIMKPT_TRB)

0.e. Metadata update

2023-07-10

0.f. Related indicators

Indicator 2.5.1a on plant genetic resources and 2.5.2 on animal genetic resources

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The two components of the indicator 2.5.1, plant (a) and animal (b) GRFA, are separately counted.

Animal genetic resources

The animal component is calculated as the number of local (i.e. being reported to exist only in one country) and transboundary (i.e. being reported to exist in more than one country) breeds with material stored within a genebank collection with an amount of genetic material which is required to reconstitute the breed in case of extinction (further information on “sufficient material stored to reconstitute a breed” can be found in the Guidelines on Cryoconservation of Animal Genetic Resources, FAO, 2012, accessible at http://www.fao.org/docrep/016/i3017e/i3017e00.htm). The guidelines have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture at its Thirteenth Regular Session (http://www.fao.org/docrep/meeting/024/mc192e.pdf).

Concepts:

Animal genetic resources

Breed: A breed is either a sub-specific group of domestic livestock with definable and identifiable external characteristics that enable it to be separated by visual appraisal from other similarly defined groups within the same species, or a group for which geographical and/or cultural separation from phenotypically similar groups has led to acceptance of its separate identity.

Medium- or long-term conservation facilities: Biological diversity is often conserved ex situ, outside its natural habitat, in facilities called genebanks. In the case of domestic animal diversity, ex situ conservation includes both the maintenance of live animals (in vivo) and cryoconservation.

Cryoconservation is the collection and deep-freezing of semen, ova, embryos or tissues for potential future use in breeding or regenerating animals.

2.b. Unit of measure

Number of local breeds and number of transboundary breeds

2.c. Classifications

International standards and classifications used have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture at its Thirteenth Regular Session (http://www.fao.org/docrep/meeting/024/mc192e.pdf).

3.a. Data sources

National Coordinators for Management of Animal Genetic Resources, nominated by their respective government, provide data to the Domestic Animal Diversity Information System (DAD-IS) (http://dad.fao.org/). DAD-IS allows countries the storage of data on animal genetic resources being secured in either medium- or long-term conservation facilities as needed for the indicator.

3.b. Data collection method

The indicator is related to a monitoring framework endorsed by the FAO Commission on Genetic Resources for Food and Agriculture in which the status and trends of plant and animal genetic resources are described through globally agreed indicators and regular country-driven assessments. Officially appointed National Focal Points (NFPs)/National Coordinators report directly to FAO, using a format agreed by the FAO Commission on Genetic Resources for Food and Agriculture.

Sessions of the intergovernmental technical working groups on plant and on animal genetic resources for food and agriculture allow for formal consultation processes.

3.c. Data collection calendar

Data in DAD-IS can be updated throughout the whole year.

3.d. Data release calendar

First quarter of the year.

3.e. Data providers

The officially nominated National Focal Points / National Coordinators. For information by country see for animal genetic resources http://www.fao.org/dad-is/national-coordinators/en/.

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

The National Coordinators for Management of Animal Genetic Resources are responsible for the provision of national data the indicator is based on. Their Terms of Reference have been endorsed by the Commission on Genetics Resources for Food and Agriculture and are described in more detail in: Developing the institutional framework for the management of animal genetic resources.

FAO Animal Production and Health Guidelines. No. 6. Rome. (Accessible at http://www.fao.org/3/ba0054e/ba0054e00.pdf).

4.a. Rationale

4.b. Comment and limitations

Information on cryo-conserved material in the Domestic Animal Diversity Information System DAD-IS needs to be updated on a regular base.

4.c. Method of computation

For the animal component the indicator is calculated as the number of local breeds and transboundary breeds with enough genetic material stored within genebank collections allowing to reconstitute the breed in case of extinction (based on the Guidelines on Cryoconservation of animal genetic resources, FAO, 2012, http://www.fao.org/docrep/016/i3017e/i3017e00.htm). Numbers for local and transboundary breeds are presented separately. To decide whether the material stored is sufficient on regional or global levels the numbers provided to DAD-IS for each type of material (e.g. semen samples, embryos, somatic cells) conserved within the framework of a cryconservation programme, as well as the number of the respective male and female donor animals, must be summed across the countries belonging to the respective region of interest.

4.d. Validation

There is no validation process in place.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For animals, for a given breed, if no data are provided for a respective year, it is assumed that the storage status remains the same as for the last year for which data have been reported. In this case the nature of data is considered to be estimated.

At regional and global levels

Missing values are treated as such and not replaced by estimates.

4.g. Regional aggregations

Aggregates are the sum of country values.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

For the animal component the National Coordinators for the Management of Animal Genetic Resources provide the type of material (e.g. semen samples, embryos, somatic cells) cryo-conserved within the framework of a cryoconservation programme, as well as the number of the respective male and female donors to the Domestic Animal Diversity Information System DAD-IS. FAO provides internationally endorsed guidelines on the definition of “sufficient” material (see FAO. 2012. Cryo-conservation of animal genetic resources. FAO Animal Production and Health Guidelines No. 12. Rome. (available at http://www.fao.org/docrep/016/i3017e/i3017e00.pdf)

4.i. Quality management

FAO provides regular training to National Coordinators related to data collection and entering data into the official system, DAD-IS. The indicators itself is automatically calculated in DAD-IS.

4.j. Quality assurance

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets.

FAO. 2012. Cryo-conservation of animal genetic resources. FAO Animal Production and Health Guidelines No. 12. Rome. (available at http://www.fao.org/docrep/016/i3017e/i3017e00.pdf)

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Animal genetic resources

The analysis of country reports to FAO provided by 128 countries in 2014 for the preparation of ‘The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture’ provided a first baseline with regard to the number of national breed populations where sufficient material is stored. However, currently genetic material is cryo-conserved for only a very low proportion (less than 10 percent), whereas the quantity of stored material is sufficient for population reconstitution for an even smaller percentage of local breeds.

Time series:

DAD-IS data are available since 2000.

Disaggregation:

For both plant and animal components geographic disaggregation (national, regional, global) is made. Grouping by sex, age etc. is not applicable.

6. Comparability/deviation from international standards

Sources of discrepancies:

There are no internationally estimated data. Data on this indicator are all produced by countries and regional or international centres.

7. References and Documentation

Preparation of the First Report on the State of the World's Animal Genetic Resources

Guidelines for the Development of Country Reports. Annex 2. Working definitions for use in developing country reports and providing supporting data.

http://www.fao.org/docrep/004/y1100m/y1100m03.htm

Guidelines on Cryoconservation of Animal Genetic Resources, FAO, 2012, accessible at http://www.fao.org/docrep/016/i3017e/i3017e00.htm

National Coordinator for Management of Animal Genetic Resources.

http://dad.fao.org/cgi-bin/EfabisWeb.cgi?sid=-1,contacts

Status of Animal Genetic Resources – 2016, CGRFA/WG-AnGR-9/16/Inf.3.

http://www.fao.org/3/a-mq950e.pdf

Guidelines on In vivo Conservation of Animal Genetic Resources, FAO, 2013. http://www.fao.org/docrep/018/i3327e/i3327e.pdf

The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture.

http://www.fao.org/3/a-i4787e.pdf

2.5.1

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

0.c. Indicator

Indicator 2.5.1: Number of (a) plant and (b) animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilities

0.d. Series

Primary series:

Number of local breeds for which sufficient genetic resources are stored for reconstitution (ER_GRF_ANIMRCNTN)

Auxiliary series:

Number of local breeds kept in the country (ER_GRF_ANIMKPT)

Number of transboundary breeds for which sufficient genetic resources are stored for reconstitution (ER_GRF_ANIMRCNTN_TRB)

Number of transboundary breeds (including extinct ones) (ER_GRF_ANIMKPT_TRB)

0.e. Metadata update

2023-07-10

0.f. Related indicators

Indicator 2.5.1a on plant genetic resources and 2.5.2 on animal genetic resources

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The two components of the indicator 2.5.1, plant (a) and animal (b) GRFA, are separately counted.

Animal genetic resources

Concepts:

Animal genetic resources

Cryoconservation is the collection and deep-freezing of semen, ova, embryos or tissues for potential future use in breeding or regenerating animals.

2.b. Unit of measure

Number of local breeds and number of transboundary breeds

2.c. Classifications

3.a. Data sources

3.b. Data collection method

The indicator is related to a monitoring framework endorsed by the FAO Commission on Genetic Resources for Food and Agriculture in which the status and trends of plant and animal genetic resources are described through globally agreed indicators and regular country-driven assessments. Officially appointed National Focal Points (NFPs)/National Coordinators report directly to FAO, using a format agreed by the FAO Commission on Genetic Resources for Food and Agriculture.

Sessions of the intergovernmental technical working groups on plant and on animal genetic resources for food and agriculture allow for formal consultation processes.

3.c. Data collection calendar

Data in DAD-IS can be updated throughout the whole year.

3.d. Data release calendar

First quarter of the year.

3.e. Data providers

The officially nominated National Focal Points / National Coordinators. For information by country see for animal genetic resources http://www.fao.org/dad-is/national-coordinators/en/.

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

FAO Animal Production and Health Guidelines. No. 6. Rome. (Accessible at http://www.fao.org/3/ba0054e/ba0054e00.pdf).

4.a. Rationale

4.b. Comment and limitations

Information on cryo-conserved material in the Domestic Animal Diversity Information System DAD-IS needs to be updated on a regular base.

4.c. Method of computation

4.d. Validation

There is no validation process in place.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

At regional and global levels

Missing values are treated as such and not replaced by estimates.

4.g. Regional aggregations

Aggregates are the sum of country values.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

4.i. Quality management

FAO provides regular training to National Coordinators related to data collection and entering data into the official system, DAD-IS. The indicators itself is automatically calculated in DAD-IS.

4.j. Quality assurance

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets.

FAO. 2012. Cryo-conservation of animal genetic resources. FAO Animal Production and Health Guidelines No. 12. Rome. (available at http://www.fao.org/docrep/016/i3017e/i3017e00.pdf)

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Animal genetic resources

Time series:

DAD-IS data are available since 2000.

Disaggregation:

For both plant and animal components geographic disaggregation (national, regional, global) is made. Grouping by sex, age etc. is not applicable.

6. Comparability/deviation from international standards

Sources of discrepancies:

There are no internationally estimated data. Data on this indicator are all produced by countries and regional or international centres.

7. References and Documentation

Preparation of the First Report on the State of the World's Animal Genetic Resources

Guidelines for the Development of Country Reports. Annex 2. Working definitions for use in developing country reports and providing supporting data.

http://www.fao.org/docrep/004/y1100m/y1100m03.htm

Guidelines on Cryoconservation of Animal Genetic Resources, FAO, 2012, accessible at http://www.fao.org/docrep/016/i3017e/i3017e00.htm

National Coordinator for Management of Animal Genetic Resources.

http://dad.fao.org/cgi-bin/EfabisWeb.cgi?sid=-1,contacts

Status of Animal Genetic Resources – 2016, CGRFA/WG-AnGR-9/16/Inf.3.

http://www.fao.org/3/a-mq950e.pdf

Guidelines on In vivo Conservation of Animal Genetic Resources, FAO, 2013. http://www.fao.org/docrep/018/i3327e/i3327e.pdf

The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture.

http://www.fao.org/3/a-i4787e.pdf

2.5.2

0.a. Goal

Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture

0.b. Target

0.c. Indicator

Indicator 2.5.2: Proportion of local breeds classified as being at risk of extinction

0.d. Series

Proportion of local breeds classified as being at risk of extinction as a share of local breeds with known level of extinction risk (ER_RSK_LBREDS)

Number of local breeds (not extinct) (ER_NOEX_LBREDN)

Number of local breeds with unknown risk status (ER_UNK_LBREDN)

0.e. Metadata update

2023-05-15

0.f. Related indicators

Indicator 2.5.1b on animal genetic resources

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The indicator presents the percentage of local livestock breeds among local breeds with known risk status classified as being at risk of extinctions at a certain moment in time, as well as the trends for this percentage.

Concepts:

A similar indicator was originally proposed for the Target 15.5, and it serves also as an indicator for the Aichi Target 13 “Genetic Diversity of Terrestrial Domesticated Animals” under the Convention on Biological Diversity (CBD). It is described on the webpage of the Biodiversity Indicators Partnership (BIP), a network of organizations, which have come together to provide the most up-to date biodiversity information possible for tracking progress towards the Aichi Targets (http://www.bipindicators.net/domesticatedanimals). Further, it is presented in the Global Biodiversity Outlook 4, page 91 (see https://www.cbd.int/gbo/gbo4/publication/gbo4-en.pdf ) which is an output of the processes under the CBD.

2.b. Unit of measure

Percent (%)

2.c. Classifications

International standards and classifications used have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture and are provided in more detail in: FAO. 2013. In vivo conservation of animal genetic resources (accessible at http://www.fao.org/3/a-i3327e.pdf).

3.a. Data sources

DAD-IS is the Domestic Animal Diversity Information System maintained and developed by FAO (http://www.fao.org/dad-is/en/). It provides access to searchable databases of breed-related information and photos and links to other online resources on livestock diversity. It allows to analyze the diversity of livestock breeds on national, regional and global levels including the status of breeds regarding their risk of extinction. DAD-IS currently contains data from 182 countries and 38 species. It contains information on more than 8,800 mammalian and avian breeds, among those about 7,700 are considered local (i.e. reported to occur in only one country).

3.b. Data collection method

Livestock census on breed level or data derived from national herdbooks or national surveys.

3.c. Data collection calendar

Data entry into DAD-IS is possible all over the year.

3.d. Data release calendar

The indicator is updated in the first quarter of each year.

3.e. Data providers

The data are provided by the National Coordinators for the Management of Animal Genetic Resources (NCs). The NC is officially nominated by the country (usually by the Ministry of Agriculture). FAO provides the password for entering/updating the country’s data within the global data information system DAD-IS directly to the NC, after having received the official nomination letter.

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

FAO Animal Production and Health Guidelines. No. 6. Rome. (Accessible at http://www.fao.org/3/ba0054e/ba0054e00.pdf).

4.a. Rationale

The indicator has a direct link to “biodiversity” as animal or livestock genetic resources represent an integral part of agricultural ecosystems and biodiversity as such. Further there are indirect links to “malnutrition”: Animal genetic resources for food and agriculture are an essential part of the biological basis for world food security, and contribute to the livelihoods of over a thousand million people. A diverse resource base is critical for human survival and well-being, and a contribution to the eradication of hunger: animal genetic resources are crucial in adapting to changing socio-economic and environmental conditions, including climate change. They are the animal breeder’s raw material and amongst the farmer’s most essential inputs. They are essential for sustainable agricultural production.

No increase of the percentage of breeds being at risk or being extinct is directly related to “halt the loss of biodiversity”.

4.b. Comment and limitations

Breed-related information remains far from complete. Across the world, when excluding extinct breeds, 61 percent of local breeds are classified as of unknown status because of missing population data or lack of recent updates.

Generally, data collection should be possible in all countries. Updating of population size data at least each 10 years is needed for the definition of the risk classes.

4.c. Method of computation

The indicator is based on the data contained in FAO’s Global Databank for Animal Genetic Resources DAD-IS (http://dad.fao.org/). Risk classes are defined based on population sizes of breeds reported to DAD-IS. The risk class is considered to be “unknown” if (i) no population sizes are reported or (ii) the most recent population size reported refers to a year more than 10- years before the year of calculation (10 year cut off point).

Species are assigned to two groups. The first group comprises species that have high reproductive capacity, such as pigs, rabbits, guinea pigs and avian species, and the second comprises species that

have low reproductive capacity, i.e. those belonging to the taxonomical families Bovidae, Equidae, Camelidae and Cervidae.

The risk status categories are defined as follows (see also FAO. 2013. In vivo conservation of animal genetic resources. FAO Animal Production and Health Guidelines. No. 14. Rome. Accessible at http://www.fao.org/docrep/018/i3327e/i3327e.pdf):

Extinct. A breed is categorized as extinct when there are no breeding males or breeding females remaining and any cryoconserved genetic material that may be available is insufficient for breed reconstitution.

Cryoconserved only. Breeds that have no living male or female animals remaining, but for which there is sufficient cryopreserved material to allow for reconstitution of the breed, are assigned to the category cryoconserved only. The ability to reconstitute an otherwise extinct breed depends on the amount of and type of stored germplasm. Requirements differ greatly according to species. Guidance on what constitutes “sufficient cryopreserved material” is provided in the FAO guidelines Cryoconservation of animal genetic resources (FAO, 2012).

Critical. A breed is categorized as critical if:

• the total number of breeding females is less than or equal to 100 (300 for species with low reproductive capacity); or

• the overall population size is less than or equal to 80 (240) and the population trend is increasing and the proportion of females being bred to males of the same breed is greater than 80 percent (i.e. cross-breeding is equal to or less than 20 percent); or

• the overall population size is less than or equal to 120 (360) and the population trend is stable or decreasing; or

• the total number of breeding males is less than or equal to five (i.e. ΔF is 3 percent or greater).

If the population trend is unknown, then it is assumed to be stable. Breeds for which demographic characteristics suggest a critical risk of extinction, but that have active conservation programmes (including cryoconservation) in place, or populations that are maintained by commercial companies or research institutions are considered to be “critical-maintained” for reporting purposes.

Endangered. A breed is categorized as endangered if:

• the total number of breeding females is greater than 100 (300 for species with low reproductive capacity) and less than or equal to 1 000 (3 000); or

• the overall population size is greater than 80 (240) and less than 800 (2 400) and increasing in size and the percentage of females being bred to males of the same breed is above 80 percent; or

• the overall population size is greater than 120 (360) and less than or equal to 1 200 (3 600) and the trend is stable or decreasing; or

• the total number of breeding males is less than or equal to 20 and greater than five (i.e. ΔF is between 1 and 3 percent).

Once again, if the population trend is unknown, then it is assumed to be stable. Endangered breeds will be assigned to the subcategory “endangered-maintained” if active conservation programmes are in place or if their populations are maintained by commercial companies or research institutions.

Vulnerable. A breed is categorized as vulnerable if:

• the total number of breeding females is between 1 000 and 2 000 (3 000 and 6 000 for species with low reproductive capacity); or

• the overall population size is greater than 800 (2 400) and less than or equal to 1 600 (4 800) and increasing and the percentage of females being bred to males of the same breed is greater than 80 percent; or

• the overall population size is greater than 1 200 (3 600) and less than or equal to 2 400 (7 200) but stable or decreasing; or

• the total number of breeding males is between 20 and 35 (i.e. the ΔF is between 0.5 and 1 percent).

Unreported population trends are assumed to be stable.

Not at risk. A breed is categorized as not at risk if the population status is known and the breed does not fall in the critical or endangered categories (including the respective subcategories) or the vulnerable category.

Unknown. This category is self-explanatory and calls for action. A population survey is needed; the breed could be critical, endangered or vulnerable.

A breed is considered to be at risk if it has been classified as either critical, critical-maintained, endangered, endangered-maintained or vulnerable.

The indicator is calculated as follows:

Risk status of local breeds	Number
At risk	$n_{R}$
Not at risk	$n_{N R}$
Unknown	$n_{U}$
All risk classes	$n = n_{R} + n_{N R} + n_{U}$

SDG indicator for country i: $p_{i}$

$p_{i} = \frac{n_{R i}}{n_{R i} + n_{N R i}}$

4.d. Validation

Consistency of data uploaded for computation of risk status is automatically checked by DAD-IS (e.g. number of females not exceeding total population size).

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At breed level

If no population data are provided for a respective year, it is assumed that the risk status remains the same as for the last year for which population data have been reported. In this case the nature of data is considered to be estimated. However, if the most recent reporting refers to a year more than 10- years before, the risk status is considered “unknown”.

• At country level

Country information is considered to be missing if 100% percent of a country’s local breeds do have risk status “unknown”. If 100% of a country’s breed risk status values are estimates (see above), the nature of country data is also considered to be an estimate.

• At regional and global levels

See aggregation rules under 4.g

4.g. Regional aggregations

Aggregated SDG indicator Pj for k countries (with at least one local breed with known risk status) in region j with total number of local breeds in k countries: $N = \sum_{i = 1}^{k} n_{i}$

$P_{j}$ = $\sum_{i = 1}^{k} (p_{i} ∙ \frac{n_{i}}{N})$

Regional and global results are only reported if more than 50% of the countries within the respective region or globally are not missing

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Livestock census on breed level or data derived from national herdbooks or national surveys.

FAO. 2011. Surveying and monitoring of animal genetic resources. FAO Animal Production and Health Guidelines. No. 7. Rome. (available at http://www.fao.org/docrep/014/ba0055e/ba0055e00.htm)

4.i. Quality management

FAO provides regular training to National Coordinators related to data collection and entering data into the official system, DAD-IS. The indicators itself is automatically calculated in DAD-IS.

There is an automatic check of data consistency when uploaded into DAD-IS.

4.j. Quality assurance

Described in section 7 of FAO. 2011. Surveying and monitoring of animal genetic resources. FAO Animal Production and Health Guidelines. No. 7. Rome. (available at http://www.fao.org/docrep/014/ba0055e/ba0055e00.htm)

The guidelines were presented to and endorsed by the Commission on Genetic Resources for Food and Agriculture at its Thirteenth Regular Session in July 2011.

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets.

4.k. Quality assessment

Each second year, FAO is organizing a global National Coordinators’ Workshops to assess and discuss the collection of data the indicator is based on. The indicators itself is automatically calculated in DAD-IS.

5. Data availability and disaggregation

Data availability:

Data are publicly available through DAD-IS (see http://dad.fao.org/).

Time series:

DAD-IS data are available since 2000 up to 2022.

Disaggregation:

Data are available by country.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable

7. References and Documentation

URL:

http://dad.fao.org/

References:

FAO. 2013. In vivo conservation of animal genetic resources.

FAO Animal Production and Health Guidelines. No. 14. Rome. Accessible at http://www.fao.org/docrep/018/i3327e/i3327e.pdf

3.a.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate

0.c. Indicator

Indicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and older

0.d. Series

Not applicable

0.e. Metadata update

2021-12-06

0.f. Related indicators

3.4.1 Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease

0.g. International organisations(s) responsible for global monitoring

World Health Organization; Secretariat of the WHO Framework Convention on Tobacco Control (co-custodians)

1.a. Organisation

World Health Organization; Secretariat of the WHO Framework Convention on Tobacco Control (co-custodians)

2.a. Definition and concepts

Definition:

The indicator is defined as the percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.

Concepts:

Tobacco use means use of smoked and/or smokeless tobacco products. “Current use” means use within the previous 30 days at the time of the survey, whether daily or non-daily use.

Tobacco products means products entirely or partly made of the leaf tobacco as raw material intended for human consumption through smoking, sucking, chewing or sniffing.

“Smoked tobacco products” include cigarettes, cigarillos, cigars, cheroots, bidis, pipes, shisha (water pipes), roll-your-own tobacco, kretek, heated tobacco products and any other form of tobacco that is consumed by smoking.

"Smokeless tobacco product" includes moist snuff, creamy snuff, dry snuff, plug, dissolvables, gul, loose leaf, red tooth powder, snus, chimo, gutkha, khaini, gudakhu, zarda, quiwam, dohra, tuibur, nasway, naas, naswar, shammah, toombak, paan (betel quid with tobacco), iq’mik, mishri, tapkeer, tombol and any other tobacco product that consumed by sniffing, holding in the mouth or chewing.

Prevalence estimates have been “age-standardized” to make them comparable across all countries no matter the demographic profile of the country. This is done by applying each country’s age-and-sex specific prevalence rates to the WHO Standard Population. The resulting rates are hypothetical numbers which are only meaningful when comparing rates obtained for one country

with those obtained for another country.

2.b. Unit of measure

Proportion (per cent)

2.c. Classifications

“Tobacco products” are defined in Article 1 (f) of the WHO FCTC, see https://www.who.int/fctc/text_download/en/. Heated tobacco products are classified as tobacco products in decision FCTC/CIO8(22), see https://www.who.int/fctc/cop/sessions/cop8/FCTC__COP8(22).pdf

WHO Standard population is used for age-standardisation, see https://www.who.int/healthinfo/paper31.pdf

World Population Prospects (population aged 15 years or more per country) is used in the denominator of the indicator, see https://population.un.org/wpp/

3.a. Data sources

Prevalence rates by age-by-sex from national representative population surveys conducted since 1990:

• officially recognized by the national health authority;

• of randomly selected participants representative of the general population; and

• reporting at least one indicator measuring current tobacco use, daily tobacco use, current tobacco smoking, daily tobacco smoking, current cigarette smoking or daily cigarette smoking.

Official survey reports are gathered from Member States by one or more of the following methods:

• reporting system of the WHO FCTC on the progress in implementation of the Convention;

• review of surveys conducted under the aegis of the Global Tobacco Surveillance System;

• review of other surveys conducted in collaboration with WHO such as STEPwise surveys and World Health Surveys;

• scanning of international surveillance databases such as those of the Demographic and Health Survey (DHS), Multiple Indicator Cluster Survey (MICS) and the World Bank Living Standards Measurement Survey (LSMS); and

• identification and review of country-specific surveys that are not part of international surveillance systems.

3.b. Data collection method

Reports either downloaded from websites, submitted through the WHO FCTC reporting platform or emailed by national counterparts. WHO shares and makes public the methodologies for its estimates through the WHO global report on trends in tobacco use 2000-2025 and the WHO Report on the Global Tobacco Epidemic. The WHO estimates undergo country consultation prior to publication.

3.c. Data collection calendar

Continual data collection.

3.d. Data release calendar

Biennial release via the WHO Global Report on Trends in Tobacco Use 2000-2025, the WHO Global Health Observatory and the Global Progress Report on Implementation of the WHO FCTC.

3.e. Data providers

WHO Member States, Parties to the WHO FCTC.

3.f. Data compilers

WHO Tobacco Free Initiative; Secretariat of the WHO Framework Convention on Tobacco Control and the Protocol to Eliminate Illicit Trade in Tobacco Products.

3.g. Institutional mandate

The WHO Framework Convention on Tobacco Control (WHO FCTC) was adopted by the World Health Assembly on 21 May 2003 (Resolution 56.1) and entered into force on 27 February 2005. In 2010, Conference of the Parties adopted Decision FCTC/COP4(16), which requests the Convention Secretariat, in cooperation with competent authorities within WHO, in particular the Tobacco Free Initiative, to further standardize definitions and indicators and facilitate regular review of progress in implementation of the Convention. See https://apps.who.int/gb/fctc/PDF/cop4/FCTC_COP4_DIV6-en.pdf

4.a. Rationale

Tobacco use is a major contributor to illness and death from non-communicable diseases (NCDs). There is no proven safe level of tobacco use or of second-hand smoke exposure. All daily and non-daily users of tobacco are at risk of a variety of poor health outcomes across the life-course, including NCDs. Reducing the prevalence of current tobacco use will make a large contribution to reducing premature mortality from NCDs (Target 3.4). Routine and regular monitoring of this indicator is necessary to enable accurate monitoring and evaluation of the impact of implementation of the WHO Framework Convention on Tobacco Control (WHO FCTC), or tobacco control policies in the countries that are not yet Parties to the WHO FCTC, over time. Tobacco use prevalence levels are an appropriate indicator of implementation of SDG Target 3.a “Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate”.

4.b. Comment and limitations

Raw data collected through nationally representative population-based surveys in the countries are used to calculate comparable estimates for this indicator. Information from subnational surveys are not used.

In some countries, all tobacco use and tobacco smoking may be equivalent, but for many countries where other forms of tobacco are also being consumed, smoking rates will be lower than tobacco use rates to some degree.

The comparability, quality and frequency of household surveys affects the accuracy and quality of the estimates. Non-comparability of data can arise from the use of different survey instruments, sampling and analysis methods, and indicator definitions across Member States. Surveys may cover a variety of age ranges (not always 15+) and be repeated at irregular intervals. Surveys may include a variety of different tobacco products, or sometimes only one product such as cigarettes, based on the country’s perception of which products are important to monitor. Unless both smoked and smokeless products are monitored simultaneously, tobacco use prevalence will be underreported. Countries have begun to monitor use of e-cigarettes and other emerging products, which may confound countries’ definitions of tobacco use. The definition of current use may not always be restricted to the 30 days prior to the survey. In addition, surveys ask people to self-report their tobacco use, which can lead to under-reporting of tobacco use.

There is no standard protocol used across Member States to ask people about their tobacco use. WHO’s Tobacco Questions for Surveys (TQS) have been adopted in many surveys, which helps improve comparability of indicators across countries.

4.c. Method of computation

A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys are compiled into a dataset, the model is fit to calculate trend estimates from the year 2000 to 2030. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. To fill data gaps, information is “borrowed” from countries in the same UN subregion.

The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population.

Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution.

4.d. Validation

The results of the modelling described in the Method of Computation are compared with the input data to assure a good model fit. The results and input data are shared with countries via the tobacco control focal point for country consultation prior to publication in the biennial reports WHO global report on trends in tobacco use 2000-2025 and WHO Report on the Global Tobacco Epidemic. During country consultation, sometimes additional data are made available to WHO by the country for the purposes of modelling indicator 3.a.1.

4.e. Adjustments

Except for adjustments made during modelling as described in the Method of Computation, no other adjustments are made.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

 At country level

For countries with less than two national surveys completed in different years since 1990, no estimate is calculated, since no trend can be determined. For countries with data from two or more national surveys, data gaps, if any, are filled as described in the Method of Computation.

 At regional and global levels

Countries where no estimate can be calculated are included in regional and global averages by assuming their prevalence rates for men and women are equal to the average rates for men and women seen in the UN subregion1 in which they are located. Where fewer than 50% of a UN subregion’s population was surveyed, UN subregions are grouped with neighbouring subregions until at least 50% of the grouped population has contributed data to the region’s average rates.

4.g. Regional aggregations

Average prevalence rates for regions are calculated by population-weighting the age-specific prevalence rates in countries, then age-standardizing the age-specific average rates of the region.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries use a variety of population-based survey protocols to monitor tobacco use at national level. Examples of internationally supported protocols include Tobacco Questions for Surveys (https://www.gtssacademy.org/survey-tools/tqs/); the Global Adult Tobacco Survey (https://www.gtssacademy.org/survey-tools/gats/); the WHO STEPS survey (https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps); the World Health Survey (https://www.who.int/data/data-collection-tools/world-health-survey-plus); the Multiple Indicator Cluster survey (https://mics.unicef.org/tools); and the Demographic and Health Survey (https://www.dhsprogram.com/Methodology/index.cfm). Sampling for national representativeness is the preserve of National Statistics Offices. Survey data submitted by the WHO FCT Parties biennially to the Convention Secretariat via the WHO FCTC Reporting Instrument (https://fctc.who.int/who-fctc/reporting/reporting-instrument) are shared with WHO. Additional data are obtained by WHO through contact with tobacco focal points at the Ministries of Health or by searching in the public domain.

4.i. Quality management

Clearance of statistical methods and publications through WHO Division of Data, Analytics, and Delivery for Impact. Adherence to GATHER guidelines (http://gather-statement.org/) is required for clearance. Data, estimates and metadata are published through the Global Health Observatory.

4.j. Quality assurance

The survey data reported by WHO member states and by Parties to the WHO-FCTC is checked against published reports and for internal consistency. Modelling results, together with input data, are shared with tobacco surveillance and policy experts in WHO Regions prior to being shared with tobacco focal points in Ministries of Health. The pertinent WHO collaborating centre also reviews the results prior to publication.

5. Data availability and disaggregation

Data availability:

Availability depends on each country’s schedule for publishing their nationally representative population survey results. WHO calculates estimates every two years.

Time series:

The indicator is calculated for all countries from 2000 to the current year. Where the current year is later than the most recent national survey year, projections are made according to the Method of Computation described above.

Disaggregation:

Sex

6. Comparability/deviation from international standards

Sources of discrepancies:

WHO estimates differ from national estimates in that they are (i) age-standardised to improve international comparability and (ii) calculated using one standard method for all countries. Infrequent surveys or unavailability of recent surveys lead to more reliance on modelling. As the data set for each country improves over time with addition of new surveys, recent estimates may seem inconsistent with earlier estimates. WHO estimates undergo country consultation prior to release.

7. References and Documentation

URL:

http://www.who.int/gho/en/

http://apps.who.int/fctc/implementation/database/

Notes:

¹ For a listing of countries by UN region, please refer to World Population Prospects, published by the UN Department of Economic and Social Affairs. For the purposes of tobacco use analysis, the following adjustments were made: (i) Eastern Africa subregion was divided into two regions: Eastern Africa Islands and Remainder of Eastern Africa; (ii) Armenia, Azerbaijan, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Tajikistan, Uzbekistan and Turkmenistan were classified with Eastern Europe, (iii); Cyprus, Israel and Turkey were classified with Southern Europe, and (iv) Melanesia, Micronesia and Polynesia subregions were combined into one subregion.

3.b.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.b: Support the research and development of vaccines and medicines for the communicable and non‑communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all

0.c. Indicator

Indicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programme

0.e. Metadata update

2023-03-31

0.f. Related indicators

Target 3.8 Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all. Indicator 3.8.1: Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population)

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO), United Nations Children’s Fund (UNICEF)

1.a. Organisation

World Health Organization (WHO), United Nations Children’s Fund (UNICEF)

2.a. Definition and concepts

Definition:

Coverage of DTP containing vaccine (3^rd dose): Percentage of surviving infants who received the 3 doses of diphtheria and tetanus toxoid with pertussis containing vaccine in a given year.

Coverage of Measles containing vaccine (2^nd dose): Percentage of children who received two dose of measles containing vaccine according to nationally recommended schedule through routine immunization services in a given year.

Coverage of Pneumococcal conjugate vaccine (last dose in the schedule): Percentage of surviving infants who received the nationally recommended doses of pneumococcal conjugate vaccine in a given year.

Coverage of HPV vaccine (last dose in the schedule): Percentage of 15 years old girls who received the recommended doses of HPV vaccine. Currently performance of the programme in the previous calendar year based on target age group is used.

Concepts:

In accordance with its mandate to provide guidance to Member States on health policy matters, WHO provides global vaccine and immunization recommendations for diseases that have an international public health impact. National programmes adapt the recommendations and develop national immunization schedules, based on local disease epidemiology and national health priorities. National immunization schedules and number of recommended vaccines vary between countries, with only DTP polio and measles containing vaccines being used in all countries.

The target population for given vaccine is defined based on recommended age for administration. The primary vaccination series of most vaccines are administered in the first two years of life.

Coverage of DTP containing vaccine measure the overall system strength to deliver infant vaccination

Coverage of Measles containing vaccine ability to deliver vaccines beyond first year of life through routine immunization services.

Coverage of Pneumococcal conjugate vaccine: adaptation of new vaccines for children

Coverage of HPV vaccine: life course vaccination

2.b. Unit of measure

Percent

3.a. Data sources

National Health Information Systems or National Immunization systems

National immunization registries

High quality household surveys with immunization module (e.g. Demographic and Health Surveys (DHS), Multiple-Indicator Health Surveys (MICS), other national surveys)

3.b. Data collection method

Annual data collection through established mechanism. Since 1998, in an effort to strengthen collaboration and minimize the reporting burden, WHO and UNICEF jointly collect information through a standard questionnaire (the Joint Reporting Form) sent to all Member States http://www.who.int/immunization/monitoring_surveillance/routine/reporting/en/

3.c. Data collection calendar

Annual data collection March-May each year. Country consultation June each year

3.d. Data release calendar

15 July each year for time series 1980 – release year -1. (on 17 July 2023 estimates from 1980-2022)

3.e. Data providers

Ministries of Health, Immunization programmes, DHS and MICS websites

3.f. Data compilers

WHO and UNICEF

4.a. Rationale

This indicator aims to measure access to vaccines, including the newly available or underutilized vaccines, at the national level. In the past decades all countries added numerous new and underutilised vaccines in their national immunization schedule and there are several vaccines under final stage of development to be introduced by 2030. For monitoring diseases control and impact of vaccines it is important to measure coverage from each vaccine in national immunization schedule. A system is already in place to monitor immunization coverage for all national programmes, however direct measurement for proportion of population covered with all vaccines in the programme is only feasible if the country has a well-functioning national electronic immunization registry allowing coverage by cohort to be easily estimated. While countries will develop and strengthen immunization registries there is a need for an alternative measurement.

4.b. Comment and limitations

The rational to select a set of vaccines reflects the ability of immunization programmes to deliver vaccines over the life cycle and to adapt new vaccines. Coverage for other WHO recommended vaccines are also available and can be provided.

Given that HPV vaccine is relatively new and vaccination schedule varies from countries to country coverage estimate will be made for girls vaccinated by ag 15 and at the moment data is limited to very few countries therefore reporting will start later.

4.c. Method of computation

WHO and UNICEF jointly developed a methodology to estimate national immunization coverage form selected vaccines in 2000, and this approach has been refined and reviewed by expert committees over time. The methodology was published and reference is available under the reference section. Estimates time series for WHO recommended vaccines produced and published annually since 2001.

The methodology uses data reported by national authorities from countries administrative systems as well as data from immunization or multi indicator household surveys. The WHO/UNICEF estimates of national immunization coverage have been assessed using the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) checklist.

4.d. Validation

WHO and UNICEF encourage countries to review and comment on the draft coverage estimates shared following the draft production. In past years, regional or sub-regional consultations have been held during May/June to go through select country data and estimates.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

The first data point is the first reporting year after vaccine introduction. When country data are not available interpolation is used between 2 data points and extrapolation from the latest available data point.

At regional and global levels

Any needed imputation is done at country level. These country values are then used to compute regional and global estimates.

4.g. Regional aggregations

Weighted average of the country-level coverage rates where the weights are the country target population sizes based on World Population Prospects: 2022 revision from the UN Population Division. All Member States from the region are included. For HPV 15 year old girls are used for calculation weighted average.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable

4.i. Quality management

Not applicable

4.j. Quality assurance

Not applicable

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

Coverage data for different vaccines are collected annually and reviewed by WHO and UNICEF inter agency expert group and estimates made for each country and each year. Data are published both on WHO and UNICEF web sites.

http://www.who.int/immunization/ monitoring_surveillance/routine/coverage/en/index4.html http://www.data.unicef.org/child-health/immunization

Coverage for 2021 (in %)

	DTP3	MCV2	PCV3	HPV
Global	81	71	51	12
Australia and New Zealand	94	92	96	63
Central Asia and Southern Asia	86	83	45	2
Eastern Asia and South-eastern Asia	84	83	14	1
Latin America & the Caribbean	75	68	70	32
Northern America and Europe	93	91	80	37
Oceania	70	63	70	35
Sub-Saharan Africa	70	40	64	20
Western Asia and Northern Africa (M49)	88	83	56	1

Disaggregation:

Geographical location, i.e. regional and national and potentially subnational estimates

6. Comparability/deviation from international standards

Sources of discrepancies:

Countries often relay on administrative coverage data, while WHO and UNICEF review and assess data from different sources including administrative systems and surveys. Differences between country produced and international estimates are mainly due to differences between coverage estimates from administrative system and survey results.

In case the vaccine is not included in national immunization schedule the coverage from private sector vaccine delivery will not be reflected.

7. References and Documentation

Burton A, Monasch R, Lautenbach B, Gacic-Dobo M, Neill M, Karimov R, Wolfson L, Jones G, Birmingham M. WHO and UNICEF estimates of national infant immunization coverage: methods and processes. Bull World Health Organ. 2009;87(7):535-41.Available at: http://www.who.int/bulletin/volumes/87/7/08-053819/en/

Burton A, Kowalski R, Gacic-Dobo M, Karimov R, Brown D. A Formal Representation of the WHO and UNICEF Estimates of National Immunization Coverage: A Computational Logic Approach. PLoS ONE 2012;7(10): e47806. doi:10.1371/journal.pone.0047806. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3485034/pdf/pone.0047806.pdf

Brown D, Burton A, Gacic-Dobo M, Karimov R. An Introduction to the Grade of Confidence in the WHO and UNICEF Estimates of National Immunization Coverage. The Open Public Health Journal 2013, 6, 73-76. Available at: http://www.benthamscience.com/open/tophj/articles/V006/73TOPHJ.pdf

Brown, David & Burton, Anthony & Gacic-Dobo, Marta. An examination of a recall bias adjustment applied to survey-based coverage estimates for multi-dose vaccines. 2015. 10.13140/RG.2.1.2086.2883.

Danovaro-Holliday MC, Gacic-Dobo M, Diallo MS et al. Compliance of WHO and UNICEF estimates of national immunization coverage (WUENIC) with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) criteria. Gates Open Res 2021, 5:77 Available at: https://doi.org/10.12688/gatesopenres.13258.1

3.b.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

0.c. Indicator

Indicator 3.b.2: Total net official development assistance to medical research and basic health sectors

0.e. Metadata update

2017-07-09

0.f. Related indicators

Other ODA indicators

0.g. International organisations(s) responsible for global monitoring

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definition:

Gross disbursements of total ODA from all donors to medical research and basic health sectors.

Concepts:

ODA: The DAC defines ODA as “those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are

provided by official agencies, including state and local governments, or by their executive agencies; and
each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and

Medical research and basic health sectors are as defined by the DAC. Medical research refers to CRS sector code 12182 and basic health covers all codes in the 122 series (see here: http://www.oecd.org/dac/stats/purposecodessectorclassification.htm)

3.a. Data sources

The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.b. Data collection method

3.c. Data collection calendar

Data are published on an annual basis in December for flows in the previous year.

3.d. Data release calendar

Detailed 2015 flows will be published in December 2016.

3.e. Data providers

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.f. Data compilers

OECD

4.a. Rationale

Total ODA flows to developing countries quantify the public effort that donors provide to developing countries for medical research and basic health.

4.b. Comment and limitations

Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete from 1995 for commitments at an activity level and 2002 for disbursements.

4.c. Method of computation

The sum of ODA flows from all donors to developing countries for medical research and basic health.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Due to high quality of reporting, no estimates are produced for missing data.

At regional and global levels

Not applicable.

4.g. Regional aggregations

Global and regional figures are based on the sum of ODA flows to medical research and basic health.

5. Data availability and disaggregation

Data availability:

On a recipient basis for all developing countries eligible for ODA.

Time series:

Data available since 1973 on an annual (calendar) basis

Disaggregation:

This indicator can be disaggregated by donor, recipient country, type of finance, type of aid, health sub-sector, etc.

6. Comparability/deviation from international standards

Sources of discrepancies:

DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.

7. References and Documentation

URL:

www.oecd.org/dac/stats

References:

See all links here: http://www.oecd.org/dac/stats/methodology.htm

3.b.3

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

0.c. Indicator

Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis

0.e. Metadata update

2019-01-01

0.f. Related indicators

3.b.1- Proportion of the target population covered by all vaccines included in their national programme

3.b.2- Total net official development assistance to medical research and basic health sectors

3.8.1-Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, new born and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population)

3.8.2-Proportion of population with large household expenditures on health as a share of total household expenditure or income

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis.

The indicator is a multidimensional index reported as a proportion (%) of health facilities that have a defined core set of quality-assured medicines that are available and affordable relative to the total number of surveyed health facilities at national level.

Concepts:

Indicator 3.b.3 is defined as the “Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis”.This indicator is based on the proportion of facilities (pharmacies, hospitals, clinics,primary care centers, public/private, etc.) where core essential medicines from the identified set are available for purchase and their prices are affordable, compared to the total number of facilities surveyed.

There are several core concepts that are used for measuring indicator 3.b.3:

Availability of medicine
Affordability of medicine

→to define affordability, additional concepts are used:

Daily dose treatment of the medicine
National poverty line
Wage of the lowest paid unskilled government worker

Core set of relevant essential medicines (defined on a global level)

→to apply a core set of relevant essential medicines defined on a global level to all countries, an additional concept is used:

global burden of disease

1)A medicine is available in a facility when it is found in this facility by the interviewer on the day of data collection. Availability is measured as a binary variable with 1=medicine is available and 0=otherwise.

2) A medicine is affordable when no extra daily wages (EDW) are needed for the lowest paid unskilled government sector worker (LPGW wage) to purchase a monthly dose treatment of this medicine after fulfilling basic needs represented by the national poverty line (NPL). Affordability is measured as a ratio of 1) the sum of the NPL and the price per daily dose of treatment of the medicine (DDD), over 2) the LPGW salary. This measures the number of extra daily wages needed to cover the cost of the medicines in the core set and that can vary between 0 and infinity.

2.a) Daily dose of treatment (DDD) is an average maintenance dose per day for a medicine used for its main indication in adults.² DDDs allow comparisons of medicine use despite differences in strength, quantity or pack size.

2.b) National poverty line (NLP) is the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. NPLs reflect local perceptions of the level and composition of consumption or income needed to be non-poor.

2.c) Wage of the lowest paid unskilled government worker (LPGW is a minimum living wage that employees are entitled to receive to ensure overcome of poverty and reduction of inequalities.

In other words, affordability of a medicine identifies how many (if any) extra daily wages are needed for an individual who earns the LPGW wage to be able to purchase a medicine. The computed EDW ratio aims to indicate whether the LPGW wage is enough for the individual who earns the lowest possible income to cover 1) the daily expenditures for food and non-food items used to define (relative or absolute) poverty using national standards (NPL) and 2) the daily needs for a medicine (DDD). This ratio then requires transformation into a binary variable where medicine is affordable when zero extra daily wages are required to purchase it and not affordable otherwise.

3)The core set of relevant essential medicines is a list of 32 tracer essential medicines for acute and chronic, communicable and non-communicable diseases in the primary health care setting.

This basket of medicines has been selected from the 2017 WHO Model List of Essential Medicines and used in primary health care. By definition, essential medicines are those that satisfy the priority health care needs of the population and are selected for inclusion on the Model List based on due consideration of disease prevalence, evidence of efficacy and safety, and consideration of cost and cost-effectiveness.

These medicines are listed in table 1 of Annex 1, where a detailed justification for including each medicine is also provided, as well as online references for the relevant treatment guidelines and sections in the WHO List of Essential Medicines.

This list of medicines is intended as a global reference. However, to address regional and country specificities in terms of medicine needs, the medicines in this basket are weighted according to the regional burden of disease.

3.a) The global burden of disease is an assessment of the health of the world's population. More specifically, disease burden provides information on the global and regional estimates of premature mortality, disability and loss of health for causes. The summary measure used to give an indication of the burden of disease is the disability adjusted life years (DALYs), which represent a person’s loss of the equivalent of one year of full health. This metric incorporates years of life lost due to death and years of life lost through living in states of less than full health (or disability).

3.a. Data sources

The indicator relies on three data sources that have been used by countries to collect information on medicine prices and availability:

Health Action International Project supported by the WHO [HAI/WHO]
The Service Availability and Readiness Assessment survey [SARA]
The WHO Medicines Price and Availability Monitoring mobile application [EMP MedMon]

Health Action International Project supported by WHO [HAI/WHO] provides data from national and sub-national surveys that have used the WHO/HAI methodology, Measuring Medicine Prices, Availability and Affordability and Price Components. The database is available at the following link: http://haiweb.org/what-we-do/price-availability-affordability/price-availability-data/

The Service Availability and Readiness Assessment [SARA] is a health facility assessment tool designed to assess and monitor availability and readiness of the services provided in the health sector and to generate evidence to support the planning and managing of a health system.

The WHO Medicines Price and Availability Monitoring mobile application [EMP MedMon] can be considered as an updated version of the HAI/WHO tool for collecting data on medicine prices and availability. This data collection tool was created based on the two previously mentioned existing and well-established methodologies. This application is used at facility level to collect information on availability and price of the agreed-upon core basket of medicines.

The EMP MedMon is easier to use, faster to conduct and consumes much fewer resources for collecting data. It also allows for a modular approach to defining the basket, which is highly useful and convenient for the purposes of this indicator.

In order to compute historical data points prior to 2018, data from HAI/WHO is used. To compute current and future data points, SARA and EMP MedMon are recommended

3.b. Data collection method

Availability and affordability of medicines

WHO obtains SARA survey data on availability and affordability from the countries’ Ministries of Health (MoH). HAI/WHO historical data collected at the facility level is available from HAI by request, as publicly available HAI/WHO data on the HAI website has already aggregated at the country level. The EMP MedMon data on availability and medicine prices is collected in collaboration between WHO and Ministries of Health of the countries.

NPLs, LPGW wages, DALYs:

National poverty reports consistently provide information on the NPLs in local currency units. The updated and recalculated NPLs are also published by the countries in these poverty reports. The wage of the LPGW is published in the ILOSTAT database. Information regarding the regional burden of diseases (DALYs) is publicly available and published by WHO.

3.c. Data collection calendar

SARA & HAI/WHO: Data collection activities have often been conducted using funds from international donors.

EMP MedMon: Data collection activities have been conducted using funds from international donors, but WHO is currently testing a sustainable regular monitoring mechanism through the integration of similar data collection during government inspection of health facilities or using country-determined sentinel monitoring sites.

3.d. Data release calendar

Based on historical data points, the first release of the SDG indicator 3.b.3 results is planned for the summer of 2019. Subsequently, updated values will be calculated and published on an annual basis.

3.e. Data providers

SARA, HAI/WHO, EMP MedMon: Data is collected by the countries’ Ministries of Health (MOH), often with the support of the WHO country office. Data is then validated by MoH-based statisticians and shared with WHO by request.

3.f. Data compilers

The World Health Organization

4.a. Rationale

Measurement and monitoring of access to essential medicines are of high priority for the global development agenda given access is an integral part of the Universal Health Coverage movement and an indispensable element of the delivery of quality health care. Access to medicines is a composite multidimensional concept that is composed of the availability of medicines and the affordability of their prices. Information on these two dimensions has been collected and analysed since the 54^th World Health Assembly in 2001, when Member States adopted the WHO Medicines Strategy (resolution WHA54.11). This resolution led to the launch of the joint project on Medicine Prices and Availability by WHO and the international non-governmental organization Health Action International (HAI/WHO), as well as a proposed HAI/WHO methodology for collecting data and measuring components of access to medicines. To this day, this methodology has been widely implemented to produce useful analyses of availability and affordability of medicines, however the two dimensions have been evaluated separately.

While the above approach has provided an overview of the countries’ performance and progress on improving the affordability and availability of medicines, it has not allowed evaluation of overall access to medicines.

This evaluation is in turn essential as country’s success in ensuring one of the dimensions (e.g. availability) does not necessarily indicate the realization of the other (e.g. affordability) and vice versa. For example, a country may focus its policy efforts on ensuring the availability of a core set of essential medicines in the event of low capacity of local production and/or challenges associated with geographic location. As a result of the proposed policies, medicines may become available but their prices may not be affordable. The opposite situation is also possible, as lowering prices of medicines to increase affordability may be too restrictive for some pharmaceutical producers and lead to a decreased supply. Therefore, given the multidimensionality of access to medicines, it is necessary to evaluate both affordability and availability of medicines at the same time.

The proposed methodology for indicator 3.b.3 allows the combination of both dimensions into a single indicator to evaluate the availability and affordability of medicines simultaneously. This methodology also allows for disaggregation so that each dimension can be analysed separately and the main driver of poor performance of the overall index can be properly identified.

Monitoring the core set of relevant essential medicines is based on the WHO Model List of Essential Medicines (EML). The 2017 WHO EML contains 433 medications deemed essential for addressing the most important public health needs globally. The current index is computed based on a subset of 32 tracer essential medicines for the treatment, prevention and management of acute and chronic, communicable and non-communicable diseases in a primary health care setting.

4.b. Comment and limitations

On basket of tracer essential medicines:
1. Although it is possible to regularly monitor all 400+ medicines on the current WHO Model List of Essential Medicines, indicator 3.b.3 requires a specific subset of this list. Over the years, several baskets of medicines have been defined for different purposes and used to conduct data collection and monitor price and availability. This core set of medicines does not replace the other existing baskets, and WHO teams and partners are encouraged and committed to continue ad hoc monitoring through other existing channels. Throughout the process of identifying the core set of medicines, one area of focus has been to balance the selection of the tracer medicines for primary health care with the size of the basket itself. The proposed basket represents a balanced approach to allow that relevant tracer medicines for primary health care are monitored yet ensuring a practical and feasible data collection and analysis. The 32 medicines listed in the basket are meant to be indicative of the access to medicines for primary health care but do not serve as a complete or exhaustive list.
2. As mentioned above, each medicine in the basket is weighted according to the regional Disability Adjusted Life Years (DALYs) for relevant disease from the WHO Global health estimates. Regional estimates are less sensitive to country-by-country variability of data quality, they sufficiently illustrate the disease distribution across countries in the region and work well due simplicity and comparability. Hence, regional weights for medicines are used to establish the associated country weights. However, this diminishes the specificity of the basket to the national context.
On the measurement of medicines’ availability:
1. The proposed approach for measuring the availability of medicines is based on the presence of the medicine on the day that the interviewer visits the facility and does not account for temporary and/or planned stock outs. The 32 medicines identified for the analysis should always be available in the facilities considering that in some (mainly rural) areas, the facility may be very difficult to reach and individuals may not have resources to travel on a daily basis. Moreover, in this proposed methodology the price of the medicine does not take into consideration the so-called indirect costs, which normally include transportation and other costs to reach the facility. Thus, the proposed measure for availability presents some limitations.

Furthermore, given the data collection occurs at the facility level and does not monitor quantities of any given medicine, an overall analysis of the available medicines compared to the national needs is not possible.

On the measurement of medicines’ affordability:
1. Affordability of a medicine is often measured as the capacity of the population of a given country to pay for this medicine either ex-ante (usually based on income) or ex-post (usually based on reported expenditures). The latter would mainly require data collected at the individual level and from household surveys. However, information on medicine expenditures in these surveys is not always collected and when collected, is not done so consistently and regularly across the countries. In addition, there is usually a large amount of missing data.

The ex-ante approach is suggested for the purposes of this indicator as it is measured at the facility level. Ex-ante analysis requires identifying a reference person or group of people for the measurement. The lowest paid unskilled government worker is suggested to serve as the reference for this indicator. In other words, if a medicine is identified as being affordable for the individual who receives the LPGW wage, it will most likely be affordable for all other individuals affiliated with that economic group and higher. This obviously does not account for people employed in the unofficial labour market.

The proposed methodology is an adjusted HAI/WHO methodology. The HAI/WHO approach suggests computing the affordability of medicine prices as the number of daily wages that are required for the lowest paid unskilled government worker (LPGW) to purchase a daily dose of a medicine (DDD). This approach is straightforward and also refers to the capacity of the reference individual to pay for the medicines. However, no threshold was identified to distinguish the maximum number of daily wages that an individual must spend on a medicine in order to still be able to afford it.

1. Information on minimum LPGW wage is available by the International Labour Organization (ILO) for 155 countries. When information is missing or when information has not been updated recently, the alternative measure suggested is to be taken from the World Development Indicators data on “minimum wage for a 19-year old worker or an apprentice”, which is often used as an alternative in ILO reports.
2. The proposed indicator, being measured at the facility level, does not account for potential reimbursement schemes/insurance coverage present at the national level. Information about insurance or other forms of cost-coverage schemes at the national level is not readily available and would require standardization to allow for comparison across countries and income levels of the population. However, as demonstrated by the OECD in its Health at a Glance report in 2015, in 31 high- and middle-income countries the out-of-pocket (OOP) expenditures on pharmaceuticals as a share of all OOP on health varies from 64 to 16%.

Moreover, there are other SDG indicators, such as 3.8.1 and 3.8.2 that capture coverage of essential health services as well as financial protection from health expenditures net of reimbursement, including expenditures for medicines.

Other dimensions on access to medicines (quality)
1. The quality of the product is another equally important dimension of access to medicines. Currently, there is no systematic and publicly available data collection on quality of a single medicine or in a single country. WHO has, however, contributed to enhanced access to quality health products through different programmes such as regulatory systems strengthening and prequalification.

A national regulatory authority (NRA) plays a key role in assuring the quality, safety, and efficacy of medical products until they reach the patient/consumer, as well as ensuring the relevance and accuracy of product information. Hence, stable, well-functioning and integrated regulatory systems are an essential component of a health system and contribute to better public health outcomes. NRA maturity and WHO prequalification of medicines can be considered as a proxy for ensuring that medicines in a country are of assured quality. The NRA maturity level is assessed using the WHO National Regulatory Authority Global Benchmarking Tool (WHO NRA GBT). After the evaluations, countries are assigned one of five levels of maturity, with a score of maturity level three representing the minimum acceptable regulatory capacity and maturity level five representing the highest level of functioning.

The importance of transparency and the disclosure of the results of assessments amongst regulators (from ML 3 up) are taken into consideration. However, the information on country-specific NRA maturity level is not currently publicly available and WHO is working to address this limitation through recent discussions on WHO Listed Authorities (WLA).

Other comments:
1. The “sustainability” dimension in this indicator can be measured only when more than one-time series of computations is available for a specific country so that a trend (tendency of a series of data points to move in a certain direction over time) can be identified.

The proposed methodology takes advantage of recognized standards and data collection methods, proposing a recombination of dimensions to allow measurement of affordability of a core set of relevant essential medicines for communicable and non-communicable diseases.

4.c. Method of computation

The index is computed as a ratio of the health facilities with available and affordable medicines for primary health care over the total number of the surveyed health facilities:

${S D G}_{3 . b . 3} = \frac{F a c i l i t i e s w i t h a v a i l a b l e a n d a f f o r d a b l e b a s k e t o f m e d i c i n e s (n)}{S u r v e y e d F a c i l i t i e s (n)}$

For this indicator, the following variables are considered for a multidimensional understanding of the components of access to medicines:

A core set of relevant essential medicines for primary healthcare
Regional burden of disease
Availability of a medicine
Price of a medicine
Treatment courses for each medicine (number of units per treatment & duration of treatment)
National poverty line and lowest-paid unskilled government worker (LPGW) wage
Proxy for quality of the core set of relevant essential medicines.

The index is measured for each facility separately. Then a proportion of facilities that have accessible medicines is computed. The following steps must be taken to compute the index at the facility level:

Review and selection of the core basket of medicines for primary health care
Estimate weights for the defined medicines based on regional burden of disease
Measure the two dimensions of the access to medicine
- - - 1. Availability
      2. Affordability
Combine the two dimensions on availability and affordability (access to medicines)
Apply weights to the medicine in the basket according to the regional prevalence of the diseases that are cured, treated, and controlled by these medicines
Identify whether a facility has a core set of relevant essential medicines available and affordable

The next two steps are calculated at the country level across all the surveyed facilities:

Calculate the indicator as the proportion of facilities with accessible medicines in the country
Consideration of the quality of the accessible medicines in the country using a proxy

Below is a more detailed procedure of the index computation.

Step 1: Review and selection of the core basket of medicines for primary health care

For some of the disease categories captured by the proposed basket of medicines, a therapeutic category of medicine has been specified (e.g. statins, beta blockers, corticosteroids, etc.) and a specific medicine must be identified for monitoring. For example, beclomethasone is used to treat non-communicable respiratory disease and if it is not supplied in a particular country for some policy or market reason, an alternative corticosteroid inhaler must be included in the analysis. In other cases, more than one medicine should be included in the basket per disease category. This will require a preliminary review of the basket before starting the data collection process.

Step 2: Estimate weights for the defined medicines based on regional burden of disease

The following points must be considered when computing medicines’ weights:

Equal weights are assigned to medicines that are used to treat, cure, and control the same disease(s) (e.g. gliclazide (or other sulfonylurea), metformin and insulin regular are assigned equal weights according to the diabetes disease burden).
For a medicine indicated for multiple diseases, DALYs values for each disease are summed.
For a medicine used for treating conditions for children (four medicines from the list) sum of DALYs is computed for males and females at the age between 0 and 14 years.
For some of the medicines which cannot be assigned to a specific disease (e.g. paracetamol) the weight is computed as $\frac{1}{T}$ (where T is a total number of medicines in the surveyed basket) assuming equal use of the medicine relative to other medicines in the core list.
For medicines not in the list but “suggested for monitoring” by the country, weight is computed as $0.5 * \frac{1}{T}$ assuming a minor relevance of these medicines for this indicator and to avoid major issues in inter-country comparison.

To estimate the weight for each medicine, the following steps have to be undertaken:

1. Assign each medicine in the basket to one or several disease(s) that are treated/cured/controlled by that medicine (Annex 1 table 2)
2. Assign to each disease the corresponding DALYs^{^[1]} (if several diseases are treated with the same medicine, compute sum of these DALYs accordingly) [ ${D A L Y s}_{M i}$ ]
3. Compute total sum of the DALYs per medicine [ ${\sum_{i = 1}^{32} D A L Y s}_{M i}$ ]
4. Compute weight of each medicine as a proportion of the medicine specific DALYs to the total sum of DALYs in the basket [ $W_{M i}$ ]:

$W_{M i} = \frac{{D A L Y s}_{M i}}{{\sum_{i = 1}^{32} D A L Y s}_{M i}}$

As an example, the weights computed across regions for year 2015 are represented in Annex 2 table 2.1 and 2.2.

Step 3: Measure the two dimensions of access to medicine

Availability and affordability of medicines must be measured and transformed (when necessary) into the format of a binary variable.

Availability is measured as a binary variable coded as “1” when the medicine is in the facility on the day of the survey and coded as “0” otherwise. This approach is currently used in the HAI/WHO methodology.^{^[2]}
Affordability is computed following these steps:

3.1 Compute daily price per dose of treatment for each medicine (price per DDD) in the selected basket of medicines

WHO treatment guidelines provide the needed information to compute DDD.

DDD of a medicine is defined using the following formula:

$p r i c e p e r D D D = \frac{M e d i c i n e p r i c e (m o n t h) * U n i t s p e r t r e a t m e n t (m o n t h)}{365 / 12}$

where:

Units per treatments are tablets/vials or other forms that are needed for an individual with the average severity of the disease per one course of treatment of a duration of one month (365 days per year / 12 months per year = 30.42 days given 30 or 31 day per month), and
Medicine prices are calculated per unit (per tablet/vial/other form) requiring adjustments for gram or milligram according to the potency.

This ratio varies between “0” and infinity and is measured in local currency units per day [LCU/d].

Information on the number of units per treatment is specified in Annex 3. The price per DDD can be measured in per day or per month.

3.2 Define National poverty line (NPL) and minimum wage of the LPGW for the analysed country

National poverty line (NLP): countries periodically recalculate and update their poverty lines based on new survey data and publish this information in their national reports on poverty. To adjust the latest available NPLs to the relevant year of analysis (when needed) information on the Consumer Price Index (CPI) in the analysed country has to be used to account for deflation/inflation.

National poverty reports consistently provide information on the NPLs in local currency units but often refer to different recall periods from country to country (NPL can be measured per day, per month or per year). For consistency, NPL has to be adjusted to be measured per day [LCU/d].

The wage of the lowest paid unskilled government worker (LPGW): is estimated and published in the ILOSTAT database. For countries with the latest available data collected in a year different from the year of analysis, LPGW wage is actualised using the CPI conversion factor.

ILO provides information on the minimum LPGW wages in local currency units per month. LPGW wage has to be adjusted to be measured per day as well [LCU/d].

The NPL and LPGW wage can be measured in per day or per month.

3.3 Compute extra daily wages (EDW)

First, the LPGW wage is compared to the NPL and if it is lower, medicine is considered unaffordable. In this case, only medicines with a price equal to zero will be considered affordable.

Next, the affordability is measured via the number of extra daily wages (EDW) that are needed for the LPGW to pay for one-month course of treatment using the formula below. In particular, the number of extra daily wages can be computed using the following formula:

$E x t r a d a i l y w a g e s (E D W) = \frac{N P L + p r i c e p e r D D D}{d a i l y w a g e o f L P G W}$

3.4 Transform EDW variable into a binary format

Following the definition, medicine is considered to be affordable when the sum of NPL and price of a daily dose of the treatment is equal to or less than the minimum daily wage of the LPGW:

$\{ \begin{matrix} i f E D W \leq 1, a f f o r d a b i l i t y = 1, \\ o t h e r w i s e, a f f o r d a b i l i t y = 0 \end{matrix})$

Hence, the affordability of medicines is also measured as a binary variable that is coded as “1” when the medicine is affordable and “0” otherwise.

When the price of the medicine is 0, there is no need for the above-mentioned computations and the medicine is considered affordable (i.e. “1”). If all medicines in the country are provided free of charge, all medicines are directly marked as affordable and further computation of the index depends on the availability of these medicines.

Step 4: Combine the two dimensions on availability and affordability (access to medicines)

In this step, the two dimensions of access to medicines (availability and affordability) are combined into a multidimensional index.

The construction of a multidimensional index is based on the union identification approach^{^[3]} proposed by S. Alkire and G. Robles.

The combination of the dimensions of medicines can be built in matrix form:

$g_{i j}^{o} = [\begin{matrix} x_{11} & \dots & x_{1 d} \\ \dots & \dots & \dots \\ x_{n 1} & \dots & x_{n d} \end{matrix}]$

This matrix contains performance for n objects of analysis (specified in rows) in d dimensions (specified in columns). The performance of any object $i$ in all $d$ dimensions is represented by the d-dimensional vector $x_{i .}$ for all $i = 1, \dots, n$ . The performance in any dimension $j$ for all $n$ objects are represented by the n-dimensional vector $x_{. j}$ for all $j = 1, \dots, d$ . Overall, an index should be computed via two main steps: identification and aggregation. An example of how to combine the 2 dimensions can be found in Annex 4.

Step 5: Apply weights to the medicine in the basket according to the regional prevalence of the diseases that are cured/treated/controlled by these medicines

After identifying the access variable, medicines in the basket have to be weighted according to the prevalence of the disease(s) that these medicines are used to cure/treat/control using the weights identified in step 2 and provided in Annex 2, tables 2.1 and 2.2. This is performed by multiplying the access variable with the medicine weights:

Figure 1. Achievement matrix of weighted access to medicine

Step 6: Identify whether a facility has a core set of relevant essential medicines available and affordable

The following computations must be undertaken in this step:

6.1 Calculate proportion of medicines that are accessible (both available and affordable) in each facility

Because medicines are weighted, the proportion is computed as a weighted sum of medicines that are both available and affordable (accessible) in each facility using the following formula:

$A c c e s s = \sum_{i = 1}^{n} w_{m i}$

This variable is then transformed into a percentage and varies from 0 to 100.

The computed number of accessible medicines accounts for the importance of the analysed medicines in the country. In particular, if a medicine with a higher weight (for example hypertension) is not accessible, the index will be sensitive to this and will demonstrate the lack of access. On the contrary, if a medicine has a low weight (i.e. approaching zero, such as antimalarial medication in a non-endemic country) and is not accessible, the index will not be affected.

6.2 Mark facilities that have 80% or more of available and affordable medicines

The computed variable “access” is then transformed into the binary format identifying facilities that have the core basket of essential medicines available and affordable versus facilities that do not. A threshold of 80% is applied in order to transform the “access” variable into a binary format. In particular, at least 80% of all the medicines surveyed in a facility have to be both available and affordable. The transformation is made using the following formula:

$\{\begin{matrix} i f {A c c e s s}_{{f a c i l i t y}_{i}} \geq 80 % F a c i l i t y = 1, \\ 0 \\ o t h e r w i s e, F a c i l i t y = 0 \end{matrix})$

This threshold is agreed upon and adopted by the WHO Global Action Plan on Non-Communicable Diseases and used as a reference in this proposed methodology.

Step 7: Calculate the indicator as the proportion of facilities with accessible medicines in the country

The proportion of facilities that have reached the 80% threshold is calculated out of the total number of surveyed facilities in a selected country using the following formula:

${S D G}_{3 . b . 3} = \frac{F a c i l i t i e s w i t h a v a i l a b l e a n d a f f o r d a b l e b a s k e t o f m e d i c i n e s (n)}{S u r v e y e d F a c i l i t i e s (n)}$

The computed indicator is a proportion that will then be converted into a percentage between 0-100%.

Step 8: Consideration of quality of the accessible medicines in the country using a proxy

The country level of medicine regulatory capacity assessed using the WHO NRA GBT is used as a proxy of the quality of the accessible medicines. The countries with a WHO Listed Authority (WLA corresponding to maturity level 3 and above) will be flagged to indicate the assured quality component.

DALYs for a disease are calculated as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences (DALYs YLL + YLD). That is why DALYs allow “calculating” consequences both from acute diseases (mortality) and from chronic diseases (disability and life with disease). http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html ↑

http://www.who.int/medicines/areas/access/OMS_Medicine_prices.pdf ↑

https://www.ophi.org.uk/wp-content/uploads/OPHIRP046a.pdf ↑

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Treatment of missing values has already been partially addressed. In particular, when a medicine is not available, its price cannot be collected. For this reason, missing price values are considered as the medicine not being available and therefore not accessible (access = 0).

Observing missing values for availability and affordability simultaneously indicates that these medicines are not provided at all in the surveyed facility. For example, in some countries medicines for in-patient care (mostly in injectable forms) are provided only in hospitals. In this case, the procedure for computing the indicator is the same except that:

Medicines that are used for inpatient care are excluded from the analysis of the data collected in pharmacies and other non-tertiary health care facilities, and
Two different versions of weights are applied to the list of medicines for hospitals and for pharmacies.

At regional and global levels

When computing regional or global aggregates of indicator 3.b.3, it is possible to accommodate missing values from countries resulting from a lack of data collection for a given country in a given year. In order to calculate a regionally aggregated 3.b.3 indicator, a 5-year period of data collection will be used as a reference to identify the available indicators for all the countries in the region. If during the defined 5-year period, one country of the region does not have even one indicator result, this country will not be included in the regional aggregate. The missing values from the countries can only be imputed when at least one data point exists for the given country in such a 5-year period.

4.g. Regional aggregations

Regional and global aggregates can be computed using national population size of a country as a proxy for the country weights in the region or globally. This is justified because medicines must be available and affordable for every individual in the population.

To compute the regional indicator, the weighted average of the country indicators (using either the actual national indicator when available for the specific year of calculation, or the imputed value that corresponds to the year closest to the year of calculation) is used.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The HAI/WHO manual on measuring medicine prices, availability, affordability and price components describes the methodology as well as the guidelines for the data collection procedure and analysis of the availability and affordability of medicines on the facility and national level:

http://www.who.int/medicines/areas/access/medicines_prices08/en/

http://www.who.int/healthinfo/systems/SARA_Reference_Manual_Full.pdf

http://www.who.int/medicines/areas/policy/monitoring/empmedmon

4.j. Quality assurance

Quality control can be performed based on the median availability and median consumer price ratio of selected generic medicines listed on the Global Health Observatory (GHO). The quality of the key components of this indicator (i.e. availability, prices, etc.) can be assured for data collected using any of the three mechanisms listed above when cross-referenced with the GHO values.

For future data collection, quality will be based on the analysis of the sample size and the number of medicines captured in the basket.

Countries will collect and share data with the WHO Secretariat. WHO will subsequently compute the indicator and return to the countries for validation. By request, WHO will also provide all background materials and training for data collection and indicator computation.

5. Data availability and disaggregation

Data availability:

SARA: 21 national surveys are currently available from 2010 to 2017 for a total of 13 countries. Two- and three-year trends are available for six countries; the other seven countries only have one data point. 67% of the SDG basket of relevant essential medicines is covered by such surveys. These data will be used to test quality on the availability dimension only.

HAI/WHO: Historical data points are available for 55 countries (28%) of all WHO Member States. The highest number of countries captured by the surveys is in the SEARO region (59%) and the smallest is in EURO region (15%). More than 60% of the medicines from the defined SDG indicator basket are captured in the HAI/WHO historical data surveys.

Table 1. Number of countries captured by the surveys across regions

WHO Region	2001-2005	2005-2010	2010-2015	Total
African Region	14	5	2	21
Region of the Americas	3	7	1	11
Eastern Mediterranean Region	8	5	3	16
European Region	5	2	3	10
South-East Asia Region	5	2	1	8
Western Pacific Region	6	2	2	10
Total	41	23	12	76

HAI/WHO surveys were conducted more than once in some of the countries for a total of 76 surveys.

EMP MedMon: In 2016 the design of the EMP MedMon tool for data collection was finalised. Since then, several pilot surveys have been conducted to test the tool. The first pilot survey was conducted across 19 countries using a basket of medicines that captures around 60% of the one currently proposed. The second pilot used a basket adjusted for the purposes of capturing non-communicable diseases only. These pilots have demonstrated that this tool is flexible and can be easily manipulated to include specialized modules of medicines for future data collection.

Time series:

Existing data has been historically collected based on available funding. The majority of existing surveys have been collected thus far using the HAI/WHO data collection tool. Most of the existing data points are from 2000 – 2005.

Table 2. Number of surveys and % of medicines from the defined basket

that are captured by HAI/WHO surveys

	2001-2005	2005-2010	2010-2015
Total number of surveys (n)	41	23	12
Medicines captured in the surveys (%)	49.8%	66.3%	72.9%

The distribution of these 76 surveys across WHO regions is represented in Table 3.

Table 3. Number of HAI/WHO surveys across regions

Overall 21 SARA surveys were conducted over the period from 2010 to 2017. 17 surveys were conducted between 2010 and 2015 and 4 surveys after 2015.

Disaggregation:

The proposed indicator will allow for the following disaggregation:

public/private/mission sectors facilities (managing authority)
geography – rural/urban areas
therapeutic group
facility type (pharmacy/hospital)
medicine.

6. Comparability/deviation from international standards

Sources of discrepancies:

Data can be received from three data sources: SARA, HAI/WHO, and the EMP MedMon. These data collection methods demonstrate the following discrepancies:

Sampling of the facilities to be surveyed,
Size of the sampling of the facilities to be surveyed, and
Questions asked at facility level to capture availability (i.e. SARA considers potentially available expired medicines as well).

WHO will use any of these three data sources available for the year of calculation as a compromise between the limitations that these discrepancies pose to the proposed methodology and the need to overcome data availability issues in order to start reporting on this critical indicator. In the unlikely case that data is available through more than one data source for a specific country, WHO will rely on the source with a larger sample size and a higher percentage of medicines from the defined core list captured by the survey.

7. References and Documentation

World Health Organization and Health Action International, Measuring medicine prices, availability, affordability and price components, 2^nd Edition (Switzerland, 2008), available from http://www.who.int/medicines/areas/access/OMS_Medicine_prices.pdf
“Defined Daily Dose: Definition and general considerations” (WHO Collaborating Centre for Drug Statistics methodology, 07 February 2018), https://www.whocc.no/ddd/definition_and_general_considera/
“How to define a minimum wage?” (International Labour Organization, 2018),https://www.ilo.org/global/topics/wages/minimum-wages/definition/lang--en/index.htm
World Health Organization, The Global Burden of Disease: 2004 Update (Switzerland, 2008), available fromhttp://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/
“WHO Global Benchmarking Tool (GBT) for evaluation of national regulatory systems” (WHO Essential medicines and health products, 2018), available fromhttp://www.who.int/medicines/regulation/benchmarking_tool/en/.
“Disease burden and mortality estimates” (WHO Health statistics and information systems, 2018), available from http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html.
Alkire, S. and Robles, G. (2016). “Measuring multidimensional poverty: Dashboards, Union identification, and the Multidimensional Poverty Index (MPI).” OPHI Research in Progress 46a, University of Oxford.
“Essential Medicines” (WHO Global Health Observatory data repository, 2016), available from http://apps.who.int/gho/data/node.main.487.
Health at a Glance 2017: OECD Indicators, OECD (2017). OECD Publishing, Paris https://doi.org/10.1787/health_glance-2017-en.

Medicine	Category (Therapeutic group)	Justification
Salbutamol (100 mcg/dose inhaler)	NCD - Respiratory	Rationale: Salbutamol, a short acting beta-2 agonist, is recommended for prophylaxis and the first-line treatment of bronchospasm in asthma and COPD. It is recommended for all patients with acute severe asthma. Treatment References: WHO PEN 5.b, WHO Guidelines for primary health care in low-resource settings More information in WHO EML 2017 Section Reference: 25.1
Beclometasone (100 mcg/dose inhaler) or other corticosteroid inhaler Alternatives would include, but not be limited to, budesonide, fluticasone, ciclesonide. Refer to ATC group R03BA -	NCD - Respiratory	Rationale: Inhaled corticosteroids are indicated for maintenance treatment of asthma symptoms by reducing inflammation and reducing airways hyper-responsiveness. These do not provide symptomatic relief in acute asthma. Beclometasone is a representative antiasthmatic in the WHO EML. Treatment References: WHO PEN 5.b, WHO Guidelines for primary health care in low-resource settings More information in WHO EML 2017 Section Reference: 25.1
Gliclazide (80 mg cap/tab) or other sulfonylurea Alternatives would include but not be limited to glibenclamide, glimepiride. Refer to ATC group A10BB	NCD - Diabetes	Rationale: Second generation sulfonylureas (SFUs) increase the release of insulin from the pancreas to relieve the hyperglycaemia associated with diabetes. SFUs are useful in patients unable to tolerate metformin, or not adequately controlled on metformin. These are among the main therapies for most patients with type 2 diabetes, but contraindicated for patients with type 1 diabetes. However, it should be noted that glibenclamide has associated with higher levels of hypoglycaemia compared with gliclazide. Gliclazide is the representative sulfonylurea in the WHO EML. Treatment References: WHO PEN 5.b, WHO Guidelines for primary health care in low-resource settings More information in WHO EML 2017 Section Reference: 18.5
Metformin (500 mg cap/tab, 850 mg cap/tab or 1 g cap/tab)	NCD - Diabetes	Rationale: Metformin, an oral anti-diabetic medicine, can be used in patients with type 2 diabetes as a monotherapy or in combination with sulfonylureas. Treatment References: WHO PEN 5.b, WHO Guidelines for primary health care in low-resource settings More information in WHO EML 2017 Section Reference: 18.5
Insulin regular, soluble (100 IU/ml injection)	NCD - Diabetes	Rationale: Regular human insulin, a rapid acting insulin, is necessary for all patients with type 1 and more than 10% of patients with type 2 diabetes. It is currently more affordable to health systems than other long-acting or analogue insulins. Treatment References: WHO PEN 5.b More information in WHO EML 2017 Section Reference: 18.5
Two of the following antihypertensive: Amlodipine (5 mg cap/tab) Enalapril (5 mg cap/tab) or other angiotensin converting enzyme inhibitor (ACEI). Refer to ATC group C09AA. Hydrochlorothiazide (25 mg cap/tab) or Chlorthalidone (25 mg cap/tab) Bisoprolol (5 mg cap/tab) or alternative betablocker (atenolol or carvedilol or metoprolol only)	NCD - Cardiovascular	Rationale: Calcium channel blockers (CBB) are among the first-line treatment options for patients with hypertension. Amlodipine is the representative CCB in the WHO EML. ACEIs are among first-line treatment options for patients with hypertension. ACEIs are also used in the management of heart failure. Enalapril is the representative ACEI in the WHO EML. Thiazide diuretics are among the first-line treatment options for patients with hypertension. Thiazides are also used as the management of heart failure. Hydrochlorothiazide is the representative thiazide diuretic in the WHO EML. Beta-blockers are among the recommended treatment options for patients with hypertension, angina, cardiac arrhythmias or heart failure. Bisoprolol is the representative beta-blocker in the WHO EML. Treatment References: WHO PEN 5.b, WHO Guidelines for primary health care in low-resource settings More information in WHO EML 2017 Section Reference: 12.3, 12.4
Simvastatin (20 mg cap/tab) or other statin. Refer to ATC group C10AA.	NCD - Cardiovascular	Rationale: Statins, lipid-lowering medicines, are used to reduce the risk of coronary heart disease, including fatal and non-fatal myocardial infarction and stroke. Simvastatin is the representative statin in the WHO EML. Treatment References: WHO PEN 5.b, WHO Guidelines for primary health care in low-resource settings More information in WHO EML 2017 Section Reference: 12.6
Acetylsalicylic acid (aspirin) (100 mg cap/tab)	NCD – Cardiovascular	Rationale: Aspirin, an anti-platelet medication, is recommended for preventing a first stroke, has an important role in preventing recurrent strokes, and can reduce the severity of an ischemic stroke. Low-dose aspirin has numerous therapeutic indications including anti-platelet therapy and can be used to reduce the risk of cardiovascular disease. Treatment References: WHO PEN 5.b More information in WHO EML 2017 Section Reference: 12.5
Furosemide 40 mg tablet	NCD - Cardiovascular	Rationale: Furosemide is a loop diuretic used in the treatment of oedema, congestive heart failure, and kidney disease. Treatment References: WHO PEN 5.b More information in WHO EML 2017 Section Reference: 12.4
Morphine (10mg tablet)	Palliative care	Rationale: Morphine, an opioid analgesic, is the first-choice opioid for treatment of strong pain, including cancer pain. It is also recommended as a preoperative medication and sedation for short-term procedures. Treatment References: WHO Model Prescribing Information: Drugs Used in Anaesthesia More information in WHO EML 2017 Section Reference: 2.2,1.3
Paracetamol (any strength)	Pain and Palliative Care	Rationale: Paracetamol, also referred to as acetaminophen or APAP, is an analgesic and antipyretic that is used widely as a first-line treatment for mild to moderate pain and fever. It is also often found in combinations with other medications to treat a cold or for severe pain. In particular, it is the preferred analgesic for pregnant women. Treatment References: WHO Model Prescribing Information: Drugs Used in Anaesthesia More information in WHO EML 2017 Section Reference: 2.1, 7.1
Fluoxetine (20 mg cap/tab) or other selective serotonin reuptake inhibitor (SSRI)	CNS	Rationale: SSRIs are among the most widely used drugs in the treatment of depressive disorders. Fluoxetine is recommended for use in depressive disorders and can be used to treat patients over 8 years old. SSRIs should be used as part of a comprehensive management plan. Treatment References: Evidence-based recommendations for management of depression in non-specialized health settings More information in WHO EML 2017 Section Reference: 24.2
Phenytoin (100mg Tablet) or Carbamazepine (200 mg cap/tab)	CNS	Rationale: Carbamazepine and phenytoin are anticonvulsant/antiepileptic medicines used in the management of generalized and partial seizures and neuropathic pain. Treatment References: Evidence-based recommendations for management of epilepsy and seizures in non-specialized health settings More information in WHO EML 2017 Section Reference: 5
Gentamicin (40 mg/mL in 2mL vial)	Anti-infective	Rationale: Gentamicin, an aminoglycoside antibiotic, is used for the systemic treatment of susceptible infections. It is classified as an ACCESS antibiotic in the WHO EML, signifying that it should widely available, affordable, and quality assured. It is the first-line treatment for community acquired pneumonia, complicated severe malnutrition, and neonatal sepsis, and second-line treatment for gonorrhoeae. Treatment References: WHO Model Prescribing Information: Drugs used in Bacterial Infections More information in WHO EML 2017 Section Reference: 6.2.2
Amoxicillin (500mg cap/tab)	Anti-infective	Rationale: Amoxicillin, a beta-lactam antibiotic, is used to treat a wide range of susceptible infections. It is classified as an ACCESS antibiotic in the WHO EML, signifying that it should widely available, affordable, and quality assured. It is the first-line treatment for specific infectious syndromes, including community acquired pneumonia, neonatal sepsis, lower urinary tract infections, and the second-line treatment for acute bacterial meningitis. Treatment References: WHO Model Prescribing Information: Drugs used in Bacterial Infections More information in WHO EML 2017 Section Reference: 6.2.1
Ceftriaxone (1g/vial Injection)	Anti-infective	Rationale: Ceftriaxone, a third generation cephalosporin, is used for the systemic treatment of susceptible infections. It is classified as a WATCH in the WHO EML, signifying it higher resistance potential and recommendation for only a specific, limited number of indications. It is the first-line treatment for specific infectious syndromes including severe community acquired pneumonia, acute bacterial meningitis, and gonorrhoeae. Treatment References: WHO Model Prescribing Information: Drugs used in Bacterial Infections More information in WHO EML 2017 Section Reference: 6.2.1
Procaine benzylpenicillin (1G = 1MU Injection) or Benzathine benzylpenicillin (900mg=1.2 MIU or 1.44g = 2.4MIU) injection	Anti-infective	Rationale: Procaine benzylpenicillin, a beta-lactam antibiotic, is used to treat syphilis in adults and children. It is classified as an ACCESS antibiotic in the WHO EML, signifying that it should widely available, affordable, and quality assured. Treatment References: WHO Model Prescribing Information: Drugs used in Bacterial Infections More information in WHO EML 2017 Section Reference: 6.2.1
One of the following contraceptives: Ethinylestradiol + levonorgestrel: tablet 30 mcg + 150 mcg (or alternative combined oral contraceptive) Levonorgestrel 30 microgram tablet. Medroxyprogesterone acetate injection IM 150 mg/mL or SC 104 mg/0.65mL Progesterone-releasing implant (etonogestrel 68 mg or levonorgestrel 150 mg) Levonorgestrel 750 mcg or 1.5 mg tablet	MCH	Rationale: Promotion of family planning – and ensuring access to preferred contraceptive methods for women and couples – is essential to securing the well-being and autonomy of women, while supporting the health and development of communities. Access to contraceptives can reduce infant and maternal mortality rates associated with closely spaced and ill-timed pregnancies. Additionally, contraceptives have be included on the WHO EML since its inception and are also listed as life-saving commodities by the UN Commission on Life-Saving Commodities for Women and Children. Treatment References: Medical eligibility criteria for contraceptive use More information in WHO EML 2017 Section Reference: 18.3
Oral rehydration (salts 1 litre)	MCH	Rationale: Oral rehydration salts (ORS), solutions containing sodium, potassium, citrate, and glucose, are used to replace fluid and electrolytes orally. ORS is used to treat acute diarrhoea in children to prevent or treat dehydration. Treatment References: Diarrhoea treatment guidelines including new recommendations for the use of ORS and zinc supplementation for clinic-based healthcare workers More information in WHO EML 2017 Section Reference: 26.1
Zinc sulphate (20mg dispersible tablet)	MCH	Rationale: Zinc supplements are recommended to reduce the severity and duration of acute diarrhoea. If given for 10 to 14 days, zinc also reduces the incidence of new episodes of diarrhoea in the 2 to 3 months following treatment. Treatment References: Diarrhoea treatment guidelines including new recommendations for the use of ORS and zinc supplementation for clinic-based healthcare workers More information in WHO EML 2017 Section Reference: 17.5.2
Oxytocin (5iu or 10iu injection)	MCH	Rationale: Oxytocin, a peptide hormone, is used for the prevention and treatment of postpartum and post-abortion haemorrhage in emergency situations. It is the recommended that all women giving birth should be offered uterotonic drugs, such as oxytocin, during the third stage of labour for the prevention of PPH. Treatment References: WHO Recommendations for the Prevention and Treatment of Postpartum Haemorrhage, UNFPA Medicines for Maternal Health More information in WHO EML 2017 Section Reference: 22.1
Magnesium sulphate 50% 10ml Injection	MCH	Rationale: Magnesium sulfate, an anticonvulsant, is used in the management and prevention of recurrent seizures in eclampsia and pre-eclampsia. Treatment References: WHO recommendation on magnesium sulfate for the prevention of eclampsia in women with severe pre-eclampsia, UNFPA Medicines for Maternal Health More information in WHO EML 2017 Section Reference: 5
Folic acid	MCH	Rationale: Single-agent folic acid is important for the prevention of neural tube defects and should be taken periconceptionally and in first trimester of pregnancy. Treatment References: WHO recommendation on periconceptional folic acid supplementation to prevent neural tube defects More information in WHO EML 2017 Section Reference: 10.1
Artemisinin-based combination therapy (ACT) for treatment of uncomplicated P. falciparum malaria. One of the following: Artemether+lumefantrine (20/120 mg cap/tab) Artesunate+amodiaquine (any strength) Artesunate+mefloquine (any strength) Dihydroartemisinin+piperaquine (any strength) Artesunate+sulfadoxine-pyrimethamine (50 mg+500mg/25mg)	Anti-malarial	Rationale: WHO Guidelines recommend treating adults and children with uncomplicated P. falciparum malaria with artemisinin-based combination therapy (strong recommendation, high-quality evidence). Treatment References: WHO Guidelines for the Treatment of Malaria More information in WHO EML 2017 Section Reference: 6.5.3.1
Artesunate (60 mg injection or 100 mg rectal dose form)	Anti-malarial	Rationale: IM or rectal artesunate is recommended pre-referral treatment of suspected cases of severe malaria pending transfer to a higher level facility. Treatment References: WHO Guidelines for the Treatment of Malaria More information in WHO EML 2017 Section Reference: 6.5.3.1
Combination anti-retroviral therapy for first line treatment of HIV One of the following combinations individually for concomitant use or in fixed-dose combination: 1. Efavirenz (400 mg or 600 mg) + Emtricitabine (200 mg) + Tenofovir disoproxil fumarate (300 mg) 2. Efavirenz (400 mg or 600 mg) + Lamivudine (300 mg) + Tenofovir disoproxil fumarate (300 mg)	Antiretroviral	Rationale: Efavirenz/Emtricitabine/Tenofovir is the preferred fixed-dose combination antiretroviral therapies for treatment of HIV in adults, pregnant or breastfeeding women, and adolescents. Treatment References: WHO Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection More information in WHO EML 2017 Section Reference: 6.4.2.4
Ibuprofen (200mg tablet)	Pain and Palliative Care	Rationale: Ibuprofen, a non-steroidal anti-inflammatory drug, is a first choice medicine in the treatment of mild pain. Treatment References: WHO Guidelines on the pharmacological treatment of persisting pain in children with medical illnesses More information in WHO EML 2017 Section Reference: 2.1
Chlorhexidine Solution or gel: 7.1% (digluconate) delivering 4% chlorhexidine	Neonatal care	Rationale: A recommended antiseptic that should be applied to the umbilical cord in cases of unclean delivery, and if the traditional practices in place increase the risk of cord infection Treatment References: Review of the available evidence on 4% chlorhexidine solution for umbilical cord care More information in WHO EML 2017 Section Reference: 29.1
Ready-to-use therapeutic food (RUTF), paste or spread (1 sachet = 92 g [500 Kcal]) or biscuit (28.4g, 500 kcal per 100g)	Nutrition	Rationale: Energy-dense, micronutrient enhanced pastes used in therapeutic feeding for the community-based management of children who are suffering from uncomplicated severe acute malnutrition and who retain an appetite. Is provided as the therapeutic food in the rehabilitation phase (following F-75 in the stabilization phase) Treatment References: WHO Guideline: Updates on the management of severe acute malnutrition in infants and children. 2013 More information in WHO EML 2017: Not currently included
Isoniazid + pyrazinamide + rifampicin (50 mg + 150 mg + 75 mg)	Antituberculosis	Rationale: Isoniazid + pyrazinamide + rifampicin is recommended as fixed-dose combination therapy for the intensive phase of treatment of drug-susceptible tuberculosis in children. Treatment References: Guidance for national tuberculosis programmes on the management of tuberculosis in children, 2014 More information in WHO EML 2017 Section Reference: 6.2.4
Erythropoiesis - stimulating agents. One of the following: Epoetin alfa (2,000 IU/mL) Darbepoetin alfa (100 mcg/mL)	Chronic kidney disease	Rationale: Erythropoiesis-stimulating agents are recommended for treatment of anaemia of chronic kidney disease in children, young people and adult patients with chronic renal disease requiring dialysis. Treatment References: WHO EML 2016-2017 - Application for erythropoietin-stimulating agents (erythropoietin type blood factors) More information in WHO EML 2017 Section Reference: 10.1
Suggested for monitoring (optional for countries) *
One of the following: Epinephrine injection 1 mg (as hydrochloride or hydrogen tartrate) in 1- mL ampoule Dexamethasone injection 4 mg/ mL in 1- mL ampoule (as disodium phosphate salt)	Antiallergics and medicine used in anaphylaxis	Rationale: Epinephrine (adrenaline) is the first line treatment for a severe allergic reaction. During anaphylactic shock, it must be administered through an intramuscular injection. Dexamethasone is a corticosteroid that prevents almost all symptoms of inflammation associated with allergy. It can also be used during emergency anaphylactic shock. Treatment References: WHO Antiallergics and Medicine Use in Anaphylaxis More information in WHO EML 2017 Section Reference: 3
Fluconazole (50 mg cap/tab) and Nystatin (tablet 500 000 IU)	Anti-fungal drugs	Rationale: Nystatin is an antifungal polyene antibiotic that is effective against infections caused by a wide range of yeasts and yeasts-like fungi. It is used for the treatment of oral, oesophageal and intestinal candidosis. Fluconazole is an orally active imidazole antifungal agent with activity against dermatophytes, yeasts, and other pathogenic fungi. It is widely used in the treatment of serious gastrointestinal and systemic mycoses as well as in the management of superficial infections. Fluconazole is also used to prevent fungal infections in immunocompromised patients. Treatment References: WHO Model Formulary 2008 WHO Model Prescribing Information Drugs used in sexually transmitted diseases More information in WHO EML 2017 Section Reference: 6.3
Levothyroxine (tablet 50 micrograms)	Thyroid hormones	Rationale: Levothyroxine is used for the management of hypothyroidism, diffuse non-toxic goitre, Hashimoto thyroiditis and thyroid cancer. Treatment References: WHO Model Formulary 2008 More information in WHO EML 2017 Section Reference: 18.8

Annex 1: Basket of core set of relevant essential medicines for primary health care and related disease category

Table 1. Basket of core set of relevant essential medicines for primary health care

* These additional medicines were suggested for monitoring during the consultations with WHO regional advisers and WHO Member States, however they do not represent major burden of disease in countries and cannot be weighted according to the same procedure as the mandatory list.

Table 2. Diseases treated with the medicines in the core list

Medicine name	Affiliated disease (code of the diseases according to the ICD-11 classification)
Salbutamol	→ Asthma (1190) → Chronic obstructive pulmonary disease (1180)
Beclometasone or other corticosteroid inhaler	→ Asthma (1190)
Gliclazide or other sulfonylurea	→ Diabetes mellitus (800)
Metformin
Insulin regular, soluble
Amlodipine	→ Hypertensive heart disease (1120)
Enalapril or other angiotensin converting enzyme inhibitor	→ Hypertensive heart disease (1120) → Cardiomyopathy, myocarditis, endocarditis (1150)
Hydrochlorothiazide or Chlorthalidone
Bisoprolol or alternative betablocker (atenolol or carvedilol or metoprolol only)	→ Hypertensive heart disease (1120) → Ischaemic heart disease (1130) → Other circulatory diseases (1160) → Cardiomyopathy, myocarditis, endocarditis (1150)
Furosemide	→ Cardiomyopathy, myocarditis, endocarditis (1150)
Simvastatin or other statin	→ Ischaemic heart disease (1130) → Stroke (1140)
Acetylsalicylic acid (aspirin)	→ Ischaemic heart disease (1130)
Morphine	→ Malignant neoplasms (610)
Paracetamol	→ weight = 1/T
Ibuprofen	→ weight = 1/T
Fluoxetine or other selective serotonin reuptake inhibitor	→ Depressive disorders (830)
Phenytoin or Carbamazepine	→ Epilepsy (970)
Gentamicin	→ Lower respiratory infections (390) → Infectious and parasitic diseases (20)
Amoxicillin	→ Infectious and parasitic diseases (20)
Ceftriaxone
Procaine benzylpenicillin or Benzathine benzylpenicillin
Ethinylestradiol + levonorgestrel (or alternative combined oral contraceptive)	→ Maternal conditions (420)
Medroxyprogesterone acetate injection
Progesterone-releasing implant (etonogestrel or levonorgestrel)
Levonorgestrel
Oral rehydration	→ Diarrhoeal diseases (110)
Zinc sulphate	→ Diarrhoeal diseases (110)
Oxytocin	→ Maternal conditions (420)
Magnesium sulphate	→ Epilepsy (970)
Folic acid	→ Iron-deficiency anaemia (580)
Artemether+lumefantrine	→ Malaria (220)
Artesunate+amodiaquine
Artesunate+mefloquine
Dihydroartemisinin+piperaquine
Artesunate+sulfadoxine-pyrimethamine
Artesunate
Efavirenz + Emtricitabine + Tenofovir disoproxil fumarate	→ HIV/AIDS (100)
Efavirenz + Lamivudine + Tenofovir disoproxil fumarate	→ HIV/AIDS (100)
Chlorhexidine	→ Neonatal sepsis and infections (520)
Ready-to-use therapeutic food (RUTF)	→ Nutritional deficiencies (540)
Isoniazid + pyrazinamide + rifampicin	→ Tuberculosis (30)
Erythropoiesis - stimulating agents	→ Other chronic kidney disease (1273)
Suggested for monitoring (optional)
Epinephrine or Dexamethasone	→ weight = *0.5(**1/T)
Fluconazole
Nystatin
Levothyroxine

Annex 2. Calculation of weights

Weights are region-specific, and the sum of the weights assigned to medicines in the basket is always equal to “1” in a given region. Since some of the medicines are weighted not according to the DALYs but according to the formula in points iii. and iv. above, the weights have to be normalized so that their sum is equal to “1”.

WHO regional data on disease burden is computed and published for 5-year intervals (e.g. 2000, 2005, 2010 and 2015 for now). As a result, for data points falling between the reference years for which DALY estimates are available the closest reference year is used to calculate medicines’ weights (either previous or following) (Figure 1).

Figure 2.1. Selection of data year for computing medicine weights

Two versions of weights are computed: one capturing 32 medicines (excluding optional medicines) and the other capturing 36 medicines (including optional medicines). For countries where the distribution of specific medicines is calculated only in specialized facilities (for example injectable medicines are provided only in hospitals), WHO suggests computing two versions of weights (1 – for pharmacies and other non-tertiary health care facilities based on a shorter list of medicines that exclude the mentioned medicines and 2 – for hospitals that includes the full list of medicines).

Annex 3: Basket of core set of relevant essential medicines for primary health care: number of units and duration per treatment

Medicine	Dose	Duration	Units
Salbutamol	100 mcg/dose inhaler	30	30
Beclometasone	100 mcg/dose inhaler	30	60
Gliclazide	80 mg cap/tab	30	30
Metformin	500 mg cap/tab OR 850 mg cap/tab OR 1 g cap/tab	30	90
Insulin regular, soluble	100 IU/ml injection	30	90
Amlodipine	5 mg cap/tab	30	30
Enalapril	5 mg cap/tab	30	30
Hydrochlorothiazide	25 mg cap/tab	30	30
Chlorthalidone	25 mg cap/tab	30	15
Bisoprolol	5 mg cap/tab	30	30
Simvastatin	20 mg cap/tab	30	30
Acetylsalicylic acid (aspirin)	100 mg cap/tab	30	30
Morphine	10mg cap/tab	30	180
Paracetamol	500 mg tab/cap	30	180
Fluoxetine	20 mg cap/tab	30	30
Phenytoin	100mg cap/tab	30	90
Carbamazepine	200 mg cap/tab	30	150
Gentamicin	40 mg/mL in 2mL vial	3	15
Amoxicillin for adults	500mg cap/tab	7	21
Ceftriaxone	1g/vial Injection	1	1
Procaine benzylpenicillin	1G = 1MU Injection	10	10
Benzathine benzylpenicillin	900mg=1.2 MIU OR 1.44g = 2.4MIU injection	1	1 or 2
Ethinylestradiol + levonorgestrel	30 mcg cap/tab + 150 mcg cap/tab	28	21
Levonorgestrel	30 mcg cap/tab	28	28
Medroxyprogesterone acetate injection	IM 150 mg/mL OR SC 104 mg/0.65mL	84	1
Progesterone-releasing implant: Etonogestrel OR Levonorgestrel	Etonogestrel 68 mg OR Levonorgestrel 150 mg	3 or 5 years	1
Levonorgestrel	750 mcg OR 1.5 mg tablet	1	2 or 1
Oral rehydration salts	1 litre	1	3
Zinc sulphate	20mg dispersible tablet	14	14
Oxytocin	5iu or 10iu injection	1	1
Magnesium sulphate	50% 10ml Injection	1	2
Folic acid	400 mcg tablet	30	30
Artemether+lumefantrine	20/120 mg cap/tab	3	24
Artesunate+amodiaquine	100 mg + 270 mg	3	6
Artesunate+mefloquine	100 mg + 220 mg	3	6
Dihydroartemisinin+piperaquine	40 mg + 320 mg	3	9
Artesunate+sulfadoxine-pyrimethamine	200 mg + 1500mg + 75mg	3	3 + 1
Artesunate	60 mg injection OR 100 mg rectal dose form	1	1
Efavirenz + Emtricitabine + Tenofovir disoproxil fumarate	400 mg OR 600 mg + 200 mg + 300 mg	30	30
Efavirenz + Lamivudine + Tenofovir disoproxil fumarate	400 mg or 600 mg + 300 mg + 300 mg	30	30
Ibuprofen for adults	200mg cap/tab	30	60
Furosemide	40 mg cap/tab	30	30
Epinephrine	1 mg injection	1	0.5
Dexamethasone	injection 4 mg/ mL in 1- mL ampoule (as disodium phosphate salt)	1	1
Fluconazole	50 mg cap/tab (depending on indication)
Nystatin	tablet 500 000 IU	2	8
Levothyroxine	tablet 50 micrograms	30	60
Chlorhexidine	Solution or gel: 7.1% (digluconate) delivering 4% chlorhexidine	7	1
Ready-to-use therapeutic food (RUTF)	paste or spread (1 sachet = 92 g [500 Kcal]) OR biscuit (28.4g, 500 kcal per 100g)	30	150 - 220 kcal/kg per day
Isoniazid + pyrazinamide + rifampicin	50 mg + 150 mg + 75 mg	30	30 (60, 90 or 120)
Epoetin alfa	2,000 IU/mL	12	50 units/kg

Annex 4 – Combination of availability and affordability

As an example, consider a simplified case of access to a basket of three medicines (Figure 2). In the matrix:

“1” indicates that a medicine is available or is affordable.
“0” indicates that a medicine is not available or not affordable. In other words, “0” in the matrix indicates that the dimension is deprived.
“.” indicates cases when medicine is not available and consequently affordability of medicine is not measured. In other words, information on prices cannot be collected when a medicine is not found by the interviewer in the facility.

Figure 4.1. Achievement matrix on access to medicine (two dimensions)

In this basket the 1^st medicine is fully accessible (i.e. it is both available and affordable), the 2^nd medicine is partially accessible (i.e. it is available but not affordable), while the 3^rd medicine is inaccessible (i.e. it is not available and thus it is not possible to collect information on prices).

In this example, the first medicine is accessible and the third medicine is not. However, the second medicine is partially deprived indicating that specific policies applied in the country may be effective for availability of the medicine but not for its affordability. Applying the union identification approach by S. Alkire and G. Robles that treats elements (medicines) in the matrix with partial deprivation as fully deprived, the second medicine is considered not accessible as well (Figure 3).

Figure 4.2. Achievement matrix of access to medicine (two dimensions & deprivation of dimensions)

At the end of this step, the variable “access” to medicines is generated, combining the 2 dimensions of availability and affordability. This variable remains binary in nature with 1 – medicine is accessible (both available and affordable) and 0 – medicine is not accessible (not available or available but not affordable).

3.c.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing States

0.c. Indicator

Indicator 3.c.1: Health worker density and distribution

0.d. Series

Health worker distribution, by sex and type of occupation (%)

Health worker density, by type of occupation (per 10,000 population)

0.e. Metadata update

2023-03-31

0.g. International organisations(s) responsible for global monitoring

Health Workforce Department, World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Health worker densities by occupation

Definition:

Density of medical doctors: The density of medical doctors is defined as the number of medical doctors, including generalists and specialist medical practitioners per 10,000 population in the given national and/or subnational area. The International Standard Classification of Occupations (ISCO) unit group codes included in this category are 221, 2211 and 2212 of ISCO-08.

Density of nursing and midwifery personnel: The density of nursing and midwifery personnel is defined as the number of nursing and midwifery personnel per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2221, 2222, 3221 and 3222.

Density of dentists: The density of dentists is defined as the number of dentists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2261.

Density of pharmacists: The density of pharmacists is defined as the number of pharmacists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2262.

Health worker distribution by sex

Percentage of male medical doctors: Male doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212.

Percentage of female medical doctors: Female doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212.

Percentage of male nursing personnel: Male nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221.

Percentage of female nursing personnel: Female nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221.

2.b. Unit of measure

Health worker densities by occupation: Per 10,000 population

Health worker distribution by sex and type of occupation: Percent (%)

2.c. Classifications

International Standard Classification of Occupations (ISCO-08)

3.a. Data sources

In response to the Sixty-ninth World Health Assembly (WHA69.19), an online National Health Workforce Accounts (NHWA) data platform was developed to facilitate national reporting. In addition to the reporting, the platform also serves as an analytical tool at the national/regional and global levels. Since Its launch in November 2017, Member States are called to use the NHWA data platform to report health workforce data. Complementing the national reporting through the NHWA data platform, additional sources such as the National Census, Labour Force Surveys and key administrative national and regional sources are also employed. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices.

3.b. Data collection method

Countries are encouraged to adopt a progressive NHWA implementation approach building on multi-stakeholder engagement at national and sub-national levels. National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.

3.c. Data collection calendar

Ongoing process

3.d. Data release calendar

Data is released yearly.

3.e. Data providers

NHWA focal point at national level

3.f. Data compilers

World Health Organization (WHO)

3.g. Institutional mandate

The Global Strategy for Human Resources for Health: 2030 agenda and the progressive implementation of NHWA adopted in Sixty-ninth World Health Assembly (WHA69.19). WHA69.19 urges Member States to share health workforce data to WHO, to increase the evidence base on health workforce statistics globally.

4.a. Rationale

For detailed metadata and definitions, refer to the National Health Workforce Accounts (NHWA) Handbook (https://www.who.int/publications/i/item/9789241513111)

4.b. Comment and limitations

Data on health workers tend to be more complete for the public health sector and may underestimate the active workforce in the private, military, nongovernmental organization and faith-based health sectors. In many cases, information maintained at the national regulatory bodies and professional councils is not updated.

As data is not always published annually for each country, the latest available data has been used. Due to the differences in data sources, considerable variability remains across countries in the coverage, periodicity, quality and completeness of the original data. Densities are calculated using the latest national population estimates from the United Nations Population Division's World Population Prospects database and may vary from densities produced by the country.

4.c. Method of computation

Health worker densities by occupation

The figures for number of medical doctors (including generalist and specialist medical practitioners) depending on the nature of the original data source may include practising medical doctors only or all registered medical doctors.

The figures for number of nursing and midwifery include nursing personnel and midwifery personnel, whenever available. In many countries, nurses trained with midwifery skills are counted and reported as nurses. This makes the distinction between nursing personnel and midwifery personnel difficult to draw.

The figures for number of dentists include dentists in the given national and/or subnational area. Depending on the nature of the original data source may include practising (active) only or all registered in the health occupation. The ISCO -08 codes included here are 2261.

The figures for number of pharmacists include in the given national and/or subnational area. Depending on the nature of the original data source may include practising (active) only or all registered in the health occupation. The ISCO-08 codes that relate to this occupation is 2262.

In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database. In cases where the official health workforce report provides density indicators instead of counts, estimates of the stock were then calculated using the latest population estimates from the United Nations Population Division's World population prospects database.

Health worker distribution by sex and type of occupation

The number of male medical doctors as reported by the country is expressed as a percentage of total male and female medical doctors reported by the country.

The number of female medical doctors as reported by the country is expressed as a percentage of total male and female medical doctors reported by the country.

The number of male nursing personnel as reported by the country is expressed as a percentage of total male and female nursing personnel reported by the country.

The number of female nursing personnel as reported by the country is expressed as a percentage of total male and female nursing personnel reported by the country.

4.d. Validation

The data recorded in the NHWA data platform is validated by country focal points. Data quality checks and country consultation are employed.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Data for the countries with missing values, if any in the last 5 years are estimated with neighbouring comparable countries.

• At regional and global levels

Not applicable

4.g. Regional aggregations

The global average density was estimated as the population weighted average of the national densities.

For the regional average density, data for the countries with missing values, if any in the last 5 years were first estimated with neighbouring comparable countries. Then the regional average was also computed as a weighted average by pooling these estimated values plus the available national densities.

The population for estimating densities at regional and global level are based on the latest available estimates from the UN Population Division.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries are requested to refer to the National Health Workforce Accounts (NHWA) Handbook (https://www.who.int/publications/i/item/9789241513111), for guidance on indicators and methodology.

4.i. Quality management

All national health occupations data is mapped to the International Standard Classification of Occupations (ISCO-08) to enable cross country comparability.

4.j. Quality assurance

Data is collected through a standardised online data entry form based on DHIS2 application. Data validations and quality checks are in-built to minimise data entry errors

4.k. Quality assessment

We perform internal validation for outliers and completeness and raise queries to countries directly to the national focal points and /or through the WHO country and regional offices, for clarification.

5. Data availability and disaggregation

Data availability:

Data available for all 194 WHO Member States

Time series:

From year 2000.

Global Health Workforce Statistics in Global Health Observatory data repository: https://apps.who.int/gho/data/node.main.HWFGRP?lang=en

NHWA data portal: https://apps.who.int/nhwaportal/

Disaggregation:

National level data

6. Comparability/deviation from international standards

Sources of discrepancies:

Population estimates utilised by countries and/or regional offices may differ from those of the UN Population Division

7. References and Documentation

URL:

https://www.who.int/activities/improving-health-workforce-data-and-evidence

References:

Sixty-ninth World Health Assembly Agenda Item 16.1. Global strategy on human resources for health: workforce 2030 (2016), available from (http://apps.who.int/gb/ebwha/pdf_files/WHA69/A69_R19-en.pdf)
WHO (2014). Global strategy on human resources for health: Workforce 2030 (https://www.who.int/publications/i/item/9789241511131)
"WHO Global Health Workforce Statistics." World Health Organization, n.d. Web. Feb. 2018. (https://apps.who.int/gho/data/node.main.HWFGRP?lang=en)
"WHO Global Health Workforce Statistics." World Health Organization, n.d. Web. Feb. 2018. (http://apps.who.int/gho/data/node.main.A1444?lang=en&showonly=HWF)
WHO, National Health Workforce Accounts: A Handbook, n.d. Wed. Feb. 2018. (https://www.who.int/publications/i/item/9789241513111)
WHO 13^th Global Programme of Work (https://www.who.int/about/what-we-do/gpw-thirteen-consultation/en/)
WHO NHWA data portal: https://apps.who.int/nhwaportal/

3.d.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks

0.c. Indicator

Indicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparedness

0.e. Metadata update

2022-09-30

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

Country Capacity Assessment and Planning Group (CAP)

Department of Health Security Preparedness (HSP)

Division of Emergency Preparedness (HEP)

WHO Health Emergency Programme

2.a. Definition and concepts

The revised International Health Regulations (IHR) were adopted in 2005 and entered into force in 2007. Under the IHR, States Parties are obliged to develop and maintain minimum core capacities for surveillance and response, including at points of entry, to detect, assess, notify, and respond to any potential public health events of international concern.

Article 54 of the IHR states, "States Parties and the Director-General shall report to the Health Assembly on the implementation of these Regulations as decided by the Health Assembly."

The State Party self-assessment and reporting tool captures the level of self-assessed national capacities. They are essential public health capacities that States Parties are required to put in place throughout their territories according to Articles 5 and 12 and Annex 1A of the IHR (2005) requirements.

Based on the lessons learned from the COVID-19 pandemic, WHO published the revised second edition of the IHR State Parties Self-Assessment Tool in 2021 with new indicators related to gender equality in health emergencies, advocacy for IHR implementation, and community engagement, to name a few. The revisions are intended to improve the assessment of the IHR core capacities and the preparedness of State parties for health emergencies. The indicator SDG 3.d.1 reflects the capacities State Parties of the International Health Regulations (2005) (IHR) had agreed and committed to developing.

2.b. Unit of measure

Percentage

2.c. Classifications

We use the WHO Official list of countries that are State Parties of the International Health Regulations (IHR2005), distributed according to the six WHO administrative regions (www.who.int ).

The second edition SPAR tool has been expanded from 13 to 15 capacities. The 15 core capacities are (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies.

The 13 core capacities of the first edition of the IHR State Parties Annual Assessment and Reporting Tool are (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies.

Both SPAR questionnaires (1st and 2nd editions) use a five-level scoring with indicators based on five cumulative levels to measure the implementation status for each capacity. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party's current status. To move to the next level, all capacities described in previous levels should be in place for each indicator.

For the years 2010 to 2017, Member States used the IHR monitoring questionnaire. The questionnaire is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards. Individual questions are grouped by components and indicators in the questionnaires. States Parties can provide additional information on the questions in the comment boxes. Responses to the questions include marking one appropriate value (Yes, No, or Not Known) or the appropriate percentages. For statistical purposes, the "Not Known" value is computed as a "No" value. The IHR monitoring questionnaire includes the following: IHR01. National legislation, policy and financing; IHR02. Coordination and National Focal Point communications; IHR03. Surveillance; IHR04. Response; IHR05. Preparedness; IHR06. Risk communication; IHR07. Human resources; IHR08. Laboratory; IHR09. Points of entry; IHR10. Zoonotic events; IHR11. Food safety; IHR12. Chemical events; IHR13. Radio nuclear emergencies.

3.a. Data sources

The data is collected annually from State Parties since 2010 and registered and available on the e-SPAR platform (https://extranet.who.int/e-spar). The actual total of IHR State Parties is 196, and all are committed to reporting annually to the WHO to report the World Health Assembly. The number of reports received has increased annually. By 2021, WHO received SPAR data from 184 (out of 196) Member States, reflecting 94% of submissions, the highest number for a SPAR reporting cycle.

3.b. Data collection method

The data is collected using an online questionnaire (https://extranet.who.int/e-spar). An optional interactive PDF and MS Excel forms for Points of Entry are available in case of limitations in internet connectivity. The multisectoral approach remains critical to completing the IHR State Party Self-assessment Annual Report. It is highly recommended that each State Party convene relevant IHR stakeholders at the outset of the SPAR process.

3.c. Data collection calendar

Data collection for 2021 was completed in July 2022. The data collection for 2022 will start in October 2022, with the deadline on the 28th of February 2023.

3.d. Data release calendar

Results of the States Parties Self-Assessment Annual Report 2021 are now available in the e-SPAR platform https://extranet.who.int/e-spar and disseminated to other WHO homepages on WHO websites, including the Strategic Partnership for Health Security and Emergency Preparedness (SPH) Portal (https://extranet.who.int/sph/), the Global Health Observatory (https://www.who.int/data/gho ), WHO GPW13 triple billion targets dashboard (https://portal.who.int/triplebillions/ ).

3.e. Data providers

All data is collected from 196 Member States and disseminated by WHO.

3.f. Data compilers

All data is compiled and disseminated by WHO.

3.g. Institutional mandate

In 2008, the World Health Assembly, through the adoption of Resolution WHA61(2), and later in 2018 with the Resolution WHA71(15), decided that "that States Parties and the Director-General shall continue to report annually to the Health Assembly on the implementation of the International Health Regulations (2005), using the self-assessment annual reporting tool". In December 2021, and under Resolution WHA75, an updated SPAR tool second edition was published.

4.a. Rationale

The indicators used represent the essential public health capacity that States Parties must have in place throughout their territories under Articles 5 and 12 and Annex 1A of the IHR (2005) requirements. Further detailed information and guidance on how to use the State Parties Self-Assessment and Reporting Tool – SPAR indicators, can be found in a guidance document at: https://extranet.who.int/e-spar

4.b. Comment and limitations

1) it is based on a self-assessment and reporting by the State Party

2) There are three datasets based on the different tools to collect data for SPAR. For the period 2010 to 2017, the questionnaire, known as the IHR monitoring questionnaire, is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards and information on the status of implementation for each capacity. The IHR monitoring questionnaire ( 2010 to 2017) was replaced by the IHR State Parties Self-Assessment Tool – SPAR, published in July 2018 also known as SPAR 1st edition. The States Parties used the questionnaire from the 2018 – 2020 SPAR reporting cycle. The current questionnaire replaced the SPAR 1st edition and was used by the Member States for 2021. Under each capacity, the indicators were either retained, replaced or added. Historical trends based on the data for similar capacity titles may be taken with caution.

4.c. Method of computation

All data are from the questionnaires submitted by States Parties annually.

For each of the 15 capacities, one to five indicators are used to measure implementation status. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party's current status. To move to the next level, all capacities described in previous levels should be in place for each indicator. The score of each indicator level is classified as a percentage of performance along the "1 to 5" scale. e.g. for a country selecting level 3 for indicator 2.1, the indicator level is expressed as: 3/5*100=60%

CAPACITY LEVEL

The level of capacity is expressed as the average of all indicators. e.g. for a country selecting level 3 for indicator 2.1 and level 4 for indicator 2.2. The indicator level for 2.1 is expressed as 3/5*100=60%, the indicator level for 2.2 will be expressed as 4/5*100=80% and the capacity level for 2 will be expressed as (60+80)/2=70%

4.d. Validation

The e-SPAR electronic platform has mechanisms and checks to monitor reports received and to proceed with quality checks. The eSPAR is also accessible to WHO staff working with the Member States on SPAR (all levels). When the national authority fills in the questionnaire, electronic checks are automatically available (pop-up alerts) to avoid potential mistakes and missing critical information on the report before final submission.

Seminars are promoted, tutorials are available (under revision) and consultation with national authorities can be made in coordination with all levels of WHO. More details with references, short videos and links in several languages at: https://extranet.who.int/e-spar/

4.e. Adjustments

No adjustments were adopted.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

Usually, no methodology is employed to replace missing reports. Eventually, on an ad-hoc basis, the last report received can be used just for a specific request for data analysis.

4.g. Regional aggregations

The regional aggregation is based on the list of WHO State Parties on each administrative region as the denominator.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

There are specific tutorials and guidance for national authorities to use the e-SPAR platform and to report using the State Parties Self-Assessment and Reporting Tool – SPAR, accessible from the e-SPAR public page at: https://extranet.who.int/e-spar/

4.i. Quality management

WHO have specific teams working in a collaborative approach to manage the quality of the statistical products and process, such as the Division of Data Analytics and Delivery for Impact (more details at https://www.who.int/data/ddi )

4.j. Quality assurance

Please see details from the statistical WHO Programmes at https://www.who.int/data/ddi

4.k. Quality assessment

Please see details from the statistical WHO Programmes at https://www.who.int/data/ddi

5. Data availability and disaggregation

Since 2010, when the IHR Annual Reporting was implemented, all 196 State Parties had reported at least once. All reports and regional breakdowns are available, including for download of excel spreadsheet with all countries capacities reported since 2010 at: https://extranet.who.int/e-spar/ , at Health Security and Emergency Preparedness (SPH) Portal (https://extranet.who.int/sph/ ) and

the Global Health Observatory (https://www.who.int/data/gho ).

6. Comparability/deviation from international standards

The national IHR annual self-assessment and reporting have specific indicators based on IHR requirements for core capacities needed to detect, assess, notify, report and respond, including at points of entry, to public health risks and acute events of domestic and international concern.

External voluntary evaluation of similar capacities can be done, by the same country, such as using the Joint external evaluation (JEE) tool, supported by several countries, in complement to the self-assessment. More details are available at the Health Security and Emergency Preparedness (SPH) Portal (https://extranet.who.int/sph/)

7. References and Documentation

International health regulations (‎2005)‎: state party self-assessment annual reporting tool, 2nd ed	English							https://www.who.int/publications/i/item/9789240040120
Международные медико-санитарные правила (‎2005 г.)‎: Инструмент ежегодной отчетности государств-участников на основе самооценки, 2-е издание		Russian					https://www.who.int/ru/publications/i/item/9789240040120
Règlement sanitaire international (‎2005)‎ : outil d’autoévaluation pour l’établissement de rapports annuels par les états parties, 2e ed	French							https://www.who.int/fr/publications/i/item/9789240040120
Regulamento Sanitário Internacional (‎2005)‎: ferramenta de auto-avaliação e relatório anual dos Estados Partes, segunda edição	Portuguese							https://www.who.int/pt/publications/i/item/9789240040120
اللوائح الصحية الدولية (2005): أداة إعداد التقارير السنوية للتقييم الذاتي للدولة الطرف ، الإصدار الثاني	Arabic							https://www.who.int/ar/publications/i/item/9789240040120
国际卫生条例（2005)‎: 缔约国自评年度报告工具, 第二版			Chinese		https://www.who.int/zh/publications/i/item/9789240040120
Reglamento sanitario internacional (‎2005)‎: instrumento de autoevaluación para la presentación anual de informes de los estados partes, 2a ed				Spanish		https://www.who.int/es/publications/i/item/9789240040120

International health regulations (‎2005)‎: state party self-assessment annual reporting tool second edition: C11. Points of entry (‎PoE)‎ and border health	English	https://www.who.int/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1
اللوائح الصحية الدولية (2005): أداة إعداد التقارير السنوية للتقييم الذاتي للدولة الطرف ، الإصدار الثاني: C11. نقاط الدخول (PoE) وصحة الحدود	Arabic
《国际卫生条例（2005）》: 缔约国自评年度报告工具, 第二版：C11。入境点 (‎PoE)‎ 和边境卫生	Chinese
Международные медико-санитарные правила (2005 г.). Инструмент ежегодной отчетности государств-участников на основе самооценки, второе издание: C11. Точки въезда (‎PoE)‎ и состояние границы	Russian
Règlement sanitaire international (2005) : outil d’autoévaluation pour l’établissement de rapports annuels par les états parties,deuxième édition: C11. Points d'entrée (‎PoE)‎ et santé aux frontières	French	https://who.int/fr/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1
Regulamento Sanitário Internacional (2005): Ferramenta de auto-avaliação e relatório anual dos Estados Partes, segunda edição: C11. Pontos de entrada (‎PoE)‎ e saúde da fronteira	Portuguese	https://who.int/pt/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1
Reglamento sanitario internacional (2005): instrumento de autoevaluación para la presentación anual de informes de los estados partes, 2ª edición: C11. Puntos de entrada (‎PoE)‎ y sanidad fronteriza	Spanish	https://who.int/es/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1

International Health Regulations (‎‎‎‎‎‎‎‎‎‎‎‎‎‎2005)‎‎‎‎‎‎‎‎‎‎‎‎‎‎: guidance document for the State Party self-assessment annual reporting tool	English	https://www.who.int/publications/i/item/WHO-WHE-CPI-2018.17
Règlement sanitaire international (‎‎‎‎2005)‎‎‎‎ : document d’orientation sur l’outil d’autoévaluation pour l’établissement de rapports annuels par les États Parties	French	https://www.who.int/fr/publications/i/item/WHO-WHE-CPI-2018.17
Reglamento Sanitario Internacional (‎‎2005)‎‎: documento de orientación sobre el instrumento de autoevaluación para la presentación anual de informes de los Estados Partes	Spanish	https://www.who.int/es/publications/i/item/WHO-WHE-CPI-2018.17
اللوائح الصحية الدولية (2005): وثيقة توجيهية بشأن أداة اإلبالغ السنوي للدول األطراف بالتقييم	Arabic	https://www.who.int/ar/publications/i/item/WHO-WHE-CPI-2018.17
Международные медико-санитарные правила (‎‎‎2005 г.)‎‎‎: руководство по инструменту ежегодной отчетности государств-участников на основе самооценки	Russian	https://www.who.int/ru/publications/i/item/WHO-WHE-CPI-2018.17
国际卫生条例（2005): 缔约国自评年度报告工具指导文件	Chinese	https://www.who.int/zh/publications/i/item/WHO-WHE-CPI-2018.17

3.d.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks

0.c. Indicator

Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organisms

0.e. Metadata update

2021-04-01

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli resistant to 3rd-generation cephalosporin (e.g., ESBL- E. coli) among patients seeking care and whose blood sample is taken and tested.

Presumptive methicillin-resistant S. aureus (MRSA) isolates as defined by oxacillin minimum inhibitory concentration (MIC) and cefoxitin disc diffusion tests according to current internationally recognized clinical breakpoints (e.g., EUCAST or CLSI)^[1]
E. coli resistant to third generation cephalosporins: E. coli isolates that are resistant as defined by current internationally recognized clinical breakpoints for third generation cephalosporins (e.g., EUCAST or CLSI), specifically ceftriaxone or cefotaxime or ceftazidime.

EUCAST guidelines for detection of resistance mechanisms and specific resistances of clinical and/or epidemiological importance. Version 2.0. 2017. Both for species identification and antimicrobial susceptibility testing (AST)

CLSI. M100 Performance Standards for Antimicrobial Susceptibility Testing. 29th ed2018 https://clsi.org/standards/products/microbiology/documents/m100/ ↑

3.a. Data sources

Preferred sources: National AMR data collected through the national AMR surveillance system and reported to GLASS.

GLASS provides a standardised approach to the collection, analysis, and sharing of AMR data by countries, and seeks to document the status of existing or newly developed national AMR surveillance systems. Furthermore, GLASS promotes a shift from surveillance approaches based solely on laboratory data to a system that includes epidemiological, clinical, and population-level data. GLASS also collaborates with regional and national AMR surveillance networks to produce timely and comprehensive data. Collaboration with the UN Food and Agriculture Organization (FAO) and the World Organisation for Animal Health (OIE) – which together with WHO form the Tripartite Collaboration – is ongoing to improve a comprehensive understanding of AMR across sectors and to promote the One Health Approach to AMR control.

GLASS also collects information on the status of national AMR surveillance systems through a short

questionnaire completed by AMR national focal points (NFPs) in each country. The questionnaire covers three main areas: 1) overall coordination; 2) surveillance system; and 3) quality control. Each area consists of a set of indicators developed to measure development and strengthening of national AMR surveillance.

Other possible data sources: Published and non-published data from national centres and research/academic institutions and from others regional surveillance networks.

3.c. Data collection calendar

Yearly

3.e. Data providers

Ministries of Health

3.f. Data compilers

WHO

4.a. Rationale

Antimicrobial resistance (AMR) is a global threat to health, livelihoods, food security and the achievement of many of the Sustainable Development Goals. Antibiotics, antivirals, antiparasitic agents and antifungals are increasingly ineffective owing to resistance developed through their excessive or inappropriate use, with serious consequences for human and animal health (terrestrial and aquatic), and plant health, and negative impacts on food production, the environment and the global economy^[2].

In particular, antimicrobial resistance will negatively impact the achievement of many of the targets listed under Goal 3 due to reduced treatment options for infections by resistant pathogens; will impact targets under Goal 2 by impacting the agricultural productivity, including food animal production; and will impact targets in Goal 1 as increased antimicrobial resistance will result in large declines in economic growth, increase economic inequality and drive an additional 24 million people into extreme poverty by 2030^[3].

Given the above context, there is an urgent need to build country capacity, especially in developing countries, to address this growing national and global multisectoral risk. The current indicator (3.d.1) for target 3.d has a focus on strengthening 13 core capacities – essential public health capacity that State Parties are required to have in place throughout their territories pursuant to IHR (2005) requirements by the year 2012. While a few of these 13 core capacities^[4] can be considered “AMR-sensitive”, they do not specifically monitor or address the significant risks associated with AMR. So, with the adoption of the Global Action Plan on AMR in 2015 by the World Health Assembly, the adoption of a Political Declaration on AMR at the high-level meeting of the UN General Assembly in 2016, and the report in 2019 of the Ad-hoc Inter-Agency Coordination Group established by the UN Secretary-General, an urgent need has been identified for an additional indicator on AMR to be considered for inclusion within the global SDG indicator framework.

This new proposed indicator, based on establishing a functional national AMR surveillance system, is considered a basic building block for AMR monitoring and response in countries. Surveillance is the cornerstone to assessing the spread of AMR, providing early warning, and informing and monitoring the impact of local, national, and global risk reduction and management strategies. The global antimicrobial surveillance system (GLASS^[5]) managed by WHO recommends the establishment of three core components to set up a well-functioning national AMR surveillance system: 1) a National Coordinating Centre (NCC); 2) a National Reference Laboratory (NRL); and 3) Sentinel surveillance sites where both diagnostic and epidemiological data are collected.

This new proposed indicator, therefore will help catalyse the establishment of national AMR surveillance systems to ensure the collection of data at the national level and can also be used for tracking progress of country capacity for early warning of outbreaks of resistant infections. The proposed indicator aims to address critical elements of the SDG target 3.d through a strategic approach derived from the evidence gathered through this indicator, as well as allows to ‘strengthen the capacity of all countries, in particular developing countries’, ‘reduction’ and ‘management of national’ and ‘global health risks’, as part of the SDG global monitoring framework. The surveillance and diagnostics data thus generated will also help countries give early warning for public health preparedness, and for appropriate response measures.

Rationale for selecting the types of AMR organisms:

(i) E. coli and S. aureus are among the most common human fast-growing bacteria causing acute human infections;

(ii) E. coli is highly prevalent in both humans, animals and environment, being an ideal indicator for monitoring AMR across the sectors in line with the One Health approach. It recognizes that the health of humans, animals and ecosystems are interconnected and therefore requires a coordinated, collaborative, multidisciplinary and cross-sectoral approach to address potential or existing risks that originate at the animal-human-ecosystems interface;

(iii) both MRSA and E. coli resistant to 3rd-generation cephalosporin are largely disseminated and found in high frequency in human infections observed in hospital settings all over the world and increasingly very frequent in the community. Infections with these types of AMR lead to increase in use of the last resort drugs (e.g., vancomycin for MRSA infections, and carbapenems for E. coli resistant to 3rd-generation cephalosporin) against which new types of AMR are emerging.

Effective control of these two types of AMR will ultimately help preserve the capacity to treat infections with available antimicrobials while new prevention and treatment solutions can be developed. WHO has well defined global infection prevention and control standards and strategies.

Retrospective cohort study. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin. 2016;21. doi: 10.2807/1560-7917.ES.2016.21.33.30319 ↑

World Bank Group, Drug-resistant Infections: A Threat to Our Economic Future – Final Report (Washington, D.C., March 2017). ↑

⁴

(1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3)Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory;; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies ↑

⁵

https://www.who.int/glass/en/ ↑

4.b. Comment and limitations

AMR is an emerging global threat and risk to public health worldwide. In its early implementation phase of the global antimicrobial resistance surveillance system (GLASS), WHO recognizes various constraints in obtaining unbiased, representative AMR data: number and distribution of surveillance sites and representativeness of surveillance data, sampling bias, poor diagnostic capacity, measurements errors, issues with data management. It is imperative that countries should have a functioning national system to support AMR surveillance and report to GLASS. More detailed GLASS methodology and limitations of data currently submitted by countries can be found in the GLASS report^[6]. AMR surveillance, country preparedness and response are now high priority for WHO and its Member States. In the next five years, WHO aims to provide intensified technical assistance. Experience gained and lessons learnt from the further implementation of the national AMR surveillance systems will increase effectiveness, address limitations, and the make the data more robust.

⁶

Global antimicrobial resistance surveillance system (GLASS) report: Early implementation 2017-2018 (2019). https://apps.who.int/iris/bitstream/handle/10665/279656/9789241515061-eng.pdf ↑

4.c. Method of computation

The WHO Global AMR Surveillance System (GLASS) supports countries to implement an AMR standardized surveillance system. Cases of AMR infection are found among patients from whom routine clinical samples have been collected for blood culture at surveillance sites (health care facility) according to local clinical practices, and antimicrobial susceptibility tests (AST) are performed for the isolated blood pathogens as per international standards^[7]. The microbiological results (bacteria identification and AST) are de-duplicated and combined with the patient data and related to population data from the surveillance sites. GLASS does collect information on the origin of the infection, either community origin (less than 2 calendar days in hospital) or hospital origin (patients hospitalized for more than 2 calendar days). Data are collated and validated at national level and reported to GLASS where epidemiological statistics and metrics are generated. GLASS has published guidelines on the set up of national AMR surveillance systems^[8] and the GLASS methodology implementation manual^[9] is available to countries.

Although national representativeness of generated AMR rates is not a strict requirement, GLASS encourages countries to derive representative national data.

Formulation of the proposed new indicator: Proportion of patients with Percentage of bloodstream infections due to selected antimicrobial resistant organisms.

This is derived from the following and multiplied by 100^[10]:

Numerator: Number of patients with growth of methicillin-resistant S. aureus or E. coli resistant to third generation cephalosporins in tested blood samples

Denominator: Total number of patients with growth of S. aureus or E. coli in tested blood samples

Stratification:

The data are stratified by gender, and age group. Data are aggregated at the country level. Data are analysed and reported according to whether specimen is within 2 calendar days of admission (community origin) or after 2 calendar days of admission (hospital origin).

⁷

EUCAST, ≪EUCAST guidelines for detection of resistance mechanisms and specific resistances of clinical

and/or epidemiological importance,≫ 2013, Available: http://www.amcli.it/wp-content/uploads/2015/10/

EUCAST_detection_resistance_mechanisms_V1.pdf .

CLSI, ≪M100 Performance Standards for Antimicrobial Susceptibility Testing,≫ 27th ed, 2017. ↑

⁸

National antimicrobial resistance surveillance systems and participation in the Global Antimicrobial Resistance Surveillance System (GLASS): A guide to planning, implementation, and monitoring and evaluation (2016). https://www.who.int/glass/resources/publications/national-surveillance-guide/en/ ↑

⁹

Global Antimicrobial Resistance Surveillance System: Manual for Early Implementation (2015). https://www.who.int/antimicrobial-resistance/publications/surveillance-system-manual/en/ ↑

¹⁰

Both for species identification and antimicrobial susceptibility testing (AST) ↑

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Countries with no data are reported as blank.

5. Data availability and disaggregation

Data availability:

Data are available by country, gender, and age group, as well as whether infection is of community or hospital origin.

7. References and Documentation

URL: http://www.who.int/glass/en/ ; http://www.who.int/gho/glass/en/

3.1.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births

0.c. Indicator

Indicator 3.1.1: Maternal mortality ratio

0.d. Series

Applies to all series

0.e. Metadata update

2023-03-31

0.f. Related indicators

3.1.2: Proportion of births attended by skilled health personnel

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO). Department of Sexual and Reproductive Health and Research.

1.a. Organisation

World Health Organization (WHO). Department of Sexual and Reproductive Health and Research.

2.a. Definition and concepts

Definition:

The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy (proxied by a single live birth).

Concepts:

In the International statistical classification of diseases and related health problems (ICD) WHO defines the following:

Maternal death: The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management (from direct or indirect obstetric death), but not from unintentional or incidental causes.

A death occurring during pregnancy, childbirth and puerperium (also known as a pregnancy-related death): The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death.

2.b. Unit of measure

Maternal deaths per 100,000 live births

2.c. Classifications

Maternal deaths are classified according to the International statistical classification of diseases and related health problems (ICD) definition. The specific codes used under ICD-10 (the 10^th revision of the ICD) to define a maternal death are: O00-O96; O98, O99 and A34.

ICD-11 (the 11th revision of the ICD) was adopted by the World Health Assembly in May 2019 and comes into effect on 1^st January 2022. Further information is available at: www.who.int/classifications/icd/en/ The coding rules related to maternal mortality are being edited to fully match the new structure of ICD-11, but without changing the resulting statistics. The ICD-11 rules can be accessed in the reference guide of ICD-11, at https://icd.who.int . Forthcoming releases from 2022 onwards will transition to use ICD-11 coding. Care has been taken to ensure that the definition of maternal death used for international comparison of mortality statistics remains stable over time, but the word “unintentional” has been used in the ICD-11 definition in place of the word “accidental” which was previously used, in ICD-10.

3.a. Data sources

Please see Sections 3.1 and 3.2 of the report: Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023.

3.b. Data collection method

The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) – comprising WHO, UNICEF, UNFPA, the World Bank Group and the United Nations Population Division (UNDESA/Population Division) maintains an input database consisting of maternal mortality data from civil registration, population-based surveys, surveillance systems, censuses, and other specialized studies/surveys. This database is updated before the release of every new round of estimates and is used to calculate the proportion of maternal deaths (PM) among women of reproductive age (WRA). The maternal mortality ratio (MMR) is then calculated as MMR = PM(D/B); where "D" is the number of all-cause deaths among women WRA and "B" is the number of live births. The number of live births is based upon the World Population Prospects published by UNDESA/Population Division.

Statistical modelling is undertaken to generate comparable country, regional, and global level estimates. Adjustments are made according to the data source type (See Section 4e below). The analysis accounts for stochastic errors, sampling error in the data source, errors during data collection and processing, and other random error. The model's fit is assessed by cross-validation.

3.c. Data collection calendar

The input datasets are updated prior to each new publication round of the maternal mortality ratio (MMR) estimates. Source data are collected by countries, typically annually for civil registration and vital statistics (CRVS) sources, every 3-5 years for specialized reviews, every 5-7 years for population-based surveys, and every 10 years for censuses.

3.d. Data release calendar

The maternal mortality estimates are updated approximately every 2-3 years.

3.e. Data providers

National-level data providers are typically statistical offices, specialized epidemiology monitoring authorities and/or Ministry of Health.

3.f. Data compilers

3.g. Institutional mandate

World Health Organization (WHO) is the custodian UN agency for the maternal mortality ratio.

4.a. Rationale

All maternal mortality indicators include a point-estimate and an 80% uncertainty interval (UI). Both point-estimates and 80% UIs should be taken into account when assessing estimates.

For example: “The estimated 2020 global MMR is 223 (UI 202 to 255).”

This means:

The point-estimate is 223 and the 80% uncertainty interval ranges 202 to 255.
There is a 50% chance that the true 2020 global MMR lies above 223, and a 50% chance that the true value lies below 223.
There is an 80% chance that the true 2020 global MMR lies between 202 and 255.
There is still a 10% chance that the true 2020 global MMR lies above 255, and a 10% chance that the true value lies below 202.

Other accurate interpretations include:

We are 90% certain that the true 2020 global MMR is at least 202.
We are 90% certain that the true 2020 global MMR is 255 or less.

The amount of data available for estimating an indicator and the quality of that data determine the width of an indicator’s UI. As data availability and quality improve, the certainty increases that an indicator’s true value lies close to the point-estimate.

4.b. Comment and limitations

The extent of maternal mortality in a population is essentially the combination of two factors:

The risk of death in a single pregnancy or a single live birth.
The fertility level (i.e. the number of pregnancies or births that are experienced by women of reproductive age).

The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100 000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures (i) above.

By contrast, the maternal mortality rate (MMRate) is calculated as the number of maternal deaths divided by person-years lived by women of reproductive age. The MMRate captures both the risk of maternal death per pregnancy or per total birth (live birth or stillbirth), and the level of fertility in the population.

In addition to the MMR and the MMRate, it is possible to calculate the adult lifetime risk of maternal mortality for women in the population. An alternative measure of maternal mortality, the proportion of deaths among women of reproductive age that are due to maternal causes (PM), is calculated as the number of maternal deaths divided by the total deaths among women aged 15–49 years.

4.c. Method of computation

The maternal mortality ratio (MMR) can be calculated by dividing recorded (or estimated) maternal deaths by total recorded (or estimated) live births in the same period and multiplying by 100 000. Measurement requires information on pregnancy status, timing of death (during pregnancy, childbirth, or within 42 days of termination of pregnancy), and cause of death.

The MMR can be calculated directly from data collected through vital registration systems, household surveys or other sources. There are often data quality problems, particularly related to the underreporting and misclassification of maternal deaths. Therefore, data are often adjusted in order to take these data quality issues into account. Some countries undertake these adjustments or corrections as part of specialized/confidential enquiries or administrative efforts embedded within maternal mortality monitoring programmes.

Bayesian maternal mortality estimation model (the BMat model):

Estimation and projection of maternal mortality indicators are undertaken using the BMat model. This model is intended to ensure that the MMR estimation approach is consistent across all countries but remains flexible in that it is based on covariate-driven trends to inform estimates in countries or country-periods with limited information; captures observed trends in countries with longer time series of observations; and takes into account the differences in stochastic and sampling errors across observations.

The model is summarized as follows:

$\log (E P M^{N A}) = b_{0} + b_{1} \log (G D P) + b_{2} \log (G F R) + b_{3} S B A + γ_{j} + φ_{k}$

Where:

$E P M^{N A}$ = the expected proportion of non-HIV-related deaths to women aged 15–49 years that are due to maternal causes [NA = non-HIV; formerly it referred to “non-AIDS”]

GDP = gross domestic product per capita (in 2011 PPP US dollars)

GFR = general fertility rate (live births per woman aged 15–49 years)

SBA = proportion of births attended by skilled health personnel

$γ_{j}$ = random intercept term for country j

φk = random intercept term for region k.

For countries with data available on maternal mortality, the expected proportion of non-HIV-related maternal deaths was based on country and regional random effects, whereas for countries with no data available, predictions were derived using regional random effects only.

The resulting estimates of the $E P M^{N A}$ were used to obtain the expected non-HIV MMR through the following relationship:

Expected non-HIV MMR =EPM^NA*(1-a)*E/B

Where:

a = the proportion of HIV-related deaths among all deaths to women aged 15–49 years

E = the total number of deaths to women of reproductive age

B = the number of births.

Estimation of HIV-related indirect maternal deaths:

For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and post-delivery. There is also some evidence from community studies that women with HIV infection have a higher risk of maternal death, although this may be offset by lower fertility. If HIV is prevalent, there will also be more incidental HIV deaths among pregnant and postpartum women. When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died (i.e. among all HIV-related deaths occurring during pregnancy, childbirth and puerperium).

The number of HIV-related indirect maternal deaths $D^{H I V}$ , is estimated by:

$D^{H I V} = a ∙ E ∙ v ∙ u$

Where:

a*E = the total number of HIV-related deaths among all deaths to women aged 15–49.

v = is the proportion of HIV-related deaths to women aged 15–49 that occur during pregnancy. The value of v can be computed as follows: v = c k GFR / [1 + c(k-1) GFR] where GFR is the general fertility rate, and where c is the average exposure time (in years) to the risk of pregnancy-related mortality per live birth (set equal to 1 for this analysis), and where k is the relative risk of dying from AIDS for a pregnant versus a non-pregnant woman (reflecting both the decreased fertility of HIV-positive women and the increased mortality risk of HIV-positive pregnant women). The value of k was set at 0.3.

u = is the fraction of pregnancy-related AIDS deaths assumed to be indirect maternal deaths. The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG)/TAG reviewed available study data on AIDS deaths among pregnant women and recommended using u = 0.3.

For observed PMs, we assumed that the total reported maternal deaths are a combination of the proportion of reported non-HIV-related maternal deaths and the proportion of reported HIV-related (indirect) maternal deaths, where the latter is given by a*v for observations with a “pregnancy-related death” definition and a*v*u for observations with a “maternal death” definition.

4.d. Validation

Estimates are reviewed with Member States through a World Health Organization (WHO) country consultation process and SDG focal points. In 2001, the WHO Executive Board endorsed a resolution (EB. 107.R8) seeking to “establish a technical consultation process bringing together personnel and perspectives from Member States in different WHO regions”. A key objective of this consultation process is “to ensure that each Member State is consulted on the best data to be used”. Since the process is an integral step in the overall estimation strategy, it is described here in brief.

The country consultation process entails an exchange between WHO and technical focal person(s) in each country. It is carried out prior to the publication of estimates. During the consultation period, WHO invites focal person(s) to review input data sources, methods for estimation and the preliminary estimates. Focal person(s) are encouraged to submit additional data that may not have been taken into account in the preliminary estimates.

4.e. Adjustments

Full details on adjustments and formulas are published/available here:

(1) Peterson E, Chou D, Gemmill A, Moller AB, Say L, Alkema L. Estimating maternal mortality using vital registration data: a Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity of reporting for population-periods without validation data. 2019 (https://arxiv.org/abs/1909.08578)

(2) Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023.2019 (https://www.who.int/reproductivehealth/publications/maternal-mortality-2000-2017/en/).

To summarise the key adjustments in brief:

Adjustments for variation in definitions of the input data:

Previous studies found incidental or accidental deaths (comprise 10% of pregnancy-related deaths (excluding HIV-related deaths) in sub-Saharan African countries, and 15% in other low- and middle-income countries. Adjustments are applied to pregnancy-related deaths to account for these non-maternal deaths.

Adjustment for crisis years:

The proportion of pregnancy-related deaths among the deaths attributable to mortality shock from crisis is assumed to be equal to the proportion of women in the population who are pregnant or postpartum at the time of the crisis. The proportion of pregnant women in the population is set equal to the general fertility rate, based on the assumption of a one-year period associated with a live birth. Additional uncertainty is added to the estimates of crisis years.

Adjustment for age distribution in population-based surveys:

Population-based surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) obtain information by interviewing respondents about the survival of their siblings. This approach, commonly referred to as the direct sisterhood method. Given the study design (based on sisters of respondents), the population exposed to risk may be atypical of the population at large. Therefore, we compute an age-standardized value of PM, based on the female population of households at the time of the survey.

Adjustment for underreporting (unregistered) and misclassification in civil registration and vital statistics (CRVS) systems:

Underreporting and misclassification in CRVS systems are accounted for with specialized studies. Model estimated country-year specific adjustment factors are obtained and applied to CRVS data.

Adjustment for under-reporting in non-CRVS and non-specialised sources:

It is widely believed that some form of upward adjustment is required for data sources that are not CRVS or specialised studies, to account for deaths early in pregnancy that might not have been captured. Therefore, an upwards adjustment of 10% was applied to maternal deaths that were not obtained from CRVS systems or specialized studies.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Missing values are treated at the country-level. This is done as follows. There is no treatment of missing values at the regional level.

Predictor variable data:

Complete and comparable predictor data is obtained by constructing time series estimates for predictor variables (covariates).

Gross domestic product (GDP) per capita, measured in purchasing power parity (PPP) equivalent international dollars using 2017 as the baseline
General fertility rate (GFR)
Skilled birth attendant (SBA)

Response variable data:

All-cause deaths for WRA, used to denominate maternal deaths in the statistic PM, are imputed when missing and in some cases overwritten.

Estimated all-cause deaths from by UNDP’s World Population Prospects 2022 lifetables were used to impute and overwrite all-cause deaths in specialized studies in which the search went beyond registration systems.
Civil Registration and Vital Statistics (CRVS) reported all-cause deaths were used to impute missing all-cause deaths in specialized studies in which the search was within registration systems.
Estimated all-cause deaths from by UNDP’s World Population Prospects 2022 Estimates were used to impute missing all-cause deaths in miscellaneous studies.

4.g. Regional aggregations

Regional aggregations are calculated by aggregating the national-level estimates. The size of a country is determined by the live births estimated by World Population Prospects. Aggregations are currently made for each of the UN Agencies that comprise the UN MMEIG.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The methodology used by countries to compile the data depends on the source input type (CRVS, specialised study etc). Useful references include:

WHO Civil Registration and Vital Statistics (CRVS) Tools & Resources: https://www.who.int/data/data-collection-tools/civil-registration-and-vital-statistics-(crvs)
World Health Organization. (‎2013)‎. WHO guidance for measuring maternal mortality from a census. World Health Organization. https://apps.who.int/iris/handle/10665/87982
World Health Organization. (‎2004)‎. Beyond the numbers : reviewing maternal deaths and complications to make pregnancy safer. World Health Organization. https://apps.who.int/iris/handle/10665/42984
World Health Organization. (2022). Maternal mortality measurement: guidance to improve national reporting. World Health Organization. https://www.who.int/publications/i/item/9789240052376
World Health Organization. (2022). Certification of deaths during pregnancy, childbirth, or the puerperium where confirmed or suspected COVID-19 is a cause of death. World Health Organization. https://www.who.int/publications/i/item/9789240049314

Support and guidance to national authorities may also be requested from the WHO Secretariat.

4.i. Quality management

For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi

4.j. Quality assurance

For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi

4.k. Quality assessment

For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi

5. Data availability and disaggregation

Data Availability

Data availability is presented by country with the country profiles, please see here: https://www.who.int/data/gho/data/themes/maternal-and-reproductive-health/maternal-mortality-country-profiles

https://www.who.int/publications/i/item/9789240068759

Disaggregation:

Current maternal mortality ratio (MMR) estimates are reported at national, regional, and global levels. Countries and territories included in the analyses are WHO Member States with populations over 100 000, plus two territories (Puerto Rico, and the occupied Palestinian Territory, including east Jerusalem).

The time series available is currently 2000 to 2020.

6. Comparability/deviation from international standards

Sources of discrepancies:

The maternal mortality ratio is defined as the number of maternal deaths divided by live births. However, to account for potential incompleteness of death recording in various data sources, the United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) first computes the fraction of deaths due to maternal causes from original data sources (referred to as the “proportion maternal”, or PM), and then applies that fraction to WHO estimates of total deaths among women of reproductive age to obtain an estimate of the number of maternal deaths.

In other words, the following fraction is first computed from country data sources:

PM= Number of maternal deaths 15-49/All female deaths at ages 15-49

and then the PM is used to compute the MMR as follows:

MMR=PM × (All female deaths at ages 15-49/Number of live births)

Where the estimate of all deaths at ages 15-49 in the second equation is derived from WHO Global Health Estimates life tables, and the number of live births is from the World Population Prospects 2019.

With this as background, a few reasons that MMEIG estimates may differ from national statistics are as follows:

Civil registration and vital statistics systems are not always complete (i.e., they do not always capture 100% of all deaths) and completeness may change over time. The MMEIG estimation approach attempts to correct for this by using the above approach, which involves first computing the PM.
The MMEIG often applies adjustment factors to the PM computed from original data to account for measurement issues (such as how the country defined “maternal” deaths; misclassification; or incompleteness).
The MMEIG uses the standardized series of live births from the United Nations Population Division, as published in World Population Prospects 2022, in the denominator of the MMR equation. To better inform the WPP, countries should discuss discrepancies directly with the United Nations Population Division: the contact address is population@un.org; this email address is monitored regularly, and messages are dispatched to the appropriate analysts for each country or concern.
Statistically speaking, maternal deaths are a relatively rare event, which can lead to noisy time trends in data over time. As the goal of the MMEIG estimates is to track long term progress in reducing maternal mortality, the estimation process involves some smoothing to generate a curve that better captures changes in underlying risk

7. References and Documentation

URL: https://www.who.int/publications/i/item/9789240068759

References:

Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023.
Peterson E, Chou D, Gemmill A, Moller AB, Say L, Alkema L. Estimating maternal mortality using vital registration data: a Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity of reporting for population-periods without validation data. 2019 (https://arxiv.org/abs/1909.08578).

3.1.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births

0.c. Indicator

Indicator 3.1.2: Proportion of births attended by skilled health personnel

0.d. Series

Proportion of births attended by skilled health personnel (%)

0.e. Metadata update

2022-03-31

0.f. Related indicators

Related to Target 3.1 on reducing maternal mortality, 3.2 on reducing neonatal mortality and 3.8 on achieving universal health coverage (coverage of essential health services)

0.g. International organisations(s) responsible for global monitoring

United Nations Children’s Fund (UNICEF) and World Health Organization (WHO)

1.a. Organisation

United Nations Children’s Fund (UNICEF) and World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Proportion of births attended by skilled health personnel (generally doctors, nurses or midwives but can refer to other health professionals providing childbirth care) is the proportion of childbirths attended by professional health personnel. According to the current definition (1) these are competent maternal and newborn health (MNH) professionals educated, trained and regulated to national and international standards. They are competent to: (i) provide and promote evidence-based, human-rights based, quality, socio-culturally sensitive and dignified care to women and newborns; (ii) facilitate physiological processes during labour and delivery to ensure a clean and positive childbirth experience; and (iii) identify and manage or refer women and/or newborns with complications.

2.b. Unit of measure

This indicator is reported in proportion (or percentage (%)

2.c. Classifications

An important aspect of this indicator is the reporting of categories or occupational titles of health providers at country level. Standard categories for the indicator include doctor, nurse and midwife. However, some additional categories are currently being reported by some countries. When that is the case, a process of verification is conducted in which the competency level of other categories of health care providers is assessed with national sources and in communication with national counterparts.

3.a. Data sources

National-level household surveys are the main data sources used to collect data for skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and other national surveys based on similar methodologies. In these surveys the respondent is asked about the last live birth and who helped during delivery for a period up to five years before the interview.

Surveys are undertaken every three to five years.

Population-based surveys are the preferred data source in countries with a low utilization of childbirth services, where private sector data are excluded from routine data collection, and/or with weak health information systems.

Routine service/facility records are a more common data source in countries where a high proportion of births occur in health facilities and are therefore recorded. These data can be used to track the indicator on an annual basis.

3.b. Data collection method

UNICEF and WHO maintain a joint database on SDG 3.1.2: “Proportion of births attended by skilled health personnel” and collaborate to ensure quality and consistency of data sources.

As part of the data harmonization process and interaction with countries, an annual country consultation is conducted by UNICEF. During the country consultation, SDG country focal points are contacted for updating and verifying values included in the database and for obtaining new data sources. New data sources are reviewed and assessed jointly with WHO. As part of the process, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctor, nurse and midwife.

3.c. Data collection calendar

UNICEF/WHO joint database is updated on an annual basis. However, not all countries report new data on annual basis. Countries reporting data from household surveys, may report a new value every three-five years, according to their data collection schedule. Data reported from routine administrative sources are regularly available on an annual basis.

3.d. Data release calendar

Country reported data and global and regional estimates are published annually; in February by UNICEF in the data website www.data.unicef.org (3) and by the World Health Organization in May in the World Health Statistics Report (http://www.who.int/whosis/whostat/en/) and the WHO Global Health Observatory (https://www.who.int/data/gho. UNICEF also reports this indicator in the State of the World’s Children report which is on a bi-annual reporting schedule (https://www.unicef.org/reports/state-of-worlds-children).

3.e. Data providers

Ministries of Health and National Statistical Offices, either through household surveys or routine sources.

3.f. Data compilers

United Nations Children’s Fund (UNICEF) andWorld Health Organization (WHO).

3.g. Institutional mandate

UNICEF and WHO are co-custodians for the compilation and reporting of this indicator.

4.a. Rationale

Having a skilled health care provider at the time of childbirth is an important lifesaving intervention for both women and newborns. Not having access to this key assistance is detrimental to women's and newborns’ health because it could cause the death of the women and/or the newborns or long lasting morbidity. Achieving universal coverage for this indicator is therefore essential for reducing maternal and newborn mortality and morbidity.

4.b. Comment and limitations

Births attended by skilled health personnel is an indicator of health care utilization. It is a measure of the health system’s functioning and potential to provide adequate coverage for childbirth. On its own, however, this indicator does not provide insight into the availability or accessibility of services, for example in cases where emergency care is needed. Neither does this indicator capture the quality of care received.

Data collection and data interpretation in many countries is challenged by lack of guidelines, standardization of professional titles and functions of the health care provider, and in some countries by task-shifting. In addition, many countries have found that there are large gaps between international standards and the competencies of existing health care professionals providing childbirth care. Lack of training and an enabling environment often hinder evidence-based management of common obstetric and neonatal complications.

4.c. Method of computation

Numerator:

Number of births attended by skilled health personnel (doctor, nurse or midwife) trained in providing quality obstetric care, including giving the necessary support and care to the mother and the newborn during childbirth and immediate postpartum period

Denominator: The total number of live births in the same period.

Births attended by skilled health personnel = (number of births attended by skilled health personnel)/(total number of live births) x 100.

4.d. Validation

As part of the data harmonization process, an annual country consultation is conducted by UNICEF. Country inputs are reviewed and assessed jointly with WHO. During the process, SDG country focal points are contacted for updating and verifying values included in the database and obtaining new sources of data. The national categories of skilled health personnel are verified, and the estimates for some countries may include additional categories of trained personnel beyond doctor, nurse, and midwife. This process serves as validation of the reported values.

Furthermore, with regard to data obtained from surveys, the validity of such data depends on the correct identification by the women of the credentials of the person attending the childbirth, which may not be obvious in certain countries.

4.e. Adjustments

In cases where reporting of skilled categories or occupational titles is not consistent with previous years or with categories considered skilled at country level, reported values may be adjusted. When this is done, the process is consulted with countries.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

There is no treatment of missing values at country level. If value is missing for a given year, then there is no reporting of that value.

At regional and global levels

Missing values are not imputed for regional and global levels. For the latest reported time period, the latest available year in the year range is used for the calculation of regional and global average.

4.g. Regional aggregations

Regional and global estimates are calculated using weighed averages. Annual number of births from United Nations Population Division, World Population Prospects (3) are used as weighing indicator. Regional values are calculated for a reference year, including a range of four to five years for each reference year or year range. For example, for 2021, the latest year available for the period 2015-2021 was used.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Definition of skilled health personnel varies between countries. The proportion of births attended by skilled health personnel is calculated as the number of births attended by skilled health personnel (doctors, nurses or midwives) expressed as a proportion of the number of live births in the same period.

In household surveys, such as DHS, MICS and RHS, the respondent is asked about the most recent birth and who helped during childbirth for a period up to five years before the interview. For consistency of reporting, survey customization teams in country are encouraged to review categories or occupational title of health care providers reported on the previous surveys and ensure comparability. Service/facility records could be used where a high proportion of births occur in health facilities and are recorded.

4.i. Quality management

Data are reported to UNICEF on an annual basis. Values are reviewed and assessed to make sure that reported indicator complies with standard definition and methodology.

4.j. Quality assurance

As part of the data harmonization process an annual country consultation process is conducted by UNICEF. Country inputs are reviewed and assessed jointly with WHO. During the process, SDG country focal points are contacted for updating and verifying values included in the data bases and obtaining new sources of data. The national categories of skilled health personnel are verified, and the reported data for some countries may include additional categories of trained personnel beyond doctor, nurse, and midwife.

4.k. Quality assessment

Data included in the database is verified through an annual country consultation process and data harmonization process conducted by the two custodian agencies: UNICEF and WHO. All values are also assessed for consistency in terms of standard definition, representativeness, source of information, quality.

5. Data availability and disaggregation

Data availability:

Data are available for over 170 countries.

The lag between the reference year and actual production of data series depends on the availability of the household survey for each country.

Time series:

2001-2021

Disaggregation:

For this indicator, when data are reported from household surveys, disaggregation is available for various socio-economic characteristics including age of the mother, residence (urban/rural), household wealth (quintiles), education level of the mother, maternal age, geographic regions. When data are reported from administrative sources, disaggregation is more limited and tend to include only residence.

6. Comparability/deviation from international standards

Sources of discrepancies:

Discrepancies are possible if there are national figures compiled at the health facility level. These would differ from the global figures, which are typically based on survey data collected at the household level.

In terms of survey data, some survey reports may present a total percentage of births attended by a skilled health professional that does not conform to the SDG definition (e.g., total includes provider that is not considered skilled, such as a community health worker). In that case, the proportion of childbirths by a physician, nurse, or a midwife are totalled, consulted with the country and included in the global database as the SDG estimate.

In some countries where the indicator on skilled health personnel is not actively reported, birth in a health facility (institutional births) is used as a proxy indicator. This is frequent in countries in the Latin America region, in European and Central Asian regions, where the proportion of births attended by health professionals is very high. Nonetheless, it should be noted that institutional births may underestimate the percentage of births assisted by skilled health professionals, particularly in cases were home births - assisted by skilled health professionals - are prevalent.

7. References and Documentation

URL: https://data.unicef.org/topic/maternal-health/delivery-care/#

References:

1. Definition of skilled health personnel providing care during childbirth 2018 joint statement by WHO, UNFPA, UNICEF, ICM, ICN, FIGO and IPA. https://www.who.int/reproductivehealth/publications/statement-competent-mnh-professionals/en/
2. Joint UNICEF/WHO database of skilled health personnel, based on population-based national household survey data and routine health systems. https://data.unicef.org/topic/maternal-health/delivery-care/#.
3. United Nations Population Division, World Population Prospects. https://population.un.org/wpp/Download/Standard/Population/.

3.2.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births

0.c. Indicator

Indicator 3.2.1: Under‑5 mortality rate

0.d. Series

Applies to all series

0.e. Metadata update

2023-03-31

0.f. Related indicators

3.2.2: Neonatal mortality rate

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

1.a. Organisation

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

The under-five mortality rate is the probability of a child born in a specific year or period dying before reaching the age of 5 years, if subject to age-specific mortality rates of that period, expressed as deaths per 1000 live births.

Concepts:

The under-five mortality rate as defined here is, strictly speaking, not a rate (i.e. the number of deaths divided by the number of population at risk during a certain period of time), but a probability of death derived from a life table and expressed as a rate per 1000 live births.

2.b. Unit of measure

Number (SH_DYN_MORTN); Deaths per 1,000 live births (SH_DYN_MORT)

2.c. Classifications

Not applicable

3.a. Data sources

Nationally representative estimates of child mortality can be derived from several different sources, including civil registration and sample surveys. Demographic surveillance sites and hospital data are excluded as they are not nationally representative. The preferred source of data is a civil registration system that records births and deaths on a continuous basis. If registration is complete and the system functions efficiently, the resulting estimates will be accurate and timely. However, many countries do not have well-functioning vital registration systems. In such cases, household surveys, such as the UNICEF-supported Multiple Indicator Cluster Surveys (MICS), the USAID-supported Demographic and Health Surveys (DHS) and periodic population censuses have become the primary sources of data on under-five mortality. These surveys ask women about the survival of their children, and it is these reports that provide the basis of child mortality estimates for a majority of low- and middle-income countries. These data are subject to sampling and non-sampling errors, which might be substantial.

Civil registration

Civil registration is the preferred data source for under-five, infant and neonatal mortality estimation. The calculation of the under-five and infant mortality rates from civil registration data is derived from a standard period abridged life table using available data on the number of deaths and mid-year populations. For civil registration data), initially annual observations were constructed for all observation years in a country.

Population census and household survey data

Most survey data come in one of two forms: the full birth history (FBH), whereby women are asked for the date of birth of each of their children, whether the child is still alive, and if not, the age at death; and the summary birth history (SBH), whereby women are asked only about the number of their children ever born and the number that have died (or equivalently the number still alive).

3.b. Data collection method

For under-five mortality, UNICEF and the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) compile data from all available data sources, including household surveys, censuses, and vital registration data. UNICEF and the UN IGME compile these data whenever they are available publicly and then conduct data quality assessment. UNICEF also collects data through UNICEF country offices by reaching national counterpart(s). The UN IGME also collects vital registration data reported by Ministries of Health or other relevant agencies to WHO.

To increase the transparency of the estimation process, the UN IGME has developed a child mortality web portal, https://childmortality.org/, which includes all available data and shows estimates for each country. Once the new estimates are finalized, the web portal will be updated to reflect all available data and the new estimates.

3.c. Data collection calendar

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) underlying database is continuously updated whenever new empirical data become available.

3.d. Data release calendar

A new round of United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates is released annually, usually in the 3^rd or 4^th quarter.

3.e. Data providers

The National Statistical Office or the Ministry of Health is the typical provider of data for generating under-five mortality estimates at the national level.

3.f. Data compilers

United Nations Children’s Fund (UNICEF)

3.g. Institutional mandate

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), led by the United Nations Children’s Fund (UNICEF) and including members from the World Health Organization (WHO), the World Bank Group and the United Nations Population Division, was established in 2004 to advance the work on monitoring progress towards the achievement of child survival goals and to augment country capacity to collect high quality data on and produce timely estimates of child mortality. Every year, the UN IGME estimates levels and trends in under-5 mortality at the global, regional and country level and provides an assessment of current progress towards the SDG targets.

4.a. Rationale

Mortality rates among young children are a key output indicator for child health and well-being, and, more broadly, for social and economic development. It is a closely watched public health indicator because it reflects the access of children and communities to basic health interventions such as vaccination, medical treatment of infectious diseases and adequate nutrition.

4.b. Comment and limitations

A civil registration system that continuously records all births and deaths in a population is the preferred source of high-quality underlying data on under-five mortality but these systems are not well developed in many low- and middle-income countries. Instead, household surveys and population censuses are the primary sources of underlying data in these countries.

The reliance on multiple data sources, i.e. surveys and census conducted several years apart and producing retrospective time series, can result in disparate mortality rates from different sources, sometimes referring to the same time period. Available data also suffer from sampling and nonsampling errors, including misreporting of age and sex, survivor selection bias, underreporting of child deaths, and recall errors as data are collected retrospectively. Further misclassifications can also impact the accuracy of data, for example early neonatal deaths may be classified as stillbirths. Thus, simply comparing two country data points from different sources and drawing a line between them is not a technically sound way to assess levels and trends. Given varying levels of data quality across different sources, this sort of trend assessment will provide misleading results. Hence, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) fits a statistical model to these data that takes into account these various data sources to produce annualized estimates.

It is important to keep these challenges in mind when looking at available country data and also when discrepancies between country data and the UN IGME estimates are being discussed. The following

points are important to highlight:

The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. Thus, UN IGME estimates are derived from country data. Notably, UN IGME assesses the quality of underlying data sources and adjusts data when necessary.
National estimates may refer to an earlier calendar year than the UN IGME estimates. This is particularly the case where estimates from the most recent national survey are used as the national estimate, since the survey estimates derived from a birth history are retrospective and typically refer to a period before the year of the survey, which may be several years behind the target year for the UN IGME estimates. National estimates may also use a different combination of data sources, or different projection or calculation methods.
In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates estimates with uncertainty bounds. When discussing the UN IGME estimates, it’s important to look at the uncertainty ranges, which might be fairly wide in the case of some countries.

4.c. Method of computation

For the underlying data mentioned above, the most frequently used methods are as follows:

Civil registration: The under-five mortality rate can be derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data to calculate death rates, which are then converted into age-specific probabilities of dying.

Census and surveys: An indirect method is used based on a summary birth history, a series of questions asked of each woman of reproductive age as to how many children she has ever given birth to and how many are still alive. The Brass method and model life tables are then used to obtain an estimate of under-five and infant mortality rates. Censuses often include questions on household deaths in the last 12 months, which can also be used to calculate mortality estimates.

Surveys: A direct method is used based on a full birth history, a series of detailed questions on each child a woman has given birth to during her lifetime. Neonatal, post-neonatal, infant, child and under-five mortality estimates can be derived from the full birth history.

4.d. Validation

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) conducts an annual country consultation whereby the draft UN IGME estimates, empirical data used to derive the estimates, and notes on methodology are sent to National Statistical Offices and to Ministries of Health or other relevant agencies for review. National Statistical Offices, Ministries of Health or other relevant agencies have the opportunity to provide feedback or comments on estimates and methods, as well as supply additional empirical data during this consultation.

4.e. Adjustments

Direct estimates from survey data are adjusted in high prevalence HIV settings for under-reporting of under-five mortality due to ‘missing mothers,’ i.e. women who have died from HIV/AIDS and cannot report on the mortality experience of their children. Furthermore, United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are also adjusted to capture rapidly changing mortality rates due to HIV/AIDS and crises/disasters that are not well captured in survey data.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are based on underlying empirical data. If the empirical data refer to an earlier reference period than the end year of the period the estimates are reported, the UN IGME extrapolates the estimates to the common end year. The UN IGME does not use any covariates to derive the estimates.

At regional and global levels

To construct aggregate estimates of under-five mortality before 1990, regional averages of mortality rates were used for country-years with missing information and weighted by the respective population in the country-year.

4.g. Regional aggregations

Global and regional estimates of under-five mortality rates are derived using the aggregated number of country-specific under-five deaths for a specific region or globally estimated by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) using a birth-week cohort approach and aggregated country-specific births from the United Nations Population Division.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Detailed methodological descriptions can be found at the following:

https://childmortality.org/methods and https://childmortality.org/wp-content/uploads/2023/01/UN-IGME-Child-Mortality-Report-2022.pdf

4.i. Quality management

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) applies a standard estimation method across all countries in the interest of comparability. This method aims to estimate a smooth trend curve of age-specific mortality rates, accounting for potential outliers and biases in data sources and averaging over the possibly many disparate data sources for a country. A more detailed descripition of the different phases of the statistical production process is available in the annual UN IGME report and at https://childmortality.org/methods.

4.j. Quality assurance

Quality is assured by applying standard statistical and demographic methods to all input data and conducting regular data quality assessments. Countries are also consulted on the draft estimates during the annual country consultation process.

4.k. Quality assessment

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) aims to produce transparent, timely and accurate annual estimates of under-five mortality. Data quality is critical to that end. The UN IGME assesses data quality using both internal and external validaity checks and does not include data sources with substantial non-sampling errors or omissions as underlying empirical data in its statistical model.

5. Data availability and disaggregation

Data availability:

This indicator is available for all countries from 1990 (or earlier depending on the availability of empirical data for each country before 1990) to the most recent target reference year, typically one or two years behind the current calendar year.

Disaggregation:

Disaggregation is available by sex, age (neonatal, infant, child) and wealth quintile.

6. Comparability/deviation from international standards

Sources of discrepancies:

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are based on nationally representative data. Countries may use a single source as their official estimate or apply methods different from the UN IGME methods to derive official national estimates. The differences between the UN IGME estimates and national official estimates are usually not large if empirical data are high quality.

Many countries lack a single source of high-quality data covering the last several decades, instead relying on multiple data sources to estimate mortality. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of under-five mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country.

Each new survey or data point must be examined in the context of all other sources, including previous data, and with respect to any sampling or non-sampling errors that may be present (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). The UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also extrapolates estimates to a common reference year.

In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. The UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates.

7. References and Documentation

URL:

All data sources, estimates and detailed methods are documented on the website https://childmortality.org/

References:

United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels and trends in child mortality. Report 2022. New York: UNICEF, 2023. Available at https://childmortality.org/wp-content/uploads/2023/01/UN-IGME-Child-Mortality-Report-2022.pdf

United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Subnational Under-five Mortality Estimates, 1990–2019 for 22 countries. New York: UNICEF, 2020. Available at https://childmortality.org/wp-content/uploads/2021/03/UN-IGME-Subnational-Under-five-Mortality-Estimates.pdf

Alkema L, New JR. Global estimation of child mortality using a Bayesian B-spline bias-reduction method. The Annals of Applied Statistics. 2014; 8(4): 2122–2149. Available at: https://arxiv.org/abs/1309.1602

Alkema L, Chao F, You D, Pedersen J, Sawyer CC. National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. The Lancet Global Health. 2014; 2(9): e521–e530.

Pedersen J, Liu J. Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories. Plos Medicine. 2012;9(8). Available at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001289

Silva R. Child Mortality Estimation: Consistency of Under-Five Mortality Rate Estimates Using Full Birth Histories and Summary Birth Histories. Plos Medicine. 2012;9(8). Available at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296

Walker N, Hill K, Zhao FM. Child Mortality Estimation: Methods Used to Adjust for Bias due to AIDS in Estimating Trends in Under-Five Mortality. Plos Medicine. 2012;9(8). Available at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296

3.2.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

0.c. Indicator

Indicator 3.2.2: Neonatal mortality rate

0.d. Series

Applies to all series

0.e. Metadata update

2023-03-31

0.f. Related indicators

3.2.1: Under-five mortality rate

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

1.a. Organisation

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

The neonatal mortality rate is the probability that a child born in a specific year or period will die during the first 28 completed days of life, if subject to age-specific mortality rates of that period, expressed per 1000 live births.

Neonatal deaths (deaths among live births during the first 28 completed days of life) may be subdivided into early neonatal deaths, occurring during the first 7 days of life, and late neonatal deaths, occurring after the 7th day but before the 28th completed day of life.

2.b. Unit of measure

Number (SH_DYN_NMRTN); Deaths per 1,000 live births (SH_DYN_NMRT)

2.c. Classifications

Not applicable

3.a. Data sources

Nationally representative estimates of child mortality can be derived from several different sources, including civil registration and sample surveys. Demographic surveillance sites and hospital data are excluded as they are not nationally representative. The preferred source of data is a civil registration system that records births and deaths on a continuous basis. If registration is complete and the system functions efficiently, the resulting estimates will be accurate and timely. However, many countries do not have well-functioning vital registration systems. In such cases household surveys, such as the UNICEF-supported Multiple Indicator Cluster Surveys (MICS), the USAID-supported Demographic and Health Surveys (DHS) and periodic population censuses have become the primary sources of data on under-five and neonatal mortality. These surveys ask women about the survival of their children, and it is these reports that provide the basis of child mortality estimates for a majority of low- and middle-income countries. These data are subject to sampling and non-sampling errors, which might be substantial.

Civil registration

Civil registration is the preferred data source for under-five, infant and neonatal mortality estimation. Neonatal mortality rates are calculated using the number of neonatal deaths and the number of live births over a period. For civil registration data, initially annual observations were constructed for all observation years in a country.

Population census and household survey data

The majority of survey data comes from the full birth history (FBH), whereby women are asked for the date of birth of each of their children, whether the child is still alive, and if not the age at death.

3.b. Data collection method

For neonatal mortality, UNICEF and the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) compile data from all available data sources, including household surveys, censuses, and vital registration data. UNICEF and the UN IGME compile these data whenever they are available publicly and then conduct data quality assessment. UNICEF also collects data through UNICEF country offices by reaching national counterpart(s). The UN IGME also collects vital registration data reported by Ministries of Health or other relevant agencies to WHO.

3.c. Data collection calendar

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) underlying database is continuously updated whenever new empirical data become available.

3.d. Data release calendar

A new round of United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates is released annually, usually in the 3^rd or 4^th quarter.

3.e. Data providers

The National Statistical Office or the Ministry of Health is the typical provider of data for generating neonatal mortality estimates at the national level.

3.f. Data compilers

United Nations Children's Fund (UNICEF)

3.g. Institutional mandate

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), led by the United Nations Children’s Fund (UNICEF) and including members from the World Health Organization (WHO), the World Bank Group and the United Nations Population Division, was established in 2004 to advance the work on monitoring progress towards the achievement of child survival goals and to augment country capacity to collect high quality data on and produce timely estimates of child mortality. Every year, the UN IGME estimates levels and trends in neonatal mortality at the global, regional and country level and provides an assessment of current progress towards the SDG targets.

4.a. Rationale

4.b. Comment and limitations

The reliance on multiple data sources, i.e. surveys and census conducted several years apart and producing retrospective time series, can result in disparate mortality rates from different sources, sometimes referring to the same time period. Available data also suffer from sampling and nonsampling errors, including misreporting of age and sex, survivor selection bias, underreporting of child deaths, and recall errors as data are collected retrospectively. Further misclassifications can also impact the accuracy of data, for example, early neonatal deaths may be classified as stillbirths. Thus, simply comparing two country data points from different sources and drawing a line between them is not a technically sound way to assess levels and trends. Given varying levels of data quality across different sources, this sort of trend assessment will provide misleading results. Hence, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) fits a statistical model to these data that takes into account these various data sources to produce annualized estimates.

It is important to keep these challenges in mind when looking at available country data and also when discrepancies between country data and the UN IGME estimates are being discussed. The following

points are important to highlight:

The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. Thus, UN IGME estimates are derived from country data. Notably, UN IGME assesses the quality of underlying data sources and adjusts data when necessary.
National estimates may refer to an earlier calendar year than the UN IGME estimates. This is particularly the case where estimates from the most recent national survey are used as the national estimate, since the survey estimates derived from a birth history are retrospective and typically refer to a period before the year of the survey, which may be several years behind the target year for the UN IGME estimates. National estimates may also use a different combination of data sources, or different projection or calculation methods.
In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates estimates with uncertainty bounds. When discussing the UN IGME estimates, it’s important to look at the uncertainty ranges, which might be fairly wide in the case of some countries.

4.c. Method of computation

The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from nationally representative data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates (except in the case of neonatal mortality estimation, which incorporates the relatively more data-rich under-five mortality rate estimates in the modelling). It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME produces neonatal mortality rate (NMR) estimates with a Bayesian spline regression model, which models the ratio of neonatal mortality rate / (under-five mortality rate - neonatal mortality rate). Estimates of NMR are obtained by recombining the estimates of the ratio with the UN IGME-estimated under-five mortality rate. See the references for details.

For the underlying data mentioned above, the most frequently used methods are as follows:

Civil registration: The neonatal mortality rate can be calculated from the number of children who died during the first 28 days of life and the number of live births.

Censuses and surveys: Censuses and surveys often include questions on household deaths in the last 12 months, which can be used to calculate mortality estimates.

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

At regional and global levels

To construct aggregate estimates of neonatal mortality before 1990, regional averages of mortality rates were used for country-years with missing information and weighted by the respective population in the country-year.

4.g. Regional aggregations

Global and regional estimates of neonatal mortality rates are derived using the aggregated number of country-specific neonatal deaths for a specific region or globally estimated by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) using a birth-week cohort approach and aggregated country-specific births from the United Nations Population Division.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Detailed methodological descriptions can be found at the following:

https://childmortality.org/methods and https://childmortality.org/wp-content/uploads/2023/01/UN-IGME-Child-Mortality-Report-2022.pdf

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Disaggregation:

Due to data limitations, neonatal mortality rates are not estimated for any conventional disaggregation at this time.

6. Comparability/deviation from international standards

Sources of discrepancies:

7. References and Documentation

URL:

All data sources, estimates and detailed methods are documented on the website https://childmortality.org.

References:

Alexander, M. and L. Alkema, Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model Demographic Research, vol. 38, 2018, pp. 335–372.

3.3.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

0.c. Indicator

Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Achieving this target will positively impact multiple SDG goals and by reaching other goals will improve countries ability to reduce new HIV infections. The goals that are linked to HIV include goals 1 through 8, 10, 11, 16 and 17.

0.g. International organisations(s) responsible for global monitoring

The Joint United Nations Programme on HIV/AIDS (UNAIDS)

1.a. Organisation

The Joint United Nations Programme on HIV/AIDS (UNAIDS)

2.a. Definition and concepts

Definition:

The number of new HIV infections per 1,000 uninfected population, by sex, age and key populations as defined as the number of new HIV infections per 1,000 persons among the uninfected population.

2.b. Unit of measure

Number of newly infected people per 1,000 uninfected population.

2.c. Classifications

Not applicable

3.a. Data sources

Spectrum modelling is used for the data presented here which incorporates programme data, surveillance data, survey data and region-specific assumptions about the HIV epidemic. Alternative methods of measures include household or key population surveys with HIV incidence-testing, or routine surveillance among key populations.

The model development is guided by the UNAIDS Reference Group on Estimates, Modelling and Projections provides technical guidance on the development of the HIV component of the Spectrum software (www.epidem.org). The Spectrum software is developed by Avenir Health (www.avenirhealth.org)—which includes a module, the Estimates and Projections Package, which is developed by the East-West Center (www.eastwestcenter.org).

3.b. Data collection method

Country teams use UNAIDS-supported Spectrum software to develop estimates annually. The country teams are comprised primarily of national epidemiologists, demographers, monitoring and evaluation specialists and technical partners. The model incorporates data that are collected through programme information systems, surveillance and surveys.

3.c. Data collection calendar

Data sources are compiled all year long. The spectrum models are created in the first three months of every year and finalized by May.

3.d. Data release calendar

Data are released every year in July

3.e. Data providers

The estimates are produced by a team of national experts consisting of ministry of health, national AIDS advisory groups and development partners. The results are signed off on by senior managers at the ministries of health.

3.f. Data compilers

After the data review process, the national experts share their results with UNAIDS who compiles the data for all countries and calculates regional and global estimates.

3.g. Institutional mandate

The UN Political Declarations on HIV/AIDS (from 2001, 2011, 2016 and 2021) have mandated for UNAIDS to support countries to produce these data and for UNAIDS to report on the status of the Global HIV epidemic annually as well as through the UN Secretary General.

4.a. Rationale

The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Although other indicators are also very important to the HIV epidemic, HIV incidence reflects success in prevention programmes and, to some extent, successful treatment programmes, as those will also lead to lower HIV incidence.

4.b. Comment and limitations

The methods and limitations for estimating HIV incidence vary based on the data and surveillance systems available in countries.

Countries with high HIV prevalence in the general population have relatively strong surveillance systems with household surveys contributing to the information required to estimate incidence. In epidemics concentrated in key populations, the surveillance systems for key hard-to-reach populations are often not comparable over time due to changing survey and sampling methods. The estimated size of key populations, a critical input to the Spectrum model for concentrated epidemics, can also lead to important under or over estimation of HIV incidence in concentrated epidemics.
In many countries trends in recent new infections rely on prevalence data from routine antenatal clinic testing. If those data are biased because women with known positive HIV status are not captured when calculating prevalence, or women found to be negative at initial antenatal care visit are retested later in the pregnancy, the derived incidence trends might be biased. While some limitations of the models are reflected in the uncertainty bounds the measurement biases and the uncertainty caused by these biases are not easily quantified and are thus not included.
Although HIV prevalence and incidence among children appears to be reasonably robust in generalized epidemics, estimating the pediatric HIV epidemic in concentrated epidemics remains a challenge because no robust measures of fertility exist among key populations living with HIV.
Currently UNAIDS only supports the HIV estimates development in countries with populations greater than 250,000. This is primarily due to support capacity at UNAIDS.

4.c. Method of computation

Longitudinal data on individuals newly infected with HIV would be the most accurate source of data to measure HIV incidence, however these data are rarely available for representative populations. Special diagnostic tests in surveys or from health facilities can also be used to obtain data on HIV incidence but these require very large samples to accurately estimate HIV incidence and the latter are also rarely representative. HIV incidence is thus modelled using the Spectrum software. The software incorporates data on HIV prevalence, the number of people on treatment, demographics and other relevant indicators to estimate historical HIV incidence, among other indicators. A full description of the model is available in peer-reviewed articles and in the most recent UNAIDS Global AIDS Update Reports. https://onlinelibrary.wiley.com/toc/17582652/2021/24/S5

https://www.unaids.org/en/resources/documents/2021/2021-global-aids-update

4.d. Validation

The HIV incidence estimates are created by country teams and are signed off on by ministry of health managers, including a clear statement that these data will be provided for SDG reporting. The SDG focal point in country is copied on the requests for clearance. UNAIDS reviews the input data and results to ensure quality before requesting clearance and compiling to regional and global values.

4.e. Adjustments

No adjustments are made to the estimates.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

Estimates are not collected from countries with populations < 250,000 according to the latest world population prospects estimates. In addition, no estimates are available for 8 countries with very small HIV epidemics who do not produce estimates.

For some countries in which the estimates were not finalized at the time of publication the country-specific values are not presented.

• At regional and global levels:

The countries with populations < 250,000 and the 8 countries that do not produce estimates are not included in regional or global level estimates. For countries in which the estimates were not finalized at the time of publication, the unofficial best estimates are included in the regional and global values.

4.g. Regional aggregations

Available for the World, the SDG regional groupings, Least Developed Countries, Landlocked Developing Countries and Small Island Developing States.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

A description of the methodology is available from the latest Global AIDS Update reports in the methods annex. Resources are also available at HIVtools.unaids.org.

Countries are provided with capacity building workshops on the methods every other year. In addition, they are supported by in-country UNAIDS advisers in roughly 45 countries. Where no in-country specialists are available, remote assistance is provided. Training videos and documentation are also available at: HIVtools.unaids.org

4.i. Quality management

Development of methods is overseen by an external reference group of experts (www.epidem.org). The actual files are reviewed by UNAIDS global experts to ensure consistency between countries.

4.j. Quality assurance

Countries are fully involved in the development of the estimates. The final values are reviewed for quality by UNAIDS and approved by senior managers at national Ministries of Health.

4.k. Quality assessment

Results are routinely compared to empirical evidence when available. These empirical data include research studies, household surveys with incidence measurement, and longitudinal HIV surveillance sites when available. If inconsistencies are found modifications are considered for the models. Methods are also published in peer-reviewed journals every two years. See links to publications at www.epidem.org.

5. Data availability and disaggregation

Data availability:

172 countries in 2022. Data are available by age and sex, however there are methodological challenges in estimating incidence among key populations.

Time series:

2000 -2021

Disaggregation:

General population, Age groups (0-14, 15-24, 15-49, 50+ years, All ages), sex (male, female, both). Key population data are currently not available as methods are being developed.

6. Comparability/deviation from international standards

Sources of discrepancies:

These variations will differ by country.

7. References and Documentation

URL:

unaids.org

References:

More information on the estimates process, tools and tutorial videos on the methods

https://hivtools.unaids.org/

Journal Supplement on methods:

https://onlinelibrary.wiley.com/toc/17582652/2021/24/S5

UNAIDS Global AIDS Monitoring

https://www.unaids.org/en/global-aids-monitoring

Political Declaration on HIV and AIDS: Ending inequalities

https://www.unaids.org/en/resources/documents/2021/2021_political-declaration-on-hiv-and-aids

UNAIDS website for access to data

http://aidsinfo.unaids.org/

UNAIDS website for downloading files used to create incidence estimates https://www.unaids.org/en/dataanalysis/datatools/spectrum-epp

Consolidated Strategic Information Guidelines for HIV in the Health Sector. Geneva: World Health Organization; https://www.who.int/publications/i/item/9789240000735

3.3.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

0.c. Indicator

Indicator 3.3.2: Tuberculosis incidence per 100,000 population

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Indicators associated with tuberculosis incidence: numbers: 1.1.1, 1.3.1, 2.1.1, 3.3.1, 3.4.1, 3.5.2, 3.a.1, 3.8.1, 3.8.2, 7.1.2, 8.1.1, 10.1.1, 11.1.1

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Tuberculosis (TB) incidence is defined as the estimated number of new and relapse TB cases (all forms of TB, including cases in people living with HIV) arising in a given year. It is usually expressed as a rate per 100 000 population.

Concepts:

Direct measurement requires high-quality surveillance systems in which underreporting is negligible, and strong health systems so that under-diagnosis is also negligible; otherwise, indirect estimates are produced, using either a) notification data combined with estimates of levels of underreporting and under-diagnosis, b) inventory studies combined with capture-recapture modelling, c) population-based surveys of the prevalence of TB disease or d) dynamic models fitted to monthly/quarterly notification data. Dynamic models are only used for selected countries in which major drops in TB case notifications compared with pre-2020 trends suggest major reductions in access to TB diagnosis and treatment during the COVID-19 pandemic.

2.b. Unit of measure

Number of cases per year per 100,000 population.

2.c. Classifications

Definitions and reporting framework for tuberculosis – 2013 revision (WHO/HTM/TB/2013.2). Geneva: World Health Organization; 2013 (https://www.who.int/publications/i/item/9789241505345 ).

3.a. Data sources

Details about data sources and methods are available in annex 1 and the technical appendix on methods used by WHO to estimate the global burden of tuberculosis disease published alongside the most recent WHO Global Tuberculosis Report at https://www.who.int/teams/global- tuberculosis-programme/data

3.b. Data collection method

National Tuberculosis (TB) Programmes report their annual TB data to WHO every year between April and June using a standardized web-based data reporting system maintained at WHO. The system includes real-time checks for data consistency. Estimates of TB burden are prepared in July-August and shared with countries for review in August-September; revisions are made based on feedback received. In selected countries with new survey data, estimates are updated separately during the year. The final set of estimates is reviewed in WHO before publication in October, for compliance with specific international standards and harmonization of breakdowns for age and sex groups.

3.c. Data collection calendar

April-June each year

3.d. Data release calendar

October each year

3.e. Data providers

National TB Programmes, Ministries of Health

3.f. Data compilers

World Health Organization (WHO)

3.g. Institutional mandate

Several World Health Organization resolutions endorsed by Member States at different World Health Assemblies have given the World Health Organization responsibility for monitoring the burden of TB globally and reporting on the response:

Global strategy and targets for tuberculosis prevention, care and control after 2015, World Health Organization, 67th World Health Assembly, Resolutions and decisions, Resolution WHA 67.11, Geneva, Switzerland, 2014.

https://apps.who.int/gb/ebwha/pdf_files/WHA67-REC1/A67_2014_REC1-en.pdf#page=25

Prevention and control of multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis, World Health Organization, 62nd World Health Assembly, Resolutions and decisions, Resolution WHA 62.15, Geneva, Switzerland, 2009.

https://apps.who.int/gb/ebwha/pdf_files/WHA62-REC1/WHA62_REC1-en-P2.pdf#page=25

Tuberculosis control: progress and long-term planning

World Health Organization. 60th World Health Assembly. Resolutions and decisions.

Resolution WHA 60.19. Geneva, Switzerland: WHO; 2007.

https://apps.who.int/gb/ebwha/pdf_files/WHASSA_WHA60-Rec1/E/WHASS1_WHA60REC1-en.pdf#page=67

Sustainable financing for tuberculosis prevention and control

World Health Organization. 58th World Health Assembly. Resolutions and decisions.

Resolution WHA 58.14. Geneva, Switzerland: WHO; 2005. https://apps.who.int/gb/ebwha/pdf_files/WHA58-REC1/english/A58_2005_REC1-en.pdf#page=96

Stop Tuberculosis Initiative

World Health Organization. 53rd World Health Assembly. Resolutions and decisions.

Resolution WHA 53.1. Geneva, Switzerland: WHO; 2000. https://apps.who.int/gb/ebwha/pdf_files/WHA53-REC1/WHA53-2000-REC1-eng.pdf#page=18

Tuberculosis control programme

World Health Organization. 44th World Health Assembly. Resolutions and decisions.

Resolution WHA44.8. Geneva, Switzerland: WHO, 1991.

4.a. Rationale

Following two years of consultations, a post-2015 global tuberculosis strategy was endorsed by the World Health Assembly in May 2014. Known as the End TB Strategy, it covers the period 2016-2035. The overall goal is to “End the global tuberculosis epidemic”, and correspondingly ambitious targets for reductions in tuberculosis deaths and cases are set for 2030 (80% reduction in incidence rate compared with the level of 2015) and 2035 (90% reduction in incidence rate), in the context of the SDGs.

The tuberculosis incidence rate was selected as an indicator for measuring reductions in the number of cases of disease burden. Although this indicator was estimated with considerable uncertainty in most countries in 2014, notifications of cases to national authorities provide a good proxy if there is limited under-reporting of detected cases and limited under or over-diagnosis of cases.

4.b. Comment and limitations

TB incidence has been used for over a century as a main indicator of TB burden, along with TB mortality. The indicator allows comparisons over time and between countries. Improvement in the quality of TB surveillance data result in reduced uncertainty about indicator values.

4.c. Method of computation

Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are derived from annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease, national inventory studies and information from death (vital) registration systems.

For the period 2000-2019, estimates of incidence for each country are derived using one or more of the following approaches, depending on available data: (i) incidence = case notifications/estimated proportion of cases detected; (ii) capture-recapture modelling, (iii) incidence = prevalence/duration of condition.

For 2020 and 2021 specifically, these methods were retained for most countries. However, for countries with large absolute reductions in the reported number of people newly diagnosed with TB in 2020 or 2021 relative to pre-2020 trends (which suggested major disruptions to access to TB diagnosis and treatment during the COVID-19 pandemic), dynamic models were used in replacement of the methods used for 2000-2019.

Uncertainty bounds are provided in addition to best estimates.

Details are provided in the technical appendix on methods used by WHO to estimate the global burden of tuberculosis disease published alongside the most recent WHO global tuberculosis report at https://www.who.int/teams/global-tuberculosis- programme/data.

4.d. Validation

Estimates of TB burden are prepared in July-August and shared with countries for review. In selected countries with new survey data, estimates are updated separately during the year. All estimates are communicated in August-September and revisions are made based on feedback. The final set of estimates is reviewed in WHO before publication in October, for compliance with specific international standards and harmonization of breakdowns for age and sex groups.

4.e. Adjustments

The final set of estimates is reviewed in WHO before publication in October, for compliance with specific international standards and harmonization of breakdowns for age and sex groups.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Details are provided in the technical appendix of each WHO Global Tuberculosis Report at https://www.who.int/teams/global-tuberculosis-programme/data

• At regional and global levels

Details are provided in the technical appendix of each WHO Global Tuberculosis Report at

https://www.who.int/teams/global-tuberculosis-programme/data

4.g. Regional aggregations

Country estimates of case counts are aggregated. Uncertainty is propagated assuming independence of country estimates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Available at Definitions and reporting framework for tuberculosis – 2013 revision (WHO/HTM/TB/2013.2). Geneva: World Health Organization; 2013 (https://www.who.int/publications/i/item/9789241505345)

4.i. Quality management

All health statistics published by WHO undergo a systematic internal review process from the Data Division, including TB burden statistics. External review of specific statistics is conducted in various ways, including through country consultations and reviews by technical review bodies such as the WHO Global Task Force on TB Impact Measurement. A report of a 2022 review by a subgroup of the Task Force is available at https://www.who.int/publications/i/item/9789240057647.

4.j. Quality assurance

The underlying TB data reported by WHO member states is carefully checked for completeness and internal consistency. Additional data sources are used in the process of disease burden estimation, including survey results, according to methods published in WHO documents mentioned in previous sections and cited in section 7.

4.k. Quality assessment

TB surveillance data are assessed systematically through so-called epidemiological reviews, which provide data quality scores used to update plans for strengthening TB surveillance and are used in models to estimate the burden of TB. In addition, the data are reviewed internally for consistency. Data and estimates are published in the form of country profiles, which are published following their review by countries, as mentioned in previous sections and cited in section 7. Results are published in detail in publicly available annual global TB reports.

5. Data availability and disaggregation

Data availability:

All countries

Time series:

2000 onwards

Disaggregation:

The indicator is disaggregated by country, sex and age group and five risk factors.

6. Comparability/deviation from international standards

Sources of discrepancies:

Population denominators may differ between national sources and United Nations Population Division (UNPD). WHO uses UNPD population estimates.

7. References and Documentation

URL:

https://www.who.int/teams/global-tuberculosis-programme/data

References:

The latest WHO Global Tuberculosis Report: https://www.who.int/teams/global-tuberculosis- programme/data).

Definitions and reporting framework for tuberculosis – 2013 revision (WHO/HTM/TB/2013.2). Geneva: World Health Organization; 2013 (https://www.who.int/publications/i/item/9789241505345).

World Health Assembly governing body documentation: official records. Geneva: World Health Organization (https://apps.who.int/gb/or/ ).

3.3.3

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

0.c. Indicator

Indicator 3.3.3: Malaria incidence per 1,000 population

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.g. International organisations(s) responsible for global monitoring

Global Malaria Programme at World Health Organization(WHO)

1.a. Organisation

Global Malaria Programme at World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Incidence of malaria is defined as the number of new cases of malaria per 1,000 people at risk each year.

Concepts:

A case of malaria is defined as the occurrence of malaria infection in a person in whom the presence of malaria parasites in the blood has been confirmed by a diagnostic test. The population considered is the population at risk of the disease.

2.b. Unit of measure

Cases per 1000 population at risk.

2.c. Classifications

N.A.

3.a. Data sources

Cases reported by the NMCP are obtained from each country surveillance system. This include among others information on the number of suspected cases, number of tested cases, number of positive cases by method of detection and by species as well as number of health facilities that report those cases. This information is summarized in a DHIS2 application developed for this purpose. Data for representative household surveys are publicly available and included National Demographic Household Surveys (DHS) or Malaria Indicator Survey (MIS).

3.b. Data collection method

The official counterpart for each country is the National Malaria Control Program at the Ministry of Health.

3.c. Data collection calendar

Data is collected every year.

3.d. Data release calendar

Data is released yearly.

3.e. Data providers

The National Malaria Control Program is the responsible to collect the information at each country.

3.f. Data compilers

The Surveillance, Monitoring and Evaluation Unit of the Global Malaria Control Programme is the responsible to compile and process all the relevant information. National estimates for some countries are estimated in collaboration with the Malaria Atlas Project which has been designated a WHO collaborating centre in geospatial disease modelling.

3.g. Institutional mandate

The Global technical strategy and targets for malaria 2016–2030 was adopted by The 68 World Health Assembly (https://apps.who.int/iris/bitstream/handle/10665/253469/A68_R1_REC1-en.pdf?sequence=1&isAllowed=y). The Assembly requested WHO to monitor the progress toward the GTS milestones and targets. The World Malaria Report is the process by which the GTS is monitored by country, WHO region and globally.

4.a. Rationale

To measure trends in malaria morbidity and to identify locations where the risk of disease is highest. With this information, programmes can respond to unusual trends, such as epidemics, and direct resources to the populations most in need. These data also serves to inform global resource allocation for malaria such as when defining eligibility criteria for Global Fund finance.

4.b. Comment and limitations

The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by:

the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100%
the extent of malaria diagnostic testing (the number of slides examined or RDTs performed)
the use of private health facilities which are usually not included in reporting systems.
the indicator is estimated only where malaria transmission occurs.

4.c. Method of computation

Malaria incidence (1) is expressed as the number of new cases per 100,000 population per year with the population of a country derived from projections made by the UN Population Division and the total proportion at risk estimated by a country’s National Malaria Control Programme. More specifically, the country estimates what is the total proportion of the population at risk of malaria and then, for each year, the total population at risk is estimated as the UN Population for that year, times the proportion of the population at risk at baseline. The same proportion of the population at risk is used for the entire time series to ensure comparability of estimates through time.

The total number of new cases, T, is estimated from the number of malaria cases reported by a Ministry of Health which is adjusted to take into account (i) incompleteness in reporting systems (ii) patients seeking treatment in the private sector, self-medicating or not seeking treatment at all, and (iii) potential over-diagnosis through the lack of laboratory confirmation of cases. The procedure, which is described in the World malaria report 2009 (2), combines data reported by NMCPs (reported cases, reporting completeness and likelihood that cases are parasite positive) with data obtained from nationally representative household surveys on health-service use. Briefly,

T=(a+(c × e)/d)×(1+h/g+((1−g−h)/2)/g)

where:
a is the number of malaria cases confirmed in public sector
b is the number of suspected cases tested
c is the number of presumed cases (not tested but treated as malaria)
d is the reporting completeness
e is the test positivity rate (malaria positive fraction) = a/b
f is the estimated cases in public sector, calculated by (a + (c x e))/d
g is the fraction seeking treatment in public sector
h is the fraction seeking treatment in private sector
i is the fraction not seeking treatment, calculated by (1-g-h)/2
j is the cases in private sector, calculated as f x h/g
k is the cases not in private and not in public, calculated by f x i/g
T is total cases, calculated by f + j + k

To estimate the uncertainty around the number of cases, the test positivity rate was assumed to have a normal distribution centred on the Test positivity rate value and standard deviation defined as

0.244 × Test positivity rate^0.5547

and truncated to be in the range 0, 1. Reporting completeness was assumed to have one of three distributions, depending on the range or value reported by the NMCP. If the value was reported as a range greater than 80%, the distribution was assumed to be triangular, with limits of 0.8 and 1.0, and the peak at 0.8. If the value was more than 50% but less than or equal to 80%, the distribution was assumed to be rectangular, with limits of 0.5 and 0.8. Finally, if the value was less than or equal to 50%, the distribution was assumed to be triangular, with limits of 0 and 0.5, and the peak at 0.5 (3). If the reporting completeness was reported as a value and was more than 80%, a beta distribution was assumed, with a mean value of the reported value (maximum of 95%) and confidence intervals (CIs) of 5% around the mean value. The fraction of children brought for care in the public sector and in the private sector was assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation being calculated from the range of the estimated 95% CIs. The fraction of children not brought for care was assumed to have a rectangular distribution, with the lower limit being 0 and the upper limit calculated as 1 minus the proportion that were brought for care in the public and private sectors. The three distributions (fraction seeking treatment in public sector, fraction seeking treatment in private sector only and fraction not seeking treatment) were constrained to add up to 1.

Sector-specific care-seeking fractions were linearly interpolated between the years that had a survey and were extrapolated for the years before the first or after the last survey. The parameters used to propagate uncertainty around these fractions were also imputed in a similar way or, if there was no value for any year in the country or area, were imputed as a mixture of the distributions of the region for that year. CIs were obtained from 10 000 draws of the convoluted distributions. The data were analysed using the R statistical software (4). This method was used was used for countries and areas outside the WHO African Region, and for low-transmission countries and areas in the African Region: Afghanistan, Bangladesh, Bolivia (Plurinational State of), Botswana, Brazil, Cambodia, Colombia, the Dominican Republic, Eritrea, Ethiopia, French Guiana, the Gambia, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, the Lao People’s Democratic Republic, Madagascar, Mauritania, Myanmar, Namibia, Nepal, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, the Philippines, Rwanda, Senegal, Solomon Islands, Timor-Leste, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Yemen and Zimbabwe. Bangladesh, Bolivia (plurinational State of), Botswana, Brazil, Colombia, Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Myanmar (since 2013), Rwanda, and Venezuela (Bolivarian Republic of) report cases from the private and public sector together; therefore, no adjustment for private sector seeking treatment was made while for Indonesia, 25% of the private was assumed to be reported in the public sector since 2017. For India, the values were obtained at subnational level using the same methodology, but adjusting the private sector for an additional factor due to the active case detection, estimated as the ratio of the test positivity rate in the active case detection over the test positivity rate for the passive case detection. This factor was assumed to have a normal distribution, with mean value and standard deviation calculated from the values reported in 2010. An additional adjustment was applied in several states in India, to control for the reductions in reported testing rates associated with disruptions in health services related to the COVID-19 pandemic.

For some high-transmission African countries the quality of case reporting is considered insufficient for the above formulae to be applied. In such cases estimates of the number of malaria cases are derived from information on parasite prevalence obtained from household surveys. First, data on parasite prevalence from nearly 60 000 survey records were assembled within a spatiotemporal Bayesian geostatistical model, along with environmental and sociodemographic covariates, and data distribution on interventions such as ITNs, antimalarial drugs and IRS. The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2–10 years, at a resolution of 5 × 5 km2, throughout all malaria endemic African countries for each year from 2000 to 2016 (see https://malariaatlas.org/ for methods on the development of maps by the Malaria Atlas Project). Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence. The model was then applied to the estimated parasite prevalence in order to obtain estimates of the malaria case incidence at 5 × 5 km2 resolution for each year from 2000 to 2020. Data for each 5 × 5 km2 area were then aggregated within country and regional boundaries to obtain both national and regional estimates of malaria cases (5). In 2020, additional cases estimated using this method were added, to account for the disruptions in malaria prevention, diagnostic and treatment services as a result of the COVID-19 pandemic and other events that occurred during this year. Disruption information was reported per country and was obtained from the national pulse surveys on continuity of essential health services during the COVID-19 pandemic conducted by WHO (first round in May–July 2020 and second in January–March 2021). This method was applied in the following countries: Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, the Congo, Côte d’Ivoire, the Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Malawi, Mali, Mozambique, the Niger, Nigeria, Sierra Leone, Somalia, South Sudan, the Sudan, Togo, Uganda, the United Republic of Tanzania and Zambia

For most of the elimination or near elimination countries, the number of indigenous cases registered by the NMCPs are reported without further adjustments. (Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Cabo Verde, China, Comoros, Costa Rica, Democratic People’s Republic of Korea, Djibouti, Ecuador, Egypt, El Salvador, Eswatini, Georgia, Iran (Islamic Republic of), Iraq, Kazakhstan, Kyrgyzstan, Malaysia, Mexico, Morocco, Oman, Paraguay, Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, Syrian Arab Republic, Thailand, Turkey, Turkmenistan, United Arab Emirates and Uzbekistan).

4.d. Validation

Burden estimates presented in the World Malaria Report are sent to the countries via regional offices for consultation and approval.

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For missing values of the parameters (test positivity rate and reporting completeness) a distribution based on a mixture of the distribution of the available values is used, if any value exists for the country or from the region otherwise. Values for health seeking behaviour parameters are imputed by linear interpolation of the values when the surveys where made or extrapolation of the first or last survey. When no reported data is available the number of cases is interpolated taking into account the population growth.

At regional and global levels

Not Applicable

4.g. Regional aggregations

Number of cases are aggregated by region, and uncertainty obtained from the aggregation of each country’s distribution. Population at risk is aggregated without any further adjustment. Estimation at global level is obtained from aggregation of the regional values.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Information is provided by each country’s NMCP using a DHIS 2 application created specifically for this purpose.

4.i. Quality management

Burden estimates are first reviewed internally by GMP and WHO regional and country offices. These are then shared to country for validation. Final approval is received from the WHO division of Data, Analytics.

4.j. Quality assurance

We collect data using a standardize form depending on the status of malaria control, elimination or prevention of reintroduction. We work closely with the collaborators centres and external reviewers to assure quality.

4.k. Quality assessment

We perform internal validation for outliers and completeness and raise queries to countries through the regional offices for clarification. When necessary we rely on data quality assessment information from external sources such as partners working in malaria monitoring and evaluation.

5. Data availability and disaggregation

Data availability:

109 countries

Time series:

Annually since 2000

Disaggregation:

The indicator is estimated at country level.

6. Comparability/deviation from international standards

Sources of discrepancies:

The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by:

the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100%
the extent of malaria diagnostic testing (the number of slides examined or RDTs performed)
the use of private health facilities which are usually not included in reporting systems.

7. References and Documentation

URL:

https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021

References:

1. World Health Organization. World Malaria Report 2021.

2. World Health Organization. World Malaria Report 2008 [Internet]. Geneva: World Health Organization; 2008. Available from: http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf

3. Cibulskis RE, Aregawi M, Williams R, Otten M, Dye C. Worldwide Incidence of Malaria in 2009: Estimates, Time Trends, and a Critique of Methods. Mueller I, editor. PLoS Med. 2011 Dec 20;8(12):e1001142.

4. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: http://www.R-project.org/

5. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015 Oct 8;526(7572):207–11.

3.3.4

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

0.c. Indicator

Indicator 3.3.4: Hepatitis B incidence per 100,000 population

0.d. Series

Not applicable

0.e. Metadata update

2021-04-01

0.g. International organisations(s) responsible for global monitoring

World Health Organization

1.a. Organisation

World Health Organization

2.a. Definition and concepts

Definition:

This indicator is measured indirectly through the proportion of children 5 years of age who have developed chronic HBV infection (i.e. the proportion that tests positive for a marker of infection called hepatitis B surface antigen [HBsAg]).¹

Hepatitis B surface antigen: a protein from the virus’s coat. A positive test for HBsAg indicates active HBV infection. The immune response to HBsAg provides the basis for immunity against HBV, and HBsAg is the main component of HepB.²

Concepts:

It is not possible, on clinical grounds, to differentiate hepatitis B from hepatitis caused by other viral agents, hence, laboratory confirmation of the diagnosis is essential. The Hepatitis B surface antigen is the most common hepatitis B test. The presence of HBsAg in serum indicates that the patient has contracted HBV infection. The measurement of HBsAg levels have been standardized in IU/ml. The test is used to identify those at risk of spreading the disease. HBsAg, an HBV viral coat antigen, is produced in large quantities in infected-cell cytoplasm and continues to be produced in patients with chronic, active HBV infection. Documented HBsAg positivity in serum for 6 or more months suggests chronic HBV with a low likelihood of subsequent spontaneous resolution.

2.b. Unit of measure

Prevalence of the Hepatitis b surface antigen in children under five years of age (proportion with chronic infection)

3.a. Data sources

A systematic search on articles published between Jan 1, 1965, and Oct 30, 2018. in the databases Embase, PubMed, Global Index Medicus, Popline, and Web of Science.

Following full text review, we extracted data from each study using the following variables: study characteristics (study and sample collection dates, study locations i.e., city, subnational [an area, region, state, or province in a country], or national level), participant characteristics (age range, sex, year, and population group), and prevalence of the HBV marker, type of laboratory tests, and number of participants the HBV marker prevalence was based on.

Data of eligible articles were entered into a Microsoft EXCEL® and/or Distiller databank by two reviewers independently. Information was extracted for author name, year, age, gender, marker, laboratory test used, number of individuals tested, prevalence of each marker when reported, the population group (general population, HCWs, or blood donors) and whether the data reported was for a city, sub-national (an area, region, state or province in a country) or national level, GDP per capita. In addition to HBsAg, HBeAg was recorded, as available for individuals when HBsAg was also reported. In order to record information on methodological quality and study bias resulting from non-representativeness, an additional variable was used: samples likely to be representative for the country/area specified were coded as 0 and others, e.g. convenience samples in certain communities or tribes in the country were assigned a 1, supplemented by additional information. The risk of bias/non-representativeness information was applied if the population was neither HCW nor blood donor (see description below).3 In the following, variables extracted from the studies and assumptions made are described in detail:

Author, Date
Year start/end of study conduct: Year of study begin and end was extracted. If this information was not available from the studies, we used the commonly used assumption that the study was conducted two years prior to the year of publication (e.g. author, 2000, year of study conduct: 1998).
Sex: Sex-specific values were extracted. If only an overall (all) estimate was provided, the share of females in the study was specified in the column additional information.
Age start/end: The most specific age-group provided by the data was extracted. If the age-group on which the parameter value was based on was not available, assumptions were made based on the context of the study. Therefore, the following was applied in case of missing information on age-groups in the study population:
If the study was conducted in the general population without further specification and if only one prevalence estimate is provided, the age-group was considered to be 0-85 years. Subsequently, if the beginning and last age-group is missing, the lower value of the youngest age-group is 1 year, the upper value of the oldest age-groups is 85 years.
If the study was conducted among adult populations but no age-range is provided, the age-group is considered to be 17-65 years.
If the study was conducted among pupils but no age-range is provided, the age-group is considered to be 5-15 years.
If the study was conducted among pregnant women but no age-range is provided, the age-group is considered to be 15-49 years (reproductive age).
If the study was conducted among blood donors but no age-range is provided, the age-group is considered to be 17-65 years.
If the study was conducted among army recruits or soldiers but no age-range is provided, the age-group is considered to be 18-45 years.
If the study was conducted among the working population but no age-range is provided, the age-group is considered to be 16-65 years.
HBsAg Prevalence: The most specific prevalence estimate provided by the data was extracted (defined by age-/sex-/year-prevalence). Separate lines for each marker were used in the data extraction file (e.g. one for HBeAg and one line for HBsAg, even if the study group/publication was the same)
HBeAg Prevalence (optional marker): The most specific prevalence estimate (defined by age-/sex-/year-prevalence) of HBeAg among HBsAg-positive individuals was extracted and, if applicable was calculated to reflect prevalence among HBsAg carriers.
anti-HBc Prevalence (optional marker): The most specific prevalence estimate provided by the data was extracted (defined by age-/sex-/year-prevalence).
Laboratory method: Testing immune response markers of HBV infection began in the 1970s by counter-immuno-electrophoresis technique (CIEP). Since then, different detection methods have been developed (RIA, EIA, …). The most applied method in prevalence studies is the ELISA (enzyme-linked immunosorbent assay). Five categories were established to record the method/test used for prevalence detection in the studies: ELI new (ELISA -2, -3, EIA, …), EIA old (CMIA, CIEP, RPHA), NAT (qPCR/real-time PCR, nested PCR, multiplex PCR), other (e.g. RIA); Unknown/not specified.
Country: Country names were recorded according to www.who.int and, for additional analysis purpose, were grouped according to the six WHO regions: the African Region, the Region of the Americas, the Eastern Mediterranean Region, the European Region, the South East-Asia Region and the Western Pacific Region.
Sample size of individuals blood drawn from; of individuals involved in analyses/bases for parameter estimate: As a quality indicator of the study, we distinguished the effective sample size, i.e. the number of individuals involved in the analysis/on which the parameter estimate is based on, from the number of individuals from which blood was drawn from (separate column) and the initially calculated/planed sample size (separate column).
Population: Although focus was on the general population, two additional groups were included and specified. These include: HCW and blood donor (plus subgroups unspecified, paid, unpaid/voluntary). If in this column “population” was specified as HCW or blood donor and not as general population, the risk of bias column (following) remains empty.
Level: Information is provided if the study was conducted on a national, sub-national, city level or if the level was not further specified (four categories).
Study Location: This free-text variable specifies the city/area within the country where the included study was conducted. The variables/columns Level and Study Location were additionally included following the WHO Meeting on Impact of Hepatitis B Vaccination at WHO, Geneva, in March 2014.

Additional data from other sources than the eligible studies:

Year of vaccine introduction in the entire country: data is derived from official reports by WHO Member States and unless otherwise stated, data is reported annually through the WHO/UNICEF joint reporting process. http://www.who.int/entity/immunization/monitoring_surveillance/data/year_vaccine_introduction.xls?ua=1
Period when the study was conducted: pre- vaccination or post vaccination. This is determined according the year of introduction in the whole country.
Coverage estimates series: data is obtained from WUENIC: http://apps.who.int/immunization_monitoring/globalsummary/timeseries/tswucoveragebcg.html
GDP per capita was used form UN data that compiles information from the World Bank Source http://data.un.org/Data.aspx?q=GDP&d=SNAAMA&f=grID%3a101%3bcurrID%3aUSD%3bpcFlag%3a1 ),
Longitude and latitude data (source: www.google.com).
Population structure and size data for each country was from the UN population division:

http://www.un.org/en/development/desa/population/

3.b. Data collection method

WHO provides Member States the opportunity to review and comment on data as part of the so called country consultation process. Member States receive an annex with their country specific estimates, the serosurveys used to inform the mathematical model and the summary of the methodology. They are provided with sufficient time to provide any additional study to be screened according to the inclusion and inclusion criteria.

3.c. Data collection calendar

The systematic review of published serosurveys and model estimates are updated on an annual basis. Planned for the last quarter of 2019.

3.d. Data release calendar

Second quarter of each year

3.e. Data providers

World Health Organization

3.f. Data compilers

World Health Organization

4.a. Rationale

The purpose is to describe the reduction in chronic hepatitis b infections. Most of the burden of disease from HBV infection comes from infections acquired before the age of 5 years. Therefore, prevention of HBV infection focuses on children under 5 years of age. The United Nations selected the cumulative incidence of chronic HBV infection at 5 years of age as an indicator of the Sustainable Development Goal target for “combating hepatitis”. This indicator is measured indirectly through the proportion of children 5 years of age who have developed chronic HBV infection (i.e. the proportion that tests positive for a marker of infection called hepatitis B surface antigen [HBsAg]).

4.b. Comment and limitations

The main Limitations of the analysis is that despite the thorough and in-depth literature search and access, there are fewer data on post vaccination studies than pre- vaccination studies. The model is largely informed by pre-vaccination studies in adults.

The quality of studies and data was assessed by reviewing representativeness of sampling. Bias factor is a dichotomous variable.

Potential important biases included geographical representation of the data points. Also, studies were from many different sources such as blood donors and pregnant women. The former possibly having a lower proportion of Hep B prevalence than the general population as donor questionnaires often exclude individuals with risk factors for blood-borne diseases and the pregnant women possibly having a higher prevalence as were in studies to see the effect of a birth dose of vaccine to prevent vertical transmission. As the proportion of studies and size of studies that were from blood donors was significantly greater than those on pregnant women, we may presume that our estimates of prevalence of pre- vaccination may be on the low side.

4.c. Method of computation

The data was modelled using a Bayesian logistic regression looking at the proportion of individuals that tested positive for HBsAg in each study, weighting each study by its size and using a conditional autoregressive (CAR) model accounting for spatial and economic correlations between similar countries. This model uses data from well sampled countries to estimate prevalence in more data poor countries with effects such as sex, age and vaccination status, these are also informed by the geographic and countries GDP proximity to other countries (CAR model). Under the assumption that countries that are close together economically and/or geographically will have more similar prevalence due to similar social structure and health care capabilities.

The response variable in the model was the prevalence of Hepatitis surface antigen (HBsAg) with the explanatory variables being age (three categories, under 5, juvenile (5-15) and adult (16+), split using the average age of participants in the study), sex (proportion female in the study), study bias (e.g. a high fraction of study participants from indigenous populations), 3 dose vaccine coverage, birth dose of the vaccine and country of study. The coverage of routine 3 dose vaccination and birth dose vaccination in each study was calculated by cross referencing the year of and age of participants in each study with the corresponding WHO-UNICEF vaccine coverage estimates for that country. The WHO-UNICEF estimates are annual data for the country as a whole, and did not contain information on vaccine efficacy which was not used in the analysis as no data on this was obtained. The vaccine efficacy would be implicitly estimated in the analysis as we see vaccination having a variable effect across time and space across the studies. The coverage of routine 3 dose vaccination and birth dose vaccination in each study was calculated by cross referencing the year of and age of participants in each study with the corresponding WHO-UNICEF vaccine coverage estimates for that country. The coverage of routine 3 dose vaccination and birth dose vaccination in each study was calculated by cross referencing the year of and age of participants in each study with the corresponding WHO-UNICEF vaccine coverage estimates for that country. More explicitly, the model uses the ages and timing of the study to calculate the years across which the participants are born, so if the if there was an age group range of 10-15 in a study that was undertaken in 2015, the birth years would be from 2000-2005, we then average the vaccination coverage from the WHO-UNICEF estimates across those 5 years assuming that each age was evenly represented in that age group in the study. The same process was used for the 3 dose and birth dose vaccination.

The general logistic model equation is described below,

Yi ~Binomial (πi, Ni), logπi1−πi= β0+ ∑j=1pβjxij+ui

Where βj are the fixed effects of the explanatory variables xii. With the spatial random effects described by

ui~ N(u−i,σ2u/ni)

where,

u−i= ∑j ∈ neigh(i)wiuj/ni

Where ni is the number of neighbours for country i and weights wi, are 1.

The model was simulated in the Bayesian statistical package WinBUGS, and data manipulation and model initialisation run from R (3.3.1) using R2WinBUGS. The model considers the parameters of age, sex, study bias (e.g. a high fraction of study participants from indigenous populations), vaccine coverage, birth dose of the vaccine and country of study.

The model uses the CAR-normal function, in WinBUGS, to model the spatial and economic autocorrelation related to neighbouring countries. For each country that had prevalence data, a weighted central position was calculated using the size and location of each study. For those countries with no data, we used the population centroid. In a novel approach, we considered 3 dimensions in the country adjacency matrix; we used the usual geographic dimensions, latitude and longitude and also combined these with the natural log of the country’s GDP per capita. This was to measure not only geographic but also the developmental proximity of countries. The adjacency matrix for the geo-economic distance gives a score between each country to every other country. Those countries which are close geographically and economically would have a low score and those further apart either geographically or economically would have a high score/distance. Therefore, those countries that are more alike will have a low score and those countries which are alike would have a high score.

The way we proportioned the geographic and economic distance to produce the adjacency matrix was then explored, this is because geographic distance may be more or less important than economic similarities. Thus, by creating a number of different adjacency matrices (not definitive) we could select the most suitable matrix that explains reality best. We normalised the geographic and GDP distance and then calculated the distance between these two normalised figures. This creates a smoothed Gaussian surface that is dependent on both spatial proximity and GDP per-capita proximity. We compared ratios of, 1:0, 1:1, 2:1, 1:2 (Geographic:GDP).

For each different adjacency matrix, we also had to select a neighbourhood distance, i.e. over what distance can a country be effected by another. Thus, we also varied the radius of distance from which to select neighbours for the neighbourhood network, we used the maximum minimum distance, twice the maximum minimum and three times the maximum minimum, thus varying the number of neighbours each country would have.

Finally, to decide the magnitude of the effect one country has on another in the neighbourhood network we varied the weights of pairs of countries in the adjacency matrix, using either a neutral weighting of 1, so that each neighbour has an equal effect on each other (not dependent on the distance in the network), or decaying weights over distance with 1/distance, and 1/distance2, where the closer the country is the greater the effect it has on another country. The outcome of these 36 different combinations led to minimum DIC (Deviance Information Criterion) being found for a ratio of 1:2 (Geographic:GDP), the neighbourhood networks minimum distance being twice the maximum minimum distance and an even weighting of 1/distance for each adjacent country.

This model structure produces estimates for all fixed effects and also individual country level risk, this provides information on which are significantly at greater or lower risk to the average risk.

All parameters were given un-informative priors. Simulations were run with 3 MCMC chains with 50,000 burn in iterations and each parameter estimated from 1000 samples taken from a thinned 250,000 iterations to produce the posterior distribution. Convergence was attained, with r̂ values all very close to 1.000. Due to the Bayesian framework and WinBUGS software it was possible to gain estimates for countries where we had no data on prevalence, using their GDP and geographic proximity to inform this estimate. Those countries with the largest number of studies provided the estimates with the tightest confidence intervals and those with few or no data were less well defined, often producing a log normal distributed posterior distribution, giving estimates with long tails.

Posterior distributions of parameters were inspected for convergence and to check for covariance between parameters. Where necessary parameters were centred and scaled to N (0, 1) to aid parameter convergence and remover covariance. This was done for the sex parameter, which was entered as the proportion of the sample that was female; this was seen to co-vary with the intercept and bias parameters before re-centring and scaling. However, the covariance of routine vaccination and birth dose persisted even after re-centring. This is in part unsurprising as there a few instances where birth dose is administered without the routine vaccination. Here we tried to reduce this interaction of the terms by transforming the birth dose data. We modelled birth dose using only data where the birth dose was greater than 60, 70, 80 & 90% respectively, we also modelled birth dose to the square, thus increasing the effect of high birth doses over smaller doses. Model selection dependent on which one both reduced the covariance between the parameters and returned the lowest DIC score.

Model validation was conducted using 90% of randomly selected data against the remaining 10%, and by comparing model estimates of prevalence against observed data (Figure 3). Figure 4 shows the average prevalence in each country from all the studies plotted against the models estimate. Figure 5 shows the marginal and joint posterior distributions for the fitted parameters. Table 1 gives the estimated parameter values with associated credible intervals.

During the validation exercise (in which countries were consulted over their estimates) it was pointed out that China had undertaken three very large-scale population-based serological surveys in order to establish baseline prevalence and progress towards HBV elimination. There were a large number of other surveys from China, that are less representative than these three nationwide surveys. We conducted a sensitivity analysis by restricting the data from China to the three nationally representative surveys. The effect of this change in input data was that the effect of vaccination was more distinct, but the estimated age effects (change in prevalence in children under 5, or juveniles (children 5-15 years)) were no longer significantly different from zero (see Table 2 and Figure 6). The deviance was significantly reduced, suggesting a much better fitting model (Table 2), albeit on a somewhat reduced dataset.

4.e. Adjustments

Estimates are provided for the 194 WHO Member States and grouped accordingly to the six WHO regions. We also provide estimates according to income classification and follow UN Regional Groupings and Compositions as much as possible.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

All values represent the best estimates for the hepatitis B surface antigen indicator and aim to facilitate comparability across countries and over time. The estimates are not always the same as the official national estimates, because of the use of different methodologies and data sources. Estimates are provided for 194 WHO Member States. The analysis was carried out for the age groups 0-5 years and for the general population. Due to scarcity of data from some countries, the estimates are more robust at global and regional level than at country level, therefore, we suggest countries focus on the 95% Credible Intervals and not only on the reported point estimates.

A thorough and robust literature review was undertaken to find studies across the 194 WHO Member States and across age groups and vaccination status. We updated the systematic review by Schweitzer et al, 2015 that included a systematic search on articles published between Jan 1, 1965, and Oct 23, 2013. We updated the systematic search to include articles published between Oct 23, 2013, and October 30, 2018 in the databases Embase, PubMed, Global Index Medicus, Popline, and Web of Science.

For each country that had prevalence data, a weighted central position was calculated using the size and location of each study. For those countries with no data, we used the population centroid. Please see detailed explanation above.

At regional and global levels

Same as above

4.g. Regional aggregations

Sources of discrepancies:

The estimates are not always the same as the official national estimates, because of the use of different methodologies and data sources. The study selection criteria were similar to (Schweitzer, et al., 2015). Observational studies on chronic HBV infection seroprevalence (HBsAg prevalence), done in the general population or among blood donors, health-care workers (HCWs), and pregnant women were considered for inclusion in this systematic review. Studies were excluded if they were systematic reviews or meta-analyses, surveillance reports, case studies, letters or correspondence, or did not contain HBsAg seroprevalence data. Studies were also excluded if they exclusively reported prevalence estimates for high-risk population groups (e.g., migrants and refugees).

Country estimates may come from selected serosurveys.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Non applicable. Estimates come from the mathematical model.

Gather checklist of information that should be included in new reports of global health estimates. Gather promotes best practices in reporting health estimates. A range of health indicators are used to monitor population health and guide resource allocation throughout the world. But the lack of data for some regions and differing measurement methods present challenges that are often addressed by using statistical modelling techniques to generate coherent estimates based on often disparate sources of data. http://gather-statement.org/

4.j. Quality assurance

Quality assurance

WHO’s estimates use a methodology reviewed by the Immunization and Vaccines Related Implementation Research Advisory Committee (IVIR-AC) and presented to the Strategic Advisory Group of Experts (SAGE). These estimates have been documented following the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER).

WHO provided Member States the opportunity to review and comment on data and estimates as part of the so called country consultation process.

5. Data availability and disaggregation

Data availability:

Estimates are available for 194 Member States and for the six WHO Regions, as well as at global level.

Time series:

Estimates are available for pre- vaccine era, 2015 and 2018 and 2020

Disaggregation:

age groups (i.e. under five years of age, 5 years and older (although these estimates are not reported) and the general population); sex/gender if possible. Although the data for the latter is scarce. In addition, data at national, regional and global level.

6. Comparability/deviation from international standards

This dataset represents the best estimates for the hepatitis B surface antigen indicator and aims to facilitate comparability across countries and over time. The estimates are not always the same as the official national estimates, because of the use of different methodologies and data sources e.g. special populations or populations at risk are not included in the hepatitis b seroprevalence model. Estimates are provided for 194 WHO Member States. The conditional autoregressive model uses data from well sampled countries to estimate prevalence in more data-poor countries taking account of effects such as sex, age and vaccination status. Due to scarcity of data from some countries, the estimates are more robust at global and regional level than at country level, therefore focus should be on the 95% Credible Intervals and not only on the reported point estimates.

Sources of discrepancies:

Inclusion or exclusion criteria of the type of seroprevalence studies. Observational studies on chronic HBV infection seroprevalence (HBsAg prevalence), done in the general population or among blood donors, health-care workers (HCWs), and pregnant women were considered for inclusion. Studies were excluded if they were systematic reviews or meta-analyses, surveillance reports, case studies, letters or correspondence, or did not contain HBsAg seroprevalence data. Studies were also excluded if they exclusively reported prevalence estimates for high-risk population groups (e.g., migrants and refugees).

7. References and Documentation

Serosurveys are available for each member states and reference provided for each data point.

URL: http://whohbsagdashboard.com/#global-strategies

3.3.5

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

0.c. Indicator

Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseases

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

NTDs are formally recognized as targets for global action in SDG target 3.3, which calls to “end the epidemics of ... neglected tropical diseases” by 2030, as part of Goal 3 (Ensure healthy lives and ensure well-being for all at all ages). Successful interventions against NTDs contribute to meeting other SDGs, such as alleviating poverty (Goal 1) and hunger (Goal 2), enabling people to pursue an education (Goal 4) and lead productive working lives (Goal 8) and promoting equality, for example with regard to gender (Goals 5 and 10). Progress towards other Goals can accelerate the achievement of NTD goals. For example, wider provision of clean water, sanitation and hygiene (WASH) (Goal 6) is believed to help to eliminate or control NTDs; the availability of resilient infrastructure (Goal 9) should facilitate delivery of medicines and outreach to remote communities; the goals of sustainable cities (Goal 11) and climate action (Goal 13) can support the environmental management necessary for control of disease vectors. Attaining all SDGs and NTD goals is founded on strong global partnerships (Goal 17).

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Number of people requiring treatment and care for any one of the neglected tropical diseases (NTDs) targeted by the WHO NTD Roadmap and World Health Assembly resolutions and reported to WHO.

Concepts:

Treatment and care is broadly defined to allow for preventive, curative, surgical or rehabilitative treatment and care. In particular, it includes both:

1) Average annual number of people requiring mass treatment known as preventive chemotherapy (PC) for at least one PC-NTD; and

2) Number of new cases requiring individual treatment and care for other NTDs.

Other key interventions against NTDs (e.g. vector management, veterinary public health, water, sanitation and hygiene) are to be addressed in the context of other targets and indicators, namely Universal Health Coverage (UHC) and universal access to water and sanitation.

2.b. Unit of measure

Number of people

2.c. Classifications

Not applicable

3.a. Data sources

Description:

The number of people requiring treatment and care for NTDs is measured by existing country systems, and reported through joint request and reporting forms for donated medicines, the WHO Integrated Data Platform, and other reports to WHO.

https://www.who.int/teams/control-of-neglected-tropical-diseases/data-platforms-and-tools

Country data are published via the WHO Global Health Observatory.

https://www.who.int/data/gho/data/themes/neglected-tropical-diseases

3.b. Data collection method

NTDs requiring preventive chemotherapy (PC-NTDs)

As part of global efforts to accelerate expansion of preventive chemotherapy for elimination and control of lymphatic filariasis (LF), schistosomiasis (SCH) and soil-transmitted helminthiases (STH), WHO facilitates the supply of the following medicines donated by the pharmaceutical industry: diethylcarbamazine citrate, albendazole, mebendazole, and praziquantel. WHO also collaborates to supply ivermectin for onchocerciasis (ONCHO) and LF elimination programmes, and azithromycin for trachoma (TRA) through the Trachoma Elimination Monitoring Form.

A joint mechanism and a set of forms have been developed to facilitate the process of application, review and reporting as well as to improve coordination and integration among different programmes.

Joint Request for Selected PC Medicines (JRSM) – designed to assist countries in quantifying the number of tablets of the relevant medicines required to reach the planned target population and districts in a coordinated and integrated manner against multiple diseases during the year for which medicines are requested.

Joint Reporting Form (JRF) – designed to assist countries in reporting annual progress on integrated and coordinated distribution of medicines across PC-NTDs in the reporting year in a standardized format.

PC Epidemiological Data Reporting Form (EPIRF) – designed to standardize national reporting of epidemiological data on LF, ONCHO, soil-transmitted helminthiases and SCH. National authorities are encouraged to complete this form and submit it to WHO on a yearly basis, together with the JRF.

The reports generated in the JRSM and in the JRF (SUMMARY worksheets) must be printed and signed by the NTD coordinator or a Ministry of Health representative to formally endorse the country’s request for these medicines and the reported annual progress of the national programme(s). The date of signature must also be included. Once signatures have been obtained, the scanned copies of the two worksheets, together with the full Excel versions of the JRSM, the JRF and the EPIRF can be jointly submitted to WHO.

The forms are submitted to the WHO Representative of the concerned WHO Country office with electronic copies to PC_JointForms@who.int and the concerned Regional focal point. The relevant submission deadline depends on the time of planned implementation dates as follows:

the final report should be submitted within 3 months after the last round was implemented and no later than 31 March of the next implementation year;
to ensure the medicines are delivered on time, the request for PC medicines should be submitted at least 9 months before the first date of MDA planned in the calendar year of the request.

https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package

NTDs requiring individual diagnosis and treatment

Countries are invited to report on Buruli ulcer, Chagas disease, leprosy, the leishmaniases, mycetoma, rabies, snakebite envenoming and yaws cases using Excel templates or directly into the WHO integrated data platform (https://extranet.who.int/dhis2). Modules are under development to collect information on, echinococcosis and taeniasis cases through the same platform.

Cases of human African trypanosomiasis (HAT) and other key HAT indicators are reported at village level by national sleeping sickness control programmes through annual reports and entered in the Atlas of HAT (https://www.who.int/publications/i/item/1476-072X-8-15), but annual cases aggregated at country level are also entered in the WHO integrated data platform.

3.c. Data collection calendar

Data for the reporting year is being collected and reported during first 3 quarters of the next year.

3.d. Data release calendar

Data reported for the preceding year is released during the last quarter of the year

3.e. Data providers

National NTD programmes within Ministries of Health

3.f. Data compilers

World Health Organization (WHO)

3.g. Institutional mandate

A process of data reporting by national NTD programmes implemented according to the WHO Data Sharing Policy on use and sharing of data collected in Member States by the World Health Organization (WHO) outside the context of public health emergencies (https://www.who.int/about/policies/publishing/data-policy). The department of control of Neglected tropical diseases at WHO is then responsible for processing and disseminating the statistics for this indicator.

4.a. Rationale

The average annual number of people requiring treatment and care for NTDs is the number that is expected to decrease toward “the end of NTDs” by 2030 (target 3.3), as NTDs are eradicated, eliminated or controlled. The number of people requiring other interventions against NTDs (e.g. vector management, veterinary public health, water, sanitation and hygiene) are expected to be maintained beyond 2030 and are therefore to be addressed in the context of other targets and indicators, namely Universal Health Coverage (UHC) and universal access to water and sanitation.

This number should not be interpreted as the number of people at risk for NTDs. It is in fact a subset of the larger number of people at risk. Mass treatment is limited to those living in districts above a threshold level of prevalence; it does not include all people living in districts with any risk of infection. Individual treatment and care is for those who are or have already been infected; it does not include all contacts and others at risk of infection. This number can better be interpreted as the number of people at a level of risk requiring medical intervention – that is, treatment and care for NTDs.

4.b. Comment and limitations

Country reports may not be perfectly comparable over time. Improved surveillance and case-finding may lead to an apparent increase in the number of people known to require treatment and care. Some further estimation may be required to adjust for changes in surveillance and case-finding. Missing country reports may need to be imputed for some diseases in some years.

4.c. Method of computation

Some estimation is required to aggregate data across interventions and diseases. There is an established methodology that has been tested and an agreed international standard. [https://apps.who.int/iris/bitstream/handle/10665/241869/WER8702.PDF]

1) Average annual number of people requiring mass treatment known as PC for at least one PC-NTD (lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases and trachoma). People may require PC for more than one PC-NTD. The number of people requiring PC is compared across the PC-NTDs, by age group and implementation unit (e.g. district). The largest number of people requiring PC is retained for each age group in each implementation unit. The total is considered to be a conservative estimate of the number of people requiring PC for at least one PC-NTD. Prevalence surveys determine when an NTD has been eliminated or controlled and PC can be stopped or reduced in frequency, such that the average annual number of people requiring PC is reduced.

2) Number of new cases requiring individual treatment and care for other NTDs: The number of new cases is based on country reports, whenever available, of new and known cases of Buruli ulcer, dengue, dracunculiasis, echinococcosis, human African trypanosomiasis (HAT), leprosy, the leishmaniases, rabies and yaws. Where the number of people requiring and requesting surgery for PC-NTDs (e.g. trichiasis or hydrocele surgery) is reported, it can be added here. Similarly, new cases requiring and requesting rehabilitation (e.g. leprosy or lymphoedema) can be added whenever available.

Populations referred to under 1) and 2) may overlap; the sum would overestimate the total number of people requiring treatment and care. The maximum of 1) or 2) is therefore retained at the lowest common implementation unit and summed to get conservative country, regional and global aggregates. By 2030, improved co-endemicity data and models will validate the trends obtained using this simplified approach.

4.d. Validation

Data is jointly validated by the three levels of the organization – countries, regions and global.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

We do not impute missing values for countries that have never reported data for any NTD. For countries that have reported data in the past, we impute missing values only for those NTDs that have been reported in the past but that have not been reported in the current year.

For reproducibility, we employ multiple imputation techniques using the freely available Amelia package in R. We impute 100 complete datasets using all available cross-sectional data (countries and years), applying a square root transformation to exclude negative values of incidence, as well as categorical variables denoting regions and income groups, and allowing for country-specific linear time effects. We aggregate across diseases and extract the mean and 2.5th and 97.5th centile values to report best estimates and uncertainty intervals for each country.

At regional and global levels

Using the 100 imputed datasets, we aggregate across diseases and regions, extract the mean and 2.5th and 97.5th centile values to report best estimates and uncertainty intervals at the regional and global levels.

4.g. Regional aggregations

Global and regional estimates are simple aggregates of the country values, with no particular weighting. There is no further adjustment for global and regional estimates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

This indicator is based on national-level data reported to WHO by its Member States and disseminated via the WHO Global Health Observatory (https://www.who.int/data/gho/data/themes/neglected-tropical-diseases) and PC Data Portal (http://apps.who.int/gho/cabinet/pc.jsp). Some adjustment is required to aggregate country-reported data on individual neglected tropical diseases across all NTDs included in this indicator. There is an established methodology to standardize this aggregation: https://apps.who.int/iris/bitstream/handle/10665/241869/WER8702.PDF

For NTDs requiring preventive chemotherapy, a joint reporting mechanism and set of reporting forms have been developed to facilitate the process of requesting donated medicines and reporting progress as well as to improve coordination and integration among programmes. More information is available here, https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package

For the other NTDs, the number of new cases should be reported by the health facilities to the national level in order to compile them. If active case search activities are organized (e.g. for integrated skin NTDs, human African trypanosomiasis, etc.), the country must ensure that the number of new cases detected through these activities are also reported, either through the health facilities or directly to the national level. A strong health information system is essential for countries to be able to collect, compile and analyse good quality information on these NTDs.

4.i. Quality management

A framework for monitoring and evaluating progress of the road map for neglected tropical diseases guides activities involving the development of standards, tools and methods for generating, collecting, compiling, analysing, using and disseminating high-quality data on NTDs. At WHO, the department of control of neglected tropical diseases is responsible for curating and generating the statistics on NTDs, which will be checked and validated internally by the Division of Data and Analytics before publication and dissemination.

4.j. Quality assurance

A user guide and video tutorial for the joint reporting mechanism and set of reporting forms are available here: https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package

Details about individual NTD data are available via: https://www.who.int/data/gho/data/themes/neglected-tropical-diseases. For NTDs requiring preventive chemotherapy, reports are signed by the NTD coordinator or a Ministry of Health representative to formally endorse the country’s request for medicines (when applicable) and data. They are submitted to the WHO Representative of the concerned WHO Country office.

4.k. Quality assessment

A data quality review toolkit has been developed by WHO to provide a multi-pronged approach that ensures a comprehensive and holistic review of the quality of health facility data. WHO has also developed a field manual to guide national NTD programmes in using tools to improve data quality and information, through coverage evaluation surveys, data quality assessments and a supervisors’ coverage tool (https://apps.who.int/iris/bitstream/handle/10665/329376/9789241516464-eng.pdf).

5. Data availability and disaggregation

Data availability:

Data are currently being reported by 191 countries, with good coverage of all regions.

Time series:

2010-2021

Disaggregation:

Disaggregation by disease is required; ending the epidemic of NTDs requires a reduction in the number of people requiring interventions for each NTD.

Disaggregation by age is required for PC: preschool-aged children (1-4 years), school-aged (5-14 years) and adults (= 15 years+).

6. Comparability/deviation from international standards

Sources of discrepancies:

Countries do not typically aggregate their data across NTDs, but if they applied the aggregation method as described above, they would obtain the same number. The only exceptions would be countries with one or more missing values for individual NTDs. In these exceptional cases, internationally estimated aggregates will be higher than country produced aggregates that assume missing values are nil. We present best estimates with uncertainty intervals to highlight those missing values that have a significant impact on country aggregates, until such time that missing values are reported.

7. References and Documentation

URL:

https://www.who.int/teams/control-of-neglected-tropical-diseases/overview

References:

Global report on neglected tropical diseases 2023. Geneva: World Health Organization; 2023

(https://www.who.int/publications/i/item/9789240067295, accessed 8 February 2023).

Ending the neglect to attain the Sustainable Development Goals: A road map for neglected tropical diseases 2021–2030. Geneva: World Health Organization; 2021 (https://www.who.int/publications/i/item/9789240010352, accessed 8 February 2023).

A compendium of indicators for monitoring and evaluating progress of the road map for neglected tropical diseases 2021–2030

3.4.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being

0.c. Indicator

Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease

0.e. Metadata update

2021-03-01

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definitions:

Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease.

Probability of dying between the ages of 30 and 70 years from cardiovascular diseases, cancer, diabetes or chronic respiratory diseases, defined as the per cent of 30-year-old-people who would die before their 70th birthday from cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS). This indicator is calculated using life table methods (see further details in section 3.3).

Concepts:

Probability of dying: The likelihood that an individual would die between two ages given current mortality rates at each age, calculated using life table methods. The probability of death between two ages may be called a mortality rate.

Life table: A table showing the mortality experience of a hypothetical group of infants born at the same time and subject throughout their lifetime to a set of age-specific mortality rates.

Cardiovascular disease, cancer, diabetes or chronic respiratory diseases: ICD-10 underlying causes of death I00-I99, COO-C97, E10-E14 and J30-J98.

2.b. Unit of measure

Probability

2.c. Classifications

The four noncommunicable causes of death are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See 2.a)

3.a. Data sources

The preferred data source is death registration systems with complete coverage and medical certification of cause of death. Other possible data sources include household surveys with verbal autopsy, and sample or sentinel registration systems.

3.b. Data collection method

WHO conducts a formal country consultation process before releasing its cause-of-death estimates.

3.c. Data collection calendar

WHO annually requests tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis.

3.d. Data release calendar

End of 2020.

3.e. Data providers

National statistics offices and/or ministries of health.

3.f. Data compilers

WHO

3.g. Institutional mandate

According to Article 64 of its constitution, WHO is mandated to request each Member State to provide statistics on mortality. Furthermore, the WHO Nomenclature Regulations of 1967 affirms the importance of compiling and publishing statistics of mortality and morbidity in comparable form. Member States started to report mortality data to WHO since the early fifties and this reporting activity is continuing until today.

4.a. Rationale

Disease burden from non-communicable diseases (NCDs) among adults is rapidly increasing globally due to ageing and epidemiological transitions. Cardiovascular diseases, cancer, diabetes and chronic respiratory diseases are the four main causes of NCD burden. Measuring the risk of dying from these four major causes is important to assess the extent of burden from premature mortality due NCDs in a population.

4.b. Comment and limitations

Cause of death estimates have large uncertainty ranges for some causes and some regions. Data gaps and limitations in high-mortality regions reinforce the need for caution when interpreting global comparative cause of death assessments, as well as the need for increased investment in population health measurement systems. The use of verbal autopsy methods in sample registration systems, demographic surveillance systems and household surveys provides some information on causes of death in populations without well-functioning death registration systems, but there remain considerable challenges in the validation and interpretation of such data, and in the assessment of uncertainty associated with diagnoses of underlying cause of death.

4.c. Method of computation

The methods used for the analysis of causes of death depend on the type of data available from countries:

For countries with a high-quality vital registration system including information on cause of death, the vital registration that member states submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths.

For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies and surveillance systems. In most cases, these data sources are combined in a modelling framework.

The probability of dying between ages 30 and 70 years from the four main NCDs was estimated using age-specific death rates of the combined four main NCD categories. Using the life table method, the risk of death between the exact ages of 30 and 70, from any of the four causes and in the absence of other causes of death, was calculated using the equation .provided in the document below. The ICD codes used are: Cardiovascular disease: I00-I99, Cancer: C00-C97, Diabetes: E10-E14, and Chronic respiratory disease: J30-J98

Formulas to (1) calculate age-specific mortality rate for each five-year age group between 30 and 70, (2) translate the 5-year death rate into the probability of death in each 5-year age range, and (3) calculate the probability of death from age 30 to age 70, independent of other causes of death, can be found on page 6 of this document:

NCD Global Monitoring Framework: Indicator Definitions and Specifications. Geneva: World Health Organization, 2014 (http://www.who.int/nmh/ncd-tools/indicators/GMF_Indicator_Definitions_FinalNOV2014.pdf?ua=1)

4.d. Validation

The number of deaths were country consulted with country designated focal points (usually at the Ministry of Health or National Statistics Office) as part of the full set of causes of death prior to the release.

4.e. Adjustments

Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For countries with high-quality cause-of-death statistics, interpolation/extrapolation was done for missing country-years; for countries with only low-quality or no data on causes of death, modelling was used. Complete methodology may be found here:

WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf )

At regional and global levels

4.g. Regional aggregations

Aggregation of estimates of deaths by cause, age and sex by country, and aggregation of population by age, sex and country as denominator where needed.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The cause of death categories (including suicides) follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10). Please see Annex Table A of the WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

4.i. Quality management

The World Health Organization (WHO) established a Reference Group on Health Statistics in 2013 to provide advice on population health statistics to WHO with a focus on methodological and data issues related to the measurement of mortality and cause-of-death patterns. The group facilitated interaction between multilateral development institutions and other independent academic groups with WHO expert groups in specific subject areas including methods to the estimation on causes of death

4.j. Quality assurance

The data principles of the World Health Organization (WHO) provide a foundation for continually reaffirming trust in WHO’s information and evidence on public health. The five principles are designed to provide a framework for data governance for WHO. The principles are intended primarily for use by WHO staff across all parts of the Organization in order to help define the values and standards that govern how data that flows into, across and out of WHO is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with WHO.

4.k. Quality assessment

All statements and claims made officially by WHO headquarters about population-level (country, regional, global) estimates of health status (e.g. mortality, incidence, prevalence, burden of disease), are cleared by the Department of Data and Analytics (DNA) through the executive clearance process. This includes the GATHER statement. GATHER promotes best practices in reporting health estimates using a checklist of 18 items that should be reported every time new global health estimates are published, including descriptions of input data and estimation methods. Developed by a working group convened by the World Health Organization, the guidelines aim to define and promote good practice in reporting health estimates.

5. Data availability and disaggregation

Data availability:

Almost 70 countries currently provide WHO with regular high-quality data on mortality by age, sex and causes of death, and another 58 countries submit data of lower quality. However, comprehensive cause-of-death estimates are calculated by WHO systematically for all of its Member States (with a certain population threshold).

Time series:

2000-2019

Disaggregation:

Sex

6. Comparability/deviation from international standards

Sources of discrepancies:

In countries with high quality vital registration systems, point estimates sometimes differ primarily for two reasons: 1) WHO redistributes deaths with ill-defined cause of death; and 2) WHO corrects for incomplete death registration.

7. References and Documentation

URL:

http://www.who.int/gho/en/

References:

WHO indicator definition (http://apps.who.int/gho/indicatorregistry/App_Main/view_indicator.aspx?iid=3354)

WHO methods and data sources for global causes of death, 2000–2019

(https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

World Health Assembly Resolution, WHA66.10 (2014): Follow-up to the Political Declaration of the High-level Meeting of the General Assembly on the Prevention and Control of Non-communicable Diseases. Including Appendix 2: Comprehensive global monitoring framework, including 25 indicators, and a set of nine voluntary global targets for the prevention and control of noncommunicable diseases. (http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R10-en.pdf?ua=1)

WHO Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 (http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf?ua=1)

3.4.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being

0.c. Indicator

Indicator 3.4.2: Suicide mortality rate

0.e. Metadata update

2021-05-01

0.f. Related indicators

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definitions:

The Suicide mortality rate as defined as the number of suicide deaths in a year, divided by the population, and multiplied by 100 000.

Concepts:

2.b. Unit of measure

Rate per 100 000 population

2.c. Classifications

Suicides are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See 3.a)

3.a. Data sources

The preferred data source is death registration systems with complete coverage and medical certification of cause of death, coded using the international classification of diseases (ICD). The ICD-10 codes for suicide are: X60-X84, Y87.0. Other possible data sources include household surveys with verbal autopsy, sample or sentinel registration systems, special studies and surveillance systems.

3.b. Data collection method

WHO conducts a formal country consultation process before releasing its cause-of-death estimates.

3.c. Data collection calendar

WHO annually requests tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis.

3.d. Data release calendar

End of 2020

3.e. Data providers

National statistics offices and/or ministries of health.

3.f. Data compilers

WHO

3.g. Institutional mandate

4.a. Rationale

Mental disorders occur in all regions and cultures of the world. The most prevalent of these disorders are depression and anxiety, which are estimated to affect nearly 1 in 10 people. At its worst, depression can lead to suicide. In 2019, there were over 700,000 estimated suicide deaths worldwide.

4.b. Comment and limitations

The complete recording of suicide deaths in death-registration systems requires good linkages with coronial and police systems, but can be seriously impeded by stigma, social and legal considerations, and delays in determining cause of death. Less than one half of WHO Member States have well-functioning death-registration systems that record causes of death.

4.c. Method of computation

Suicide mortality rate (per 100,000 population) = (Number of suicide deaths in a year x 100,000) / Mid-year population for the same calendar year

The methods used for the analysis of causes of death depend on the type of data available from countries:

4.d. Validation

The number of suicide deaths were country consulted as part of the full set of causes of death prior to the release.

4.e. Adjustments

Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level:

WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf) )

At regional and global levels

4.g. Regional aggregations

Country estimates of number of deaths by cause, along with corresponding population estimates, are summed to obtain regional and global aggregates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Time series:

From 2000 to 2019

Disaggregation:

Sex, age group

6. Comparability/deviation from international standards

Sources of discrepancies:

In countries with high quality vital registration systems, point estimates sometimes differ primarily for two reasons: 1) WHO redistributes deaths with ill-defined cause of death (i.e. injuries of unknown intent, ICD codes Y10-Y34 and Y872) to suicide; and 2) WHO corrects for incomplete death registration.

7. References and Documentation

URL:

http://www.who.int/gho/en/

References:

WHO indicator definition (http://apps.who.int/gho/indicatorregistry/App_Main/view_indicator.aspx?iid=4664)

WHO methods and data sources for global causes of death, 2000–2019

(https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

World Health Assembly Resolution WHA66.8 (2013): Comprehensive mental health action plan 2013–2020, including Appendix 1: Indicators for Measuring Progress Towards Defined Targets of the Comprehensive Mental Health Action Plan 2013-2020 (http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R8-en.pdf?ua=1)

3.5.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol

0.c. Indicator

Indicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders

0.e. Metadata update

2019-09-20

0.f. Related indicators

Indicator 3.5.2: Harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Office on Drugs and Crime (UNODC)

1.a. Organisation

World Health Organization (WHO)

United Nations Office on Drugs and Crime (UNODC)

2.a. Definition and concepts

Definitions:

The coverage of treatment interventions for substance use disorders is defined as the number of people who received treatment in a year divided by the total number of people with substance use disorders in the same year. This indicator is disaggregated by two broad groups of psychoactive substances: (1) drugs, (2) alcohol and other psychoactive substances.

Whenever possible, this indicator is additionally disaggregated by type of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services). The proposed indicator will be accompanied, with contextual information on availability coverage, i.e. treatment capacity for substance use disorders generated at national level to provide additional information for interpretation of the contact coverage data.

Concepts:

The central concept of “substance abuse” in the SDG health target 3.5 implies the use of psychoactive substances that, when taken in or administered into one's system, affect mental processes, e.g. perception, consciousness, cognition or affect. The concept of “substance use disorders” includes both “drugs use disorders” and “alcohol use disorders” according to the International Classification of Diseases (ICD-10 and ICD-11).

The term “drugs” refers to controlled psychoactive substances as scheduled by the three Drug Control Conventions (1961, 1971 and 1988), substances controlled under national legislation and new psychoactive substances (NPS) that are not controlled under the Conventions, but may pose a public health threat. “Alcohol” refers to ethanol - a psychoactive substance with dependence producing properties that is consumed in ethanol-based or alcoholic beverages.

People with substance use disorders are those with harmful substance use and/or affected by substance dependence. Harmful substance use is defined in the ICD-11 as a pattern of use of substances that has caused damage to a person’s physical or mental health or has resulted in behaviour leading to harm to the health of others. According to ICD-11, dependence arises from repeated or continuous use of psychoactive substances. The characteristic feature is a strong internal drive to use psychoactive substance, which is manifested by impaired ability to control use, increasing priority given to use over other activities and persistence of use despite harm or negative consequences.

Treatment of substance use disorder -any structured intervention that is aimed specifically to a) reduce substance use and cravings for substance use; b) improve health, well-being and social functioning of the affected individual, and c) prevent future harms by decreasing the risk of complications and relapse. These may include pharmacological treatment, psychosocial interventions and rehabilitation and aftercare. All evidence-based used for treatment of substance use disorders are well defined in WHO and UNODC related documents.

Pharmacological treatment refers to interventions that include detoxification, opioid agonist maintenance therapy (OAMT) and antagonist maintenance (WHO, UNODC International Standards for the treatment of drug use disorders, 2016).

Psychosocial interventions refer to programs that address motivational, behavioral, psychological, social, and environmental factors related to substance use and have been shown to reduce drug use, promote abstinence and prevent relapse. For different drug use disorders, the evidence from clinical trials supports the effectiveness of treatment planning, screening, counselling, peer support groups, cognitive behavioral therapy (CBT), motivational interviewing (MI), community reinforcement approach (CRA), motivational enhancement therapy (MET), family therapy (FT) modalities, contingency management (CM), counselling, insight-oriented treatments, housing and employment support among others. (UNODC WHO International Standards for the Treatment of Drug Use Disorders, 2016).

Rehabilitation and aftercare (Recovery Management and Social Support) refers to interventions that are based on scientific evidence and focused on the process of rehabilitation, recovery and social reintegration dedicated to treat drug use disorders.

3.a. Data sources

The sources include:

Household surveys
Surveys among people using substances – using for instance respondent driven sampling
Indirect methods such as capture/recapture or multiplier benchmark method

Surveys should be nationally representative, with a sample size sufficiently large to capture relevant events and compute needed disaggregation, and they should be based on a solid sample design. The use of indirect questions for network scale-up methods in household surveys is encouraged.

Treatment registries are the main source of data for the number of people receiving treatment. They should cover the entire national territory and be linked to all relevant agencies providing treatment services.

To estimate the number of people with alcohol use disorders, preferred data sources are population-based surveys targeting the adult population (15+ years). International surveys such as WHS, STEPS, GENACIS, and ECAS represent good practices.

3.b. Data collection method

WHO and UNODC will use existing data collections to gather available statistics from member states:

UNODC Annual Report Questionnaire ;
WHO Global Survey on Progress on SDG Health Target 3.5;

Drugs:

Data on people with drug use disorders and the number of people in treatment are collected through a standardised questionnaire sent to countries, the Annual Report Questionnaire (ARQ). This questionnaire provides specific definitions of data to be collected and it collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.). At the national level, countries are required to have standardized treatment reporting system.
A revised ARQ will be used from 2021 onwards. Data on drug use disorders and treatment, with the relevant disaggregations will continue to be collected through this tool.
Countries will be requested to nominate national focal points to ensure technical supervision at country level
Automated and substantive validation procedures are in place to assess data consistency and compliance with standards
When data from national official sources are missing or not complying with methodological standards, data from other sources are also considered and processed by using the same quality assurance procedures.

Alcohol and other substances:

In the periodical WHO Global Surveys on Alcohol and Health, alcohol focal points officially nominated by the Ministry of Health provide data or links or contacts through which the data can be accessed.
These focal points provide national government statistics.
In addition, data are accessed from country-specific industry data sources in the public domain and other databases as well as systematic literature reviews.
WHO global surveillance activities generate population-based country data used for estimation of the number of people with substance use disorders in populations (such as World Mental Health Survey and STEPS surveys)
Data on service utilization and contextual information are being collected by WHO Global Survey on SDG 3.5 that has been previously piloted and through specific activities such as service mapping surveys implemented in collaboration with UNOD
The collected, collated and analysed data is included in the process of country consultations.

After the validation process, the data will be sent to national focal points for their review before publication.

3.c. Data collection calendar

Countries are encouraged to conduct general population surveys on substance use regularly, but at least every four-five years. Also, countries are encouraged to use less costly alternatives to estimate the number of people with substance use disorders and service utilization, taking advantage of the availability of administrative data through the use of indirect estimation methods. Collection of data from countries is planned on annual or biannual basis.

3.d. Data release calendar

Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually. Data are then reported to UNSD through the regular reporting channels annually.

3.e. Data providers

Drug use disorders data are collected through national focal points. Data providers vary by country and they can be institutions such as Drug Control Agencies, National Drug Observatories, Ministries of Health and/or National Statistical Offices.

3.f. Data compilers

Data will be compiled by the co-custodians for this indicator (UNODC and WHO).

4.a. Rationale

According to UNODC and WHO data, around 271 million people aged 15 to 64 years worldwide used an illicit drug at least once in 2017, about 2.3 billion people are current drinkers of alcohol, some 35 million of people suffer from drug use disorders and 289 million from alcohol use disorders.

Substance use disorders are serious health conditions that present a significant burden for affected individuals, their families and communities. Untreated substance use disorders trigger substantial costs to society including lost productivity, increased health care expenditure, and costs related to criminal justice, social welfare, and other social consequences. Strengthening treatment services entails providing access to a comprehensive set of evidence-based interventions (-laid down in the international standards and guidelines) that should be available to all population groups in need. The indicator will inform the extent to which a range of evidence-based interventions for treatment of substance use disorder are available and are accessed by the population in need at country, regional and global level.

Even though effective treatment exists, only a small amount of people with substance use disorders receive it. For instance, it is estimated that globally one out of 7 people with drug use disorders have access to or provided drug treatment services (World Drug Report 2019). WHO ATLAS-Substance Use data showed that in 2014 only 11.9 % (out of 103 responding) countries reported high coverage (40% or more) for alcohol dependence. SDG indicator 3.5.1 is crucial for measurement the progress towards strengthening the treatment of substance abuse worldwide as formulated in the Target 3.5.

4.b. Comment and limitations

The two main challenges in terms of computing the SDG 3.5.1 indicator are the limited availability of household surveys on substance use and the under-reporting of use among survey respondents.

Data reported from household surveys are one of the sources of information on of the number of people with substance use disorders. There are issues of under-reporting for certain psychoactive substances, in countries where stigma is associated to substance use and when a considerable proportion of the drug or alcohol using population is institutionalized, homeless or unreachable by population-based surveys. Additionally, being a relatively rare event, household surveys on substance use disorders require a large sample and can be costly. In order to address these issues, additional approaches (e.g. scale up methods) are increasingly used in household surveys to address undercount issues. These can be used in conjunction with special studies and/or additional information, in order to obtain reasonable estimates via indirect methods, such as benchmark/multiplier or capture-recapture methods.

An additional step in data validation and country capacity building for monitoring treatment coverage for substance use disorders will be implemented during the next couple of years for in-depth data generation in a sample of countries from different regions and representing different levels of health system development. A rapid assessment tool for in-depth data generation is in the process of development by WHO.

The indicator stresses on type, availability and coverage of services but does not necessarily provide information on the actual quality of the interventions/services provided. To address this, at national level, the proposed treatment indicator will be accompanied with contextual information on availability coverage, i.e. treatment capacity for substance use disorders to provide additional information for interpretation of the contact coverage data.

4.c. Method of computation

The indicator will be computed by dividing the number of people receiving treatment services at least once in a year by the total number of people with substance use disorders in the same year:

${C o v e r a g e}_{S U D} = \frac{ n u m b e r o f p e o p l e i n t r e a t m e n t f o r S U D }{n u m b e r o f p e o p l e w i t h S U D} X 100$

Where: SUD – Substance use disorders

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For drug use disorder, data will be provided for countries where information is available for both numerator and denominator. No data estimates will be done at the national level.

For alcohol, when information on service utilization is missing in a country, several approaches will be used to produce estimates based on all available pieces of contextual service capacity data in the country and regionally. Link to be established between service availability and service utilization to get rough understanding on number of people who might be using services for countries where no direct information on number of people using services is available at all.

At regional and global level

Sub-regional and regional aggregates are produced when enough data at the country level are available (a minimum number of countries and a minimum percentage of population coverage). When data are available, sub-regional estimates are created first and then aggregated at regional level. The global level is computed as aggregation of regional estimates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

UNODC has published a series of methodological guidelines on several issues related to the drug problem, entitled “Global Assessment Program (GAP)”. These guidelines consist of 8 modules, covering different aspects of monitoring the drug situation including setting up drug information systems, estimating drug prevalence using indirect methods, setting up treatment monitoring and reporting systems, etc. The modules can be found at: https://www.unodc.org/unodc/en/GAP/. It is planned to update these guidelines in the near future.

As part of the ARQ review process, UNODC is planning to enhance its capacity building tools by complementing regional and national capacity building activities with:

E-learning training modules with incorporated training curricula
Creating methodological guidelines and tools on drug-related issues, including drug use disorders and treatment
promoting national coordination mechanisms on drugs data, including national drug observatories

WHO has published series of documents on alcohol monitoring in populations (e.g. International Guide for Monitoring Alcohol Consumption and Related Harm), and established a Global Information System on Alcohol and Health (GISAH) that provides easy and rapid access to a wide range of alcohol-related health indicators. It is an essential tool for assessing and monitoring the health situation and trends related to alcohol consumption, alcohol-related harm, and policy responses in countries. GISAH is a further development of the Global Alcohol Database which has been built since 1997 by the WHO Department of Mental Health and Substance Abuse. The main purpose of GISAH is to serve WHO Member States and governmental and nongovernmental organizations by making alcohol-related health data available. These data can help to analyse the state of the health situation related to alcohol in a country, a WHO region or sub-region, or the world. The Indicator Code Book has been prepared to assist countries in collecting the data.

4.j. Quality assurance

At UNODC, quality assurance measures are in place to collect, process and disseminate statistical data. They build on the ‘Principles governing international statistical activities’ and regulate the collection, processing, publication and dissemination of data.

All data for SDG indicators as compiled by the Office are sent to countries (through the relevant national focal points) for their review before statistical data are officially released by UNODC. When countries provide feedback/comments on the data, a technical discussion is conducted to identify a common position.

At WHO quality assurance measures are in place for producing the health statistics that include the main indicators on alcohol consumption and its health consequences. WHO Technical Advisory Group on Alcohol and Drug Epidemiology provides technical advice and input to WHO activities on monitoring alcohol consumption and treatment capacity for substance use disorders in its Member States.

Data compilation is to be performed centrally by WHO and UNODC based on data collected from countries that later will be validated through official focal points.

5. Data availability and disaggregation

Data availability:

During the reporting period 2013-2017, 62 countries have provided data on drug use disorders and 98 countries provided data on drug treatment. The availability and accuracy of data on the number of people with drug use disorders and people in treatment for the use of drugs is gradually increasing.

For the number of alcohol use disorders data are currently available for 188 Member States (for 2016) and validated through the process of country consultation. Data are regularly updated and presented through WHO Global Health Observatory. For utilization of treatment by people with alcohol use disorders, data are currently available for at least 30 countries and further data collection is ongoing

For contextual information on treatment services, WHO has collected data from more than 85 countries; data collection for other is ongoing and to be accomplished till the end of 2019.

Time series:

During 2013-2017, 34 countries have provided at least two datapoints for both numerator and denominator necessary for the calculation of the SDG indicator on drug use disorders. With the improved ARQ, it is expected that the number of responses and quality of data reported will increase after 2021. For the alcohol, data on denominator are available for a long period since establishment of GISAH in 1997 and the indicator has been tentatively calculated for at least 30 countries in 2019, with contextual information available for 85.

Disaggregation:

Given the policy importance, the indicator will be disaggregated to provide data for drugs and alcohol. Depending on data availability, it will be additionally disaggregated by following:

by treatment interventions (pharmacological, psychosocial, rehabilitation and aftercare)
by sex
by age groups

In relation to drug use disorders, the following types of drugs should be considered:

cannabis (including herb and resin)
opioids (opium, heroin, medicinal products containing opioids and other opioids)),
cocaine type,
amphetamines (amphetamine, methamphetamine, medicinal products containing ATS),
ecstasy-type substances,
sedatives and tranquilizers,
hallucinogens
solvents and inhalants
NPS

6. Comparability/deviation from international standards

Sources of discrepancies:

Given the heterogeneity of national data collection systems, there is potential for discrepancies related either to the differences in recording the number of people in treatment and for people with substance use disorders. For this purpose, the ARQ has recently been improved to allow for countries to specify the nature of the data reported and to enable UNODC to assess the accuracy and comparability of data.

Apart from evaluating the consistency of data and addressing data discrepancies by using additional sources, UNODC is in continuous communication and discusses technical issues with reporting countries in order to minimize discrepancies and inconsistency of data.

7. References and Documentation

URLs:

https://www.who.int/gho/substance_abuse/en/

https://wdr.unodc.org/wdr2019/

https://www.who.int/healthinfo/global_burden_disease/about/en/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608813/

https://icd.who.int/browse10/2016/en

https://www.unodc.org/unodc/en/commissions/CND/conventions.html

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2395571/

https://www.who.int/healthinfo/systems/monitoring/en/

https://www.unodc.org/unodc/en/GAP/

https://www.unodc.org/documents/pakistan/Survey_Report_Final_2013.pdf

https://www.unodc.org/documents/data-and-analysis/statistics/Drugs/Drug_Use_Survey_Nigeria_2019_BOOK.pdf

https://www.who.int/substance_abuse/activities/gisah/en/

https://www.who.int/substance_abuse/activities/gisah_indicatorbook.pdf?ua=1

3.5.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol

0.c. Indicator

Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Goal 8; Targets 3.4, 3.6

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definitions:

Harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol.

Total alcohol per capita (15+ years) consumption (APC) is defined as the total (sum of three-year average recorded APC and unrecorded APC adjusted for tourist consumption) amount of pure alcohol consumed per adult (15+ years), in a calendar year, in litres of pure alcohol. Recorded alcohol consumption refers to official statistics at country level (production, import, export, and sales or taxation data), while the unrecorded alcohol consumption refers to alcohol which is not taxed and is outside the usual system of governmental control, such as home or informally produced alcohol (legal or illegal), smuggled alcohol, surrogate alcohol (which is alcohol not intended for human consumption), or alcohol obtained through cross-border shopping (which is recorded in a different jurisdiction). Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries. Positive figures denote alcohol consumption of outbound tourists being greater than alcohol consumption by inbound tourists, negative numbers the opposite. Tourist consumption is based on UN statistics, and data are provided by the Institute for Health Metrics and Evaluation.

Concepts:

Recorded alcohol per capita (15+) consumption of pure alcohol is calculated as the sum of beverage-specific alcohol consumption of pure alcohol (beer, wine, spirits, other) from different sources. The first priority in the decision tree is given to government national statistics; second are country-specific alcohol industry statistics in the public domain based on interviews or fieldwork (GlobalData (formerly Canadean), International Wine and Spirit Research (IWSR), Wine Institute; historically World Drink Trends) or data from the International Organisation of Vine and Wine (OIV); third is the Food and Agriculture Organization of the United Nations' statistical database (FAOSTAT), and fourth is data from alcohol industry statistics in the public domain based on desk review.

For countries where the data source is FAOSTAT, the unrecorded consumption may be included in the recorded consumption. As for the beverage-specific categories, beer includes malt beers, wine includes wine made from grapes and vermouth, spirits include all distilled beverages, and other includes one or several other alcoholic beverages, such as fermented beverages made from sorghum, maize, millet, rice, or cider, fruit wine, fortified wine, etc. For unrecorded APC, the first priority in the decision tree is given to nationally representative empirical data; these are often general population surveys in countries where alcohol is legal. Second are specific empirical investigations, and third is expert opinion supported by periodic survey of experts at country level using modified Delphi-technique.

For recorded APC, if beverage volumes are not available in litres of pure alcohol, they are transformed into litres of pure alcohol. The alcohol content (% alcohol by volume) is considered to be as follows: beer (barley beer 5%), wine (grape wine 12%; must of grape 9%, vermouth 16%), spirits (distilled spirits 40%; spirit-like 30%), and other (sorghum, millet, maize beers 5%; cider 5%; fortified wine 17% and 18%; fermented wheat and fermented rice 9%; other fermented beverages 9%).

Unrecorded APC is estimated using a regression analysis. Fractional response random intercepts regression models, which account for clustering of data points within countries, are used to estimate what percentage of total APC is due to unrecorded APC. Univariate models are fitted for alcohol consumption statistics and other predictors.

The litres of alcohol consumed by tourists (15 years of age and older) in a country are based on the number of tourists who visited a country, the average amount of time they spent in the country, and how much these people drink on average in their countries of origin (estimated based on per capita consumption of recorded and unrecorded alcohol). Furthermore, tourist alcohol consumption also accounts for the inhabitants of a country consuming alcohol while visiting other countries (based on the average time spent outside of their country (for all people 15 years and older) and the amount of alcohol consumed in their country of origin). These estimations assume the following: (1) that people drink the same amounts of alcohol when they are tourists as they do in their home countries, and (2) that global tourist consumption is equal to 0 (and thus tourist consumption can be either net negative or positive).

2.b. Unit of measure

Litres of pure alcohol

2.c. Classifications

Not applicable

3. Data source type and collection method

3.a. Data sources (SOURCE_TYPE)

Recorded: Government statistics or, alternatively, alcohol industry statistics in the public domain, FAOSTAT.

Unrecorded: Nationally representative empirical data or, alternatively, specific empirical investigations, expert opinion.

Tourist: UN tourist statistics

3.b. Data collection method

The Global Survey on Alcohol and Health is conducted periodically in collaboration with all six WHO regional offices. National counterparts or focal points in all WHO Member States are officially nominated by the respective ministries of health. They are provided with the online survey data collection tool for completion. Where online completion is not feasible, a hard copy of the tool is forwarded to those who requested it. The survey submissions are checked and whenever information is incomplete or in need of clarification, the questionnaire is returned to the focal point or national counterpart in the country concerned for revision. Amendments to the survey responses are resubmitted by e-mail or electronically. Data submitted from countries is triangulated with data from key industry-supported data providers at annual meetings organized by WHO with an objective to identify discrepancies and solutions. Estimates for key indicators, such as APC, are compiled into country profiles which are sent to the focal point or national counterpart in the country for validation and endorsement.

3.c. Data collection calendar

Ongoing updates from data sources on the web. The next WHO global surveys on alcohol and health involving data collection from WHO Member States are in 2022 and 2025.

3.d. Data release calendar

Annually

3.e. Data providers

Ministries of Health; National statistical bureau/agencies (data on alcohol production and trade/sales); National monitoring centres on alcohol and drug use; National academic and monitoring centres concerned with population-based surveys of risk factors to health.

3.f. Data compilers

World Health Organization (WHO)

3.g. Institutional mandate

Monitoring public health risks and generate, collate, compile and disseminate reliable information on the health impact of alcohol, drugs and addictive behaviours as well as health policy and health system responses.

4.a. Rationale

Alcohol consumption can have an impact not only on the incidence of diseases, injuries and other health conditions, but also on the course of disorders and their outcomes in individuals. Alcohol consumption has been identified as a component cause for more than 200 diseases, injuries and other health conditions. Per capita alcohol consumption is widely accepted as the best possible indicator of alcohol exposure in populations and the key indicator for estimation of alcohol-attributable disease burden and alcohol-attributable deaths. Its correct interpretation requires the use of additional population-based indicators such as prevalence of drinking, and, as a result, stimulates development of national monitoring systems on alcohol and health involving contributions from a wide range of stakeholders, including alcohol production and trade sectors.

4.b. Comment and limitations

The indicator is feasible and suitable for monitoring purposes as evidenced by availability of data from 190 countries and inclusion of this indicator in global, regional and national monitoring frameworks. This is the key indicator for alcohol exposure in populations. The data available (based on production, import, export, and sales or taxation) do not enable the disaggregation of alcohol per capita consumption (APC) by sex or age; to this end, other data sources, such as survey data, are needed. The estimation of unrecorded APC remains a challenge, and triangulation of data from different sources as well as Delphi-techniques are used for increasing validity of estimates. In recent time, the number of research activities focused on improvement of the estimates of unrecorded alcohol consumption as well as their geographical coverage have increased substantially. As a result, it leads to a more accurate assessment of the total amount of alcohol consumed per person per year in a given country.

4.c. Method of computation

Numerator: The sum of the amount of recorded alcohol consumed per capita (15+ years), average during three calendar years, in litres of pure alcohol, and the amount of three-year average unrecorded alcohol per capita consumption (15+ years), during a calendar year, in litres of pure alcohol, adjusted for tourist consumption.

Denominator: Midyear resident population (15+ years) for the same calendar year, UN World Population Prospects, medium variant.

4.d. Validation

Estimates are sent to focal points or national counterparts in the country through WHO Regional Offices for validation and endorsement.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

The values of missing countries (e.g. Monaco, San Marino) are that small that they would not affect global or regional figures.

At regional and global levels

The values of missing countries (e.g. Monaco, San Marino) are that small that they would not affect global or regional figures.

4.g. Regional aggregations

Regional and global aggregates are population weighted averages from country values (weighted by population of inhabitants 15+ years of the respective countries).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Global Status Report on Alcohol and Health 2018 (https://www.who.int/publications/i/item/9789241565639).

4.i. Quality management

Steering Committee of Global Information System on Alcohol and Health; Technical Advisory Group on Alcohol and Drug Epidemiology.

4.j. Quality assurance

Statistics clearance by Data, Analytics and Delivery for Impact Unit.

4.k. Quality assessment

Data, Analytics and Delivery for Impact Unit.

5. Data availability and disaggregation

Data availability:

Global, by WHO and SDG regions, by World Bank income groups, by country. The data are available for 190 WHO Member States.

Time series:

Recorded alcohol per capita consumption since 1960s, and total alcohol per capita consumption since 2000.

Disaggregation:

Sex, age.

6. Comparability/deviation from international standards

Sources of discrepancies:

Population estimates, alcohol content by volume across different alcoholic beverage categories, age distributions, requirements for survey data used in producing the estimates, estimates of unrecorded alcohol consumption.

7. References and Documentation

URL:

https://apps.who.int/gho/data/node.gisah.GISAH?showonly=GISAH

References:

https://apps.who.int/gho/data/node.gisah.GISAH?showonly=GISAH

https://www.who.int/data/gho/data/themes/global-information-system-on-alcohol-and-health

http://www.who.int/substance_abuse/publications/global_alcohol_report/en/

3.6.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidents

0.c. Indicator

Indicator 3.6.1: Death rate due to road traffic injuries

0.e. Metadata update

2021-03-01

0.f. Related indicators

3.5, 11.2

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Death rate due to road traffic injuries as defined as the number of road traffic fatal injury deaths per 100,000 population.

Concepts:

Numerator: Number of deaths due to road traffic crashes

Absolute figure indicating the number of people who die as a result of a road traffic crash.

Denominator: Population (number of people by country)

2.b. Unit of measure

Rate per 100 000 population

2.c. Classifications

Road injuries are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See Annex A of the WHO methods and data sources for global causes of death, 2000–2019)

3.a. Data sources

For the road traffic deaths we have two sources of data. Data from Global Status Report on Road Safety survey and Vital registration or certificate deaths data that WHO receive every year from member states (ministries of health).

For the population, we used data from the United Nations / Department of Economic and Social Affairs/ Population division.

3.b. Data collection method

The methodology involved collecting data from a number of different sectors and stakeholders in each country is as follows. National Data Coordinators (NDCs), who were nominated by their governments, were trained in the project methodology. As representatives of their ministries, they were required to identify up to eight other road safety experts within their country from different sectors (e.g. health, police, transport, nongovernmental organizations and/or academia) and to facilitate a consensus meeting of these respondents. While each expert responded to the questionnaire based on their expertise, the consensus meeting facilitated by NDCs allowed for discussion of all responses, and the group used this discussion to agree on one final set of information that best represented their country’s situation at the time (up to 2014, using the most recent data available). This was then submitted to the World Health Organization (WHO). More details are in the Global Status Report on Road Safety 2018 and the WHO methods and data sources for global causes of death, 2000–2019.

3.c. Data collection calendar

WHO annually requests tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis.

3.d. Data release calendar

End of 2020

3.e. Data providers

The road traffic deaths data were provided nationally by mainly three ministries, namely, ministry of health, ministry of interior and ministry of transport.

3.f. Data compilers

WHO is the organization responsible for compilation and reporting on this indicator at the global level.

3.g. Institutional mandate

4.a. Rationale

Road traffic injuries remain an important public health problem, particularly for low-income and middle-income countries.

4.b. Comment and limitations

There are no vital registration data for all countries to make comparison against the data received on the survey. Also we cannot collect road traffic data every year using this methodology outlined in the Global status report.

4.c. Method of computation

The methods used for the analysis of causes of death depend on the type of data available from countries:

4.d. Validation

The number of deaths due to road injury were country consulted with country designated focal points (usually at the Ministry of Health or National Statistics Office) as part of the full set of causes of death prior to the release.

4.e. Adjustments

Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf )

At regional and global levels

4.g. Regional aggregations

Country estimates of number of deaths by cause are summed to obtain regional and global aggregates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The cause of death categories (including road injury) follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10). Please see Annex Table A of the WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Time series:

From 2000 to 2019

Disaggregation:

Sex, age group

6. Comparability/deviation from international standards

Sources of discrepancies:

WHO's estimation of road traffic rates are, in many countries, different to the official estimates for the reasons described above that relate to our methodology.

There are also differences in the data used for population between the national data and the estimates produced by the United Nations department of population.

7. References and Documentation

URL:

http://www.who.int/violence_injury_prevention

References:

Global status report on road safety 2018 (https://www.who.int/publications/i/item/9789241565684)

WHO methods and data sources for global causes of death, 2000–2019

(https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

3.7.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes

0.c. Indicator

Indicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods

0.d. Series

Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years)

0.e. Metadata update

2023-03-31

0.f. Related indicators

This indicator is linked to Target 3.8 (Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all) because the provision of family planning information and methods to all individuals who want to prevent pregnancy is an important component of achieving universal health coverage.

This indicator is also linked to Target 5.6 (Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences) because meeting the demand for family planning is facilitated by increasing access to sexual and reproductive health-care services, and also improves sexual and reproductive health and the ability to exercise reproductive rights.

0.g. International organisations(s) responsible for global monitoring

Population Division, Department of Economic and Social Affairs (DESA)

United Nations Population Fund (UNFPA)

1.a. Organisation

Population Division, Department of Economic and Social Affairs (DESA)

United Nations Population Fund (UNFPA)

2.a. Definition and concepts

Definition:

The percentage of women of reproductive age (15-49 years) currently using a modern method of contraception among those who desire either to have no (additional) children or to postpone the next pregnancy. The indicator is also referred to as the demand for family planning satisfied with modern methods.

Concepts:

The percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods is also referred to as the proportion of demand satisfied by modern methods. The components of the indicator are contraceptive prevalence (any method and modern methods) and unmet need for family planning.

Contraceptive prevalence is the percentage of women who are currently using, or whose partner is currently using, at least one method of contraception, regardless of the method used.

For analytical purposes, contraceptive methods are often classified as either modern or traditional. Modern methods of contraception include female and male sterilization, the intra-uterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap and spermicidal foam, jelly, cream and sponge), lactational amenorrhea method (LAM), emergency contraception and other modern methods not reported separately (e.g., the contraceptive patch or vaginal ring). Traditional methods of contraception include rhythm (e.g., fertility awareness-based methods, periodic abstinence), withdrawal and other traditional methods not reported separately.

Unmet need for family planning is defined as the percentage of women of reproductive age who want to stop or delay childbearing but are not using any method of contraception. The standard definition of unmet need for family planning includes women who are fecund and sexually active in the numerator, and who report not wanting any (more) children, or who report wanting to delay the birth of their next child for at least two years or are undecided about the timing of the next birth, but who are not using any method of contraception. The numerator also includes pregnant women whose pregnancies were unwanted or mistimed at the time of conception; and postpartum amenorrheic women who are not using family planning and whose last birth was unwanted or mistimed. Further information on the operational definition of the unmet need for family planning, as well as survey questions and statistical programs needed to derive the indicator, can be found at the following website of the USAID Demographic and Health Surveys Program: http://measuredhs.com/Topics/Unmet-Need.cfm.

2.b. Unit of measure

Percent (%)

2.c. Classifications

The classification of contraceptive methods is presented in World Health Organization Department of Reproductive Health and Research (WHO/RHR) and Johns Hopkins Bloomberg School of Public Health/Center for Communication Programs (CCP) (2018).

3.a. Data sources

This indicator is calculated from nationally-representative household survey data. Multi-country survey programmes that include relevant data for this indicator are: Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programmes and national surveys.

For information on the source of each estimate, see United Nations, Department of Economic and Social Affairs, Population Division (2022). World Contraceptive Use 2022. (https://www.un.org/development/desa/pd/data/world-contraceptive-use)

3.b. Data collection method

Data are compiled based on systematic searches of websites of international survey programmes, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms and ad hoc queries in addition to utilization of the country-specific information from UNFPA country offices.

3.c. Data collection calendar

Data are compiled in the period from October to April.

3.d. Data release calendar

Updated data compilations on the indicator are released by the Population Division biennially in July as a comprehensive compilation of data and model-based annual estimates and projections up to 2030 at the national, regional and global level. See:

United Nations, Department of Economic and Social Affairs, Population Division (2022). World Contraceptive Use 2022. New York: United Nations. (https://www.un.org/development/desa/pd/data/world-contraceptive-use)

United Nations, Department of Economic and Social Affairs, Population Division (2022). Estimates and Projections of Family Planning Indicators 2022. New York: United Nations. (https://www.un.org/development/desa/pd/data/family-planning-indicators)

The data are also available in the interactive data portal of the Population Division (https://population.un.org/dataportal/home)

3.e. Data providers

3.f. Data compilers

This indicator is produced at the global level by the Population Division, Department of Economic and Social Affairs, United Nations in collaboration with the United Nations Population Fund (UNFPA).

3.g. Institutional mandate

The Population Division of the Department of Economic and Social Affairs conducts demographic research in the area of population and development and assists countries in developing their capacity to produce and analyse population data and information. The Population Division compiles global datasets of family planning indicators and provides analysis of levels and trends in contraceptive use and the need for family planning. The Population Division monitors progress in ensuring universal access to sexual and reproductive health-care services, as called for in the 2030 Agenda for Sustainable Development, and is the custodian agency for Sustainable Development Goal (SDG) indicator 3.7.1.

4.a. Rationale

The proportion of demand for family planning satisfied with modern methods is useful in assessing overall levels of coverage for family planning programmes and services. Access to and use of an effective means to prevent pregnancy helps enable women and their partners to exercise their rights to decide freely and responsibly the number and spacing of their children and to have the information, education and means to do so. Meeting demand for family planning with modern methods also contributes to maternal and child health by preventing unintended pregnancies and closely spaced pregnancies, which are at higher risk for poor obstetrical outcomes.

Levels of demand for family planning satisfied with modern methods of 75 percent or more are generally considered high, and values of 50 percent or less are generally considered as very low. The indicator has no global numerical ‘target’ value set to be achieved by 2030. Looking at the highest values of the indicator, in 22 countries representing regions such as Europe and Northern America, Latin America and the Caribbean and Eastern and South-Eastern Asia, more than 85 percent of women who want to avoid pregnancy are using a modern contraceptive method but for no country is this estimate above 91 percent.

Even in these countries, specific sub-populations (for example, adolescents or the poor) can still face barriers of access to family planning information and services. It should also be recognized that reaching 100 percent may not be a necessary or even desirable outcome with respect to reproductive rights. Some women may prefer to use a traditional method, even while having access to a full range of modern methods and being aware of the typical differences in effectiveness of methods in preventing pregnancies. Other women might have ambivalent preferences regarding their next pregnancy which may influence their contraceptive choice.

4.b. Comment and limitations

Differences in the survey design and implementation, as well as differences in the way survey questionnaires are formulated and administered can affect the comparability of the data. The most common differences relate to the range of contraceptive methods included and the characteristics (age, sex, marital or union status) of the persons for whom contraceptive prevalence is estimated (base population). The time frame used to assess contraceptive prevalence can also vary. In most surveys there is no definition of what is meant by “currently using” a method of contraception.

In some surveys, the lack of probing questions, asked to ensure that the respondent understands the meaning of the different contraceptive methods, can result in an underestimation of contraceptive prevalence, in particular for traditional methods. Sampling variability can also be an issue, especially when contraceptive prevalence is measured for a specific subgroup (by age-group, level of educational attainment, place of residence, etc.) or when analysing trends over time.

When data on women aged 15 to 49 are not available, information for married or in-union women is reported. Illustrations of base populations that are sometimes presented are: married or in-union women aged 15-44, sexually active women (irrespective of marital status), or ever-married women. Notes in the data set indicate any differences between the data presented and the standard definitions of contraceptive prevalence or unmet need for family planning or where data pertain to populations that are not representative of women of reproductive age.

4.c. Method of computation

The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CPAny) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NSMod).

${N S}_{M o d} = \frac{{C P}_{M o d}^{}}{U M N {+ C P}_{A n y}^{}} \times 100$

4.d. Validation

For surveys with microdata sets, the indicators are calculated following the definitions and concepts described above. These results are compared with the indicators published in survey reports, SDG national reporting platforms, or obtained from ad hoc queries. In some cases of discrepancies, the results are consulted with the national institutions that conducted the survey.

For model-based estimates and projections, out-of-sample validation methods are described in Kantorová et al (2020).

4.e. Adjustments

Generally, there is no discrepancy between data presented and data published in survey reports. However, some published national data have been adjusted by the Population Division to improve comparability. Notes are used in the data set to indicate when adjustments were made and where data differed from standard definitions. Surveys might differ in the classification of modern and traditional methods. To improve comparability of data over time and across countries, method classifications used in some survey are adjusted to follow the classification described above.

The global indicator represents all women of reproductive age. Some survey estimates represent women who are married or in a union and this is indicated in a note.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

There is no attempt to provide estimates for individual countries or areas when country or area data are not available.

For analytical and comparative purposes, country-level model-based estimates and projections are generated using a Bayesian hierarchical model (see references below).

At regional and global levels

In order to generate regional and global estimates for any given reference year, the Population Division/DESA uses a Bayesian hierarchical model, described in detail in:

Alkema et al (2013) and Kantorová et al (2020).

Country-level, model-based estimates are only used for computing regional and global averages and are not used for global SDG reporting of trends at the country level. However, the model-based estimates are recommended to be used for analytical and comparative purposes. Since the model takes into account the relationship of family planning indicators - contraceptive use of any, modern and traditional methods, unmet need for family planning – the information from surveys that only provide data on contraceptive use (and have no information on unmet need for family planning) is considered as well. The model is providing estimates of the indicator for countries and years without direct survey data by extrapolating underlying trends determined using data across all countries. The model implicitly weights observations from other countries such that higher weights are given to observations from more similar countries. The fewer the number of observations for the country of interest, the more its estimates are driven by the experience of other countries, whereas for countries with many observations the results are determined to a greater extent by those empirical observations.

4.g. Regional aggregations

The Bayesian hierarchical model is used to generate regional and global estimates and projections of the indicator. Aggregate estimates and projections are weighted averages of the model-based country estimates, using the number of women aged 15-49 for the reference year in each country. The number of women aged 15-49 are taken from United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022. Numbers of women who are married or in a union are taken from United Nations, Department of Economic and Social Affairs, Population Division (2022). Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union: 2022 Revision. New York: United Nations, which are estimates and projections based on data from United Nations, Department of Economic and Social Affairs, Population Division (2019). World Marriage Data 2019.

Details of the methodology are described in Kantorová et al (2020) and United Nations, Department of Economic and Social Affairs, Population Division (2022).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

E-Learning video for SDG indicator 3.7.1 on the website of the Population Division (https://www.un.org/development/desa/pd/file/10712)

Information on the operational definitions and calculations of family planning indicators from surveys, as well as survey questions and statistical programs needed to derive the indicator, can be found at the website of the USAID Demographic and Health Surveys Program: https://dhsprogram.com/topics/Family-Planning.cfm and the website of UNICEF MICS: https://mics.unicef.org/

4.i. Quality management

Detailed guidelines are established for data compilation, data checking, and the production of model-based estimates and projections. Data compilations and model-based estimates and projections of family planning indicators are compliant with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) (http://gather-statement.org/).

4.j. Quality assurance

Not applicable

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

Data for the percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods are available for 140 countries or areas for the 2000-2021 time period. For 115 countries or areas, there are at least two available data points.

Table 1: The regional breakdown of data availability is as follows:

Region	At least one data point	Two or more data points
WORLD	140	115
Central and Southern Asia	13	10
Central Asia	4	4
Southern Asia	9	6
Eastern and South-Eastern Asia	12	11
Eastern Asia	3	2
South-Eastern Asia	9	9
Europe and Northern America	16	11
Eastern Europe	6	4
Northern America	1	1
Northern Europe	3	2
Southern Europe	5	4
Western Europe	1	0
Latin America and the Caribbean	25	20
Caribbean	8	5
Central America	8	8
South America	9	7
Northern Africa and Western Asia	17	15
Northern Africa	6	6
Western Asia	11	9
Oceania (excluding Australia and New Zealand)	9	6
Melanesia	3	2
Micronesia	3	1
Polynesia	3	3
Sub-Saharan Africa	48	42
Eastern Africa	16	14
Middle Africa	10	8
Southern Africa	6	5
Western Africa	16	15
Landlocked developing countries (LLDCs)	33	29
Least developed countries (LDCs)	47	42
Small island developing States (SIDS)	27	19

Time series:

Not applicable

Disaggregation:

Age, marital status, geographic location, socioeconomic status and other categories, depending on the data source and number of observations.

6. Comparability/deviation from international standards

Sources of discrepancies:

Generally, there is no discrepancy between data presented and data published in survey reports. However, some published national data have been adjusted by the Population Division to improve comparability. Notes are used in the data set to indicate when adjustments were made and where data differed from standard definitions. Surveys might differ in the classification of modern and traditional methods. To improve comparability of data over time and across countries, method classifications used in some surveys are adjusted to follow the classification described above.

The global indicator represents all women of reproductive age. Some survey estimates represent women who are married or in a union and this is indicated in a note.

7. References and Documentation

URL:

https://www.un.org/development/desa/pd/; https://population.un.org/dataportal/home; https://www.unfpa.org/data

References:

Alkema, L., Kantorova, V., Menozzi, C., & Biddlecom, A. (2013). National, regional, and global rates and trends in contraceptive prevalence and unmet need for family planning between 1990 and 2015: a systematic and comprehensive analysis. The Lancet, 381(9878), 1642-1652.

Bradley, S. E. K., Croft, T. N., Fishel, J. D., & Westoff, C. F. (2012). Revising Unmet Need for Family Planning: DHS Analytical Studies No. 25. ICF International, Calverton, Maryland. http://dhsprogram.com/pubs/pdf/AS25/AS25[12June2012].pdf

Every Woman Every Child (2016). Commitments to Every Woman Every Child’s Global Strategy for Women’s Children’s and Adolescents’ Health (2016-2030), https://www.everywomaneverychild.org/global-strategy/

Every Woman Every Child (2020). United Nations EWEC 2020 Progress Report – Protect the Progress: Rise, Refocus, Recover. https://protect.everywomaneverychild.org/

Kantorová V., M. C. Wheldon, P. Ueffing., A. N. Z. Dasgupta (2020). Estimating progress towards meeting women’s contraceptive needs in 185 countries: A Bayesian hierarchical modelling study. PLoS Medicine 17(2):e1003026.

United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022. (https://population.un.org/wpp/)

United Nations, Department of Economic and Social Affairs, Population Division (2019). World Marriage Data 2019. (https://www.un.org/development/desa/pd/data/world-marriage-data)

United Nations, Department of Economic and Social Affairs, Population Division (2022). Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union: 2022 Revision. New York: United Nations.

United Nations, Department of Economic and Social Affairs, Population Division (2022). World Contraceptive Use 2022. See also methodology with technical details available at

(https://www.un.org/development/desa/pd/data/world-contraceptive-use)

United Nations Department of Economic and Social Affairs, Population Division (2022). World Family Planning 2022: Meeting the changing needs for family planning: Contraceptive use by age and method. (https://www.un.org/development/desa/pd/content/family-planning-0)

United Nations Department of Economic and Social Affairs, Population Division (2020). E-Learning for SDG indicator 3.7.1. (https://www.un.org/development/desa/pd/content/family-planning-0)

United Nations, Department of Economic and Social Affairs, Population Division (2022). World Contraceptive Use 2022 and Estimates and Projections of Family Planning Indicators 2022. Methodology report. UN DESA/POP/2022/DC/NO. 5. (https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2022_wcu_fp-indicators_documentation.pdf)

World Health Organization Department of Reproductive Health and Research (WHO/RHR) and Johns Hopkins Bloomberg School of Public Health/Center for Communication Programs (CCP), Knowledge for Health Project. Family Planning: A Global Handbook for Providers (2018 update). Baltimore and Geneva: CCP and WHO, 2018. (https://www.who.int/reproductivehealth/publications/fp-global-handbook/en/)

World Health Organization (2020). Family planning/contraception methods. https://www.who.int/news-room/fact-sheets/detail/family-planning-contraception

World Health Organization (2022). World Health Statistics 2022. https://www.who.int/data/gho/publications/world-health-statistics

3.7.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

0.c. Indicator

Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age group

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Indicator is linked to Target 5.6 (Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences) because reductions in adolescent childbearing that are brought about by increasing access to sexual and reproductive health-care services are also reflective of improvements in sexual and reproductive health and the exercise of reproductive rights per se. This indicator is linked to Target 17.19 (By 2030 build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product and support statistical capacity-building in

developing countries) because the estimates of the adolescent birth rate are based in part on birth registration and census data. Strengthened civil registration and vital statistics systems reaching universal coverage of birth registration and conducting at least one census every 10 years are directly relevant for measuring progress on target 3.7.

0.g. International organisations(s) responsible for global monitoring

Population Division, Department of Economic and Social Affairs (DESA)

United Nations Population Fund (UNFPA)

1.a. Organisation

Population Division, Department of Economic and Social Affairs (DESA)

United Nations Population Fund (UNFPA)

2.a. Definition and concepts

Definition:

Annual number of births to females aged 10-14 or 15-19 years per 1,000 females in the respective age group.

Concepts:

The adolescent birth rate represents the level of childbearing among females in the particular age group. The adolescent birth rate among women aged 15-19 years is also referred to as the age-specific fertility rate for women aged 15-19.

2.b. Unit of measure

Annual number of births to females aged 10-14 or 15-19 years per 1,000 females in the respective age group.

2.c. Classifications

Not Applicable

3.a. Data sources

Civil registration is the preferred data source. Census and household survey are alternate sources when there is no reliable civil registration.

Data on births by age of mother are obtained from civil registration systems covering 90 percent or more of all live births, supplemented eventually by census or survey estimates for periods when registration data are not available. For the numerator, the figures reported by National Statistical Offices (NSOs) to the United Nations Statistics Division (UNSD) have first priority. When they are not available or present problems, use is made of data from the regional statistical units or directly from NSOs. For the denominator, first priority is given to the latest revision of World Population Prospects (WPP) produced by the Population Division, Department of Economic and Social Affairs, United Nations. In cases where the numerator does not cover the complete de facto population, an alternative appropriate population estimate is used if available. When either the numerator or denominator is missing, the direct estimate of the rate produced by the NSO is used. Information on sources is provided at the cell level. When the numerator and denominator come from two different sources, they are listed in that order.

In countries lacking a civil registration system or where the coverage of that system is lower than 90 percent of all live births, the adolescent birth rate is obtained from household survey data and census data. Registration data regarded as less than 90 percent complete are exceptionally used for countries where the alternative sources present problems of compatibility and registration data can provide an assessment of trends. In countries with multiple survey programmes, large sample surveys conducted on an annual or biennial basis are given precedence when they exist.

For information on the source of each estimate, see United Nations, Department of Economic and Social Affairs, Population Division: DemoData: Data Browser (online database of empirical demographic data and selected tabulations). https://popdiv.dfs.un.org/demodata/web/#!#%2Fhome

3.b. Data collection method

For civil registration data, data on births or the adolescent birth rate are obtained from country-reported data from the United Nations Statistics Division or regional Statistics Divisions or statistical units (ESCWA, ESCAP, CARICOM, SPC). The population figures are obtained from the last revision of the United Nations Population Division World Population Prospects and only exceptionally from other sources.

Survey data are obtained from national household surveys that are internationally coordinated—such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS)—and other nationally-sponsored surveys. Other national surveys conducted as part of the European Fertility and Family Surveys (FFS) or the Pan-Arab Project for Family Health (PAPFAM) may be considered as well. The data are taken from published survey reports or, in exceptional cases, other published analytical reports. Whenever the estimates are available in the survey report, they are directly taken from it. If clarification is needed, contact is made with the survey sponsors or authoring organization, which occasionally may supply corrected or adjusted estimates in response. In other cases, if microdata is available, estimates are produced by the Population Division based on national data.

For census data, the estimates are preferably obtained directly from census reports. In such cases, adjusted rates are only used when reported by the National Statistical Office (NSO). In other cases, the adolescent birth rate is computed from tables on births in the preceding 12 months by age of mother, and census population distribution by sex and age.

In addition to obtaining data and estimates directly from the websites of NSOs, the

following databases and websites are utilized: the DHS (http://api.dhsprogram.com/#/index.html), Demographic Yearbook database of the Statistics Division of the Department of Economic and Social Affairs (DESA) of the United Nations Secretariat (http://data.un.org/), internal databases of the Population Division of DESA of the United Nations Secretariat (see latest public release here: https://population.un.org/wpp/Publications/Files/WPP2022_Data_Sources.pdf

Eurostat (https://ec.europa.eu/eurostat/data/database), the Human Fertility Database (http://www.humanfertility.org), the Human Fertility Collection (http://www.fertilitydata.org), and the MICS (http://mics.unicef.org/). Survey databases (e.g., the Integrated Household Survey Network (IHSN) database) are also consulted in addition to searches for data on websites of National Statistical Offices and ad hoc queries.

3.c. Data collection calendar

Data are compiled and updated on a regular basis.

3.d. Data release calendar

Updated data on the adolescent birth rate for SDG monitoring are released by the Population Division annually. The next release is expected in 2024.

3.e. Data providers

For civil registration data, data on births or the adolescent birth rate are obtained from country-reported data from the United Nations Statistics Division (UNSD) or regional Statistics Divisions or statistical units (ESCWA, ESCAP, CARICOM, SPC). The population figures are obtained from the last revision of the United Nations Population Division World Population Prospects and only exceptionally from other sources. Survey data are obtained from national household surveys that are internationally coordinated—such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS)—and other nationally-sponsored surveys. Data from censuses are obtained from country-reported data from the UNSD or regional Statistics Divisions or statistical units (ESCWA, ESCAP, CARICOM, SPC) or directly from census reports.

3.f. Data compilers

This indicator is produced at the global level by the Population Division, Department of Economic and

Social Affairs, United Nations in collaboration with the United Nations Population Fund (UNFPA).

3.g. Institutional mandate

The Population Division of the Department of Economic and Social Affairs provides the international community with timely and accessible population data and analysis of population trends and development outcomes for all countries and areas of the world. It is the custodian agency for Sustainable Development Goal (SDG) indicator 3.7.2.

4.a. Rationale

Reducing adolescent fertility and addressing the multiple factors underlying it are essential for improving sexual and reproductive health and the social and economic well-being of adolescents. There is substantial agreement in the literature that women who become pregnant and give birth very early in their reproductive lives are subject to higher risks of complications or even death during pregnancy and birth and their children are also more vulnerable. Therefore, preventing births very early in a woman’s life is an important measure to improve maternal health and reduce infant mortality. Furthermore, women having children at an early age experience reduced opportunities for socio-

economic advancement, particularly because young mothers are less likely to complete their education and, if they need to work, may find it especially difficult to combine family and work responsibilities. The adolescent birth rate also provides indirect evidence on access to pertinent health services since young people, and in particular unmarried adolescent women, often experience difficulties in access to sexual and reproductive health services.

4.b. Comment and limitations

Discrepancies between estimates obtained from different national data are common.

For civil registration, rates are subject to limitations which depend on the completeness of birth registration, the treatment of infants born alive but die before registration or within the first 24 hours of life, the quality of the reported information relating to age of the mother, and the inclusion of births from previous periods. The population estimates may suffer from limitations connected to age misreporting and coverage.

For survey and census data, both the numerator and denominator come from the same population. The main limitations concern age misreporting, the omission of births, misreporting the date of birth of the child, and, in the case of surveys, sampling size and variability.

With respect to estimates of the adolescent birth rate among females aged 10-14 years, comparative evidence suggests that a very small proportion of births in this age group occur to females below age 12. Other evidence based on retrospective birth history data from surveys indicates that women aged 15-19 years are less likely to report first births before age 15 than women from the same birth cohort when asked five years later at ages 20–24 years.

The adolescent birth rate is commonly reported as the age-specific fertility rate for ages 15-19 years in the context of calculation of total fertility estimates. It has also been called adolescent fertility rate. A related measure is the proportion of adolescent fertility measured as the percentage of total fertility contributed by women aged 15-19.

4.c. Method of computation

The adolescent birth rate is computed as a ratio. The numerator is the number of live births to women aged 15-19 years, and the denominator an estimate of exposure to childbearing by women aged 15-19 years. The computation is the same for the age group 10-14 years. The numerator and the denominator are calculated differently for civil registration, survey and census data.

Computation formula:

Adolescent Birth Rate (15-19) = (number of births to women ages 15-19/mid-year population of women ages 15-19) * 1,000

In the case of civil registration data, the numerator is the registered number of live births born to women aged 15-19 years during a given year, and the denominator is the estimated or enumerated population of women aged 15-19 years.

In the case of survey data, the numerator is the number of live births obtained from retrospective birth histories of the interviewed women who were 15-19 years of age at the time of the births during a reference period before the interview, and the denominator is person-years lived between the ages of 15 and 19 years by the interviewed women during the same reference period. The reported observation

year corresponds to the middle of the reference period. For some surveys without data on retrospective birth histories, computation of the adolescent birth rate is based on the date of last birth or the number of births in the 12 months preceding the survey.

With census data, the adolescent birth rate is computed on the basis of the date of last birth or the number of births in the 12 months preceding the enumeration. The census provides both the numerator and the denominator for the rates. In some cases, the rates based on censuses are adjusted for under- registration based on indirect methods of estimation. For some countries with no other reliable data, the own-children method of indirect estimation provides estimates of the adolescent birth rate for a number of years before the census.

Whenever data are available, adolescent fertility at ages 10-14 years are also computed.

For a thorough treatment of the different methods of computation, see Handbook on the Collection of Fertility and Mortality Data, United Nations Publication, Sales No. E.03.XVII.11, (https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf)..Indirect methods of estimation are analyzed in Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2.

4.d. Validation

The Population Division maintains an online database on of empirical demographic data and selected tabulations (including fertility rates) from different sources including estimates produced by National Statistical Offices (NSOs) and regional statistical units. Data since the last round of SDG reporting are updated from various sources on a regular basis. Newly available demographic data are subjected to quality analyses and evaluated by examining the consistency in the patterns, levels and trends of the data within and across countries and regions. The Population Division has often reached out to the United Population Fund (UNFPA) to request its country offices to assist in obtaining data that might be available but not yet published. Also, through the UNFPA, data that the Population Division deems questionable are verified by the NSO or other relevant government agency. The process of data compilation and selection for SDG reporting is available at: https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd-egm-fer-2020-10-backgroundpaper_newtitle_final_oct9.pdf

4.e. Adjustments

When data are available from civil registration systems covering 90 percent or more of all live births, the adolescent birth rate is calculated by dividing the annual number of live births to females aged 19 years or younger (10-14 and 15-19 age groups) by the female population in the pertinent age group taken from the latest revision of World Population Prospects produced by the Population Division. The adolescent birth rate from other sources are not adjusted.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

There is no attempt to provide estimates for individual countries or areas when country or area data are not available.

• At regional and global levels

The regional or global aggregates of the adolescent birth rate for the age group 15-19 years are from the latest revision of World Population Prospects produced by the Population Division.

In cases where data are missing or assessed as unreliable, estimates for individual countries or areas are generated either through expert-based opinion, reviewing and weighting each observation analytically, or, in more recent years, using automated statistical methods, or by using a bias-adjusted data model to control for systematic biases between different types of data. See United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections (UN DESA/POP/2022/TR/NO. 4. ), available at: https://population.un.org/wpp/Publications/Files/WPP2022_Methodology.pdf

4.g. Regional aggregations

The adolescent birth rates reported for global and regional aggregates are based on the average of estimated adolescent birth rates for two, contiguous five-year periods (e.g., 2015-2020 and 2020-2025 for year 2020) published in United Nations, Department of Economic and Social Affairs, Population Division (2022), World Population Prospects 2022 (http://esa.un.org/unpd/wpp/)

The age-specific fertility rates for global and regional aggregates from World Population Prospects (WPP) are based on population reconstruction at the country level and provide a best estimate based on all the available demographic information. WPP considers potentially as many types and sources of empirical estimates as possible (including retrospective birth histories, direct and indirect fertility estimates), and the final estimates are derived to ensure as much internal consistency as possible with all other demographic components and intercensal cohorts enumerated in successive censuses.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Handbook on the Collection of Fertility and Mortality Data, United Nations Publication (ST/ESA/STAT/SER.F/92), (https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf)

Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2. (https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf)

Indicator and Monitoring Framework for the Global Strategy for Women’s, Children’s and Adolescents’ Health (2016-2030), (https://www.who.int/life-course/publications/gs-Indicator-and-monitoring-framework.pdf)

4.i. Quality management

The Population Division maintains an online database of empirical demographic data and selected tabulations (including fertility rates) from different sources including estimates produced by National Statistical Offices and regional statistical units. Data since the last round of SDG reporting are continuously updated from various sources: DemoData: Data Browser (online database of empirical demographic data and selected tabulations). https://popdiv.dfs.un.org/demodata/web/#!#%2Fhome

With each revision of World Population Prospects (WPP), the Population Division carries out a re-estimation of historical demographic trends for countries and territories of the world. These demographic estimates are based on the most recently available data sources, such as censuses, demographic surveys, registries of vital events, population registers and various other sources. Newly available demographic data and information are subjected to quality analyses and evaluated by examining the consistency in the patterns, levels and trends of the data across countries and regions. The description of the guidelines for managing the quality of the data and process is available at: https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd-egm-fer-2020-10-backgroundpaper_newtitle_final_oct9.pdf; https://population.un.org/wpp/Publications/Files/WPP2022_Methodology.pdf

4.j. Quality assurance

See 4.d and 4.i

4.k. Quality assessment

See 4.d and 4.i

5. Data availability and disaggregation

Data availability:

Data selected for the adolescent birth rate for women aged 10-14 years and 15-19 years are available for 215 and 229 countries or areas, respectively, for the 2000-2021 time period. For women aged 10-14, , there are at least two available data points for 212 countries or areas. Only three countries have one data point , one in Europe and Northern America (Isle of Man),one in Eastern and South-eastern Asia (Democratic Republic of Korea) and one in Latin America and the Caribbean (Saint Kitts and Nevis). For women aged 15-19, only four countries have one data point for this age group , one in Oceania (Tokelau), and two in Europe and Northern America (Isle of Man, and Channel Islands-Jersey ) and one in Western Asia and Northern Africa (Lebanon). .

Table 1: The regional breakdown of data availability is as follows:

	At least one data point between 2000 and 2020
World and SDG regions	ABR women aged 10-14 years	ABR women aged 15-19 years
WORLD	215	20953 46 14 19 25 50 21 2 32 46 54
Europe and Northern America	51
Latin America and the Caribbean	45
Central Asia and Southern Asia	13
Eastern Asia and South-eastern Asia	18
Northern Africa and Western Asia	20
Sub-Saharan Africa	49
Oceania excluding Australia and New Zealand	17
Australia and New Zealand	2
Landlocked developing countries (LLDCs)	31
Least Developed Countries (LDCs)	43
Small island developing States (SIDS)	51

Time series:

See table 1 above

Disaggregation:

Age, education, number of living children, marital status, socioeconomic status, geographic location and other categories, depending on the data source and number of observations.

6. Comparability/deviation from international standards

Sources of discrepancies:

Estimates based on civil registration are only provided when the country reports at least 90 percent coverage and when there is reasonable agreement between civil registration estimates and survey estimates. Small discrepancies might arise due to different denominators or the inclusion of births to women under 15 years of age. Survey estimates are only provided when there is no reliable civil registration. There might be discrepancies on the dating and the actual figure if a different reference period is being used. In particular, many surveys report rates both for a three-year and a five-year reference period. For countries where data are scarce, reference periods located more than five years before the survey might be used.

7. References and Documentation

URL:

https://www.un.org/development/desa/pd/; https://www.unfpaopendata.org/libraries/aspx/Home.aspx

Expert group meeting on the evaluation of adolescent fertility data and estimates | Population Division (un.org)

References:

United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4. World Population Prospects 2022: Methodology of the United Nations population estimates and projections

United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022. http://esa.un.org/unpd/wpp/

3.8.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all

0.c. Indicator

Indicator 3.8.1: Coverage of essential health services

0.d. Series

Applies to all series

0.e. Metadata update

2023-01-24

0.f. Related indicators

The universal health coverage (UHC) service coverage index is designed to summarize existing indicators of health service coverage to ensure consistency with the SDGs and other global initiatives and reduce duplication and reporting burden.

Indicator 3.8.1 should always be interpreted together with the other SDG UHC indicator, 3.8.2, which measures financial protection.

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population).

Concepts:

The index of health service coverage is computed as the geometric means of 14 tracer indicators. The 14 indicators are listed below and detailed metadata for each of the components is given in Annex 1. The tracer indicators are as follows, organized by four broad categories of service coverage:

I. Reproductive, maternal, newborn and child health

1. Family planning: Percentage of women of reproductive age (15−49 years) who are married or in-union who have their need for family planning satisfied with modern methods

2. Pregnancy care: Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times

3. Child immunization: Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine

4. Child treatment: Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider

II. Infectious diseases

5. Tuberculosis: Percentage of incident TB cases that are detected and treated

6. HIV/AIDS: Percentage of adults and children living with HIV currently receiving antiretroviral therapy

7. Malaria: Percentage of population in malaria-endemic areas who slept under an insecticide-treated net the previous night [only for countries with high malaria burden]

8. Water, sanitation and hygiene: Percentage of population using at least basic sanitation services.

III. Noncommunicable diseases

9. Hypertension: Prevalence of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)

10. Diabetes: Age-standardized mean fasting plasma glucose (mmol/L) for adults aged 18 years and older

11. Tobacco: Age-standardized prevalence of adults >=15 years currently using any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis (SDG indicator 3.a.1, metadata available here)

IV. Service capacity and access

12. Hospital access: Hospital beds density, relative to a maximum threshold of 18 per 10,000 population

13. Health workforce: Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre (partial overlap with SDG indicator 3.c.1, see metadata here)

14. Health security: International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained (SDG indicator 3.d.1, see metadata here)

2.b. Unit of measure

The indicator is an index reported on a unitless scale of 0 to 100.

2.c. Classifications

Not applicable

3.a. Data sources

Many of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in more detail in Annex 1.

In terms of values used to compute the index, values are taken from existing published sources. This includes assembled data sets and estimates from various UN agencies. This is summarized in the above link.

3.b. Data collection method

The mechanisms for collecting data from countries vary across the 14 tracer indicators, however in many cases a UN agency or interagency group has assembled and analysed relevant national data sources and then conducted a formal country consultation with country governments to review or produce comparable country estimates. For the universal health coverage (UHC) service coverage index, once this existing information on the 14 tracer indicators is collated, WHO conducts a country consultation with nominated focal points from national governments to review inputs and the calculation of the index. WHO does not undertake new estimation activities to produce tracer indicator values for the service coverage index; rather, the index is designed to make use of existing and well-established indicator data series to reduce reporting burden.

3.c. Data collection calendar

Data collection varies from every 1 to 5 years across tracer indicators. For example, country data on immunizations and HIV treatment are reported annually, whereas household surveys to collect information on child treatment may occur every 3-5 years, depending on the country. More details about individual tracer indicators are available in Annex 1.

3.d. Data release calendar

The first release of baseline values for the universal health coverage (UHC) service coverage index took place in December 2017. Updates are released every two years.

3.e. Data providers

In most cases, Ministries of Health and National Statistical Offices oversee data collection and reporting for health service coverage indicators.

3.f. Data compilers

The World Health Organization, drawing on inputs from other international agencies such as UNICEF, UNAIDS, UN DESA, OECD, Eurostat, World Bank Group.

3.g. Institutional mandate

WHO support for monitoring the service coverage dimension of Universal Health Coverage (UHC) (target 3.8, indicator 3.8.1 specifically) is underpinned by Resolution WHA69 that requests the Secretariat to track progress towards achieving UHC as part of the SDG 2030 agenda for Sustainable Development.

4.a. Rationale

Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1 is for health service coverage and indicator 3.8.2 focuses on health expenditures in relation to a household’s budget to identify financial hardship caused by direct health care payments. Taken together, indicators 3.8.1 and 3.8.2 are meant to capture the service coverage and financial protection dimensions, respectively, of target 3.8. These two indicators should be always monitored jointly.

Countries provide many essential services for health protection, promotion, prevention, treatment and care. Indicators of service coverage – defined as people receiving the service they need – are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, an index is constructed from 14 tracer indicators selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden. The index is reported on a unitless scale of 0 to 100, with 100 being the optimal value.

4.b. Comment and limitations

These tracer indicators are meant to be indicative of service coverage, not a complete or exhaustive list of health services and interventions that are required for universal health coverage. The 14 tracer indicators were selected because they are well-established, with available data widely reported by countries (or expected to become widely available soon). Therefore, the index can be computed with existing data sources and does not require initiating new data collection efforts solely to inform the index.

4.c. Method of computation

The index is computed with geometric means, based on the methods used for the Human Development Index. The calculation of the 3.8.1 indicator requires first standardizing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values.

The 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators, conversion and/or rescaling is required to obtain appropriate values from 0 to 100, as follows:

Conversion

The prevalence of tobacco use is converted into prevalence of tobacco non-use, so that an increase means an improvement.

Rescaling

Rescaling based on a non-zero minimum to obtain finer resolution (this “stretches” the distribution across countries): prevalence of non-use of tobacco is rescaled using a minimum value of 30%, which indicate a realistic range of prevalence levels for the indicator.

$r e s c a l e d t o b a c c o n o n u s e = (X - 30) / (100 - 30) * 100$

Rescaling for a continuous measure: mean fasting plasma glucose, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.4 mmol/L).

$r e s c a l e d v a l u e = (7.4 - o r i g i n a l v a l u e) / (7.4 - 5.1) * 100$

Maximum thresholds for rate indicators: hospital bed density and health workforce density are both capped at maximum thresholds, and values above this threshold are held constant at 100. These thresholds are based on minimum values observed across OECD countries (2015 edition of OECD Health Statistics Database).

$r e s c a l e d h o s p i t a l b e d s p e r 10, 000 = m i n i m u m (100, o r i g i n a l v a l u e / 18 * 100)$

$r e s c a l e d p h y s i c i a n s p e r 1, 000 = m i n i m u m (100, o r i g i n a l v a l u e / 0.9 * 100)$

$r e s c a l e d p s y c h i a t r i s t s p e r 100, 000 = m i n i m u m (100, o r i g i n a l v a l u e / 1 * 100)$

$r e s c a l e d s u r g e o n s p e r 100, 000 = m i n i m u m (100, o r i g i n a l v a l u e / 14 * 100)$

Once all tracer indicator values are on a scale of 0 to 100, geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. If the value of a tracer indicator happens to be zero or beyond 100, it is set to 1 (out of 100) or 100 (out of 100) respectively before computing the geometric mean. The following diagram illustrates the calculations.

Note that in countries with low malaria burden, the tracer indicator for use of insecticide-treated nets is dropped from the calculation.

4.d. Validation

The data obtained to calculate the index have typically already been checked for quality through separated processes. However, a quality assessment is performed before consulting countries (i.e. detection of important outliers or substantial difference between last update and next update for the same year). The index estimates are included in a consultation to obtain country’s feedback. Data are revised as needed for antenatal care coverage and hospital beds densities. The revision of all the other indicators should follow the reporting mechanism already in place.

Information on the validation of the index construction can be found in the following paper: https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(17)30472-2/fulltext

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

The starting point for computing the index is to assemble existing information for each tracer indicator. In many cases, this involves using country time series that have been produced or collated by UN agencies in consultation with country governments (e.g., immunization coverage, access to sanitation, HIV treatment coverage, etc). Some of these published time series involve mathematical modelling to reconcile multiple data sources or impute missing values, and these details are summarized in Annex 1.

After assembling these inputs, there are still missing values for some country-years for some indicators. Calculating the universal health coverage (UHC) service coverage index requires values for each tracer indicator for a country, so some imputation is necessary to fill these data gaps. The current approach involves a simple imputation algorithm. For each indicator:

If a country has missing values between two years with values, linear interpolation is used to fill missing values for the intervening years
If a country has historical years with values, but no current value, constant extrapolation is used to fill missing values to the current year
If a country has no values, a value is imputed. For pneumonia care-seeking and density of surgeons, a regression is fit to impute missing values (see Annex 1 for details). For all other indicators, a regional median is calculated to impute missing values. By default, regions are based on UN SDG subregions. However, when there are not enough countries within UN SDG subregions with available data, other groupings can be used.

Given the timing and distribution of various health surveys and other data collection mechanisms, countries do not collect and report on all 14 tracer indicators of health service coverage on an annual basis. In addition, monitoring at country level is most suitably done at broader time intervals, e.g., every 5 years, to allow for new data collection across indicators. Therefore, the extent to which imputation has been used to fill missing information should be communicated along with the index value.

At regional and global levels

Any needed imputation is done at country level. These country values can then be used to compute regional and global values.

4.g. Regional aggregations

Regional and global aggregates use United Nations population estimates at the country level to compute a weighted average of country values for the index. This is justified because universal health coverage (UHC) is a property of countries, and the index of essential services is a summary measure of access to essential services for each country’s population. United Nations population estimates at country level are used to ensure consistency and comparability of estimates within countries and between countries over time.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable

4.i. Quality management

Not applicable

4.j. Quality assurance

Not applicable

4.k. Quality assessment

See 4.d Validation

5. Data availability and disaggregation

Data availability:

Summarizing data availability for the universal health coverage (UHC) service coverage index is not straightforward, as different data sources are used across the 14 tracer indicators. Additionally, for many indicators comparable estimates have been produced, in many cases drawing on different types of underlying data sources to inform the estimates while also using projections to impute missing values.

Time series:

A baseline value for the UHC service coverage index for 2015 across 183 countries was published in late 2017. As part of this process, data sources going back to 2000 were assembled. In 2019, UHC service coverage index were estimates for the years: 2000, 2005, 2010, 2015 and 2017. From 2021, the index is estimated every two years for all countries (i.e. 194 WHO member states).

Disaggregation:

Equity is central to the definition of UHC, and therefore the UHC service coverage index should be used to communicate information about inequalities in service coverage within countries. This can be done by presenting the index separately for the national population vs disadvantaged populations to highlight differences between them.

For countries, geographic location is likely the most feasible dimension for sub-national disaggregation based on average coverage levels measured with existing data sources. To do this, the UHC index can be computed separately by, e.g., province or urban vs rural residence, which would allow for subnational comparisons of service coverage. Currently, the most readily available data for disaggregation on other dimensions of inequality, such as household wealth, is for indicators of coverage within the reproductive, maternal, newborn and child health services category. Inequality observed in this dimension can be used as a proxy to understand differences in service coverage across key inequality dimensions. This approach should be replaced with full disaggregation of all 14 tracer indicators once data are available to do so.

6. Comparability/deviation from international standards

Sources of discrepancies:

The service coverage index draws on existing, publicly available data and estimates for tracer indicators. These numbers have already been through a country consultation process (e.g., for immunization coverage), or are taken directly from country reported data.

7. References and Documentation

URL: https://www.who.int/health-topics/universal-health-coverage

References: https://www.who.int/publications/i/item/tracking-universal-health-coverage

http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf

https://www.who.int/health-topics/universal-health-coverage

For historical development of methods, see:

https://www.who.int/publications/i/item/9789241565264

https://www.who.int/publications/i/item/monitoring-progress-towards-universal-health-coverage-at-country-and-global-levels-framework-measures-and-targets

http://collections.plos.org/uhc2014

Annex 1: Metadata for tracer indicators used to measure the coverage of essential health services for monitoring SDG indicator 3.8.1.

Please send any comments or queries to: uhc_stats@who.int

Tracer area	Family planning
Indicator definition	Percentage of women of reproductive age (15−49 years) who are married or in-union who have their need for family planning satisfied with modern methods.
Numerator	Number of women aged 15-49 who are married or in-union who are currently using, or whose partner is currently using a modern method of contraception
Denominator	Number of women aged 15-49 who are married or in-union with a need for family planning
Main data sources	Population-based health surveys
Method of measurement	Household surveys include a series of questions to measure the modern contraceptive prevalence rate and need for family planning. The number of women with a need for family planning is defined as the sum of the number of women of reproductive age (15–49 years) who are married or in a union and who are currently using, or whose sexual partner is currently using, at least one contraceptive method (modern or traditional), and the number of women of reproductive age with an unmet need for family planning. Unmet need for family planning is the proportion of women of reproductive age (15–49 years) either married or in a consensual union, who are fecund and sexually active but who are not using any method of contraception (modern or traditional), and report not wanting any more children or wanting to delay the birth of their next child for at least two years. Included are: all pregnant women (married or in a consensual union) whose pregnancies were unwanted or mistimed at the time of conception; all postpartum amenorrhoeic women (married or in consensual union) who are not using family planning and whose last birth was unwanted or mistimed; all fecund women (married or in consensual union) who are neither pregnant nor postpartum amenorrhoeic, and who either do not want any more children (want to limit family size), or who wish to postpone the birth of a child for at least two years or do not know when or if they want another child (want to space births), but are not using any contraceptive method. Modern methods include female and male sterilization, the intra-uterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap and spermicidal foam, jelly, cream and sponge), lactational amenorrhea method (LAM), emergency contraception and other modern methods not reported separately.
Method of estimation	The United Nations Population Division produces a systematic and comprehensive series of annual estimates and projections of the proportion of need for family planning among women of reproductive age (15-49) satisfied with modern methods. A Bayesian hierarchical model is applied to a comprehensive global dataset of a country-specific data to generate the estimates and projections. The model accounts for differences by data source, sample population, and survey questions. See here for details: https://www.un.org/development/desa/pd/data/family-planning-indicators Data compilation of country-specific survey data in World Contraceptive Use: https://www.un.org/development/desa/pd/node/3285
UHC-related notes

Tracer area	Pregnancy care
Indicator definition	Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times
Numerator	Number of women aged 15−49 years with a live birth in a given time period who received antenatal care four or more times
Denominator	Total number of women aged 15−49 years with a live birth in the same period.
Main data sources	Household surveys and routine facility information systems.
Method of measurement	Data on four or more antenatal care visits is based on questions that ask if and how many times the health of the woman was checked during pregnancy. Household surveys that can generate this indicator include DHS, MICS, RHS and other surveys based on similar methodologies. Service/facility reporting systems can be used where the coverage is high, usually in higher income countries.
Method of estimation	WHO maintains a data base on coverage of antenatal care: http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4
UHC-related notes	Ideally this indicator would be replaced with a more comprehensive measure of pregnancy care, for example the proportion of women who have a skilled provider attend the birth or an institutional delivery. A challenge in measuring skilled attendance at birth is determining which providers are “skilled”.

Tracer area	Child immunization
Indicator definition	Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine
Numerator	Children 1 year of age who have received three doses of diphtheria-tetanus-pertussis containing vaccine
Denominator	All children 1 year of age
Main data sources	Household surveys and facility information systems.
Method of measurement	For survey data, the vaccination status of children aged 12–23 months is collected from child health cards or, if there is no card, from recall by the care-taker. For administrative data, the total number of doses administered to the target population is extracted.
Method of estimation	Together, WHO and UNICEF derive estimates of DTP3 coverage based on data officially reported to WHO and UNICEF by Member States, as well as data reported in the published and grey literature. They also consult with local experts - primarily national EPI managers and WHO regional office staff - for additional information regarding the performance of specific local immunization services. Based on the available data, consideration of potential biases, and contributions from local experts, WHO/UNICEF determine the most likely true level of immunization coverage. For details, see here: https://www.who.int/teams/immunization-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage
UHC-related notes	There is variability in national vaccine schedules across countries. Given this, one option for monitoring full child immunization is to monitor the fraction of children receiving vaccines included in their country’s national schedule. A second option, which may be more comparable across countries and time, is to monitor DTP3 coverage as a proxy for full child immunization. Diphtheria-tetanus-pertussis containing vaccine often includes other vaccines, e.g., against Hepatitis B and Haemophilus influenza type B, and is a reasonable measure of the extent to which there is a robust vaccine delivery platform within a country.

Tracer area	Child treatment
Indicator definition	Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider
Numerator	Number of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider
Denominator	Number of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey
Main data sources	Household surveys
Method of measurement	The indicator is captured by household surveys including DHS, MICS and other national population-based surveys and is intended for use in high under-5 mortality settings to monitor efforts to reduce mortality from acute respiratory infections (including pneumonia) which are a leading cause of death for children under the age of 5 years. The Child Health Accountability Tracking Technical Advisory Group (CHAT TAG), convened by WHO and UNICEF, has ratified this indicator and is working to standardize its use across household surveys. WHO/UNICEF maintains a database of country-level observations from household surveys that can be accessed here: https://data.unicef.org/topic/child-health/pneumonia/
Method of estimation	UNICEF and WHO maintain a data base on this indicator and work on ensuring that values presented are comparable, using the same indicator definition.
UHC-related notes	This indicator is not typically measured in higher income countries with well-established health systems. For countries without observed data, coverage was estimated from a regression that predicts coverage of care-seeking for symptoms of acute respiratory infection (on the logit scale), obtained from the WHO data base described above, as a function of the log of the estimated under-five all-causes mortality rate, which can be found here: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates

Tracer area	Tuberculosis treatment
Indicator definition	Percentage of incidence TB cases that are detected and treated in a given year
Numerator	Number of new and relapse cases detected and treated in a given year
Denominator	Number of new and relapse cases in the same year
Main data sources	Facility information systems, surveillance systems, population-based health surveys with TB diagnostic testing, TB register and related quarterly reporting system (or electronic TB registers)
Method of measurement	This indicator requires two main inputs: (1) The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO in a given year. (2) The number of incident TB cases for the same year, typically estimated by WHO. The final indicator = (1)/(2)
Method of estimation	Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and information from death (vital) registration systems. Estimates of incidence for each country are derived, using one or more of the following approaches depending on available data: 1. incidence = case notifications/estimated proportion of cases detected; 2. incidence = prevalence/duration of condition; 3. incidence = deaths/proportion of incident cases that die. Dynamic and statistical models were introduced to produce estimates for 2020 and 2021 that account for the major disruptions to the provision of and access to TB diagnostic and treatment services that have occurred in the context of the coronavirus (COVID-19) pandemic. These estimates of TB incidence are combined with country-reported data on the number of cases detected and treated, and the percentage of cases successfully treated, as described above.
UHC-related notes	To compute the indicator using WHO estimates, one can access necessary files here: http://www.who.int/tb/country/data/download/en/, and compute the indicator as = c_cdr

Tracer area	HIV treatment
Indicator definition	Percentage of adults and children living with HIV currently receiving antiretroviral therapy (ART)
Numerator	Number of adults and children who are currently receiving ART at the end of the reporting period
Denominator	Number of adults and children living with HIV during the same period
Main data sources	Facility reporting systems, sentinel surveillance sites, population-based surveys
Method of measurement	Numerator: The numerator is generated by counting the number of adults and children who received ART at the end of the reporting period. Data can be collected from facility-based ART registers or drug supply management systems. These are then tallied and transferred to cross sectional monthly or quarterly reports which will then be aggregated for national totals. Patients receiving ART in the private sector and public sector should be included in the numerator. Denominator: Data on the number of people with HIV infection may come from epidemic models and population-based surveys or, as is common in sub-Saharan Africa, surveillance systems based on antenatal care clinics.
Method of estimation	Estimates of antiretroviral treatment coverage among people living with HIV for 2000-2018 are derived as part of the 2019 UNAIDS' estimation round. To estimate the number of people living with HIV across time in high burden countries, UNAIDS in collaboration with countries use an epidemic model (Spectrum) that combines surveillance data on prevalence with the current number of patients receiving ART and assumptions about the natural history of HIV disease progression. Since ART is now recommended for all individuals living with HIV, monitoring ART coverage is less complicated than before, when only those with a certain level of disease severity were eligible to receive ART. Estimates of ART coverage can be found here: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)
UHC-related notes	Comparable estimates of ART coverage in high income countries, in particular time trends, are not always available.

Tracer area	Malaria prevention
Indicator definition	Percentage of population in malaria-endemic areas who slept under an ITN the previous night.
Numerator	Number of people in malaria-endemic areas who slept under an ITN.
Denominator	Total number of people in malaria endemic areas.
Main data sources	Data on household access and use of ITNs come from nationally representative household surveys such as Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and Malaria Indicator Surveys. Data on the number of ITNs delivered by manufacturers to countries are compiled by Milliner Global Associates, and data on the number of ITNs distributed within countries are reported by National Malaria Control Programs.
Method of measurement	Many recent national surveys report the number of ITNs observed in each respondent household. Ownership rates can be converted to the proportion of people sleeping under an ITN using a linear relationship between access and use that has been derived from 62 surveys that collect information on both indicators.
Method of estimation	Mathematical models can be used to combine data from household surveys on access and use with information on ITN deliveries from manufacturers and ITN distribution by national malaria programmes to produce annual estimates of ITN coverage. WHO uses this approach in collaboration with the Malaria Atlas Project. Methodological details can be found in pages 122-123 of the World Malaria Report 2021: https://www.who.int/publications/i/item/9789240040496.
UHC-related notes	WHO produces comparable ITN coverage estimates for 40 of the 47 malaria endemic countries or areas of sub-Saharan Africa. The islands of Mayotte (for which no ITN delivery or distribution data were available) and Cabo Verde (which does not distribute ITNs) were excluded, as were the low transmission countries of Eswatini, Namibia, Sao Tome and Principe, and South Africa, for which ITNs comprise a small proportion of vector control. Analyses were limited to populations categorized by NMPs as being at risk. For other countries, ITN coverage is not included in the UHC service coverage index due to data limitations.

Tracer area	Water, sanitation and hygiene
Indicator definition	Percentage of population using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households
Numerator	Number of people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush toilets connected to piped sewer systems, septic tanks or pit latrines; pit latrines with slabs (including ventilated pit latrines), and composting toilets
Denominator	Total population
Main data sources	Population-based household surveys and censuses
Method of measurement	Data on improved sanitation facilities are routinely collected in household surveys and censuses. These data sources may also collect information on sharing of sanitation facilities are shared among two or more households, and on emptying of on-site sanitation facilities. Household-level responses, weighted by household size, are used to compute population coverage.
Method of estimation	The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) is responsible for SDG reporting on drinking water, sanitation and hygiene (WASH) and has produced regular estimates of coverage of the population using at least basic sanitation services since 2000. The JMP assembles, reviews and assesses national data collected by statistics offices and other relevant institutions including sectoral authorities. Linear regression is used to provide estimates of the population using improved sanitation facilities, as well as the proportion practising open defecation. Regressions are also made to estimate the population using improved sanitation facilities connected to sewers and septic tanks; these are constrained to not exceed the estimates for total improved facilities. The proportion of the population sharing sewered and non-sewered sanitation facilities is estimated by making a linear regression on all available data on sharing from household surveys and censuses. Basic sanitation services are calculated by multiplying the proportion of the population using improved sanitation facilities by the proportion of improved sanitation facilities which are not shared among two or more households. Separate estimates are made for urban and rural areas, and national estimates are generated as weighted averages of the two, using population data from the most recent report of the United Nations Population Division. The most recent household survey or census available for most countries was typically conducted two to six years ago. The JMP extrapolates regressions for two years beyond the last available data point. Beyond this point the estimates remain unchanged for up to four years unless coverage is below 0.5 per cent or above 99.5 per cent, in which case the line is extended indefinitely. For more information see https://washdata.org/monitoring/methods/estimation-methods
UHC-related notes	The SDG global indicator of “proportion of population using safely managed sanitation services” (SDG 6.2.1a) is an expanded version of the MDG indicator, which additionally considers safe management of excreta along the entire sanitation chain, including treatment and disposal This indicator is not used for UHC monitoring due to lower data availability.

Tracer area	Prevention of cardiovascular disease
Indicator definition	Prevalence of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)
Numerator	Number of adults aged 30-79 years who took medication for hypertension
Denominator	Number of adults aged 30-79 years with hypertension (defined as having systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or taking medication for hypertension)
Main data sources	Population-based surveys and surveillance systems
Method of measurement	Data sources recording measured blood pressure are used (self-reported data are excluded). If multiple blood pressure readings are taken per participant, the first reading is dropped and the remaining readings are averaged. Whether medication is taken for hypertension may be assessed using questions worded as variations of “Are you currently taking any medicines, tablets, or pills for high blood pressure?” or “In the past 2 weeks, have you taken any drugs (medication) for raised blood pressure prescribed by a doctor or other health worker?” In studies that gather information on prescribed medicines, survey information may be used to establish that the purpose of taking a blood pressure-lowering drug was specifically to treat hypertension.
Method of estimation	Full details of input and data methods are available at: NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet S0140-6736(21)01330-1 (https://www.thelancet.com/article/S0140-6736(21)01330-1/fulltext). A total of 1,201 population-based studies that included measured blood pressure and data on blood pressure treatment in 104 million individuals aged 30–79 years were used to estimate trends in hypertension and hypertension diagnosis, treatment and control from 1990 to 2019. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.
UHC-related notes

Tracer area	Management of diabetes
Indicator definition	Age-standardized mean fasting plasma glucose for adults aged 18 years and older
Main data sources	Population-based surveys and surveillance systems
Method of measurement	Fasting plasma glucose (FPG) levels are determined by taking a blood sample from participants who have fasted for at least 8 hours. Other related biomarkers, such as hemoglobin A1c (HbA1c), were used to help calculate estimates (see below).
Method of estimation	For producing comparable national estimates, data observations based on mean FPG, oral glucose tolerance test (OGTT), HbA1c, or combinations therein, are all converted to mean FPG. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country specific prevalences, which accounts for national vs. subnational data sources, urban vs. rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Methodological details can be found here: https://www.who.int/diabetes/global-report/en/
UHC-related notes	An individual’s FPG may be low because of effective treatment with glucose-lowering medication, or because the individual is not diabetic as a result of health promotion activities or other factors such as genetics. Mean FPG is thus a proxy for both effective promotion of healthy diets and behaviors and effective treatment of diabetes. The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed. The indicator, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.41 mmol/L). rescaled value = (7.41 - original value) / (7.41-5.1) * 100

Tracer area	Tobacco control
Indicator definition	Age-standardized percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.
Numerator	Estimated number of adults 15 years and older who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis
Denominator	Total number of adults 15 years and older
Main data sources	Household surveys
Method of measurement	Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”.
Method of estimation	A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution.
UHC-related notes	Prevalence of tobacco non-use is computed as 1 minus the prevalence of tobacco use. The indicator is then rescaled based on a non-zero minimum to obtain finer resolution : rescaled tobacco non-use = (X-30)/(100-30)*100.

Tracer area	Hospital access
Indicator definition	Hospital beds per capita, relative to a maximum threshold of 18 per 10,000 population
Numerator	Number of hospital beds (should exclude labor and delivery beds)
Denominator	Total population
Main data sources	Administrative systems / Health facility reporting system
Method of measurement	Country administrative systems are used to total the number of hospital beds, which are divided by the total estimated population, and multiplied by 10,000.
Method of estimation	Using available data, the indicator is computed relative to a threshold value of 18 hospital beds per 10,000 population. This threshold is below the observed OECD high income country minimum (since year 2000) of 20 per 10,000 (OECD Health Statistics database, 2015 edition) and tends to correspond to an inpatient hospital admission rate of around 5 per 100 per year. This indicator is designed to capture low levels of hospital capacity; the maximum threshold is used because very high hospital bed densities are not necessary an efficient use of resources. The indicator is computed as follows, using country data on hospital bed density (x), which results in values ranging from 0 to 100: Country with a hospital bed density x < 18 per 10,000 per year, the indicator = x /18*100. Country with a hospital bed density x >= 18 per 10,000 per year, the indicator = 100.
UHC-related notes	This indicator is used as proxy for the full coverage of inpatient care services. An alternative indicator could be hospital in-patient admission rate, relative to a maximum threshold. However, that indicator is currently not reported widely across regions, in particular the African Region. In countries where both hospital beds per capita and in-patient admission rates are available, they are highly correlated.

Tracer area	Health workforce
Indicator definition	Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre
Numerator	Number of physicians, psychiatrists and surgeons
Denominator	Total population
Main data sources	National Health Workforce Accounts. This includes reported data from Member States based on national registry of health workers, ideally coupled with regular assessment of completeness using census data, labour force surveys, professional association registers, or facility censuses.
Method of measurement	The classification of health workers is based on criteria for vocational education and training, regulation of health professions, and activities and tasks of jobs, i.e., a framework for categorizing key workforce variables according to shared characteristics. The WHO framework largely draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations), United Nations Educational, Scientific and Cultural Organization (International Standard Classification of Education), and the United Nations Statistics Division (International Standard Industrial Classification of All Economic Activities). Methodological details can be found here: https://www.who.int/activities/improving-health-workforce-data-and-evidence Health workforce data can be accessed on the NHWA data portal: https://apps.who.int/nhwaportal/
Method of estimation	Using available data, the indicator is computed by first rescaling, separately, health worker density ratios for each of the three cadres (physicians, psychiatrists and surgeons) relative to the minimum observed values across OECD countries since 2000 (OECD Health Statistics database, 2015 edition), which are as follows: physicians = 0.9 per 1000, psychiatrists = 1 per 100,000, and surgeons = 14 per 100,000. This rescaling is done in the same way as that for the hospital bed density indicator described above, resulting in indicator values that range from 0 to 100 for each of the three cadres. For example, using country data on physicians per 1000 population (x), the cadre-specific indicator would be computed as: Country with x < 0.9 per 1000 per year, the cadre-specific indicator = x /0.9*100. Country with x >= 0.9 per 1000 per year, the cadre-specific indicator = 100. As a final step, the geometric mean of the three cadre-specific indicator values is computed to obtain the final indicator of health workforce density.
UHC-related notes	Due to major challenges measuring coverage in all health areas, which leaves major gaps for important areas such as routine medical exams, treatment for mental illnesses, emergency care and surgical procedure, proxies are used. Physician, psychiatrist and surgeon densities are used as proxies for the full coverage of outpatient care, mental health care and emergency/surgical care services, respectively. It should be noted that those measures are difficult to interpret because the optimal level for those indicators is unknown and they do not relate to a specific need for services. Despite this fact, low levels for these indicators are indicative of poor access to and use of essential health services.

Tracer area	Health security
Indicator definition	International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of all core capacities that have been attained at a specific point in time. The second edition SPAR tool has been expanded from 13 to 15 capacities. The 15 core capacities are (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies. The 13 core capacities of the first edition of the IHR State Parties Annual Assessment and Reporting Tool are (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies. Both SPAR questionnaires (1st and 2nd editions) use a five-level scoring with indicators based on five cumulative levels to measure the implementation status for each capacity. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party's current status. To move to the next level, all capacities described in previous levels should be in place for each indicator. For the years 2010 to 2017, Member States used the IHR monitoring questionnaire. The questionnaire is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards. Individual questions are grouped by components and indicators in the questionnaires. States Parties can provide additional information on the questions in the comment boxes. Responses to the questions include marking one appropriate value (Yes, No, or Not Known) or the appropriate percentages. For statistical purposes, the "Not Known" value will be computed as a "No" value. The IHR monitoring questionnaire includes the following: IHR01. National legislation, policy and financing; IHR02. Coordination and National Focal Point communications; IHR03. Surveillance; IHR04. Response; IHR05. Preparedness; IHR06. Risk communication; IHR07. Human resources; IHR08. Laboratory; IHR09. Points of entry; IHR10. Zoonotic events; IHR11. Food safety; IHR12. Chemical events; IHR13. Radio nuclear emergencies.
Numerator	Number of attributes attained
Denominator	Total number of attributes
Main data sources	Key informant survey
Method of measurement	Key informants report on attainment of a set of attributes for each of the core capacities using a standard WHO instrument. This instrument is based on a self-assessment and self-reporting by the State Party. There are three datasets based on the different tools to collect data for SPAR. For the period 2010 to 2017, the questionnaire, known as the IHR monitoring questionnaire, is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards and information on the status of implementation for each capacity. The IHR monitoring questionnaire ( 2010 to 2017) was replaced by the IHR State Parties Self-Assessment Tool – SPAR, published in July 2018 also known as SPAR 1st edition. The States Parties used the questionnaire from the 2018 – 2020 SPAR reporting cycle. The current questionnaire replaced the SPAR 1st edition and was used by the Member States for 2021. Under each capacity, the indicators were either retained, replaced or added. Historical trends based on the data for similar capacity titles may be taken with caution.
Method of estimation	The score of each indicator level is classified as a percentage of performance along the “1 to 5” scale. e.g. for a country selecting level 3 for indicator 2.1, the indicator level will be expressed as: 3/5100=60% CAPACITY LEVEL The level of the capacity is expressed as the average of all indicators. e.g. for a country selecting level 3 for indicator 2.1 and level 4 for indicator 2.2. Indicator level for 2.1 will be expressed as: 3/5100=60%, indicator level for 2.2 will be expressed as: 4/5*100=80% and capacity level for 2 will be expressed as: (60+80)/2=70%
UHC-related notes	Countries began reporting IHR core capacity attainment to WHO for the year 2010. The earliest available IHR score for each country is used for all years 2000-2009.

3.8.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

0.c. Indicator

Indicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or income

0.d. Series

Applies to all series (SH_XPD_EARN25 and SH_XPD_EARN10)

0.e. Metadata update

2023-05-15

0.f. Related indicators

SDG indicators: 3.8.1; 1.1.1 and 1.2.1

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO) and the World Bank

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Proportion of the population with large household expenditure on health as a share of total household expenditure or income. Two thresholds are used to define “large household expenditure on health”: greater than 10% and greater than 25% of total household expenditure or income.

Concepts:

Indicator 3.8.2 is defined as the “Proportion of the population with large household expenditure on health as a share of total household expenditure or income”. In effect, it is based on a ratio exceeding a threshold. The two main concepts of interest behind this ratio are household expenditure on health (numerator) and total household consumption expenditure or, when unavailable, income (denominator).

Numerator

Household expenditure on health is defined as any expenditure incurred at the time of service use to get any type of care (promotive, preventive, curative, rehabilitative, palliative or long-term care), including all medicines, vaccines and other pharmaceutical preparations, as well as all health products, from any type of provider and for all members of the household. These health expenditures are characterized by direct payments that are financed by a household’s income (including remittances), savings or loans but do not include any third-party payer reimbursement. They are labelled Out-Of-Pocket (OOP) payments in the classification of health care financing schemes (HF) of the International Classification for Health Accounts (ICHA). They are the most inequitable source of funding for the health system as they are solely based on the willingness and ability to pay of the household; they only grant access to the health services and health products individuals can pay for, without any solidarity between the healthy and the sick beyond the household^{^[1]}, the rich and the poor; they represent a barrier to access for those people who are unable to find the economic resources need to pay out of their own pocket.

The components of household expenditure on health should be consistent with division 06 on the health of the UN Classification of Individual Consumption According to Purpose (COICOP) on medicines and medical products (06.1), outpatient care services (06.2), inpatient care services (06.3) and other health services (06.4)^{^[2]}.

Further information on definitions and classifications of health expenditures should be consistent with the International Classification for Health Accounts (ICHA) and its family of classifications (for example, by type of provider).

Denominator

Expenditure on household consumption and household income are both monetary welfare measures. Household consumption is a function of permanent income, which is a measure of a household’s long-term economic resources that determine living standards. Consumption is generally defined as the sum of the monetary values of all items consumed by the household on a domestic account during a common reference period^[3]. It includes monetary expenditures on food and non-food non-durable goods and services consumed as well as the imputed values of goods and services that are not purchased but procured otherwise for consumption (value of in-kind consumption); the value use of durables, and the value use of owner-occupied housing. Information on household consumption is usually collected in household surveys that may use different approaches to measure ‘consumption’ depending on whether items refer to durable or non-durable goods and/or are directly produced by households.

The most relevant measure of income is disposable income, as it is close to the maximum available to the household for consumption expenditure during the accounting period. Disposable income is defined as total income less direct taxes (net of refunds), compulsory fees and fines. Total income is generally composed of income from employment, property income, income from household production of services for own consumption, transfers received in cash and goods, and transfers received as services^{^[4]}.

Income is more difficult to measure accurately due to its greater variability over time. Consumption is less variable over time and easier to measure. Therefore, it is recommended that whenever there is information on household consumption and income, the former is used (see the “comments and limitations” section to learn more about the sensitivity of 3.8.2 to the income/expenditure choice in the denominator). Statistics on 3.8.2 currently produced by WHO and the World Bank predominantly rely on consumption (see the section on data sources).

Thresholds

Two thresholds are used for global reporting to identify large household expenditure on health as a share of total household consumption or income: a lower threshold of 10% (3.8.2_10) and a higher threshold of 25% (3.8.2_25). With these two thresholds, the indicator measures financial hardship (see the section on comments and limitations).

http://www.oecd-ilibrary.org/social-issues-migration-health/a-system-of-health-accounts/classification-of-health-care-financing-schemes-icha-hf_9789264116016-9-en ↑

Agenda item 3(l) available at https://unstats.un.org/unsd/statcom/49th-session/documents/; http://unstats.un.org/unsd/cr/registry/regcs.asp?Cl=5&Lg=1&Co=06.1 ↑

https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e ↑

⁴

http://www.ilo.org/public/english/bureau/stat/download/17thicls/r2hies.pdf ↑

2.b. Unit of measure

Percent (%) (proportion of people)

2.c. Classifications

For the definition of health expenditures (numerator)

http://www.oecd-ilibrary.org/social-issues-migration-health/a-system-of-health-accounts/classification-of-health-care-financing-schemes-icha-hf_9789264116016-9-en

For the components of health expenditures (numerator)

division 06 of the UN Classification of Individual Consumption According to Purpose (COICOP) https://unstats.un.org/unsd/class/revisions/coicop_revision.asp;

For the components of household total consumption (preferred denominator)

UN Classification of Individual Consumption According to Purpose (COICOP) https://unstats.un.org/unsd/class/revisions/coicop_revision.asp;

3.a. Data sources

The recommended data sources for the monitoring of the “Proportion of the population with large household expenditure on health as a share of total household expenditure or income” are household surveys with information on both household consumption expenditure on health and total household consumption expenditures, which are routinely conducted by national statistical offices. Household budget surveys (HBS) and household income and expenditure surveys (HIES) typically collect these as they are primarily undertaken to provide inputs to the calculation of consumer price indices or the compilation of national accounts. Another potential source of information is socio-economic or living standards surveys; however, some of these surveys may not collect information on total household consumption expenditures – for example, when a country measures poverty using income as the welfare indicator^{^[5]}. The most important criterion for selecting a data source to measure SDG indicator 3.8.2 is the availability of both household consumption expenditure on health and total household consumption expenditures.

⁵

http://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf ↑

3.b. Data collection method

The World Health Organizaiton (WHO) and the World Bank contact Ministries of Health and/or National statistical offices for two purposes: a) request access to the household survey microdata in order to produce SDG indicator 3.8.2; b) request estimates produced by the country itself.

A) The first type of request is done by each organization separately. WHO obtains access to the household survey microdata from national statistical offices through its regional offices or country offices. The access request is often part of technical assistance programs on health financing issues.

The World Bank also typically receives data from National Statistical Offices (NSOs) directly. In other cases, it uses NSO data received indirectly. For example, it receives data from Eurostat and LIS (Luxembourg Income Study), which provide the World Bank NSO data in its original form or harmonized for comparability. The Universidad Nacional de La Plata, Argentina and the World Bank jointly maintain the SEDLAC (Socio-Economic Database for Latin American and Caribbean) database that includes harmonized statistics on poverty and other distributional and social variables from 24 Latin American and Caribbean countries, based on microdata from household surveys conducted by NSOs. Data is obtained through country-specific programs, including technical assistance programs and joint analytical and capacity-building activities. The World Bank has relationships with NSOs on work programs involving statistical systems and data analysis. Poverty economists from the World Bank typically engage with NSOs broadly on poverty measurement and analysis as part of technical assistance activities.

The World Health Organization and the World Bank regularly undertake training events on the measurement of lack of financial protection coverage to produce SDG 3.8.2 indicator. This type of activity involves participants from the Ministry of Health as well as from the National Statistical Office.

All the country-year estimates produced by both organizations are assembled in a joint database following a quality assessment process (see section 4.j). Such estimates are included in a country consultation conducted to give an opportunity to i) review the estimates, the data sources and the methods used for computation; ii) provide information about additional data sources; iii) build a mutual understanding of the strengths and weaknesses of available data and ensure broad ownership of the results; and iv) request estimates produced by the country as further explained hereafter.

B) Estimates produced by each country are requested through a country consultation conducted by the World Health Organization. Following the WHO Executive Board resolution (EB107.R8), this process starts with WHO sending a formal request to ministries of health to nominate a focal point for the consultation. WHO sends draft estimates and methodological descriptions to them, copying countries’ focal points for SDG reporting where nominated at the request of the UN Statistics Division. Codes are available to reproduce the estimates shared. The focal points then send to WHO their comments, often including new data or revised country estimates that are used to update the country estimates. Estimates produced by the countries are subject to the same quality assessment process and included in the joint database if they are not flagged in consumption or the health budget share (see section 4.j).

3.c. Data collection calendar

A country consultation on SDG 3.8.2 estimates is typically conducted between January and March every two years.

3.d. Data release calendar

SDG 3.8.2 estimates at country, regional and global levels are released every two years either on December 12 (Universal Health Coverage day) or in September (UN General Assembly).

3.e. Data providers

National Statistical Offices in collaboration with Ministries of Health. See 3.a Data sources for further details.

3.f. Data compilers

The World Health Organization and the World Bank.

3.g. Institutional mandate

WHO support for monitoring the financial protection dimension of Universal Health Coverage (target 3.8, indicator 3.8.2 specifically) is underpinned by Resolution WHA58.33 on sustainable health financing, universal coverage and social health insurance.

4.a. Rationale

Target 3.8 is about universal health coverage (UHC) and is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The concern is with all people and communities receiving the quality health services they need (including medicines and other health products) without financial hardship. Financial hardship is a key consequence of inadequate financial risk protection mechanisms and can be experienced in any country, regardless of the income level and type of health system. Indicator 3.8.2 is about identifying people with out-of-pocket health spending on health exceeding their ability to pay, which might lead to cutting spending on other basic needs such as education, food, housing and utilities. Reducing financial hardship in health is important on the global development agenda as well as a priority of the health sector of many countries across all regions.

4.b. Comment and limitations

It is feasible to monitor indicator 3.8.2 on a regular basis using the same household survey data that is used to monitor SDG targets 1.1 and 1.2 on poverty^{^[6]}. These surveys are also regularly conducted for other purposes, such as calculating weights for the Consumer Price Index. These surveys are typically undertaken by National Statistical Offices (NSOs). Thus, monitoring the proportion of the population with large household expenditures on health as a share of total household consumption or income does not add any additional data collection burden so long as the health expenditure component of the household non-food consumption data can be identified. While this is an advantage, indicator 3.8.2 suffers from the same challenges of timeliness, frequency, data quality and comparability of surveys as SDG indicator 1.1.1. However, indicator 3.8.2 has its own conceptual and empirical limitations.

First, challenges to track out-of-pocket health spending (numerator): indicator 3.8.2 attempts to identify financial hardship that individuals face when using their income, savings or taking loans to pay for health care. However, most household surveys fail to identify the source of funding used by a household that is reporting health expenditure. In countries where there is no retrospective reimbursement of household spending on health, this is not a problem. If a household does report any expenditure on health, it would be because it will not be reimbursed by any third-party payer. It is, therefore, consistent with the definition given for direct health care payments (the numerator). For those countries, on the other hand, where there is retrospective reimbursement – for example, via a contributory health insurance scheme - the amount reported by a household on health expenditures might be totally or partially reimbursed at some later point, perhaps outside the recall period of the household survey.

Clearly, more work is needed to ensure that survey instruments gather information on the sources of funding used by the household to pay for health care or that the household survey instrument always specifies that health expenditures should be net of any reimbursement. The survey instrument and sample design should also be carefully reviewed to minimize measurement errors due to both non-sampling errors such as very short or very long recall periods precluding proper data collection of all health care components (overnight stay, medicines, etc.); or sampling errors such as over-sample of areas with a particularly low burden of disease.

Second, the sensitivity of the indicator to the choice of the welfare metric for disaggregation (consumption or income in the denominator): in the current definition of indicator 3.8.2, large health expenditures can be identified by comparing how much household spend on health to either household income or total household expenditure. Expenditure is the recommended measure of a household’s resources (see concept section), but recent empirical work has demonstrated that while statistics on 3.8.2 at the country level are fairly robust to such choice, their disaggregation by income group is pretty sensitive to it. Income-based measures show a greater concentration of the proportion of the population with large household expenditure on health among the poor than expenditure-based measures (see Chapter 2 in the WHO and World Bank 2017 report on tracking universal health coverage as well as Wagstaff et al. 2018).

Third, cut-off values to identify large health expenditures: indicator 3.8.2. relies on a single cut-off point to identify what constitutes ‘large health expenditure as a share of total household expenditure or income’. People just below such threshold are not taken into account, which is always the problem with measures based on cut-offs. This is simply avoided by plotting the cumulative distribution function of the health expenditure ratio behind 3.8.2. By doing so, it is possible to identify for any threshold the proportion of the population that is devoting any share of its household’s budget to health.

Fourth, there are other indicators used to measure financial hardship, all based on the same data sources. The current definition of SDG indicator 3.8.2 is based on methodologies dating back to the 1990s developed in collaboration with academics at the World Bank and the World Health Organization. It corresponds to an indicator of the incidence of catastrophic health spending using a budget share approach (see references). In addition to SDG indicator 3.8.2, WHO also defines large health expenditure in relation to non-subsistence spending^{^[7],^[8],^[9],} and both WHO and the World Bank use indicators of impoverishing health spending to assess to what extent OOP health spending deters efforts to “End poverty in all its form everywhere” (SDG 1).

Fifth, SDG indicator 3.8.2. needs to be tracked jointly with SDG indicator 3.8.1, as well as indicators of barriers to access. Two indicators have been chosen to monitor target 3.8 on Universal Health Coverage within the SDG framework. SDG indicator 3.8.1 is for the health service coverage dimension of universal health coverage (UHC), and SDG indicator 3.8.2 tracks the financial protection dimensions. These two indicators should always be monitored jointly. Indeed, some of the people seeking care face barriers to access related to financial constraints, acceptability issues, unavailability of services, or accessibility. Those unable to overcome such barriers (financial and non-financial ones) will not report any spending on health, which will tend to reduce SDG indicator 3.8.2 rates. When this happens, SDG indicator 3.8.1 levels should also be low as the tracer indicators of service coverage should reflect that large fractions of the population are unable to get the services they need. But specific indicators on barriers to access ought to be tracked to understand which type of barriers is precluding access to needed services.

⁶

http://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf ↑

⁷

Chapter 2 in “Tracking universal health coverage: 2017 global monitoring report”, World Health Organization and International Bank for Reconstruction and Development/ The World Bank; 2017; http://www.who.int/healthinfo/indicators/2015/en/ ; ↑

⁸

Xu, K., Evans, D. B., Carrin, G., Aguilar-Rivera, A. M., Musgrove, P., and Evans, T. (2007), “Protecting Households From Catastrophic Health Spending,” Health Affairs, 26, 972–983. Xu, K., Evans, D., Kawabata, K., Zeramdini, R., Klavus, J., and Murray, C. (2003), “Households Catastrophic Health Expenditure: A Multi-Country Analysis,” The Lancet, 326, 111–117. ↑

⁹

http://www.euro.who.int/en/health-topics/Health-systems/health-systems-financing/publications/clusters/universal-health-coverage-financial-protection;http://applications.emro.who.int/dsaf/EMROPUB_2016_EN_19169.pdf?ua=1 ; http://apps.searo.who.int/uhc http://www.paho.org/hq/index.php?option=com_content&view=article&id=11065%3A2015-universal-health-coverage-latin-america-caribbean&catid=3316%3Apublications&Itemid=3562&lang=en ↑

4.c. Method of computation

Population weighted average number of people with large household expenditure on health as a share of total household expenditure or income

$\frac{\sum_{i} m_{i} ω_{i} 1 (\frac{h e a l t h e x p e n d i t u r e o f t h e h o u s e h o l d i}{t o t a l e x p e n d i t u r e o f t h e h o u s e h o l d i} > τ)}{\sum_{i} m_{i} ω_{i}}$

where i denotes a household, 1() is the indicator function that takes on the value 1 if the bracketed expression is true, and 0 otherwise, m_i corresponds to the number of household members of i, $ω_{i}$ corresponds to the sampling weight of household i, $τ$ is a threshold identifying large household expenditure on health as a share of total household consumption or income (i.e., 10% and 25%).

Household health expenditure and household expenditure or income are defined as explained in the 2.a Definitions and concepts section. For more information about the methodology, please refer to Wagstaff et al. (2018) and Chapter 2 in the WHO and World Bank 2017 report on tracking universal health coverage.

4.d. Validation

The microdata obtained by WHO is requested to National Statistical Offices with the denominator (household total consumption expenditure) already constructed following their own guidelines and follows those guidelines when the denominator is not provided. WHO generates the numerator (household total health spending) following the definitions and classifications described in 2.a and 2.c.

The microdata obtained by the World Bank is provided by country governments and typically includes the denominator and the numerator already constructed. Sometimes, the World Bank has to construct the welfare aggregate or adjust the aggregate provided by the country.

The microdata obtained by both institutions to track SDG indicator 3.8.2 has typically already been checked for quality to track other important indicators (e.g. SDG indicator 1.1.1). A quality assessment is performed before consulting countries on SDG 3.8.2 estimates (see section 4.k).

The estimates produced by both organizations are included in a consultation to obtain the country’s feedback and revise as needed.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

At the country level, no imputation is attempted to produce estimates. The proportion of the population with large household expenditure on health as a share of total household expenditure or income is estimated for all years for which a nationally representative survey on the household budget, household income and expenditure, socio-economic conditions or living standards is available with information on both total household expenditure or income and total household expenditure on health. When there are multiple surveys over time for the same country from different collections, a preference is given to estimates produced based on the same type of survey. A series of tests is performed to retain the best performing series (see 4.k).

• At regional levels

Because surveys are not conducted yearly in most countries, SDG 3.8.2 estimates across countries are computed for different years. To compute regional and global aggregates for a common reference year (i.e. every five years between 2000 and 2015; every two years from 2015.), survey-based country estimates are “lined-up” using one of the following different methods depending upon the availability of information for that country around or at the reference year (T*): In countries for which there is an observed incidence rate of the SDG indicator 3.8.2 in the reference year T*, this point is used. When there are at least two observed incidence rates of the SDG indicator 3.8.2 around the reference over a 5-year window around the reference year [T*–5; T*+5], linear interpolation is used to project the value of SDG indicator 3.8.2 in the reference year. If these conditions are not met but there are at least two observed incidences rates of the SDG indicator 3.8.2, a multilevel model is estimated using the aggregate share of out-of-pocket health spending over total consumption expenditure as the explanatory variable if that information is available. If such information is not available or there aren’t two incidence rates of the SDG indicator 3.8.2, the incidence rate is imputed in the reference year with the median incidence in that year among countries within the same income group (low, lower-middle, upper-middle, or high) as classified by the World Bank. If such classification is missing, the regional median value of the SDG indicator 3.8.2 at the 10% threshold is used. The regional classification used for the imputation is M49 level 1. The country estimates for the reference year are then aggregated up to the regional and global levels to compute the “Total population with household expenditures on health greater than 10% of total household income or expenditure” in millions. The proportion of the total population at the global and regional levels is then calculated by expressing these numbers as a share of the relevant population, equivalent to taking a population-weighted average of the relevant country rates. For more information, pleas consult the WHO Global Health Observatory metadata registry (https://www.who.int/data/gho/indicator-metadata-registry/imr-details/4844).

The aggregate proportion of the population with large household expenditure on health as a share of total household expenditure or income for a region corresponds to the total number of people across all the countries in that region with such large expenditures divided by the total number of people in that region.

4.g. Regional aggregations

Regional and global aggregates correspond to population-weighted averages of the “lined-up” country estimates (see 4.f).

The World Bank and the World Health Organization use their own regional grouping in addition to the regional breakdown used for SDG reporting.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

All documentation needed to compile the data at the national level is shared with nominated focal points every two years. It can be requested by National Statistical Offices as well as Ministries of Health along with Stata codes, to uhc_stats@who.int, subject: package to produce SDG indicator 3.8.2.

4.i. Quality management

The quality of the estimates is managed through WHO Health Financing and Economics unit and the World Bank Health, Nutrition and Population Global Practice, Global Engagement Unit

4.j. Quality assurance

The estimates released by the World Health Organization and the World Bank are quality checked by members of the WHO Health Financing and Economics unit and the World Bank Health, Nutrition and Population Global Practice, Global Engagement Unit and submitted to a country consultation composed of members of the relevant National Statistical Offices and Ministry of health every two years.

4.k. Quality assessment

The World Health Organization and the World Bank generate indicator 3.8.2 following the methods, validation and treatment of missing values described in sections 4.c. to 4.d. Both institutions combine estimates at the meso-level. Eligibility of the estimates included in a joint global database at a country level and used to produce regional and global estimates is based on the following quality assessment:

For the denominator of the health expenditure ratio

Compare the average monthly total household per capita consumption or income in a benchmark source with the average monthly value estimated from the survey. The comparison is based on the ratio of both averages (benchmark source to the survey-based estimate). If the ratio is greater than 20% (when both averages are based on consumption) or 30% (when the benchmark source estimate is based on income and the survey-based one on consumption), the survey point is identified as an outlier in terms of consumption per capita and flagged for possible exclusion. Both averages are expressed in interntional dollars. The source for the benchmark average is either the Poverty and Inequality Platform^{^[10]} (already expressed in international dollars), or derived from the World Development Indicators (WDI)^{^[11]} andcomputed as the household final consumption expenditures in constant international dollars divided by the total population. The average estimated from the survey is available in local nominal currency units. It is converted into internation dollars using purchasing power parities (PPP) for private consumption and consumer index prices. PPP data are downloadable from the World Bank’s (WDI) data website14 and the Poverty and Inequality Platform (PIP). Data on CPIs is also downloadable from the Poverty and Inequality Platform (PIP). PIP is the preferred data source for both CPIs and PPPs.
Compare the poverty headcount estimated from the survey using international poverty lines with the poverty incidence reported in Poverty and Inequality Platform at the same poverty lines (benchmark value). When the absolute difference between the benchmark value and the survey-based estimate exceeds 10 percentage points, the survey-based point is identified as an outlier to track poverty using international poverty lines and flagged for possible exclusion. An extreme and moderate poverty line are used for this assessment. The latest value of international extreme poverty line is $2.15 per day per capita using 2017 purchasing power parities (PPPs) for private consumption and replaces the $1.90 poverty line based on 2011 PPPs. The latest value of the moderate international poverty line is $3.65 per person per day is based on 2017 PPPs which replaces the $3.20 poverty line based on 2011 PPPs. It corresponds to the typical standard used to assess national poverty levels in lower-middle-income countries. For more information about the latest purchasing power parity revision (PPP), please consult https://www.worldbank.org/en/news/factsheet/2022/05/02/fact-sheet-an-adjustment-to-global-poverty-lines

For the numerator of the health expenditure ratio

Compare the average health expenditure ratio in the survey to a benchmark average health budget share. The latter is constructed from national health accounts data as the ratio of the aggregate measure of household out-of-pocket expenditures to the final consumption expenditure of households and profit institutions serving households, both in current local currency. When the absolute difference exceeds 5 percentage points, the survey point is identified as an outlier in terms of household budget share spent on health and flagged for possible exclusion. The macro-indicators are available from the Global Health Expenditure Database (GHED)^{^[12]}.

These benchmarks are also used to decide which estimates to accept between two estimates for those countries and the years for which both institutions have the same data source. For a survey-based estimate of SDG indicator 3.8.2 to be included in the joint database and, therefore, in the country consultation conducted every two years previously described, it cannot be an outlier in consumption, nor in terms of the health budget share.

Estimates produced by the countries and shared through the country consultation are subject to the same quality assurance process. They are included in the joint database if they are not flagged neither in consumption nor in the health budget share.

¹⁰

https://pip.worldbank.org/home ↑

¹¹

https://datacatalog.worldbank.org/dataset/world-development-indicators ↑

¹²

https://apps.who.int/nha/database ↑

5. Data availability and disaggregation

Data availability:

The number of countries or territories with SDG 3.8.2 data increases over time as more surveys become available.. For more information and to get the latest updates, please use WHO and World Bank dedicated data portals:

https://www.who.int/data/gho/data/themes/topics/financial-protection and

https://datatopics.worldbank.org/universal-health-coverage/

Time series:

The frequency of such data is similar to the frequency of the data used to produce SDG indicator 1.1.1. It varies across countries but on average, this ranges from an annual 1-year basis to 3 to 5 years.

Disaggregation:

The following disaggregation is possible in so far as the survey has been designed to provide representative estimates and/or there are enough observations collected at such level:

Geographic location (rural/urban)
Sex of the head of the household (male/female);
Age and sex of the head of the household (below 60 years old/ 60 years or older; male/female);
Age composition of the household based on the following grouping: “Adults only (20-59 years old)” - households that consist of members aged between 20 and 59 years old; “Adults with children and adolescents (below 60 years old members)” - households that consist of members aged below 60 only as follows: at least one member below 20 years old AND at least one member aged between 20 and 59 years old; “Multigenerational households (all ages)” - households that include at least one person below 20 years old AND at least one person aged between 20 and 59 years old AND at least one person >= 60 years old; “Adults with older persons (from 20 years old)” - households that consist of members aged >=20 only as follows: at least one person aged between 20 and 59 years old AND at least one person >= 60 years old; “Only older adults (>=60 years old)” - households that consist of members aged >=60 years old only; “Only members below 20 years old” - households that consist of members aged below 20 years old only.
Geographic location (rural/urban)
Other possible disaggregation are possible such as by quintiles of the household welfare measures (total household consumption expenditure or income). See section 4.b on comments and limitations for the sensitivity of the disaggregation to the choice of the welfare measure.

6. Comparability/deviation from international standards

Sources of discrepancies:

Country-level estimates are all based on nationally representative surveys with information on both household total expenditure or income and household expenditure on health (see data sources). In most cases, such data come from non-standard household surveys, and ex-post-standardization processes can be designed to increase the degree of comparability across countries. For instance, regional teams from the World Bank produce standardized versions of raw datasets following common regional proceduressuch as the Eastern Europe and Central Asia poverty harmonized datasets (ECAPOV^{^[13]}); the Survey based Harmonized Indicators (SHIP) collection results from a poverty program on harmonized household surveys in the World Bank’s African region, while the Standardized Household Economic Survey (SHES) collection was developed by the World Bank for the international comparison program. The Luxembourg income study (LIS) datasets result from an effort to harmonize datasets from many high and middle-income countries^{^[14]}.

In some cases, the raw data is accessible to produce country-level estimates. In some countries, both raw data and standardized versions are available; in some countries, only the standardized version is available. When multiple versions of the same survey are available, the estimate which performed best in a series of quality assurance tests is retained (see collection process). When a standardized version of a nationally designed survey instrument is chosen, there are differences between expenditure variables generated using the raw data and the expenditure variables generated using the harmonization procedures, which might result in the different estimated incidence of the population with large household expenditure on health as a share of household total expenditure or income.

¹³

https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.15/2016/Wshp/Session_A._LEAD_PRESENTATION_WB_ENG.pdf ↑

¹⁴

http://www.lisdatacenter.org/ ↑

7. References and Documentation

URL:

https://www.who.int/data/gho/data/themes/topics/financial-protection; http://datatopics.worldbank.org/universal-health-coverage/

References:

Global monitoring reports (e.g. 2015, 2017, 2019, 2021)

https://www.who.int/teams/health-systems-governance-and-financing/global-monitoring-report

Methodology:

Chapter 2 on Financial protection in “Tracking universal health coverage: 2017 global monitoring report”, World Health Organization and International Bank for Reconstruction and Development/ The World Bank; 2017;
Wagstaff, A., Flores, G., Hsu J., Smitz, M-F., Chepynoga, K., Buisman, L.R., van Wilgenburg, K. and Eozenou, P., (2018), “Progress on catastrophic health spending in 133 countries: a retrospective observational study”, the Lancet Global Health, volume 6, issue 2, e169-e179.

http://dx.doi.org/10.1016/S2214-109X(17)30429-1

Chapter 18 of “Analyzing health equity using household survey data”. Washington, DC: World Bank Group; 2008, http://www.worldbank.org/en/topic/health/publication/analyzing-health-equity-using-household-survey-data

3.9.1

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination

0.c. Indicator

Indicator 3.9.1: Mortality rate attributed to household and ambient air pollution

0.d. Series

Applies to all series

0.e. Metadata update

2023-07-10

0.f. Related indicators

11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)

7.1.2: Proportion of population with primary reliance on clean fuels and technology

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

The mortality rate attributable to the joint effects of household and ambient air pollution can be expressed as: crude death rate or age-standardized death rate. Crude rates are calculated by dividing the brut number of deaths by the total population (or indicated if a different population group is used, e.g. children under 5 years), while the age-standardized rates adjust for differences in the age distribution of the population by applying the observed age-specific mortality rates for each population to a standard population.

Evidence from epidemiological studies have shown that exposure to air pollution is linked, among others, to the important underlying causes of death taken into account in this estimate:

- Acute lower respiratory infections (estimated in all age groups; ICD-10: J09-J22, P23, U04 );

- Cerebrovascular diseases (stroke) in adults (estimated above 25 years; ICD-10: I60-I69);

- Ischaemic heart diseases (IHD) in adults (estimated above 25 years; ICD-10: I20-I25);

- Chronic obstructive pulmonary disease (COPD) in adults (estimated above 25 years; ICD-10: J40-J44); and

- Lung cancer in adults (estimated above 25 years; ICD-10: C33-C34).

Concepts:

The mortality resulting from the exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuels used for cooking and/or heating was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood kerosene, wood, coal, animal dung, charcoal, and crop wastes.

2.b. Unit of measure

Deaths per 100,000 population

2.c. Classifications

Not applicable

3.a. Data sources

A. Exposure:

Household air pollution: Indicator 7.1.2 was used as exposure indicator Ambient air pollution: Annual mean concentration of particulate matter of less than 2.5 µm was used as exposure indicator for ambient air pollution. The data is modelled according to methods described for Indicator 11.6.2.

B. Exposure-response function:

The integrated exposure-response functions (IER) developed for the Global Burden of Disease (GBD) project 2010 and 2013 (Burnett et al, 2014 and Forouzanfar et al, 2015) were used. These IERs were updated using the most recent epidemiological evidence identified through a systematic search of studies on particulate matter and mortality, for the five outcomes of interest.

The exposure-response function captures the magnitude of the death risks due to the exposure to air pollution by integrating epidemiological evidence from four sources of PM: ambient air pollution, household air pollution, active smoking, and second-hand smoking; and excluding the possible effects of other risk factors on the outcomes of interest. Due that, it is possible to assess the attributable burden due to household and ambient air pollution using the same IERs.

The IER has recently been included and is available for download in the AirQ+ software tool for health risk assessment of air pollution, version 2.2 (released in March the 14^th, 2023).

C. Background health burden: The total number of deaths by country, disease, sex and age group have been developed by the World Health Organization’s (WHO 2019b) Global Health Estimates (GHE).

3.b. Data collection method

A. Exposure:

Household air pollution: As reported for Indicator 7.1.2
Ambient air pollution: As reported for Indicator 11.6.2.

B. Exposure-response function:

Modelled by the WHO Air Quality and Health Unit with input from epidemiological studies on particulate matter and mortality, collected through a systematic search.

C. Background health burden: collected from the WHO Global Health Estimates (GHE).

3.c. Data collection calendar

Not applicable

3.d. Data release calendar

Not applicable

3.e. Data providers

WHO Global Health Estimates

Global Burden of Disease project

WHO as a custodial agency of the SDG 11.6.2

WHO as a custodial agency of the SDG 7.1.2

3.f. Data compilers

World Health Organization (WHO)

3.g. Institutional mandate

Not applicable

4.a. Rationale

As part of a broader project to assess major risk factors to health, the mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuel use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood as wood, coal, animal dung, charcoal, and crop wastes, as well as kerosene.

Air pollution is the biggest environmental risk to health. The majority of the burden is borne by the populations in low and middle-income countries.

4.b. Comment and limitations

An approximation of the combined effects of risk factors (i.e., ambient and household air pollution) is possible if independence and little correlation between risk factors with impacts on the same diseases can be assumed (Ezzati et al, 2003). In the case of air pollution, however, there are some limitations to estimate the joint effects: limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions (Lim et al, 2012; Smith et al, 2014). In several regions, however, household air pollution remains mainly a rural issue, while ambient air pollution is predominantly an urban problem. Also, in some continents, many countries are relatively unaffected by household air pollution, while ambient air pollution is a major concern. If assuming independence and little correlation, a rough estimate of the total impact can be calculated, which is less than the sum of the impact of the two risk factors.

On the other hand, as the IER function integrates epidemiological evidence from four sources of PM (i.e., ambient air pollution, household air pollution, active smoking and second-hand smoking), some assumptions are assumed. Specifically, the relative risk at any concentration is independent of the source of PM2.5, and only dependent on the magnitude of the total exposure from all sources together (Burnett et al, 2020).

4.c. Method of computation

Attributable mortality is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed, proportion of population relying primarily on polluting fuels for cooking).

This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in that case of both the annual mean concentration of particulate matter and exposure to polluting fuels for cooking).

Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths), gives the total number of deaths that results from exposure to that particular risk factor (in the example given above, to ambient and household air pollution).

To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).

The mortality associated with household and ambient air pollution was estimated based on the calculation of the joint population attributable fractions assuming independently distributed exposures and independent hazards as described in (Ezzati et al, 2003).

The joint population attributable fraction (PAF) were calculated using the following formula:

$P A F = 1 - P R O D U C T (1 - P A F i)$

Where PAFi is PAF of individual risk factors.

The PAF for ambient air pollution and the PAF for household air pollution were assessed separately, based on the Comparative Risk Assessment (Ezzati et al, 2002) and expert groups for the Global Burden of Disease (GBD) 2010 study (Lim et al, 2012; Smith et al, 2014).

For exposure to ambient air pollution, annual mean estimates of particulate matter of a diameter of less than 2.5 um (PM25) were modelled as described in (Shaddick et al, 2018; Shaddick et al, 2021)), or for Indicator 11.6.2.

For exposure to household air pollution, the proportion of population with primary reliance on polluting fuels use for cooking was modelled (see Indicator 7.1.2 [polluting fuels use=1-clean fuels use]). Details on the model are published in (Bonjour et al, 2013).

The integrated exposure-response functions (IER) developed for the GBD 2010 and 2013 (Burnett et al, 2014 and Forouzanfar et al, 2015) were used. These IERs were updated using the most recent epidemiological evidence identified through a systematic search of studies on particulate matter and mortality for the five outcomes of interest.

The percentage of the population exposed to a specific risk factor (here ambient air pollution, i.e. PM2.5) was provided by country and by increment of 1 µg/m3; relative risks were calculated for each PM2.5 increment, based on the IER. The counterfactual concentration was selected to be between 2.4 and 5.9 µg/m³, as described elsewhere (Cohen et al, 2017). The country population attributable fraction for ALRI, COPD, IHD, stroke and lung cancer were calculated using the following formula:

$P A F = S U M (P i (R R - 1) / (S U M (R R - 1) + 1)$

Where i is the level of PM2.5 in ug/m³, and Pi is the percentage of the population exposed to that level of air pollution, and RR is the relative risk.

The calculations for household air pollution are similar and are explained in detail elsewhere (WHO 2014a).

4.d. Validation

Not applicable

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Countries with no data are reported as blank.

At regional and global levels

Countries with no data are not considered to estimate the regional and global averages.

4.g. Regional aggregations

Number of deaths by country are summed and divided by the population of countries included in the region (regional aggregates) or by the total population (global aggregates).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable

4.i. Quality management

Not applicable

4.j. Quality assurance

Not applicable

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

Data is available by country, sex, disease and age.

Disaggregation:

The data is available by country, by sex, by disease, and by age.

6. Comparability/deviation from international standards

Sources of discrepancies:

Underlying differences between country produced and internationally estimated data may due to :

- Different exposure data (annual mean concentration of particulate matter of less than 2.5 µm of diameter, proportion of population using clean fuels and technology for cooking)

- Different exposure-risk estimates

- Different underlying mortality data

7. References and Documentation

URL:

https://www.who.int/data/gho/data/themes/air-pollution

References:

Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. (2013). Solid fuel use for household cooking: country and regional estimates for 1980-2010. Environ Health Perspect. 121(7):784-90. doi: 10.1289/ehp.1205987.

Burnett RT, Pope CA 3rd, Ezzati M, Olives C, Lim SS, Mehta S, Shin HH, Singh G, Hubbell B, Brauer M, Anderson HR, Smith KR, Balmes JR, Bruce NG, Kan H, Laden F, Prüss-Ustün A, Turner MC, Gapstur SM, Diver WR, Cohen A. (2014). An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ Health Perspect. 122(4):397-403. doi: 10.1289/ehp.1307049.

Burnett R, Cohen A. (2020). Relative Risk Functions for Estimating Excess Mortality Attributable to Outdoor PM2.5 Air Pollution: Evolution and State-of-the-Art. Atmosphere, 11, 589. https://doi.org/10.3390/atmos11060589

Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V, Freedman G, Hubbell B, Jobling A, Kan H, Knibbs L, Liu Y, Martin R, Morawska L, Pope CA 3rd, Shin H, Straif K, Shaddick G, Thomas M, van Dingenen R, van Donkelaar A, Vos T, Murray CJL, Forouzanfar MH. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 389(10082):1907-1918. doi: 10.1016/S0140-6736(17)30505-6.

Ezzati M, Hoorn SV, Rodgers A, Lopez AD, Mathers CD, Murray CJ. (2003). Comparative Risk Assessment Collaborating Group. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 362(9380):271-80. doi: 10.1016/s0140-6736(03)13968-2.

Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, Burnett R, Casey D, Coates MM, Cohen A, Delwiche K, Estep K, Frostad JJ, Astha KC, Kyu HH, Moradi-Lakeh M, Ng M, Slepak EL, Thomas BA, Wagner J, Aasvang GM, Abbafati C, Abbasoglu Ozgoren A, Abd-Allah F, Abera SF, Aboyans V, Abraham B, Abraham JP, Abubakar I, Abu-Rmeileh NM, Aburto TC, Achoki T, Adelekan A, Adofo K, Adou AK, Adsuar JC, Afshin A, Agardh EE, Al Khabouri MJ, Al Lami FH, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Aleman AV, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Ali MK, Alla F, Allebeck P, Allen PJ, Alsharif U, Alvarez E, Alvis-Guzman N, Amankwaa AA, Amare AT, Ameh EA, Ameli O, Amini H, Ammar W, Anderson BO, Antonio CA, Anwari P, Argeseanu Cunningham S, Arnlöv J, Arsenijevic VS, Artaman A, Asghar RJ, Assadi R, Atkins LS, Atkinson C, Avila MA, Awuah B, Badawi A, Bahit MC, Bakfalouni T, Balakrishnan K, Balalla S, Balu RK, Banerjee A, Barber RM, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Barrientos-Gutierrez T, Basto-Abreu AC, Basu A, Basu S, Basulaiman MO, Batis Ruvalcaba C, Beardsley J, Bedi N, Bekele T, Bell ML, Benjet C, Bennett DA, Benzian H, Bernabé E, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bikbov B, Bin Abdulhak AA, Blore JD, Blyth FM, Bohensky MA, Bora Başara B, Borges G, Bornstein NM, Bose D, Boufous S, Bourne RR, Brainin M, Brazinova A, Breitborde NJ, Brenner H, Briggs AD, Broday DM, Brooks PM, Bruce NG, Brugha TS, Brunekreef B, Buchbinder R, Bui LN, Bukhman G, Bulloch AG, Burch M, Burney PG, Campos-Nonato IR, Campuzano JC, Cantoral AJ, Caravanos J, Cárdenas R, Cardis E, Carpenter DO, Caso V, Castañeda-Orjuela CA, Castro RE, Catalá-López F, Cavalleri F, Çavlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chen Z, Chiang PP, Chimed-Ochir O, Chowdhury R, Christophi CA, Chuang TW, Chugh SS, Cirillo M, Claßen TK, Colistro V, Colomar M, Colquhoun SM, Contreras AG, Cooper C, Cooperrider K, Cooper LT, Coresh J, Courville KJ, Criqui MH, Cuevas-Nasu L, Damsere-Derry J, Danawi H, Dandona L, Dandona R, Dargan PI, Davis A, Davitoiu DV, Dayama A, de Castro EF, De la Cruz-Góngora V, De Leo D, de Lima G, Degenhardt L, del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, deVeber GA, Devries KM, Dharmaratne SD, Dherani MK, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Durrani AM, Ebel BE, Ellenbogen RG, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Fahimi S, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Feigl AB, Fereshtehnejad SM, Ferrari AJ, Ferri CP, Flaxman AD, Fleming TD, Foigt N, Foreman KJ, Paleo UF, Franklin RC, Gabbe B, Gaffikin L, Gakidou E, Gamkrelidze A, Gankpé FG, Gansevoort RT, García-Guerra FA, Gasana E, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Gillum RF, Ginawi IA, Giroud M, Giussani G, Goenka S, Goginashvili K, Gomez Dantes H, Gona P, Gonzalez de Cosio T, González-Castell D, Gotay CC, Goto A, Gouda HN, Guerrant RL, Gugnani HC, Guillemin F, Gunnell D, Gupta R, Gupta R, Gutiérrez RA, Hafezi-Nejad N, Hagan H, Hagstromer M, Halasa YA, Hamadeh RR, Hammami M, Hankey GJ, Hao Y, Harb HL, Haregu TN, Haro JM, Havmoeller R, Hay SI, Hedayati MT, Heredia-Pi IB, Hernandez L, Heuton KR, Heydarpour P, Hijar M, Hoek HW, Hoffman HJ, Hornberger JC, Hosgood HD, Hoy DG, Hsairi M, Hu G, Hu H, Huang C, Huang JJ, Hubbell BJ, Huiart L, Husseini A, Iannarone ML, Iburg KM, Idrisov BT, Ikeda N, Innos K, Inoue M, Islami F, Ismayilova S, Jacobsen KH, Jansen HA, Jarvis DL, Jassal SK, Jauregui A, Jayaraman S, Jeemon P, Jensen PN, Jha V, Jiang F, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Kany Roseline SS, Karam NE, Karch A, Karema CK, Karthikeyan G, Kaul A, Kawakami N, Kazi DS, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khang YH, Khatibzadeh S, Khonelidze I, Kieling C, Kim D, Kim S, Kim Y, Kimokoti RW, Kinfu Y, Kinge JM, Kissela BM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kose MR, Kosen S, Kraemer A, Kravchenko M, Krishnaswami S, Kromhout H, Ku T, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kwan GF, Lai T, Lakshmana Balaji A, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larson HJ, Larsson A, Laryea DO, Lavados PM, Lawrynowicz AE, Leasher JL, Lee JT, Leigh J, Leung R, Levi M, Li Y, Li Y, Liang J, Liang X, Lim SS, Lindsay MP, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Logroscino G, London SJ, Lopez N, Lortet-Tieulent J, Lotufo PA, Lozano R, Lunevicius R, Ma J, Ma S, Machado VM, MacIntyre MF, Magis-Rodriguez C, Mahdi AA, Majdan M, Malekzadeh R, Mangalam S, Mapoma CC, Marape M, Marcenes W, Margolis DJ, Margono C, Marks GB, Martin RV, Marzan MB, Mashal MT, Masiye F, Mason-Jones AJ, Matsushita K, Matzopoulos R, Mayosi BM, Mazorodze TT, McKay AC, McKee M, McLain A, Meaney PA, Medina C, Mehndiratta MM, Mejia-Rodriguez F, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mendoza W, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Misganaw A, Mishra S, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Montañez Hernandez JC, Montico M, Moore AR, Morawska L, Mori R, Moschandreas J, Moturi WN, Mozaffarian D, Mueller UO, Mukaigawara M, Mullany EC, Murthy KS, Naghavi M, Nahas Z, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nash D, Neal B, Nejjari C, Neupane SP, Newton CR, Ngalesoni FN, Ngirabega Jde D, Nguyen G, Nguyen NT, Nieuwenhuijsen MJ, Nisar MI, Nogueira JR, Nolla JM, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Olusanya BO, Omer SB, Opio JN, Orozco R, Pagcatipunan RS Jr, Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Parry CD, Paternina Caicedo AJ, Patten SB, Paul VK, Pavlin BI, Pearce N, Pedraza LS, Pedroza A, Pejin Stokic L, Pekericli A, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Perry SA, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phua HP, Plass D, Poenaru D, Polanczyk GV, Polinder S, Pond CD, Pope CA, Pope D, Popova S, Pourmalek F, Powles J, Prabhakaran D, Prasad NM, Qato DM, Quezada AD, Quistberg DA, Racapé L, Rafay A, Rahimi K, Rahimi-Movaghar V, Rahman SU, Raju M, Rakovac I, Rana SM, Rao M, Razavi H, Reddy KS, Refaat AH, Rehm J, Remuzzi G, Ribeiro AL, Riccio PM, Richardson L, Riederer A, Robinson M, Roca A, Rodriguez A, Rojas-Rueda D, Romieu I, Ronfani L, Room R, Roy N, Ruhago GM, Rushton L, Sabin N, Sacco RL, Saha S, Sahathevan R, Sahraian MA, Salomon JA, Salvo D, Sampson UK, Sanabria JR, Sanchez LM, Sánchez-Pimienta TG, Sanchez-Riera L, Sandar L, Santos IS, Sapkota A, Satpathy M, Saunders JE, Sawhney M, Saylan MI, Scarborough P, Schmidt JC, Schneider IJ, Schöttker B, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serdar B, Servan-Mori EE, Shaddick G, Shahraz S, Levy TS, Shangguan S, She J, Sheikhbahaei S, Shibuya K, Shin HH, Shinohara Y, Shiri R, Shishani K, Shiue I, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh GM, Singh JA, Skirbekk V, Sliwa K, Soljak M, Soneji S, Søreide K, Soshnikov S, Sposato LA, Sreeramareddy CT, Stapelberg NJ, Stathopoulou V, Steckling N, Stein DJ, Stein MB, Stephens N, Stöckl H, Straif K, Stroumpoulis K, Sturua L, Sunguya BF, Swaminathan S, Swaroop M, Sykes BL, Tabb KM, Takahashi K, Talongwa RT, Tandon N, Tanne D, Tanner M, Tavakkoli M, Te Ao BJ, Teixeira CM, Téllez Rojo MM, Terkawi AS, Texcalac-Sangrador JL, Thackway SV, Thomson B, Thorne-Lyman AL, Thrift AG, Thurston GD, Tillmann T, Tobollik M, Tonelli M, Topouzis F, Towbin JA, Toyoshima H, Traebert J, Tran BX, Trasande L, Trillini M, Trujillo U, Dimbuene ZT, Tsilimbaris M, Tuzcu EM, Uchendu US, Ukwaja KN, Uzun SB, van de Vijver S, Van Dingenen R, van Gool CH, van Os J, Varakin YY, Vasankari TJ, Vasconcelos AM, Vavilala MS, Veerman LJ, Velasquez-Melendez G, Venketasubramanian N, Vijayakumar L, Villalpando S, Violante FS, Vlassov VV, Vollset SE, Wagner GR, Waller SG, Wallin MT, Wan X, Wang H, Wang J, Wang L, Wang W, Wang Y, Warouw TS, Watts CH, Weichenthal S, Weiderpass E, Weintraub RG, Werdecker A, Wessells KR, Westerman R, Whiteford HA, Wilkinson JD, Williams HC, Williams TN, Woldeyohannes SM, Wolfe CD, Wong JQ, Woolf AD, Wright JL, Wurtz B, Xu G, Yan LL, Yang G, Yano Y, Ye P, Yenesew M, Yentür GK, Yip P, Yonemoto N, Yoon SJ, Younis MZ, Younoussi Z, Yu C, Zaki ME, Zhao Y, Zheng Y, Zhou M, Zhu J, Zhu S, Zou X, Zunt JR, Lopez AD, Vos T, Murray CJ. (2015). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 386(10010):2287-323. doi: 10.1016/S0140-6736(15)00128-2.

Shaddick G, Thomas ML, Green A, Brauer M, van Donkelaar A, Burnett R, Chang HH, Cohen A, Van Dingenen R, Dora C, Gumy S, Liu Y, Martin R, Waller LA, West J, Zidek JV, Prüss-Ustün A. (2018). Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society. Series C (Applied Statistics), 67(1), 231–253. http://www.jstor.org/stable/44682225

Shaddick G, Salter JM, Peuch VH, Ruggeri G, Thomas ML, Mudu P, Tarasova O, Baklanov A, Gumy S. (2021). Global Air Quality: An Inter-Disciplinary Approach to Exposure Assessment for Burden of Disease Analyses. Atmosphere, 12, 48. https://doi.org/10.3390/atmos12010048

Smith KR, Bruce N, Balakrishnan K, Adair-Rohani H, Balmes J, Chafe Z, Dherani M, Hosgood HD, Mehta S, Pope D, Rehfuess E; HAP CRA Risk Expert Group. (2014). Millions dead: how do we know and what does it mean? Methods used in the comparative risk assessment of household air pollution. Annu Rev Public Health. 35:185-206. doi: 10.1146/annurev-publhealth-032013-182356

WHO (2014a). Methods description for the burden of disease attributable to household air pollution. Access at: http://www.who.int/phe/health_topics/outdoorair/database/HAP_BoD_methods_March2014.pdf?ua=1

WHO (2019b). Global Health Estimates 2019: Deaths by Cause, Age and Sex, by Country, 2000-2019 (provisional estimates). Geneva, World Health Organization, 2019.

3.9.2

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination

0.c. Indicator

Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)

0.d. Series

Not applicable

0.e. Metadata update

2022-07-07

0.f. Related indicators

Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

The mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services) as defined as the number of deaths from unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe WASH services) in a year, divided by the population, and multiplied by 100,000.

Concepts:

Deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services, expressed per 100,000 population; The included diseases are diarrhoea (GHE code 110 which includes ICD-10 codes A00, A01, A03, A04, A06-A09), acute respiratory infections (GHE code 380 which includes ICD-10 codes H65-H66, J00-J22, P23, and U04) intestinal nematode infections (GHE codes 340, 350 and 360 which include ICD-10 codes B76-B77, and B79) and protein-energy malnutrition (GHE code 550 which includes ICD-10 codes E40-E46).

2.b. Unit of measure

Mortality rate (deaths per 100,000 population)

2.c. Classifications

Not applicable

3.a. Data sources

Data is compiled mainly from country and other databases directly. To maximize the data for robust estimates, as well as to reduce duplication of data collection to avoid further data reporting burden on countries, complementary data are used from various databases (please refer to section 4.c. for specific data sources).

3.b. Data collection method

WHO conducts a formal country consultation process before releasing its cause-of-death estimates.

3.c. Data collection calendar

Ongoing

3.d. Data release calendar

2022, second quarter

3.e. Data providers

National statistics offices, Various line ministries and databases covering civil registration with complete coverage and medical certification of cause of death.

3.f. Data compilers

WHO

3.g. Institutional mandate

The World Health Organization (WHO) is the Custodian Agency or co-Custodian Agency for reporting on several SDG indicators, including indicator 3.9.2, the mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services).

4.a. Rationale

The indicator expresses the number of deaths from inadequate water, sanitation and hygiene (with focus on WASH services) which could be prevented by improving those services and practices. It is based on both the WASH service provision in the country, as well as the related health outcomes, and therefore provides important information on the actual disease caused by the risks measured in targets 6.1 and 6.2.

4.b. Comment and limitations

Data rely on (a) statistics on WASH services (6.1 and 6.2), which are well assessed in almost all countries, and (b) data on deaths. Data on deaths are also widely available from countries from death registration data or sample registration systems, which are certainly feasible systems. Such data are crucial for improving health and reducing preventable deaths in countries. The main limitation is that not all countries do have such registration systems to date, and data need to be completed with other type of information.

4.c. Method of computation

4.c.i. Model

'WHO estimation of health impacts from environmental risks is based on comparative risk assessment (CRA) methods, which are used extensively in burden of disease assessments (Ezzati et al., 2002). This approach estimates the proportional reduction in disease or death that would occur if exposures were reduced to an alternative baseline level bearing a minimum risk (also referred to as theoretical minimum risk), while other conditions remain unchanged. The CRA methodology combines data on exposure, disease burden and the exposure-response relationship to estimate the burden of disease associated with that exposure (Ezzati et al., 2002). For each risk factor (unsafe water, sanitation, or hygiene), the population attributable fraction (PAF) is estimated by comparing current exposure distributions to a counterfactual distribution, for each exposure level, sex and age group:

$P A F = \frac{\sum_{i = 1}^{n} p_{i} ({R R}_{i} - 1)}{\sum_{i = 1}^{n} p_{i} ({R R}_{i} - 1) + 1}$

Where pi and RR_i are the proportion of the exposed population and the relative risk at exposure level i, respectively, and n is the total number of exposure levels. The joint burden of exposure to unsafe water, sanitation and hygiene was estimated by the following formula (6):

$P A F = 1 - \prod_{r = 1}^{R} (1 - {P A F}_{r})$

Where r is the individual risk factor, and R the total of risk factors accounted for in the cluster. Additional details on the methods of estimation are available from various publications (1,7).

This methodology has been used extensively to calculate the health gains from improvements in water supply, as well as sanitation and hygiene and had been published in various documents (Clasen et al., 2014; Prüss-Ustün et al., 2014; Prüss-Ustün et al., 2019)

The following four types of data are required to produce estimates for indicator 3.9.2:

Data type

Source

Population

Country level population figures

UN Population Division. https://population.un.org/wpp/

Exposure

The necessary water indicators include

safely managed drinking water services;
basic drinking water services;
population using surface water, unimproved drinking water sources, or limited drinking water services;
population practising household water treatment with filtration, chlorination, or solar disinfection.

The necessary sanitation indicators include

basic sanitation services with sewer connections;
basic sanitation services without sewer connections;
open defecation, unimproved sanitation facilities, or limited sanitation services

One hygiene indicator is used:

population practising handwashing with soap and water after potential faecal contacts.

Many of these data are available in the global database maintained by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, and several are SDG indicators. Where countries lack data for one or more indicators, missing values are imputed using multi-level logistic modelling (Wolf et al, 2013; Prüss-Ustün et al., 2014; Prüss-Ustün et al., 2019)

www.washdata.org

Diseaase burden

The total number of deaths and DALYs caused by diarrhoeal disease per year.

WHO Global Health Observatory (GHO) https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death

Exposure-response relationship

The relative risk, which links exposure with disease.

The calculation uses the exposure-response relationship for drinking water and diarrhoea calculated as part of the most recent systematic review of water and sanitation intervention studies and impacts on diarrhoea

(Wolf, J, 2022, under review).

4.d. Validation

Draft estimates are reviewed with Member States through a WHO country consultation process and SDG focal points every time new data are generated. In addition, the methods and data are published in a peer-reviewed journal. 2016 estimates were published in 2019 (see 4.c.), and the manuscript for the 2019 estimates presently being submitted is currently under development, with plans for submission to a peer-reviewed journal by April 2022.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For population data and disease burden envelopes, complete datasets are available, so there are no issues with missing data at the country level. For exposure data, many of these data are available in the global database maintained by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, and several are SDG indicators. Where data are lacking for one or more required indicators, missing values are imputed using multi-level logistic modelling (Wolf et al, 2013).

At regional and global levels

Not applicable

4.g. Regional aggregations

Country estimates of number of deaths by cause are summed to obtain regional and global aggregates. Populations published by the UNPD’s World Population Prospects are aggregated to regional and global levels. The mortality rate is then calculated at the regional and global levels.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Data for this indicator are not routinely collected by countries. Rather, they are modelled using Comparative Risk Assessment methods^[1] (For further information please see section 4.c.). However, while countries do not routinely collect these data to feed into the global figures for indicator 3.9.2, there have been a small number of requests for technical assistance from WHO country offices for support in the country-level calculation of WASH-attributable disease burden. A country tool is in development to enable countries to calculate the estimated burden of disease associated with WASH for their own country, and this will be available later this year.

Prüss-Ustün A, Wolf J, Bartram J, Clasen T, Cumming O, Freeman MC, Gordon B, Hunter PR, Medlicott K, Johnston R. Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low- and middle-income countries. International journal of hygiene and environmental health. 2019 Jun 1; 222(5): 765-77. ↑

4.i. Quality management

For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi

4.j. Quality assurance

For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi

4.k. Quality assessment

For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi

5. Data availability and disaggregation

Data availability:

Data are available for 183 UN Member States, and can be accessed through the WHO Global Health Observatory: https://apps.who.int/gho/data/view.main.INADEQUATEWSHv?lang=en

Time series:

Previous rounds of estimates have been published with reference years of 2012, 2015, and 2016. As there have been changes in methods for diarrhoea, they have limited comparability.

Disaggregation:

National, regional and global data are available at the total population; disaggregated into male and female populations; and for the population under age five.

6. Comparability/deviation from international standards

Sources of discrepancies:

WHO is required by World Health Assembly resolution to consult on all WHO statistics, and seek feedback from countries on data about countries and territories. Before publishing, all estimates undergo country consultations.

7. References and Documentation

URL:

WHO indicator definition https://www.who.int/data/gho/indicator-metadata-registry/imr-details/2260

WHO methods and data sources for global causes of death, 2000–2012 https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf

References:

Clasen, T., Prüss-Ustün, A., Mathers, C. D., Cumming, O., Cairncross, S., & Colford, J. M. (2014). Estimating the impact of unsafe water, sanitation and hygiene on the global burden of disease: evolving and alternative methods. Trop Med Int Health, 19(8), 884-893. https://doi.org/10.1111/tmi.12330

Ezzati, M., Lopez, A. D., Rodgers, A., Vander Hoorn, S., Murray, C. J., & Group, C. R. A. C. (2002). Selected major risk factors and global and regional burden of disease. Lancet, 360(9343), 1347-1360. https://doi.org/10.1016/S0140-6736(02)11403-6

'Prüss-Ustün, A., Bartram, J., Clasen, T., Colford, J. M., Cumming, O., Curtis, V., . . . Cairncross, S. (2014). Burden of disease from inadequate water, sanitation and hygiene in low- and middle-income settings: a retrospective analysis of data from 145 countries. Trop Med Int Health, 19(8), 894-905. https://doi.org/10.1111/tmi.12329

Prüss-Ustün A, Wolf J, Bartram J, Clasen T, Cumming O, Freeman MC, Gordon B, Hunter PR, Medlicott K, Johnston R. (2019) Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low- and middle-income countries. International journal of hygiene and environmental health. 222(5): 765-77.

https://doi.org/10.1016/j.ijheh.2019.05.004

'WHO (2014). Preventing diarrhoea through better water, sanitation and hygiene: exposures and impacts in low- and middle-income countries. https://www.who.int/publications/i/item/9789241564823

Wolf, J., Bonjour, S., & Prüss-Ustün, A. (2013). An exploration of multilevel modeling for estimating access to drinking-water and sanitation. Journal of Water and Health, 11(1), 64-77

https://doi.org/10.2166/wh.2012.107

3.9.3

0.a. Goal

Goal 3: Ensure healthy lives and promote well-being for all at all ages

0.b. Target

Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination

0.c. Indicator

Indicator 3.9.3: Mortality rate attributed to unintentional poisoning

0.d. Series

Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population) SH_STA_POISN

0.e. Metadata update

2022-03-31

0.f. Related indicators

Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

The mortality rate attributed to unintentional poisoning as defined as the number of deaths of unintentional poisonings in a year, divided by the population, and multiplied by 100,000.

Concepts:

Mortality rate in the country from unintentional poisonings per year. The International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding to the indicator includes X40, X43, X46-X48, X49.

2.b. Unit of measure

Rate per 100,000 population

2.c. Classifications

Poisonings are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See 2.a).

3.a. Data sources

Data inputs to the estimate include (a) data on Water, Sanitation, and Hygiene services and practices, and (b) cause-of-death data, of which the preferred data source is death registration systems with complete coverage and medical certification of cause of death. Other possible data sources include household surveys with verbal autopsy, sample or sentinel registration systems, special studies and surveillance systems.

3.b. Data collection method

WHO collects data directly from country sources, and following established method, estimates are shared with countries to receive their feedback before publication. See Indicator 6.1 for more details.

3.c. Data collection calendar

WHO sends an e-mail twice annually requesting tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis.

3.d. Data release calendar

End of 2020

3.e. Data providers

National statistics offices, various line ministries and databases covering civil registration with complete coverage and medical certification of cause of death.

3.f. Data compilers

World Health Organization (WHO)

3.g. Institutional mandate

4.a. Rationale

The measure of mortality rate from unintentional poisonings provides an indication of the extent of inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system.

4.b. Comment and limitations

Data on deaths are widely available from countries from death registration data or sample registration systems, which are feasible systems, but good quality data are not yet available in all countries. Such data are crucial for improving health and reducing preventable deaths in countries. For countries that do not have such registration systems, data need to be completed with other types of information.

4.c. Method of computation

The methods with agreed international standards have been developed, reviewed and published in various documents.

For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. Complete methodology may be found here: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf

4.d. Validation

4.e. Adjustments

Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

At regional and global levels

Not applicable

4.g. Regional aggregations

Country estimates of number of deaths by cause are summed to obtain regional and global aggregates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The cause of death categories (including unintentional poisoning follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10). Please see Annex Table A of the WHO methods and data sources for global causes of death, 2000–2019 (https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

4.i. Quality management

The World Health Organization (WHO) established a Reference Group on Health Statistics in 2013 to provide advice to it on population health statistics with a focus on methodological and data issues related to the measurement of mortality and cause-of-death patterns. The group facilitated interaction between multilateral development institutions and other independent academic groups with WHO expert groups in specific subject areas including methods to the estimation on causes of death.

4.j. Quality assurance

The data principles of the World Health Organization (WHO) provide a foundation for continually reaffirming trust in its information and evidence on public health. The five principles are designed to provide a framework for data governance for the organization. The principles are intended primarily for use by all staff in order to help define the values and standards that govern how data that flows into, across and out of the organization is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with the organization.

4.k. Quality assessment

All statements and claims made officially by WHO headquarters about population-level (country, regional, global) estimates of health status (e.g. mortality, incidence, prevalence, burden of disease), are cleared by the Department of Data and Analytics (DNA) through the executive clearance process. These include the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement. GATHER promotes best practices in reporting health estimates using a checklist of 18 items that should be reported every time new global health estimates are published, including descriptions of input data and estimation methods. Developed by a working group convened by the World Health Organization, the guidelines aim to define and promote good practice in reporting health estimates.

5. Data availability and disaggregation

Data availability:

Time series:

From 2000 to 2019

Disaggregation:

Data can be disaggregated by age group, sex and disease.

6. Comparability/deviation from international standards

Sources of discrepancies:

WHO is required by World Health Assembly resolution to consult on all its statistics, and seek feedback from countries on data about countries and territories before publishing all estimates.

7. References and Documentation

URL:

https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates

References:

WHO indicator definition (http://apps.who.int/gho/data/node.imr.SDGPOISON?lang=en)

WHO methods and data sources for global causes of death, 2000–2019

(https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf)

4.a.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for all

0.c. Indicator

Indicator 4.a.1: Proportion of schools offering basic services, by type of service

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

6.1, 6.2, 7.1, 9.c, 17.8

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UIS)

1.a. Organisation

UNESCO Institute for Statistics (UIS)

2.a. Definition and concepts

Definitions:

The percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service.

Concepts:

Electricity: Regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes.

Internet for pedagogical purposes: Internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer) and thus can also be accessed by mobile telephone, tablet, personal digital assistant, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network.

Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types:

- A desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard;

- A laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and

- A tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard.

Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building's potential users with an assurance of individual health, safety and welfare during the course of those activities.

Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment.

Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations.

Basic drinking water is defined as a functional drinking water source (MDG ‘improved’ categories) on or near the premises and water points accessible to all users during school hours.

Basic sanitation facilities are defined as functional sanitation facilities (MDG ‘improved’ categories) separated for males and females on or near the premises.

Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys.

2.b. Unit of measure

Percent (%)

2.c. Classifications

The International Standard Classification of Education (ISCED) is used to define primary, lower secondary and upper secondary education.

3.a. Data sources

(1) Administrative data from schools and other providers of education or training

(2) Cross-national learning assessments

3.b. Data collection method

For administrative sources:

The UNESCO Institute for Statistics (UIS) produces time series based on data reported by Ministries of Education or National Statistical Offices (NSOs). The data are gathered through the annual Survey of Formal Education (on access to electricity, drinking water, sanitation and handwashing facilities) and through the Survey on ICTs in Education (on access to electricity, Internet and computers). Data on adapted infrastructure are not collected currently. Countries are asked to report data according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.

The data received are validated using electronic error detection systems that check for arithmetic errors and inconsistencies and trend analysis for implausible results. Queries are taken up with the country representatives reporting the data so that corrections can be made (of errors) or explanations given (of implausible but correct results). During this process, countries are also encouraged to provide estimates for missing or incomplete data items.

In addition, countries also have an opportunity to see and comment on the main indicators the UIS produces in an annual “country review” of indicators.

For cross-national learning assessments:

Data is acquired from the administrators of cross-national assessment; typically, these are available for download publicly. UIS analyses this data to provide estimates of the indicator. When there is more than one data point available for a given level of schooling, an average is used as the indicator. Annex Table 2 presents the questionnaire used to collect data in the cross-national assessments included.

3.c. Data collection calendar

For administrative sources: Annual UIS survey (usually launched in the 4^th quarter) and UOE survey (usually launched in June).

For cross-national assessments: as data is released publicly.

3.d. Data release calendar

Biannual UIS data release (March and September).

3.e. Data providers

For administrative sources: Ministries of Education and/or National Statistical Offices.

For cross-national learning assessments: International student assessment programme administrators.

3.f. Data compilers

UNESCO Institute for Statistics (UIS)

3.g. Institutional mandate

4.a. Rationale

The indicator measures access in schools to key basic services and facilities necessary to ensure a safe and effective learning environment for all students.

A high value indicates that schools have good access to the relevant services and facilities. Ideally, each school should have access to all these services and facilities.

4.b. Comment and limitations

The indicator measures the existence in schools of the given service or facility but not its quality or operational state.

4.c. Method of computation

The number of schools in a given level of education with access to the relevant facilities is expressed as a percentage of all schools at that level of education.

${P S}_{n, f} = \frac{S_{n, f}}{S_{n}} \times 100$

where:

PS_n,f = percentage of schools at level n of education with access to facility f

S_n,f = schools at level n of education with access to facility f

S_n = total number of schools at level n of education

4.d. Validation

The UIS shares all indicator values and notes on methodology with NSOs, Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data.

4.e. Adjustments

Data should be reported according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

The UIS estimates certain key items of data that may be missing or incomplete in order to have publishable estimates at the country level. Where this is not possible, the UIS imputes missing values for use only for calculating regional and global aggregates.

In all cases, estimates are based on evidence from the country itself (e.g., information from the data provider on the size of the missing component, via correspondence, publications or data on the Ministry’s or National Statistical Office’s Webpage, or via surveys conducted by other organizations) or on data from the country for a previous year.

Where data are available for a country for both an earlier and a more recent year than the missing year, a simple linear interpolation is made. Where data are only available for an earlier year, the most recent value is used as an estimate. Similarly, where data are only available for a more recent year, the last value is used as an estimate.

Where the relevant data are not available at all for a country, estimates may be based on another variable which is clearly linked to the item being estimated. For example, schools with access to basic services or facilities may be estimated from the total number of schools.

Where no data are available for the country in any year that can inform the estimate, the unweighted average for the region in which the country lies is used.

Currently no estimates are made for this indicator for the purpose of having publishable country-level data.

• At regional and global levels

Regional and global aggregates are derived from both publishable and imputed national data. Publishable data are the data submitted to the UIS by Member States or the result of an explicit estimation made by the Institute based on pre-determined standards. In both cases, these data are sent to Member States for review before they are considered publishable by the UIS.

When data are not available for all countries, the UIS imputes national data for the sole purpose of calculating regional averages. These imputed data are not published nor otherwise disseminated.

The regional and global aggregates are then calculated as weighted averages using the denominator of the indicator as the weight.

4.g. Regional aggregations

Regional and global aggregates are calculated as weighted averages using the denominator of the indicator as the weight. As described previously, where publishable data are not available for a given country or year, values are imputed for the purpose of calculating the regional and global aggregates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator. ISCED mappings that help countries report their data in an internationally comparable framework are available on the website of the UNESCO Institute for Statistics (http://uis.unesco.org/en/isced-mappings).

4.i. Quality management

The UIS maintains the global database used to produce this indicator. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database. Quality assurance of information produced by the cross-national assessment programs are described in their manuals.

4.j. Quality assurance

The process for quality assurance includes review of survey documentation, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers

4.k. Quality assessment

The indicator should be calculated based on data from accurate and comprehensive enumeration of schools or training institutions by level of education with and without access to the given facilities, whether these schools or training institutions are from public or private sector. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.

5. Data availability and disaggregation

Data availability:

For administrative data sources:

140 countries for electricity, 113 countries for computers, 106 countries for Internet, 109 countries for water, 103 countries for sanitation, 105 countries for hand-washing facilities and 50 countries for adapted infrastructure that have at least one data point in the period 2010-2019.

For student assessment sources:

Annex Table 1 presents indicator availability by suggested cross-national learning assessment included in the data as well as number of countries which participate in the assessment programme.

Time series:

2000-2019

Disaggregation:

By level of education

6. Comparability/deviation from international standards

Sources of discrepancies:

Nationally-published figures may differ from the international ones because of differences between national education systems and the International Standard Classification of Education (ISCED); or differences in coverage (i.e. the extent to which different types of education – e.g. private or special education – are included in one rather than the other).

7. References and Documentation

URL:

http://uis.unesco.org/

References:

The proportion of schools with access to electricity, the Internet for pedagogical purposes and computers for pedagogical purposes: see Guide to Measuring Information and Communication Technologies (ICT) in Education, UIS Technical Paper No. 2.

WASH Monitoring Indicators: https://www.unicef.org/wash

UIS Questionnaires on Statistics of Information and Communication Technologies (ICT) in Education and the Regional Module for Africa: http://uis.unesco.org/en/uis-questionnaires

Annex: methods used to estimate indicator values using cross-national assessments

Cross national assessments are sample-based and, as such, provide estimates of the proportion of schools with the given facility. Estimation methods followed those suggested by the respective organization providing the cross-national assessment data. All surveys utilized a two-stage sampling procedure, randomly selecting schools and within those classes or students. School-level (first stage) data was used to estimate the percentages of schools with the given facilities. Data was weighted by school sampling weights. The population which the sample of schools represented are presented in Annex Table 1.

Annex Table 1. Data on school environment indicators collected by suggested cross-national learning assessment
			Data collected on the following
Assessment	Number of participants (includes sub-national entities in some cases; data may not be available for all countries for a given indicator)	Target population	electricity	internet for pedagogical purposes	computers for pedagogical purposes	adapted infrastructure for students with disabilities	basic drinking water	single-sex basic sanitation facilities	basic hand-washing facilities
PISA 2018	80	secondary schools with 15 year-old students		X	X
TIMSS 2015	54 4^th grade; 46 8^th grade	schools with 8th grade; schools with 4th grade			X
PASEC 2014	10 both grades	schools with 2nd grade; schools with 6th grade	X				X
LLECE (TERCE) 2013	16 both grades	schools with 3rd grade; schools with 6th grade	X	X	X		X

Annex Table 2. School questionnaire items related to SDG 4.a.1
Survey	Population	Questionnaire item	SDG 4.a.1 sub-indicator
LLECE 2013	schools with 3rd grade students; schools with 6th grade students	¿Con cuáles de estos servicios cuenta la escuela? Luz eléctrica. Sí / No Agua potable. Sí / No	Electricity and basic drinking water
LLECE 2013		¿Cuántos computadores hay en la escuela para uso de los estudiantes? Con conexión a Internet: No hay / Entre 1 y 10 / Entre 11 y 20 / Entre 21 y 30 / Más de 30 Sin conexión a Internet: No hay / Entre 1 y 10 / Entre 11 y 20 / Entre 21 y 30 / Más de 30	Internet for pedagogical purposes; computers for pedagogical purposes
PASEC 2014	schools with 2nd grade; schools with 6th grade	65.Is there in the school...? Electricity: yes/no Piped-in water: yes/no Another source of drinking water (well, borehole…): yes/no	Electricity; drinking water
PISA 2018	secondary schools with 15 year-old students	The goal of the following set of questions is to gather information about the student-computer ratio for students in the <national modal grade for 15-year-olds> at your school. (Please enter a number for each response. Enter “0” (zero) if there are none.) At your school, what is the total number of students in the <national modal grade for 15-year-olds>? Approximately, how many computers are available for these students for educational purposes? Approximately, how many of these computers are connected to the Internet/World Wide Web?	Internet for pedagogical purposes; computers for pedagogical purposes
TIMSS 2015 4^th & 8^th grade	Math and science teachers’ classes of 4^th grade & 8^th grade students (can be aggregated to school level)	Do the students in this class have computers (including tablets) available to use during their mathematics lessons? Yes / No Do the students in this class have computers (including tablets) available to use during their science lessons? Yes / No	Computers for pedagogic use

4.b.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countries

0.c. Indicator

Indicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of study

0.e. Metadata update

2017-07-09

0.f. Related indicators

Other ODA indicators.

0.g. International organisations(s) responsible for global monitoring

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definition:

Gross disbursements of total ODA from all donors for scholarships.

Concepts:

ODA: The DAC defines ODA as “those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are

i) provided by official agencies, including state and local governments, or by their executive agencies; and

ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and

Scholarships: Financial aid awards for individual students and contributions to trainees. The beneficiary students and trainees are nationals of developing countries. Financial aid awards include bilateral

grants to students registered for systematic instruction in private or public institutions of higher education to follow full-time studies or training courses in the donor country. Estimated tuition costs

of students attending schools financed by the donor but not receiving individual grants are not included here, but under item imputed student costs (CRS sector code 1520). Training costs relate to contributions

for trainees from developing countries receiving mainly non-academic, practical or vocational training in the donor country.

3.a. Data sources

Data for scholarships are only available since 2010 when the new typology of aid was introduced in DAC statistics.

The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.b. Data collection method

3.d. Data release calendar

Data are published on an annual basis in December for flows in the previous year.

Detailed 2015 flows was published in December 2016.

3.e. Data providers

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.f. Data compilers

OECD

4.a. Rationale

Total ODA flows to developing countries quantify the public effort that donors provide to developing countries for scholarships.

4.b. Comment and limitations

Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete from 1995 for commitments at an activity level and 2002 for disbursements.

Data for scholarships are only available since 2010 when the new typology of aid was introduced in DAC statistics.

4.c. Method of computation

The sum of ODA flows from all donors to developing countries for scholarships.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Due to high quality of reporting, no estimates are produced for missing data.

• At regional and global levels

Not applicable.

4.g. Regional aggregations

Global and regional figures are based on the sum of ODA flows for scholarships.

5. Data availability and disaggregation

Data availability:

On a recipient basis for all developing countries eligible for ODA.

Time series:

Data are available from 2010.

Disaggregation:

This indicator can be disaggregated by donor, recipient country, type of finance, etc.

6. Comparability/deviation from international standards

Sources of discrepancies:

DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.

7. References and Documentation

URL:

www.oecd.org/dac/stats

References:

See all links here: http://www.oecd.org/dac/stats/methodology.htm

4.c.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing States

0.c. Indicator

Indicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education level

0.d. Series

Not applicable.

0.e. Metadata update

2021-12-06

0.f. Related indicators

1.2, 1.4, 1.a, 2.1, 2.2, 2.3, 3.7, 3.c, 3.d, 5.1, 5.5, 5.b, 8.6, 8.7, 10.2, 12.8, 13.3, 13.b

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UNESCO-UIS)

1.a. Organisation

UNESCO Institute for Statistics (UNESCO-UIS)

2.a. Definition and concepts

Definition:

The percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have received at least the minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level in a given country.

Concepts:

A teacher is trained if they have received at least the minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level in a given country.

2.b. Unit of measure

Proportion (values between 0% and 100%).

2.c. Classifications

The International Standard Classification of Education (ISCED) is used as reference to define and classify educational programmes across countries in a comparative manner.

The minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level is defined according to national standards.

The UIS is developing an International Standard Classification of Teacher Training Programmes (ISCED-T) to support the production of internationally comparable data on teacher training programmes, and to improve the availability and quality of teacher statistics, especially in reference to national programmes for pre-service teacher education. ISCED-T will also aid explore the development of an international standard for “trained” and “qualified” teachers that could be used alongside the national standards currently used for the monitoring of this target. A draft proposal of ISCED-T is submitted to the 41st Session of the UNESCO General Conference for consideration and adoption in November 2021.

3.a. Data sources

Administrative data from schools and other organized learning centres.

3.b. Data collection method

The UNESCO Institute for Statistics produces time series based on teachers’ data reported by Ministries of Education or National Statistical Offices. The data are gathered through the annual Survey of Formal Education. Countries are asked to report data according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.

3.c. Data collection calendar

Annual UIS survey (latest launched in October 2020).

3.d. Data release calendar

Biannual UIS data release (February and September).

3.e. Data providers

Ministries of Education and/or National Statistical Offices.

3.f. Data compilers

UNESCO Institute for Statistics.

3.g. Institutional mandate

The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the United Nations depository for global statistics in the fields of education, science, technology and innovation, culture and communication. The UIS is the official source of internationally comparable data used to monitor progress towards the Sustainable Development Goal on education (SDG4) and key targets related to science, culture and communication, and gender equality. The Institute also produces standards and methodologies to support the monitoring of these goal and targets.

Moreover, as part of UIS mandate attribution, Education 2030 Framework for Action stressed that “[…] Countries should seek to improve the quality, levels of disaggregation and timeliness of reporting to the UNESCO Institute for Statistics […]” (http://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf, &18). The Education 2030 Framework for Action also stated that: “In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO’s mandate, working in coordination with the SDG-Education 2030 SC” (http://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf, &100).

4.a. Rationale

Teachers play a key role in ensuring the quality of education provided. Ideally all teachers should receive adequate, appropriate and relevant pedagogical training to teach at the chosen level of education and be academically well-qualified in the subject(s) they are expected to teach. This indicator measures the share of the teaching work force which is pedagogically well-trained.

A high value indicates that students are being taught by teachers who are pedagogically well-trained to teach.

4.b. Comment and limitations

It is important to note that national minimum training requirements can vary widely from one country to the next. This variability between countries lessens the usefulness of global tracking because the indicator would only show the percent reaching national standards, not whether teachers in different countries have similar levels of training. Further work would be required if a common standard for teacher training is to be applied across countries.

4.c. Method of computation

The number of teachers in a given level of education who are trained is expressed as a percentage of all teachers in that level of education.

PTT_n = TT_n/T_n

where:

PTT_n = percentage of trained teachers at level n of education

TT_n = trained teachers at level n of education

T_n = total teachers at level n of education

n = 02 (pre-primary), 1 (primary), 2 (lower secondary), 3 (upper secondary) and 23 (secondary)

4.d. Validation

Teachers’ data used to produce this indicator are gathered through the annual Survey of Formal Education. The data received are validated using electronic error detection systems that check for arithmetic errors and inconsistencies and trend analysis for implausible results. Queries are taken up with the relevant agencies in individual countries or country representatives reporting the data so that corrections can be made (of errors) or explanations given (of implausible but correct results). During this process countries are also encouraged to provide estimates for missing or incomplete data items.

In addition, countries have an opportunity to review, comment on, and validate the main indicators the UIS produces in an annual “country review” of indicators before the publication of the data by the UIS.

4.e. Adjustments

Data should be reported according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

The UIS estimates certain key items of data that may be missing or incomplete in order to have publishable estimates at the country level. Where this is not possible the UIS imputes missing values for use only for calculating regional and global aggregates.

For the purposes of calculating the percentage of trained teachers, the UIS may make one or more of the following:

• An adjustment to account for over- or under-reporting, for example:

o To include teachers in a type of education – such as private education or special education – not reported by the country; and/or

o To include teachers in a part of the country not reported by the country.

• An estimate of the number of trained teachers in each level of education if the country only reported data for combined levels (eg total secondary rather than lower and upper secondary separately).

In all cases estimates are based on evidence from the country itself (eg information from the data provider on the size of the missing component, via correspondence, publications or data on the Ministry’s or National Statistical Office’s Webpage, or via surveys conducted by other organizations) or on data from the country for a previous year. These figures may be published: (i) as observed data if the missing items are found in a national source; (ii) as national estimates if the country is persuaded to produce estimates and submit them in place of missing data; or (iii) as UIS estimates, if the estimates are made by the UIS.

At regional and global levels

When data are not available for all countries, the UIS imputes national data for the sole purpose of calculating regional averages. These imputed data are not published nor otherwise disseminated.

Where the relevant data are not available at all for a country, estimates may be based on another variable which is clearly linked to the item being estimated. For example, trained teachers may be based on total teachers.

Where no data are available for the country in any year that can inform the estimate, the unweighted average for the region in which the country lies is used.

4.g. Regional aggregations

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Administrative teachers’ data from schools and other organized learning centres are gathered through the national annual schools census. The collected data are usually stored in the national Education Management Information System (EMIS) according to procedures in place in each country.

To assist countries to make a more informed choice in relation to EMIS, by developing standards about what an EMIS must be able to do in order to supply accurate and valid information to education sector policymakers, school managers, and international organisations as part of international data reporting, the UIS in collaboration with the Global Partnership for Education, has developed EMIS user’s and buyer’s guides ( http://emis.uis.unesco.org/buyers-and-users-guide/).

4.i. Quality management

4.j. Quality assurance

The indicator should be based on available data on trained teachers for the given level of education, from all types of educational institutions in the country (public and private). The process for quality assurance includes review of survey documentation, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.

4.k. Quality assessment

Accurate data on the number of teachers at each level of education who have the minimum required qualifications and the total number of teachers at each level in a given academic year are essential for calculating this indicator. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.

5. Data availability and disaggregation

Data availability:

124 countries for pre-primary education, 141 countries with data for primary education, 103 countries for lower secondary education and 97 countries for upper secondary education with at least one data point in the period 2010-2021.

Time series:

1998-2021 in UIS database; 2000-2021 in the SDG global database.

Disaggregation:

By sex, level of education and type of institution (public/private).

6. Comparability/deviation from international standards

Sources of discrepancies:

7. References and Documentation

URL:

http://www.uis.unesco.org

References:

EMIS user’s and buyer’s guides:

http://emis.uis.unesco.org/buyers-and-users-guide/

The Survey of Formal Education Instruction Manual http://uis.unesco.org/sites/default/files/documents/instruction-manual-survey-formal-education-2017-en.pdf

The International Standard Classification of Education (ISCED): http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf

UIS Questionnaire on Students and Teachers (ISCED 0-4)

http://uis.unesco.org/en/uis-questionnaires

4.1.1a

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes

0.c. Indicator

Indicator 4.1.1: Proportion of children and young people: (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex

0.e. Metadata update

Last updated: November 2020

0.f. Related indicators

Related indicators

The parity indices for this indicator are reported in SDG indicator 4.5.1.

0.g. International organisations(s) responsible for global monitoring

Institutional information

Organization(s):

UNESCO Institute of Statistics (UIS)

2.a. Definition and concepts

Concepts and definitions

Definition:

Percentage of children and young people achieving at least a minimum proficiency level in (i) reading and (ii) mathematics during primary education (Grade 2 or 3), at the end of primary education, and at the end of lower secondary education. The minimum proficiency level will be measured relative to new common reading and mathematics scales currently in development.

Concepts:

Minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. In September 2018, an agreement was reached on a verbal definition of the global minimum proficiency level of reference for each of the areas and domains of Indicator 4.1.1 as described in the Minimum Proficiency Levels (MPLs): Outcomes of the consensus building meeting.

Minimum proficiency levels defined by each learning assessment

To ensure comparability across learning assessments, a verbal definition of MPL for each domain and levels between cross-national assessments (CNAs) was established by conducting an analysis of the performance level descriptors (PLDs)^[1] of cross-national, regional, and community-led tests in reading and mathematics. The analysis was led and completed by the UIS and a consensus among experts on the proposed methodology was deemed adequate and pragmatic.

The global MPL definitions for the domains of reading and mathematics are presented in Table 1.

Table 1. Minimum proficiency levels defined by each learning assessment

Reading
Educational Level	Descriptor
Grade 2	They read and comprehend most of written words, particularly familiar ones, and extract explicit information from sentences.
Grade 3	Students read aloud written words accurately and fluently. They understand the overall meaning of sentences and short texts. Students identify the texts’ topic
Grades 4 & 6	Students interpret and give some explanations about the main and secondary ideas in different types of texts. They establish connections between main ideas on a text and their personal experiences as well as general knowledge.
Grades 8 & 9	Students establish connections between main ideas on different text types and the author’s intentions. They reflect and draw conclusions based on the text.

Mathematics
Educational Level	Descriptor
Grades 2-3	Students demonstrate skills in number sense and computation, shape recognition and spatial orientation.
Grades 4-6	Students demonstrate skills in number sense and computation, basic measurement, reading, interpreting, and constructing graphs, spatial orientation, and number patterns.
Grades 8 & 9	Students demonstrate skills in computation, application problems, matching tables and graphs, and making use of algebraic representations.

PLD: Performance level descriptors are descriptions of the performance levels to express the knowledge and skills required to achieve each performance level, by domain. ↑

3.a. Data sources

Data sources

Description:

Type of data sources: In school and population-based learning assessments.

Table 2. How interim reporting is structured?

	In-school based		Household Based Surveys	Grade
	Cross-national	National	Household Based Surveys	Grade
Grade 2 or 3	LLECE	Yes	MICS6	2/3 plus one year when primary lasts more than 4 years according to ISCED level of the country
	PASEC		EGRA
	TIMSS		EGMA
	PIRLS		PAL network
End of primary	LLECE	Yes	PAL network	plus or minus one year of last year of primary according to ISCED level of the country
	PASEC
	TIMSS
	PIRLS
	PILNA
	SEAMEO
	SACMEQ
End of lower secondary	PISA	Yes	Young Lives	plus two or minus one of last year of lower secondary according to ISCED level of the country
	PISA-D
	TIMSS
Definition of minimum level until 2018 release		Those defined by each assessment by point of measurement and domain
Definition of minimum level from 2019		According to alignment as adopted by Global Alliance to Monitoring Learning (GAML) and Technical Cooperation Group (TCG)
Grade for end of primary and end of lower secondary		As defined by the ISCED levels in each country
Validation		Sent from UIS for countries’ approval

3.b. Data collection method

Collection process:

Information not available.

3.c. Data collection calendar

Calendar

Data collection:

Data collection is ongoing.

3.d. Data release calendar

Data release:

February 2020

3.e. Data providers

Data providers

School Based assessments

International Large Scale Assessments are reported to the UIS by cross-national organisations (LLECE, PASEC, TIMSS, and PIRLS). Typically, Cross National Large Scale Assessment, either regional or international, define various performance levels, and report as well the mean and standard deviation. They choose as well one level as the cut-off point that defines what children/youth are below or above level.
Regional assessments: PASEC, SACMEQ, ERCE, PILNA, SEAMEO.
National Large-Scale Assessments either sample- or census- based. Countries should report the proportion of students by level of competency for each domain indicating as well the minimum proficiency level, when it is defined by the national assessment. EGRA and EGMA as reported by USAID or individual countries.

Household-Based survey

MICS6: reported to the UIS by UNICEF
Pal Network: reported to the UIS by Pal Network

3.f. Data compilers

Data compilers

UNESCO Institute of Statistics (UIS)

4.a. Rationale

Rationale:

The indicator aims to measure the percentage of children and young people who have achieved the minimum learning outcomes in reading and mathematics during or at the end of the relevant stages of education.

The higher the figure the higher the proportion of children and/or young people reaching at least minimum proficiency in the respective domain (reading or mathematic) with the limitations indicated under the “Comments and limitations” section.

4.b. Comment and limitations

Comments and limitations:

The comparability of learning outcomes over time has additional complications, which require, ideally, to design and implement a set of comparable items as anchors in advance. Methodological developments are underway to address comparability of assessments outcomes over time.

4.c. Method of computation

Methodology

Computation method:

The number of children and/or young people at the relevant stage of education n in year t achieving or exceeding the pre-defined proficiency level in subject s expressed as a percentage of the number of children and/or young people at stage of education n, in year t, in any proficiency level in subject s.

MPL_t,n,s, = MP_t,n,s / P_t,n

where:

MP_t,n,s = the number of children and young people at stage of education n, in year t, who have achieved or exceeded the minimum proficiency level in subject s.

P_t,n= the number of children and young people at stage of education n, in year t, in any proficiency level in subject s.

n = the stage of education that was assessed

s = the subject that was assessed (reading or mathematics).

Harmonize various data sources

To address the challenges posed by the limited capacity of some countries to implement cross-national, regional, and national assessments, actions have been taken by the UIS and its partners. The strategies are used according to its level of precision and following a reporting protocol that includes the national assessments under specific circumstances.

Out-of-school children

In 2016, 263 million children, adolescents and youth were out of school, representing nearly one-fifth of the global population of this age group. 63 million, or 24% of the total, are children of primary school age (typically 6 to 11 years old); 61 million, or 23% of the total, are adolescents of lower secondary school age (typically 12 to 14 years old); and 139 million, or 53% of the total, are youth of upper secondary school age (about 15 to 17 years old). Not all these kids will be permanently outside school, some will re-join the educational system and, eventually, complete late, while some of them will enter late. The quantity varies per country and region and demands some adjustment in the estimate of Indicator 4.1.1. There is currently a discussion on how to implement these adjustments to reflect all the population. In 2017, the UIS proposed to make adjustments using the out-of-school children (OOSC)^{^[2]} and the completion rates.

UIS (2017a). More than one-half of children and adolescents are not learning worldwide. Montreal and UIS (2017b). Counting the number of children not learning: Methodology for a global composite indicator for education. Montreal. ↑

4.f. Treatment of missing values (i) at country level and (ii) at regional level

Treatment of missing values:

At country level:

Missing values are not imputed.

At regional and global levels:

Missing values are not imputed.

4.g. Regional aggregations

Regional aggregates:

Not yet applicable. Data are reported at the national level only. Population weighted average by region to be reported in 2020.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Methods and guidance available to countries for the compilation of the data at the national level:

Information not available.

4.j. Quality assurance

Quality assurance:

Information not available.

5. Data availability and disaggregation

Data availability

Time series:

Data available since 2000. The indicator will be reported annually.

Disaggregation:

Indicator is published disaggregated by sex. Other disaggregation such as location, socio-economic status, immigrant status, ethnicity and language of the test at home are based on data produced by international organizations administering cross learning assessment detailed in the expanded metadata document and validated by countries. Parity indexes are estimated in the reporting of Indicator 4.5.1. Information on the disaggregation of variable for Indicator 4.1.1 are presented in the following tables.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not yet applicable. Data are reported at the national level only.

7. References and Documentation

References

Minimum Proficiency Levelshttp://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/07/MPLs_revised_doc_20190506_v2.pdf

Costs and Benefits of Different Approaches to Measuring the Learning Proficiency of Students (SDG Indicator 4.1.1)

http://uis.unesco.org/sites/default/files/documents/ip53-costs-benefits-approaches-measuring-proficiency-2019-en.pdf

Protocol for Reporting on SDG Global Indicator 4.1.1

http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf

Global Proficiency Framework for Reading and Mathematics - Grade 2 to 6

http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/Global-Proficiency-Framework-18Oct2019_KD.pdf

4.1.1bc

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes

0.c. Indicator

0.e. Metadata update

Last updated: July 2016

0.f. Related indicators

Related indicators

This metadata covers part (b) and (c) of indicator 4.1.1: proportion of children and young people (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex

1.2, 1.4, 1.5, 2.1, 2.2, 2.3, 3.1, 3.3, 3.4, 3.7, 3.c, 4.5, 5.3, 5.4, 5.5, 5.b, 7.a, 8.6, 8.7, 8.b, 10.2, 10.6, 12.8, 13.3, 13.b, 16.a

0.g. International organisations(s) responsible for global monitoring

Institutional information

Organization(s):

UNESCO Institute for Statistics (UNESCO-UIS)

2.a. Definition and concepts

Concepts and definitions

Definition:

Percentage of children and young people in Grade 2 or 3 of primary education, at the end of primary education and the end of lower secondary education achieving at least a minimum proficiency level in (a) reading and (b) mathematics. The minimum proficiency level will be measured relative to new common reading and mathematics scales currently in development.

Concepts:

Minimum proficiency level is the benchmark of basic knowledge in a domain (mathematics or reading) measured through learning assessments. For example, the Programme for International Student Assessment (PISA) reading test has six proficiency levels, of which Level 2 is described as the minimum proficiency level. In Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS), there are four proficiency levels: Low, Intermediate, High and Advanced. Students reaching the Intermediate benchmark are able to apply basic knowledge in a variety of situations, similar to the idea of minimum proficiency. Currently, there are no common standards validated by the international community or countries. The indicator shows data published by each of the agencies and organizations specialised in cross-national learning assessments.

3.a. Data sources

Data sources

Description:

Various cross-national learning assessments including: Programme d'analyse des systèmes éducatifs de la CONFEMEN (PASEC), Progress in International Reading Literacy Study (PIRLS), Programme for International Student Assessment (PISA), Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ), Tercer Estudio Regional Comparativo y Explicativo (TERCE) and Trends in International Mathematics and Science Study (TIMSS). (a) Short-term strategy: Use national large-scale representative assessment data from cross-national assessments even though the performance levels may not be directly comparable. (b) Medium-term strategy: Use a global reporting scale based on either a new test or the statistical linking of national, regional and cross-national assessments.

3.b. Data collection method

Collection process:

For cross-national learning assessments, data were provided by the respective organizations responsible for each assessment.

3.c. Data collection calendar

Calendar

Data collection:

Various. Each learning assessment has its own data collection cycle.

3.d. Data release calendar

Data release:

July 2016

3.e. Data providers

Data providers

Name:

Bodies responsible for conducting learning assessments (including Ministries of Education, National Statistical Offices and other data providers). For cross-national assessments, the data providers are the International Association for the Evaluation of Educational Achievement (IEA), Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE), the Organisation for Economic Co-operation and Development (OECD), Programme d'Analyse des Systèmes Educatifs de la CONFEMEN (PASEC) and Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ).

3.f. Data compilers

Data compilers

UNESCO Institute for Statistics

4.a. Rationale

Rationale:

The indicator is a direct measure of the learning outcomes achieved in the two subject areas at the end of the relevant stages of education. The three measurement points will have their own established minimum standard. There is only one threshold that divides students into above and below minimum:

Below minimum is the proportion or percentage of students who do not achieve a minimum standard as set up by countries according to the globally-defined minimum competencies.
Above minimum is the proportion or percentage of students who have achieved the minimum standards. Due to heterogeneity of performance levels set by national and cross-national assessments, these performance levels will have to be mapped to the globally-defined minimum performance levels. Once the performance levels are mapped, the global education community will be able to identify for each country the proportion or percentage of children who achieved minimum standards.

4.b. Comment and limitations

Comments and limitations:

While data from many national assessments are available now, every country sets its own standards so the performance levels might not be comparable. One option is to link existing regional assessments based on a common framework. Furthermore, assessments are typically administered within school systems, the current indicators cover only those in school and the proportion of in-school target populations might vary from country to country due to varied out-of-school children populations. Assessing competencies of children and young people who are out of school would require household-based surveys. Assessing children in households is under consideration but may be very costly and difficult to administer and unlikely to be available on the scale needed within the next 3-5 years. Finally, the calculation of this indicator requires specific information on the ages of children participating in assessments to create globally-comparable data. The ages of children reported by the head of the household might not be consistent and reliable so the calculation of the indicator may be even more challenging. Due to the complication in assessing out-of-school children and the main focus on improving education system, the UIS is taking a stepping stone approach. It will concentrate on assessing children in school in the medium term, where much data are available, then develop more coherent implementation plan to assess out-of-school children in the longer term.

4.c. Method of computation

Methodology

Computation method:

The indicator is calculated as the percentage of children and/or young people at the relevant stage of education achieving or exceeding a pre-defined proficiency level in a given subject.

Performance above the minimum level, PLtn,s, above minimum = p

where p is the percentage of students in a learning assessment at stage of education n, in subject s in any year (t-i) where 0 ? i ? 5, who has achieved the level of proficiency that is greater than a pre-defined minimum standard, Smin. The minimum standard is defined by the global education community taking into consideration regional differences.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

Treatment of missing values:

At country level:

None by data compiler.

At regional and global levels:

None by data compiler.

4.g. Regional aggregations

Regional aggregates:

Regional and global aggregates are not currently available for this indicator.

5. Data availability and disaggregation

Data availability

Description:

79 countries

Time series:

Latest year available in the period 2010-2015.

Disaggregation:

By age or age-group of students, sex, location, socio-economic status, migrant status and ethnicity. Disability status is not currently available in most national and cross-national learning assessments but could be considered for future assessments.

7. References and Documentation

References

URL:

http://www.uis.unesco.org/Pages/default.aspx

References:

Programme d’analyse des systems éducatifs de la CONFEMEN (PASEC): http://www.pasec.confemen.org/

Progress in International Reading Literacy Study (PIRLS): http://www.iea.nl/pirls_2016.html

Programme for International Student Assessment (PISA): https://www.oecd.org/pisa/aboutpisa/

The Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ): http://www.sacmeq.org/?q=sacmeq-projects/sacmeq-iv

Tercer Estudio Regional Comparativo y Explicativo (TERCE): http://www.unesco.org/new/es/santiago/education/education-assessment-llece/third-regional-comparative-and-explanatory-study-terce/

Trends in International Mathematics and Science Study (TIMSS): http://www.iea.nl/timss_2015.html

4.1.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes

0.c. Indicator

Indicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

The parity indices for this indicator are reported in SDG indicator 4.5.1.

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute of Statistics (UIS)

1.a. Organisation

UNESCO Institute of Statistics (UIS)

2.a. Definition and concepts

Definition:

Concepts:

Minimum proficiency level (MPL) is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. In September 2018, an agreement was reached on a verbal definition of the global minimum proficiency level of reference for each of the areas and domains of Indicator 4.1.1 as described in the Minimum Proficiency Levels (MPLs): Outcomes of the consensus building meeting.

Minimum proficiency levels defined by each learning assessment

To ensure comparability across learning assessments, a verbal definition of MPL for each domain and levels between cross-national assessments (CNAs) was established by conducting an analysis of the performance level descriptors (PLDs)^{^[1]} of cross-national, regional, and community-led tests in reading and mathematics. The analysis was led and completed by the UIS and a consensus among experts on the proposed methodology was deemed adequate and pragmatic.

The global MPL definitions for the domains of reading and mathematics are presented in Table 1.

Table 1. Minimum proficiency levels defined by each learning assessment

Reading
Educational Level	Descriptor
Grade 2	They read and comprehend most of written words, particularly familiar ones, and extract explicit information from sentences.
Grade 3	Students read aloud written words accurately and fluently. They understand the overall meaning of sentences and short texts. Students identify the texts’ topic.
Grades 4 & 6	Students interpret and give some explanations about the main and secondary ideas in different types of texts. They establish connections between main ideas on a text and their personal experiences as well as general knowledge.
Grades 8 & 9	Students establish connections between main ideas on different text types and the author’s intentions. They reflect and draw conclusions based on the text.

Mathematics
Educational Level	Descriptor
Grades 2-3	Students demonstrate skills in number sense and computation, shape recognition and spatial orientation.
Grades 4-6	Students demonstrate skills in number sense and computation, basic measurement, reading, interpreting, and constructing graphs, spatial orientation, and number patterns.
Grades 8 & 9	Students demonstrate skills in computation, application problems, matching tables and graphs, and making use of algebraic representations.

PLD: Performance level descriptors are descriptions of the performance levels to express the knowledge and skills required to achieve each performance level, by domain. ↑

2.b. Unit of measure

Percent (%)

2.c. Classifications

This indicator expresses a Minimum proficiency level (MPL) that is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. In September 2018, an agreement was reached on a verbal definition of the global minimum proficiency level of reference for each of the areas and domains of Indicator 4.1.1 as described in the Minimum Proficiency Levels (MPLs): Outcomes of the consensus building meeting.

3.a. Data sources

Type of data sources: In school and population-based learning assessments.

Table 2. How reporting is structured?

	In-school based		Household Based Surveys	Grade
	Cross-national	National
Grade 2 or 3	LLECE	Yes	MICS6	2/3 plus one year when primary lasts more than 4 years according to ISCED level of the country, except for TIMSS/PIRLS grade 4, which are mapped to the end of primary when primary lasts six or less years.
	PASEC		EGRA
	TIMSS		EGMA
	PIRLS		PAL network
End of primary	LLECE	Yes	PAL network	plus or minus one year of last year of primary according to ISCED level of the country except for TIMSS/PIRLS grade 4, which are mapped to the end of primary when primary lasts six or less years.
	PASEC
	TIMSS
	PIRLS
	PILNA
	SEAMEO
	SACMEQ
End of lower secondary	PISA	Yes	Young Lives	plus two or minus one of last year of lower secondary according to ISCED level of the country
	PISA-D
	TIMSS
Definition of minimum level until 2018 release		Those defined by each assessment by point of measurement and domain.
Definition of minimum level from 2019		According to alignment as adopted by Global Alliance to Monitoring Learning (GAML) and Technical Cooperation Group (TCG)
Grade for end of primary and end of lower secondary		As defined by the ISCED levels in each country
Validation		Sent from UIS for countries’ approval

3.b. Data collection method

The UIS compiles information from data source providers at international level and from countries at the national level.

3.c. Data collection calendar

Data collection is rolling during the year.

3.d. Data release calendar

Biannual UIS data release (March and September)

3.e. Data providers

School-Based assessments

International Large-Scale Assessments are reported to the UIS by cross-national organisations (LLECE, PASEC, TIMSS, and PIRLS). Typically, Cross-National Large-Scale Assessment, either regional or international, define various performance levels, and report as well the mean and standard deviation. They choose as well one level as the cut-off point that defines what children/youth are below or above level.
Regional assessments: PASEC, SACMEQ, ERCE, PILNA, SEAMEO.
National Large-Scale Assessments either sample- or census-based. Countries should report the proportion of students by level of competency for each domain indicating as well the minimum proficiency level, when it is defined by the national assessment. EGRA and EGMA as reported by USAID or individual countries.

Household-Based surveys

MICS6: reported to the UIS by UNICEF
Pal Network: reported to the UIS by Pal Network

3.f. Data compilers

UNESCO Institute of Statistics (UIS)

3.g. Institutional mandate

4.a. Rationale

The higher the figure, the higher the proportion of children and/or young people reaching at least minimum proficiency in the respective domain (reading or mathematic) with the limitations indicated under the “Comments and limitations” section.

4.b. Comment and limitations

Learning outcomes from cross-national learning assessment are directly comparable for all countries which participated in the same cross-national learning assessments. However, these outcomes are not comparable across different cross-national learning assessments or with national learning assessments. A level of comparability of learning outcomes across assessments could be achieved by using different methodologies, each with varying standard errors. The UIS has implemented a mechanism of comparability through a consensus on the definition of the skills and contents. The comparability of learning outcomes over time has additional complications, which require, ideally, to design and implement a set of comparable items as anchors in advance. Methodological developments are underway to address comparability of assessments outcomes over time.

4.c. Method of computation

${M P L}_{t, n, s} = \frac{M P t, n, s}{P_{t, n}}$

where:

MP_t,n,s = the number of children and young people at stage of education n, in year t, who have achieved or exceeded the minimum proficiency level in subject s.

P_t,n = the total number of children and young people at stage of education n, in year t.

n = the stage of education that was assessed.

s = the subject that was assessed (reading or mathematics).

Harmonize various data sources

Completion status

Combining completion rates with learning outcomes improves our understanding of progress towards Target 4.1. Almost all information regarding learning is school-based and does not take into account the completion of the level. The inclusion of completion in the global list offers to report according to completion status. The greatest differences between the SDG 4.1.1 on learning before completion and the disaggregation by completion are found in regions or countries with lower completion and enrolment rates because the adjusted (or children completing and learning) indicator is based on a quality-adjusted completion rate. This also explains why the largest differences occur at the lower-secondary level. Globally, 47% of lower-secondary students achieve minimum proficiency in reading according to the original SDG 4.1.1 Indicator, but the value for the adjusted indicator would fall to 34% of adolescents completing lower secondary and achieving minimum proficiency in mathematics. References here.

4.d. Validation

The quality control is granted by the setting of a Review Panel to discuss any problem/disagreement on implementation. The Review Panel is constituted by regionally representative experts on learning.

4.e. Adjustments

As currently measured, most learning assessments have different methodologies for establishing a Minimum Proficiency level (MPL). The UIS and GAM establish standardization guidelines to guide the choice of the minimum thresholds based on the frameworks of each assessment program. The most critical decision is to choose in each assessment a level for international reporting that is consistent with the international definition of MPL. In the case of some assessment program, it means choosing a different level than the one the assessment program had been using for reporting results.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Missing values are not imputed.

• At regional and global levels

Missing values are not imputed.

4.g. Regional aggregations

Population weighted averages.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The UIS has elaborated guidance for the countries regarding the contents, the procedures and the reporting in the Global Alliance to monitor learning microsite.

In terms of selection of data sources, the Protocol for Reporting on SDG Global Indicator 4.1.1 is guiding the countries about the selection of the assessment program.

4.i. Quality management

The UIS maintains a global database on learning assessments in basic education. For transparency purposes, the inclusion

of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database.

4.j. Quality assurance

Information produced by the cross-national and national assessment programs are described in their manuals.

4.k. Quality assessment

The criteria to ensure the quality and standardization of the data are: the data sources must include adequate documentation; data values should be representative at the national population level and should otherwise be included in a footnote; data values are based on a sufficiently large sample; the learning assessment framework covers the minimum set of content in the global content framework and the proficiency levels are aligned with the minimum proficiency level (MPL) as defined in the global proficiency framework; and the data are plausible and based on trends and consistency with previously published or reported estimates for the indicator.

5. Data availability and disaggregation

Data availability:

Data available at the national level.

Time series:

Data available since 2000.

Disaggregation:

Indicator is published disaggregated by sex and completion status (Global Indicator 4.1.2). Other disaggregation such as location, socio-economic status, immigrant status, ethnicity and language of the test at home are based on data produced by international organizations administering cross learning assessment detailed in the expanded metadata document and validated by countries. Parity indexes are estimated in the reporting of Indicator 4.5.1.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not yet applicable. Data are reported at the national level only.

7. References and Documentation

Minimum Proficiency Levels

http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/07/MPLs_revised_doc_20190506_v2.pdf

Costs and Benefits of Different Approaches to Measuring the Learning Proficiency of Students (SDG Indicator 4.1.1)

http://uis.unesco.org/sites/default/files/documents/ip53-costs-benefits-approaches-measuring-proficiency-2019-en.pdf

Protocol for Reporting on SDG Global Indicator 4.1.1

http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf

Global Proficiency Framework for Reading and Mathematics - Grade 2 to 6

http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/Global-Proficiency-Framework-18Oct2019_KD.pdf

4.1.2

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes

0.c. Indicator

Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

4.1.1

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UIS)

1.a. Organisation

UNESCO Institute for Statistics (UIS)

2.a. Definition and concepts

Definition:

Percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of each level of education who have completed that grade.

Concepts:

The intended age for the last grade of each level of education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade.

For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate.

2.b. Unit of measure

Percent (%)

2.c. Classifications

The International Standard Classification of Education (ISCED) is used to define primary, lower secondary and upper secondary education.

3.a. Data sources

The data can be obtained from population censuses and household surveys that collect information on the highest level of education and/or grade completed by children and young people in a household. Typical questions in a survey to collect data on educational attainment are:

- What is the highest level of education [name of household member] has attended?

- What is the highest grade of education [name of household member] has completed at that level?

Sources include publicly available data from Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), European Union Statistics on Income and Living Condition (EU-SILC), the Integrated Public Use Microdata Series (IPUMS), and national household surveys and censuses.

3.b. Data collection method

Data from all publicly available household surveys and censuses with the required information are compiled and used to calculate the completion rate. For international comparability, national data are mapped to the ISCED before indicator calculation.

Indicator values intended for dissemination and addition to the global SDG Indicators Database are submitted by the UNESCO Institute for Statistics to National Statistical Offices (NSOs), Ministries of Education or other relevant agencies in individual countries for their review and feedback.

3.c. Data collection calendar

Household survey and census datasets are publicly available from the sources described above and do not follow any particular release calendar.

3.d. Data release calendar

Household survey and census datasets are publicly available from the sources described above and do not follow any particular release calendar.

3.e. Data providers

Household survey and census datasets are publicly available from the sources described above and national statistical agencies.

3.f. Data compilers

UNESCO Institute for Statistics (UIS)

3.g. Institutional mandate

4.a. Rationale

The indicator is explicitly referenced in the text of target 4.1: ‘ensure that all girls and boys complete […] primary and secondary education’. A completion rate at or near 100% indicates that all or most children and adolescents have completed a level of education by the time they are 3 to 5 years older than the official age of entry into the last grade of that level of education. A low completion rate indicates low or delayed entry into a given level of education, high drop-out, high repetition, late completion, or a combination of these factors.

The completion rate can be used either as a self-standing indicator or in combination with SDG indicator 4.1.1 (proportion of children and young people (a) in Grade 2 or 3; (b) at the end of primary education; and (c) at the end of lower secondary education achieving at least a minimum proficiency level in (i) reading and (ii) mathematics). Combining the completion rate with indicator 4.1.1 provides information on the percentage of children or young people in a cohort who achieve a minimum level of proficiency, and not only on the percentage of children in school who achieve minimum proficiency.

4.b. Comment and limitations

Three common issues affect the indicator. First, the age group 3-5 years above the official age of entry into the last grade for a given level of education was selected for the calculation of the completion rate to allow for some delayed entry or repetition. In countries where entry can occur very late or where repetition is common, some children or adolescents in the age group examined may still attend school and the eventual rate of completion may therefore be underestimated. Second, as the indicator is calculated from household survey data, it is subject to time lag in the availability of data. Third, when multiple surveys are available, they may provide conflicting information due to the possible presence of sampling and non-sampling errors in survey data.

Responding to a request by the Technical Cooperation Group (TCG) on the Indicators for SDG 4 - Education 2030, a refinement of the methodology to model completion rate estimates has been developed (Barakat et al. 2021), following an approach similar to that used for the estimation of child mortality rates. The model ensures that these common challenges with household survey data, such as timeliness and sampling or non-sampling errors are addressed to provide annual, up-to-date (through short-term projections) and more robust data, including for children and youth who complete each level later than 3-5 years above the official age of entry into the last grade.

4.c. Method of computation

The number of persons in the relevant age group who have completed the last grade of a given level of education is divided by the total population (in the survey sample) of the same age group.

Formula:

${C R}_{n} = \frac{P_{C_{n}, {A G}_{a + 3 t 5}}}{P_{{A G}_{a + 3 t 5}}}$

where:

${C R}_{n} =$ completion rate for level n of education

$P_{C_{n}, {A g e}_{a + 3 t 5}} =$ population aged 3 to 5 years above the official entrance age a into the last grade of level n of education who completed level n

$P_{{A g e}_{a + 3 t 5}} =$ population aged 3 to 5 years above the official entrance age a into the last grade of level n of education

$n =$ ISCED level 1 (primary education), 2 (lower secondary education), or 3 (upper secondary education)

4.d. Validation

The UNESCO Institute for Statistics shares all indicator values and notes on methodology with National Statistical Offices (NSO), Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data.

In a different validation and capacity building exercise, the completion rate model estimates will be consulted with countries. This annual consultation process will give each country’s education ministry and NSO the opportunity to review and provide feedback on all data inputs, the estimation methodology and the draft estimates.

4.e. Adjustments

Description of any adjustments with respect to use of standard classifications and harmonization of breakdowns for age group and other dimensions, or adjustments made for compliance with specific international or national definitions. To take into account countries where the eventual rate of completion is underestimated because entry occurs very late or repetition is common, estimated completion rates are also available for cohorts of children or young people aged up to 8 years above the intended age for the last grade of each level of education.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

The completion rate can be calculated from older cohorts who are outside of the age bracket specified in the definition of the indicator to obtain estimates for different years. Gaps in national time series can be imputed using the aforementioned model to estimate the completion rate.

• At regional and global levels

See above.

4.g. Regional aggregations

Global and regional estimates of the primary, lower secondary and upper secondary completion rate are derived by using the national population in the respective age groups as weights for aggregation of national values.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries can calculate the completion rate using the methodology described in this document. ISCED mappings that help countries report their data in an internationally comparable framework are available on the website of the UNESCO Institute for Statistics (http://uis.unesco.org/en/isced-mappings).

4.i. Quality management

The global database with completion rates is maintained by the UIS and the Global Education Monitoring Report. The UIS sets standards, develops questionnaires and quality control protocols for country data reporting, and maintains the global database on the structure of national education systems.

4.j. Quality assurance

The process for quality assurance includes review of survey documentation, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.

Before its annual data release and addition to the global SDG Indicators Database, the UNESCO Institute for Statistics submits all indicator values and notes on methodology to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.

4.k. Quality assessment

Accurate data on the structure of the national education system and on educational attainment by single year of age are needed for calculating this indicator. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data values are based on a sufficiently large sample; data are plausible and based on trends and consistency with previously published/reported values for the indicator.

5. Data availability and disaggregation

Data availability:

The primary completion rate is currently available for 150 Member States, representing 77% of all Member States. The lower secondary completion rate is available for 155 Member States, representing 80% of all Member States. Coverage for the upper secondary completion rate is similar, with data for 155 Member States, representing 80% of all Member States. The countries with completion rates are home to more than 90% of the global population.

Time series:

The completion rate is available for the years since 2000. National time series of raw data are incomplete due to the infrequent implementation of household surveys and censuses but time series without gaps are available through the aforementioned reconstructed model-based estimates of the completion rate.

Disaggregation:

The indicator is disaggregated by sex, location, wealth and other dimensions specified in global indicator 4.5.1 (parity index). Model-based estimates are disaggregated by sex.

6. Comparability/deviation from international standards

Sources of discrepancies:

None.

7. References and Documentation

Barakat, B., Dharamshi, A., Alkema, L., & Antoninis, M. (2021). Adjusted Bayesian Completion Rates (ABC) Estimation. SocArXiv. https://doi.org/https://doi.org/10.31235/osf.io/at368

UNESCO Institute for Statistics (UIS). 2019. UIS.Stat online database.

Primary Completion rate, Lower secondary completion rate, and Upper secondary completion rate: http://data.uis.unesco.org/?lang=en&SubSessionId=c3260ae3-3921-4be9-bae6-e0138dbff642&themetreeid=-200

UNESCO Institute for Statistics (UIS) and Global Education Monitoring Report. 2019. World Inequality Database on Education (WIDE).

Primary completion rate: https://www.education-inequalities.org/indicators/comp_prim_v2
Lower secondary completion rate: https://www.education-inequalities.org/indicators/comp_lowsec_v2
Upper secondary completion rate: https://www.education-inequalities.org/indicators/comp_upsec_v2

4.2.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education

0.c. Indicator

Indicator 4.2.1: Proportion of children aged 24–59 months who are developmentally on track in health, learning and psychosocial well-being, by sex

0.d. Series

Proportion of children who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning

0.e. Metadata update

2022-03-31

0.f. Related indicators

Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sex

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

1.a. Organisation

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

The proportion of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being.

Concepts:

The domains included in the indicator for SDG indicator 4.2.1 include the following concepts:

Health: gross motor development, fine motor development and self-care.
Learning: expressive language, literacy, numeracy, pre-writing, and executive functioning.
Psychosocial well-being: emotional skills, social skills, internalizing behavior, and externalizing behavior.

The recommended measure for SDG 4.2.1 is the Early Childhood Development Index 2030 (ECDI2030) which is a 20-item instrument to measure developmental outcomes among children aged 24 to 59 months in population-based surveys. The indicator derived from the ECDI2030 is the proportion of children aged 24 to 59 months who have achieved the minimum number of milestones expected for their age group, defined as follows:

Children age 24 to 29 months are classified as developmentally on-track if they have achieved at least 7 milestones;
Children age 30 to 35 months are classified as developmentally on-track if they have achieved at least 9 milestones;
Children age 36 to 41 months are classified as developmentally on-track if they have achieved at least 11 milestones;
Children age 42 to 47 months are classified as developmentally on-track if they have achieved at least 13 milestones;
Children age 48 to 59 months are classified as developmentally on-track if they have achieved at least 15 milestones.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Not applicable

3.a. Data sources

In 2015, UNICEF initiated a process of methodological development that involved extensive consultations with experts, partner agencies and national statistical authorities. Over the following five years, a sequence of carefully planned technical steps were executed, incorporating both qualitative and quantitative methods to identify the best items to measure indicator 4.2.1. This process led to the development of the ECDI2030.

The ECDI2030 addresses the need for nationally representative and internationally comparable data on early childhood development, collected in a standardized way. It captures the achievement of key developmental milestones by children between the ages of 24 and 59 months. Mothers or primary caregivers are asked 20 questions about the way their children behave in certain everyday situations, and the skills and knowledge they have acquired.

The ECDI2030 can be integrated into existing national data collection efforts, including international household survey programmes such as UNICEF-supported Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS).

The ECDI2030 is meant to replace the Early Childhood Development Index (or ECDI) which collects data on the proxy indicator for SDG 4.2.1 that has been in use since 2015. The former ECDI and the new ECDI2030 target different age groups and measure slightly different development domains. Therefore, the indicators generated by both instruments may not be fully comparable and caution is needed when interpreting estimates produced by the two measures.

3.b. Data collection method

1. UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).

As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators for which it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicits feedback directly from National Statistical Offices (NSOs), as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

3.c. Data collection calendar

UNICEF will undertake an annual country consultation likely between December and January every year to allow for review and processing of the feedback received in order to meet global SDG reporting deadlines.

3.d. Data release calendar

Updated data on 4.2.1 as measured by the ECDI2030 will be available in the SDG reporting period every February/March.

3.e. Data providers

National Statistical Offices (in most cases)

3.f. Data compilers

United Nations Children's Fund (UNICEF)

3.g. Institutional mandate

UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on early childhood development (ECD), including through the UNICEF-supported MICS household survey programme. UNICEF also compiles ECD statistics with the goal of making internationally comparable datasets publicly available, and it analyses ECD statistics which are included in relevant data-driven publications, including in its flagship publication, The State of the World’s Children.

4.a. Rationale

Early childhood development (ECD) sets the stage for life-long thriving. Investing in ECD is one of the most critical and cost-effective investments a country can make to improve adult health, education and productivity in order to build human capital and promote sustainable development. ECD is equity from the start and provides a good indication of national development. Efforts to improve ECD can bring about human, social and economic improvements for both individuals and societies.

4.b. Comment and limitations

SDG 4.2.1 was initially classified as Tier 3 and was upgraded to Tier 2 in 2019; additionally, changes to the indicator were made during the 2020 comprehensive review. In light of this and given that the ECDI2030 was officially released in March 2020, it will take some time for country uptake and implementation of the new measure and for data to become available from a sufficiently large enough number of countries. Therefore, in the meantime, a proxy indicator (children aged 36-59 months who are developmentally on-track in at least three of the following four domains: literacy-numeracy, physical, social-emotional and learning) will be used to report on 4.2.1, when relevant. This proxy indicator has been used for global SDG reporting since 2015 but is not fully aligned with the definition and age group covered by the SDG indicator formulation. When the proxy indicator is used for SDG reporting on 4.2.1 for a country, it will be footnoted as such in the global SDG database.

4.c. Method of computation

The number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100.

4.d. Validation

A wide consultative process is undertaken to compile, assess and validate data from national sources.

The consultation process solicits feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed by UNICEF as the custodian agency. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

4.e. Adjustments

The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

When data for a country are entirely missing, UNICEF does not publish any country-level estimate.

• At regional and global levels

The regional average is applied to those countries within the region with missing values for the purposes of calculating regional aggregates only but are not published as country-level estimates. Regional aggregates are only published when at least 50 percent of the regional population for the relevant age group are covered by the available data.

4.g. Regional aggregations

The global aggregate is a weighted average of all countries with available data. Global aggregates are published regardless of population coverage, but the number of countries and the proportion of the relevant population group represented by the available data are clearly indicated.

Regional aggregates are weighted averages of all the countries within the region.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries gather prevalence data on children’s developmental status through household surveys such as UNICEF-supported MICS or Demographic and Health Surveys.

4.i. Quality management

The process behind the production of reliable statistics on ECD is well established within UNICEF. The quality and process leading to the production of the SDG indicator 4.2.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.

4.j. Quality assurance

UNICEF maintains the global database on ECD that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator.

As of 2018, UNICEF undertakes an annual consultation with government authorities on 10 of the child-related SDG indicators in its role of sole or joint custodian, and in line with its global monitoring mandate and normative commitments to advancing the 2030 Agenda for children. This includes indicator 4.2.1.

4.k. Quality assessment

Data consistency and quality checks are regularly conducted for validation of the data before dissemination.

5. Data availability and disaggregation

Data availability:

Data on the indicator collected through implementation of the ECDI2030 are expected to become available, beginning in 2022. Comparable data collected by the ECDI are currently available for around 80 countries. Countries with data on the proxy indicator collected with the ECDI will continue to be used for global SDG reporting until new data using the ECDI2030 are available.

Time series:

Not available

Disaggregation:

Sex

6. Comparability/deviation from international standards

Sources of discrepancies:

The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.

7. References and Documentation

URL:

data.unicef.org

References:

http://data.unicef.org/ecd/development-status.html

4.2.2

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education

0.c. Indicator

Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sex

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

1.4, 4.5

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UIS)

1.a. Organisation

UNESCO Institute for Statistics (UIS)

2.a. Definition and concepts

Definition:

The participation rate in organized learning (one year before the official primary entry age), by sex is defined as the percentage of children in the given age range who participate in one or more organized learning programme, including programmes which offer a combination of education and care. Participation in early childhood and in primary education are both included. The age range will vary by country depending on the official age of entry to primary education.

Concepts:

An organized learning programme is one which consists of a coherent set or sequence of educational activities designed with the intention of achieving pre-determined learning outcomes or the accomplishment of a specific set of educational tasks. Early childhood and primary education programmes are examples of organized learning programmes.

Early childhood and primary education are defined in the 2011 revision of the International Standard Classification of Education (ISCED 2011). Early childhood education is typically designed with a holistic approach to support children’s early cognitive, physical, social and emotional development and to introduce young children to organized instruction outside the family context. Primary education offers learning and educational activities designed to provide students with fundamental skills in reading, writing and mathematics and establish a solid foundation for learning and understanding core areas of knowledge and personal development. It focuses on learning at a basic level of complexity with little, if any, specialisation.

The official primary entry age is the age at which children are obliged to start primary education according to national legislation or policies. Where more than one age is specified, for example, in different parts of a country, the most common official entry age (i.e. the age at which most children in the country are expected to start primary) is used for the calculation of this indicator at the global level.

2.b. Unit of measure

Percent (%)

2.c. Classifications

The International Standard Classification of Education (ISCED) is used to define early childhood and primary education.

3.a. Data sources

Administrative data from schools and other centres of organized learning or from household surveys on enrolment by single year of age in early learning programmes; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment); administrative data from ministries of education on the official entrance age to primary education.

3.b. Data collection method

The UNESCO Institute for Statistics produces time series based on enrolment data reported by Ministries of Education or National Statistical Offices and population estimates produced by the UN Population Division. The enrolment data are gathered through the annual Survey of Formal Education. Countries are asked to report data according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.

In addition, countries also have an opportunity to see and comment on the main indicators the UIS produces in an annual “country review” of indicators.

3.c. Data collection calendar

Annual UIS survey (usually launched in the 4^th quarter) and UOE survey (usually launched in June).

3.d. Data release calendar

Biannual UIS data release (March and September).

3.e. Data providers

Ministries of Education and/or National Statistical Offices.

3.f. Data compilers

UNESCO Institute for Statistics (UIS)

3.g. Institutional mandate

4.a. Rationale

The indicator measures children’s exposure to organized learning activities in the year prior to the start of primary school. A high value of the indicator shows a high degree of participation in organized learning immediately before the official entrance age to primary education.

4.b. Comment and limitations

Participation in learning programmes in the early years is not full time for many children, meaning that exposure to learning environments outside of the home will vary in intensity. The indicator measures the percentage of children who are exposed to organized learning but not the intensity of the programme, which limits the ability to draw conclusions on the extent to which this target is being achieved. More work is needed to ensure that the definition of learning programmes is consistent across various surveys and defined in a manner that is easily understood by survey respondents, ideally with complementary information collected on the amount of time children spend in learning programmes.

4.c. Method of computation

The number of children in the relevant age group who participate in an organized learning programme is expressed as a percentage of the total population in the same age range. The indicator can be calculated both from administrative data and from household surveys. If the former, the number of enrolments in organized learning programmes are reported by schools and the population in the age group one year below the official primary entry age is derived from population estimates. For the calculation of this indicator at the global level, population estimates from the UN Population Division are used. If derived from household surveys, both enrolments and population are collected at the same time.

${P R O L}_{0 t 1, A G (a - 1) = } \frac{E_{0 t 1, A G (a - 1)}}{{S A P}_{A G (a - 1)}}$

where:

PROL_0t1,AG(a-1) = participation rate in organized learning one year before the official entry age a to primary education

E_0t1,AG(a-1) = enrolment in early childhood or primary education (ISCED levels 0 and 1) aged one year below the official entry age a to primary education

SAP_AG(a-1) = school-age population aged one year below the official entry age a to primary education

4.d. Validation

4.e. Adjustments

Data should be reported according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

For the purposes of calculating participation rates by age, the UIS may make one or more of the following:

• An adjustment to account for over- or under-reporting, for example:

o To include enrolments in a type of education – such as private education or special education – not reported by the country; and/or

o To include enrolments in a part of the country not reported by the country.

• An estimate of the number of enrolments in the given age group if the age distribution was not reported by the country.

• A redistribution of enrolments of unknown age (across known ages).

• An estimate of the population in the official age group for small countries (if neither the UN Population Division (UNPD) nor the country itself can provide estimates of their own).

In all cases estimates are based on evidence from the country itself (e.g. information from the data provider on the size of the missing component, via correspondence, publications or data on the Ministry’s or National Statistical Office’s (NSO’s) Webpage, or via surveys conducted by other organizations) or on data from the country for a previous year. These figures may be published: (i) as observed data if the missing items are found in a national source; (ii) as national estimates if the country is persuaded to produce estimates and submit them in place of missing data; or (iii) as UIS estimates, if the estimates are made by the UIS.

The age distribution of enrolments is most commonly estimated from the age distribution reported in a previous year. If the country has never reported the age distribution of enrolments, the age distribution reported in another survey, if available, is used (such as Multiple Indicator Cluster Surveys (MICS) or Demographic Health Surveys (DHS)).

Enrolments of unknown age are redistributed across known ages if they constitute more than 5% of the total enrolments in that level of education. No estimation is made if they are 5% or less.

Population estimates by age for countries with small population – produced only where there are no other suitable estimates available either from UNPD or from the country itself – are made only for countries which have reported education data to the UIS and for which population estimates from a reliable source are available in some years.

• At regional and global levels

When data are not available for all countries, the UIS imputes national data for the sole purpose of calculating regional averages. These imputed data are neither published nor otherwise disseminated.

Where the relevant data are not available at all for a country, estimates may be based on another variable which is clearly linked to the item being estimated. For example, enrolments by age may be based on total enrolments.

Where no data are available for the country in any year that can inform the estimate, the unweighted average for the region in which the country lies is used.

4.g. Regional aggregations

4.h. Methods and guidance available to countries for the compilation of the data at the national level

4.i. Quality management

The UIS maintains the global database used to produce this indicator. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database. The international reporting of enrolment data should be based on the 2011 International Standard Classification of Education maintained by the UIS. Population data are produced and maintained by the United Nations Population Division (UNPD).

4.j. Quality assurance

The process for quality assurance includes review of survey documentation to make sure that the definition of organized learning programmes is consistent across various surveys, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.

4.k. Quality assessment

The indicator should be based on enrolment by single year of age in early learning programmes in all types of education institutions, including public, private and all other institutions that provide organized educational programmes. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.

5. Data availability and disaggregation

Data availability:

167 countries with at least one data point in the period 2010-2019.

Time series:

1998-2019 in UIS database; 2000-2019 in SDG global database.

Disaggregation:

By age and sex from administrative sources, and by age, sex, location and income from household surveys.

6. Comparability/deviation from international standards

Sources of discrepancies:

7. References and Documentation

URL:

http://www.uis.unesco.org

References:

The Survey of Formal Education Instruction Manual http://uis.unesco.org/sites/default/files/documents/instruction-manual-survey-formal-education-2017-en.pdf

UIS Questionnaire on Students and Teachers (ISCED 0-4)

http://uis.unesco.org/en/uis-questionnaires

4.3.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university

0.c. Indicator

Indicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sex

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

1.4, 4.4, 4.5, 5.b, 8.5, 9.2

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UIS)

1.a. Organisation

UNESCO Institute for Statistics (UIS)

2.a. Definition and concepts

Definition:

The percentage of youth and adults in a given age range (15-24 years, 25-54 years, 55-64 years, 15-64 years) participating in formal or non-formal education and training in the previous 12 months.

Concepts:

Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous ‘ladder’ of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this ‘ladder’ are organized programmes of joint part-time employment and part-time participation in the regular school and university system.

Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Not applicable

3.a. Data sources

The SDG 4.3.1 indicator is calculated by the UIS based on the household-based survey data compiled by the Department of Statistics of the International Labour Organisation (ILO), which maintains a global database on national Labour Force Surveys or other relevant household surveys that cover labour market.

3.b. Data collection method

Data are collected from the respective organizations responsible for each survey.

3.c. Data collection calendar

Various depending on survey and country.

3.d. Data release calendar

Various depending on survey and country.

3.e. Data providers

Ministries of Education and /or National Statistical Offices (NSOs).

3.f. Data compilers

UNESCO Institute for Statistics (UIS)

3.g. Institutional mandate

4.a. Rationale

To show the level of participation of youth and adults in education and training of all types. A high value indicates a large share of the population in the relevant age group is participating in formal and non-formal education and training.

4.b. Comment and limitations

Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer.

4.c. Method of computation

The number of people in selected age groups participating in formal or non-formal education or training is expressed as a percentage of the population of the same age.

${P R}_{A G i} = \frac{E_{A G i}}{P_{A G i}}$

where:

PR_AGi = participation rate of the population in age group i in formal and non-formal education and training

E_AGi = enrolment of the population in age group i in formal and non-formal education and training

P_AGi = population in age group i

i = 15-24, 25-54 years, 55-64 years, 15-64 years

4.d. Validation

4.e. Adjustments

Not applicable.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

None by data compiler.

• At regional and global levels:

None by data compiler.

4.g. Regional aggregations

Regional and global aggregates are not currently available for this indicator.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator.

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

Accurate data on participation in formal and non-formal education and training by age or specific age-groups and by sex, and the corresponding population data from all types of educational institutions (public and private), formal and non-formal, are essential for calculating this indicator. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.

5. Data availability and disaggregation

Data availability:

154 countries with at least one data point for the period 1976-2022.

Time series:

1976-2022 in UIS database; 2000-2022 in SDG global database.

Disaggregation:

By age and sex.

6. Comparability/deviation from international standards

Sources of discrepancies:

None

7. References and Documentation

URL:

uis.unesco.org

References:

Department of Statistics of the International Labour Organisation (ILO) (global database on national Labour Force Surveys and other relevant household surveys that cover labour market):

https://ilostat.ilo.org/

European Adult Education Survey (AES): http://www.eui.eu/Research/Library/ResearchGuides/Economics/Statistics/DataPortal/AES.aspx

European Continuing Vocational Training Survey:

https://ec.europa.eu/eurostat/web/microdata/continuing-vocational-training-survey

European Labour Force Survey: http://ec.europa.eu/eurostat/cache/metadata/en/trng_lfs_4w0_esms.htm

4.4.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship

0.c. Indicator

Indicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skill

0.d. Series

Proportion of youth andadults with information and communications technology (ICT) skills by type of skill (%)

0.e. Metadata update

2023-03-31

0.f. Related indicators

4.5.1, 9.c.1, 17.6.1, 17.8.1

0.g. International organisations(s) responsible for global monitoring

International Telecommunication Union (ITU)

1.a. Organisation

International Telecommunication Union (ITU)

2.a. Definition and concepts

Definition:

The proportion of youth and adults with Information and Communications Technology (ICT) skills, by type of skill defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The indicator is expressed as a percentage.

Concepts:

The indicator on the proportion of individuals with ICT skills, by type of skills refers to individuals that have undertaken certain activities in the last three months. (Please note however, that until 2019 this data refer to computer-related activities and response categories, as explained below.)

Computer-related activities to measure ICT skills are as follows:

Copying or moving a file or folder
Using copy and paste tools to duplicate or move information within a document
Sending e-mails with attached files (e.g. document, picture, video)
Using basic arithmetic formulas in a spreadsheet
Connecting and installing new devices (e.g. a modem, camera, printer)
Finding, downloading, installing and configuring software
Creating electronic presentations with presentation software (including images, sound, video or charts)
Transferring files between a computer and other devices
Writing a computer program using a specialized programming language

A computer refers to a desktop computer, a laptop (portable) computer or a tablet (or similar handheld computer). It does not include equipment with some embedded computing abilities, such as smart TV sets, and devices with telephony as their primary function, such as smartphones.

Most individuals will have carried out more than one activity and therefore multiple responses are expected. The tasks are broadly ordered from less complex to more complex, although there is no requirement for a respondent to select simpler tasks before selecting a more complex task.

From 2020, the data refer to skills irrespective of the device used. The new skills categories are:

Using copy and paste tools to duplicate or move data, information and content in digital environments (e.g. within a document, between devices, on the cloud)
Sending messages (e.g. e-mail, messaging service, SMS) with attached files (e.g. document, picture, video)
Using basic arithmetic formulae in a spreadsheet
Connecting and installing new devices (e.g. a modem, camera, printer) through wired or wireless technologies
Finding, downloading, installing and configuring software and apps
Creating electronic presentations with presentation software (including text, images, sound, video or charts)
Transferring files or applications between devices (including via cloud-storage)
Setting up effective security measures (e.g. strong passwords, log-in attempt notification) to protect devices and online accounts
Changing privacy settings on your device, account or app to limit the sharing of personal data and information (e.g. name, contact information, photos)
Verifying the reliability of information found online
Programming or coding in digital environments (e.g. computer software, app development)

2.b. Unit of measure

Percent (%)

2.c. Classifications

Activities are classified according to agreement at the Expert Group meeting on information and communications technology (ICT) Household Indicators (EGH).

Furthermore, for countries that collect this data through an official survey, and if data allow breakdown and disaggregation, the indicator can be broken down by region (urban/rural), by sex, by age group, by educational level (ISCED), by labour force status (ILO), and by occupation (ISCO). International Telecommunication Union (ITU) collects data for all of these breakdowns from countries.

3.a. Data sources

Countries can collect data on this indicator through national household surveys. Data for different countries are compiled by the International Telecommunication Union (ITU).

3.b. Data collection method

Data for different countries are compiled and provided by the International Telecommunication Union (ITU).

3.c. Data collection calendar

Various. Each survey has its own data collection cycle. The International Telecommunication Union (ITU) collects data twice a year from Member States, in Q1 and in Q3.

3.d. Data release calendar

The International Telecommunication Union (ITU) releases data twice per year on ICT skills.

3.e. Data providers

Bodies responsible for conducting household surveys (including National Statistical Offices and Government Ministries) in which information on the use of ICT skills is collected. Data is compiled by the International Telecommunication Union (ITU).

3.f. Data compilers

International Telecommunication Union (ITU)

3.g. Institutional mandate

As the United Nations (UN) specialized agency for ICTs, the International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States.

4.a. Rationale

ICT skills determine the effective use of information and communication technology, so this indicator may therefore assist in making the link between ICT usage and impact. The lack of such skills continues to be one of the key barriers keeping people from fully benefitting from the potential of information and communication technologies. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society.

This is also a core indicator of the Partnership on Measuring ICT for Development's Core List of Indicators, which has been endorsed by the UN Statistical Commission (in 2020).

4.b. Comment and limitations

This indicator is relatively new but based on an internationally-agreed definition and methodology, which have been developed under the coordination of International Telecommunications Union (ITU), through its Expert Groups and following an extensive consultation process with countries. It was also endorsed by the UN Statistical Commission in 2014^[1], and again in 2020.

The indicator is based on the responses provided by interviewees regarding certain activities that they have carried out in a reference period of time. However, it is not a direct assessment of skills nor do we know if those activities were undertaken effectively.

As one of the Core List of Indicators of the Partnership on Measuring ICT for Development. ↑

4.c. Method of computation

This indicator is calculated as the proportion of in-scope who have carried out each activity in the past 3 months, regardless of where that activity took place. The indicator is expressed as a percentage.

[ $(n u m b e r o f i n - s c o p e i n d i v i d u a l s b y t y p e o f s k i l l s)$

$/ (n u m b e r o f i n - s c o p e i n d i v i d u a l s)] * 100$

Figures supplied are expressed as a proportion of the in-scope population.

4.d. Validation

Data are submitted by Member States to the International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States.

4.e. Adjustments

No adjustments are made to the data submitted by countries.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

None by data compiler.

• At regional and global levels

None by data compiler.

4.g. Regional aggregations

Regional and global aggregates are not currently available for this indicator.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

International Telecommunication Union (ITU) Manual for Measuring Information and Communications Technology (ICT) Access and Use by Households and Individuals 2020:

https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx

4.i. Quality management

Data are checked and validated by the ICT Data and Analytics (IDA) Division of the International Telecommunication Union (ITU). Countries are contacted to clarify and correct their submissions.

4.j. Quality assurance

The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.

4.k. Quality assessment

The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.

5. Data availability and disaggregation

Data availability:

Overall, the indicator is available for more than 90 countries from at least one survey.

Time series:

2005 onwards

Disaggregation:

Since data for the indicator on the proportion of individuals with ICT skills, by type of skills are collected through a survey, classificatory variables for individuals can provide further information on the differences in ICT skills among men/women, children/adults (age groups), employed/unemployed, etc., according to national requirements These data may be used to inform targeted policies to improve ICT skills, and thus contribute to the development of an inclusive information society.

6. Comparability/deviation from international standards

Sources of discrepancies:

None

7. References and Documentation

URL:

International Telecommunication Union:

https://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx

References:

ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:

https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx

4.5.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situations

0.c. Indicator

Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregated

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

All equity targets and targets associated with the underlying indicators.

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UIS)

1.a. Organisation

UNESCO Institute for Statistics (UIS)

2.a. Definition and concepts

Definition:

Parity indices require data for the specific groups of interest. They represent the ratio of the indicator value for one group to that of the other. Typically, the likely more disadvantaged group is placed in the numerator. A value of exactly 1 indicates parity between the two groups.

Concepts:

See metadata for relevant underlying indicator.

2.b. Unit of measure

Ratio. This indicator is expressed as the ratio of the value of the indicator for the likely more disadvantaged group to that of the likely more advantaged group.

2.c. Classifications

Not applicable

3.a. Data sources

The sources are the same as for the underlying indicators for this goal.

3.b. Data collection method

The same as the underlying indicator.

3.c. Data collection calendar

Depends on underlying indicator.

3.d. Data release calendar

Depends on underlying indicator.

3.e. Data providers

The same as the underlying indicator.

3.f. Data compilers

UNESCO Institute for Statistics (UIS)

3.g. Institutional mandate

4.a. Rationale

To measure the general level of disparity between two sub-populations of interest with regard to a given indicator. The further from 1 the parity index lies, the greater the disparity between the two groups of interest.

4.b. Comment and limitations

The indicator is not symmetrical about 1 but a simple transformation can make it so (by inverting ratios that exceed 1 and subtracting them from 2). This will make interpretation easier.

4.c. Method of computation

The indicator value of the likely more disadvantaged group is divided by the indicator value of the other sub-population of interest.

$D P I = \frac{{[{l n d}_{i}]}_{d}}{{[{l n d}_{i}]}_{a}}$

where:

DPI = the Dimension (Gender, Wealth, Location, etc.) Parity Index

Ind_i = the Education 2030 Indicator i for which an equity measure is needed.

d = the likely disadvantaged group (e.g. female, poorest, etc.)

a = the likely advantaged group (e.g. male, richest, etc.)

4.d. Validation

4.e. Adjustments

The same as the underlying indicator.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

The same as the underlying indicator.

• At regional and global levels

The same as the underlying indicator.

4.g. Regional aggregations

The same as the underlying indicator.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator.

4.i. Quality management

Quality management for this indicator is the same as quality management of the underlying indicators.

4.j. Quality assurance

Quality assurance for this indicator is the same as quality assurance of the underlying indicators.

4.k. Quality assessment

Quality assessment for this indicator is the same as quality assessment of the underlying indicators.

5. Data availability and disaggregation

Data availability:

Depends on underlying indicator.

Time series:

Depends on underlying indicator.

Disaggregation:

None because the parity indices directly compare two sub-populations of interest.

6. Comparability/deviation from international standards

Sources of discrepancies:

The same as the underlying indicator.

7. References and Documentation

URL:

http://www.uis.unesco.org

References:

See references for each underlying indicator.

4.6.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracy

0.c. Indicator

Indicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex

0.d. Series

Not applicable.

0.e. Metadata update

2022-03-31

0.f. Related indicators

1.2, 1.5, 2.1, 2.2, 2.3, 3.1, 3.3, 3.4, 3.7, 4.5, 5.3, 5.4, 5.5, 5.6, 8.5, 8.6, 8.b, 10.2, 12.8, 13.3, 13.b

0.g. International organisations(s) responsible for global monitoring

UNESCO Institute for Statistics (UIS)

1.a. Organisation

UNESCO Institute for Statistics (UIS)

2.a. Definition and concepts

Definition:

The proportion of youth (aged 15-24 years) and of adults (aged 15 years and above) who have achieved or exceeded a fixed level of proficiency in (a) literacy and (b) numeracy.

Concepts:

The fixed level of proficiency (FLP) is the benchmark of basic knowledge in a domain (literacy or numeracy) measured through learning assessments. Currently, the FLP for global reporting is the Programme for the International Assessment of Adult Competencies (PIAAC) level 2 descriptor.

The concepts of functional literacy and functional numeracy are based on the UNESCO definitions, which cover a continuum of proficiency levels rather than a dichotomy. A person is functionally literate if he/she can engage in all those activities in which literacy is required for the effective functioning of his/her group and community and also which enables them to continue to use reading, writing and calculation for his/her own and the community’s development.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Level 2 descriptor of PIAAC

Literacy:

At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to:

cycle through or integrate two or more pieces of information based on criteria;
compare and contrast or reason about information requested in the question; or
navigate within digital texts to access and identify information from various parts of a document.

Numeracy:

Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percentages and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs.

3.a. Data sources

This indicator is collected via skills' assessment surveys of the adult population (e.g., PIAAC, STEP, LAMP^[1], RAMAA) and national adult literacy surveys.

Literacy Assessment and Monitoring Programme.

Note: the full forms of PIAAC and STEP can be found in the rest of the document. ↑

3.b. Data collection method

Data are collected from the respective organizations responsible for each assessment.

3.c. Data collection calendar

Various depending on survey and country.

3.d. Data release calendar

Biannual UIS data release (March and September)

3.e. Data providers

This indicator is collected via skills national or international assessment surveys of youth and adult populations. The Organization for Economic Co-operation and Development’s (OECD) Survey of Adult Skills in its Programme for the International Assessment of Adult Competencies (PIAAC) and the World Bank’s Skills Towards Employment and Productivity (STEP) measurement programme, both based on the PIAAC framework and scale, and bodies responsible for conducting national learning assessments (including Ministries of Education, National Statistical Offices and other data providers) are sources of data of this indicator

3.f. Data compilers

UNESCO Institute for Statistics (UIS)

3.g. Institutional mandate

”

The Education 2030 Framework for Action §100 has clearly states that: “Inrecognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO’s mandate, working in coordination with the SDG-Education 2030 SC”.

4.a. Rationale

The indicator is a direct measure of the skill levels of youth and adults in the two areas: literacy and numeracy.

4.b. Comment and limitations

Functional literacy and numeracy are related to context, thus survey programs need further development in order to frame questions in a way that are meaningful to different economic and social-settings and could be more efficient to reflect population level of skills.

4.c. Method of computation

Proportion of youth and adults who have achieved at least a fixed level of proficiency as defined for large-scale (sample representative) adult literacy and numeracy assessments:

${P F L P}_{t, a, d} = \frac{{F L P}_{t, a, d}}{P_{t, a, d}}$

where:

PFLP_t,a,d = the proportion of people in a skills survey in age group a, in year t, who have achieved or exceeded the fixed level of proficiency in domain d.

FLP_t,a,d = the number of people in a skills survey in age group a, in year t, who have achieved or exceeded the fixed level of proficiency in domain d.

P_t,a,d = the total number of people in age group a, in year t, who participated in the skills survey of domain d.

a = 16-65 years (youth and adults).

d = the domain which was assessed (literacy or numeracy).

4.d. Validation

In each data update period, surveys of recent publications of results of national and international assessments are carried out. Then, consultations are made with national references and UIS technical focal points to verify the availability and validity of the data.

4.e. Adjustments

Not applicable.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

None by data compiler.

• At regional and global levels

None by data compiler.

4.g. Regional aggregations

Regional and global aggregates are not currently available for this indicator.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The UIS has elaborated guidance for the countries regarding the contents, the procedures and the reporting in the Global Alliance to monitor learning microsite.

4.i. Quality management

The UIS maintains a global database on learning assessments. The inclusion of a data point in the database to show transparency is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database.

4.j. Quality assurance

OECD is the data compiler for PIAAC and the World Bank Group is the compiler for STEP, both used the PIAAC framework and skills level descriptors.

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

45 countries with at least one data point for the period 2010-2017.

Time series:

2006 onwards.

Disaggregation:

By age-group, sex, location, income and type of skill. Disability status is not currently available in most national and cross-national learning assessments.

6. Comparability/deviation from international standards

Sources of discrepancies:

None.

7. References and Documentation

URL:

http://uis.unesco.org/

References:

Programme for the International Assessment of Adult Competencies (PIAAC):

https://www.oecd.org/skills/piaac/

STEP Skills Measurement Programme:

https://microdata.worldbank.org/index.php/catalog/step/about

Action Research: Measuring Literacy Programme Participants’ Learning Outcomes (RAMAA):

https://uil.unesco.org/literacy/learning-outcomes-ramaa/action-research-measuring-literacy-programme-participants-learning

4.7.1

0.a. Goal

Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

0.b. Target

Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable development

0.c. Indicator

Indicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessment

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

12.8.1 and 13.3.1

0.g. International organisations(s) responsible for global monitoring

UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD)

UNESCO Institute for Statistics (UNESCO-UIS)

1.a. Organisation

UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD) and UNESCO Institute for Statistics (UNESCO-UIS),

2.a. Definition and concepts

Definition:

Indicator 4.7.1/12.8.1/13.3.1 measures the extent to which countries mainstream Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) in their education systems. This is an indicator of characteristics of different aspects of education systems: education policies, curricula, teacher training and student assessment as reported by government officials, ideally following consultation with other government ministries, national human rights institutes, the education sector and civil society organizations. It measures what governments intend and not what is implemented in practice in schools and classrooms.

For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. (See methodology section for full details).

The indicator and its methodology have been reviewed and endorsed by UNESCO’s Technical Cooperation Group on the Indicators for SDG 4-Education 2030 (TCG), which is responsible for the development and maintenance of the thematic indicator framework for the follow-up and review of SDG 4. The TCG also has an interest in education-related indicators in other SDGs, including global indicators 12.8.1 and 13.3.1. The TCG is composed of 38 regionally representative experts from UNESCO Member States (nominated by the respective geographic groups of UNESCO), as well as international partners, civil society, and the Co-Chair of the Education 2030 Steering Committee. The UNESCO Institute for Statistics acts as the Secretariat.

Concepts:

Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) nurture respect for all, build a sense of belonging to a common humanity, foster responsibility for a shared planet, and help learners become responsible and active global citizens and proactive contributors to a more peaceful, tolerant, inclusive, secure and sustainable world. They aim to empower learners of all ages to face and resolve local and global challenges and to take informed decisions and actions for environmental integrity, economic viability and a just society for present and future generations, while respecting cultural diversity.

2.b. Unit of measure

Index (between 0.000 and 1.000)

2.c. Classifications

Not applicable

3.a. Data sources

Responses to the quadrennial reporting by UNESCO Member States on the implementation of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms. The most recent round of reporting took place in 2020-21. The results were published in the Global SDG Indicator Database in July and September 2021. (See methodology section for details of questions asked).

3.b. Data collection method

Responses are submitted by national governments, typically by officials in Ministries of Education. Respondents are asked to consult widely across other government ministries, with national human rights institutes, the education sector and civil society organizations in compiling their responses. Respondents are also asked to submit supporting evidence in the form of documents or links (e.g. to education policies or laws, curricula, etc.), which will be made publicly available during 2022.

3.c. Data collection calendar

2020-21 round (covering 2017-2020) completed in April 2020. Next round foreseen in 2023-24 (covering 2021-2023).

3.d. Data release calendar

Q2 and Q3 of 2021 (from 2020-21 reporting round) The next data release is not foreseen until at least Q2 of 2024.

3.e. Data providers

Requests for reports are submitted to Ministers Responsible for Relations with UNESCO who are typically Education Ministers. Reports are usually completed by government officials in Ministries of Education. Countries are requested to consult widely before submitting their reports. To assist with this, requests for reports are also copied to NGOs in official partnership with UNESCO and the Office of the High Commissioner for Human Rights (OHCHR).

3.f. Data compilers

UNESCO’s Sections for Education for Sustainable Development and Global Citizenship and Peace Education.

3.g. Institutional mandate

In 1974, UNESCO Member States adopted the Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms, which encapsulates many of the aims of SDG targets 4.7, 12.8 and 13.3. Every four years, countries report on the implementation of the Recommendation. This well-established formal mechanism is the data source for indicator 4.7.1/12.8.1/13.3.1. The seventh quadrennial reporting round took place in 2020-2021.

4.a. Rationale

In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for governments to ensure that ESD and GCED and their sub-themes are fully integrated in all aspects of their education systems. Students will not achieve the desired learning outcomes if Education for Sustainable Development (ESD) and Global Citizenship Education (GCED) have not been identified as priorities in education policies or laws, if curricula do not specifically include the themes and sub-themes of ESD and GCED, and if teachers are not trained to teach these topics across the curriculum.

This indicator aims to give a simple assessment of whether the basic infrastructure exists that would allow countries to deliver quality ESD and GCED to learners, to ensure their populations have adequate information on sustainable development and lifestyles in harmony with nature. Appropriate education policies, curricula, teacher education, and student assessment are key aspects of national commitment and effort to implement GCED and ESD effectively and to provide a conducive learning environment.

Each component of the indicator is assessed on a scale of zero to one. The closer to one the value, the better mainstreamed are ESD and GCED in that component. By presenting results separately for each component, governments will be able to identify in which areas more efforts may be needed.

4.b. Comment and limitations

The indicator is based on self-reporting by government officials. However, countries are asked to provide supporting evidence in the form of documents or links (e.g. education policies or laws, curricula, etc.) to back up their responses. In addition, UNESCO compares responses with available information from alternative sources and, if appropriate, raise queries with national respondents. At the end of the reporting cycle, country responses and the supporting documents will be made publicly available.

4.c. Method of computation

Information collected with the questionnaire for monitoring the implementation by UNESCO Member States of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms is used for the construction of the global indicator. For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. Only information for primary and secondary education are used for calculation of indicator 4.7.1/12.8.1/13.3.1.

Laws and policies

The following questions are used to calculate the policies component of the indicator:

A2: Please indicate which global citizenship education (GCED) and education for sustainable development) ESD themes are covered in national or sub-national laws, legislation or legal frameworks on education.

Response categories are no = 0, yes = 1, unknown, which is treated as zero, and not applicable, which is ignored. Blanks are also treated as zeros.

If more than half of responses are unknown or blank the question score is not calculated.

Note that ‘not applicable’ is used where only one level of government is responsible for education.

Question score = simple mean of the 0 and 1 scores, excluding not applicables (i.e., if eight of the 16 responses are ‘not applicable’, the sum of the 0 and 1 scores is divided by 8 to get the mean and not by 16).

A4. Please indicate which GCED and ESD themes are covered in national or sub-national education policies, frameworks or strategic objectives.

Response categories are no = 0, yes = 1, and unknown (treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

A5. Please indicate whether national or sub-national education policies, frameworks or strategic objectives on education provide a mandate to integrate GCED and ESD.

There are two levels of government (national, sub-national) and five areas of integration (curricula, learning objectives, textbooks, teacher education, and student assessment) = 10 responses.

Response categories are no = 0, yes = 1, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros.

If more than half of responses excluding not applicables are unknown or blank, the question score is not calculated.

Note that ‘not applicable’ is used where only one level of government is responsible for education.

Question score = simple mean of the 0 and 1 scores, excluding not applicables (i.e., if five of the 10 responses are ‘not applicable’, the sum of the 0 and 1 scores is divided by 5 to get the mean and not by 10).

E1a. Based on your responses to questions in the previous section (laws and policies) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed^[1] in education laws and policies in your country.

There are two levels of government (national, sub-national) = 2 responses.

Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros.

If more than half of responses excluding not applicables are unknown or blank, the question score is not calculated.

Note that ‘not applicable’ is used where only one level of government is responsible for education.

Question score = half the simple mean of the 0, 1 and 2 scores, excluding not applicables (i.e., if one of the two responses is ‘not applicable’, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1 as do the scores for the other three questions in this section.

Policy component score = simple mean of the scores for questions A2, A4, A5 and E1a. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.

Curricula

The following questions are used to calculate the curricula component of the indicator:

B2: Please indicate which global citizenship education (GCED) and education for sustainable development (ESD) themes are taught as part of the curriculum.

Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

B3. Please indicate in which subjects or fields of study GCED and ESD are taught in primary and secondary education.

There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and twelve subjects in which they may be taught (arts; civics, civil or citizenship education; ethics/moral studies; geography; health, physical education and sports; history; languages; mathematics; religious education; science; social studies and integrated studies) = 96 responses.

Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank the question score is not calculated.

Note that responses to ‘other subjects, please specify’ in the question are ignored. If appropriate, during quality assurance answers in this category may be recoded to one of the other 12 subjects.

Question score = simple mean of the 0 and 1 scores.

B4. Please indicate the approaches used to teach GCED and ESD in primary and secondary education.

There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses

Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

E1b. Based on your responses to questions in the previous section (curricula) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed^[2] in curricula in your country.

There are two levels of government (national, sub-national) = 2 responses.

Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros.

If more than half of responses excluding ‘not applicables’ are unknown or blank, the question score is not calculated.

Note that ‘not applicable’ is used where only one level of government is responsible for education.

Question score = half the simple mean of the 0, 1 and 2 scores, excluding ‘not applicables’ (i.e., if one of the two responses is ‘not applicable’, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.

Curricula component score = simple mean of the scores for questions B2, B3, B4 and E1b. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.

Teacher education

The following questions are used to calculate the teacher education component of the indicator:

C2: Please indicate whether teachers, trainers and educators are trained to teach global citizenship education (GCED) and education for sustainable development (ESD) during initial or pre-service training and/or through continuing professional development.

There are two types of training (initial/pre-service and continuing professional development) and two types of teachers (of selected subjects in which ESD/GCED are typically taught, and of other subjects) = 4 responses.

Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

C3. Please indicate on which GCED and ESD themes pre-service or in-service training is available for teachers, trainers and educators.

Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

C4. Please indicate whether teachers, trainers and educators are trained to teach the following dimensions of learning in GCED and ESD.

There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses.

Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

C5. Please indicate whether teachers, trainers and educators are trained to use the following approaches to teach GCED and ESD in primary and secondary education.

There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses.

Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

E1c. Based on your responses to questions in the previous section (teacher education), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed^[3] in teacher education in your country.

There are two levels of government (national, sub-national) = 2 responses.

Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable (which is ignored). Blanks are also treated as zeros.

If more than half of responses excluding ‘not applicables’ are unknown or blank, the question score is not calculated.

Note that ‘not applicable’ is used where only one level of government is responsible for education.

Teacher education component score = simple mean of the scores for questions C2, C3, C4, C5 and E1c. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.

Student assessment

The following questions are used to calculate the student assessment component of the indicator:

D2: Please indicate whether the global citizenship education (GCED) and education for sustainable development (ESD) themes below are generally included in student assessments or examinations.

Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

D3. Please indicate which of the dimensions of learning in GCED and ESD below are generally included in student assessments or examinations.

There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses.

Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros.

If more than half of responses are unknown or blank, the question score is not calculated.

Question score = simple mean of the 0 and 1 scores.

E1d. Based on your responses to questions in the previous section (student assessment), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed^[4] in student assessment in your country.

There are two levels of government (national, sub-national) = 2 responses.

Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros.

If more than half of responses excluding ‘not applicables’ are unknown or blank, the question score is not calculated.

Note that ‘not applicable’ is used where only one level of government is responsible for education.

Student assessment component score = simple mean of the scores for questions D2, D3 and E1d. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.

The component scores all lie between zero and one and are presented as a dashboard of four scores. They are not combined to create a single overall score for the indicator. The higher the score, the more GCED and ESD are mainstreamed in the given component. In this way, users can make a simple assessment in which component area more efforts may be needed.

GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. ↑

GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers), as appropriate. ↑

⁴

GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers) as appropriate. ↑

4.d. Validation

Responses are reviewed by UNESCO for consistency and credibility and, if necessary, queries are raised with national respondents. Where feasible, reference is made to national documents and links supplied by respondents and to available alternative sources of information.

Any proposed changes in response values in the questionnaire as a result of quality assurance procedures are communicated and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and with national SDG indicator focal points where they exist.

4.e. Adjustments

The only adjustments made are where question response categories are not valid and responses between different questions are inconsistent. In those circumstances, proposed changes are communicated to and verified with countries.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

A small number of missing values – unknown responses and/or blanks – are treated as zeros in the calculation of the question scores. Where they represent more than 50% of the responses to a single question, the component score is not calculated. In such cases, the component score is reported as not available when results are disseminated.

At regional level

Regional values are not calculated.

4.g. Regional aggregations

Regional aggregates are not calculated.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

• Countries wishing to calculate this indicator for themselves should follow the steps described in section 4.c. Method of computation above.

• The questionnaire for the monitoring of the implementation of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms is approved by the Member States of the Executive Board of UNESCO. The questionnaire contains guidelines for completion and a glossary of key terms. In addition, UNESCO provides direct support to Member States in completing the questionnaire and responds to queries in a quality and timely manner.

4.i. Quality management

None related to the processing of qualitative data collected principally for non-statistical purposes.

4.j. Quality assurance

UNESCO reviews country responses for consistency and credibility and, if necessary, raises queries with national respondents. To assist with this, countries are asked to provide, in addition to completed questionnaires, supporting evidence of their responses in the form of documents or links (e.g. to education policies, laws, curricula, etc.). These will be made publicly available during 2022 along with completed questionnaires. UNESCO also takes into account alternative sources of information, where available. These may include national responses to similar intergovernmental consultation processes, such as the Council of Europe’s consultations on the Charter on Education for Democratic Citizenship and Human Rights Education, the UN Economic Commission for Europe’s consultations on the Strategy for Education for Sustainable Development, or other information on education for sustainable development (ESD) and global citizenship education (GCED) in countries’ national education systems.
Any proposed changes to response values in the questionnaire as a result of quality assurance procedures are communicated to and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and SDG indicator focal points.

4.k. Quality assessment

None related to the processing of qualitative data collected principally for non-statistical purposes.

5. Data availability and disaggregation

Data availability:

During the last consultation on the implementation of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms carried out in 2020-2021, 75 countries provided reports: Central and Southern Asia (4), Eastern and South-Eastern Asia (7), Europe and Northern America (32), Latin America and the Caribbean (10), Northern Africa and Western Asia (14), Oceania (2), and sub-Saharan Africa (6).

Time series:

The first data are available for the time period 2017-2020 (as a single time point).

Disaggregation:

None

6. Comparability/deviation from international standards

Sources of discrepancies:

There should be no difference as the indicator values are calculated from the responses submitted by countries. If any changes are proposed to responses as a result of quality assurance procedures, these are communicated to and verified with countries.

7. References and Documentation

URL: http://uis.unesco.org/.

References:

Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms.

5.a.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.a: Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources, in accordance with national laws

0.c. Indicator

Indicator 5.a.1: (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure

0.d. Series

Proportion of total agricultural population with ownership or secure rights over agricultural land (SP_LGL_LNDAGSEC) Share of women among owners or rights-bearers of agricultural land, by type of tenure (SP_GNP_WNOWNS)

0.e. Metadata update

2023-05-15

0.f. Related indicators

SDG indicator 1.4.2.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The indicator consists of two sub-indicators.

Sub-indicator 5.a.1 (a):

No. of people in agricultural population with ownership or secure rights over agricultural land	$\times$ 100, by sex
Total agricultural population

Sub-indicator 5.a.1 (a) is a prevalence measure. It measures the prevalence of people in the agricultural population with ownership or secure rights over agricultural land, disaggregated by sex.

Land ownership is a legally recognised right to acquire, to use and to transfer land. “Secure rights” in the context of indicator 5.a.1 is defined as secure tenure rights, i.e., rights to use, manage and control land, fisheries and forests, in the sense of the Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security^[1]. Operationally, for the purposes of measurement of this indicator, secure tenure rights comprise both land ownership and two key alienation rights: the right to sell and the right to bequeath agricultural land.

Sub-indicator 5.a.1 (b):

Number of women in the agricultural population with ownership or secure rights over agricultural land	$\times$ 100, by type of tenure
Total in the agricultural population with ownership or secure rights over agricultural land

Sub-indicator 5.a.1 (b) focuses on gender parity, measuring the extent to which women are disadvantaged in ownership or secure rights over agricultural land.

Broad types of tenure identified by the IAEG-SDG are freehold, customary and leasehold.

Concepts and terms:

The basic concepts and terms essential to collecting data needed to compute SDG indicator 5.a.1 are the following:

Agricultural land
Agricultural household
Agricultural population
Ownership or secure rights over agricultural land

(1) Agricultural land

Land is considered ‘agricultural land’ according to its use. The classes and definitions of land use are based on the classification of land use for the agricultural census recommended by the World Programme for the Census of Agriculture 2020^[2].

As shown in Figure 1 below, agricultural land is a subset of the total land of a country. In particular, agricultural land includes:

LU1- land under temporary crops^[3]
LU2- land under temporary meadows and pastures^[4]
LU3- land temporarily fallow^[5]
LU4- land under permanent crops^[6]
LU5- land under permanent meadows and pastures^[7]

Figure 1. Classification of land use (WCA 2020)

Since indicator 5.a.1 focuses on agricultural land, it excludes all the forms of land that are not considered ‘agricultural’, namely:

LU6- land under farm buildings and farmyards
LU7- forest and other wooded land
LU8- area used for aquaculture (including inland and coastal waters if part of the holding)
LU9- other area not elsewhere classified

The land use class of agricultural land is with respect to a specific reference period; thus, the reference period should be specified when collecting data on land use. As further discussed below, the reference period should cover a 12-month period. In agricultural censuses and surveys, this is generally the preceding 12 months.

(2) Agricultural household

Ownership or secure rights over agricultural land are specifically relevant to individuals whose livelihood relies on agriculture. These individuals are identified by way of whether their household^[8] can be classified as an agricultural household which for purposes of calculating indicator 5.a.1 is characterized by the following:

Criterion 1: A member or members of the household operated land for agricultural purposes or raised livestock over the past 12 months regardless of the final purpose of production

and

Criterion 2: At least one member of the household operated land for agricultural purposes or raised livestock as an own-account worker.

The definition considers that since agricultural land includes both crop land (LU1-LU4) and meadows and pastures (LU5), ownership or secure rights over agricultural land are relevant for households operating land and/or raising or tending livestock. Engagement in forestry, logging, fishing and aquaculture activities is not included because the focus of the indicator is on agricultural land.

Households who own or have secure rights over agricultural land but did not farm the land nor used the land in raising/tending livestock during the reference period are excluded, because the indicator focuses on households whose livelihood is linked to practicing agriculture.

The long reference period-- previous 12 months-- allows to capture agricultural households even when data collection occurs during the off-season or when households are not engaged in agricultural activity at the time of the survey. That is, since agricultural work is highly irregular and strongly affected by seasonality, a short reference period would exclude such households.

The specification “regardless of the final purpose of production” ensures the inclusion of households that produce only for own consumption. It addresses a common problem where agriculture practiced only or mainly for own consumption, without any market orientation (so, with no or little income) is not perceived as an economic activity by respondents.

The second criterion for a household to be classified as an agricultural household for purposes of computing the sub-indicators 5.a.1(a) and 5.a.1(b) is that at least one household member farms or raises livestock as an own-account worker. Thus, information on the status in employment and, for those employed, the industry in which they are employed, and their occupation need to be collected for each member of the household.

(3) Agricultural population

The reference population for indicator 5.a.1 is the population of adult individuals living in agricultural households (as defined above). For purposes of international comparability, the recommended definition of “adult” is a person who is 18 years old or older. However, countries could use their own definitions of adult but allow for the calculation of statistics based on the 18-years old definition.

Once a household is classified as an ‘agricultural household’, all the adult household members are considered as part of the reference population (to be referred to simply as the “agricultural population” in this document).

The adoption of a household perspective is particularly important from the gender perspective, because in many agricultural households, women often consider themselves as not being involved in agriculture, even though they provide substantive support to the household’s agricultural activities. In addition, the individual’s livelihood cannot be completely detached from the livelihood of the other household members; and in particular, for households operating agricultural land or raising livestock, land is an important asset for all the individuals and protects them in case the household dissolves.

When the data is collected in agricultural surveys or censuses, usually the statistical unit is the agricultural holding or farm. The WCA 2020 classifies holdings into two types: (i) holdings in the household sector; i.e., those operated by household members and (ii) holdings in the non-household sector, such as corporations. For a given household sector holding, there may be one or more producers and the agricultural population is defined as the adult members of the households of the producers. It is important to note, that someone employed in the agricultural holding is not a producer. Holdings in the non-household sector are not relevant for the estimation of indicator 5.a.1.

(4) Ownership of agricultural land and secure rights over agricultural land

It is challenging to operationalize the definition of ownership of and secure rights to agricultural land for purposes of data collection. In addition, differences in legal systems and how legal systems protect rights to agricultural land across countries poses challenges in providing comparable statistics across countries. The discussion below:

Land ownership is a legally recognised right to acquire, to use and to transfer land. For purposes of specifying the data that needs to be collected, it is useful to recognize three broad typologies of land ownership systems:

Private property systems, where land ownership is predominantly a right akin to a freehold tenure.
Systems where land is owned by the State, where “land ownership” in the sense of private property systems does not exist but refers to possession of the rights most akin to ownership in a private property system. In this context, it is more appropriate to speak of tenure rights that capture an individual’s capacity to control and take decisions over the land-- for instance, long-term leases, occupancy, tenancy or use rights granted by the State that are transferable and are granted to users for several decades (e.g., 99 years).
Communal land tenure system, where land is primarily held under a tribal, communal, or traditional form of tenure. Such arrangements usually involve land being held on a tribal, village, kindred or clan basis, with land ownership being communal in character but with certain individual rights being held by virtue of membership in the social unit.

In many countries, a combination of systems of ownership as well as secure tenure rights to land may exist. A common combination would be where the private property system prevails, but with pockets of state-owned and/or communal land. For some countries, the system may primarily be that of state-owned land or communal land.

Considering the above, as well as the need for comparability of estimates across countries, to determine whether an individual is said to have ownership or secure rights to agricultural land three conditions (proxies) are considered:

Formal documentation:

Proxy 1- Presence of legally recognised documents in the name of the individual

Alienation rights:

Proxy 2- Right to sell

Proxy 3- Right to bequeath

These proxies are further described below.

Formal documentation

Proxy 1 refers to the existence of any document that an individual can use to claim property rights before the law over an asset by virtue of the individual’s name being listed as owner/co-owner or holder/co-holder on the document.

It is not possible to provide an exhaustive list of documents that could be considered as formal proof of ownership (for private property systems) or secure tenure rights (for state-owned or communal land systems) across countries. Examples of common relevant legal documents are provided in the discussion below. It is recommended that the list of documents be customised in accordance with land ownership laws of the country. It is further recommended that:

Private property systems

For private property systems, the following are typically considered as formal written proof of ownership:

Title deed: “a written or printed instrument that effects a legal disposition”^[9]
Certificate of occupancy or land certificate: “A land certificate is a certified copy of an entry in a land title system and provides proof of the ownership and of encumbrances on the land at that time”^[10]
Purchase agreement: a contract between a seller and a buyer to dispose of land
Registered certificate of hereditary acquisition
Certificate issued for adverse possession or prescription: is a certificate indicating that the adverse possessor acquires the land after a prescribed statutory period.

It is to be noted that agricultural land possessed or used under a rental contract or leasehold is outside the coverage of indicator 5.a.1. Ownership of land confers on the holder a series of crucial benefits leading to economic empowerment - from being able to use it as collateral to having a higher propensity to invest in one’s own asset – these benefits are drastically reduced or even absent in the case of rentals or leases.

Customary/communal land tenure

For land covered by customary tenure laws, the types of tenure and associated rights vary considerably. Thus, it is recommended that the list of relevant documents be prepared according to each country’s customary laws. An example of a relevant document is:

Certificate of customary tenure: an official state document indicating the owner or holder of the land because customary law has recognized that particular person as the rightful owner. It can be used as proof of legal right over the land. These certificates include, among others, certificates of customary ownership and customary use.

Systems where land is owned by the state

Similarly, for state-owned land, associated formal documents of ownership-like possession should be specified according to the country’s land laws. An example of a relevant document is:

Registered certificate of perpetual / long term lease: “a contractual agreement between the state and a tenant for the tenancy of land. A lease or tenancy agreement is the contractual document used to create a leasehold interest or tenancy” ^[11]

Note that findings from the Evidence and Data for Gender Equality (EDGE) project^[12] clearly show that using legally recognized documents alone to establish ownership is not sufficient to analyse the complexity of rights related to land, especially in developing countries and from the gender perspective. The main factor limiting the universal applicability of legally recognized documents to define ownership is the diverse penetration of such legally binding documents.

Alienation rights

In the absence of formal written documentation alienation rights over land, which can be present even in contexts where tenure rights are not formally documented, can serve as a proxy for ownership or secure rights. Alienation is defined as the ability to transfer a given asset during lifetime (Proxy 2- right to sell) or after death (Proxy 3- right to bequeath).

The “right to sell” refers to the ability of an individual to permanently transfer the asset in question in return for cash or in-kind benefits.

The “right to bequeath” refers to the ability of an individual to pass on the asset in question to other person(s) after their death, by written will, oral will (if recognized by the country) or when the deceased left no will, through intestate succession.

The right to sell and the right to bequeath are considered as objective facts that carry legal force as opposed to a simple self-reported declaration of tenure rights over land.

For purposes of data collection for 5.a.1, countries should clearly indicate whether these two alienation rights are relevant to the concept of land ownership in their legal contexts. This is particularly important in relation to land use under systems where land is owned by the state and customary/communal land.

It is recommended that data on all three proxies be collected for purposes of compiling indicator 5.a.1. The decision to rely on the three proxies is based on the results of seven field tests conducted by the EDGE project. The tests demonstrated:

The lower reliability of data on reported ownership/possession. Data on ownership/possession are often collected through a question on whether the individual owns any agricultural land. The data collected captures the self-perception of the respondent’s ownership or possession status of the land, irrespective of whether the respondent has formal documentation. The study showed that such data was often neither supported by any kind of documentation nor by the possession of any alienation right.
The need to consider as ‘owners’ or ‘holders of tenure rights’ only the individuals who are linked to the agricultural land by an objective right over it, including both formal legal possession and alienation rights.
The need to combine different proxies, as no single proxy is universally applicable in defining land ownership or secure tenure rights.

A Note on “(Self) Reported Ownership/Possession of Agricultural Land”

As mentioned above, reported ownership or possession is relatively less reliable than documented ownership. However, in a situation where a country has scarce data on formal documentation along with missing information on alienation rights, reported ownership could still be a temporarily useful alternative for comparing ownership between men and women. However, estimates computed based mainly on reported ownership weakens the international comparability of estimates across countries. Therefore, it is highly recommended that the survey questionnaire be modified in a manner that both documented ownership and alienation rights are included, as defined above, in order to calculate the indicator using the correct methodology.

Refer to https://www.fao.org/tenure/voluntary-guidelines/en/ ↑

FAO. 2015. World Programme for the Census of Agriculture 2020- Volume 1: Programme, concepts and definitions. FAO Statistical Development Series 15, paras 8.2.13 – 8.2.28. ↑

Defined as: “all land used for crops with a less than one-year growing cycle” (WCA 2020). Temporary crops comprise all the crops that need to be sown or planted after each harvest for new production (e.g., cereals). The full list of crops classified as ‘temporary’ is provided in the WCA 2020 (page 165, http://www.fao.org/3/a-i4913e.pdf) ↑

⁴

Defined as: “land that has been cultivated for less than five years with herbaceous or forage crops for mowing or pasture”. ↑

⁵

When arable land is kept at rest for at least one agricultural year because of crop rotation or other reasons, such as the impossibility to plant new crops, this is defined as temporarily fallow. This category does not include the land that it is not cultivated at the time of the survey but will be sowed and planted before the end of the agricultural year. ↑

⁶

Area that is cultivated with long term crops that do not need to be replanted every year, such as fruits and nuts, some types of stimulant crops, etc. ↑

⁷

Land cultivated with herbaceous forage crops or is left as wild prairie or grazing land for more than five years. ↑

⁸

Household is defined according to the United Nations Principles and Recommendations for Population and Housing Censuses, Revision 3 @ https://unstats.un.org/unsd/publication/seriesM/Series_M67rev3en.pdf ↑

⁹

Source: “Multilingual thesaurus on land tenure”, FAO 2003 ↑

¹⁰

Source: “Multilingual thesaurus on land tenure”, FAO 2003 ↑

¹¹

Source: “Multilingual thesaurus on land tenure”, FAO 2003 ↑

¹²

Source: “UN Methodological Guidelines on the Production of Statistics on Asset Ownership from a Gender Perspective” Draft Guidelines submitted at the UN Statistical Commission in March 2017 ↑

2.b. Unit of measure

5.a.1 (a): percent (%)

5.a.1 (b): percent (%)

2.c. Classifications

Classification of land use - World Census of Agriculture 2020 (WCA 2020).

3.a. Data sources

Recommended data sources

Indicator 5.a.1 focuses on adult individuals living in agricultural households, as defined above. Thus, the data required to estimate the indicator, can be collected through agricultural surveys/ censuses or national household-based surveys having a suitable coverage of agricultural households.

Agricultural Survey: Agricultural surveys are a recommended data source for two main reasons:

The unit of analysis is the agricultural holding, and, in most countries, the relationship between the household-sector agricultural holding and the agricultural households is known. Therefore, agricultural surveys capture well the reference population of indicator 5.a.1
Agricultural surveys can easily accommodate questions on ownership or secure rights to agricultural land since they frequently collect data regarding tenure of agricultural land of the holding as well as data on agricultural producers households.

General household survey (GHS)^{^[13]}: Nationally representative general household surveys are a recommended data source for indicator 5.a.1 for the following reasons:

Nationally representative general household surveys are the most common data source available in both developed and developing countries.
Countries that have an integrated household survey system can integrate the data requirements for 5.a.1 as part of the core survey or as a module in one of the rounds of the survey.
Nationally representative general household surveys generate social, demographic, health and economic statistics (depending on their particular focus). When data requirements for 5.a.1 are integrated in the survey, it allows for exploring associations between the individual status on indicator 5.a.1 and other individual or household characteristics, such as education, health, income level, etc.

However, if a GHS is used to collect data to generate estimates for indicator 5.a.1, it is necessary to have a representative sample of agricultural households in the full sample. In countries where a low proportion of households is engaged in agricultural production, oversampling may be needed, especially in urban and peri-urban areas and procedures for doing so need to be part of the survey design.

Also, some household surveys may have limitations in relation to the population coverage as defined by the age classes typically used in these surveys-- for example, having upper bound age cut-offs.

Agricultural Census: In the absence of agricultural or household-based surveys, agricultural censuses can be used for collecting data on SDG 5.a.1. However, the Census presents some disadvantages:

The Census is usually conducted every 10 years; therefore, it cannot provide data to closely monitor the progress on indicator 5.a.1.
It is much more expensive to add the questions for 5.a.1 in agricultural censuses than in surveys as the number of holdings to be enumerated is much larger.
With the need for a much larger number of interviewers in a census, the quality of interviewers selected may be adversely affected.

¹³

Examples of GHS that could be used to generate the indicator 5.a.1 are: Household Budget Surveys (HBS), Living Standard Measurement Surveys (LSMS), Living Conditions Surveys, Labour Force Surveys (LFS), Multipurpose Household Surveys, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). ↑

3.b. Data collection method

The data should be collected through surveys that collect information on an individual’s land ownership and tenure rights.

In collecting data for indicator 5.a.1 through an agricultural survey, agriculture census or general household survey, two decisions need to be made:

i) Determine the number of adult members of an agricultural household (eligible respondents) on whom information is to be collected, and

ii) Determine who should report this information

Possible options are shown in Table 1 below:

Table 1. Options and respondent approaches for data collection

Number of eligible respondents	Who should report
Number of eligible respondents	Self-Respondent	Proxy-Respondent
All	Option 1	Option 3
Randomly selected number, n	Option 2	Option 4

When collecting data on asset ownership from a gender perspective, the self-respondent approach where the concerned individuals themselves are interviewed is recommended over the proxy respondent approach, where the most knowledgeable household member is interviewed to collect information on all the household members^[14]. Thus, among the possible options, Option 1 and Option 2 are recommended:

Option 1: Self-respondent approach applied to all members. Each adult member of the household is interviewed on their ownership / secure rights over agricultural land.
Option 2: Self-respondent approach applied to a random sample of adult members of the household. Randomly selected adult household members are interviewed on their ownership / secure rights over agricultural land.

In practice, due to budget constraints and interview time limitations, interviewing only n = 1 eligible respondent per household or a proxy respondent are the most viable options. Furthermore, in agricultural surveys and censuses, only the producers respond to the whole questionnaire, so using a self-respondent approach is not viable. However, if a country wants to study intra-household dynamics or to increase the accuracy of the 5.a.1 estimates, it may decide to collect information about two or more and even all adult household members.

Minimum Set of Data

The minimum set of data needed to calculate the indicator is summarized in Table 2 below:

Table 2. Minimum set of data for indicator 5.a.1

Data Item	Purpose
Whether or not the household has operated land for agricultural purposes and/or raised livestock over the past 12 months regardless of final purpose of production	To identify agricultural households
Whether operating land or raising livestock was done only as wage labour	To identify agricultural households
Sex of agricultural household members	To identify adult agricultural population, by sex
Age of agricultural household members	To identify adult agricultural population, by sex
*For adult agricultural population, data on ownership or secure rights to agricultural land based on the three proxies*
Whether or not the individual owns or holds secure rights to any agricultural land	Filter question on whether owns/has secure rights to agricultural land. Also provides data on (self-) reported ownership
Proxy 1: Whether or not any of the land owned or held by the individual has a legally recognized document that allows protecting his/her ownership/secure rights over the land	To determine ownership/secure rights based on legally recognized document
If yes to Proxy 1: Whether or not the individual is listed as an owner or holder on any of the legally recognized documents, either alone or jointly with someone else
(Proxy 2) Whether or not the individual has the right to sell any of the agricultural land, either alone or jointly with someone else	To determine ownership/secure rights based on possession of alienation rights
(Proxy 3) Whether or not the individual has the right to bequeath any of the agricultural land, either alone or jointly with someone else

Question formulation to collect minimum data items required for indicator 5.a.1

Questions to Identify agricultural households and adult Individuals in the agricultural population

As mentioned above, the reference population (denominator) for indicator 5.a.1 are the adult individuals living in agricultural households. The first step to identify the agricultural population is to identify agricultural households.

The module presented in Table 3 suggests how to identify agricultural households from among households covered by the data collection vehicle (survey/census) for purposes of indicator 5.a.1. The questions aim to capture the household’s involvement in agriculture over the preceding 12 months and screen out households where all members are involved in agricultural activity only as wage workers. The respondent to the questions in the module should be the most knowledgeable member of the household.

Table 3. Module for identifying agricultural households

Question		Function
Check Criterion 1 defining an agricultural household
Q1	Did anyone in this household operate any land⁽¹⁾ for agricultural purposes in the past 12 months⁽²⁾? 1. Yes 2. No	Screening (farming) (Response = 1)
Q2	Did anyone in this household raise or tend any livestock (e.g., cattle, goats, etc.) in the last 12 months? 1. Yes 2. No (If Q1 = 2 and Q2 = 2, questions end. Else, go to Q3.)	Screening (livestock) (Response = 1)
Check Criterion 2 defining an agricultural household
Q3	Identify all people in the household roster who operated land for agricultural purposes and/or raise or tend livestock in the last 12 months (i.e., Q1=1 and/or Q2=1).	List members of agricultural households engaged in farming or raising livestock
Q4	For each individual in the household who operated land for agricultural purposes and/or raise or tend livestock in the last 12 months, was this performed… (tick all that apply) For use / consumption of the household? For profit / trade? As wage work for others?	Filter out households where agricultural activities were done only as wage labor (Response = 3)

⁽¹⁾Including orchards and kitchen gardens

⁽²⁾ Alternative phrasings:

Did anyone in this household cultivate/use any land for agricultural purposes in the last 12 months?
Did anyone in this household operate any land to produce crops in the last 12 months?
Did anyone in this household cultivate/use any land to produce crops in the last 12 months?

Specific application to agricultural surveys or censuses

When we collect data using an agricultural survey or an agricultural census, the agricultural population will be all the adult members of the household of the agricultural holder. As per the World Programme for the Census of Agriculture 2020 Volume 1, the agricultural holder is defined as “the civil person, group of civil persons or juridical person who makes the major decisions regarding resource use and exercises management control over the agricultural holding operation. The agricultural holder has technical and economic responsibility for the holding and may uptake all responsibilities directly, or delegate responsibilities related to the day-to-day work management to a hire manager.”

As the indicator refers to individuals, only household sector holdings—i.e., holdings for which the agricultural holder is a civil person (i.e., one person) or group of civil persons-- should be considered. When the agricultural holder is a (single) civil person, the adult members of the household of the single holder are part of the agricultural population. When the holder is a group of civil persons, adult members of households of each of the persons in the group belong to the agricultural population.

Questions to identify owners of, or holders of secure rights to agricultural land from among the agricultural population

Data on ownership of, or secure rights to agricultural land of members of the agricultural population for purposes of estimating indicator 5.a.1 refers to individual members of agricultural households (as defined above) whose age is 18 years old or over.

An example of a module that can be utilized for collecting the data using the self-respondent approach is presented in Table 4.

Table 4. Example of minimum set of questions for collecting data on ownership of or secure rights to agricultural land at the person/individual level

Questions

Function

Q1. Do you own or hold secure rights^[15] to any agricultural land, either alone or jointly with someone else?

1 - Yes

2 – No (end of the module)

This question refers to whether the respondent, not the respondent’s household, holds any agricultural land. It measures reported possession, which captures the respondent’s self-perception of his/her possession status, irrespective of whether the respondent has a formal or legal documentation of ownership.

Q2. Is there a formal document for any of the agricultural land you own or hold secure rights to that is issued by or registered at the Land Registry/Cadastral Agency, such as a title deed, certificate of ownership, or certificate of hereditary acquisition?

1 - Yes

2 – No >> Q4

This question identifies whether there is a legally recognized document for any of the agricultural land the respondent reports having.

Documented ownership/secure rights refer to the existence of any document an individual can use to claim ownership or secure rights in law over the land.

Q3a. What type of documents are there for the agricultural land you own?

LIST UP TO 3.

CODES FOR DOCUMENT TYPE:

TITLE DEED.........................1

CERTIFICATE OF CUSTOMARY

OWNERSHIP..........................2

CERTIFICATE OF OCCUPANCY....3

CERTIFICATE OF HEREDITARY ACQUISITION LISTED IN REGISTRY............................4

SURVEY PLAN......................5

OTHER (SPECIFY).................6

The list of options presented here is indicative. It is of utmost importance that the list includes all the legal documents recognized/ enforceable by law according to the national land tenure system. Refer to discussion in Section 2.a on formal documentation.

Q3b. Is your name listed on any of the documents as owner?

1 – Yes

2 – No

98 - Don’t know

99 - Refusal

Because individual names can be listed as witnesses on a document, it is important to ask if the respondent is listed “as an owner” or “holder” on the document. The respondent does not need to show the document to the enumerator.

Q4. Do you have the right to sell any of the agricultural land held (alternatively ‘land possessed, used or occupied’), either alone or jointly with someone else?

1 - Yes

2 – No >> Q5

98 - Don’t know

99 - Refuses to respond

Alienation rights- Proxy 2

This question obtains information on whether the respondent believes that he/she has the right to sell any of the agricultural land s/he reports possessing. When a respondent has the right to sell the land, it means that he or she has the right to permanently transfer the land to another person or entity for cash or in-kind benefits.

Q5. Do you have the right to bequeath any of the agricultural land held (alternatively ‘land possessed, used or occupied’), alone or jointly with someone else?

1 - Yes

2 - No

98 - Don’t know

99 - Refuses to respond

Alienation rights- Proxy 3

This question obtains information on whether the respondent believes that he/she has the right to bequeath any of the agricultural land he/she reports possessing.

When a respondent has the right to bequeath the land, it means that he/she has the right to give the land by oral or written will to another person upon his/her death his/her death.

In agricultural surveys or censuses

In agricultural surveys and censuses, usually there is a question about land tenure^[16] of land used for agricultural activities. The data to calculate SDG indicator 5.a.1 can be collected by adding a few questions to the land tenure question as shown in Table 5 in the example below which uses a proxy-respondent approach:

Example

Usual question on land tenure in agricultural surveys/census:

Q1. Of the total Agricultural Area Utilized (AAU) of the agricultural holding, how much is:	AREA
a. Owned with written documentation (such as title deeds, wills, purchase agreements)
b. Owned without written documentation
c. Rented-in, leased or sharecropped with written agreement
d. Rented-in, leased or sharecropped without written agreement
e. State or communal land used with written agreement (certified use rights)
f. State or communal land used without written agreement (uncertified use rights)
g. Occupied/squatted without any permission
Control Total land (total of options a to g)

Add the following questions to obtain the data needed for 5.a.1:

Table 5. Q2. If Q1 = a, b, e or f, please fill the table below.

a- List all the household members of the agricultural holder/s (producers) of the holding				b- Sex of the person	c- If Q1= a or e, Is this person´s name listed as owner in the written documentation?	d- If Q1= a, b, e or f: does this person have the right to sell any of the agricultural land owned, either alone or jointly with someone else?		e- If Q1= a, b, e or f: does this person have the right to bequeath any of the agricultural land owned, either alone or jointly with someone else?
Agricultural holder/producer				F/M	Y/N	Y/N		Y/N
Member 1	F/M	Y/N	Y/N	Y/N
Member 2		F/M	Y/N		Y/N		Y/N
...

¹⁴

Findings from the EDGE pilot studies reveal that data from proxy respondents yield different estimates than self-reported data, with variations by asset, by type of ownership and by the sex of the owner. In particular, it was found that proxy-reported data decrease both women’s and men’s reported ownership of agricultural land. Such underestimation is greater for men (-15 percentage points) than for women (-10 percentage points) and is less pronounced when we consider documented ownership (-7 percentage points for men and -2 percentage points for women). ↑

¹⁵

Alternatively ‘do you have, use or occupy’ … ↑

¹⁶

Refer to the WCA 2020 Volumes @ https://www.fao.org/world-census-agriculture/wcarounds/wca2020/en/ or Handbook on the Agricultural Integrated Survey @ https://www.fao.org/in-action/agrisurvey/resources/resource-detail/en/c/1198081/ ↑

3.c. Data collection calendar

3.d. Data release calendar

The data release depends highly on the frequency of surveys required to compute the indicators.

3.e. Data providers

National Statistical Offices. If agricultural surveys or censuses are used, the responsible organization may be the Ministry of Agriculture or, more generally, the organization responsible for agricultural surveys or censuses in the country.

3.f. Data compilers

Food and Agricultural Organization (FAO)

3.g. Institutional mandate

4.a. Rationale

Indicator 5.a.1 aims to monitor the gender balance on ownership/secure rights over agricultural land. Sub-indicator (a) and sub-indicator (b) are based on the same data and they monitor ownership/rights from two different angles. While sub-indicator (a) uses the total male/female agricultural population as reference population, and it tells us how many male/female own land, sub-indicator (b) focuses on the agricultural population with land ownership/secure rights, and it tells us how many of them are women.

Therefore, it is sufficient to have:

The number in the agricultural population with ownership or secure rights over agricultural land (by sex), and
The total agricultural population

An illustration of how to compute the sub-indicators is presented here, using the data in Table 6.

Table 6. Data for Illustrative example

Variable	Women	Men	Total
Number in agricultural population with ownership/secure rights over agricultural land	10	100	110
Number in agricultural population	100	200	300

Sub-indicator 5.a.1 (a): Percentage of the agricultural population with ownership or secure rights over agricultural land, by sex

The sub-indicator 5.a.1 (a) measures the percentage of individuals with ownership or secure rights over agricultural land among the total agricultural population, by sex. In this example, overall, 37 percent (110/300*100) of the agricultural population has ownership or secure rights over agricultural land. When the indicator is disaggregated by sex, gender disparities become visible: 50 per cent of the adult men living in agricultural households ((100/200)*100) own or hold secure rights over agricultural land compared to 10 per cent of adult women (10/100*100).

To construct 5.a.1 (b) we divide the number of women in the agricultural population who own or hold secure rights to agricultural land by the total number of the agricultural population who own or hold secure rights to agricultural land. In the example above the indicator value is 9 percent ((10/110)*100).

4.b. Comment and limitations

One recommendation is for countries to take into consideration the impact of the expected sample size on the precision of the estimates. One way of attaining a large enough sample size is to consider collecting information on all eligible respondents through a proxy respondent, as this can be relatively easily done using the household rosters in the surveys. However, it is important to keep in mind that when a proxy respondent provides the information for the member of the household, it is likely that some bias or response errors are introduced

It is critical that the list of legally binding documents of ownership proposed to be included in questions relating to proxy 1 in this document are customized to consider only documents that are enforceable before the law and that guarantee individual’s rights in the national context.

4.c. Method of computation

How the indicator is calculated:

The indicator 5.a.1 considers as owners or holders of secure rights to agricultural land all the individuals in the reference population who:

Are listed as ‘owners’ or ‘holders’ on a written legal document that testifies security of rights over agricultural land

Have the right to sell agricultural land

Have the right to bequeath agricultural land

The presence of one of the three proxies is sufficient to define a person as ‘owner’ or ‘holder’ of secure tenure rights over agricultural land. The advantage of this approach is its applicability to different countries. Indeed, based on the analysis of the seven EDGE pilot countries, these proxies provide the most robust measure of ownership/tenure rights that is comparable across countries. In fact, individuals may still have the right to sell or bequeath an asset in the absence of legally recognized document, therefore the indicator combines documented ownership / tenure rights with the right to sell or bequeath to render it comparable across countries.

Operationalization of indicator 5.a.1 expressed through mathematical formulas

Sub-indicator 5.a.1 (a)

Total agricultural population with:

Legally recognized document of ownership of agricultural land OR the right to sell it OR the right to bequeath it

$\times$ 100, by sex

Total agricultural population

Sub-indicator 5.a.1 (b)

Number of women in the agricultural population with:

Legally recognized document of ownership of agricultural land OR the right to sell it OR the right to bequeath it

$\times$ 100, by type of tenure

Number of people in the agricultural population with:

Legally recognized document on agricultural land OR the right to sell it OR the right to bequeath it

Use of Sampling Weights

When the data source is a sample survey, the appropriate survey sampling weights—base weights, non-response adjustments and poststratification adjustments—should be used in estimating the sub-indicators. Further, if subsampling of eligible respondents to the 5.a.1 questions is done in a census or survey, the weights need to account for this.

4.d. Validation

FAO is responsible to check the syntaxes used in the computation of the indicator as well as the questions.

4.e. Adjustments

No adjustment with respect to use of standard classification and harmonization of breakdown for age groups and other dimension is performed.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

No imputation of data is made at country level.

At regional and global levels

No imputation of data is made at the regional and global level.

4.g. Regional aggregations

Weighted regional aggregates will be generated only if a sufficient number of countries in the region report on the indicator. This will be the case if (1) at least 50 percent of countries have a value or (2) if enough countries have a value as to cover 50 percent of the population in the region.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries can rely on the background paper describing the methodology and other relevant documents available at http://www.fao.org/sustainable-development-goals/indicators/5a1/en/?ADMCMD_view=1 as well as the e-learning available at https://elearning.fao.org/course/view.php?id=363

4.i. Quality management

Logical and arithmetic control of reporting data is carried out.

4.j. Quality assurance

FAO is collaborating with the countries to design/complete/improve the survey questionnaires and contributing to develop and check the syntaxes used to compute the indicator. The microdata of surveys utilized in the computation of indicators are collected by the national institutions, hence their quality rests with the data producers.

4.k. Quality assessment

Quality assessments are performed on the final estimation of the indicator when it is updated and compared with previous results. Some countries have data that needs to be assessed further, either check on the raw data and/or the processing of data.

5. Data availability and disaggregation

Data availability:

Data availability is currently limited (though growing) around the world, and most of the available data points derive from suitable surveys in countries in Africa and Asia. The limited data availability does not yet allow for producing regional and global aggregates.

Disaggregation:

We can distinguish between levels of disaggregation which are ‘mandatory’ for the global monitoring and levels of disaggregation which are recommended especially for the country level analysis, as they provide insights for policy making.

‘Mandatory’ levels of disaggregation

‘Recommended’ levels of disaggregation

(not exhaustive list)

[for sub-indicator (a)] sex of the individuals
[for sub-indicator (b)] type of tenure

[for both sub-indicators]

Income level
age group
ethnic group
geographic location (urban/rural)
type of legally recognized document

If the country collects data by type of tenure, the disaggregation is required by type of tenure. However, if the country does not do this, the disaggregation by type of tenure would not be possible as the information will be collected at an aggregated level.

6. Comparability/deviation from international standards

Sources of discrepancies:

There is currently no known source of difference.

7. References and Documentation

1- URL: http://www.fao.org/sustainable-development-goals/indicators/5.a.1/en/

2- AGRIS handbook on the integrated agricultural surveys, https://www.fao.org/in-action/agrisurvey/resources/resource-detail/en/c/1198081/

3- Measuring Individuals’ Rights to Land. An Integrated Approach to Data Collection for SDG Indicators 1.4.2 and 5.a.1. https://www.fao.org/publications/card/en/c/CA4885EN/

4- World Programme for the Census of Agriculture 2020 Volume 1. https://www.fao.org/3/i4913e/i4913e.pdf

5.a.2

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

0.c. Indicator

Indicator 5.a.2: Proportion of countries where the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Goal 1, specifically indicator 1.4.2, and Goal 5, specifically 5.a.1 and 5.1.1.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

Indicator 5.a.2 assesses the extent to which the national legal frameworks (including customary law) guarantee women’s equal rights to land ownership and/or control.

The indicator “measures” the level to which a country’s legal framework supports women’s land rights, by testing that framework against six proxies drawn from international law and internationally accepted good practices , in particular the Convention on the Elimination of Discrimination Against Women (CEDAW) ratified by 189 countries, and the Voluntary Guidelines for the Responsible Governance of the Tenure of Land Fisheries and Forestry (VGGT) endorsed unanimously by Committee of Food Security (CFS) members in 2012.

The six proxies through which indicator 5.a.2 is monitored are the following:

Proxy A: Joint registration of land is compulsory or encouraged through economic incentives

Proxy B: Compulsory spousal consent for land transactions

Proxy C: Women’s and girls’ equal inheritance rights

Proxy D: Allocation of financial resources to increase women’s ownership and control over land

Proxy E: In legal systems that recognize customary land tenure, the existence of explicit protection of the land rights of women

Proxy F: Mandatory quotas for women’s participation in land management and administration institutions

Concepts:

The indicator tracks progress on legal reforms that guarantee women’s land rights (including customary law) in terms of ownership and/or control.

The customary dimension of the indicator is very important because in many contexts in which customary law prevails, women’s land rights tend to be denied or insecure. However, the enormous diversity of customs and social norms that govern customary land among and within countries and their unwritten nature, create a significant challenge for assessing whether the proxies are present in these systems. Therefore, the customary dimension will only be considered in the case it has been incorporated the legal system.

Finally, the indicator refers to ownership and/or control of land which are two critical but different dimensions of women’s land rights. Land ownership refers to the legally recognized right to acquire, use and transfer land property, while control over land is associated with the ability to make decisions over land.

Key definitions are the following:

Land

Land is defined as all immovable property – for instance the house, the land upon which a house is built and land which is used for other purposes, such as agricultural production. It also encompasses any other structures built on land to meet permanent purposes. Legal frameworks commonly use the terms ‘immovable property’ or ‘real property’ when referring to land.

Land ownership

Land ownership is a legally recognized right to acquire, use and transfer land. In private property systems, this is a right akin to freehold tenure. In systems where land is owned by the state, the term “land ownership” refers to possession of the rights most akin to ownership in a private property system – for instance, long-term leases, occupancy, tenancy or use rights granted by the state that are transferrable and are granted to users for several decades (for instance 99 years).

Control over land

Control over land is the ability to make decisions over land. It may include rights to make decisions about how the land should be used, including what crops should be planted, and to benefit financially from the sale of crops.

Customary land tenure

Customary land tenure is defined as the bodies of rules and institutions governing the way land and natural resources are held, managed, used and transacted within customary legal systems.

Customary legal systems

Customary legal systems are systems that exist at the local or community level, that have not been set up by the state, and that derive their legitimacy from the values and traditions of the indigenous or local group. Customary legal systems may or may not be recognized by national law.

Legal and policy framework

The legal and policy framework comprises a set of publicly available legal and policy instruments governing land and family matters in force when conducting the assessment, including the Constitution, primary - and secondary legislation and policies. It includes customary legal systems where they have been recognized by statutory law.

Personal laws

Personal law is defined as a set of codified rules and norms applying to a group of people sharing a common religious faith about personal matters. These laws usually cover family relations, marriage, and inheritance. The term can be used interchangeably with ‘religious laws’.

Primary legislation

Primary legislation refers to (i) acts or statutes that have been formally adopted at the national level following the official parliamentary procedure for the passage of laws (in parliamentary systems); (ii) other acts at the national level with the force of law, such as decree-laws and legislative decrees and otherwise (in parliamentary systems); (iii) other legal instruments that have been formally endorsed by a law-making body, for instance presidential and royal orders or presidential and royal decrees (in non-parliamentary systems or systems where law-making power lies in an additional institution to the parliament). In all cases, primary legislation must have the force of law, be binding. For this assessment primary legislation includes the Constitution.

Secondary legislation

Secondary legislation includes subsidiary, delegated or subordinate legal instruments that have the force of law, are binding, and shall not be in contradiction with primary legislation. They are usually passed by the executive, such as national regulations, rules, by-laws, determinations, directions, circulars, orders, and implementing decrees.

Joint registration

Joint registration is where the names of both spouses or both partners in an unmarried couple, are entered into the land registry as the owners or principal users of the land being registered. Joint registration signifies a form of shared tenure over the land – usually either a joint tenancy/occupancy or a tenancy in common). In legal systems which include a framework for land titling, joint registration is commonly referred to as joint titling.

Unmarried couples

Unmarried couples are defined as couples who live together (cohabit) in an intimate relationship, but who are not married following the marriage law of the country. It refers to couples who were married under custom or religious laws, where such marriages are not recognized or do not comply with the requirements of the formal law. It may also refer to relationships that are recognized by the state but that are not considered a marriage – for instance a civil partnership and a de facto relationship that is registered with the state. The term ‘unmarried couples’ is often used interchangeably with ‘de facto unions’, ‘consensual unions’ or ‘irregular unions’. The members of an unmarried couple are referred to as ‘partners’.

Land transactions

Land transactions for the methodology are major land transactions, specifically the sale and encumbrance (mortgage) of land.

Inheritance

Inheritance is defined as property passing at the owner's death to the heir or those entitled to succeed.

Deceased’s estate

The deceased’s estate encompasses the legal rights, interests and entitlements, to property of any kind (not only land) which the deceased spouse or partner enjoyed at the time of death, less any liabilities. Depending on the legal system, marital property may be excluded fully from the calculation of deceased’s estate, or the deceased’s 50% share in the marital property will be included.

Equal inheritance rights for sons and daughters

Equal inheritance rights for sons and daughters require the law on intestate inheritance to either be gender-neutral or provide for both an equal rank and equal shares in the inheritance for brothers and sisters (or daughters and sons).

2.b. Unit of measure

The proportion of countries where the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control is the unit for measuring progress at the global and/or regional level.

At the national level, it ‘measures’ the extent to which the legal and policy framework protects women’s land rights against the 6 proxies defined for monitoring SDG indicator 5.a.2. According to the number of proxies identified countries are classified in a band system ranging from 1=No evidence to 6=Highest levels of guarantees.

2.c. Classifications

The 6 proxies are drawn from international law and internationally accepted good practices, in particular the Convention on the Elimination of Discrimination Against Women (CEDAW) ratified by 189 countries, and the Voluntary Guidelines for the Responsible Governance of the Tenure of Land Fisheries and Forestry (VGGT) endorsed unanimously by Committee of Food Security (CFS) members in 2012.

3.a. Data sources

Sources of data for measuring Indicator 5.a.2 are the official versions of national policies, primary law and secondary legislation which must be publicly available. More specifically, the relevant laws include the following: land, family, marriage, inheritance, land registration, gender equality laws, constitution, agrarian reform. Relevant policies include land, agriculture and gender policies.

3.b. Data collection method

For the official reporting ONLY the proxies localized in the primary and/or secondary law will be reported because of their binding nature. The only exception to this rule is Proxy D where also national land/agrarian reform or titling programs are considered for the purpose of the assessment. However, for the meaningfulness of the assessment, relevant policies are considered for the analysis, but recorded only in the additional information section, because they represent the foundations of the law setting out the principles that indicate the direction towards which the country aims to move and very often suggest reforms that need to be adopted in the legal framework. In this sense, if the proxies are present in these types of instruments they constitute an important step towards a more gender sensitive legal framework.

The data are extracted directly from the laws in force when the assessment is carried out. Data collection/provision entails the assessment of the relevant laws to determine if the six proxies are present or not in the legal framework. For proxies D and F, in case that no provisions are identified in the legal and policy framework, they can be considered equally present if official national statistics showing that at least 40 percent of those who own or have secure rights to land are women. This is because these proxies are associated with special temporary measures for ensuring equal women’s and men’s land ownership and/or control.

Data will be compiled in an electronic questionnaire organized as follows:

Section 1: General Instructions

•. Respondent Information

• Instructions for filling the questionnaire

Section 2: Legal Assessment

•. Checklist of policy and legal instruments relevant for the assessment to guide the expert in the identification of the proxies in the policy and legal framework of the country analyzed.

•. Form 1 “Policy and legal instruments, including provisions for Proxy (x)”. This form is composed of a set of questions to be answered (Yes or No) to determine if the proxy is present. The details of the instruments containing the Proxy are to be provided in this form.

•. Form 2 “Results of Assessment – Proxy (x)”. This form summarizes the results of the assessment for each proxy.

Section 3: Summary of the Assessment (Country Results)

To complete the indicator 5.a.2 assessment, national legal experts must examine the national legal and policy framework and complete the electronic questionnaire following the methodological guidelines. This involves three steps that must be repeated for each proxy.

1. Collect all the relevant policy and legal documents, using the checklist contained in the questionnaire as a guide.

2. Using the detailed methodological guidelines, determine whether the proxy exists in the legal and policy framework and in which instruments.

3. Complete the questionnaire for each proxy, citing the instrument and the relevant provisions where the proxy was located in Form 1, and any relevant information or exception directly associated with the proxy in the additional information box (Form 2) such as policies and/or adopted bills. Include a hyperlink to the text of the legal and policy instrument.

After these three steps have been undertaken for all six proxies the responsible national institution will identify the level of protection to women’s land rights present in the legal framework according to the number of proxies located.

The filled questionnaire will be communicated to FAO for the quality control and global reporting to the UN SDGs Secretariat.

3.c. Data collection calendar

As policy and law reforms usually take a long time, countries should report on this indicator only every four years. However, if countries that have already submitted their report experience legal reforms that change their scores, those countries should send to FAO an updated questionnaire with the revised assessment for quality control and re-classification in the band system.

3.d. Data release calendar

All countries are able to start reporting on the first year, as the source of data (the laws and policies in force when the assessment takes place) are publicly available in all of them and “measuring” the indicator is done by conducting a legal analysis. Moreover, the assessment can be conducted by a legal expert in a very short timeframe (about 15 days).

3.e. Data providers

Governments should nominate a national entity responsible for the process of monitoring and reporting on indicator 5.a.2. The designation of the responsible institution should be guided by nature of the information required in particular relevant provisions from land and family laws. In view of this, the most adequate national institutions that could be designated for having this responsibility are land related institutions (i.e. Ministries of Land or the national institution governing land matters), and/or the national gender institution (i.e. Gender Equality Commissions, Women´s Affairs or Gender Ministries).

3.f. Data compilers

FAO is responsible for compilation and reporting on this indicator at the global level. After checking and validating the results, the responsible national entity submits the questionnaire to FAO. Upon receipt of the questionnaire, FAO will undertake a quality check, and revert to the responsible national institution in case clarifications or revisions are needed. FAO will then compute the indicator based on the information supplied by countries and communicate the results to the UN SDGs Secretariat.

3.g. Institutional mandate

Article I of the FAO constitution requires that the Organization collects, analyses, interprets and disseminates information relating to nutrition, food and agriculture.

http://www.fao.org/3/K8024E/K8024E.pdf

4.a. Rationale

Indicator 5.a.2 measures the extent to which countries’ legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control.

The focus on land of Indicator 5.a.2 reflects the recognition that land is a key economic resource inextricably linked to access to, use of and control over other economic and productive resources. It is a key input for agricultural production; it can facilitate access to financial and extension services or to join producer organisations. Moreover, it can generate income directly if rented or sold. It also acknowledges that women’s ownership of and/or control of land is critical for poverty reduction, food security, inclusiveness and overall sustainable development objectives. Finally, gender equality in land ownership and control is a human right. For example, the International Covenant on Civil and Political Rights (ICCPR) guarantees equality between women and men, and prohibits discrimination based on sex in Article 2. Article 26 of the ICCPR enshrines equality before the law and can be applied to defend women’s right to non-discrimination and equality, including economic and social rights. Further, the Convention on the Elimination of Discrimination Against Women (CEDAW), emphasizes that discrimination against women “violates the principles of equality of rights and respect for human dignity".

The following paragraphs describe the scope and rationale of the proxies, as well as their specific content.

For guidance on the meaning of the terms used in the proxies please refer to the terminology in section 2.a “Definitions and concepts” of this document. For detailed information on the conditions determining whether the proxy exists in the legal framework please refer to the methodological guidelines “Realizing women’s rights to land in the law. A Guide for reporting on SDG Indicator 5.a.2”.

Proxy A: Is the joint registration of land compulsory or encouraged through economic incentives?

Without the inclusion of their names on the land title, deed or certificate, women’s property rights remain insecure, especially in the context of land registration programs and property acquired by the spouses during the marriage. This is particularly the case for married women who separate, divorce, are abandoned, or become widows.

The proxy therefore assesses whether the legal and policy framework includes provisions requiring joint registration of land or encouraging joint registration through economic incentives for both married and unmarried couples. For the proxy to be present it is sufficient that joint registration is provided at least for married couples.

Proxy B: Does the legal and policy framework require spousal consent for land transactions?

Major land transactions, such as the sale, mortgage or lease of family land or the family home, can directly affect women’s land rights if they do not participate in the decisions. Therefore, spousal or partner consent requirements for such transaction strengthen women’s control rights over land by protecting them against unilateral actions taken by their husband or, in the case of unmarried couples, partner. Provisions that support equality in marriage relations and that provide for joint administration of matrimonial property including land, directly contribute to gender equality in the control over land.

The proxy examines whether national laws provide for mandatory spouse or partner consent for land transactions. As with proxy A, the assessment covers both married and unmarried couples. Yet, for proxy B to be present it is sufficient that spousal consent is provided at least for married couples.

Proxy C: Does the legal and policy framework support women’s and girls’ equal inheritance rights?

Inheritance is one of the main channels through which women acquire property and secure independent land rights. However, the persistence of discriminatory cultural and legal norms often denies women’s and girls’ equal inheritance rights and hinders women’s opportunity to acquire property on an equal footing to men. Personal laws and customary laws, in particular, often deny women’s right to inherit or to inherit in equal shares. However, many post-colonial governments have incorporated these rules in the formal legal architecture. In some cases, daughters may only be entitled to inherit in the absence of a traceable male relative.

Proxy C examines the extent to which national laws on intestate inheritance establish equal inheritance rights for surviving children and the surviving spouse(s) regardless of sex.

This proxy aims to identify if the legal and policy framework of a country provides that:

1. Sons and daughters have equal inheritance rights and equal shares; and

2. Male and female surviving spouse and/or partner are entitled to an equal right of the deceased spouse’s estate and/or to a lifetime user right to the family home.

The law must prescribe both equal inheritance rights for sons and daughters and for the surviving spouse and/or partner for Proxy to be present.

Proxy D: Does the legal and policy framework provide for the allocation of financial resources to increase women’s ownership and control over land?

Legal reforms to support gender equality in land ownership and/or control and access to other productive resources have not always translated into practice. The poor implementation of land and agriculture related policies and laws geared towards enhancing gender equality, is partially due to the lack or insufficiency of financial resources.

For this reason, this proxy identifies any legal provision that commits the government to allocate financial resources to increase women’s ownership and control over land or access to productive resources, including land. Such provisions are widely regarded as innovative measures to support women’s land rights and have been consistently endorsed by the CEDAW Committee in its deliberations and comments on state parties’ reports under the treaty. For this proxy to be present, the fund must be anchored into a national law that explicitly mentions the purpose of improving women’s land rights.

Since Proxy D amounts to a “special measure”, as per Art. 4 of CEDAW, countries that do not include this measure in their legal framework, may provide official statistical data that show, nationally, at least 40 percent of those who own or have secure rights to land are women to satisfy the proxy.

Proxy E: In legal systems that recognize customary land tenure, does the legal and policy framework explicitly protect the land rights of women?

Many countries have incorporated customary land tenure rights into the formal legal system, in effect ‘formalizing’ them. The legal recognition of customary land tenure however may reinforce discriminatory practices where there is no explicit protection for women’s customary land rights. Further, the use of gender-neutral provisions in the context of formalization of customary land tenure has in practice been associated with a lack of protection of women’s rights. To avoid such outcomes explicit provisions protecting the land rights of women should accompany legal provisions recognizing customary land rights.

Proxy E assesses whether the Constitution and/or any land related law that recognizes customary land tenure, explicitly protect s women’s land rights.

It is important to note that for those countries where customary law has not been incorporated into the legal framework, Proxy E is not applicable and will not be assessed in the computation. As noted above, the customary dimension of this indicator will only be considered when it has been legally recognized.

Proxy F: Does the legal and policy framework mandate women’s participation in land management and administration institutions?

Land related institutions are responsible for governing the land tenure systems and are in charge of land administration and management. Women are often excluded from participating in the day-to-day processes of land governance at all levels, and therefore have limited capacity to influence decision-making. A lack of women’s representation in land governance tends to lead to biased outcomes in land recording and registration processes and the hindering of women’s land claims, for instance by overlooking women’s rights on common lands.

Proxy F aims to identify provisions within the legal framework requiring mandatory participation of women (quotas) in land related management and administration institutions.

Since Proxy F amounts to a “special measure”, as per Art. 4 of CEDAW, countries that do not include this measure in their legal and policy framework, yet provide official statistical data that show, nationally, at least 40 percent of those who own or have secure rights to land are women, will equally satisfy the proxy.

4.b. Comment and limitations

Customary law

Since customary law is not a homogenous system of law, assessing whether it establishes equal rights to land ownership and/or control for women and men is very challenging. Therefore, the methodology determines that customary law will only be considered to the extent that it has been recognized in the legal framework. However, this also means that reporting data does not cover the legal systems where customary law has not been formalized but continues to govern family and land matters, possibly constituting a major factor of discrimination against women. Further, given that customary law does not exist in all countries, it is not universally applicable. The methodology has addressed this issue by creating a dual system of computation of the results, which is explained below in section 4.

Geographical scope.

The data collected for the SDG indicator 5.a.2 is collected at the national level to ensure that it adequately represents the national legal system. This means that the 5.a.2 assessment to determine the existence of the proxies should focus on legal and policy instruments that have nationwide authority. In countries where law-making power for land or gender matters does not reside with the central authority (or is shared between the national government and a sub-national government), the assessment may require analysing laws at state, provincial or county level. However, any research at sub-national level can only be undertaken after mapping and analysing the relevant provisions in the overarching legal framework at constitutional and federal level for a focused and efficient data collection process.

In case the assessment requires data collection and data analysis at the sub-national level, a sample of the states, provinces or counties will be established, including the most populous states up until reaching 50 percent of the total country’s population. Since the results must have nationwide authority, the proxy should be located in the laws of each state, province or county that is part of the sample. If it is not the case, the proxy is not present.

4.c. Method of computation

The qualitative and legal nature of this indicator required the development of a nuanced and articulated methodology that could be feasible, universally relevant and meaningful.

The computation of results under Indicator 5.a.2 involves two steps: (1) classification of the country according to the number of proxies located in primary or primary and secondary legislation and (2) consolidation of all country results for global reporting.

Step 1: Classification categories of country

The country will be classified according to the total number of proxies found in primary legislation or primary and secondary legislation. Given that not all countries recognize customary land tenure or customary law (related to proxy E), a dual approach for computing national results has been developed:

For countries where customary land tenure is NOT recognized in the legal framework (either via statute or the constitution), regardless of whether it exists de facto or not, Proxy E is marked non-applicable and the country will be assessed out of the five remaining proxies.
For countries where customary land tenure is recognized in the legal framework, the country will be assessed against all six proxies,

The table below describes the dual approach for computing results and the classification bands. As is shown below, in countries where customary law is applicable (Proxy E) the presence of five or six proxies are included in the same band (band 6 - very high levels of guarantees). This is due to the necessity of making universal the calculation of the component of customary law, which is not universal and not always formalized in the legal system.

Table 1: Classification Band System

Result of assessment Where Proxy E is applicable	Result of assessment Where Proxy E is not applicable	Classification
None of the six proxies are present in the primary or primary and secondary legislation	None of the five proxies are present in the primary or primary and secondary legislation	Band 1: No evidence of guarantees of gender equality in the land ownership and/or control in the legal framework.
One of the proxies present in primary or primary and secondary legislation	One of the proxies present in primary or primary and secondary legislation	Band 2: Very low levels of guarantees of gender equality in land ownership and/or control in the legal framework.
Two the proxies present in primary or primary and secondary legislation	Two of the proxies present in primary and secondary legislation	Band 3: Low levels of guarantees of gender equality in land ownership and/or control in the legal framework.
Three of the proxies are present in primary legislation or primary and secondary legislation	Three of the proxies are present in primary legislation or primary and secondary legislation	Band 4: Medium levels of guarantees of gender equality in land ownership and/or control in the legal framework.
Four of the proxies are present in primary legislation or primary and secondary legislation	Four of the proxies are present in primary legislation or primary and secondary legislation	Band 5: High levels of guarantees of gender equality in land ownership and/or control in the legal framework.
Five or six proxies are present in primary legislation or primary and secondary legislation	All five proxies are present in primary legislation or primary and secondary legislation	Band 6: Very high levels of guarantees of gender equality in land ownership and/or control in the legal framework.

Under the methodology all proxies have an equal weight. This implies that no dimension is more important than another in terms of supporting gender equality in land ownership and/or control.

4.d. Validation

As with all the SDG targets and indicators, the monitoring and reporting process for target 5.2 a is global in scope and country-led.

FAO provides technical support to the designated focal point(s) and national legal expert to carry out the assessment and fill the questionnaire. To facilitate the process FAO also shares with them relevant materials, including the methodological guidelines “Realizing women’s rights to land in the law” (https://www.fao.org/3/i8785en/I8785EN.pdf), the questionnaire and the e-learning platform (https://elearning.fao.org/course/view.php?id=364). The key materials currently exist in English, French, Spanish, and Arabic.

When the assessment has been finalized, the responsible institution submits the questionnaire to FAO for quality control to ensure that the assessment fulfil the criteria and thresholds established in the methodology. The reviewed questionnaire is sent back to the country for validation and official submission.

4.e. Adjustments

Not Applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Although all UN countries are expected to report, this might not be the case. Different countries may report at different times and a non-negligible share of countries may choose not to report on the indicator at all during the reporting period creating missing values.

The missing values will be treated in the following way:

a) For countries, which have reported in only 1 period, FAO does not have information on whether they are making progress on the indicator. However, FAO can alleviate the problem with missing values. First, FAO can assume that there was no progress on the indicator over the reporting periods and keep the same results until a reviewed questionnaire is submitted.

b) Not imputed. The only way to include countries that will never report is to cluster them in a category of missing information. This is because no assumption can be done regarding the status of each country’s laws. However, it is important to keep track of the countries which do not report rather than limit the analysis to the reporting countries

• At regional and global levels

Not imputed. The regional and global aggregates will be based solely on those countries for which data are available, but at no point will countries with missing data be treated as if they were the same as those for which data are available. The global or regional aggregates would be valid for the reporting countries but not necessarily for the region as whole or at the global level as a whole. Missing values for individual countries or areas cannot be imputed or estimated to derive regional or global aggregates of the indicator because no assumption can be done regarding the status of each country’s laws.

4.g. Regional aggregations

The band classification system used at the country level illustrated in table 1 also applies for regional and global aggregates for this indicator. Once 50% of the countries of a particular region has officially reported, the mean/average score for an SDG region will be calculated without weighting national scores. The region will be classified into a particular band reflecting the extent to which the relevant national laws recognise and protect women’s rights to land. The same applies to the global aggregation which will be calculated based on the unweighted regional average/mean score, once 50% of the regions have been classified in a particular band.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The Methodology used by countries for the compilation of the data at the national level (https://www.fao.org/3/i8785en/I8785EN.pdf) and the

questionnaires provided to countries include guidance, definitions and instructions.

Technical support provided by FAO to the representatives and legal experts from the designated responsible institutions
E-learning available in the FAO learning academy https://elearning.fao.org/course/view.php?id=364

4.i. Quality management

This is a qualitative, legal indicator. Upon submission of the reporting questionnaire by the focal point in the responsible institution, FAO performs a quality assessment based on the methodology. This ensures that the reporting is carried out consistently across all reporting countries. During this quality review, FAO may provide methodological clarifications to ensure conformity with the methodological guidelines.

4.j. Quality assurance

The assessment of laws is initially carried out by national counterparts, and legal practitioners in the relevant areas of law (land, land registration, land programmes, matrimonial property, inheritance, quota’s ensuring women’s participation in land administration and management bodies, for all types of land -including agrarian, customary, housing-). The data is checked and verified by the FAO. The data is then sent to the designated focal points/country counterparts to review and validate. Please refer to section 3 above on Data source type and data collection method for more details.

4.k. Quality assessment

See section 4.d. on validation. The methodological guidelines are used to set criteria that are applied equally to all countries for the purposes of ensuring comparability across countries and regions.

5. Data availability and disaggregation

Data availability:

Not applicable

Time series:

Not applicable

Disaggregation:

Not applicable

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable.

7. References and Documentation

https://www.fao.org/3/i8785en/I8785EN.pdf

http://www.fao.org/nr/tenure/voluntary-guidelines/en/

http://www.un.org/womenwatch/daw/cedaw/

http://www.fao.org/gender-landrights-database/en/

5.b.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.b: Enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of women

0.c. Indicator

Indicator 5.b.1: Proportion of individuals who own a mobile telephone, by sex

0.d. Series

Proportion of individuals who own a mobile telephone, by sex (%)

0.e. Metadata update

2023-03-31

0.f. Related indicators

4.4.1, 9.c.1, 17.6.1, 17.8.1

0.g. International organisations(s) responsible for global monitoring

International Telecommunication Union (ITU)

1.a. Organisation

International Telecommunication Union (ITU)

2.a. Definition and concepts

Definition:

The proportion of individuals who own a mobile telephone, by sex is defined as the ‘proportion of individuals who own a mobile telephone, by sex’.

Concepts:

An individual owns a mobile cellular phone if he/she has a mobile cellular phone device with at least one active SIM card for personal use. Mobile cellular phones supplied by employers that can be used for personal reasons (to make personal calls, access the Internet, etc.) are included. Individuals who have only active SIM card(s) and not a mobile phone device are excluded. Individuals who have a mobile phone for personal use that is not registered under his/her name are also included. An active SIM card is a SIM card that has been used in the last three months.

A mobile (cellular) telephone refers to a portable telephone subscribing to a public mobile telephone service using cellular technology, which provides access to the Public Switched Telephone Network (PSTN). This includes analogue and digital cellular systems and technologies such as IMT-2000 (3G) and IMT-Advanced. Users of both postpaid subscriptions and prepaid accounts are included.

2.b. Unit of measure

Percent (%) (of individuals)

2.c. Classifications

For countries that collect this data through an official survey, and if data allow breakdown and disaggregation, the indicator can be broken down by region (urban/rural), sex, age group, educational level (International Standard Classification of Education (ISCED) ), by labour force status (International Labour Organization (ILO)), and by occupation (International Standard Classification of Occupation (ISCO)). The International Telecommunication Union (ITU)) collects data for all these breakdowns from countries.

3.a. Data sources

This indicator is a newly developed International Telecommunication Union (ITU) indicator that was approved by the World Telecommunication/ICT Indicators Symposium (WTIS) in 2014. The indicator’s definition and methodology were developed under the coordination of ITU, through its Expert Groups, and following an extensive consultation process with countries. Data for the proportion of individuals owning a mobile phone were first collected in 2015 through an annual questionnaire that ITU sends to National Statistical Offices (NSO). In this questionnaire, through which ITU already collects several ICT indicators, ITU collects absolute values. The percentages are calculated a-posteriori. The survey methodology is verified to ensure that it meets adequate statistical standards. The data are verified to ensure consistency with previous years’ data and other relevant country-level indicators (ICT and economic).

Data are usually not adjusted, but discrepancies in the definition, age scope of individuals, reference period, or the break in comparability between years are noted in a data note. For this reason, data are not always strictly comparable.

3.b. Data collection method

The International Telecommunication Union (ITU) collects data on this indicator through an annual questionnaire that it sends to the heads of the National Statistical Offices (NSO). In this questionnaire, through which ITU already collects several ICT indicators, ITU collects absolute values. The percentages are calculated a-posteriori. The survey methodology is verified to ensure that it meets adequate statistical standards. The data are verified to ensure consistency with previous years’ data and other relevant country-level indicators (ICT and economic).

3.c. Data collection calendar

The data are collected using the ITU Short and Long ICT Household questionnaires. . Each survey has its own data collection cycle. The International Telecommunication Union (ITU) collects data twice a year from Member States, in Q1 using the short questionnaire and in Q3 using the long questionnaire.

3.d. Data release calendar

Data are released twice a year, In July and December, in the World Telecommunication/ICT Indicators Database (WTID) and in the ITU DataHub, see https://datahub.itu.int/.

3.e. Data providers

National Statistical Offices (NSOs).

3.f. Data compilers

International Telecommunication Union (ITU)

3.g. Institutional mandate

As the UN specialized agency for information and communication technologies (ICTs), the International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States.

4.a. Rationale

Mobile phone networks have spread rapidly over the last decade and the number of mobile-cellular subscriptions is quasi equal to the number of people living on earth. However, not every person uses or owns a mobile-cellular telephone. Mobile phone ownership, in particular, is important to track gender equality since the mobile phone is a personal device that, if owned and not just shared, provides women with a degree of independence and autonomy, including for professional purposes. Several studies have highlighted the link between mobile phone ownership and empowerment, and productivity growth.

Existing data on the proportion of women owning a mobile phone suggest that fewer women than men own a mobile phone. This indicator highlights the importance of mobile phone ownership to track and improve gender equality, and monitoring will help design targeted policies to overcome the gender divide. The collection of this indicator was proposed by the Task Group on Gender of the Partnership on Measuring ICT for Development.

4.b. Comment and limitations

While the data on the ‘proportion of individuals who own a mobile telephone’ currently only exist for very few countries, ITU is encouraging all countries to collect data on this indicator through national household surveys and the indicator is expected to be added to the Partnership on Measuring ICT for Development’s Core List of Indicators. The number of countries with official data for this indicator is expected to increase in the near future.

4.c. Method of computation

Countries can collect data on this indicator through national household surveys. This indicator is calculated by dividing the total number of in-scope individuals who own a mobile phone by the total number of in-scope individuals.

$[(n u m b e r o f i n - s c o p e i n d i v i d u a l s o w n i n g a m o b i l e p h o n e) / (t o t a l n u m b e r o f i n - s c o p e$

$i n d i v i d u a l s)] * 100$

4.d. Validation

Data are submitted by Member States to The International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States.

4.e. Adjustments

No adjustments are made to the data submitted by countries.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Missing values are not estimated.

• At regional and global levels

In the absence of official household surveys, International Telecommunication Union (ITU) estimates the percentage of individuals owning mobile phones (owners of mobile phones as a percentage of total population) using various techniques, such as hot-deck imputation, regression models and time series forecast, using data such as Internet use, income, education and other ICT indicators.

4.g. Regional aggregations

Country-level data on the percentage of individuals owning mobile phones (owners of mobile phones as a percentage of total population) are first estimated using various techniques, such as hot-deck imputation, regression models and time series forecast. Hot-deck imputation uses data from countries with “similar” characteristics, such as GNI per capita and geographic location. In cases when it is not possible to find an adequate imputation based on similar cases, regression models are applied.

Once the country-level percentages are available for all countries, the number of mobile phone owners are calculated by multiplying the percentages to the population of the country. The regional and world total mobile phone owners were calculated by summing the country-level data. The aggregate percentages were calculated by dividing the regional totals by the population of respective groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:

https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx

4.i. Quality management

Data are checked and validated by the ICT Data and Analytics (IDA) Division of the International Telecommunication Union (ITU). Countries are contacted to clarify and correct their submissions.

4.j. Quality assurance

The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.

4.k. Quality assessment

The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.

5. Data availability and disaggregation

Data availability:

Overall, the indicator is available for more than 80 countries at least from one survey.

Time series:

2015 onwards

Disaggregation:

For countries that collect this indicator through a national household survey, and if data allow breakdown and disaggregation, the indicator can be broken down not only by sex but also by region (urban/rural), age group, educational level, labour force status, and occupation. Estimates of regional aggregates by sex are also calculated.

6. Comparability/deviation from international standards

Sources of discrepancies:

None. The International Telecommunication Union (ITU) uses the data provided by countries, including the in-scope population that is used to calculate the percentages.

7. References and Documentation

URL:

http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx

References:

ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:

https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx

5.c.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.c: Adopt and strengthen sound policies and enforceable legislation for the promotion of gender equality and the empowerment of all women and girls at all levels

0.c. Indicator

Indicator 5.c.1: Proportion of countries with systems to track and make public allocations for gender equality and women’s empowerment

0.d. Series

Applies to all series

0.e. Metadata update

2023-03-31

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

United Nations Entity for Gender Equality and the Empowerment of Women (UN Women) in collaboration with Organisation for Economic Co-operation and Development (OECD) and United Nations Development Programme (UNDP)

1.a. Organisation

2.a. Definition and concepts

Definitions:

Sustainable Development Goal (SDG) Indicator 5.c.1 seeks to measure government efforts to track budget allocations for gender equality throughout the public finance management cycle and to make these publicly available. This is an indicator of the characteristics of the fiscal system. It is not an indicator of the quantity or quality of finance allocated for gender equality and women’s empowerment (GEWE). The indicator measures three criteria. The first focuses on the intent of a government to address GEWE by identifying if it has programs/policies and resource allocations for GEWE. The second assesses if a government has planning and budget tools to track resources for GEWE throughout the public financial management cycle. The third focuses on transparency by identifying if a government has provisions to make allocations for GEWE publicly available.

The indicator aims to encourage national governments to develop appropriate budget tracking and monitoring systems and commit to making information about allocations for gender equality readily available to the public. The system should be led by the Ministry of Finance in collaboration with the sectoral ministries and National Women’s Machineries and overseen by an appropriate body such as Parliament or Public Auditors.

Concepts:

To determine if a country has a system to track and make public allocations for gender equality and women’s empowerment, the following questionnaire is sent to its Ministry of Finance, or agency in charge of the government budget:

Criterion 1. Which of the following aspects of public expenditure are reflected in your government programs and its resource allocations? (In the last completed fiscal year)

Question 1.1. Are there policies and/or programs of the government designed to address well-identified gender equality goals, including those where gender equality is not the primary objective (such as public services, social protection, and infrastructure) but incorporates action to close gender gaps? (Yes=1/No=0)

Question 1.2. Do these policies and/or programs have adequate resources allocated within the budget, sufficient to meet both their general objectives and their gender equality goals? (Yes=1/No=0)

Question 1.3. Are there procedures in place to ensure that these resources are executed according to the budget? (Yes=1/No=0)

Criterion 2. To what extent does your Public Financial Management system promote gender-related or gender-responsive goals? (In the last completed fiscal year)

Question 2.1. Does the Ministry of Finance/budget office issue call circulars, or other such directives, that provide specific guidance on gender-responsive budget allocations? (Yes=1/No=0)

Question 2.2. Are key policies and programs, proposed for inclusion in the budget, subject to an ex-ante gender impact assessment? (Yes=1/No=0)

Question 2.3. Are sex-disaggregated statistics and data used across key policies and programs in a way which can inform budget-related policy decisions? (Yes=1/No=0)

Question 2.4. Does the government provide, in the context of the budget, a clear statement of gender-related objectives (i.e. gender budget statement or gender responsive budget legislation)? (Yes=1/No=0)

Question 2.5. Are budgetary allocations subject to “tagging” including by functional classifiers, to identify their linkage to gender-equality objectives? (Yes=1/No=0)

Question 2.6. Are key policies and programs subject to ex-post gender impact assessment? (Yes=1/No=0)

Question 2.7. Is the budget as a whole subject to independent audit to assess the extent to which it promotes gender-responsive policies? (Yes=1/No=0)

Criterion 3. Are allocations for gender equality and women’s empowerment made public? (In the last completed fiscal year)

Question 3.1. Is the data on gender equality allocations published? (Yes=1/No=0)

Question 3.2. If published, has this data been published in an accessible manner on the Ministry of Finance (or office responsible for budget) website and/or related official bulletins or public notices? (Yes=1/No=0)

Question 3.3. If so, has the data on gender equality allocations been published in a timely manner? (Yes=1/No=0)

Concept Definitions:

For Criterion 1:

“Programs or policies of the government, that are designed to address well-identified gender equality goals” can be defined as:
- Programs or policies that specifically target only women and/or girls. For example, a government program that provides scholarships for girls only, or a prenatal care program, or a National Action Plan on Gender Equality; or
- Programs or policies that target both women or girls and men or boys and have gender equality as the primary objective. For example, a national public information campaign against gender violence, or on-the-job training programs on gender equality; or
- Programs or policies where gender equality is not the primary objective, but the program includes action to close gender gaps. These programs could include the provision of infrastructure, public services, and social protection. For example, an infrastructure program that has a provision for using women’s labour, or a public transportation program that takes into consideration the mobility needs of women in its design.
“Programs or policies have adequate resources allocated within the budget, sufficient to meet both their general objectives and their gender equality goals” can be defined as:
- The programs or policies that are designed to address well-identified gender equality goals are allocated sufficient resources to cover the costs of meeting those goals from funding that is included in the budget rather than from off-budget sources.
“Procedures in place to ensure that these resources are executed according to the budget” can be defined as:
- There are procedures established in laws or regulations so that resources for programs or policies that are designed to address well-identified gender equality goals are executed as specified in the budget or if there are deviations in the exercise from the budgeted allocations, government agencies must justify to a supervising entity (e.g. ministries of finance, parliaments, audit bodies, or other relevant authorities) the reason for not executing resources according to budget.

For Criterion 2:

“Call circulars” can be defined as:
- Call circulars are the official notices that are issued by the Ministry of Finance or Budget Office in a country towards the beginning of each annual budget cycle. The circular instructs government agencies how they must submit their bids or demands for budget allocations for the coming year (in some countries the notice may have another name, such as budget guidelines or Treasury guidelines). It may inform each agency what its budget “ceiling” for the next fiscal year.^[1]
“Key programs and policies” can be defined as:
- Programs or policies of the government, that are designed to address well-identified gender equality goals (as identified in Criterion 1).
“Ex-ante gender impact assessment” can be defined as:
- Assessing individual resource allocations, in advance of their inclusion in the budget, specifically for their impact on gender equality.^[2] For example, before its inclusion in the budget, there is an estimate of how a conditional cash transfer program will impact school attendance of girls.
“Sex-disaggregated statistics and data are available in a systematic manner across all key programs and policies” can be defined as:
- There is routine availability of gender-specific data sets and statistics that would greatly facilitate the evidential basis for the identification of gender equality gaps, design of policy interventions, and the evaluation of impacts.^[3]
“Gender budget statements” can be defined as:
- A document that, either as part of the budget documentation or separately, provides a clear statement of gender-related goals. It is a document produced by a government agency, usually, the Ministry of Finance or Budget Office, to show what its programs and budgets are doing in respect of gender. It is generally prepared after government agencies have completed the process of drawing up the budget and allocating resources to different programs in response to the annual call circular.^[4]
“Functional classifiers” can be defined as^[5]:
- Categorization of expenditure according to the purposes and objectives for which they are intended. A functional classifier on gender would identify expenditure that goes to programs or activities that address gender issues.
“Ex-post gender impact assessment” can be defined as:^[6]
- Assessing individual resource allocations, after their implementation, specifically for their impact on gender equality. For example, once the resources are spent and the program executed, how did a conditional cash transfer program affect the school attendance rate of girls when compared to boys’ attendance rate?
“The budget as a whole is subject to independent audit, to assess the extent to which it promotes gender-responsive policies” can be defined as:
- Independent, objective analysis, conducted by a competent authority different from the central budget authority, of the extent to which gender equality is effectively promoted and/or attained through the policies set out in the annual budget.^[7]

For Criterion 3:

“Published in an accessible manner” can be defined as:
- Allocations for gender equality and women’s empowerment are published on the Ministry of Finance (or office responsible for budget) website and/or related official bulletins or public notices in a way that is clearly signalled and/or made available in hard copies that are distributed to parliamentarians and NGOs.
“Published in a timely manner” can be defined as:
- Allocations for gender equality and women’s empowerment and/or its exercise are published in the same quarter as when approved/exercised.

Ibid. ↑

“Gender Budgeting in OECD Countries,” OECD, 2016. ↑

Ibid. ↑

⁴

“Budget Call Circulars and Gender Budget Statements in the Asia Pacific: A Review,” UN Women, 2015. ↑

⁵

“Budget Classification,” IMF, 2009. ↑

⁶

Ibid. ↑

⁷

Ibid. ↑

2.b. Unit of measure

Percent (%) (Proportion of countries that have a system in place to track budget allocations to gender equality out of the total number of reporting countries)

2.c. Classifications

Not applicable

3.a. Data sources

An electronic questionnaire composed of thirteen binary questions with accompanying monitoring guidance will be used to collect data on this indicator.

3.b. Data collection method

Data collection is undertaken as part of the country-level monitoring of effective development cooperation where the Global Partnership monitoring framework provides a useful platform and mechanism. The Global Partnership monitoring is led by national coordinators appointed by their respective government to coordinate data collection and validation across relevant government ministries, departments, and agencies. Where countries are not reporting through the Global Partnership, efforts are made to expand country coverage by reaching out to national coordinators/focal points directly or through custodian/co-custodian country offices.

For this indicator, the national coordinator/focal point will liaise with the Ministry of Finance, the Ministry of Women, and other relevant ministries to complete the questionnaire. UN Women’s country office focal points will be available for support.

3.c. Data collection calendar

Data collected every 3 years

3.d. Data release calendar

First quarter, every 3 years

3.e. Data providers

Response to questionnaire completed by the Ministries of Finance—as part of national statistical systems—or Budget Office in coordination with National Statistical Offices and relevant sectoral ministries and national women’s machineries.

3.f. Data compilers

UN Women, with UNDP and the OECD.

3.g. Institutional mandate

UN Women is committed through its work at the global, regional, and county level to support the Member States in filling critical gaps in generating and using data, statistics, evidence, and analysis on gender equality. As part of its triple mandate, UN Women supports the Member States in setting norms. UN Women also assists in implementing norms and standards through its country programmes. In addition, UN Women leads and coordinates the UN system’s work in support of gender equality and the empowerment of women.

4.a. Rationale

Adequate and effective financing is essential to achieve SDG 5 and gender-related targets across the SDG framework. By tracking and making public gender equality allocations, governments promote greater transparency which can support stronger accountability. The indicator encourages governments to put in place a system to track and make public resource allocations which can then inform policy review, better policy formulation, and more effective public financial management.

The principle of adequate financing for gender equality is rooted in the Beijing Declaration and Platform of Action (para 345 and 346) adopted in 1995. However, the Secretary General’s report on the twenty-year review and appraisal of the Platform for Action found that underinvestment in gender equality and women’s empowerment has contributed to slow and uneven progress in all 12 critical areas of concern. Inadequate financing hinders the implementation of gender-responsive laws and policies. Data shows that financing gaps are sometimes as high as 90% with critical shortfalls in infrastructure, productive and economic sectors.

The 2030 Agenda for Sustainable Development Agenda commits to a “significant increase in investments to close the gender gap.” Ensuring requisite resources for gender equality is central to implementing and achieving SDG 5 and all gender targets across the framework. Tracking these allocations and making the data publicly available are important steps to assessing progress towards meeting these goals. This has been reaffirmed at the Third International Conference on Financing for Development, where member states adopted the Addis Ababa Action Agenda which commits to tracking gender equality allocations and increasing transparency on public spending.^[8] Furthermore, the Commission on the Status of Women at its 60th session called upon states to support and institutionalize gender-responsive budgeting and tracking across all sectors of public expenditure to address gaps in resourcing for gender equality and the empowerment of women and girls.

Indicator 5.c.1 measure the proportion of governments with systems to track and make public resource allocations for gender equality. It builds on Indicator 8 of the Global Partnership for Effective Development Co-operation (GPEDC) that has been piloted, tested, and rolled out in 81 countries. Indicator 8 allowed, for the first time, the systematic collection of data on government efforts to track resource allocations for gender equality across countries. Indicator 5.c.1 is defined in almost identical terms to Indicator 8 of the Global Partnership for Effective Development Co-operation (GPEDC). In addition, Indicator 5.c.1 is the only indicator in the SDG monitoring framework that links national budgeting systems with the implementation of legislation and policies for gender equality and women’s empowerment.

The refined methodology for Indicator 5.c.1 is an improvement over the original methodology for Indicator 8. The increased specificity of the criteria provides a greater level of detail and therefore, captures the variability in countries’ gender equality policies and public financial management systems. The application of a tiered scoring approach with specific thresholds increases the indicator’s rigor and gives incentive to countries to improve these systems over time.

Further, it is envisaged that the Organisation for Economic Co-operation and Development (OECD) Survey of Budget Practices and Procedures, conducted regularly among OECD countries, will be modified, and updated to align closely with Indicator 5.c.1. This will allow greater global coverage by strengthening the indicator’s relevance to ministries of finance in OECD countries.

⁸

Addis Ababa Action Agenda paragraphs 30 and 53. ↑

4.b. Comment and limitations

The indicator does not measure allocation of resources but the existence of mechanisms to track resource allocations and that make that information available publicly. However, there is an optional question in the questionnaire (not scored) that requests countries to report the percentage of the government budget allocated for gender equality programs.

Another limitation is that the indicator, which is process oriented, does not provide data on the adequacy or quality of resource allocations.

4.c. Method of computation

Data is collected via a questionnaire comprising 13 binary (Yes/No) questions to assess whether a country has a system in place to track and make public allocations for gender equality and women’s empowerment.

Scoring:

Each criterion is weighted equally. A country would need to satisfy the threshold of “yes” responses per criterion. A country will be considered to satisfy each criterion as follows:

	Requirements per criterion
A country will satisfy Criterion 1	if it answers “Yes” to 2 out of 3 questions in Criterion 1
A country will satisfy Criterion 2	if it answers “Yes” to 4 out of 7 questions in Criterion 2
A country will satisfy Criterion 3	if it answers “Yes” to 2 out of 3 questions in Criterion 3

Countries then will be classified as ‘fully meets requirements’, ‘approaches requirements’, and ‘does not meet requirements’ per the following matrices (There are 8 possible combinations of criteria being satisfied, Cases A-G below):

Fully meets requirements
	Criterion 1	Criterion 2	Criterion 3
Case A	ü	ü	ü

Note: “Checked” boxes represent satisfied criteria;

“unchecked” boxes represent unsatisfied criteria.

Approaches requirements
	Criterion 1	Criterion 2	Criterion 3
Case B	ü
Case C		ü
Case D			ü
Case E	ü	ü
Case F	ü		ü
Case G		ü	ü

Note: “Checked” boxes represent satisfied criteria;

“unchecked” boxes represent unsatisfied criteria.

Does not meet requirements
	Criterion 1	Criterion 2	Criterion 3
Case H

Note: “Checked” boxes represent satisfied criteria;

“unchecked” boxes represent unsatisfied criteria.

Because the three criteria are equally important, a country would need to satisfy the three to fully meet requirements.

The method of computation is as follows:

$I n d i c a t o r 5 . c . 1 = \frac{(N u m b e r o f c o u n t r i e s t h a t f u l l y m e e t r e q u i r e m e n t s ) \times 100}{T o t a l n u m b e r o f c o u n t r i e s}$

Unit:

Percent (%)

Disaggregation:

In addition to reporting Indicator 5.c.1 as described above; the following two country classification global proportions will also be reported:

$\frac{(N u m b e r o f c o u n t r i e s t h a t d o n o t m e e t r e q u i r e m e n t s ) \times 100}{T o t a l n u m b e r o f c o u n t r i e s}$

$\frac{(N u m b e r o f c o u n t r i e s t h a t a p p r o a c h a p p r o a c h r e q u i r e m e n t s ) \times 100}{T o t a l n u m b e r o f c o u n t r i e s}$

Additional disaggregation by region as follows:

$\frac{(N u m b e r o f c o u n t r i e s i n r e g i o n x w i t h c o u n t r y c l a s s i f i c a t i o n y ) \times 100}{T o t a l n u m b e r o f c o u n t r i e s i n r e g i o n x}$

Where x refers to the region of analysis and y refers to the country classification based on the questionnaire.

4.d. Validation

Guidance and instructions for reporting on the indicator recommend coordination between the Ministry of Finance, national women’s machineries and/or national statistical institution. The validation process is led by country governments, in-line with existing standards and mechanisms. UN Women, as lead custodian, supports validation through review of questionnaire submissions and direct follow-up with government focal points. Further, qualitative data is requested to support the validation of ‘yes’ responses by a country.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Not Imputed

• At regional and global levels

Not Imputed

4.g. Regional aggregations

Global aggregates are weighted averages of all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries within the region.

Country-level data are updated on a periodic basis. Where data are not updated, the last reported year may be used for the global and/or regional aggregates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Methodology used by countries for the compilation of the data at the national level: questionnaire with monitoring guidance that includes definitions and instructions.

4.i. Quality management

See 4.d on validation

4.j. Quality assurance

See 4.d on validation

4.k. Quality assessment

See 4.d on validation

5. Data availability and disaggregation

Data availability:

As identified in the pilot exercise for Indicator 5.c.1, the information that is collected through administering the questionnaire is readily available by Ministries of Finance and/or Budget Offices.

Time series:

First release of data was 2019

Disaggregation:

Not applicable

6. Comparability/deviation from international standards

Sources of discrepancies:

Since data is reported by countries via a validated questionnaire, there should be no discrepancies.

7. References and Documentation

The Sustainable Development Goals Report 2022 (Glossy Report; Extended Report; Gender Snapshot 2022)

Organisation for Economic Co-operation and Development (OECD) and UN Women (2021). Gender responsive COVID-19 recovery. https://www.oecd-ilibrary.org/development/gender-responsive-covid-19-recovery_edb0172d-en

Global Partnership for Effective Development Corporation: https://effectivecooperation.org/4thMonitoringRound

Technical materials on how to incorporate gender equality into public finance management systems: http://gender-financing.unwomen.org/en

IMF research on gender responsive budgeting and tracking systems: https://www.imf.org/external/np/res/dfidimf/topic7.htm

https://www.imf.org/external/pubs/ft/wp/2016/wp16149.pdf

Gender budgeting and tracking in OECD countries:

https://www.imf.org/external/pubs/ft/wp/2016/wp16149.pdf

https://www.oecd.org/gender/Gender-Budgeting-in-OECD-countries.pdf

5.1.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.1: End all forms of discrimination against all women and girls everywhere

0.c. Indicator

Indicator 5.1.1: Whether or not legal frameworks are in place to promote, enforce and monitor equality and non‑discrimination on the basis of sex

0.d. Series

SG_LGL_GENEQLFP, Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 1: overarching legal frameworks and public life

SG_LGL_GENEQVAW, Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 2: violence against women

SG_LGL_GENEQEMP, Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 3: employment and economic benefits

SG_LGL_GENEQMAR, Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 4: marriage and family

0.e. Metadata update

2022-03-31

0.f. Related indicators

There are other legal SDGs indicators:

• Indicator 5.a.2, ‘Proportion of countries where the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control’; and

• Indicator 5.6.2, ‘Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education’.

To avoid duplication, indicator 5.1.1 does not cover areas of law that are addressed under indicators 5.a.2 and 5.6.2. Indicator 5.1.1 complements these other indicators.

Legal frameworks that advance gender equality generally relate to all of Goal 5 as well as other Goals since gender equality is central to the achievement of all SDGs. See UN Women and UN Statistics Division’s annual Progress on the Sustainable Development Goals: The Gender Snapshot which each year uses latest available data to demonstrate how gender equality, including progress on Target 5.1, is fundamental to achievement of all 17 Goals.

0.g. International organisations(s) responsible for global monitoring

UN Women, World Bank Group, OECD Development Centre

1.a. Organisation

UN Women, World Bank Group, OECD Development Centre

2.a. Definition and concepts

Definitions:

Indicator 5.1.1 measures Government efforts to put in place legal frameworks that promote, enforce and monitor gender equality.

The indicator is based on an assessment of legal frameworks that promote, enforce and monitor gender equality. The assessment is carried out by national counterparts, including National Statistical Offices (NSOs) and/or National Women’s Machinery (NWMs), and legal practitioners/researchers on gender equality, using a questionnaire comprising 42 yes/no questions under four areas of law: (i) overarching legal frameworks and public life; (ii) violence against women; (iii) employment and economic benefits; and (iv) marriage and family^[1]. The areas of law and questions are drawn from the international legal and policy framework on gender equality, in particular the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, and the Beijing Platform for Action. As such, no new internationally agreed standard on equality and non-discrimination on the basis of sex was needed. The primary sources of information relevant for indicator 5.1.1 are legislation and policy/action plans.

The 42 questions in the questionnaire are:

Area 1: Overarching legal frameworks and public life

Promote

If customary law is a valid source of law under the constitution, is it invalid if it violates constitutional provisions on equality or non-discrimination?
If personal law is a valid source of law under the constitution, is it invalid if it violates constitutional provisions on equality or non-discrimination?
Is there a discrimination law that prohibits both direct and indirect discrimination against women?
Do women and men enjoy equal rights and access to hold public and political office (legislature, executive, judiciary)?
Are there quotas for women (reserved seats) in, or quotas for women in candidate lists for, national parliament?
Do women and men have equal rights to confer citizenship to their spouses and their children?

Enforce and monitor

Does the law establish a specialized independent body tasked with receiving complaints of discrimination based on gender (e.g., national human rights institution, women’s commission, ombudsperson)?
Is legal aid mandated in criminal matters?
Is legal aid mandated in civil/family matters?
Does a woman’s testimony carry the same evidentiary weight in court as a man’s?
Are there laws that explicitly require the production and/or dissemination of gender statistics?
Are there sanctions for noncompliance with mandated candidate list quotas, or incentives for political parties to field women candidates in national parliamentary elections?

Area 2: Violence against women

Promote

Is there legislation specifically addressing domestic violence?
Have provisions exempting perpetrators from facing charges for rape if the perpetrator marries the victim after the crime been removed, or never existed in legislation?
Have provisions reducing penalties in cases of so-called honor crimes been removed, or never existed in legislation?
Are laws on rape based on lack of consent, without requiring proof of physical force or penetration?
Does legislation explicitly criminalize marital rape or does legislation entitle a woman to file a complaint about rape against her husband or partner?
Is there legislation that specifically addresses sexual harassment?

Enforce and monitor

Are there budgetary commitments provided for by government entities for the implementation of legislation addressing violence against women by creating an obligation on the government to provide a budget or allocation of funding for the implementation of relevant programs or activities?
Are there budgetary commitments provided by government entities for the implementation of legislation addressing violence against women by allocating a specific budget, funding, and/or incentives to support non-governmental organizations for activities to address violence against women?
Is there a national action plan or policy to address violence against women that is overseen by a national mechanism with the mandate to monitor and review implementation?

Area 3: Employment and economic benefits

Promote

Does the law mandate non-discrimination based on gender in employment?
Does the law mandate equal remuneration for work of equal value?
Can women work in jobs deemed hazardous, arduous, or morally inappropriate in the same way as men?
Are women able to work in the same industries as men?
Are women able to perform the same tasks as men?
Does the law allow women to work the same night hours as men?
Does the law provide for maternity or parental leave available to mothers in accordance with the ILO standards?
Does the law provide for paid paternity or parental leave available to fathers or partners?

Enforce and monitor

Is there a public entity that can receive complaints on gender discrimination in employment?
Is childcare publicly provided or subsidized?

Area 4: Marriage and family

Promote

Is the minimum age of marriage at least 18, with no legal exceptions, for both women and men?
Do women and men have equal rights to enter marriage (i.e., consent) and initiate divorce?
Do women and men have equal rights to be the legal guardian of their children during and after marriage?
Do women and men have equal rights to be recognized as head of household or head of the family?
Do women and men have equal rights to choose where to live?
Do women and men have equal rights to choose a profession?
Do women and men have equal rights to obtain an identity card?
Do women and men have equal rights to apply for passports?
Do women and men have equal rights to own, access, and control marital property including upon divorce?

Enforce and monitor

Is marriage under the legal age void or voidable?
Are there dedicated and specialized family courts?

Concepts:

Article 1 of CEDAW provides a comprehensive definition of discrimination against women covering direct and indirect discrimination and article 2 sets out general obligations for States, in particular on required legal frameworks, to eliminate discrimination against women. Article 1 of CEDAW states: “… the term "discrimination against women" shall mean any distinction, exclusion or restriction made on the basis of sex which has the effect or purpose of impairing or nullifying the recognition, enjoyment or exercise by women, irrespective of their marital status, on a basis of equality of men and women, of human rights and fundamental freedoms in the political, economic, social, cultural, civil or any other field”. Article 2 of CEDAW states: States Parties condemn discrimination against women in all its forms, agree to pursue by all appropriate means and without delay a policy of eliminating discrimination against women and, to this end, undertake: (a) To embody the principle of the equality of men and women in their national constitutions or other appropriate legislation if not yet incorporated therein and to ensure, through law and other appropriate means, the practical realization of this principle; (b) To adopt appropriate legislative and other measures, including sanctions where appropriate, prohibiting all discrimination against women; (c) To establish legal protection of the rights of women on an equal basis with men and to ensure through competent national tribunals and other public institutions the effective protection of women against any act of discrimination; (d) To refrain from engaging in any act or practice of discrimination against women and to ensure that public authorities and institutions shall act in conformity with this obligation; (e) To take all appropriate measures to eliminate discrimination against women by any person, organization or enterprise; (f) To take all appropriate measures, including legislation, to modify or abolish existing laws, regulations, customs and practices which constitute discrimination against women; (g) To repeal all national penal provisions which constitute discrimination against women”.

The term “legal frameworks” is defined broadly to encompass laws, mechanisms, and policies/plans to ‘promote, enforce and monitor’ gender equality.

Legal frameworks that “promote” are those that establish women’s equal rights with men and enshrine non-discrimination based on sex. Legal frameworks that “enforce and monitor’ are directed to the realization of equality and non-discrimination and implementation of laws, such as policies/plans, the establishment of enforcement and monitoring mechanisms, and allocation of financial resources.

The areas of law were agreed at the expert workshop, held on 14 and 15 June 2016, to discuss the methodological development of SDG indicator 5.1.1. ↑

2.b. Unit of measure

Percent (%) of legal frameworks that promote, enforce, and monitor gender equality

2.c. Classifications

Not applicable

3.a. Data sources

The data for the indicator are derived from an assessment of legal frameworks using primary sources/official government documents, in particular laws, policies and action plans. The assessment is carried out by national counterparts, including National Statistical Offices (NSOs) and/or National Women’s Machinery (NWMs), and legal practitioners/researchers on gender equality, using a questionnaire comprising 42 yes/no questions under four areas of law: (i) overarching legal frameworks and public life; (ii) violence against women; (iii) employment and economic benefits; and (iv) marriage and family. The areas of law and questions are drawn from the international legal and policy framework on gender equality, in particular the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, and the Beijing Platform for Action.

3.b. Data collection method

Countries are asked to designate a focal point to undertake the coordination at the country level necessary for the collection and validation of the data. Most designated focal points are within the NWMs, a number are within the NSOs, and some are within both the NWMs and the NSOs. After verification,^[2] the data with relevant laws, policies, and other sources included are sent to the designated focal points/country counterparts to review and validate. Final answers are arrived at after the process of validation with country counterparts.

Verification includes information (eg national legal sources) compiled under World Bank Group and OECD Development Centre procedures by legal practitioners/researchers on gender equality. The World Bank Group’s Women, Business and the Law and the OECD Development Centre’s Social Institutions and Gender Index are two well-known global databases on national legal frameworks that promote gender equality which have been collecting data in this area for 10 and 9 years respectively. ↑

3.c. Data collection calendar

Data will be compiled every two years starting in 2018.

3.d. Data release calendar

First quarter, every two years.

3.e. Data providers

National counterparts, including National Statistical Offices and National Women’s Machinery.

3.f. Data compilers

The World Bank Group, the OECD Development Centre, UN Women

3.g. Institutional mandate

The World Bank works closely with international agencies, regional development banks, donors, and other partners to develop frameworks, guidance, and standards of good practice for statistics, build consensus and define internationally agreed indicators, establish data exchange processes and methods, and help countries improve statistical capacity. Since 2009, the World Bank Group’s Women, Business and the Law project has contributed to the study of gender equality and informed discussions on improving women's economic opportunities and empowerment through a unique dataset that measures the legal differences in access to economic opportunities between men and women in 190 economies.

The OECD Development Centre’s core mission is to provide a platform for evidence-based policy dialogue between OECD and non-OECD countries to design better policies, by identifying policy solutions to improve lives in developing countries. Through its Gender Programme, particularly since the creation of the Social Institutions and Gender Index (SIGI) in 2009, the OECD Development Centre has played an instrumental role in highlighting the data gaps and fostering policy dialogue and mutual learning on the social institutions that discriminate against women and girls across their life cycle. It is also building the capacity of member states in data collection through the SIGI Country Studies, and advocates for more, better, and comparable data through its SIGI Global and Regional Reports and policy dialogue events.

UN Women is committed through its work at the global, regional, and county level to support Member States in filling critical gaps in generating and using data, statistics, evidence, and analysis on gender equality in crucial areas. As part of its mandate, the organization supports Member States in setting norms. It conducts research, and compiles and provides evidence, including good practices and lessons learned, to inform intergovernmental debates and decisions. It also assists in implementing norms and standards through its country programs. In addition, it leads and coordinates the UN system’s work in support of gender equality and the empowerment of women.

4.a. Rationale

Equality and non-discrimination based on sex are core principles under the international legal and policy framework, including the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, and the Beijing Platform for Action. This framework sets out the commitments of States to eliminate discrimination against women and promote gender equality, including in the area of legal frameworks.

In the Beijing Platform for Action, States pledged to revoke any remaining laws that discriminate based on sex. The five-year review and appraisal of the Beijing Platform for Action (Beijing +5) established 2005 as the target date for the repeal of laws that discriminate against women. This deadline has come and gone. While there has been progress in reforming laws to promote gender equality, discrimination against women in the law continues in many countries. Even where legal reforms have taken place, gaps in implementation persist.

Removing discriminatory laws and putting in place legal frameworks that advance gender equality are prerequisites to ending discrimination against women and achieving gender equality (Goal 5, Target 5.1). Indicator 5.1.1 will be crucial in accelerating progress on the implementation of SDG 5 and all other gender-related commitments in the 2030 Agenda for Sustainable Development.

4.b. Comment and limitations

To avoid duplication, the indicator does not cover areas of law that are addressed under indicator 5.a.2, ‘Proportion of countries where the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control’, and indicator 5.6.2, ‘Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education’. Indicator 5.1.1 complements these indicators.

4.c. Method of computation

Scoring:

The indicator is based on an assessment of legal frameworks that promote, enforce, and monitor gender equality using a questionnaire comprising 42 Yes/No questions under four areas of law drawn from the international legal and policy framework on gender equality, in particular, CEDAW and the Beijing Platform for Action.

The answers to the questions are coded with simple “Yes/No” answers with “1” for “Yes” and “0” for “No”. For questions 1 and 2 only, they may be scored “N/A” in which case they are not included as part of the overall score calculation for the area.^{^[3]}

The scoring methodology is the unweighted average of the questions under each area of law calculated by: $A_{i} = \frac{q_{1} + \dots + q_{m_{i}}}{m_{i}}$ .

Where A_i refers the area of law i; m_i refers to the total number of questions under the area of law i;^{^[4]} q₁+...+q_mi refers to the sum of the coded questions under the area of law and where q_i=”1” if the answer is “Yes” and q_i=”0” if the answer is “No”.

Results of the four areas are reported as percentages as a dashboard: $〈A_{1,}, A_{2}, A_{3}, A_{4}〉$ . The score for each area (a number between 0 and 100) therefore represents the percentage of achievement of that country in that area, with 100 being best practice met on all questions in the area.

The choice of presenting all four area scores without further aggregation is the result of adopting the posture that high values in one area in a given country need not compensate in any way the country having low values in some other area, and that a comprehensive examination of the value of those four numbers for each country is potentially more informative than trying to summarize all four numbers into a single index.

For questions 1 and 2, the methodology does not attribute a score (positive or negative) to the existence of customary or personal law but does score whether they are subject to constitutional principles of equality or non-discrimination. Therefore, in countries where customary or personal law does not apply, these questions are scored as “N/A” and are not included as part of the overall score calculation for the area ‘overarching legal frameworks and public life’. ↑

⁴

If a question is coded as “N/A”, it will not be counted in the total number of questions in an area of the law. ↑

4.d. Validation

After verification, the data with relevant laws, polices and other sources included, are sent to the designated focal points/country counterparts to review and validate. Final answers are arrived at after the process of validation with country counterparts.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

Not imputed

• At regional and global levels:

Not imputed

4.g. Regional aggregations

The regional and global aggregate calculations will be the unweighted average of the scores of each country in that region (or globally), per area of law.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

• Methodology used by countries for the compilation of the data at the national level: The questionnaires provided to countries include guidance, definitions and instructions.

• International recommendations and guidelines: The areas of law and questions are drawn from the international legal and policy framework on gender equality, in particular the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, (http://www.ohchr.org/EN/HRBodies/CEDAW/Pages/CEDAWIndex.aspx), and the Beijing Platform for Action (http://www.unwomen.org/en/how-we-work/intergovernmental-support/world-conferences-on-women). The attached Methodological Note sets out the international standards supporting the areas of law and questions and also attaches the background paper for the expert workshop which provides a useful summary of the international legal and policy framework on equality and non-discrimination on the basis of sex and the relevance for SDG indicator 5.1.1.

4.i. Quality management

See section 4.d. on validation.

4.j. Quality assurance

The assessment of laws is initially carried out by national counterparts, and legal practitioners and researchers on gender equality. The data is checked and verified by the World Bank Group, OECD Development Centre, and UN Women. The data is then sent to the designated focal points/country counterparts to review and validate. Please refer to section 3 above on Data source type and data collection method for more details.

4.k. Quality assessment

See section 4.d. on validation. In addition, coding guidelines are used to set criteria that are applied equally to all countries for the purposes of ensuring comparability across countries.

5. Data availability and disaggregation

Data availability:

Pilot data collection and validation was carried out for 14 countries.

Time series:

First release of data was in 2019.

Disaggregation:

The indicator captures and is disaggregated into four areas of law: (i) overarching legal frameworks and public life; (ii) violence against women; (iii) employment and economic benefits; and (iv) marriage and family. Data in the global database corresponds to these disaggregations.

6. Comparability/deviation from international standards

Sources of discrepancies:

There should be no discrepancies. Data is collected through validated surveys.

7. References and Documentation

World Bank Group: http://wbl.worldbank.org/

OECD Development Centre: http://www.genderindex.org/

UN Women: https://data.unwomen.org/data-portal/sdg

UN Women and UN Statistics Division annual SDG and gender monitoring report: Progress on the Sustainable Development Goals: The Gender Snapshot

5.2.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.2: Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitation

0.c. Indicator

Indicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age

0.d. Series

Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%) VC_VAW_MARR

0.e. Metadata update

2022-03-31

0.f. Related indicators

5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence

5.6.1: Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care. ( as includes a component on saying no to sex.)

11.7.2: Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months

16.1.3: Proportion of population subjected to physical, psychological, or sexual violence in the previous 12 months

16.2.3: Proportion of young women and men aged 18-29 years who experienced sexual violence by age 18

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)

United Nations Office on Drugs and Crime (UNODC)

United Nations Population Fund (UNFPA)

United Nations Statistics Division (UNSD)

1.a. Organisation

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)

United Nations Office on Drugs and Crime (UNODC)

United Nations Population Fund (UNFPA)

United Nations Statistics Division (UNSD)

2.a. Definition and concepts

Definition:

This indicator measures the percentage of ever-partnered women and girls aged 15 years and older who have been subjected to physical, sexual, or psychological violence by a current or former intimate partner, in the previous 12 months. The definition of violence against women and girls (VAWG) and the forms of violence specified under this indicator are presented in the next section (Concepts).

NOTE: References to “violence against women” (VAW) throughout also include adolescent girls (15-19 years old).

Concepts:

According to the UN Declaration on the Elimination of Violence against Women (1993), violence against women is “Any act of gender-based violence that results in, or is likely to result in, physical, sexual, or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life. Violence against women shall be understood to encompass, but not be limited to, the following: Physical, sexual and psychological violence occurring in the family […]”. See here for the full definition: https://undocs.org/en/A/RES/48/104

Intimate partner violence (IPV) against women includes any abuse perpetrated by a current or former partner within the context of marriage, cohabitation, or any other formal or informal union.

The different forms of violence included in the indicator are defined as follows:

1. Physical violence consists of acts aimed at physically hurting the victim and include, but are not limited to acts like pushing, grabbing, twisting the arm, pulling hair, slapping, kicking, biting, hitting with a fist or object, trying to strangle or suffocate, burning or scalding on purpose, or threatening or attacking with some sort of weapon, gun or knife.

2. Sexual violence is defined as any sort of harmful or unwanted sexual behaviour that is imposed on someone, whether by use of physical force, intimidation, or coercion. It includes acts of abusive sexual contact, forced sexual acts, attempted or completed sexual acts (intercourse) without consent (rape or attempted rape), non-contact acts such as being forced to watch or participate in pornography, etc. In intimate partner relationships, sexual violence is commonly operationally defined in surveys as being physically forced to have sexual intercourse, having sexual intercourse out of fear for what the partner might do or through coercion, and/or being forced to do something sexual that the woman considers humiliating or degrading.

3. Psychological violence consists of any act that induces fear or emotional distress. It includes a range of behaviours that encompass acts of emotional abuse such as being frequently humiliated in public, intimidated or having things you care for destroyed, etc. These often coexist with acts of physical and sexual violence by intimate partners. In addition, surveys often measure controlling behaviours (e.g., being kept from seeing family or friends, or from seeking health care without permission). These are also considered acts of psychological abuse, although usually measured separately. .

For a more detailed definition of physical, sexual, and psychological violence against women, see Guidelines for Producing Statistics on Violence against Women- Statistical Surveys (UN, 2014), and the International Classification of Crime for Statistical Purposes ICCS (UNODC, 2015), and Violence against Women Prevalence Estimates, 2018. Global, regional, and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women (WHO, 2021).

2.b. Unit of measure

Percent (%)

2.c. Classifications

The ‘gold standard’ and operational definitions applied to the generation of the 2018 global, regional and national estimates for intimate partner violence against women (WHO, 2021) reference the UN Guidelines for Producing Statistics on Violence against Women(UN, 2014) and the UNODC International Classification of Crime for Statistical Purposes ICCS (UNODC, 2015. These international standards on measurement and reporting include

standardized definitions of physical, sexual, and psychological IPV against women;
measurement of these forms of violence using acts-based questions
Appropriate sample size
disaggregation by age groups
application of the appropriate denominator/target population (ever-partnered women)
reporting by type of perpetrator
comprehensive interviewer training to administer violence against women questions, and following internationally agreed ethical and safety guidelines, including on privacy, confidentiality and support service information.

Survey measurement, should be guided by these international standards and documentation should report on all of the above to allow for an overall assessment of data quality.

However, to date, individual studies and surveys use different measures, methodologies and reporting standards. This makes it challenging to compare the prevalence across studies and requires the use of adjusted estimates for international comparability (see section 4b).

3.a. Data sources

The SDG 5.2.1 Indicator Database comprises data from population-based household surveys representative at the national and/or sub-national level and implementing a methodology that uses act-based questions. All sources date from 2000 to 2018.

A significant proportion of data from low- and middle-income countries are obtained from the Domestic Violence Module of the Demographic and Health Surveys (DHS). Some data come from dedicated surveys on violence against women in countries that have implemented, for example, WHO’s violence against women survey methodology or other methodologies consistent with international guidelines and best practices. In the case of higher-income countries, data were obtained from Crime Victimisation Surveys (CVS) or dedicated surveys.

3.b. Data collection method

Data are collated by the WHO on behalf of the Inter-Agency Working Group on Violence against Women.

Data come from publicly available survey data or data provided by National Statistics Offices (NSOs) or other relevant national entities through the consultation process with countries. For efficiency, some data are collated using existing data-compiling online platforms (e.g., DHS StatCompiler and the EU-wide Survey on Violence Against Women (Fundamental Rights Agency) Data Explorer)).

3.c. Data collection calendar

Countries are encouraged to conduct surveys at regular intervals. The recommended interval, depending on available resources, is three (3) to five (5) years which will allow countries to effectively measure progress. The prevalence database will be updated on an annual basis.

3.d. Data release calendar

Data on SDG indicator 5.2.1 were collected, compiled, and sent back to countries alongside the country estimates for their review. It is expected that the modeled estimates will be updated every 2 years.

3.e. Data providers

Data are provided by nationally or sub-nationally representative surveys on violence against women conducted by National Statistical Offices (in most cases),line ministries/other national institutions or other entities.

3.f. Data compilers

Data are compiled and reviewed by the Interagency Working Group on Violence against Women Data (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC).

3.g. Institutional mandate

WHO is the directing and coordinating authority on international health within the United Nations System. It supports countries as they coordinate the efforts of multiple sectors of the government and partners to attain their health objectives and support their national health policies and strategies, including through developing norms and standards, and strengthening data collection, reporting, and use. The organization produces estimates and statistics for a wide range of diseases and health conditions including in its annual world health statistics report. It has led work on the measurement of violence against women since 1998, developed and tested new instruments for measuring VAW cross-culturally, as well as ethical and safety standards for research on VAW.

In 2016, WHO Member States endorsed the Global plan of action on strengthening the health system role in addressing violence, in particular against women and girls, and against children(WHA Resolution 69.5) Improving the collection and use of data was one of its four strategic directions and included: a) Developing and disseminating harmonized indicators and measurement tools to support Member States in collecting standardized information on VAWG; b) Supporting Member States to implement population-based surveys on VAW; c) Building capacity in the collection, analysis and use of data; d) Regularly updating estimates of the prevalence of VAW.

UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to the Member States to support their efforts to collect quality data on violence against children, including through the UNICEF-supported Multiple Indicator Cluster Surveys (MICS) household survey program. UNICEF develops standards, tools, and guidelines for data collection. Furthermore, it compiles statistics on violence to make internationally comparable datasets publicly available, and it analyses such data which are included in relevant publications, including in its flagship publication, The State of the World’s Children.

UN Women is committed through the conjunction of its triple mandate of normative support, UN coordination, and operational activities to work at the global, regional, and country-level to support the Member States in filling critical gaps in generating and using data, statistics, evidence, and analysis on gender equality in crucial areas. The organization supports the Member States in setting norms that include global standards. It conducts research, and compiles and provides evidence, including good practices and lessons learned, to inform intergovernmental debates and decisions; that help design specific policies and development plans at the regional, national, and local levels as part of its operational activities. It also assists in implementing norms and standards through its country programmes. In addition, UN Women leads and coordinates the UN system’s work in support of gender equality and the empowerment of women.

The Statistics Division of the Department of Economic and Social Affairs (UNSD) helps the Member States to build sound national statistical systems, which includes solid institutional infrastructures, systematic data collection activities, the compilation of aggregate macroeconomic, social, and environment statistics according to global standards and norms, and a multichannel data dissemination system. In the area of methodological work, the Division develops international statistical standards and methods essential for the compilation of reliable and comparable statistics and methodological guidelines for the collection, processing, analysis, and dissemination of data. the Division has unparalleled recognition around gender statistics. Over the past 4 decades, it has supported countries in their efforts to produce and use high quality and timely gender data for better evidence-based policymaking; developed and promoted standards and methodological guidelines addressing emerging issues of gender concern; produced the World’s Women report every 5 years, and compiled gender statistics and facilitated access to data. (https://unstats.un.org/unsd/demographic-social/gender/).

UNODC – as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists the Member States in reforming their criminal justice systems to be effective, fair, and humane for the entire population, including women and girls. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports the Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programs and the UNODC field office network.

UNFPA - is the United Nations sexual and reproductive health agency. Our mission is to deliver a world where every pregnancy is wanted, every childbirth is safe and every young person's potential is fulfilled. The agency collects and facilitates the gathering of the most accurate population data available to empower countries to make informed decisions on crucial development issues and humanitarian response. Its Population Data strategy addresses long-standing shortfalls in population data and related human capacity. The strategy seeks to expand the scope and quality of modern census and registry data, increase the use of geo-referenced population data to accelerate progress towards the SDGs, and advance the objectives of its mandate. The agency provides census technical support to more than 125 countries, through strong partnerships with governments, UN country teams, the US Census Bureau, and the population and data sectors worldwide. Census data provide the denominators for computation of many of the Sustainable Development Goals (SDGs) and a basis for weights when calculating regional and global aggregates of various indicators, including SDGs.

4.a. Rationale

Intimate partner violence is one of the most common forms of violence that women face globally. Given prevailing social norms that sanction male dominance over women, male violence towards their female intimate partners is often perceived as an ordinary/normal element of relationships in the context of marriage or other unions/relationships. Violence against women is an extreme manifestation of gender inequality and discrimination.

Prevalence data are required to measure the magnitude of the problem; understand the various forms of violence and their consequences; identify groups at high risk and explore the barriers to seeking help to ensure that the appropriate responses are being provided. These data are the starting point for informing laws, policies and developing effective responses and prevention programs. They also allow countries to monitor change over time and optimally target resources to maximize the effectiveness of interventions (especially in resource-constrained settings).

4.b. Comment and limitations

Comparability:

The availability of comparable data remains a challenge in this area as many data collection efforts have relied on different survey methodologies, or used different definitions of partner or spousal violence and recall periods (e.g., different definitions of “lifetime”). Many survey measures and/or reports lack disaggregation by different forms of intimate partner violence (physical, sexual, psychological). There are often differences in survey question formulations and/or denominators e.g. all women [various age ranges], or only ever-married/partnered or currently married/partnered women). There is also heterogeneity in age groups sampled and reported on. The quality of interviewer training also likely varies, although this is difficult to quantify. Willingness to discuss experiences of violence and understanding of relevant concepts may also differ according to how the survey is implemented and the social/cultural context, and this can affect reported prevalence levels.

Given the wide variations in methodologies, measurement, and quality across studies from different countries, statistically adjusted estimates are currently needed to ensure comparability across countries and regions. However, generating estimates is an interim solution and individual countries need to collect robust, internationally comparable, high-quality data that reflect the relevant socio-economic, political and cultural risk, and protective factors associated with the prevalence of violence against women (VAW) to inform appropriate policy responses and programmatic decision-making. As more countries adopt international recommendations and guidelines, including the key elements described in this document, the need for adjustments for estimates for global monitoring will be greatly reduced.

Regularity of data production:

Since 2000, only about 78 countries have conducted more than one survey on VAW. Obtaining data on VAW is a costly and time-consuming exercise, whether they are obtained through stand-alone dedicated surveys or modules in other surveys. Demographic and Health Surveys (DHS), the main source of data for low- and lower-middle Income Countries (LMICs), are conducted every 5 years or so and dedicated surveys, if repeated, are conducted usually with less periodicity than this. Monitoring this indicator with certain periodicity may be a challenge if sustained capacities are not built and financial resources are not available for regular surveys. At the same time prevalence is unlikely to change from year to year so, depending on resources, every 3-5 years is recommended.

Feasibility:

This indicator calls for global reporting on three types of intimate partner violence (IPV): physical, sexual, and psychological. While there is global consensus on how physical and sexual IPV are generally defined and measured, psychological partner violence—is conceptualized differently across cultures and in different contexts. This indicator therefore currently reports on physical and/or sexual intimate partner violence only. Efforts are underway by custodian agencies to develop a global standard for measuring and reporting psychological IPV. This will enable reporting on the three stipulated types of partner violence in the future.

Similarly, this indicator calls for global reporting of violence ever-partnered women aged 15 years and above have been subjected to. Most data come from DHS, which typically sample only women aged 15-49, and there is a lack of consistency in the age range of sample populations across other country surveys. For those surveys that interview a sample of women from a different age group, the prevalence for the 15-49 age group is often published or can be calculated from available data. The global indicator therefore currently reports on both violence ever-partnered women 15-49 years of age and 15 years and older have been subjected to. Given the existing limited data availability on violence against women aged 50 years and older, efforts are underway by the custodian agencies to improve the measurement and encourage increased availability of data for women of this age group. This will enable a better estimation of the extent of this problem and understanding the experiences of partner violence for women over 50.

4.c. Method of computation

This indicator calls for breakdown by form of violence and by age group. Countries are encouraged to compute prevalence data for each form of violence as detailed below to assist comparability at the regional and global levels:

1. Physical intimate partner violence:

Number of ever-partnered women (aged 15 years and above) subjected to any act of physical violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women and girls (aged 15 years and above) in the population multiplied by 100 .

2. Sexual intimate partner violence:

Number of ever-partnered women (aged 15 years and above) subjected to any act of sexual violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) in the population multiplied by 100.

3. Psychological intimate partner violence:

Number of ever-partnered women (aged 15 years and above) subjected to psychological violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) multiplied by 100.

4. Any form of physical and/or sexual intimate partner violence:

Number of ever-partnered women (aged 15 years and above) who experience physical and/or sexual violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) multiplied by 100.

5. Any form of physical, sexual and/or psychological intimate partner violence:

Number of ever-partnered women (aged 15 years and above) subjected to any act of physical, sexual and/or psychological violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) multiplied by 100.

NOTE: To assist comparability at the regional and global level, and due to more comparable data available, countries are encouraged to additionally compute the above figures for ever-partnered women aged 15 to 49. Regional and global reporting on this indicator currently only includes data computed by countries for #4 above (i.e., any form of physical and/or sexual partner violence), and for both the 15-49 and the 15 years and older age groups). For further details, see Feasibility section above.

4.d. Validation

A country consultation on the intimate partner violence (IPV) estimates was conducted in early 2020. All countries received their country profile which included their data sources, estimate, and a technical note explaining the methodology (available in six official languages). The consultations ensured: i) countries had the opportunity to review their nationally modeled IPV estimate and the data sources (surveys/studies) used in the production of these estimates, ii) the identification of any additional surveys/studies that met the inclusion criteria (i.e published between 2000-2018, used acts-based measures of IPV, nationally or subnationally representative) but which may not have been previously identified; and iii) familiarize countries with the statistical modeling approach used to derive the global, regional, and national estimates.

4.e. Adjustments

There have been significant improvements in the measurement, availability, and quality of population-based survey data on intimate partner violence (IPV) globally. However, substantial heterogeneity remains in how national surveys and studies measure the different forms of intimate partner violence against women (VAW). For international comparability, data are statistically adjusted to ensure harmonization concerning: definitions (e.g. severity); age groupings (5 year age groups and aggregate 15 to 49 or 15 years and above), type of IPV (physical IPV only or sexual IPV only), the perpetrator of partner violence (spouse only or spouse/partner; current or most recent spouse/partner only or any current or previous spouse/partner), sample profile (ever-married/partnered women or currently-married/partnered women or all women) and geographical scope (national or sub-national, rural, urban).

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

When data for a country are entirely missing, no country-level estimate is published.

• At regional and global levels

Imputations are made in cases where country data are not available for the purposes of regional and global figures. The number of countries included in the average and with data available is clearly indicated by SDG region.

4.g. Regional aggregations

Global aggregates are weighted averages of all the countries that make up the world. Regional aggregates are weighted averages of all the countries within the region. Weights used are the population of women aged 15 to 49 from the most recent 2019 revision of the World Population Prospects. Where data are not available for all countries in any given region, regional aggregates may still be calculated. The number of countries included in the average is indicated.

It should be noted that regional and global figures should be interpreted with caution, as they do not necessarily represent with accuracy the region or world, especially for regions where population coverage is below 50 percent.

Custodian agencies, in consultation with the Member States, have produced-to-date global, regional, and country estimates, enhancing the quality and accuracy of 5.2.1 reporting and addressing the comparability challenges outlined above. These new regional and global estimates (2018) are included in this round and form a baseline for the monitoring of progress. These are also available for World Bank, Global Burden of Disease and individual agency regions.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries gather data on intimate partner violence (IPV) through (1) specialized national prevalence surveys dedicated to measuring violence against women (VAW), (2) VAW modules that are added to international/national household surveys, such as the DHS; and (3) crime victimization surveys.

Although administrative data from health, police, courts, justice, and social services, among other services used by survivors of violence, can provide information on VAW, these do not provide prevalence data, but rather incidence data or service use (i.e., number of cases received in/who seek services). Many women who are subjected to abuse do not report or seek help for the violence and those who do, tend to be the most serious cases. Therefore, administrative data are not recommended as a data source for this indicator.

For more information on recommended practices in the production of VAW statistics, see UN Guidelines for Producing Statistics on Violence against Women- Statistical Surveys (UN, 2014). The WHO with other co-custodians are finalizing a “Quality checklist for surveys on IPV against women” as a tool for strengthening country capacity in collecting and reporting high-quality data on violence against women.

4.i. Quality management

The identification of surveys and entering of data in the database was independently checked by 2 or 3 people and consistency checks were carried out by 2 analysts. The estimates followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) and were reviewed by WHO’s Data Department and reviewed by the other co-custodians.

4.j. Quality assurance

The Interagency Working Group on Violence against Women Data, which comprises all custodian agencies for this SDG indicator, thoroughly reviews all country data, including its primary source when deemed necessary, to assess quality and comparability based on exclusion/inclusion criteria agreed upon a priori. These criteria refer to, inter-alia, survey population coverage, operational definitions, methodology, and period. All data points have been discussed and a consensual decision made for every data point included/excluded from the current SDG Indicators Database.

In 2020, a country consultation and validation process of data compiled by custodian agencies for this indicator was undertaken, including with identified SDG indicators focal points and focal points in other relevant ministries.

4.k. Quality assessment

The e estimates on physical and/or sexual intimate partner violence against women were based on the Global Database on the Prevalence of Violence against Women (housed at WHO). Studies included within this database were identified through a comprehensive systematic review of published global prevalence data, metadata repositories of national statistics offices and through the country consultation process as explained below.

Informed by international guidelines on the survey measurement of violence against women, including the Guidelines for Producing Statistics on Violence against Women- Statistical Surveys (UN, 2014) only population-based studies, representative at national or subnational level that used the ‘gold standard’ act-specific measures of IPV were eligible for inclusion. This criterion aimed to minimise the under-estimation of the prevalence of IPV that is associated with the use of broader non-acts-based measures. Data extractions were conducted by two data analysts independently and underwent additional quality control and rigorous consistency checks by a third reviewer.

The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) guided the process of developing the estimates and reviewed the Technical Note for the country consultation and published estimates’ report. The independent external Technical Advisory Group (TAG) to the VAW-IAWGED provided expert advice and input throughout the process of developing the methodology and the estimates.

In addition to the above, and in line with WHO’s quality standards for data production and publication a formal country consultation process was conducted with 194 Member States and one territory (occupied Palestinian territory). The purpose this consultation process was to (i) to ensure that countries had the opportunity to review their national modelled intimate partner violence estimates and the data sources (surveys/studies) used in the production of these estimates; (ii) to ensure the inclusion of any additional surveys/studies that met these inclusion criteria but not been previously identified; and (iii) to familiarize countries with the statistical modelling approach used to derive the global, regional and national estimates.

5. Data availability and disaggregation

Data availability:

Since 2000, 161 countries have conducted violence against women (VAW) national or subnational prevalence surveys or have included a module on VAW in a DHS or other national household surveys. However, not all these data are comparable and in many cases, they are not collected on a regular basis.

Time series:

Some countries (~77) have data on physical and/or sexual intimate partner violence for two or more time points. Global time series with comparable data are not yet available.

Disaggregation:

In addition to form of violence and age, income/wealth, education, ethnicity (including indigenous status), disability status, marital/partnership status, relationship with the perpetrator (i.e., current/former partner), geographic location, migration status, and frequency of violence are suggested as desired variables for disaggregation for this indicator. Though disaggregated data by these variables is not yet feasible to report on at regional and global levels, countries are encouraged to report these levels of disaggregation in their national reports; and—whenever possible—include these data for the age group 15 to 49.

6. Comparability/deviation from international standards

Sources of discrepancies:

All available survey data sources that are representative at the national and subnational level, are used to generate the prevalence estimates. The data are from published survey reports and/or data and datasets provided by countries. In cases where only data disaggregated by violence type were presented in the report, microdata was used to calculate the aggregate measure of physical and/or sexual intimate partner violence (IPV). As there is variability in the measurement across surveys and countries, relevant covariate adjustments were made to enhance comparability. These include adjustments for case definitions (e.g. severity), type of violence (i.e. physical IPV only or sexual IPV only), population surveyed (e.g. currently married women only or all women), reference partners (e.g. current/most recent partners), and geographical strata (rural or urban), aggregate measure of physical and/or sexual IPV where only one of the two forms were available.

7. References and Documentation

URL:

https://srhr.org/vaw-data

http://evaw-global-database.unwomen.org/en

data.unicef.org

http://unstats.un.org/unsd/gender/default.html

References:

1. World Health Organization, 2021. Violence against Women Prevalence Estimates, 2018. Global, regional, and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women. Available at: https://www.who.int/publications/i/item/9789240022256

2. United Nations, 2014. Guidelines for Producing Statistics on Violence against Women- Statistical Surveys. Available at: https://unstats.un.org/unsd/gender/docs/guidelines_statistics_vaw.pdf

3. United Nations, 2015. The World’s Women 2015, Trends and Statistics. Available at: https://unstats.un.org/unsd/gender/downloads/worldswomen2015_report.pdf

4. World Health Organization, Department of Reproductive Health and Research, London School of Hygiene and Tropical Medicine, South African Medical Research Council, 2013. Global and regional estimates of violence against women: prevalence and health effects of intimate partner violence and non-partner sexual violence. Available at: https://www.who.int/publications/i/item/9789241564625

5. UN Women. 2016. Global Database on Violence against Women. Available at: http://evaw-global-database.unwomen.org/en

6. UNICEF Data portal: http://data.unicef.org/child-protection/violence.html

7. UNSD Portal on the minimum set of gender indicators: https://genderstats.un.org/#/home

8. UNSD dedicated portal for data and metadata on violence against women: http://unstats.un.org/unsd/gender/vaw/

9.UNODC, 2015. International Classification of Crime for Statistical Purposes. Available at: https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html

5.2.2

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.2: Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitation

0.c. Indicator

Indicator 5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence

0.e. Metadata update

2017-07-09

0.f. Related indicators

5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age

11.7.2: Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and pace of occurrence, in the previous 12 months

16.1.3: Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months

16.2.3: Proportion of young women and men aged 18-29 years who experienced sexual violence by age 18

0.g. International organisations(s) responsible for global monitoring

United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)

United Nations Children's Fund (UNICEF)

United Nations Statistics Division (UNSD)

World Health Organization (WHO)

United Nations Population Fund (UNFPA)

1.a. Organisation

United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)

United Nations Children's Fund (UNICEF)

United Nations Statistics Division (UNSD)

World Health Organization (WHO)

United Nations Population Fund (UNFPA)

2.a. Definition and concepts

Definition:

This indicator measures the percentage of women and girls aged 15 years and older who have experienced sexual violence by persons other than an intimate partner, in the previous 12 months.

Definition of sexual violence against women and girls is presented in the next section (Concepts).

Concepts:

According to the UN Declaration on the Elimination of Violence against Women (1993), Violence against Women is “Any act of gender-based violence that results in, or is likely to result in, physical, sexual or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life. Violence against women shall be understood to encompass, but not be limited to, the following: […], Physical, sexual and psychological violence occurring within the general community, including rape, sexual abuse, sexual harassment and intimidation at work, in educational institutions and elsewhere, trafficking in women and forced prostitution […]”. See here for full definition: http://www.un.org/documents/ga/res/48/a48r104.htm

Sexual violence is defined as any sort of harmful or unwanted sexual behaviour that is imposed on someone. It includes acts of abusive sexual contact, forced engagement in sexual acts, attempted or completed sexual acts without consent, incest, sexual harassment, etc. However, in most surveys that collect data on sexual violence against women and girls by non-partners the information collected is limited to forcing someone into sexual intercourse when she does not want to, as well as attempting to force someone to perform a sexual act against her will or attempting to force her into sexual intercourse.

For a more detailed definition of sexual violence against women see Guidelines for Producing Statistics on Violence against Women- Statistical Surveys (UN, 2014).

3.a. Data sources

The main sources of intimate partner violence prevalence data are (1) specialized national surveys dedicated to measuring violence against women and (2) international household surveys that include a module on experiences of violence by women, such as the DHS.

Although administrative data from health, police, courts, justice and social services, among other services used by survivors of violence, can provide information on violence against women and girls, these do not produce prevalence data, but rather incidence data or number of cases received in/reported to these services. We know that many abused women do not report violence and those who do, tend to be only the most serious cases. Therefore, administrative data should not be used as a data source for this indicator.

For more information on recommended practices in production of violence against women statistics see: UN Guidelines for Producing Statistics on Violence against Women- Statistical Surveys (UN, 2014).

3.b. Data collection method

An Inter-Agency Group on Violence against Women Data and its Technical Advisory Group is currently being established (jointly by WHO, UN Women, UNICEF, UNSD and UNFPA) to establish a mechanism for compiling harmonized country level data on this indicator.

3.c. Data collection calendar

3.d. Data release calendar

3.e. Data providers

National Statistical Offices (in most cases) or line ministries/other government agencies that have conducted national surveys on violence against women and girls.

3.f. Data compilers

UN Women, UNICEF, UNSD, WHO, UNFPA

4.a. Rationale

Violence against women and girls is one of the most pervasive forms of human rights violations in the world. Evidence has shown that globally, an estimated 7% of women have been sexually assaulted by someone other than a partner at some point in their lives (WHO et al., 2013). Having data on this indicator will help understand the extent and nature of this form of violence and develop appropriate policies and programmes.

4.b. Comment and limitations

Comparability:

The availability of comparable data remains a challenge in this area as many data collection efforts have relied on different survey methodologies and used different definitions of sexual violence and different survey question formulation. Diverse age groups are also often utilized. Willingness to discuss experiences of violence and understanding of relevant concepts may also differ according to the cultural context and this can affect reported prevalence levels.

Efforts and investment will be required to develop an internationally-agreed standard and definition of sexual violence by non-partners that will enable comparison across countries.

Regularity of data production:

Since 1995, only some 40 countries have conducted more than one survey on violence against women and girls. Obtaining data on violence against women and girls is a costly and time-consuming exercise, no matter if they are obtained through stand-alone dedicated surveys or through modules inserted in other surveys. Not all VAW surveys, however, collect information on non-intimate partner violence. Monitoring this indicator with certain periodicity may be a challenge if sustained capacities are not built and financial resources are not available.

4.c. Method of computation

This indicator calls for disaggregation by age group and place of occurrence. No standard definitions and methods have been globally agreed yet to collect data on the place where the violence occurs, therefore this is not presented at this point in the computation method below.

Number of women and girls aged 15 years and above who experience sexual violence by persons other than an intimate partner in the previous 12 months divided by the number of women and girls aged 15 years and above in the population multiplied by 100.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

When data for a country are entirely missing, no country-level estimate is published.

• At regional and global levels

No imputations are made in cases where country data are not available. Where regional and global figures are presented, clear notes on data limitations are provided. The number of countries included in the average is clearly indicated.

4.g. Regional aggregations

Global aggregates are weighted averages of all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries within the region. Where data are not available for all countries in a given region, regional aggregates may still be calculated if the minimum threshold for population coverage is met. The number of countries included in the average is clearly indicated.

5. Data availability and disaggregation

Data availability:

About 100 countries have conducted violence against women national prevalence surveys or have included a module on violence against women in a national household survey on other topic, although not all include data on non-partner sexual violence. Moreover, not all these data are comparable and in many cases they are not collected on a regular basis.

Comparable data are available for a sub-sample of women and girls aged 15-49 for 37 low- and middle-income countries.

Time series:

Time series are available for some countries. Global time series with comparable data not yet available.

Disaggregation:

In addition to age and place of occurrence, income/wealth, education, ethnicity (including indigenous status), disability status, geographic location, relationship with the perpetrator (including sex of perpetrator) and frequency and type of sexual violence (as proxy to severity) are suggested as desired variables for disaggregation for this indicator.

6. Comparability/deviation from international standards

Sources of discrepancies:

Only figures published by countries are used.

7. References and Documentation

URL:

http://evaw-global-database.unwomen.org/en

data.unicef.org

http://unstats.un.org/unsd/gender/default.html

References:

1. United Nations, 2014. Guidelines for Producing Statistics on Violence against Women- Statistical Surveys.

2. United Nations, 2015. The World’s Women 2015, Trends and Statistics.

3. World Health Organization, Department of Reproductive Health and Research, London School of Hygiene and Tropical Medicine, South African Medical Research Council, 2013. Global and regional estimates of violence against women: prevalence and health effects of intimate partner violence and non-partner sexual violence.

4. UN Women. 2016. Global Database on Violence against Women. Available at: http://evaw-global-database.unwomen.org/en

5. UNICEF Data portal: http://data.unicef.org/child-protection/violence.html

6. UNSD Portal on the minimum set of gender indicators: http://genderstats.un.org/beta/index.html#/home

7. UNSD dedicated portal for data and metadata on violence against women: http://unstats.un.org/unsd/gender/vaw/

5.3.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation

0.c. Indicator

Indicator 5.3.1: Proportion of women aged 20–24 years who were married or in a union before age 15 and before age 18

0.d. Series

Proportion of women aged 20-24 years who were married or in a union before age 15 (%)

Proportion of women aged 20-24 years who were married or in a union before age 18 (%)

0.e. Metadata update

2022-03-31

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

1.a. Organisation

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

Proportion of women aged 20-24 years who were married or in a union before age 15 and before age 18.

Concepts:

Both formal (i.e., marriages) and informal unions are covered under this indicator. Informal unions are generally defined as those in which a couple lives together for some time, intends to have a lasting relationship, but for which there has been no formal civil or religious ceremony (i.e., cohabitation).

2.b. Unit of measure

Percent (%)

2.c. Classifications

The indicator captures all formal and informal cohabiting unions. For comparability, age 18 is used as a standard across countries as the common age of majority, though the threshold age between childhood and adulthood varies across countries, as does the legal age at marriage.

3.a. Data sources

Household surveys such as UNICEF-supported Multiple Indicator Cluster Survey (MICS) and Demographic and Health Surveys (DHS) have been collecting data on this indicator in low- and middle-income countries since around the late 1980s. In some countries, such data are also collected through national censuses, other national household surveys, or administrative data.

3.b. Data collection method

1. UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism it used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).
2. As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators it is custodian or co-custodian for, to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicited feedback directly from National Statistical Offices (NSOs), as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

3.c. Data collection calendar

3.d. Data release calendar

Annually in March.

3.e. Data providers

National Statistical Offices (in most cases)

3.f. Data compilers

United Nations Children's Fund (UNICEF)

3.g. Institutional mandate

UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on child marriage, including through the UNICEF-supported MICS household survey programme. UNICEF also compiles child marriage statistics with the goal of making internationally comparable datasets publicly available, and it analyses child marriage statistics which are included in relevant data-driven publications, including in its flagship publication, The State of the World’s Children.

4.a. Rationale

Marriage before the age of 18 is a fundamental violation of human rights. Child marriage often compromises a girl’s development by resulting in early pregnancy and social isolation, interrupting her schooling, limiting her opportunities for career and vocational advancement and placing her at increased risk of intimate partner violence. In many cultures, girls reaching puberty are expected to assume gender roles associated with womanhood. These include entering a union and becoming a mother. The practice of early/child marriage is a direct manifestation of gender inequality.

The issue of child marriage is addressed in a number of international conventions and agreements. Although marriage is not mentioned directly in the Convention on the Rights of the Child, child marriage is linked to other rights – such as the right to freedom of expression, the right to protection from all forms of abuse, and the right to be protected from harmful traditional practices.

4.b. Comment and limitations

There are existing tools and mechanisms for data collection that countries have implemented to monitor the situation with regards to this indicator. The modules used to collect information on marital status among women and men of reproductive age (15-49 years) in the DHS and MICS have been fully harmonized.

The measure of child marriage is retrospective in nature by design, capturing age at first marriage among a population that has completed the risk period (i.e., adult women). While it is also possible to measure the current marital status of girls under age 18, such measures would provide an underestimate of the level of child marriage, as girls who are not currently married may still do so before they turn 18. For more details on interpretation and common pitfalls for this indicator, see: A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework (UNICEF 2020).

4.c. Method of computation

Number of women aged 20-24 who were first married or in union before age 15 (or before age 18) divided by the total number of women aged 20-24 in the population multiplied by 100.

4.d. Validation

A wide consultative process is undertaken to compile, assess and validate data from national sources.

The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed by UNICEF as the custodian agency. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

When data for a country are entirely missing, UNICEF does not publish any country-level estimate.

• At regional and global levels

4.g. Regional aggregations

Global aggregates are weighted averages of all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries within the region.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries gather data on child marriage through household surveys such as UNICEF-supported MICS or Demographic and Health Surveys. In some countries, such data are also collected through other national household surveys.

4.i. Quality management

The process behind the production of reliable statistics on child marriage is well established within UNICEF. The quality and process leading to the production of the SDG indicator 5.3.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.

4.j. Quality assurance

UNICEF maintains the global database on child marriage that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator.

4.k. Quality assessment

Data consistency and quality checks are regularly conducted for validation of the data before dissemination.

5. Data availability and disaggregation

Data availability:

Comparable data on this indicator are currently available for 126 countries.

Time series:

At the country level, the latest available data for indicator 5.3.1 are published. At the regional and global levels, time series estimates are published for 5-year intervals beginning from 2000.

Disaggregation:

None

6. Comparability/deviation from international standards

Sources of discrepancies:

The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.

7. References and Documentation

URL:

https://data.unicef.org/topic/child-protection/child-marriage/

References:

https://data.unicef.org/topic/child-protection/child-marriage/

https://data.unicef.org/resources/a-generation-to-protect/

5.3.2

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation

0.c. Indicator

5.3.2 Proportion of girls and women aged 15–49 years who have undergone female genital mutilation, by age

0.e. Metadata update

2023-03-31

0.f. Related indicators

The prevalence of female genital mutilation can be interpreted alongside other indicators about women’s well-being, including those on women’s health under Goal 3, those on the status of women under Goal 5, and those around violence against women under Goal 16.

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

1.a. Organisation

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

Proportion of girls and women aged 15-49 years who have undergone female genital mutilation.

This indicator can be measured among smaller age groups, with the experience of younger women representing FGM/C that has occurred more recently and the experience of older women representing levels of the practice in the past. At the regional and global level, this indicator is currently being reported as the proportion of adolescent girls aged 15-19 years who have undergone female genital mutilation.

Concepts:

Female genital mutilation (FGM) refers to “all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons" (World Health Organization, Eliminating Female Genital Mutilation: An interagency statement, WHO, UNFPA, UNICEF, UNIFEM, OHCHR, UNHCR, UNECA, UNESCO, UNDP, UNAIDS, WHO, Geneva, 2008, p.4)

2.b. Unit of measure

Proportion

2.c. Classifications

The indicator captures all experiences of FGM, regardless of type.

3.a. Data sources

Household surveys such as UNICEF-supported MICS and DHS have been collecting data on this indicator in low- and middle-income countries since the late 1980s. In some countries, such data are also collected through other national household surveys.

3.b. Data collection method

1. UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).

As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

3.c. Data collection calendar

3.d. Data release calendar

March 2021

3.e. Data providers

National Statistical Offices (in most cases)

3.f. Data compilers

UNICEF

3.g. Institutional mandate

UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on FGM, including through the UNICEF-supported MICS household survey programme. UNICEF also compiles FGM statistics with the goal of making internationally comparable datasets publicly available, and it analyses FGM statistics which are included in relevant data-driven publications, including in its flagship publication, The State of the World’s Children.

4.a. Rationale

FGM is a violation of girls’ and women’s human rights. There is a large body of literature documenting the adverse health consequences of FGM over both the short and long term. The practice of FGM is a direct manifestation of gender inequality

FGM is condemned by a number of international treaties and conventions. Since FGM is regarded as a traditional practice prejudicial to the health of children and is, in most cases, performed on minors, it violates the Convention on the Rights of the Child. Existing national legislation in many countries also include explicit bans against FGM.

4.b. Comment and limitations

There are existing tools and mechanisms for data collection that countries have implemented to monitor the situation with regards to this indicator. The modules used to collect information on the circumcision status of girls aged 0-14 and girls and women aged 15-49 in the DHS and MICS have been fully harmonized.

Data on FGM inform policymakers of critically important variables in an effort to better understand the practice and develop policies for its abandonment. That said, these data must be analysed in light of the extremely delicate and often sensitive nature of the topic. Self-reported data on FGM need to be treated with caution for several reasons. Women may be unwilling to disclose having undergone the procedure because of the sensitivity of the issue or the illegal status of the practice in their country. In addition, women may be unaware that they have been cut or of the extent of the cutting, particularly if FGM was performed at an early age.

Data users should also keep in mind the retrospective nature of these data, which results in this indicator not being sensitive to recent change. For more details on interpretation and common pitfalls for this indicator, see: A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework (UNICEF 2020).

4.c. Method of computation

Number of girls and women aged 15-49 who have undergone FGM divided by the total number of girls and women aged 15-49 in the population multiplied by 100

4.d. Validation

A wide consultative process is undertaken to compile, assess and validate data from national sources.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

When data for a country are entirely missing, UNICEF does not publish any country-level estimate

• At regional and global levels

Regional aggregates are only published when at least 50 per cent of the regional population for the relevant age group are covered by the available data.

4.g. Regional aggregations

Global aggregates are not presented for this indicator as data are only collected in a subset of countries where the practice is sufficiently widespread to warrant national-level data collection. Regional aggregates are weighted averages of countries with available data within the region.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries gather data on FGM through household surveys such as UNICEF-supported MICS or Demographic and Health Surveys. In some countries, such data are also collected through other national household surveys.

4.i. Quality management

The process behind the production of reliable statistics on FGM is well established within UNICEF. The quality and process leading to the production of the SDG indicator 5.3.2 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.

4.j. Quality assurance

UNICEF maintains the global database on FGM/C that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator.

4.k. Quality assessment

Data consistency and quality checks are regularly conducted for validation of the data before dissemination

5. Data availability and disaggregation

Data availability:

Nationally representative prevalence data are currently available for 30 low- and middle-income countries

Time series:

At the country level, the latest available data for indicator 5.3.2 are published. At the regional level, time series estimates for indicator 5.3.2 (as measured among adolescent girls aged 15-19 years) are published for 5-year intervals beginning from 2000.

Disaggregation:

Age (15-49 years at the national level, 15-19 years at the regional level)

6. Comparability/deviation from international standards

Sources of discrepancies:

The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.

7. References and Documentation

URL:

data.unicef.org

References:

https://data.unicef.org/topic/child-protection/female-genital-mutilation/https://data.unicef.org/resources/a-generation-to-protect/

5.4.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.4: Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate

0.c. Indicator

Indicator 5.4.1: Proportion of time spent on unpaid domestic and care work, by sex, age and location

0.d. Series

Applies to all series

0.e. Metadata update

2023-03-31

0.f. Related indicators

Time-use information collected and analyzed around the world has shown that there is a very close link between economic poverty (SDG 1) and time poverty; most of health care is provided by households (SDG 3) and these activities are socially allocated to women in general; the provision of early childhood education services (SDG 4) not only prepares children for primary education but also frees up time for their caregivers; the sexual division of labour is a structural challenge of gender inequalities (SDG 5, 8 and 10); and the lack of services such as drinkable water, electricity or transport infrastructure increases unpaid work time and disproportionately affects women (SDG 6, 7, 9,11). Please see the section “Time-use data crucial for monitoring the 2030 Agenda for Sustainable Development: going beyond SDG 5” of the Policy relevance: Making the case for time-use data collections in support of SDGs monitoring paper submitted to the SC of 2020^[1].

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-PolicyRelevance-E.pdf ↑

0.g. International organisations(s) responsible for global monitoring

UN Statistics Division (UNSD) and UN WOMEN

1.a. Organisation

UN Statistics Division (UNSD)

2.a. Definition and concepts

Definition:

This indicator is defined as the proportion of time spent in a day on unpaid domestic and care work by men and women. Unpaid domestic and care work refers to activities related to the provision of services for own final use by household members, or by family members living in other households. These activities are listed in the International Classification of Activities for Time-Use Statistics 2016 (ICATUS 2016)^[2] under the major divisions “3. Unpaid domestic services for household and family members” and “4. Unpaid caregiving services for household and family members”.

Concepts:

Unpaid domestic work refers to activities including food and meals management preparation, cleaning and maintaining of own dwelling and surroundings, , do-it-yourself decoration, maintenance and repair of personal and household goods, care and maintenance of textiles and footwear, household management, pet care, shopping for own household and family members and travel related to previous listed unpaid domestic services.

Unpaid care work refers to activities related to childcare and instruction,, care of the sick, elderly, or disabled household and family members, and travel related to these unpaid caregiving services..

Concepts and definitions for this indicator are based on the following international standards:

International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016)
System of National Accounts 2008 (SNA 2008)
The Resolution concerning statistics of work, employment, and labour underutilization, adopted by the International Conference of Labour Statisticians (ICLS) at its 19th Session in 2013

As much as possible, statistics compiled by UNSD are based on the International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016), which classifies activities undertaken by persons during the survey period. ICATUS 2016 was adopted by the United Nations Statistical Commission for use as an international statistical classification at its 48th session, 7-10 March 2017.

https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf ↑

2.b. Unit of measure

Percent (%) (proportion of time in a day)

2.c. Classifications

The data for SDG 5.4.1 is as much as possible, in line with relevant international standards, including

▪ The International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016)

▪ System of National Accounts 2008 (SNA 2008)

▪ Resolution concerning statistics of work, employment, and labour underutilization, adopted by the International Conference of Labour Statisticians (ICLS) at its 19th Session in 2013

3.a. Data sources

Most data on time use are collected through dedicated time use surveys or from time-use modules integrated into multi-purpose household surveys, conducted at the national level.

Data on time-use can be collected through a 24-hour diary (light diary) or a stylized questionnaire. With diaries, respondents are asked to report on what activity they were performing when they started the day, what activity followed and the time that activity began and ended (in most of the cases based on fixed intervals), and so forth through the 24 hours of the day. Stylized time-use questions ask respondents to recall the amount of time they allocated to a certain activity over a specified period, such as a day or a week.

3.b. Data collection method

Data are collected by national statistical offices, the official counterparts at the country level. Data are compiled and validated by UNSD. If there are inconsistencies or issues with the data, UNSD consults the focal point in the national statistical office. The data for SDG 5.4.1 is, as much as possible, in line with relevant international standards, or properly footnoted. International standards include:

The International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016)
System of National Accounts 2008 (SNA 2008)
Resolution concerning statistics of work, employment, and labour underutilization, adopted by the International Conference of Labour Statisticians (ICLS) at its 19th Session in 2013
Guide to Producing Statistics on Time-Use: Measuring Paid and Unpaid Work

3.c. Data collection calendar

Once national time-use data become available, they are added to the UNSD database.

3.d. Data release calendar

Data are released regularly as soon as they are updated

3.e. Data providers

National Statistical Offices

3.f. Data compilers

United Nations Statistics Division

3.g. Institutional mandate

The United Nations Statistics Division is committed to the advancement of the global statistical system. UNSD compiles and disseminates global statistical information, develops standards and norms for statistical activities, and supports countries' efforts to strengthen their national statistical systems. UNSD facilitates the coordination of international statistical activities and supports the functioning of the United Nations Statistical Commission as the apex entity of the global statistical system. The Social and Gender Statistics Section of UNSD works on migration statistics, gender statistics, and time use statistics.

The Global Gender Statistics Programme is mandated by the United Nations Statistical Commission, implemented by the United Nations Statistics Division (UNSD, and coordinated by the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS).

The Programme encompasses:

improving coherence among existing initiatives on gender statistics through international coordination
developing and promoting methodological guidelines in existing domains as well as in emerging areas of gender concern
strengthening national statistical and technical capacity for the production, dissemination, and use of gender-relevant data
facilitating access to gender-relevant data and metadata through a gender data portal^[3].

UNSD serves as the Secretariat of the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS), the coordinating, and guiding body of the Global Gender Statistics Programme. The IAEG-GS was first convened in 2006, meets annually and functions through advisory groups. Presently, the main advisory group's work concentrates on examining emerging and unaddressed key gender issues and related data gaps with the aim to develop proposals on how to fill these gaps.

In addition, UNSD serves as Secretariat of the United Nations Expert Group on Innovative and Effective Ways to Collect Time-Use Statistics (EG-TUS), which initiated its work in June 2018 with the overall objective of taking stock and reviewing country practices in time-use surveys and providing technical guidance and recommendations to improve the collection and use of time use data, in line with international standards and in support of SDGs implementation. In particular, the Group was established to develop methodological guidelines on how to operationalize ICATUS 2016 and produce time-use statistics using the latest technologies, as requested by the United Nations Statistical Commission at its forty-eighth session in 2017 in its decision 48/109. The 51st Session of the Statistical Commission in 2020 (decision 51/115) endorsed the work of UNSD and the EG-TUS, approved the terms of reference of the Expert Group, and congratulated the group on the progress made in developing a conceptual framework to modernize time-use surveys. The 53rd Statistical Commission in 2022(decision 53/111b) endorsed the work of UNSD and the EG-TUS. This included the minimum harmonized instrument for time-use data collection, quality considerations for time-use surveys, and options to modernize time-use data production. These three documents are the core components of the upcoming revision of the United Nations guidelines for producing time-use statistics.

For more information and resources on the work of UNSD and the EG-TUS, please visit UNSD — Demographic and Social Statistics.

https://gender-data-hub-2-undesa.hub.arcgis.com/ ↑

4.a. Rationale

The purpose of the indicator is to measure the amount of time women and men spend doing unpaid work, to ensure that all work, whether paid or unpaid, is valued. In addition, it also provides an assessment of gender equality, by highlighting discrepancies between how much time women and men spend on unpaid work, like cooking, cleaning, or taking care of children.

This indicator measures the average amount of time as a proportion in a day, so that if for a given country it is reported that women aged 15+ spend 10% of their day on unpaid domestic chores while men in the same age group spend 1%, it indicates that women spend 2.4 hours (2 hours and 24 minutes), while men spend 14.4 minutes on it a day, on average. As explained further in 4.c, this daily average is obtained from an average taken over the reference period for the data collection, and thus not mean that women and men spend these given amounts of time every single day.

4.b. Comment and limitations

Time use statistics have been used: (1) to provide a measure of the quality of life or general well-being of individuals and households; (2) to offer a more comprehensive measurement of all forms of work, including unpaid household service work; (3) to produce data relevant for monitoring gender equality and the empowerment of women and girls and are essential inputs for the policy and political dialogue on gender equality.

International comparability of time-use statistics is limited by several factors, including:

Diary versus stylized time-use survey. Data on time-use can be collected through a 24-hour diary (light diary) or a stylized questionnaire. With diaries, respondents are asked to report on what activity they were performing when they started the day, what activity followed, the time that activity began and ended, and so forth through the 24 hours of the day. Stylized time-use questions ask respondents to recall the amount of time they allocated to a certain activity over a specified period, such as a day or week. Data obtained from these two different data collection methods are usually not comparable, and even data collected with different stylized questions might not be comparable given that the level of detail asked about activities performed might differ from one instrument to another, thus impacting the total time spent on a given activity.
Time-use activity classification. Regional and national classifications of time-use activities may differ from the The International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016), resulting in data that are not comparable across countries.
Time-use data presented refer to the “main activity” only. Any “secondary activity” performed simultaneously with the main activity is not reflected in the average times shown. For instance, a woman may be cooking and looking after a child simultaneously. For countries reporting cooking as the main activity, time spent caring for children is not accounted for and reflected in the statistics. This may affect the international comparability of data on time spent caring for children; it may also underestimate the time women spend on this activity.
Different target age populations used by countries and age groups used also make time use data difficult to compare across countries.

4.c. Method of computation

Data presented for this indicator are expressed as a proportion of time in a day. In the case when the reference period is one week, weekly data is averaged over seven days of the week to obtain the daily average time.

Proportion of time spent on unpaid domestic and care work is calculated by dividing the daily average number of hours spent on unpaid domestic and care work by 24 hours.

Proportion of time spent on unpaid domestic and care work ( $I n d i c a t o r 5.4.1$ ) is calculated as:

$I n d i c a t o r 5.4.1 = \frac{D a i l y n u m b e r o f h o u r s s p e n t o n d o m e s t i c w o r k + D a i l y n u m b e r o f h o u r s s p e n t o n c a r e w o r k}{24} \times 100$

where,

$D a i l y n u m b e r o f h o u r s s p e n t o n r e l e v a n t a c t i v i t i e s = \frac{\begin{matrix} T o t a l n u m b e r o f h o u r s s p e n t b y \\ t h e p o p u l a t i o n o n r e l e v a n t a c t i v i t i e s \end{matrix}}{\begin{matrix} T o t a l p o p u l a t i o n (r e g a r d l e s s o f w h e t h e r \\ t h e y p a r t i c i p a t e d i n t h e a c t i v i t y) \end{matrix}}$

If data on time spent are weekly, data are averaged over seven days of the week to obtain daily time spent.

Average number of hours spent on unpaid domestic and care work derives from time-use statistics that are collected through stand-alone time-use surveys or a time-use module in multi-purpose household surveys. Data on time-use may be summarized and presented as either (1) average time spent for participants (in each activity) only or (2) average time spent for all populations of a certain age (total relevant population). In the former type of average, the total time spent by the individuals who performed the activity is divided by the number of persons who performed it (participants). In the latter type of averages, the total time is divided by the total relevant population (or a sub-group thereof), regardless of whether people performed the activity or not.

SDG indicator 5.4.1 is calculated based on the average number of hours spent on unpaid domestic and unpaid care work for the total relevant population. This type of measure can be used to compare groups and assess changes over time. Differences among groups or over time may be due to a difference (or change) in the proportion of those participating in the specific activity or a difference (or change) in the amount of time spent by participants, or both.

4.d. Validation

Most of the data are provided and validated by national statistical offices. In some cases, data have been obtained from publicly available national databases and publications. The United Nations Statistics Division (UNSD) communicates with countries if there are inconsistencies or possible errors in the data.

4.e. Adjustments

No adjustments concerning international standards are made.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

UNSD does not produce estimates for missing values

At regional and global levels

No imputation is done. Aggregates are computed based on available data only.

4.g. Regional aggregations

The number of countries conducting such surveys is insufficient to allow the computation of annual regional aggregates for SDG reporting. Furthermore, limited comparability across national data hampers the computation of regional aggregates. Nevertheless, UNSD regularly produces regional estimates to monitor and report on global trends. This is done by using the latest available data from each country in the region. In the case of insufficient data from a region, regional aggregates are not reported for the region. The SDG regions of “Australia and New Zealand” and “Europe and North America” are combined to produce a single aggregate for “Developed region.” In addition, the ratio of time spent by women and men are computed separately for each country and then averaged over the countries in the region to ensure comparability.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

International Classification of Activities for Time Use Statistics 2016: https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf

Guide to Producing Statistics on Time-Use: Measuring Paid und Unpaid Work: https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf

System of National Accounts 2008 (SNA 2008): https://unstats.un.org/unsd/nationalaccount/sna2008.asp

The Resolution concerning statistics of work, employment and labour underutilization:

http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm

4.i. Quality management

Details on quality management are available in the data quality for time use statistics paper, presented to the Statistical Commission in 2020:

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Defining_Quality-E.pdf

This technical report was updated and further developed by the Expert Group on Innovative and Effective Ways to Collect Time-Use Statistics (EG-TUS)and was presented at the 53^rd session of the Statistical Commission in March 2022. The updated report is available at: BG-3h-Quality_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf

4.j. Quality assurance

The United Nations Statistics Division (UNSD) has been reviewing in detail the survey methodology followed to collect time use data and the classification of activities used by countries, to assess the level of comparability across countries and over time in each country.

4.k. Quality assessment

UNSD reviews and assesses the quality of the data received from countries and reverts to the data providers for clarifications if needed. The data received are compared to previous years to ensure consistency over time. In addition, the indicator calculations are verified, and data are checked for anomalies.

5. Data availability and disaggregation

Data availability:

92 countries with data between 2000 and 2022

By Year:

From 2000 – 2004: 41 countries

From 2005 – 2009: 38 countries

From 2010 - 2019: 66 countries

From 2020: 4 countries

Time series:

From 2000 to 2022

Disaggregation:

This indicator should be disaggregated by the following dimensions: sex, age, and location.

The categories for disaggregation, by dimension, are as follows:

Sex: female/male.

Age: the recommended age groups are 15+, 15-24, 25-44, 45-54, 55-64 and 65+

Location: urban/rural (following national definitions given the lack of international definition).

These categories have been recommended by the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS) during its 11th meeting in Rome, Italy on 30-31 October 2017.

Available data are currently disaggregated by sex, age, and location

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable

7. References and Documentation

URL:

http://unstats.un.org/unsd/gender/default.html

References:

Guide to Producing Statistics on Time-Use: Measuring Paid and Unpaid Work (https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf)
International Classification of Activities for Time Use Statistics 2016 (https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf
Minimum Set of Gender Indicators (http://genderstats.un.org)
Modernization of the production of time-use statistics: A placemat linking priority components of the conceptual framework:

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Placemat-E.pdf

Policy relevance: Making the case for time-use data collections in support of SDGs monitoring:

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-PolicyRelevance-E.pdf

Time use Concepts and Definitions:

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Concepts_and_definitions-E.pdf

Minimum Harmonized Instrument for the collection of time-use data:

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-MinimumHarmonizedInstrument-E.pdf

https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-TimeUseStats-rev2-E.pdf

Towards defining quality for data and statistics on time use:

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Defining_Quality-E.pdf

Quality considerations for Time-use Surveys

https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-Quality_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf

Modernization of the Production of Time-use Statistics

BG-3h-Modernization_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf

5.5.1a

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life

0.c. Indicator

Indicator 5.5.1: Proportion of seats held by women in (a) national parliaments and (b) local governments

0.d. Series

Number of seats held by women in national parliaments (number) SG_GEN_PARLN

Current number of seats in national parliaments (number) SG_GEN_PARLNT

Proportion of seats held by women in national parliaments (% of total number of seats) SG_GEN_PARL

0.e. Metadata update

2023-03-31

0.f. Related indicators

Indicator 16.7.1: Proportions of positions (by age group, sex, persons with disabilities and population groups) in public institutions (national and local), including (a) the legislatures; (b) the public service; and (c) the judiciary, compared to national distributions.

0.g. International organisations(s) responsible for global monitoring

Inter-Parliamentary Union (IPU)

1.a. Organisation

Inter-Parliamentary Union (IPU)

2.a. Definition and concepts

Definition:

The proportion of seats held by women in national parliaments, currently as of 1 January of reporting year, is currently measured as the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats.

National parliaments can be bicameral or unicameral. This indicator covers the single chamber in unicameral parliaments and the lower chamber in bicameral parliaments. It does not cover the upper chamber of bicameral parliaments. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members, and by-election.

Seats refer to the number of parliamentary mandates or the number of members of parliament.

Concepts:

Seats refer to the number of parliamentary mandates, also known as the number of members of parliament. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members, and by-election.

2.b. Unit of measure

Number:

Number of seats held by women in national parliaments (number)

Current number of seats in national parliaments (number)

Percent (%):

Proportion of seats held by women in national parliaments (% of total number of seats)

2.c. Classifications

Not applicable

3.a. Data sources

The data used are official statistics received from national parliaments.

3.b. Data collection method

The data are provided by national parliaments and updated after an election or parliamentary renewal. National parliaments also transmit their data to the Inter-Parliamentary Union (IPU) at least once a year and when the numbers change significantly. IPU member parliaments provide information on changes and updates to the IPU secretariat. After each general election or renewal, a questionnaire is dispatched to parliaments to solicit the latest available data. If no response is provided, other methods are used to obtain the information, such as from the electoral management body, parliamentary websites, or Internet searches. Additional information gathered from other sources is regularly crosschecked with parliament.

3.c. Data collection calendar

Data are updated on a monthly basis, up to the last day of the month.

3.d. Data release calendar

Data are updated on a monthly basis, up to the last day of the month.

3.e. Data providers

National parliaments

3.f. Data compilers

Inter-Parliamentary Union (IPU)

3.g. Institutional mandate

The Inter-Parliamentary Union (IPU) is the global organization of parliaments. It was founded in 1889 as the first multilateral political organization in the world, encouraging cooperation and dialogue between all nations. Today, the Inter-Parliamentary Union (IPU) comprises 179 national Member Parliaments and 13 regional parliamentary bodies. It promotes democracy and helps parliaments become stronger, younger, gender-balanced, and more diverse.

The IPU recognizes gender equality as a key component of democracy. It works to achieve equal participation of men and women in politics and supports parliaments in advancing gender equality. This includes the collection and dissemination of quantitative and qualitative data on women in politics. In particular, the IPU has tracked the percentage of women in national parliaments since 1945 and is the authority for this data. See historical and comparative data on women in parliament at https://data.ipu.org/historical-women.

4.a. Rationale

The indicator measures the degree to which women have equal access to parliamentary decision-making. Women’s participation in parliaments is a key aspect of women’s opportunities in political and public life and is linked to women’s empowerment. Equal numbers of women and men in lower chambers would give an indicator value of 50 percent.

A stronger presence of women in parliament allows new concerns to be highlighted on political agendas, and new priorities to be put into practice through the adoption and implementation of policies and laws. The inclusion of the perspectives and interests of women is a prerequisite for democracy and gender equality and contributes to good governance. A representative parliament also allows the different experiences of men and women to affect the social, political, and economic future of societies.

Changes in the indicator have been tracked over time. Although the international community has supported and promoted women’s participation in political decision-making structures for several decades, improvement in women’s access to parliament has been slow. This has led to the introduction of special policies and legal measures to increase women’s shares of parliamentary seats in several countries. Those countries that have adopted special measures generally have greater representation of women in parliament than countries without special measures.

4.b. Comment and limitations

- The number of countries covered varies with suspensions or dissolutions of parliaments. As of 1 February 2016, 193 countries are included.

- There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events that are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are.

- The data excludes the numbers and percentages of women in the upper chambers of parliament. The information is available on the Inter-Parliamentary Union (IPU) website at https://data.ipu.org/women-ranking.

- Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women’s contribution to political decision-making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.

4.c. Method of computation

The proportion of seats held by women in the national parliament is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.

There is no weighting or normalising of statistics.

4.d. Validation

Inter-Parliamentary Union (IPU) member parliaments provide information on changes and updates to the IPU secretariat via IPU Groups within each parliamentary chamber or via the Parline Correspondent’s Network.

Parline Correspondents are staff members of national parliaments who act as the IPU focal point for IPU’s Parline database within each chamber or parliament. Their main role is to make sure that all the data in Parline for their parliament is up‑to‑date and correct, including for this indicator. If no response is provided to questionnaires, other methods are used to obtain the information, such as from the electoral management body, parliamentary websites, or Internet searches. Additional information gathered from other sources is regularly crosschecked with parliaments.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

No adjustments are made for missing values.

• At country level

• At regional and global levels

4.g. Regional aggregations

Regional aggregations are a simple sum of country and chamber level data. A weighting structure is not applied.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Guidance is not required to provide information for this indicator (i.e. current number of members and the total number of women members in a given single or lower chamber of a national parliament).

A “Checklist for Parline Correspondents” is provided to remind parliaments to inform the Inter-Parliamentary Union (IPU) of changes to the number of seats or the total number of women in a parliamentary chamber, every time there is a change.

4.i. Quality management

Data for this indicator is input and housed within the Parline database (data.ipu.org).

The Inter-Parliamentary Union (IPU) has dedicated staff for data collection and management, a Network of Parline Correspondents to provide data updates and a constant exchange with parliaments via IPU groups housed within member parliaments.

4.j. Quality assurance

There is no significant statistical processing required for this indicator aside from checking coherence over time.

4.k. Quality assessment

The Inter-Parliamentary Union’s (IPU) data is housed within the Parline database which automatically generates calculations on the number and percentage of women to ensure accuracy. Exports from the database are utilised for SDG reporting.

5. Data availability and disaggregation

Data availability:

Data are available for 193 countries. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period.

Time series:

According to the Inter-Parliamentary Union (IPU) website the data extraction has changed over time as follows;

2020 – Present As at 1 January

2013 – 2019 As at 1 February

1999 As at 5 February

2002 As at 4 February

2003, 2005 – 2007, 2009 - 2012 As at 31 January

2001, 2004 As at 30 January

2008 As at 29 January

1998, 2000 As at 25 January

1997 As at 1 January

Prior to 1997 Unknown

Disaggregation:

The indicator can be disaggregated for analysis by geographical region and sub-region, legislature type (single or lower, parliamentary or presidential), the method of filling seats (directly elected, indirectly elected, appointed), and the use of special measures.

6. Comparability/deviation from international standards

Sources of discrepancies:

Data are not adjusted for international comparability. Though, for international comparisons, generally only the single or lower house is considered in calculating the indicator.

7. References and Documentation

URL:

https://data.ipu.org/women-ranking

http://www.ipu.org/wmn-e/classif-arc.htm

References:

Inter-parliamentary Union (2008). Equality in Politics: A Survey of Women and Men in Parliaments. Geneva. Available from http://www.ipu.org/english/surveys.htm#equality08

Inter-parliamentary Union (2010). Is Parliament Open to Women? Available from http://www.ipu.org/PDF/publications/wmn09-e.pdf

Inter-parliamentary Union (2011). Gender-Sensitive Parliaments. A Global Review of Good Practice. Available from http://www.ipu.org/pdf/publications/gsp11-e.pdf

Inter-parliamentary Union (2020). Women in parliament: 1995–2020 - 25 years in review. Available from https://www.ipu.org/resources/publications/reports/2020-03/women-in-parliament-1995-2020-25-years-in-review

Inter-parliamentary Union and UN Women (2021). Women in Politics: 2021. Available from https://www.ipu.org/women-in-politics-2021

5.5.1b

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life

0.c. Indicator

Indicator 5.5.1: Proportion of seats held by women in (a) national parliaments and (b) local governments

0.d. Series

Proportion of elected seats held by women in deliberative bodies of local government (%) SG_GEN_LOCGELS

0.e. Metadata update

2023-07-10

0.f. Related indicators

Indicator 5.5.1(a): proportion of seats held by women in single or lower chambers of national parliaments.

0.g. International organisations(s) responsible for global monitoring

United Nations Entity for Gender Equality and the Empowerment of Women (UN-Women)

1.a. Organisation

UN-Women

2.a. Definition and concepts

Definition:

Indicator 5.5.1(b) measures the proportion of positions held by women in local government.

It is expressed as a percentage of elected positions held by women in legislative/ deliberative bodies of local government.

Concepts:

Local government is a result of decentralization, a process of transferring political, fiscal, and administrative powers from the central government to sub-national units of government to regulate and/or run certain government functions or public services, on their own, in the administrative-territorial areas of a country.

The definition of local government follows the 2008 System of National Accounts (SNA) distinction between central, state, and local government (para 4.129). Local government consists of local government units, defined in the SNA as “institutional units whose fiscal, legislative and executive authority extends over the smallest geographical areas distinguished for administrative and political purposes” (para 4.145). What constitutes the local government of a given country is defined by that country’s national legal framework, including national constitutions and local government acts or equivalent legislation.

Each local government unit typically includes a legislative/ deliberative body and an executive body. Legislative/ deliberative bodies, such as councils or assemblies, are formal entities with a prescribed number of members as per national or state legislation. They are usually elected by universal suffrage and have decision-making power, including the ability to issue by-laws, on a range of local aspects of public affairs.

Executive bodies, consisting of an executive committee or a mayor, may be elected or appointed. They prepare and execute decisions made by the legislative/ deliberative body.

Elected positions are the most common manner of selection of local government members. They are selected in local elections, based on a system of choosing political office holders in which the voters cast ballots for the person, persons, or political party that they desire to see elected. The category of elected positions includes both elected persons who competed on openly contested seats and persons selected during the electoral processes on reserved seats or through a candidate quota.

By comparison, members selected for appointed positions (the least common manner of selection of local government members) are nominated, typically by government officials from higher-ranking tiers of government. Appointed members of local government are more frequent among the leadership positions, such as the heads of the executive body, representatives of specific groups (e.g., women, disadvantaged groups, youth); and temporary committees/delegations/caretakers appointed by government officials when a council has been dissolved.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Relevant concepts, definitions and classifications are based the 2008 System of National Accounts (SNA) and the 2020 Handbook on Governance Statistics (Praia City Group on Governance Statistics).

3.a. Data sources

Administrative data based on electoral records are the main source of data on elected members of local government, and the recommended data source for Indicator 5.5.1(b). Electoral records are produced and upheld by Electoral Management Bodies (EMBs) or equivalent bodies tasked with organizing elections at the local level. EMBs are part of the National Statistical Systems and are often specifically mentioned in the national statistics acts as producers of official statistics.

The use of electoral records to measure women’s representation in local government and monitoring of Indicator 5.5.1(b) is cost-effective, straightforward, and timely. No adjustments or estimates are necessary to transform the administrative information into statistics for monitoring the indicator. The conceptual framework at the basis of Indicator 5.5.1(b) is consistent with the conceptual framework at the basis of local elections, as both are provided by the national legal framework. The data used to calculate Indicator 5.5.1(b) refers to information on election winners, disaggregated by sex, and the coverage of the reference population (in this case, the elected officials) should be complete. In countries where electoral records are electronic and centralized, information on the numbers of women and men in elected positions can be made available as soon as the official results of elections are released.

Two other types of sources of data may be used in the few instances where electoral records are not electronic or not centralized. One additional type of source is also administrative and refers to public administration data available to line ministries overseeing local government. However, its use for statistics may be less straightforward compared to centralized electoral records. The scope of public administration records is beyond the elected positions, and information on women and men in elected positions of local government may be mixed with information on public administration employees, which are not covered by this indicator. Therefore, additional data processing and resources may be required to carefully extract the information needed. In some cases, the forms used as the basis for administrative records may need to be modified to ensure recording of the positions as being elected, in legislative/deliberative bodies, as well as the sex of persons in those positions. In other cases, some elected positions may not be covered in the records maintained, for example, if the administrative records are restricted to only those positions that are on the government payroll.

Another type of data source that may provide information on women and men in local government in the absence of centralized electronic election records, refers to existing surveys or censuses using local government units as units of observation. These surveys or censuses may be undertaken by National Statistical Offices and/or line ministries and may take the form of (a) local government censuses or surveys; (b) establishment surveys; and (c) municipality surveys. These surveys/censuses may already include, in the data collection tool dedicated to their main purpose, a few questions on the number of members of local legislative/deliberative and executive bodies by sex and other individual characteristics such as age and education; or may require the integration of such questions. Like other censuses and surveys, a low response rate can result in bias in the statistics obtained. Sampling errors may also add to the bias, in ways that cannot be assessed in the absence of a good understanding of the distribution of women’s and men’s representation across different local government units across the territory of a country.

3.b. Data collection method

The compilation of data, coordinated by UN Women and undertaken with the support of UN Regional Commissions, uses two mechanisms:

Data request forms sent to Electoral Management Bodies (EMBs) and National Statistical Offices (NSOs) directly or through UN Regional Commissions;
On-line dissemination of data by NSS entities who are the primary source of data or in charge with coordination of SDGs, including EMBs and/or NSOs. This process is done in a transparent manner, based on communication with NSS focal points, so that the NSS has a chance to validate or dismiss a country’s compiled data.

3.c. Data collection calendar

After establishing the global baseline, the data will be compiled every year, in January of each year, and/or after local elections have taken place. Countries with new local elections are targeted to avoid overburdening national stakeholders reporting data.

3.d. Data release calendar

Second quarter of the year.

3.e. Data providers

Data are provided by Electoral Management Bodies and/or in coordination with National Statistical Offices.

3.f. Data compilers

UN Women with the support of UN Regional Commissions.

3.g. Institutional mandate

UN Women is committed through its work at the global, regional, and country level to support Member States in filling critical gaps in generating data and using data-based analysis to advance gender equality and women’s empowerment. As part of its mandate, the organization supports Member States in setting and implementing normative standards, coordinates the UN system’s work on gender equality, conducts data-based research, and develops practice-based tools to inform the design of policies and programmes.

4.a. Rationale

Women’s and men’s right to exercise their political rights on an equal basis, and at all levels of decision-making, is recognized in the SDGs and enshrined in many human and political rights declarations, conventions and resolutions agreed to by most countries in the world. Indicator 5.5.1(b) measures the degree to which gender balance has been achieved, and women have equal access to, political decision-making in local government.

Indicator 5.5.1(b) complements Indicator 5.5.1(a) on women in national parliaments, and accounts for the representation of women among the millions of members of local governments that influence (or have the potential to influence) the lives of local communities around the world. All tiers of local government are covered by the indicator, consistent with national legal frameworks defining local government.

4.b. Comment and limitations

Indicator 5.5.1(b) refers to the representation of women among elected positions of legislative/deliberative bodies of local government. This is a strength, because it ensures comparability across countries, at a low cost, and mirrors the SDG indicator measuring women’s representation at the national level, in parliament. This is also a limitation in that the indicator does not consider other positions in local government. Local government officials holding executive positions who are not simultaneously holding a position within the legislative/deliberative body, or who are appointed and not elected, are not considered in this indicator.

It is recommended that women’s representation in executive positions, particularly at the level of the head of the executive (such as the mayor), is monitored separately at national and global levels, but not as a headline SDG indicator.

Importantly, the indicator refers to representation among members of local government and not the quality of their participation. Countries may therefore consider assessing political participation through national or subnational studies involving qualitative and/or quantitative methods of research. Additional indicators of political participation may also be monitored at the national level, such as women’s share among voters and candidates in local elections, to monitor the closing of other gaps in women’s political participation.

Finally, aspects of local governance beyond the formal institutions of local government, such as public administration staff, are not included in indicator 5.5.1(b) and may be covered by other indicators in the SDG framework, particularly within Goal 16 on inclusive societies.

4.c. Method of computation

The method of computation is as follows:

$I n d i c a t o r 5.5.1 (b) = \frac{(N u m b e r o f s e a t s h e l d b y w o m e n) \times 100}{T o t a l n u m b e r o f s e a t s h e l d b y w o m e n a n d m e n}$

Unit:

Percent (%)

4.d. Validation

Data obtained from national stakeholders are checked for consistency against the information on local government organization, electoral and quota systems maintained by UN Women for each country and additional government (Women in Local Government website) and research-based publicly available information provided by UN Women country offices and UN country teams as needed. In addition, in partnership with UN Regional Commissions, new data are checked against data previously reported by country. Potential discrepancies are solved in collaboration with the National Statistical Offices before data are disseminated or used for analysis.

4.e. Adjustments

There are no adjustments to the country data.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Not Imputed

• At regional and global levels

Not Imputed

4.g. Regional aggregations

Regional averages are computed as weighted averages by number of elected local councillors in each country with available data. The following data are not included in the calculations: outdated data (data older than 7 years in countries with new elections) and data on positions that are not covered by the indicator (appointed positions for example). The number of countries with data used to calculate the averages is indicated in data footnotes. The reference date for global and regional averages is 1 January.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

- Webinars and workshops with national data providers are organized periodically by UN Women in collaboration with UN Regional Commissions.

- International guidance on data sources relevant for the indicator is provided in handbooks and manuals under the coordination of the Praia City Group on Governance Statistics, including in the 2020 Handbook on Governance Statistics.

- Information on local government organization, electoral systems and legislated electoral quotas, for each country, is available at Women in Local Government Website.

4.i. Quality management

UN Women and UN Regional Commissions have teams dedicated to data compilation and validation. See also section 4.d for details on data validation and quality checks.

4.j. Quality assurance

See sections 4.d and 4.i. In addition, UN Women is committed to quality assurance of data-based and other knowledge products through internal coordination and peer-review processes.

4.k. Quality assessment

See sections 4.d to 4.j.

5. Data availability and disaggregation

Data availability:

Data on women’s and men’s representation in elected positions of legislative/ deliberative bodies of local government are currently estimated as available for 89 countries in the world. This estimate is based on a count of countries covered by regional databases in Europe, Latin America, and the Caribbean, and ad-hoc studies in Asia and the Pacific. However, the indicator used varies from one region to another. The count of countries is expected to change after the methodology of the indicator is used consistently across countries and regions.

Time series:

Each year in the time series shows data for countries with new elections or new data sources in previous year. By default, reference date is 1 January and exceptions are indicated in the footnotes.

Disaggregation:

Data on elected positions in legislative/deliberative bodies of local government must be disaggregated by sex to enable the calculation of the indicator. No additional disaggregation is required for SDG reporting.

6. Comparability/deviation from international standards

Sources of discrepancies:

There are no discrepancies. Data are reported by entities of National Statistical Systems, including Electoral Management Bodies and National Statistical Offices.

7. References and Documentation

ECLAC, 2016a. CEPALSTAT: Databases and statistical publications. https://oig.cepal.org/en/autonomies/autonomy-decision-making (accessed January-April 2016)

European Commission, 2016a. Database on women and men in decision-making (WMID). http://ec.europa.eu/justice/gender-equality/gender-decision-making/database/index_en.htm (accessed January-April 2016)

European Commission, International Monetary Fund, Organization for Economic Co-operation, and Development, United Nations, and the World Bank, 2009. The 2008 System of National Accounts.

Praia City Group on Governance Statistics, 2020. Handbook on Governance Statistics. https://localgov.unwomen.org/sites/default/files/resource-pdf/2021-04/Handbook_governance_statistics.pdf

UNECE, 2016a. Public life and decision-making database. http://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT__30-GE__05-PublicAnddecision (accessed January-April 2016).

UNDP, 2014. Gender Equality: Women’s participation and leadership in governments at the local level. Asia and the Pacific 2013. Bangkok, UNDP.

United Nations, 2011. Using Administrative and Secondary Sources for Official Statistics: A Handbook of Principles and Practices. UNECE.

UN Women, 2021. Women in Local Government Website. https://localgov.unwomen.org/ (accessed May 2023).

UN Women and UNDP, 2015. Inclusive Electoral Processes: A guide for Electoral Management Bodies on Promoting Gender Equality and Women’s Participation.

5.5.2

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life

0.c. Indicator

Indicator 5.5.2: Proportion of women in managerial positions

0.d. Series

Proportion of managerial positions held by women (13^th ICLS)

Proportion of women in senior and middle management positions (13^th ICLS)

Proportion of managerial positions held by women (19^th ICLS)

Proportion of women in senior and middle management positions (19^th ICLS)

0.e. Metadata update

2023-03-31

0.f. Related indicators

8.5.1

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

This indicator refers to the proportion of females in the total number of persons employed in managerial positions. It is recommended to use two different measures jointly for this indicator: the share of females in (total) management and the share of females in senior and middle management (thus excluding junior management). The joint calculation of these two measures provides information on whether women are more represented in junior management than in senior and middle management, thus pointing to an eventual ceiling for women to access higher-level management positions. In these cases, calculating only the share of women in (total) management would be misleading, in that it would suggest that women hold positions with more decision-making power and responsibilities than they actually do.

Concepts:

- Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the 13^th and 19^th ICLS series for a given country is the operational criteria used to define employment, with two series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other two series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.

- Employment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08), which organizes jobs into a clearly defined set of groups based on the tasks and duties undertaken in the job. For the purpose of this indicator, it is preferable to refer separately to senior and middle management only, and to total management (including junior management). The share of women tends to be higher in junior management than in senior and middle management, so limiting the indicator to a measure including junior management may introduce bias. Senior and middle management correspond to sub-major groups 11, 12 and 13 in ISCO-08 and sub-major groups 11 and 12 in ISCO-88. If statistics are not available disaggregated at the sub-major group level (two-digit level of ISCO), then major group 1 of ISCO-88 and ISCO-08 can be used as a proxy and the indicator would then refer only to total management (including junior management).

2.b. Unit of measure

Percent (%)

2.c. Classifications

Employment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08) as described above.

3.a. Data sources

The recommended source for this indicator is a labour force survey or, if not available, other similar types of household surveys, including a module on employment. In the absence of any labour-related household survey, establishment surveys or administrative records may be used to gather information on the female share of employment by the required ISCO groups. In cases where establishment surveys or administrative records are used, the coverage is likely to be limited to formal enterprises or enterprises of a certain size. Information on the enterprises covered should be provided with the figures. When comparing figures across years, any changes in the versions of ISCO that are used should be taken into account.

3.b. Data collection method

The ILO Department of Statistics processes national household survey microdata in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians (ICLS). For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office (NSO), labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

National statistical offices

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally-comparable datasets, and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

The indicator provides information on the proportion of women who are employed in decision-making and management roles in government, large enterprises and institutions, thus providing some insight into women’s power in decision making and in the economy (especially compared to men's power in those areas).

4.b. Comment and limitations

This indicator's main limitation is that it does not reflect differences in the levels of responsibility of women in these high- and middle-level positions or the characteristics of the enterprises and organizations in which they are employed. Its quality is also heavily dependent on the reliability of the employment statistics by occupation at the ISCO two-digit level.

4.c. Method of computation

Using ISCO-08:

$P r o p o r t i o n o f w o m e n i n s e n i o r a n d m i d d l e m a n a g e m e n t = \frac{(W o m e n e m p l o y e d i n I S C O 08 c a t e g o r y 1 - W o m e n e m p l o y e d i n I S C O 08 c a t e g o r y 14)}{(P e r s o n s e m p l o y e d i n I S C O 08 c a t e g o r y 1 - P e r s o n s e m p l o y e d i n I S C O 08 c a t e g o r y 14)} \times 100$

Which can be also expressed as:

$P r o p o r t i o n o f w o m e n i n s e n i o r a n d m i d d l e m a n a g e m e n t = \frac{(W o m e n e m p l o y e d i n I S C O 08 c a t e g o r i e s 11 + 12 + 13)}{(P e r s o n s e m p l o y e d i n I S C O 08 c a t e g o r i e s 11 + 12 + 13)} \times 100$

And

$P r o p o r t i o n o f w o m e n i n m a n a g e m e n t = \frac{W o m e n e m p l o y e d i n I S C O 08 c a t e g o r y 1}{P e r s o n s e m p l o y e d i n I S C O 08 c a t e g o r y 1} \times 100$

Using ISCO-88:

$P r o p o r t i o n o f w o m e n i n s e n i o r a n d m i d d l e m a n a g e m e n t : = \frac{(W o m e n e m p l o y e d i n I S C O 88 c a t e g o r y 1 - W o m e n e m p l o y e d i n I S C O 88 c a t e g o r y 13)}{(P e r s o n s e m p l o y e d i n I S C O 88 c a t e g o r y 1 - P e r s o n s e m p l o y e d i n I S C O 88 c a t e g o r y 13)} \times 100$

Which can also be expressed as:

$P r o p o r t i o n o f w o m e n i n s e n i o r a n d m i d d l e m a n a g e m e n t : = \frac{(W o m e n e m p l o y e d i n I S C O 88 c a t e g o r i e s 11 + 12)}{(P e r s o n s e m p l o y e d i n I S C O 88 c a t e g o r i e s 11 + 12)} \times 100$

And

$P r o p o r t i o n o f w o m e n i n m a n a g e r i a l p o s i t i o n s : = \frac{W o m e n e m p l o y e d i n I S C O 88 c a t e g o r y 1}{P e r s o n s e m p l o y e d i n I S C O 88 c a t e g o r y 1} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

For a more detailed methodological description, please refer to the ILO modelled estimates methodological overview, available at https://www.ilo.org/ilostat-files/Documents/TEM.pdf.

• At regional and global levels

Regional and global figures are aggregates of the country-level figures including the imputed values.

4.g. Regional aggregations

The aggregates are derived from the ILO modelled estimates that are used to produce global and regional estimates of, amongst others, employment by occupation and gender, with employment based on the 13^th ICLS standards. These models use multivariate regression and cross-validation techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. The regional and global proportions of women in managerial positions are obtained by first adding up, across countries, the numerator and denominator of the formula that defines the proportion of women in managerial positions - outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to compute the rate for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://www.ilo.org/ilostat-files/Documents/TEM.pdf.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

In order to calculate this indicator, data on employment by sex and occupation is needed, using at least the 2-digit level of the ISCO. This data are collected at the national level mainly through labour force surveys (or other types of household surveys with an employment module). For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question.

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators ( https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm)
ILO Manual – Decent Work Indicators, Concepts and Definitions – Chapter 8, Equal opportunity and treatment in employment http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm (second version, page 146)
Resolution concerning statistics of work, employment and labour underutilization https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
International Standard Classification of Occupations 2008 (ISCO-08) http://www.ilo.org/public/english/bureau/stat/isco/isco08/
ILOSTAT Database (https://ilostat.ilo.org/)

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published.

5. Data availability and disaggregation

Data availability:

Data on proportion of women in managerial positions is available for 189 countries and territories in the 13^th ICLS series and 107 countries and territories in the 19^th ICLS series.

Data on women in senior and middle management positions is available for 139 countries and territories in the 13^th ICLS series and 80 countries and territories in the 19^th ICLS series.

Time series:

Data for this indicator is available as of 2000 in the SDG Indicators Global Database, but time series going back several decades are available in ILOSTAT.

Global and regional data on proportion of women in managerial positions are available from 2000 to 2021.

Disaggregation:

This indicator requires no disaggregation per se, although employment statistics by both sex and occupation are needed to calculate it. If statistics are available and the sample size permits, it may be of interest to cross-tabulate this indicator by economic activity (ISIC) or disaggregate further to observe the share of women across more detailed occupational groups.

6. Comparability/deviation from international standards

Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the ICLS.

Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13^th or 19^th ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series.

7. References and Documentation

ILOSTAT database: https://ilostat.ilo.org
Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm
Decent work indicators - ILO Manual: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---integration/documents/publication/wcms_229374.pdf
ISCO-08: http://www.ilo.org/public/english/bureau/stat/isco/isco08/
Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19^th ICLS: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the 13^th International Conference of Labour Statisticians (October 1982), available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases, available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf

5.6.1

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

Target 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences

0.c. Indicator

Indicator 5.6.1: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care

0.d. Series

Proportion of women who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women aged 15-49 years) SH_FPL_INFM

Proportion of women who make their own informed decisions regarding reproductive health care (% of women aged 15-49 years) SH_FPL_INFMRH

Proportion of women who make their own informed decisions regarding contraceptive use (% of women aged 15-49 years) SH_FPL_INFMCU

Proportion of women who make their own informed decisions regarding sexual relations (% of women aged 15-49 years) SH_FPL_INFMSR

0.e. Metadata update

2022-03-31

0.f. Related indicators

Indicator 3.1.1: Maternal mortality ratio

Indicator 3.1.2: Proportion of births attended by skilled health personnel

Indicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods

Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age group

Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education

0.g. International organisations(s) responsible for global monitoring

United Nations Population Fund (UNFPA)

1.a. Organisation

United Nations Population Fund (UNFPA)

2.a. Definition and concepts

Definition:

Proportion of women aged 15-49 years (married or in union) who make their own decision on all three selected areas i.e. decide on their own health care; decide on use of contraception; and can say no to sexual intercourse with their husband or partner if they do not want. Only women who provide a “yes” answer to all three components are considered as women who make their own decisions regarding sexual and reproductive health. A union involves a man and a woman regularly cohabiting in a marriage-like relationship.

Women’s autonomy in decision-making and exercise of their reproductive rights is assessed from responses to the following three questions:

1. Who usually makes decisions about health care for yourself?

– RESPONDENT

– HUSBAND/PARTNER

– RESPONDENT AND HUSBAND/PARTNER JOINTLY

– SOMEONE ELSE

– OTHER, SPECIFY

2. Who usually makes the decision on whether or not you should use contraception?

– RESPONDENT

– HUSBAND/PARTNER

– RESPONDENT AND HUSBAND/PARTNER JOINTLY

– SOMEONE ELSE

– OTHER, SPECIFY

3. Can you say no to your husband/partner if you do not want to have sexual intercourse?

– YES

– NO

– DEPENDS/NOT SURE

A woman is considered to have autonomy in reproductive health decision making and to be empowered to exercise their reproductive rights if they (1) decide on health care for themselves, either alone or jointly with their husbands or partners, (2) decide on use or non-use of contraception, either alone or jointly with their husbands or partners; and (3) can say no to sex with their husband/partner if they do not want to.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Adopted by 179 governments, the 1994 International Conference on Population and Development (ICPD) Programme of Action marked a fundamental shift in global thinking on population and development issues. It moved away from a focus on reaching specific demographic targets to a focus on the needs, aspirations and rights of individual women and men. The Programme of Action asserted that everyone counts, that the true focus of development policy must be the improvement of individual lives and the measure of progress should be the extent to which we address inequalities. For more information on ICPD Programme of Action, please visit

https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf.

3.a. Data sources

Data are mainly derived from nationally representative Demographic and Health Surveys (DHS). Data sources increasingly include Multiple Indicator Cluster Surveys (MICS) and Generations and Gender Surveys (GGS), and other country-specific household surveys.

3.b. Data collection method

Data is collected in line with the methodology used for the relevant national survey. Data for SDG indicator 5.6.1 may be collected through existing country-specific surveys. For existing national household surveys, it must be ascertained that the sampling design does not systematically exclude subgroups of the population that are important to SDG 5.6.1, specifically, women of reproductive age (15-49) that are currently married or in a union. Surveys that cover only certain population subgroups, such as women who speak the dominant language or women from the main ethnic group, may exclude the experiences of many women. Data on the ethnicity and religion of the survey participants should be collected whenever available. The survey should have a large sample size (usually between 5,000 and 30,000 households), be nationally representative, and be representative, at least, at one administrative level below the national level.

Surveys on unrelated topics may not be good candidates for the incorporation of the SDG 5.6.1 questions. The sensitivity of the topics addressed in health surveys those examining women’s health, makes them a feasible instrument for incorporating questions on women’s experience of decision making in health care, use of contraceptives, and sexual relations for themselves.

To generate data for SDG 5.6.1, all three questions must be included in the survey. The three questions in the Definition section provide generic questions that can be used in country-specific surveys. The first and the second questions should include distinct categories for women making decisions themselves, and women making decisions jointly with their husband/partner.

3.c. Data collection calendar

As per DHS, MICS, GGS and country-specific survey cycles

3.d. Data release calendar

Annual

3.e. Data providers

Agencies responsible for household surveys at national level.

3.f. Data compilers

United Nations Population Fund (UNFPA)

3.g. Institutional mandate

The mandate of UNFPA, as established by the United Nations Economic and Social Council (ECOSOC) in 1973 and reaffirmed in 1993, is (1) to build the knowledge and the capacity to respond to needs in population and family planning; (2) to promote awareness in both developed and developing countries of population problems and possible strategies to deal with these problems; (3) to assist their population problems in the forms and means best suited to the individual countries' needs; (4) to assume a leading role in the United Nations system in promoting population programmes, and to coordinate projects supported by the Fund.

At the International Conference on Population and Development (ICPD), held in Cairo in 1994, these broad ideas were elaborated to emphasize the gender and human rights dimensions of population. UNFPA was given the lead in helping countries carry out the Programme of Action adopted by 179 governments at the Cairo Conference. In 2010, the United Nations General Assembly extended the ICPD beyond 2014, which was the original end date for the 20-year Programme of Action.

4.a. Rationale

Women’s and girls’ autonomy in decision-making about sexual and reproductive health services, contraceptive use, and consensual sexual relations is key to their empowerment and the complete exercise of their reproductive rights.

Women who make their own decision regarding seeking healthcare for themselves are considered empowered to exercise their reproductive rights.

Regarding decision-making on the use of contraception, a clearer understanding of women empowerment is obtained by looking at the indicator from the perspective of decisions being made “mainly by the partner”, as opposed to a decision being made “by the woman alone” or “by the woman jointly with the partner”. Depending on the type of contraceptive method being used, a decision by the woman “alone” or “jointly with the partner” does not always entail that the woman is empowered or has bargaining skills. Conversely, it is safe to assume that a woman that does not participate, at all, in making contraceptive choices is disempowered as far as sexual and reproductive decisions are concerned.

A woman’s ability to say no to her husband/partner if she does not want to have sexual intercourse is well aligned with the concept of sexual autonomy and women’s empowerment.

4.b. Comment and limitations

Until recently, the indicator captured results for married and in-union women and adolescent girls of reproductive age (15–49 years old) who are using any type of contraception. In the phase of the national Demographic and Health Survey (DHS–7) and later rounds, as well as in other data collection instruments including the MICS and GGS, the questionnaire is extended to respondents whether they are using contraception or not. The measure does not cover women and girls that are not married or in a union, as they do not usually make “joint decisions” on their health care with their partners.

As of early 2022, a total of 64 countries, the majority in sub-Saharan Africa, have at least one survey with data on all three questions necessary for calculating Indicator 5.6.1. 28 countries have at least 2 data points between 2006 and 2020. Broader data sources are needed, and efforts to increase data coverage are underway.

In many national contexts, household surveys, which are the main data source for this indicator, exclude the homeless and are likely to under-enumerate linguistic or religious minority groups.

4.c. Method of computation

Numerator: Number of married or in union women and girls aged 15-49 years old:

– for whom decision on health care for themselves is not usually made by the husband/partner or someone else; and

– for whom the decision on contraception is not mainly made by the husband/partner; and

– who can say no to sex.

Only women who satisfy all three empowerment criteria are included in the numerator.

Denominator: Total number of women and girls aged 15-49 years old, who are married or in union.

Proportion = (Numerator/Denominator) * 100

4.d. Validation

Annual country consultation on new and existing data that were calculated from survey microdata sets was conducted in the first three year of the SDG reporting. Countries are encouraged to publish indicator data in the survey reports.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

No attempt from UNFPA to provide and publish estimates for individual countries or areas when country or area data are not available.

• At regional and global levels

Regional aggregates are based on countries where data are available within the region. They should not be treated as country-level estimates for countries with missing values within the region.

4.g. Regional aggregations

Global and regional aggregates are computed as weighted averages of country-level data. The weighting is based on the estimated population of married women aged 15-49 who are using any type of contraception in the reporting year. The estimates of the number of women married/ in union and contraceptive prevalence rate are obtained from UN Population Division.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

For more information, please refer to https://www.unfpa.org/sdg-5-6. Further guidelines on collecting data for SDG 5.6.1 in national household surveys is available upon request.

4.i. Quality management

UNFPA has released technical guidance on core questions for collecting data for SDG indicator 5.6.1, and provides technical support through UNFPA regional and country offices to strengthen national monitoring of women's decision making on sexual and reproductive health.

4.j. Quality assurance

UNFPA maintains the global database on SDG 5.6.1. Before including any national data in the global database, UNFPA technical focal points thoroughly assess the survey methodology used to collect SDG 5.6.1 data to determine the level of comparability across countries and over time in a specific country.

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

Currently, 64 countries have at least one survey with data on all the questions above which are necessary for calculating Indicator 5.6.1. The 64 countries with data are distributed as follows:

Central Asia and Southern Asia (7)
Eastern Asia and South-eastern Asia (5)
Northern America and Europe (5)
Western Asia and Northern Africa (3)
Latin America and the Caribbean (7)
Sub-Saharan Africa (36)
Oceania (1)

Several countries have only one or two of the three questions needed to calculate Indicator 5.6.1. UNFPA engages with major international and regional survey programs, as well as national and international organizations and agencies to incorporate the questions in relevant household surveys to cover all countries on a global scale.

Time series:

Currently data comes from household surveys which have three to five- year cycles.

Disaggregation:

Based on available household survey data, disaggregation is possible by age, geographic location, place of residence, education, and wealth quintile.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable

7. References and Documentation

URL:

https://www.unfpa.org/sdg-5-6

References:

International Conference on Population and Development Programme of Action

https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf.

5.6.2

0.a. Goal

Goal 5: Achieve gender equality and empower all women and girls

0.b. Target

0.c. Indicator

0.d. Series

Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education (%) SH_LGR_ACSRHE

(S.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 1: Maternity Care (%) SH_LGR_ACSRHES1

(S.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 2: Contraceptive and Family Planning (%) SH_LGR_ACSRHES2

(S.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 3: Sexuality Education (%) SH_LGR_ACSRHES3

(S.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 4: HIV and HPV (%) SH_LGR_ACSRHES4

(S.1.C.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 1: Maternity Care (%) SH_LGR_ACSRHEC1

(S.1.C.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 2: Life Saving Commodities (%) SH_LGR_ACSRHEC2

(S.1.C.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 3: Abortion SH_LGR_ACSRHEC3

(S.1.C.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 4: Post-Abortion Care (%) SH_LGR_ACSRHEC4

(S.2.C.5) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 5: Contraceptive Services (%) SH_LGR_ACSRHEC5

(S.2.C.6) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 6: Contraceptive Consent (%) SH_LGR_ACSRHEC6

(S.2.C.7) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 7: Emergency Contraception (%) SH_LGR_ACSRHEC7

(S.3.C.8) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 8: Sexuality Education Curriculum Laws (%) SH_LGR_ACSRHEC8

(S.3.C.9) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 9: Sexuality Education Curriculum Topics (%) SH_LGR_ACSRHEC9

(S.4.C.10) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 10: HIV Counselling and Test Services SH_LGR_ACSRHEC10

(S.4.C.11) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 11: HIV Treatment and Care Services (%) SH_LGR_ACSRHEC11

(S.4.C.12) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 12: HIV Confidentiality (%) SH_LGR_ACSRHEC12

(S.4.C.13) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 13: HPV Vaccine (%) SH_LGR_ACSRHEC13

0.e. Metadata update

2022-03-31

0.f. Related indicators

Indicator 3.1.1: Maternal mortality ratio

Indicator 3.1.2: Proportion of births attended by skilled health personnel

Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations

Indicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods

Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age group

Indicator 5.6.1: Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care.

0.g. International organisations(s) responsible for global monitoring

United Nations Population Fund (UNFPA)

1.a. Organisation

United Nations Population Fund (UNFPA)

2.a. Definition and concepts

Definition:

Sustainable Development Goal (SDG) Indicator 5.6.2 seeks to measure the extent to which countries have national laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information, and education.

The indicator is a percentage (%) scale of 0 to 100 (national laws and regulations exist to guarantee full and equal access), indicating a country’s status and progress in the existence of such National laws and regulations. Indicator 5.6.2 measures only the existence of laws and regulations; it does not measure their implementation.

Concepts:

Laws: laws and statutes are official rules of conduct or action prescribed, or formally recognized as binding, or enforced by a controlling authority that governs the behavior of actors (including people, corporations, associations, government agencies). They are adopted or ratified by the legislative branch of government and may be formally recognized in the Constitution or interpreted by courts. Laws governing sexual and reproductive health are not necessarily contained in one law.

Regulations: are executive, ministerial, or other administrative orders or decrees. At the municipal level, regulations are sometimes called ordinances. Regulations and ordinances issued by governmental entities have the force of law, although circumscribed by the level of the issuing authority. Under this methodology, only regulations with the national-level application are considered.

Restrictions: many laws and regulations contain restrictions in the scope of their applicability. Such restrictions, which include, though are not limited to, those by age, sex, marital status, and requirement for third party authorization, represent barriers to full and equal access to sexual and reproductive health care, information, and education.

Plural legal systems: are defined as legal systems in which multiple sources of law co-exist. Such legal systems have typically developed over a period because of colonial inheritance, religion, and other socio-cultural factors. Examples of sources of law that might co-exist under a plural legal system include English common law, French civil or other law, statutory law, and customary and religious law. The co-existence of multiple sources of law can create fundamental contradictions in the legal system, which result in barriers to full and equal access to sexual and reproductive health care, information, and education.

“Guarantee” (access): for this methodology, “guarantee” is understood as a law or regulation that assures a particular outcome or condition. The methodology recognizes that laws can only guarantee “in principle”; for the outcomes to be fully realized in practice, additional steps, including policy and budgetary measures will need to be in place.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Adopted by 179 governments, the 1994 International Conference on Population and Development (ICPD) Programme of Action (PoA) marked a fundamental shift in global thinking on population and development issues. It shifted from an emphasis on meeting particular demographic targets to a focus on individual women's and men's needs, aspirations, and rights.. The PoA asserted that everyone counts, that the true focus of development policy must be the improvement of individual lives and the measure of progress should be the extent to which we address inequalities. For more information on ICPD PoA, please visit https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf

3.a. Data sources

Indicator 5.6.2 is calculated based on official government responses collected through the United Nations Inquiry among Governments on Population and Development. The Inquiry has been conducted since 1963. All questions required for indicator 5.6.2 are integrated into Module II on fertility, family planning, and reproductive health of the Inquiry.

3.b. Data collection method

The Inquiry is sent to the Permanent Missions by UN Population Division (DESA). UNFPA then follow-up with UNFPA Country Offices to facilitate the data submissions from national governments.

3.c. Data collection calendar

Baseline data was collected in 2019 through the 12^th Inquiry and a second round was collected in 2021-2022 through the 13^th inquiry. Further data collection will be scheduled every 4 years.

3.d. Data release calendar

Every 4 years.

3.e. Data providers

Data will be provided by relevant government ministries, departments and agencies.

3.f. Data compilers

United Nations Population Fund (UNFPA), in collaboration with UN Population Division.

3.g. Institutional mandate

At the International Conference on Population and Development (ICPD), held in Cairo in 1994, these broad ideas were elaborated to emphasize the gender and human rights dimensions of population. UNFPA was given the lead in helping countries carry out the Programme of Action (PoA) adopted by 179 governments at the Cairo Conference. In 2010, the United Nations General Assembly extended the ICPD beyond 2014, which was the original end date for the 20-year PoA.

4.a. Rationale

Indicator 5.6.2 seeks to provide the first comprehensive global assessment of legal and regulatory frameworks in line with the 1994 International Conference on Population and Development (ICPD) Programme of Action (PoA)^[1], the Beijing Platform for Action^[2], and international human rights standards^[3]. The indicator measures the legal and regulatory environment across four thematic sections, defined as the key parameters of sexual and reproductive health care, information and education according to these international consensus documents and human rights standards:

Maternity care
Contraception services
Sexuality education
HIV and HPV

Each of the four thematic areas (sections) is represented by individual components, reflecting topics that are: i) critical from a substantive perspective, ii) span a broad spectrum of sexual and reproductive health care, information, and education, and iii) the subject of national legal and regulatory frameworks. In total, Indicator 5.6.2 measures 13 components, categorized as follows:

SECTION I: MATERNITY CARE

Component 1. Maternity care

Component 2. Life-saving commodities

Component 3. Abortion

Component 4. Post-abortion care

SECTION II: CONTRACEPTION SERVICES

Component 5. Contraception

Component 6. Consent for contraceptive services

Component 7. Emergency contraception

SECTION III: SEXUALITY EDUCATION

Component 8. CSE law

Component 9. CSE curriculum

SECTION IV: HIV and HPV

Component 10. HIV testing and counselling

Component 11. HIV treatment and care

Component 12. Confidentiality of health status for men and women living with HIV

Component 13. HPV vaccine

For each of the 13 components, information is collected on the existence of i) specific legal enablers (positive laws, and regulations) and ii) specific legal barriers^[4]. Such barriers encompass restrictions to positive laws, and regulations (e.g. by age, sex, marital status and requirement for third party authorization), as well as plural legal systems that contradict co-existing positive laws and regulations. For each component, the specific enablers and barriers on which data are collected are defined as the principle enablers and barriers for that component. Even where positive laws are in place, legal barriers can undermine full and equal access to sexual and reproductive health care, information, and education; the methodology is designed to capture this.

The percentage value reflects a country’s status and progress in the existence of national laws and regulations that guarantee full and equal access to sexual and reproductive health care, information, and education. By reflecting the “extent to which” countries guarantee full and equal access to sexual and reproductive health care, information, and education, this indicator allows cross-country comparison and within-country progress over time to be captured.

United Nations (1994) International Conference on Population and Development: Programme of Action. Cairo, Egypt. ↑

United Nations (1995) Fourth World Conference on Women: Programme of Action. Beijing, China. ↑

CEDAW General Recommendation no. 24. Accessed online 24 May 2018: http://www.refworld.org/docid/453882a73.html; CEDAW General Comment no. 35 (2017). Accessed online 23May 2018: http://tbinternet.ohchr.org/Treaties/CEDAW/Shared%20Documents/1_Global/CEDAW_C_GC_35_8267_E.pdf; CESCR General Comment no. 14. Accessed online 23 May 2018: http://www.refworld.org/pdfid/4538838d0.pdf; CESCR General Comment no. 20. Accessed 24 May 2018: http://www.refworld.org/docid/4a60961f2.html; CESCR General Comment no. 22. Accessed online 23May 2018: https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health; CRC General Comment No. 15. Accessed 24 May 2018: http://www.refworld.org/docid/51ef9e134.html; CRPD Articles 23 and 25. Accessed online 24 May 2018: https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities/convention-on-the-rights-of-persons-with-disabilities-2.html. ↑

⁴

Legal barriers are not deemed applicable for the two operational components: C2: life-saving commodities and C9: CSE curriculum. ↑

4.b. Comment and limitations

Indicator 5.6.2 measures exclusively the existence of laws and regulations and their barriers. It does not measure the implementation of such laws/regulations. In addition, the 13 components are intended to be indicative of sexual and reproductive health care, information, and education, instead of a complete or exhaustive list of the care, information, and education. These components were selected because they were identified as key parameters according to international consensus documents and human rights standards.

4.c. Method of computation

The indicator measures specific legal enablers and barriers for 13 components across four sections. The calculation of the indicator requires data for all 13 components.

The 13 components are placed on the same scale, with 0% being the lowest value and 100% being the most optimal value. Each component is calculated independently and weighted equally. Each component is calculated as:

$C i = (\frac{e i}{E i} - \frac{b i}{B i}) \times 100$

where;

$C i$ : Data for component i

$E i$ : Total number of enablers in component i

$e i$ : Number of enablers that exist in component i

$B i$ : Total number of barriers in component i

$b i$ : Number of barriers that exist in component i

As legal barriers are not deemed applicable for C2: life-saving commodities and C9: CSE curriculum, they are calculated as:

$C i = \frac{e i}{E i} \times 100$

where;

$C i$ : Data for component i

$E i$ : Total number of enablers in component i

$e i$ : Number of enablers that exist in component i

In addition, as C3: Abortion collects information on four types of legal ground (to save a woman’s life, to preserve a woman’s health, in cases of rape, and in cases of fetal impairment), and that the legal barriers apply to each type, it is calculated as:

$C i = \frac{e i}{E i} (1 - \frac{b i}{B i}) \times 100$

where;

$C i$ : Data for component i

$E i$ : Total number of enablers in component i

$e i$ : Number of enablers that exist in component i

$B i$ : Total number of barriers in component i

$b i$ : Number of barriers that exist in component i

Value for Indicator 5.6.2 is calculated as the arithmetic mean of the 13-component data. Similarly, the value for each section is calculated as the arithmetic mean of its constituent component data.

4.d. Validation

Country consultation is conducted for every round of data collection. Indicator data and methodology are shared back with National governments together with the original submissions. Indicator 5.6.2 relies on official responses provided by National governments. UNFPA may follow up with national governments and request further information if the responses differ from country-specific information on legal and regulatory developments on issues about respective mandates of key stakeholders including UN Country teams and UN agencies. UNFPA also encourages each country to establish a national validation committee to review and validate all input from the Inquiry.

4.e. Adjustments

No adjustments are made at the global level.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

No imputation will be made for a country with missing data.

• At regional and global levels:

No imputation will be made at regional and global levels.

4.g. Regional aggregations

Global and regional aggregates are computed as unweighted averages of country-specific data for constituent countries.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Indicator 5.6.2 is calculated based on official government responses collected through the United Nations Inquiry among Governments on Population and Development. The Inquiry, mandated by the General Assembly in its resolution 1838 (XVII) of 18 December 1962, has been conducted by the Secretary-General since 1963. All questions required for indicator 5.6.2 are integrated into Module II on fertility, family planning and reproductive health of the Inquiry.

4.i. Quality management

Not applicable

4.j. Quality assurance

Indicator 5.6.2 relies on official responses provided by national governments. UNFPA performs quality checks and follows-up with national governments, requesting further information if the responses differ from country-specific information on legal and regulatory developments on issues about respective mandates of key stakeholders including UN Country teams and UN agencies, or if the responses are incomplete or differ from the government’s responses to a previous Inquiry. UNFPA also encourages each country to establish a national validation committee to review and validate all input from the Inquiry.

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

153 countries have complete or partial data for indicator 5.6.2, covering 89 percent of the world’s population. A total of 115 countries have complete data, allowing calculation of data for indicator 5.6.2.

Time series:

Not applicable

Disaggregation:

Data will be disaggregated by section and component. This will enable countries to identify the areas of sexual and reproductive health care, information and education in which progress is required.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable, as indicator 5.6.2 relies on official data provided by National governments, and no estimation is produced at the international level.

7. References and Documentation

https://www.unfpa.org/sdg-5-6

References:

United Nations (1994) International Conference on Population and Development: Programme of Action. Cairo, Egypt.

United Nations (1995) Fourth World Conference on Women: Programme of Action. Beijing, China.

6.a.1

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.a: By 2030, expand international cooperation and capacity-building support to developing countries in water- and sanitation-related activities and programmes, including water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies

0.c. Indicator

Indicator 6.a.1: Amount of water- and sanitation-related official development assistance that is part of a government-coordinated spending plan

0.e. Metadata update

2017-07-11

0.f. Related indicators

6.5:

Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

7.a:

Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

13.b:

Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

15.9:

Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

Comments:

6.5 (implement integrated water resources management at all levels, including transboundary cooperation as appropriate) 7.a (enhance international cooperation to facilitate access to clean energy research and technology) 13.b (mechanisms for raising capacity for climate change-related planning and management, focusing on women, youth and local and marginalized communities) 15.9 (integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts).

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Environment Programme (UNEP)

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

World Health Organization (WHO)

United Nations Environment Programme (UNEP)

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definition:

Amount of water- and sanitation-related official development assistance that is part of a government-coordinated spending plan is defined as the proportion of total water and sanitation-related Official Development Assistance (ODA) disbursements that are included in the government budget.

Concepts:

“International cooperation and capacity-building support” implies aid (most of it quantifiable) in the form of grants or loans by external support agencies. The amount of water and sanitation-related Official Development Assistance (ODA) can be used as a proxy for this, captured by OECD Creditor Reporting System (CRS). ODA is defined as flows of official financing administered with the promotion of the economic development and welfare of developing countries as the main objective, and which are concessional in character with a grant element of at least 25 per cent (using a fixed 10 per cent rate of discount). By convention, ODA flows comprise contributions of donor government agencies, at all levels, to developing countries (“bilateral ODA”) and to multilateral institutions. ODA receipts, from a recipient perspective, comprise disbursements by bilateral donors and multilateral institutions. Lending by export credit agencies—with the pure purpose of export promotion—is excluded (see http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm).

“Developing countries” refer to countries, which are eligible to receive official development assistance (see http://www.oecd.org/dac/stats/daclist.htm). This limits the scope of reporting to those countries receiving water and sanitation ODA, and the number of such countries is expected to decrease going forward.

Water and sanitation-related activities and programmes include those for water supply, sanitation and hygiene (WASH) (targets 6.1, 6.2), wastewater and water quality (6.3), water efficiency (6.4), water resource management (6.5), and water-related ecosystems (6.6). As per target 6.a wording, it includes activities and programmes for water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies.

A government coordinated spending plan is defined as a financing plan/budget for the water and sanitation sector, clearly assessing the available sources of finance and strategies for financing future needs.

3.a. Data sources

The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) provides information on governance, monitoring, human resources, and financing in the water, sanitation, and hygiene (WASH) sector. The UN-Water GLAAS survey is currently conducted on a biennial basis, led by WHO, and collected data from 94 countries (predominantly low and lower-middle income countries) in the most recent cycle in 2013-2014. The scope of the question on external funding has been expanded beyond WASH for the 2016-17 GLAAS cycle to include wastewater and water quality, water efficiency, water resource management, and the status of water-related ecosystems. GLAAS has completed three full cycles (2009-2010, 2011-2012, and 2013-2014), as well as a pilot conducted in 2008.

National governments participating in the GLAAS survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process, it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation.

The OECD Development Assistance Committee (DAC) has been collecting data on aid flows since 1973 through the OECD Creditor Reporting System based on a standard methodology and agreed definitions from member countries and other aid providers. The data are generally obtained on an activity level, and include numerous parameters to allow disaggregation by provider and recipient country, by type of finance, and by type of resources provided. Data are available for essentially all high-income countries as bilateral donors, and for an increasing number of middle-income aid providers, as well as multi-lateral lending institutions. Methodology on ODA data collection by OECD can be found here: http://www.oecd.org/dac/stats/methodology.htm

The data will be complemented by Integrated Water Resources Management (IWRM) reporting in SDG target 6.5 (for wastewater and water quality, water efficiency, water resource management, and the status of water-related ecosystems) (UNEP 2016). The analysis of IWRM has been done in the past by UN-Water in 2008 (led by UN-DESA) and in 2012 (led by UNEP, UNDP, GWP and SIWI) as requested by the UN Commission for Sustainable Development (UN-Water 2008, 2012).

3.b. Data collection method

National governments participating in the UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process (e.g. Ministry of Water, Ministry of Environment, etc. depending on country), it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation. For each GLAAS submission, information on the country processes is collected (number of ministries involved, whether a national meeting was held to support the filling of the questionnaire, stakeholder validation, use of documentation, etc.). Once received, the country submission undergoes a thorough data validation process, which is often an iterative process requiring communication and feedback with regional and country counterparts.

Countries are also requested to provide consent to publish individual, validated data responses as supplied to GLAAS. Thus through the data collection, validation and consultation processes, the results are expected to be comparable and no further adjustments are foreseen.

3.c. Data collection calendar

The current round of GLAAS has been launched and data for 2015 ODA disbursements channelled through national government budgets will be available by end-2016. OECD data on ODA disbursements for 2015 will be made available through CRS in December 2016. (From NA to NA)

3.d. Data release calendar

Q1 2017

3.e. Data providers

Ministries with responsibilities related to finance, water supply and sanitation, agriculture, water resources development and management, environment, and foreign affairs

3.f. Data compilers

WHO and OECD, with support from UNEP

4.a. Rationale

The amount of water and sanitation-related Official Development Assistance (ODA) is a quantifiable measurement as a proxy for “international cooperation and capacity development support” in financial terms. It is essential to be able to assess ODA in proportion with how much of it is included in the government budget to gain a better understanding of whether donors are aligned with national governments while highlighting total water and sanitation ODA disbursements to developing countries over time.

A low value of this indicator (near 0%) would suggest that international donors are investing in water and sanitation related activities and programmes in the country outside the purview of the national government. A high value (near 100%) would indicate that donors are aligned with national government and national policies and plans for water and sanitation.

4.b. Comment and limitations

Data on water and sanitation-related ODA included in the government budget will be available by end-2016 with the current cycle of UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) data. Until then, total water and sanitation-related ODA (denominator) will be reported. Total water and sanitation-related ODA will continue to be reported as an additional indicator going forward.

In addition, the proportion of ODA channelled through the government treasury will be reported as an additional indicator. ODA channelled through treasury indicates a high level of cooperation and alignment between donors and national government in which the donors channel funds through the national budget process.

The OECD Creditor Reporting System (CRS) currently disaggregates ODA for the water and sanitation among several categories including: sector policy and administration, water resources protection, large and basic water and sanitation systems, river basin infrastructure, waste management, agricultural water resources, and education and training. While these categories do not align directly with the target areas of SDG 6 individually, which limits the disaggregation of ODA among the SDG target areas, the combined ODA from these categories does align with a majority of the reported ODA to the water sector.

As the numerator and denominator come from different sources, there is the possibility of different underlying assumptions regarding what should be included/excluded in the ODA figures. This could lead to situations in which the proportion of ODA included in government budget is greater than 1 (100%) if total ODA reported to OECD is lower than ODA reported to be included the budget. To guard against this possibility, the OECD will supply GLAAS with the reported ODA figures, broken down to the project level, so that respondents can match these with their on-budget project data.

ODA represents only one aspect of international cooperation. To capture other dimensions, additional supporting indicators are available, including indicators for the Collaborative Behaviours identified by the Sanitation and Water for All (SWA) partnership. Each behaviour has one or two key indicators for governments and for development partners. If the behaviours are jointly adapted by governments and development partners, long-term sector performance and sustainability would improve. For additional information on the Collaborative Behaviours see: http://sanitationandwaterforall.org/about/the-four-swa-collaborative-behaviours/

4.c. Method of computation

The indicator is computed as the proportion of total water and sanitation-related ODA that is included in the government budget, i.e. the amount of water and sanitation-related ODA in the government budget divided by the total amount of water and sanitation-related ODA.

The numerator on water and sanitation-related ODA in the government budget will be obtained from the UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) survey for the 2016-2017 cycle. The question on external funding collects data on the amount of donor funds that were included in government budget. Data for 2015 ODA disbursements through GLAAS will be available by end-2016. The scope of the question on external funding has been expanded beyond WASH for the 2016-17 cycle to address all targets under SDG 6, including wastewater and water quality, water efficiency, water resource management, and water-related ecosystems.

The denominator on total water and sanitation-related ODA disbursements will be obtained through OECD Creditor Reporting System (CRS) (purpose codes 14000-series for the water sector and purpose code 31140 for agricultural water resources). Data on ODA disbursements for 2015 will be made available through CRS in December 2016.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Due to the highly country- and context-specific nature of ODA disbursements and whether they are aligned with national government plans, no estimates are produced for countries that are missing data.

• At regional and global levels

If no data is provided for the amount of ODA included in the budget, then the country is excluded from the regional and/or global analysis.

4.g. Regional aggregations

Global and regional aggregates for ODA are derived based on summation of recipient country ODA disbursement for the water sector (purpose codes 14000- series) and agricultural water resources (purpose code 31140) from the OECD Creditor Reporting System.

Global and regional proportions of ODA disbursements as part of a government budget are derived for countries based on a summation of ODA for the water sector that is included in the budget divided by a summation of total ODA for water sector. The calculation of global and regional aggregates would only be performed for those countries reporting the amount of ODA for the water sector that is included in the budget. If no data is provided for the amount of ODA in the budget, then the country is excluded from the regional and/or global analysis.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Questionnaires for providers of development cooperation are available at the following link: http://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/ The data included in the indicator are ODA flows from all donors to developing countries eligible for ODA for the water sector (water and sanitation (purpose codes 14000- series), agricultural water resources (purpose code 31140), flood prevention/control (purpose code 41050), and hydroelectric power plants (purpose code 23220)).

4.j. Quality assurance

Data are collected using a converged reporting system whereby bilateral and multilateral providers of development co-operation use a single file format (Creditor Reporting System – CRS) to report at item level on all flows of resources to developing countries. Item-level reporting is validated against key aggregates also reported by donors and then serves as the basis for producing various other aggregate statistics. For further details, see: http://www.oecd.org/dac/stats/methodology.htm

5. Data availability and disaggregation

Data availability:

Asia and Pacific: Most countries (at least 80% of the countries covering 90% of the population from the region)

Africa: Most countries (at least 80% of the countries covering 90% of the population from the region)

Latin America and the Caribbean: Most countries (at least 80% of the countries covering 90% of the population from the region)

Europe, North America, Australia, New Zealand and Japan: Some countries

Please note that these reflect availability of data on total water and sanitation ODA. Data on proportion included in government budget will be available through the current cycle of GLAAS (cf. 7.1, 10.1, and 10.2).

Time series:

Time series of parameters under the indicator are available for 2008, 2010, 2012, and 2014.

Disaggregation:

Subsector disaggregation (basic vs. large systems)

6. Comparability/deviation from international standards

Sources of discrepancies:

There may be differences in how much development aid is reported by a recipient country and the amount of ODA disbursed to that country as reported by the OECD-CRS. While OECD captures a significant amount of the aid flows (as reported by external donors) to the water and sanitation sector, countries may receive development aid for water and sanitation from national and international donors that do not report to the OECD-CRS data system. Other differences may occur if recipient countries define development aid more or less rigorously than OECD’s definition of ODA, or use different timeframes (e.g. fiscal year instead of calendar year) to report aid flows. In order to ensure data is as consistent as possible, the OECD will supply the reported ODA figures broken down to the project level, so that respondents can match these with their on-budget project data.

7. References and Documentation

URL:

http://www.who.int/water_sanitation_health/glaas/en/

http://www.unep.org/

http://www.oecd.org/dac/stats/data.htm

References:

- UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water. http://www.who.int/water_sanitation_health/glaas/en/

- UN-Water 2008: Status Report on IWRM for CSD-16, http://www.unwater.org/publications/publications-detail/en/c/206480/UNEP-DHI

- UN-Water 2012: Status Reports on IWRM. http://www.unwater.org/publications/status-report-on-integrated-water-resources-management/en/

- Data from the 2012 Survey on the Application of Integrated Approaches to Water Resources Management. http://www.unepdhi.org/rioplus20

- UNEP 2016. Degree of implementation of integrated water resources management. Draft survey to support SDG indicator 6.5.1 http://www.unepdhi.org/whatwedo/gemi .

Organisation for Economic Co-operation and Development Creditor Reporting System

http://www.oecd.org/dac/stats/data.htm

6.b.1

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.b: Support and strengthen the participation of local communities in improving water and sanitation management

0.c. Indicator

Indicator 6.b.1: Proportion of local administrative units with established and operational policies and procedures for participation of local communities in water and sanitation management

0.e. Metadata update

2017-07-11

0.f. Related indicators

6.5:

Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

15.9:

Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

Comments:

6.5 (implement integrated water resources management at all levels, including transboundary cooperation as appropriate) 15.9 (integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts)

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Environment Programme (UNEP)

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

World Health Organization (WHO)

United Nations Environment Programme (UNEP)

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definition:

The indicator assesses the percentage of local administrative units (as defined by the national government) that have an established and operational mechanism by which individuals and communities can meaningfully contribute to decisions and directions about water and sanitation management.

The indicator Proportion of local administrative units with established and operational policies and procedures for participation of local communities in water and sanitation management is currently being measured by the Proportion of countries with clearly defined procedures in law or policy for participation by service users/communities in planning program in water and sanitation management, and hygiene promotion and the Proportion of countries with high level of users/communities participating in planning programs in water and sanitation management, and hygiene promotion.

Concepts:

Stakeholder participation is essential to ensure the sustainability of water and sanitation management options over time, e.g. the choice of appropriate solutions for a given social and economic context, and the full understanding of the impacts of a certain development decision. Defining the procedures in policy or law for the participation of local communities is vital to ensure needs of all the community is met, including the most vulnerable and also encourages ownership of schemes which in turn contributes to their sustainability.

Local administrative units refers to non-overlapping sub-districts, municipalities, communes, or other local community-level units covering both urban and rural areas to be defined by the government.

Policies and procedures for participation of local communities in water and sanitation management would define a formal mechanism to ensure participation of users in planning water and sanitation activities.

A policy or procedure is considered to be established if the mechanism for participation of local communities is defined in law or has been formally approved and published. It is considered to be operational if the policy or procedure is being implemented, with appropriate funding in place and with means for verifying that participation took place.

‘Water and sanitation’ includes all areas of management related to each of the targets under SDG 6, namely: water supply (6.1), sanitation and hygiene (6.2), wastewater treatment and ambient water quality (6.3), efficiency and sustainable use (6.4), integrated water resources management (6.5) and water-related ecosystems (6.6).

3.a. Data sources

The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) provides information on governance, monitoring, human resources, and financing in the water, sanitation, and hygiene (WASH) sector. The UN-Water GLAAS survey is currently conducted on a biennial basis, led by WHO, and collected data from 94 countries (predominantly low and lower-middle income countries) in the most recent cycle in 2013-2014. The scope of the question on community and user participation has been expanded beyond WASH for the 2016-17 GLAAS cycle to address all targets in SDG 6, including water quality, water rights/allocation, water resource management, and the status of water-related ecosystems. GLAAS has completed three full cycles (2009-2010, 2011-2012, and 2013-2014), as well as a pilot conducted in 2008.

National governments participating in the GLAAS survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process, it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation.

The data will be complemented by Integrated Water Resources Management (IWRM) reporting in SDG target 6.5 (for wastewater and water quality, water efficiency, water resource management, and the status of water-related ecosystems) (UNEP 2016). A key component of IWRM is community participation and management of water resources at the local level. The analysis of IWRM has been done in the past by UN-Water in 2008 (led by UN-DESA) and in 2012 (led by UNEP, UNDP, GWP and SIWI) as requested by the UN Commission for Sustainable Development (UN-Water 2008, 2012).

The OECD Water Governance Initiative (WGI), a technical platform gathering 100+ members from the public, private and non-for-profit sectors, is currently developing a set of Water Governance Indicators, within the implementation strategy of the OECD Principles on Water Governance (OECD 2015a). The Water Governance Indicators are expected to be able to provide additional information on local participation on the basis of an indicators system proposed in OECD (2015b) for measuring “stakeholder engagement for inclusive water governance”. An indicator providing metrics on local participation will be developed and tested by 2017. Data will be made available through interactive platforms and databases in a format to foster policy dialogue and peer learning by 2018. A dedicated publication on “Water Governance at a Glance” will be launched at the 8th World Water Forum in Brasilia (2018).

3.b. Data collection method

National governments participating in the GLAAS survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process (e.g. Ministry of Water, Ministry of Environment, etc. depending on country), it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation. For each GLAAS submission, information on the country processes are collected (number of ministries involved, whether a national meeting was held to support the filling of the questionnaire, stakeholder validation, use of documentation, etc.) Once received, the country submission undergoes a thorough data validation process, which is often an iterative process requiring communication and feedback with regional and country counterparts.

3.c. Data collection calendar

The current round of UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) has been launched and data will be available by end-2016. (From NA to NA)

3.d. Data release calendar

Q1 2017

3.e. Data providers

Ministries with responsibilities related to water supply and sanitation, agriculture, water resources development and management, and environment

3.f. Data compilers

Name:

WHO, OECD and UNEP

Description:

WHO, with support from OECD and UNEP

4.a. Rationale

Defining the procedures in policy or law for the participation of local communities is vital to ensure the needs of all the community are met, including the most vulnerable and also encourages ownership of schemes which in turn contributes to their sustainability.

A low value of this indicator would suggest that participation of local communities in water and sanitation management is low, whereas a high value would indicate high levels of participation, indicating greater ownership and a higher likelihood of sustainable delivery and management of water and sanitation services.

4.b. Comment and limitations

Data on local administrative units with established and operational policies and procedures for local participation is being collected through the current cycle of GLAAS, and will be available by end-2016. Until then, the presence of policies and procedures as reported at the national level for different subsectors will be reported.

Additional data, including data measuring local participation from the OECD Water Governance Indicators and administrative data, will be progressively included in the calculation of the indicator as they become available.

4.c. Method of computation

The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) questionnaire provides information on whether there are “clearly defined procedures in laws or policies for participation by service users (e.g. households) and communities in planning programs”. For countries that have data available from the local administrative unit level, they are asked to provide data on the number of local administrative units for which policies and procedures for local participation (i) exist, and (ii) are operational, as well as (iii) the number of local administrative units assessed, and (iv) the total number of units in the country. The indicator is computed as (ii) the number of local admin units with operation policies and procedures for local participation divided by (iv) the total number of local administrative units in the country.

Both numerator and denominator will be obtained through the GLAAS survey for the 2016-2017 cycle.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Due to the highly country- and context-specific nature of the indicator, no estimates are produced for countries that are missing data.

• At regional and global levels

Operational mechanism by which individuals and communities can meaningfully contribute to water and sanitation management then the country will be excluded from the regional and global estimates for this indicator.

Global and regional estimates for a related indicator on the presence and use of participation policies and procedures at the national level for different water subsectors are also derived to support the target indicator. Similarly, countries with missing values are excluded from global and regional analysis for this indicator.

4.g. Regional aggregations

For global and regional aggregates, the percentage of local administrative units that have a defined and operational mechanism by which individuals and communities can meaningfully contribute to decisions and directions about water and sanitation management will be averaged among countries, with each country’s percent value weighted based on total country population for the data year, as a proportion of the global population.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

National governments participating in GLAAS fill out the country survey, preferably supported by a multi-stakeholder review. Although one ministry leads the process, it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation. GLAAS survey documents for the current cycle can be found at the following link: http://www.who.int/water_sanitation_health/monitoring/investments/glaas-2017-survey/en/

The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) provides information on governance, monitoring, human resources, and financing in the water, sanitation, and hygiene (WASH) sector. The UN-Water GLAAS survey is currently conducted on a biennial basis, led by WHO and has completed three full cycles (2009/2010, 2011/2012, and 2013/2014), as well as a pilot conducted in 2008. GLAAS survey documents for the current cycle of data collection (2016/2017) can be found at the following link: http://www.who.int/water_sanitation_health/monitoring/investments/glaas-2017-survey/en/

4.j. Quality assurance

Once received, the country submission undergoes a thorough data validation process, which is often an iterative process requiring communication and feedback with regional and country counterparts. Quality of the submission is also assessed through an analysis of data collected on country processes (number of ministries involved, whether a national meeting was held to support the filling of the questionnaire, stakeholder validation, use of documentation, etc.) as well as supporting documentation provided. In addition, an external validation with key informants is conducted, in which WASH experts who have not participated in the GLAAS process respond to selected questions from the survey for a specific country within their area of expertise, and agreement with country responses is evaluated.

Data submitted through GLAAS are endorsed by the national government prior to submission. A form (http://www.who.int/entity/water_sanitation_health/monitoring/investments/glaas-consent-form-2016.doc?ua=1) providing consent to WHO for the release and publication of the country data is signed and submitted along with the filled survey.

5. Data availability and disaggregation

Data availability:

Asia and Pacific: Most countries (at least 50% of the countries covering 60% of the population from the region)

Africa: Some countries (approximately 50% of the countries covering 50% of the population from the region)

Latin America and the Caribbean: Most countries (at least 60% of the countries covering 80% of the population from the region)

Europe, North America, Australia, New Zealand and Japan: Most countries (at least 60% of the countries covering 60% of the population from the region)

Please note that these reflect data on presence of policies and procedures for local participation at the national level. Data at the local administrative unit level is being collected through the current cycle of

GLAAS and through administrative data that will be progressively included in the calculation of the indicator (cf. 7.1, 10.1, and 10.2).

Time series:

Time series of parameters under the indicator are available for 2008, 2010, 2012, and 2014.

6. Comparability/deviation from international standards

Sources of discrepancies:

This indicator will be generated by countries, thus no differences in global and national figures are expected.

7. References and Documentation

URL:

http://www.who.int/water_sanitation_health/glaas/en/

http://www.unep.org/

http://www.oecd.org/env/watergovernanceprogramme.htm

References:

UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water. http://www.who.int/water_sanitation_health/glaas/en/

OECD (2015a), OECD Principles on Water Governance, available at: https://www.oecd.org/gov/regional-policy/OECD-Principles-on-Water-Governance-brochure.pdf

OECD (2015b), Stakeholder Engagement for Inclusive Water Governance, OECD Studies on Water, OECD Publishing, Paris., http://dx.doi.org/10.1787/9789264231122-en

UN-Water 2008 : Status Report on IWRM for CSD-16, http://www.unwater.org/publications/publications-detail/en/c/206480/UNEP-DHI

UN-Water 2012: Status Reports on IWRM. http://www.unwater.org/publications/status-report-on-integrated-water-resources-management/en/

Data from the 2012 Survey on the Application of Integrated Approaches to Water Resources Management. http://www.unepdhi.org/rioplus20

UNEP 2016. Degree of implementation of integrated water resources management. Draft survey to support SDG indicator 6.5.1 http://www.unepdhi.org/whatwedo/gemi

OECD 2015. Stakeholder Engagement for Inclusive Water Governance. http://www.oecd-ilibrary.org/governance/stakeholder-engagement-for-inclusive-water-governance_9789264231122-en

6.1.1

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.1: By 2030, achieve universal and equitable access to safe and affordable drinking water for all

0.c. Indicator

Indicator 6.1.1: Proportion of population using safely managed drinking water services

0.e. Metadata update

2021-12-20

0.f. Related indicators

All targets under Goal 6, as well as targets 1.2, 1.4, 2.2, 3.2, 3.8, 3.9, 4a, 5.4 and 11.1

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

1.a. Organisation

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

The proportion of the population using safely managed drinking water services is defined as the proportion of population using an improved drinking water source which is accessible on premises, available when needed and free from faecal and priority chemical contamination. ‘Improved’ drinking water sources include: piped supplies, boreholes and tubewells, protected dug wells, protected springs, rainwater, water kiosks, and packaged and delivered water.

Concepts:

The term ‘drinking water source’ refers to the point where people collect water for drinking and not the origin of the water supplied. For example, water collected from a distribution network that draws water from a surface water reservoir would be classified as piped water, while water collected directly from a lake or river would be classified as surface water.

‘Improved’ drinking water sources include the following: piped water, boreholes or tubewells, protected dug wells, protected springs, rainwater, water kiosks, and packaged or delivered water.

‘Unimproved’ drinking water sources include: unprotected dug wells, unprotected springs, and surface water (rivers, reservoirs, lakes, ponds, streams, canals, and irrigation channels), all of which are by nature of their design and construction unlikely to deliver safe water.

A water source is ‘accessible on premises’ if the point of collection is within the dwelling, compound, yard or plot, or water is delivered to the household.

Drinking water is ‘available when needed’ if households report having ‘sufficient’ water, or water is available ‘most of the time’ (i.e. at least 12 hours per day or 4 days per week).

‘Free from faecal and priority chemical contamination’ requires that drinking water meets international standards for microbiological and chemical water quality specified in the WHO Guidelines for Drinking Water Quality. For the purposes of global monitoring the priority indicator of microbiological contamination is E. coli (or thermotolerant coliforms), and the priority chemical contaminants are arsenic and fluoride.

For detailed guidance on water quality, please refer to the most recent version of the WHO Guidelines for drinking water quality:

https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/water-safety-and-quality/drinking-water-quality-guidelines

2.b. Unit of measure

Proportion of population

2.c. Classifications

WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene has established international standards for classification of drinking water facilities and service levels to benchmark and compare progress across countries (see washdata.org).

3.a. Data sources

Data sources included in the JMP database are:

Censuses, which in principle collect basic data from all people living within a country and are led by national statistical offices.
Household surveys, which collect data from a subset of households. These may target national, rural, or urban populations, or more limited project or sub-national areas. An appropriate sample design is necessary for survey results to be representative, and surveys are often led by or reviewed and approved by national statistical organizations.
Administrative data, which may consist of information collected by government or non-government entities involved in the delivery or oversight of services. Examples include water and sanitation inventories and databases, and reports of regulators.
Other datasets may be available such as compilations by international or regional initiatives (e.g. Eurostat), studies conducted by research institutes, or technical advice received during country consultations.

Access to water, sanitation and hygiene are considered core socio-economic and health indicators, as well as key determinants of child survival, maternal, and children’s health, family wellbeing, and economic productivity. Drinking water, sanitation and hygiene facilities are also used in constructing wealth quintiles used by many integrated household surveys to analyse inequalities between rich and poor. Access to drinking water, sanitation and hygiene are therefore core indicators for many household surveys and censuses. In high-income countries where household surveys or censuses do not collect detailed information on the types of facilities used by households, the JMP relies on administrative records.

Data on availability and quality of drinking water are currently available from both household surveys and from government departments responsible for drinking water supply and regulators. In many low- and middle-income countries, existing water quality data from regulatory authorities is limited, especially for rural areas and populations using non-piped supplies. To complement regulatory data, an increasing number of low- and middle-income countries are collecting nationally representative data on drinking water quality through multi-topic household surveys. Beginning in 2012, a water quality module was developed standardized by the JMP in collaboration with UNICEF’s Multiple Indicator Cluster Survey (MICS) programme. Integration of water quality testing has become a feasible option due to the increased availability of affordable and accurate testing procedures and their adaptation for use by household survey experts. The growing interest in ensuring the implementation of water quality testing in these surveys can, to a large extent, be attributed to the incorporation of drinking water quality in the SDG global indicator for ‘safely managed drinking water services’. Data gaps will be reduced even more as regulation becomes more widespread in low- and middle-income countries.

Some datasets available to the JMP are not representative of national, rural or urban populations, or may be representative of only a subset of these populations (e.g. the population using piped water supplies). The JMP enters datasets into the global database when they represent at least 20% of the national, urban or rural populations. However, datasets representing less than 80% of the relevant population, or which are considered unreliable or inconsistent with other datasets covering similar populations, are not used in the production of estimates (see section 2.6, Data Acceptance in JMP Methodology: 2017 update and SDG baselines).

The population data used by the JMP, including the proportion of the population living in urban and rural areas, are those routinely updated by the UN Population Division (World Population Prospects: https://population.un.org/wpp/; World Urbanization Projects: https://population.un.org/wup))

3.b. Data collection method

The JMP team conducts regular data searches by systematically visiting the websites of national statistical offices, and key sector institutions such as ministries of water and sanitation, regulators of drinking water and sanitation services, etc. Other regional and global databases are also reviewed for new datasets. UNICEF and WHO regional and country offices provides support to identify newly available household surveys, censuses and administrative datasets.

Before publishing, all JMP estimates undergo rigorous country consultations facilitated by WHO and UNICEF country offices. Often these consultations give rise to in-country visits or virtual meetings about data on drinking water, sanitation and hygiene services and the monitoring systems that collect these data.

3.c. Data collection calendar

The JMP begins its biennial data collection cycle in October of even years and publishes estimates during the following year.

3.d. Data release calendar

The SDG Progress Report and relevant data are published every two years since the publication of the baseline report in 2017, usually between March and July of odd years.

3.e. Data providers

National statistics offices; ministries of water, health, and environment; regulators of drinking water service providers.

3.f. Data compilers

WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)

3.g. Institutional mandate

The WHO/UNICEF JMP was established in 1990 to monitor global progress on drinking water, sanitation and hygiene (see washdata.org).

4.a. Rationale

Access to safe drinking water is essential for good health, welfare and productivity and is widely recognized as a human right. Drinking water may be contaminated with human or animal faeces containing pathogens or with chemical and physical contaminants, leading to harmful effects on health. While improving water quality is critical to prevent the transmission of many diseases (such as diarrhoea which exacerbates malnutrition and remains a leading global cause of child deaths), improving the accessibility and availability of drinking water is equally important for health and welfare, particularly for women and girls who often bear the primary responsibility for collecting drinking water from distant sources.

The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) uses a simple improved/unimproved facility type classification that has been refined over time. ‘Improved’ water sources are those that have the potential to deliver safe water by nature of their design and construction, and this metric was used beginning in 2000 to track progress towards MDG target 7c. International consultations since 2011 have established consensus on the need to build on and address the shortcomings of this indicator; specifically, to address normative criteria of the human rights to water and sanitation (UN General Assembly Resolution A/RES/64/292) and concluded that global monitoring should go beyond the basic level of access. As a result, the SDG indicator 6.1.1 is designed to address safe management of drinking water services, including dimensions of accessibility, availability and quality.

4.b. Comment and limitations

Data are widely available on the type and location of drinking water sources used by households. Data on availability and safety of drinking water are increasingly available through a combination of household surveys and administrative sources including regulators, but definitions have yet to be standardized. The JMP has been collaborating with international survey programmes (such as the UNICEF Multiple Indicator Cluster Survey programme) and national survey programmes to develop standardized questions that address the SDG criteria for service levels, as well as a module for testing water quality in household surveys. The JMP gives high importance to extending these collaborations to reduce data gaps, ensure consistency and to progressively improve the quality and comparability of data used for national, regional and global estimates.

4.c. Method of computation

The production of estimates follows a consistent series of steps, which are explained in this and following sections:

1. Identification of appropriate national datasets

2. Extraction of data from national datasets into harmonized tables of data inputs

3. Use of the data inputs to model country estimates

4. Consultation with countries to review the estimates

5. Aggregation of country estimates to create regional and global estimates

The JMP compiles national data on drinking water from a wide range of different data sources. Household surveys and censuses provide information on types of drinking water sources, and also indicate if sources are accessible on premises. These data sources often have information on the availability of water and increasingly on the quality of water at the household level, through direct testing of drinking water for faecal or chemical contamination. These data are combined with data on availability and compliance with drinking water quality standards (faecal and chemical) from administrative reporting or regulatory bodies.

The JMP uses original microdata to produce its own tabulations by using populations weights (or household weights multiplied by de jure household size), where possible. However, in many cases microdata are not readily accessible so relevant data are transcribed from reports available in various formats (PDFs, Word files, Excel spreadsheets, etc.) if data are tabulated for the proportion of the population, or household/dwelling. National data from each country, area, or territory are recorded in the JMP country files, with water, sanitation, and hygiene data recorded on separate sheets. Country files can be downloaded from the JMP website: https://washdata.org/data/downloads

The JMP calculates the proportion of population using improved water sources by fitting a linear regression line to all available data inputs within the reference period, starting from the year 2000. To calculate the proportion of the population using safely managed drinking water services, three ratios must be calculated: the proportion of the population using improved water supplies which are accessible on premises, have water available when needed, and are free from contamination. Those ratios are then multiplied with the proportion of the population using improved water sources, respectively. Safely managed drinking water services is taken as the minimum of these three indicators for any given year. National estimates are generated as weighted averages of the separate estimates for urban and rural areas, using population data from the most recent report of the United Nations Population Division.

For more details on JMP rules and methods, please refer to recent JMP progress reports and “JMP Methodology: 2017 update and SDG baselines”: https://washdata.org/report/jmp-methodology-2017-update

4.d. Validation

Every two years the JMP updates its global databases to incorporate the latest available national data for the global SDG indicators. National authorities are consulted on the estimates generated from national data sources through a country consultation process facilitated by WHO and UNICEF country offices. The country consultation aims to engage national statistical offices and other relevant national stakeholders to review the draft estimates and provide technical feedback to the JMP team.

The purpose of the consultation is not to compare JMP and national estimates of WASH coverage but rather to review the completeness or correctness of the datasets in the JMP country file and to verify the interpretation of national data in the JMP estimates. The JMP provides detailed guidance to facilitate country consultation on the estimates contained in JMP country files. The consultation focusses on three main questions:

Is the country file missing any relevant national sources of data that would allow for better estimates?
Are the data sources listed considered reliable and suitable for use as official national statistics?
Is the JMP interpretation and classification of the data extracted from national sources accurate and appropriate?

The JMP estimates are circulated for a 2 month period of consultation with national authorities starting in the fourth quarter of the year prior to publication (see https://washdata.org/how-we-work/jmp-country-consultation).

4.e. Adjustments

See method of computation.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

The JMP method uses a simple regression model to generate time series estimates for all years including for years without data points. The JMP then shares all its estimates using its country consultation mechanism to get consensus from countries before publishing its estimates.

At regional and global levels

Regional and global estimates for individual elements of safely managed services are calculated provided (non-imputed) data are available for at least 30% of the population using improved drinking water sources within the region. In order to produce estimates for regional or global levels, imputed estimates are produced for countries lacking data. Imputed country estimates are not published and only used for aggregation.

4.g. Regional aggregations

For safely managed drinking water services, the proportions of the regional population using improved drinking water sources that are accessible on premises, available when needed and free from contamination are calculated as weighted averages amongst populations using improved drinking water sources. The resulting ratios are multiplied by the proportion of the population using improved drinking water sources in each region. Following the approach taken for countries, the proportion of the population using safely managed drinking water services is then calculated at regional and global levels by taking a minimum of the three elements, or of two elements if either accessibility or availability is missing. These proportions are calculated separately for urban and rural areas and, where possible, a weighted average is made of rural and urban populations to produce total estimates for the region or world.

For more details on JMP rules and methods: JMP Methodology: 2017 update and SDG baselines:

https://washdata.org/report/jmp-methodology-2017-update

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The JMP has published guidance on core questions and indicators for monitoring WASH in households, schools and health care facilities (see https://washdata.org/monitoring/methods/core-questions) and provides technical support through WHO and UNICEF regional and country offices to strengthen national monitoring of SDG indicators relating to drinking water, sanitation and hygiene.

4.i. Quality management

The JMP has been instrumental in developing global norms to benchmark progress on drinking water, sanitation and hygiene, and has produced regular updates on country, regional, and global trends. The JMP regularly convenes expert task forces to provide technical advice on specific issues and methodological challenges related to WASH monitoring. WHO and UNICEF have also established a Strategic Advisory Group to provide independent advice on the continued development of the JMP as a trusted custodian of global WASH data (see https://washdata.org/how-we-work/about-jmp).

4.j. Quality assurance

National statistical offices are primarily responsible for assuring the quality of national data sources. A key objective of JMP country consultations is to establish whether data sources are considered reliable and suitable for use as official national statistics. The JMP has established criteria for acceptance of national data sources based on representativeness, quality and comparability.

4.k. Quality assessment

See quality assurance.

5. Data availability and disaggregation

Data availability:

As of 1 July 2021, national estimates could be produced for 138 countries, areas and territories, including 114 UN member states, and covering 45% of the global population. Estimates were available for rural areas in countries representing 55% of the global rural population, and for urban areas in countries representing 56% of the global urban population.

Time series:

Time series data are available for the basic level of drinking water service since 2000. These serve as the foundation for the safely managed drinking water service indicator. Some elements of safe management (e.g. water quality) were not collected during the MDG period (from 2000 to 2015) and for some countries and regions trend analysis is not possible for all years from 2000 to 2020.

Disaggregation:

Disaggregation by geographic location (urban/rural, sub-national regions, etc.) and by socioeconomic characteristics (wealth, education, ethnicity, etc) is possible in a growing number of countries. Drinking water services can also be disaggregated by service level (i.e. no services/surface water, unimproved, limited, basic, and safely managed services). Disaggregated data are more widely available for basic and lower levels of service than for safely managed services.

Disaggregation by individual characteristics (e.g. age, sex, disability, etc.) may also be made where data permit. Many of the datasets used for producing estimates are household surveys and censuses which collect information on drinking water at the household level. Such data cannot be disaggregated to provide information on intra-household variability (e.g. differential use of services by gender, age, or disability). The JMP seeks to highlight individual datasets which do allow assessment of intra-household variability, but these are not numerous enough to integrate into the main indicators estimated in JMP reports.

6. Comparability/deviation from international standards

Sources of discrepancies:

JMP estimates are based on national sources of data approved as official statistics. Differences between global and national figures arise due to differences in indicator definitions and methods used in calculating national coverage estimates. In some cases, national estimates are based on the most recent data point rather than from regression on all data points as done by the JMP. In order to generate national estimates, the JMP uses data that are representative of urban and rural populations and UN population estimates and projections (UN DESA World Population Prospects: https://population.un.org/wpp/; World Urbanization Projects: https://population.un.org/wup) which may differ from national population estimates.

7. References and Documentation

JMP Website: https://www.washdata.org/

https://washdata.org/report/jmp-methodology-2017-update

JMP Reports: https://washdata.org/reports

JMP Methods: https://washdata.org/monitoring/methods

JMP Methodology: 2017 update and SDG baselines

JMP Core questions on water, sanitation and hygiene for household surveys

Available in English (EN), Spanish (ES), French (FR) and Russian (RU):

EN: https://washdata.org/report/jmp-2018-core-questions-household-surveys

ES: https://washdata.org/report/jmp-2018-core-questions-household-surveys-es

FR: https://washdata.org/report/jmp-2018-core-questions-household-surveys-fr

RU: https://washdata.org/report/jmp-2018-core-questions-household-surveys-ru

JMP Integrating water quality testing into household surveys

Available in English (EN), Spanish (ES), and French (FR):

EN: https://washdata.org/report/jmp-2020-water-quality-testing-household-surveys

ES: https://washdata.org/report/jmp-2020-water-quality-testing-household-surveys-es

FR: https://washdata.org/report/jmp-2020-water-quality-testing-household-surveys-fr

JMP Report: Progress on household drinking water, sanitation and hygiene 2000-2017: Special focus on inequalities

Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):

EN: https://washdata.org/report/jmp-2019-wash-households

ES: https://washdata.org/report/jmp-2019-wash-households-es

FR: https://washdata.org/report/jmp-2019-wash-households-fr

RU: https://washdata.org/report/jmp-2019-wash-households-ru

AR: https://washdata.org/report/jmp-2019-wash-households-ar1

WHO Guidelines for Drinking Water Quality

https://www.who.int/water_sanitation_health/water-quality/guidelines/previous-guidelines/en/

The 4th edition, incorporating the first addendum (2017) is available in English (EN), Spanish (ES), and French (FR):

EN: https://www.who.int/publications/i/item/9789241549950

ES: https://www.who.int/es/publications/i/item/9789241549950

FR: https://www.who.int/fr/publications/i/item/9789241549950

UN General Assembly Resolution A/RES/64/292 for the right to water and sanitation:

https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/64/292

The Human Right to Water and Sanitation Milestones:

https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf

For queries: info@washdata.org

6.2.1a

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.2: By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations

0.c. Indicator

Indicator 6.2.1: Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water

0.d. Series

Metadata description refers to 6.2.1.a Proportion of population using safely managed sanitation services. Separate metadata description available for 6.2.1.b Proportion of population with handwashing facilities with soap and water available at home.

0.e. Metadata update

2021-12-20

0.f. Related indicators

All targets under Goal 6, as well as targets 1.2, 1.4, 2.2, 3.2, 3.8, 3.9, 4a, 5.4 and 11.1

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

1.a. Organisation

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

The proportion of the population using safely managed sanitation services is defined as the proportion of the population using an improved sanitation facility which is not shared with other households and where excreta are safely disposed of in situ or removed and treated off-site. ‘Improved’ sanitation facilities are those designed to hygienically separate human excreta from human contact. These include wet sanitation technologies such as flush and pour flush toilets connected to sewers, septic tanks or pit latrines, and dry sanitation technologies such as dry pit latrines with slabs, ventilated improved pit latrines and composting toilets.

Concepts:

An ‘improved sanitation facility’ is defined as one designed to hygienically separate human excreta from human contact. Improved sanitation facilities include wet sanitation technologies such as flush or pour flush toilets connected to sewer systems, septic tanks or pit latrines; and dry sanitation technologies such as dry pit latrines with slabs (constructed from materials that are durable and easy to clean), ventilated improved pit (VIP) latrines, pit latrines with a slab, composting toilets and container based sanitation. If a household uses a flush or pour flush toilet but does not know where it is flushed to, the sanitation facility is considered to be improved since the household may not be aware about whether it flushes to a sewer, septic tank or pit latrine.

‘Unimproved sanitation facilities’ include flush or pour flush toilets connected to open drains; pit latrines without slabs; open pits; buckets, pans, ‘trays’ or other unsealed containers; hanging toilets/latrines; defecation in the bush or field or ditch and defecation into surface water (drainage channels, beaches, rivers, streams or the sea). If a household uses a flush or pour flush toilet and survey respondents report that it is not flushed to sewer systems, septic tanks or pit latrines but elsewhere, the sanitation facility is considered to be unimproved.

Improved sanitation refers only to the type of facility used, irrespective of whether the facilities are shared. Public toilets, as well as privately owned sanitation facilities which are shared by two or more families, are classified as ‘shared facilities’. Use of improved sanitation facilities which are not shared is defined as a ‘basic sanitation service’, while use of improved sanitation facilities which are shared is defined as a ‘limited sanitation service’. ‘Basic sanitation services’ may also be counted as ‘safely managed sanitation services’, but additional information is required about the management of excreta.

For monitoring of safely managed sanitation services, excreta from different types of sanitation facilities are tracked through stages of the ‘sanitation management chain’: containment, emptying, transport, treatment, and reuse or final disposal. These stages are followed separately for excreta flushed into sewer networks, and for excreta stored in on-site containers such as septic tanks and pit latrines.

Excreta from on-site storage containers (pit latrines and septic tanks) can be treated and disposed of off-site, when faecal sludge is emptied from containers and delivered to treatment plants designed to receive faecal sludge. Excreta flushed into sewer networks can also be treated off-site, if the excreta reaches treatment plants and receives a minimum level of treatment.

For the purposes of SDG monitoring, treatment of wastewater and faecal sludge is assessed based on the treatment plant design technology, using categories defined by the System of Environmental-Economic Accounting (SEEA) and the International Recommendations for Water Statistics and following a laddered approach (primary, secondary and tertiary treatment). Wastewater and faecal sludge receiving secondary or higher levels of treatment are considered ‘safely managed’. Primary treatment is not considered safely managed, unless the effluent is discharged in a way that precludes further human contact (e.g. through a long ocean outfall). If data are available for conventional classes (primary, secondary, tertiary, advanced) as well as for ambiguous categories (e.g. “other”), ambiguous categories are generally not considered as safely managed. Where treatment classes are not specified (e.g. “treated”) the JMP assumes at least secondary treatment but seeks clarification during country consultations. Treatment of excreta in faecal sludge treatment plants is classified as safely managed if both the liquid and solid fractions are treated.

Excreta stored in on-site storage containers can be safely treated and disposed of on-site (‘safe disposal in situ’) if pit latrines and septic tanks are not emptied and excreta are contained (remain isolated from human contact) such that solids degrade within the container through physical and biological processes, and liquid effluent connects to an infiltration system such as a soakaway pit or leachfield. Faecal sludge emptied from septic tanks and pit latrines and buried on-site in a covered pit is also counted as safely disposed of in situ.

For detailed guidance on safe sanitation, please refer to the most recent version of the WHO Guidelines on Sanitation and Health:

https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/sanitation-safety

2.b. Unit of measure

Proportion of population

2.c. Classifications

WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene has established international standards for classification of sanitation facilities and service levels to benchmark and compare progress across countries (see washdata.org).

3.a. Data sources

Data sources included in the JMP database are:

Censuses, which in principle collect basic data from all people living within a country and led by national statistical offices.
Household surveys, which collect data from a subset of households. These may target national, rural, or urban populations, or more limited project or sub-national areas. An appropriate sample design is necessary for survey results to be representative, and surveys are often led by or reviewed and approved by national statistical organizations.
Administrative data, which may consist of information collected by government or non-government entities involved in the delivery or oversight of services. Examples include water and sanitation inventories and databases, and reports of regulators.
Other datasets may be available such as compilations by international or regional initiatives (e.g. Eurostat), studies conducted by research institutes, or technical advice received during country consultations.

Access to water, sanitation and hygiene are considered core socio-economic and health indicators, as well as key determinants of child survival, maternal, and children’s health, family wellbeing, and economic productivity. Drinking water and sanitation facilities are also used in constructing wealth quintiles used by many integrated household surveys to analyse inequalities between rich and poor. Access to drinking water, sanitation and hygiene is therefore are core indicators for many household surveys and censuses. In high-income countries where household surveys or censuses do not collect detailed information on types of facilities used by households, the JMP relies on administrative records.

The information about the type of sanitation facilities and whether they are shared or not by other households are mostly collected through censuses and household surveys. Data on containment, emptying, transport, treatment, and reuse or final disposal of excreta may come either from population-based data sources (household surveys and censuses), or from administrative records (e.g. data from ministries, regulators). Data on off-site treatment of excreta and wastewater cannot be collected through household surveys. Data on management of wastewater in sewered systems are normally available from administrative sources such as utilities and regulators. In contrast, some data on management of on-site sanitation systems may come from households (e.g. reported emptying of septic tanks and latrine pits) while some may come from service providers (desludging companies, treatment plant operators). Frequently data are available from one but not the other of these types of sources. If data are available for the sanitation type which is used by the majority of the population (the ‘dominant sanitation type’), then an assumption is applied to the non-dominant sanitation type in order to make an estimate for safely managed sanitation services.

Some datasets available to the JMP are not representative of national, rural or urban populations, or may be representative of only a subset of these populations (e.g. the population using sewer connections). The JMP enters datasets into the global database when they represent at least 20% of the national, urban or rural populations. However, datasets representing less than 80% of the relevant population, or which are considered unreliable or inconsistent with other datasets covering similar populations, are not used in the production of estimates (see section 2.6, Data Acceptance in JMP Methodology: 2017 update and SDG baselines).

In some cases, a dataset can be used for one or more but not all indicators, because of variable data availability and quality. For example, a household survey might yield reliable data on “improved sanitation” but unreliable data distinguishing sewer connections from on-site sanitation systems, because of ambiguous question wording or inadequate training of survey teams.

The population data used by JMP, including the proportion of the population living in urban and rural areas, are those established by the UN Population Division (UN DESA World Population Prospects: https://population.un.org/wpp/).

3.b. Data collection method

The data search is largely done by systematically visiting the websites of national statistical offices, and key sector institutions such as ministries of water and sanitation, regulators of drinking water and sanitation services, etc. Other regional and global databases are also reviewed for new datasets. UNICEF and WHO regional and country offices provides support to identify newly available household surveys, censuses and administrative datasets.

3.c. Data collection calendar

The JMP begins its biennial data collection cycle in October of even years and publishes estimates during the following year.

3.d. Data release calendar

The SDG Progress Report and relevant data are published every two years since the publication of the baseline report in 2017, usually between March and July of odd years.

3.e. Data providers

National statistics offices; ministries of water, sanitation, health, environment; regulators of sanitation service providers.

3.f. Data compilers

WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)

3.g. Institutional mandate

The WHO/UNICEF JMP was established in 1990 to monitor global progress on drinking water, sanitation and hygiene (see washdata.org).

4.a. Rationale

Access to safe sanitation and hygiene services is essential for good health, welfare and productivity and is widely recognized as a human right. Unsafe management of human excreta and poor sanitation practices are closely associated with diarrhoeal diseases, which exacerbate malnutrition and remain a major public health concern and a leading global cause of child deaths, as well as parasitic infections such as soil transmitted helminths (worms) and a range of other neglected tropical diseases. While access to a hygienic toilet facility is essential for reducing the transmission of pathogens, it is equally important to ensure safe management, treatment and disposal of the excreta produced. Sharing of sanitation facilities is also an important consideration given the negative impacts on dignity, privacy and personal safety. Lack of access to suitable sanitation and hygiene facilities is a major cause of risks and anxiety, especially for women and girls. For all these reasons, access to sanitation and hygiene services that prevent disease, provide privacy and ensure dignity has been recognized as a basic human right. The SDG target 6.2 relating to sanitation and hygiene aim to achieve this right through universal access to safely managed services.

The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) uses a simple improved/unimproved facility type classification that has been refined over time. ‘Improved’ sanitation facilities are those designed to hygienically separate excreta from human contact, and this metric was used beginning in 2000 to track progress towards MDG target 7c. International consultations since 2011 have established consensus on the need to build on and address the shortcomings of this indicator; specifically, to address normative criteria of the human right to water and sanitation (UN General Assembly Resolution A/RES/64/292) and concluded that global monitoring should go beyond the basic level of access and consider safe management of faecal wastes. As a result, the SDG indicator 6.2.1.a is designed to address safe management of sanitation services along the sanitation chain, including containment, emptying, treatment and disposal of the wastes. In other words, the indicator combines information on whether households use improved and private (not shared) toilets and safe management of the faecal waste deposited in those toilets.

4.b. Comment and limitations

Data on emptying and disposal of waste from on-site containers and the treatment of wastewater from sewer connections are increasingly available through a combination of household surveys and administrative sources including regulators, but definitions have yet to be fully standardized.

The information available about wastewater transported to treatment may not always provide a complete picture. Not all excreta from toilet facilities conveyed in sewers (sewage) or emptied from pit latrines and septic tanks (faecal sludge) reaches a treatment plant. For instance, a portion may leak from the sewer itself or, due to broken pumping installations, be discharged directly to the environment. Similarly, a portion of the faecal sludge emptied from containers may be discharged into open drains, to open ground or water bodies, rather than being transported to a treatment plant. And finally, even once the excreta reaches a treatment plant a portion may remain untreated due to dysfunctional treatment equipment or inadequate treatment capacity, and be discharged to the environment. Data on the proportion of sewage and faecal sludge which is lost in transportation are rare.

4.c. Method of computation

The production of estimates follows a consistent series of steps, which are explained in this and following sections:

1. Identification of appropriate national datasets

2. Extraction of data from national datasets into harmonized tables of data inputs

3. Use of the data inputs to model country estimates

4. Consultation with countries to review the estimates

5. Aggregation of country estimates to create regional and global estimates

The JMP compiles national data on sanitation from a wide range of different data sources. Household surveys and censuses provide data on use of types of basic sanitation facilities, while information on emptying and disposal of waste from on-site facilities and the treatment of wastewater from sewer connections are increasingly available through a combination of household surveys and administrative sources including regulators.

The JMP uses original microdata to produce its own tabulations by using populations weights (or household weights multiplied by de jure household size), where possible. However, in many cases microdata are not readily accessible so relevant data are transcribed from reports available in various formats (PDFs, Word files, Excel spreadsheets, etc...) if data are tabulated for the proportion of the population, or household/dwelling. National data from each country, area, or territory are recorded in the JMP country files, with water, sanitation, and hygiene data recorded on separate sheets. Country files can be downloaded from the JMP website: https://washdata.org/data/downloads.

The percentage of the population using safely managed sanitation services is calculated by combining data on the proportion of the population using different types of basic sanitation facilities with estimates of the proportion of faecal waste which is safely disposed in situ or treated off-site.

The JMP estimates the proportion of population using improved sanitation by fitting a linear regression model to all available and validated data points within the reference period, starting from the year 2000.

In some countries data on the proportion of the population connected to sewer networks or septic tanks are only available at the national level while data on the population using improved sanitation is available at rural and urban areas. In these cases a weighted average is used for the national estimate of improved (not shared) sanitation and this is split into sewer, septic and improved latrines and other. When data are available for rural and urban areas, national estimates are generated as weighted averages of the separate estimates for those areas, using population data from the most recent report of the United Nations Population Division.

For more details on JMP rules and methods on how data on the type of sanitation facility used and the disposal and treatment of excreta are combined to compute the safely managed sanitation services indicator, please refer to recent JMP progress reports and “JMP Methodology: 2017 update and SDG baselines”: https://washdata.org/report/jmp-methodology-2017-update

4.d. Validation

Is the country file missing any relevant national sources of data that would allow for better estimates?
Are the data sources listed considered reliable and suitable for use as official national statistics?
Is the JMP interpretation and classification of the data extracted from national sources accurate and appropriate?

The JMP estimates are circulated for a 2 month period of consultation with national authorities starting in the fourth quarter of the year prior to publication (https://washdata.org/how-we-work/jmp-country-consultation).

4.e. Adjustments

See method of computation.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

At regional and global levels

Regional and global estimates for safely managed sanitation services are calculated if there are (non-imputed) data on the management of the dominant form of improved sanitation (sewer connections or on-site systems) for at least 30% of the relevant population (i.e. the population using sewer connections or on-site improved sanitation systems) within the region. In order to produce estimates for regional or global levels, imputed estimates are produced for countries lacking data. Imputed country estimates are not published and only used for aggregation.

4.g. Regional aggregations

For safely managed sanitation services, the regional population using sewer connections is used to weight estimates of the proportion of wastewater treated, while the population using improved on-site facilities is used to weight estimates of the proportion of the population with excreta disposed in situ. Where data coverage for the nondominant form of sanitation is below 30%, estimates are based only on the dominant form of sanitation.

Regional and global estimates of the population using safely managed services are then calculated by separately summing the populations with on-site and sewered safely managed services. Where data coverage for the relevant population is above 30% in both rural and urban areas, a weighted average is used to produce total regional and global estimates.

These estimates are calculated separately for urban and rural areas and, where possible, a weighted average is made of rural and urban populations to produce total estimates for the region or world.

For more details on JMP rules and methods: JMP Methodology: 2017 update and SDG baselines:

https://washdata.org/report/jmp-methodology-2017-update

4.h. Methods and guidance available to countries for the compilation of the data at the national level

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

See quality assurance.

5. Data availability and disaggregation

Data availability:

As of 1 July 2020, national estimates could be produced for 120 countries, areas, and territories, including 115 UN member states, and covering 81% of the global population. Estimates were available for rural areas in countries representing 73% of the global rural population, and for urban areas in countries representing 75% of the global urban population.

Time series:

Time series data are available for the basic sanitation level of service since 2000. These serve as the foundation for the safely managed sanitation service indicator. Some elements of safe management (e.g. wastewater treatment) were not collected during the MDG period (from 2000 to 2015) and for some countries and regions trend analysis is not possible for all years from 2000 to 2020.

Disaggregation:

Disaggregation by geographic location (urban/rural, sub-national regions, etc.) and by socioeconomic characteristics (wealth, education, ethnicity, etc.) is possible in a growing number of countries. Sanitation services are disaggregated by service level (i.e. no services/open defecation, unimproved, limited, basic, and safely managed services). Disaggregated data are more widely available for basic and lower levels of service than for safely managed services.

Disaggregation by individual characteristics (age, sex, disability, etc.) may also be made where data permit. Many of the datasets used for producing estimates are household surveys and censuses which collect information on sanitation at the household level. Such data cannot be disaggregated to provide information on intra-household variability, e.g. differential use of services by gender, age, or disability. The JMP seeks to highlight individual datasets which do allow assessment of intra-household variability, but these are not numerous enough to integrate into the main indicators estimated in JMP reports.

6. Comparability/deviation from international standards

Sources of discrepancies:

JMP estimates are based on national sources of data approved as official statistics. Differences between global and national figures arise due to differences in indicator definitions and methods used in calculating national coverage estimates. In some cases national estimates are based on the most recent data point rather than from regression on all data points as done by the JMP. In some cases national estimates draw on administrative sector data rather than the nationally representative surveys and censuses used by the JMP. In order to generate national estimates, JMP uses data that are representative of urban and rural populations and UN population estimates and projections (UN DESA World Population Prospects: https://population.un.org/wpp/; World Urbanization Projects: https://population.un.org/wup) which may differ from national population estimates.

7. References and Documentation

JMP Website: https://www.washdata.org/

https://washdata.org/report/jmp-methodology-2017-update

JMP Reports: https://washdata.org/reports

JMP Methods: https://washdata.org/monitoring/methods

JMP Methodology: 2017 update and SDG baselines

JMP Core questions on water, sanitation and hygiene for household surveys:

Available in English (EN), Spanish (ES), French (FR) and Russian (RU):

EN: https://washdata.org/report/jmp-2018-core-questions-household-surveysES: https://washdata.org/report/jmp-2018-core-questions-household-surveys-es

FR: https://washdata.org/report/jmp-2018-core-questions-household-surveys-fr

RU: https://washdata.org/report/jmp-2018-core-questions-household-surveys-ru

JMP Report: Progress on household drinking water, sanitation and hygiene 2000-2017: Special focus on inequalities

Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):

EN: https://washdata.org/report/jmp-2019-wash-households

ES: https://washdata.org/report/jmp-2019-wash-households-es

FR: https://washdata.org/report/jmp-2019-wash-households-fr

RU: https://washdata.org/report/jmp-2019-wash-households-ru

AR: https://washdata.org/report/jmp-2019-wash-households-ar1

WHO Guidelines on sanitation and health. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO. Available in EN, ES, FR, RU and AR:

https://www.who.int/water_sanitation_health/publications/guidelines-on-sanitation-and-health/en/

WHO Water, sanitation and hygiene for accelerating and sustaining progress on Neglected Tropical Diseases. A Global Strategy 2015–2020, WHO Press, Geneva, 2015.

http://apps.who.int/iris/bitstream/handle/10665/182735/WHO_FWC_WSH_15.12_eng.pdf;jsessionid=7F7C38216E04E69E7908AB6E8B63318F?sequence=1

UN General Assembly Resolution A/RES/64/292 for the human right to water and sanitation:

https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/64/292

UN General Assembly Resolution A/RES/70/169 for the human rights to safe drinking water and sanitation:

https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/169

The Human Right to Water and Sanitation Milestones:

https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf

For queries: info@washdata.org

6.2.1b

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

0.c. Indicator

Indicator 6.2.1: Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water

0.d. Series

Metadata description refers to 6.2.1.b Proportion of population with handwashing facilities with soap and water available at home. Separate metadata description available for 6.2.1.a Proportion of population using safely managed sanitation services .

0.e. Metadata update

2021-12-20

0.f. Related indicators

All targets under Goal 6, as well as targets 1.2, 1.4, 2.2, 3.2, 3.8, 3.9, 4a, 5.4 and 11.1

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

1.a. Organisation

World Health Organization (WHO)

United Nations Children's Fund (UNICEF)

2.a. Definition and concepts

Definition:

The proportion of the population with basic hygiene services is defined as the proportion of population with a handwashing facility with soap and water available at home. Handwashing facilities may be located within the dwelling, yard or plot. They may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.

Concepts:

Household handwashing facilities may be located in the dwelling, yard or plot. A handwashing facility is a device to contain, transport or regulate the flow of water to facilitate handwashing. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents. In some cultures, ash, soil, sand or other materials are used as handwashing agents, but these are less effective than soap and are therefore counted as limited handwashing facilities.

In 2008, the JMP supported a review of indicators of handwashing practice, and determined that the most practical approach leading to reliable measurement of handwashing in national household surveys was observation of the place where household members wash their hands and noting the presence of water and soap (or local alternative) at that location. This provides a measure of whether households have the necessary tools for handwashing and is a proxy for their behaviour. Observation by survey enumerators represents a more reliable, valid and efficient indicator for measuring handwashing behaviour than asking individuals to report their own behaviour.

2.b. Unit of measure

Proportion of population

2.c. Classifications

WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene has established international standards for classification of handwashing facilities and service levels to benchmark and compare progress across countries (see washdata.org).

3.a. Data sources

Data sources included in the JMP database are:

Censuses, which in principle collect basic data from all people living within a country and led by national statistical offices.
Household surveys, which collect data from a subset of households. These may target national, rural, or urban populations, or more limited project or sub-national areas. An appropriate sample design is necessary for survey results to be representative, and surveys are often led by or reviewed and approved by national statistical organizations.
Other datasets may be available such as compilations by international or regional initiatives (e.g. Eurostat), studies conducted by research institutes, or technical advice received during country consultations.

Access to water, sanitation and hygiene are considered core socio-economic and health indicators, and key determinants of child survival, maternal, and children’s health, family wellbeing, and economic productivity. Drinking water, sanitation and hygiene facilities are also used in constructing wealth quintiles used by many integrated household surveys to analyse inequalities between rich and poor. Access to drinking water, sanitation and hygiene are therefore core indicators for many household surveys and censuses.

The JMP uses data on the observation of handwashing facilities with water and soap, typically available in Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS), as well as other household surveys. Any available surveys recording observation of handwashing facilities are included in the JMP database and JMP regression rules are applied to estimate the proportion of the population with a handwashing facility, as well as the proportion with a handwashing facility with water and soap.

Household surveys increasingly include a section on hygiene practices where the surveyor visits the handwashing facility and observes if water and soap are present. Observation of handwashing materials by surveyors represents a more reliable proxy for handwashing behaviour than asking individuals whether they wash their hands. The small number of cases where households refuse to give enumerators permission to observe their facilities are excluded from JMP estimates.

Direct observation of handwashing facilities has been included as a standard module in MICS and DHS since 2009. Following the standardization of hygiene questions in international surveys, data on handwashing facilities are available for a growing number of low- and middle-income countries. This type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal.

Some datasets reviewed by the JMP are not representative of national, rural or urban populations, or may be representative of only a subset of these populations. The JMP enters datasets into the global database when they represent at least 20% of the national, urban or rural populations. However, datasets representing less than 80% of the relevant population, or which are considered unreliable or inconsistent with other datasets covering similar populations, are not used in the production of estimates (see section 2.6, Data Acceptance in JMP Methodology: 2017 update and SDG baselines).

3.b. Data collection method

Before publishing, all JMP estimates undergo rigorous country consultations facilitated by WHO and UNICEF country offices. Often these consultations give rise to in-country visits, and meetings about data on drinking water, sanitation and hygiene services and the monitoring systems that collect these data.

3.c. Data collection calendar

The JMP begins its biennial data collection cycle in October of even years and publishes estimates during the following year.

3.d. Data release calendar

The SDG Progress Report and relevant data are published every two years since the publication of the baseline report in 2017, usually between March and July of odd years.

3.e. Data providers

National statistics offices; ministries of water, health, and environment; regulators of drinking water service providers.

3.f. Data compilers

WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)

3.g. Institutional mandate

The WHO/UNICEF JMP was established in 1990 to monitor global progress on drinking water, sanitation and hygiene (see washdata.org).

4.a. Rationale

Access to safe drinking water, sanitation and hygiene services, areessential for good health, welfare and productivity and are widely recognised as human rights. Improved hygiene is one of the most important measures to prevent the spread of infectious diseases including diarrhoeal diseases and acute respiratory infections which remain leading global causes of disease. Most infectious diseases are caused by bacteria or viruses which are transmitted either through the air, via surfaces or food, or via human faeces. Because people frequently touch their face, food, and surfaces, handwashing reduces the spread of these bacteria and viruses and is widely regarded as a top priority for improving global health outcomes.

Monitoring handwashing behaviour is difficult, but household surveys increasingly include a module that involves direct observation of facilities and presence of water and soap that has been shown to be a reasonable proxy for actual handwashing practices. International consultations among WASH sector professionals identified the presence of a handwashing facility with soap and water available within the dwelling, yard or plot as a priority indicator for national and global monitoring of hygiene under SDG 6.2. The SDG indicator 6.2.1.b is therefore designed to address both access to facilities and the availability of soap and water for handwashing at the household level.

4.b. Comment and limitations

The presence of a handwashing facility with soap and water available does not guarantee that household members consistently wash hands at key times. But direct observation of handwashing is challenging, and people tend to behave differently when being observed. The presence of a handwashing facility with soap and water available has been shown to be a reasonable proxy for handwashing. Enumerators ask households to show them where members of the household most often wash their hands and record the type of facility and whether soap and water are present at the time of the survey.

Since 2016 household surveys have refined the questions asked about handwashing facilities to include separate response categories for different types of handwashing facilities, including both fixed devices like sinks and taps, and mobile devices like jugs and portable basins. These surveys have shown that mobile devices are widely used in low-income countries. Older surveys that don’t include responses for mobile devices may therefore underestimate the population with access to handwashing facilities.

Households surveys in high-income countries rarely include questions about handwashing facilities, and as such, have very low data coverage. Some countries have data on the proportion of households with piped water supplies, hot water, showers or bathrooms but further work is required to determine how many of these also have basic hygiene services.

4.c. Method of computation

The production of estimates follows a consistent series of steps, which are explained in this and following sections:

1. Identification of appropriate national datasets

2. Extraction of data from national datasets into harmonized tables of data inputs

3. Use of the data inputs to model country estimates

4. Consultation with countries to review the estimates

5. Aggregation of country estimates to create regional and global estimates

Household surveys and censuses provide data on the presence of handwashing facilities and soap and water in the home. The JMP uses data from household surveys in which the enumerator observes the handwashing facility and confirms the presence or absence of soap and water at the facility. Datasets that include availability of soap in the household (i.e. not at the handwashing facility), or self-reported availability of handwashing facilities, soap and water may be included in the JMP database and country files, but in most cases are not used for making estimates.

In some parts of the world, households sometimes do not give permission for survey enumerators to enter the premises and observe handwashing facilities. These households are excluded from calculations of the proportion of households having handwashing facilities.

The JMP uses original microdata to produce its own tabulations and estimates by using populations weights (or household weights multiplied by de jure household size), where possible. However, in many cases microdata are not readily accessible so relevant data are transcribed from reports available in various formats (PDFs, Word files, Excel spreadsheets, etc.) if data are tabulated for the proportion of the population, or household/dwelling. National data from each country, area, or territory are recorded in the JMP country files, with water, sanitation, and hygiene data recorded on separate sheets. Country files can be downloaded from the JMP website: https://washdata.org/data/downloads.

The JMP estimates the proportion of population with a basic handwashing facility with soap and water on premises by fitting a regression model to all available and validated data points within the reference period, starting from year 2000.

4.d. Validation

Is the country file missing any relevant national sources of data that would allow for better estimates?
Are the data sources listed considered reliable and suitable for use as official national statistics?
Is the JMP interpretation and classification of the data extracted from national sources accurate and appropriate?

4.e. Adjustments

See method of computation

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

At regional and global levels

Regional and global estimates for basic hygiene services are calculated if there are (non-imputed) data available for at least 50% of the relevant population within the region. In order to produce estimates for regional or global levels, imputed estimates are produced for countries lacking data. Imputed country estimates are not published and only used for aggregation.

4.g. Regional aggregations

Regional estimates for basic hygiene services are calculated by summing up the actual or imputed estimates for each country, area or territory in the region, provided data are available for at least half (50%) of the relevant population within the region. Global estimates are made by directly aggregating country (and imputed country) estimates, not by aggregating regional estimates.

These estimates are calculated separately for urban and rural areas and, where possible, a weighted average is made of rural and urban populations to produce total estimates for the region or world.

For more details on JMP rules and methods: JMP Methodology: 2017 update and SDG baselines:

https://washdata.org/sites/default/files/documents/reports/2018-04/JMP-2017-update-methodology.pdf

4.h. Methods and guidance available to countries for the compilation of the data at the national level

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

See quality assurance.

5. Data availability and disaggregation

Data availability:

As of 1 July 2021, national estimates could be produced for 79 countries, areas and territories, including 79 UN member states, and covering 50% of the global population. Estimates were available for rural areas in countries representing 67% of the global rural population, and for urban areas in countries representing 37% of the global urban population.

Time series:

Data on drinking water and sanitation services have been routinely collected for many years, but collecting data on handwashing has only recently become standardized: both the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) added handwashing questions to their standard questionnaires in 2009. Accordingly, while time series data are available for drinking water and sanitation services since 2000, time series for hygiene are only available since 2015.

Disaggregation:

Disaggregation by geographic location (urban/rural, sub-national regions, etc) and by socioeconomic characteristics (wealth, education, ethnicity, etc) is possible in a growing number of countries. Hygiene facilities are disaggregated by service level (i.e. no facility, limited, and basic facility).

Disaggregation by individual characteristics (age, sex, disability, etc) may also be made where data permit. Many of the datasets used for producing estimates are household surveys and censuses which collect information on handwashing facilities at the household level. Such data cannot be disaggregated to provide information on intra-household variability, e.g. differential use of services by gender, age, or disability. The JMP seeks to highlight individual datasets which do allow assessment of intra-household variability, but these are not numerous enough to integrate into the main indicators estimated in JMP reports.

6. Comparability/deviation from international standards

Sources of discrepancies:

JMP estimates are based on national sources of data approved as official statistics. Differences between global and national figures arise due to differences in indicator definitions and methods used in calculating national coverage estimates. In some cases, national estimates are based on the most recent data point rather than from regression on all data points as done by the JMP. In some cases, national estimates draw on administrative sector data rather than the nationally representative surveys and censuses used by the JMP. In order to generate national estimates, JMP uses data that are representative of urban and rural populations and UN population estimates and projections (UN DESA World Population Prospects: https://population.un.org/wpp/) which may differ from national population estimates.

7. References and Documentation

JMP Website: https://www.washdata.org/

https://washdata.org/report/jmp-methodology-2017-update

JMP Reports: https://washdata.org/reports

JMP Methods: https://washdata.org/monitoring/methods

JMP Methodology: 2017 update and SDG baselines

JMP Core questions on water, sanitation and hygiene for household surveys:

Available in English (EN), Spanish (ES), French (FR) and Russian (RU):

EN: https://washdata.org/report/jmp-2018-core-questions-household-surveysES: https://washdata.org/report/jmp-2018-core-questions-household-surveys-es

FR: https://washdata.org/report/jmp-2018-core-questions-household-surveys-fr

RU: https://washdata.org/report/jmp-2018-core-questions-household-surveys-ru

JMP Report: Progress on household drinking water, sanitation and hygiene 2000-2017: Special focus on inequalities

Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):

EN: https://washdata.org/report/jmp-2019-wash-households

ES: https://washdata.org/report/jmp-2019-wash-households-es

FR: https://washdata.org/report/jmp-2019-wash-households-fr

RU: https://washdata.org/report/jmp-2019-wash-households-ru

AR: https://washdata.org/report/jmp-2019-wash-households-ar1

WHO and UNICEF Hand Hygiene for All Global Initiative: https://www.who.int/water_sanitation_health/publications/200831-unicef-hand-hygiene.pdf?ua=1

WHO Guidelines on sanitation and health. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO. Available in EN, ES, FR, RU and AR:

https://www.who.int/water_sanitation_health/publications/guidelines-on-sanitation-and-health/en/

WHO Water, sanitation and hygiene for accelerating and sustaining progress on Neglected Tropical Diseases. A Global Strategy 2015–2020, WHO Press, Geneva, 2015.

http://apps.who.int/iris/bitstream/handle/10665/182735/WHO_FWC_WSH_15.12_eng.pdf;jsessionid=7F7C38216E04E69E7908AB6E8B63318F?sequence=1

Ram, P. 2013. Practical Guidance for Measuring Handwashing Behavior: 2013 Update. Global Scaling Up Handwashing. Washington DC: World Bank Press.

UN General Assembly Resolution A/RES/64/292 for the right to water and sanitation:

https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/64/292

The Human Right to Water and Sanitation Milestones:

https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf

For queries: info@washdata.org

6.3.1

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally

0.c. Indicator

Indicator 6.3.1: Proportion of domestic and industrial wastewater flows safely treated

0.e. Metadata update

2020-09-14

0.f. Related indicators

The domestic portion of wastewater treated is closely linked to indicator 6.2.1a on the “proportion of population using safely managed sanitation services”, and draws upon some of the same data sources.

The indicator is also directly linked to indicator 6.3.2 on the “proportion of bodies of water with good ambient water quality”, because unsafe wastewater treatment leads to degradation in quality of the receiving waters. It directly informs progress towards target 6.3 and is strongly linked to target 6.6 on water-related ecosystems, as well as target 14.1 on marine pollution (coastal eutrophication).

0.g. International organisations(s) responsible for global monitoring

United Nations Human Settlements Programme (UN-Habitat)

World Health Organization (WHO)

United Nations Statistics Division (UNSD)

1.a. Organisation

United Nations Human Settlements Programme (UN-Habitat)

World Health Organization (WHO)

United Nations Statistics Division (UNSD)

2.a. Definition and concepts

Definitions:

This indicator measures the volumes of wastewater which are generated through different activities, and the volumes of wastewater which are safely treated before discharge into the environment. Both of these indicators are measured in units of 1000 m³/day, although some data sources may use other units that require conversion. The ratio of the volume treated to the volume generated is taken as the ‘proportion of wastewater flow safely treated’.

Wastewater flows will be classified into industrial, services, and domestic flows, with reference to the International Standard Industrial Classification of All Economic Activities Revision 4 (ISIC). To the extent possible, the proportion of each of these waste streams that is safely treated before discharge to the environment will be calculated.

Concepts:

Total wastewater generation and treatment can be quantified at the national level, and wastewater can also be disaggregated into different types of flows, based on ISIC categories. Domestic wastewater generated by private households, as well as wastewater generated by economic activities covered by ISIC categories, may or may not be pre-treated on premises before discharge to either the sewer for further treatment or directly to the environment, as shown in Figure 1.

Diagram Description automatically generated

Figure 1: Schematic Representation of wastewater sources, collecting systems and treatment

(modified from wastewater loading diagram, OECD/Eurostat 2018).

The main sources of wastewater include wastewater from households, services and industries, i.e. point sources of one or more pollutant(s) that can be geographically located and represented as a point on a map. Diffused pollution from non-point sources such as runoff from urban and agricultural land can contribute quite significantly to wastewater flows (Figure 1), and therefore its progressive inclusion in the global monitoring framework will be important. Presently, it cannot be monitored at source and its impact on ambient water quality will be monitored under indicator 6.3.2 “Proportion of bodies of water with good ambient water quality”.

Differentiating between the different wastewater streams is important as policy decisions need to be guided by the polluter pays principle. However, wastewater conveyed by combined sewers usually combines both hazardous and non-hazardous substances discharged from different sources, but also runoff and urban stormwater, which cannot be separately tracked and monitored. As a consequence, although the flow of wastewater generated can be disaggregated by sources (domestic, services industrial), the treated wastewater statistics are most commonly disaggregated by type (e.g. urban and industrial) and/or level of treatment (e.g. secondary) rather than by sources.

Total wastewater flows can be classified into three main categories (see ‘disaggregation section’ for details:

Industrial (ISIC divisions 05-35)
Services (ISIC divisions 45-96)
Domestic (private households)

Wastewater treatment can be classified into three main categories (see ‘disaggregation section’ for details:

Primary
Secondary
Tertiary

Where possible, treatment will additionally be classified into either on-premises or off-premises treatment.

Domestic wastewater: Wastewater from residential settlements which originates predominantly from the human metabolism and from household activities.

Industrial (process) wastewater: Water discharged after being used in, or produced by, industrial production processes and which is of no further immediate value to these processes. Where process water recycling systems have been installed, process wastewater is the final discharge from these circuits. To meet quality standards for eventual discharge into public sewers, this process waste-water is understood to be subjected to ex-process in-plant treatment. Cooling water is not considered here. Sanitary wastewater and surface runoff from industries are also excluded here.

Total wastewater generated is the total volume of wastewater generated by economic activities (agriculture, forestry and fishing; mining and quarrying; manufacturing; electricity, gas, steam and air conditioning supply; and other economic activities) and households. Cooling water is excluded.

Urban wastewater: Domestic wastewater or the mixture of domestic wastewater with industrial wastewater and/or runoff rain water.

Wastewater: Wastewater is water which is of no further value to the purpose for which it was used because of its quality, quantity or time of occurrence. Cooling water is not considered here.

Wastewater discharge: The amount of water (in m3) or substance (in kg BOD/d or comparable) added/leached to a water body (Fresh or non-fresh) from a point source.

Wastewater treatment: Process to render wastewater fit to meet applicable environmental standards or other quality norms for recycling or reuse.

3.a. Data sources

A clear specification of the terminology and methodology for wastewater statistics is essential to contribute to harmonising international data collection practices and SDG 6.3.1 reporting. The objective of indicator 6.3.1 is to cover households and the entire economy, and to build on the existing international methodology for global monitoring wastewater generation and treatment. This approach reduces the monitoring burden that SDG reporting can impose on countries, and provides well-defined and internationally comparable variables for global data analysis and use by policymakers and urban/land planners.

Data are extracted from a number of pre-existing sources:

Indicator tables from the UNSD/UNEP data collection on environment statistics

https://unstats.un.org/unsd/envstats/qindicators (refer to “Inland Water Resources”)

Country files from the UNSD/UNEP data collection on environment statistics (https://unstats.un.org/unsd/envstats/country_files)
Website of Eurostat water statistics (https://ec.europa.eu/eurostat/web/environment/water)
Website of OECD water statistics (https://stats.oecd.org/index.aspx?DataSetCode=water_treat#).
Country files from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) data collection on safely managed sanitation services, and the reports referenced therein
(https://washdata.org/)

3.b. Data collection method

Total flows of wastewater generated and treated are reported by countries to UNSD and OECD/Eurostat databases. Eurostat deals with Member States of the European Union (EU) and the European Free Trade Association (EFTA) as well as the respective candidate countries. OECD works with all its Member States not contacted by Eurostat. UNSD sends the UNSD/UNEP Questionnaire to the rest of the world (approx. 165 countries). However, the response rate for the UNSD/UNEP questionnaire is around 50% and data completeness and quality remain a challenge, especially for developing countries. While efforts will continue to collect data from National Statistical Offices and Ministries of Environment at the national level, it is also critical to improve the availability and accessibility of wastewater statistics and increase training for collection of data and capacity development at the national and sub-national levels.

The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) collects and compiles national data related to use of sanitation services including wastewater treatment, for calculation of SDG indicator 6.2.1a “proportion of the population using safely managed sanitation services.” National data sources are collected from National Statistical Offices, ministries responsible for service delivery, and regulatory authorities, as well as other regional and global initiatives (e.g. the European Protocol on Water and Health). The database is updated every two years following a country consultation process facilitated by WHO and UNICEF regional offices.

These databases rely on a comparable harmonized terminology for water statistics. Wastewater data are nonetheless still relatively sparse on a global scale. UN-Habitat and WHO will disseminate information about these data collection processes, and will liaise with their technical focal points in regions and countries, to work with them to produce estimates which could then feed into the official statistical system via the NSOs. It is expected that over time, a better reporting of the wastewater data collected can be made to populate the SDG Indicator 6.3.1.

3.c. Data collection calendar

Next UNSD/UNEP and OECD/Eurostat data collection to be conducted in second half of 2020.

3.d. Data release calendar

The global databases for indicator 6.3.1 is planned to be updated in the second quarter of 2021.

3.e. Data providers

National Statistical Offices (NSOs) are the primary responsible authorities for providing data to be used for global statistics. NSOs may draw on data collected or compiled by relevant national or other authorities, such as ministries, municipalities, or regulatory authorities.

3.f. Data compilers

United Nations Human Settlements Programme (UN-Habitat), World Health Organization (WHO), and the United Nations Statistics Division (UNSD) are co-custodians for this indicator at the global level.

UNSD leads on collecting, compiling, and processing of data submitted by National Statistical Offices through the UNSD/UNEP Questionnaire on Environment Statistics for the non-OECD/Eurostat member states.

UN-Habitat leads on collecting, compilation, and processing of data from UNSD and OECD/Eurostat databases. UN-Habitat also leads on collection of additional data on industrial wastewater generation and treatment.

World Health Organization (WHO) leads on collection, compilation and processing of additional data on domestic wastewater generation and treatment.

4.a. Rationale

Wastewater data are crucial to promote strategies for sustainable and safe wastewater use or reuse to the benefit of the world’s population health and the global environment, but also to respond to growing water demands, increasing water pollution loads, and climate change impacts on water resources.

Sustainable Development Goal 6 (SDG 6) is about ensuring the availability and sustainability of water and sanitation for all by 2030. SDG Target 6.3 sets out to improve ambient water quality, which is essential to protecting both ecosystem and human health, by eliminating, minimizing and significantly reducing different streams of pollution into water bodies.

The purpose of monitoring progress using SDG indicator 6.3.1 is to provide necessary and timely information to decision makers and stakeholders to make informed decisions to accelerate progress towards reducing water pollution, minimizing release of hazardous chemicals and increasing wastewater treatment and reuse. The target wording covers wastewater recycling and safe reuse with implication on water use efficiency, although it is not fully addressed by the global indicator and methodology.

SDG indicator 6.3.1 tracks the proportion of wastewater flows from households, services and industrial economic activities that are safely treated at the source or through centralized wastewater treatment plants before being discharged into the environment, out of the total volume of wastewater generated.

4.b. Comment and limitations

There is a relative lack of knowledge about the volumes of wastewater generated and treated, because wastewater statistics are in an early stage of development in many countries and not regularly produced or reported. Monitoring is relatively complex, costly, and data are not systematically aggregated to the national level and/or accessible; especially industrial wastewater data which are in general poorly monitored and seldom aggregated at national level.

To some extent, this may be explained by the fact that a large proportion of the industrial water requirements are covered by the use of private systems using non-public/drinking water supply (groundwater, rivers and wells) which are not systematically included in the national statistics.

Diffused pollution from non-point sources such as runoff from urban and agricultural land can contribute significantly to wastewater flows, and therefore its progressive inclusion in the global monitoring framework will be important. Presently, it cannot be monitored at source and its impact on ambient water quality will be monitored indirectly under indicator 6.3.2 on the proportion of bodies of water with good ambient water quality.

Different types of wastewater have different degrees of contamination and pose different levels of threat to the environment and public health. Some data exist on the pollutant loading in terms of BOD5 and COD (kg O2/day), but these are not as widely available as data on volumes and will not be used at present for indicator 6.3.1. It is anticipated that future data drives will include more information on pollutant loadings that could be eventually featured in SDG 6.3.1 reporting.

Finally, whether wastewater is classified as safely treated or not depends on the wastewater treatment plant’s compliance rate to the effluent standards (i.e. performance). Many wastewater plants produce effluent which does not meet quality standards, due to improper design or loading. Effluent standards rely on both national and local requirements, as well as on specific water uses and potential reuse options, so that this approach may not provide strictly comparable variables between countries. For the purposes of global monitoring, in the absence of data on compliance, technology-based proxies will be used, in which compliance is assumed if the treatment plant provides at least secondary treatment.

4.c. Method of computation

The amount of wastewater generated is calculated by summing all of the wastewater generated by different economic activities and households. Wastewater flows are expressed in units of 1000 m3/day, although some data sources may use other units that require conversion.

The amount of wastewater safely treated is calculated by summing all of the wastewater flows which receive treatment considered equivalent to secondary treatment or better. This wastewater flow is expressed in units of 1000 m3/day, although some data sources may use other units that require conversion.

The proportion of wastewater flows which are safely treated is calculated as a ratio of the amount of wastewater safely treated to the amount of wastewater generated.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Outside of the UNSD and OECD/Eurostat databases, data on wastewater generation and treatment are not widely available, and what data do exist may not align with international definitions and classifications (e.g. ISIC codes).

For statistics on total wastewater generated and treated, missing values are not imputed. No estimated or modelled data are produced.

Some countries do not separately report the volume of wastewater generated by households. In the absence of reported data on domestic wastewater generation, an estimate of the wastewater generated at the household level will be made. It can be estimated that 80% of the water supply which enters private households will subsequently exit the household as wastewater. Therefore, if data are available on per capita water consumption, these can be used to estimate domestic wastewater generation. If data on per capita water consumption are not available, data from household surveys and censuses can be used to indicate the proportion of the population which has water supplies available on premises (e.g. municipal piped water, private boreholes with overhead tanks) and the proportion of the population which collects water from off-premises sources (e.g. communal standposts, community boreholes). In the absence of other data on domestic water consumption, it can be estimated that households with on-premises water supply consume approximately 120 litres per capita per day, and therefore generate 96 litres of wastewater per capita per day; those with off-premises water supply are assumed to consume approximately 20 litres per capita per day, and therefore generate 16 litres of wastewater per capita per day.

Missing values needed for calculation of the proportion of domestic wastewater which receives appropriate treatment will be handled in a similar way to the calculation of ‘safely managed sanitation services’ for SDG indicator 6.2.1. Domestic wastewater which enters sewage lines will be assumed to reach centralized wastewater treatment plants, unless national data is available about leakage from sewage lines. The volume of domestic wastewater estimated to reach treatment plants will be compared against the volume of wastewater reported to be received at wastewater plants, and the volume reportedly received will be taken as an upper limit to the amount of domestic wastewater which receives off-site treatment. If data are available on the proportion of wastewater flows received by centralized treatment plants which receive secondary treatment or better, this proportion can be assumed to apply equally to the flows generated by households, industries, and services which discharge into public sewers. Domestic wastewater which enters on-site storage and treatment systems such as septic tanks will be assumed to be safely treated if national data on compliance of on-site wastewater treatment systems to relevant standards are available. In the absence of such data, half of the wastewater discharged into on-site storage and treatment systems will be considered to receive safe treatment.

Given the data limitations, especially on non-household wastewater, data currently available on compliance with discharge permits could be used to better to estimate the industrial flows treated.

• At regional and global levels

See ‘regional aggregates’.

4.g. Regional aggregations

Regional and global aggregates are produced by combining volumes of wastewater generated and treated from countries with data. For the purpose of calculating regional aggregate statistics, values for countries without national estimates are imputed on the basis of regional averages (e.g. using M49 sub-regions). These imputed data are never published separately as national statistics.

Regional and global aggregate statistics are only produced when the data available without imputation represent at least 50% of the regional or global total. Ideally this coverage threshold would be based on wastewater volumes, but data on the volumes of wastewater generated are not available for all countries. Accordingly, as an interim measure, data coverage thresholds and weighting of national statistics will be done on the basis of national population, drawing on the latest statistics available from the UN World Population Prospects.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

To be developed.

4.j. Quality assurance

Data submitted to UNSD or OECD/Eurostat come directly from national statistical offices and/or ministries of environment. Data treatment and validation is done jointly by Eurostat and the OECD for their member states according to an agreed process and timeline. For those data submitted to UNSD a review is undertaken by the Environment Statistics Section for consistency. UNSD carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. UNSD does not make any estimation or imputation for missing values so the number of data points provided are actual country data. Only data that are considered accurate or those confirmed by countries during the validation process are included in UNSD’s environment statistics database and disseminated on UNSD’s website.

UN-Habitat and WHO use the resulting data without modification. In case of any observed discrepancies or anomalies the national authorities are consulted for clarification.

Estimates of domestic wastewater treatment are calculated based on national data and will be shared with countries for a consultation process similar to, and coordinated with, the consultation process used by WHO and UNICEF for indicators 6.1.1 and 6.2.1.

5. Data availability and disaggregation

Data availability:

In 2018 estimates of data on ‘proportion of safely treated domestic wastewater flows’ were available for 79 countries. These are available through the UN SDG Database (EN_WWT_WWDS). No regional aggregates were produced due to low data coverage.

The UNSD/UNEP Questionnaire on Environment Statistics has collected data on wastewater generation and treatment for about 7 years. The Questionnaire has been sent to more than 160 countries, covering both national and city levels. However, the response rate for the UNSD/UNEP questionnaire is hovering around 50% and data completeness and quality remain a challenge, especially for developing countries.

For those variables relevant to this indicator which are collected via the UNSD/UNEP Questionnaire, data for up to 37 countries are available in some years (wastewater treated in urban wastewater treatment plants), though for other relevant variables, for a given year, data for 30 countries or less may be available. More details on the availability of data obtained from the UNSD/UNEP Questionnaire can be found in the Report of the Secretary-General on Environment Statistics^[1] (Part C) and the Background Report^[2] (Part 1) submitted to the fifty-first session of the Statistical Commission (New York, 3-6 March 2020). Data received via the UNSD/UNEP Questionnaire have been published on the UNSD website in the form of indicator tables (UNSD Indicator Tables (inland water resources) (https://unstats.un.org/unsd/envstats/qindicators) as well as in Country Files (https://unstats.un.org/unsd/envstats/country_files).

Time series:

Some indicators have time series available for multiple years, while others currently only have most recent year availability.

Disaggregation:

Wastewater generation (Figure 2)

Wastewater can be generated through a variety of economic activities as well as through private households. The following categories of wastewater flows can be distinguished:

Agricultural (ISIC 01-03) covers crop and animal production, hunting and related service activities; forestry and logging; and fishing and aquaculture. Wastewater generated from these activities for the most part enters the environment as non-point pollution and will not be monitored as part of indicator 6.3.1.
Mining and quarrying (ISIC 05-09) includes the extraction of minerals occurring naturally as solids (coal and ores), liquids (petroleum) or gases (natural gas). Extraction can be achieved by different methods such as underground or surface mining, well operation, seabed mining etc.
Manufacturing (ISIC 10-33) includes the physical or chemical transformation of materials, substances, or components into new products. The materials, substances, or components transformed are raw materials that are products of agriculture, forestry, fishing, mining or quarrying as well as products of other manufacturing activities. Substantial alteration, renovation or reconstruction of goods is generally considered to be manufacturing.
Electricity (ISIC 35) includes electric power generation, transmission and distribution, as well as the manufacture and distribution of gas, and steam and air conditioning supply. Water used for cooling in power generation is explicitly excluded from calculations of wastewater flows.
Construction (ISIC 41-43) includes general construction and specialized construction activities for buildings and civil engineering works. It includes new work, repair, additions and alterations, the erection of prefabricated buildings or structures on the site and also construction of a temporary nature.
Services (ISIC 45-96) These Divisions are considered service industries and include a wide range of economic activities where water is mainly used for sanitary purposes, washing, cleaning, cooking, etc.
Wastewater can also be generated by private households, originating predominantly from the human metabolism and from household activities. A portion of the water which is brought into private households for domestic purposes (e.g. cooking, drinking, bathing, washing, ISIC division 36) exits the household as wastewater. Domestic wastewater flows are not directly covered by ISIC codes, unless the household generates water in the course of an economic activity. Note that wastewater generated by residents of communal institutions may be covered under ISIC divisions, e.g. 85 (education) or 87 (residential care activities).

Graphical user interface, application, Word Description automatically generated

Figure 2. OECD/Eurostat (left) and UNSD/UNEP (right) variables for the generation of wastewater flow. The variables used to populate the SDG Indicator 6.3.1 are highlighted in colour.

Wastewater treatment (Figure 3)

OECD/Eurostat databases disaggregate the flow of treated wastewater by type (e.g. urban and industrial discharges), whereas the UNSD database reports the flow of wastewater treated in other treatment plants and in urban wastewater treatment plants (see definitions below) by level of treatment (primary, secondary and tertiary). The variables and terms used for indicator 6.3.1 are listed below.

Urban wastewater treatment is all treatment of wastewater in Urban Wastewater Treatment Plants (UWWTP’s). UWWTP’s are usually operated by public authorities or by private companies working by order of public authorities. It includes wastewater delivered to treatment plants by trucks. UWWTP's are classified under ISIC 37 (Sewerage).

Independent treatment: Facilities for preliminary treatment, treatment, infiltration or discharge of domestic wastewater from dwellings generally between 1 and 50 population equivalents, not connected to an urban wastewater collecting system. Examples of such systems are septic tanks. Excluded are systems with storage tanks from which the wastewater is transported periodically by trucks to an urban wastewater treatment plant.

Other wastewater treatment corresponds to treatment of wastewater in any non-public treatment

plant, i.e., Industrial Wastewater Treatment Plants (IWWTPs). Excluded from "other wastewater treatment" is the treatment in septic tanks. IWWTPs may also be classified under ISIC 37 (Sewerage) or under the main activity class of the industrial establishment they belong to.

Non-treated wastewater is wastewater which doesn’t undergo any form of treatment before discharge to the environment.

Primary wastewater treatment: Treatment of wastewater by a physical and/or chemical process involving settlement of suspended solids, or other process in which the Biochemical Oxygen Demand (BOD₅) of the incoming wastewater is reduced by at least 20% before discharge and the total suspended solids of the incoming wastewater are reduced by at least 50%. To avoid double counting, water subjected to more than one type of treatment should be reported under the highest level of treatment only.

Secondary wastewater treatment: Post-primary treatment of wastewater by a process generally involving biological treatment with a secondary settlement or other process, resulting in a Biochemical oxygen demand (BOD) removal of at least 70% and a Chemical Oxygen Demand (COD) removal of at least 75%. Natural biological treatment processes are also considered under secondary treatment if the constituents of the effluents from this type of treatment are similar to the conventional secondary treatment. To avoid double counting, water subjected to more than one type of treatment should be reported under the highest level of treatment only.

Tertiary wastewater treatment: Treatment (additional to secondary treatment) of nitrogen and/or phosphorous and/or any other pollutant affecting the quality or a specific use of water: microbiological pollution, colour etc. The different possible treatment efficiencies ('organic pollution removal' of at least 95% for BOD₅, 85% for COD, 'nitrogen removal' of at least 70%, 'phosphorous removal' of at least 80% and 'microbiological removal') cannot be added and are exclusive. To avoid double counting, water subjected to more than one type of treatment should be reported under the highest level of treatment only.

For all of these treatment categories, some but not all countries have data available on the compliance of treatment to relevant effluent standards or targets. When available, such data are not routinely reported to UNSD or OECD/Eurostat, but may be available in other national data sources (e.g. statistical or wastewater analysis reports). Where available, data on the proportion of flows that meet relevant criteria will be used for indicator 6.3.1. In the absence of such data, treatment nominally classified as secondary or better (or equivalent) will be used as a proxy for safe treatment.

Figure 3. OECD/Eurostat (left) and UNSD/UNEP (right) variables for the treatment of wastewater flow. The variables to populate the SDG Indicator 6.3.1 are highlighted in colour.

Where it is possible to quantify both generation and treatment by source (industrial, service, or domestic), the proportion of wastewater treated will also be calculated separately by source.

https://unstats.un.org/unsd/statcom/51st-session/documents/2020-33-EnvironmentStats-E.pdf ↑

https://unstats.un.org/unsd/statcom/51st-session/documents/BG-item-4e-EnvironmentStats-E.pdf ↑

6. Comparability/deviation from international standards

Sources of discrepancies:

To be developed.

7. References and Documentation

URL:

References:

OECD/Eurostat, 2018. Data Collection Manual for the OECD/Eurostat Joint Questionnaire on Inland Waters and Eurostat regional water questionnaire.
UNSD, 2018. International Standard Industrial Classification of All Economic Activities, Revision 4. https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf
UNSD/UNEP Questionnaire 2018 on Environment Statistics. https://unstats.un.org/unsd/envstats/questionnaire
WHO and UN Habitat, 2018. Progress on Safe Treatment and Use of Wastewater 2018: Piloting the monitoring methodology and initial findings for SDG indicator 6.3.1.
https://www.unwater.org/publications/progress-on-wastewater-treatment-631/
Manual on the Basic Set of Environment Statistics https://unstats.un.org/unsd/envstats/fdes/manual_bses.cshtml (wastewater statistics - forthcoming)
UNSD Indicator Tables (inland water resources) (https://unstats.un.org/unsd/envstats/qindicators)

6.3.2

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

0.c. Indicator

Indicator 6.3.2: Proportion of bodies of water with good ambient water quality

0.d. Series

Proportion of open water bodies with good ambient water quality (%) EN_H2O_OPAMBQ

Proportion of river water bodies with good ambient water quality (%) EN_H2O_RVAMBQ

Proportion of groundwater bodies with good ambient water quality (%) EN_H2O_GRAMBQ

Proportion of bodies of water with good ambient water quality (%) EN_H2O_WBAMBQ

0.e. Metadata update

2022-07-07

0.f. Related indicators

6.3.1, 6.6.1, 14.1.1

0.g. International organisations(s) responsible for global monitoring

United Nations Environment Programme (UNEP)

1.a. Organisation

United Nations Environment Programme (UNEP)

2.a. Definition and concepts

Definition:

The indicator is defined as the proportion of water bodies in the country that have good ambient water quality. Ambient water quality refers to natural, untreated water in rivers, lakes and groundwaters and represents a combination of natural influences together with the impacts of all anthropogenic activities. The indicator relies on water quality data derived from in situ measurements and the analysis of samples collected from surface and groundwaters. Water quality is assessed by means of core physical and chemical parameters that reflect natural water quality related to climatological and geological factors, together with major impacts on water quality. The continuous monitoring of all surface and groundwaters is economically unfeasible and not required to sufficiently characterize the status of ambient water quality in a country. Therefore, countries select river, lake and groundwater bodies that are representative and significant for the assessment and management of water quality to monitor and report on indicator 6.3.2. The quality status of individual water bodies is classified based on the compliance of the available water quality monitoring data for the core parameters with target values defined by the country. The indicator is computed as the proportion of the number of water bodies classified as having good quality (i.e. with at least 80 % compliance) to the total number of assessed water bodies, expressed as a percentage.

Concepts:

The concepts and definitions used in the methodology have been based on existing international frameworks and glossaries (WMO, 2012) unless where indicated otherwise below.

Aquifer: Geological formation capable of storing, transmitting and yielding exploitable quantities of water.

Classification of water quality: If at least 80% of the monitoring values for prescribed parameters in a water body comply with their respective target values, the water body is classified as having a “good” water quality status. Each water body is classified as being of “good” or “not good” status.

Groundwater: Subsurface water occupying the saturated zone.

Groundwater body: A distinct volume of groundwater within an aquifer or aquifers (EU, 2000). Groundwater bodies that cross river basin district (RBD) boundaries should be divided at the boundary with each separate portion of the groundwater body being reported separately along with its respective RBD.

Lake: Inland body of standing surface water of significant extent.

Non-point-source pollution: Pollution of water bodies from dispersed sources such as fertilizers, chemicals and pesticides used in agricultural activities.

Parameter: Water quality variable or characteristic of water quality, also called a determinand.

Point source pollution: Pollution with a precisely located origin.

Pollution (of water): Introduction into water of any undesirable substance which renders the water unfit for its intended use.

Pollutant: Substance which disrupts and interferes with the equilibrium of a water system and impairs the suitability of using the water for a desired purpose.

Reservoir: Body of water, either natural or man-made, used for storage, regulation and control of water resources.

River: Large stream which serves as the natural drainage for a basin.

River basin: Geographical area having a common outlet for its surface runoff.

River basin district: Area of land, made up of one or more neighbouring river basins together with their associated groundwaters (EU, 2000).

River water body: A coherent section of a river that is discrete (does not overlap with another water body) and is significant rather than arbitrarily designated.

Stream: Flowing body of water in a natural surface channel.

Surface water: Water which flows over, or lies on, the ground surface.

Note: Indicator 6.3.2 does not include the monitoring of water quality in wetlands under monitoring level 1.

Target value: A value (or range) for any given water quality parameter that indicates the threshold for a designated water quality, such as good water quality rather than acceptable water quality.

Toxic substance: Chemical substance which can disturb the physiological functions of humans, animals and plants.

Transboundary waters: Surface or ground waters which mark, cross or are located on boundaries between two or more States; wherever transboundary waters flow directly into the sea, these transboundary waters end at a straight line across their respective mouths between points on the low-water line of the banks (UNECE, 1992).

Water quality index: The measured water quality results for all parameters combined into a numeric value for each monitoring location. These scores are then aggregated over the time of the assessment period. The index score can range between zero (worst) to 100 (best).

2.b. Unit of measure

Percent (%): The proportion of the number of bodies of water with good water quality compared to the total number of assessed water bodies expressed as a percent.

To classify whether a water body is of “good ambient water quality” or not, a threshold is applied where 80 percent or more of monitoring values meet their target values. The number of water bodies that are classified as either good ambient water quality or not can be reported at the Reporting Basin District, and then at the national level to generate the national indicator score.

2.c. Classifications

Classification of inland water bodies (UNEP uses this classification, but does not analyze water quality for all categories, but only for lakes and rivers.): https://unstats.un.org/unsd/classifications/Family/Detail/2002

Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)

3.a. Data sources

The recommended sources of data are water quality monitoring data derived from in situ measurements and the analysis of samples collected from surface and groundwaters in national or sub-national ambient water quality monitoring programmes implemented by governmental authorities. Additional water quality monitoring data from research or citizen-science monitoring programmes can be used to supplement the available authoritative monitoring data, provided they are authorised by the national reporting agency.

The number of monitoring locations required to determine the quality status of a water body depends on the type and size of the water body, but a minimum of one monitoring location per water body is required. The minimum data requirements for calculating this indicator are measurements for all of the recommended or alternative core parameters appropriate to the type of water body as defined in the methodology.

Measurements should be taken routinely, at prescribed intervals, or the same time of year each year, from the same locations. Even if new monitoring stations are introduced, data should continue to be collected from the original locations. This ensures that results are comparable between reports, thereby enabling trends to be established over time. The monitoring data needed for the indicator computation may be collected by different monitoring programmes involving different agencies and organizations. It is therefore important to establish and maintain centralized data repositories at the national level that collate the data from the various stakeholders, ensuring compatibility in reporting units between all agencies submitting data. Data should be compiled for each core parameter at each sampling location in order to calculate the indicator.

3.b. Data collection method

The data is collected by UNEP and its Global Environment Monitoring System for Water (GEMS/Water) through electronic reporting in the global water quality information system GEMStat. At the national level, data reports are provided by the GEMS/Water National Focal Points or any other official counterpart appointed by the respective government. GEMS/Water offers consultation and support in selecting and compiling the required monitoring data, defining suitable river basin districts and delineating water bodies, as well as computing the indicator, upon request through its helpdesk. Data reported by the countries are checked for consistency with respect to the monitoring parameters, target values and spatial units and compared with monitoring data available in GEMStat, if applicable.

3.c. Data collection calendar

First reporting cycle: 2017
Second reporting cycle: 2020
Third reporting cycle: 2023
Fourth reporting cycle: 2026
Fifth reporting cycle: 2029

3.d. Data release calendar

First reporting cycle: 2018
Second reporting cycle: 2021
Third reporting cycle: 2024
Fourth reporting cycle: 2027
Fifth reporting cycle: 2030

3.e. Data providers

GEMS/Water National Focal Points in relevant Ministries, Water Authorities, National Statistical Offices etc. or their nominated representative.

3.f. Data compilers

United Nations Environment Programme (UNEP)
UNEP GEMS/Water Data Centre, International Centre for Water Resources and Global Change (ICWRGC), German Federal Institute of Hydrology (BfG)

3.g. Institutional mandate

Identification of UNEP as custodian agency for SDG indicator 6.3.2 by Inter-agency and Expert Group on SDG Indicators. GEMS/Water is the mechanism within UNEP supporting countries on all aspects around ambient freshwater quality.

4.a. Rationale

Good ambient water quality is essential for protecting aquatic ecosystems and the services they provide, including: the preservation of biodiversity; the protection of human health during recreational use and through the provision of drinking water; the support of human nutrition through the provision of fish and water for irrigation; the enabling of a variety of economic activities; and the strengthening of the resilience of people against water-related disasters. Good ambient water quality is therefore closely linked to the achievement of many other Sustainable Development Goals.

Target 6.3 aims at improving water quality and indicator 6.3.2 provides a mechanism for determining whether, and to which extent, water quality management measures are contributing to the improvement of water quality over time. The indicator is also directly linked to indicator 6.3.1 on wastewater treatment because inadequate wastewater treatment leads to degradation in quality of the waters receiving the wastewater effluents. It directly informs progress towards target 6.3 and is strongly linked to target 6.6 on water-related ecosystems, as well as target 14.1 on marine pollution (coastal eutrophication).

The methodology recognises that countries have different capacity levels to monitor water quality, with many developed countries operating extensive and complex programmes that collect and report data to existing reporting frameworks beyond the scope of this methodology. For these countries it is recognised that this methodology will not contribute to improving their water quality; however it must be sufficiently flexible to capture data from existing monitoring frameworks without burdening countries with additional reporting obligations. Conversely, many of the least developed countries currently do not monitor water quality or operate very limited monitoring programmes. The methodology must therefore allow these countries to contribute to the global indicator, according to their national capacity and available resources.

The development of the methodology builds on best practice for water quality monitoring promoted by the UNEP GEMS/Water programme since 1978 together with testing by several pilot countries during the Integrated Monitoring Initiative Proof of Concept phase of 2016, and external review by experts and international organizations. This led to revision of the original methodology, which was then further tested through the 2017 global data drive. The feedback received has contributed to the present refined methodology.

4.b. Comment and limitations

The monitoring and reporting of SDG Indicator 6.3.2 requires considerable national financial and human capacities to regularly measure water quality parameters at sufficient spatial and temporal resolutions, and to consistently collect, quality-assure and process the monitoring data to compute the indicator. Substantial investments in monitoring and data management infrastructures, as well as targeted capacity development in water quality monitoring programme design and operation, will be required in many countries to enhance national capacities to regularly and consistently report on the indicator.

Recognizing the differences in monitoring and data processing capacities among countries, the indicator methodology offers a progressive monitoring approach allowing countries to start with reporting based on their existing capacity and progressively enhance the data coverage and indicator significance with increasing capacity.

4.c. Method of computation

Computation Method:

The indicator is computed by first classifying all assessed water bodies based on the compliance of the monitoring data collected for selected parameters at monitoring locations within the water body with parameter-specific target values:

$C_{w q} = \frac{n_{c}}{n_{m}} \times 100$

Where

$C_{w q}$ is the percentage compliance [%];

$n_{c}$ is the number of monitoring values in compliance with the target values;

$n_{m}$ is the total number of monitoring values.

A threshold value of 80% compliance is defined to classify water bodies as “good” quality. Thus, a body of water is classified as having a good quality status if at least 80% of all monitoring data from all monitoring stations within the water body comply with the respective targets.

In a second step, the classification results are used to compute the indicator as the proportion of the number of water bodies classified as having a good quality status to the total number of classified water bodies expressed in percentage:

$W B G Q = \frac{n_{g}}{n_{t}} \times 100$

Where

$W B G Q$ is the percentage of water bodies classified as having a good quality status;

$n_{g}$ is the number of classified water bodies classified as having a good quality status;

$n_{t}$ is the total number of monitored and classified water bodies.

4.d. Validation

The UNEP SDG6 Helpdesk assists countries in ensuring the quality of their submission during its preparation.

Following the initial submission, the Helpdesk undertakes several checks on the data and calrifies any irregularities with the country technical focal point until both sides agree to finalize the report.

The data is then submitted to the UNEP SDG focal point, who collates all indicators data for which UNEP is the Custodian Agency, where a further quality check is undertaken, prior to submission to the SDG Global Database.

4.e. Adjustments

In case national definitions such as water quality target values change, countries can retroactively adjust previous submissions.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Missing values are not imputed.

• At regional and global levels

Missing values are not imputed.

4.g. Regional aggregations

The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see:

https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

6.3.2 Online Support Platform with official methodology, technical materials, case studies and presentations to guide the reporting process available under: https://communities.unep.org/display/sdg632

SDG 6.3.2 Helpdesk reachable via: sdg632@un.org (Q&A, arranging of individual support calls, indicator calculation services etc.).

Various capacity development activities around the indicator: online webinars, country visits, workshops.

4.i. Quality management

The GEMS/Water Data Centre is hosted by the Federal Institute of Hydrology, a government entity of the Federal Republic of Germany and complies with the government’s quality management, assurance, and assessment procedures.

4.j. Quality assurance

See 4.i

4.k. Quality assessment

See 4.i.

5. Data availability and disaggregation

Data availability:

An initial baseline data collection has been conducted in 2017 with 48 country data submissions as of February 2018.

Time series:

Second reporting cycle 2020: 89 submissions as of February 2021.

Disaggregation:

Depending on the level of detail provided by countries in their submissions, the indicator can be disaggregated by water body type (river, lake, groundwater) and river basin district. This disaggregated data can support informed decision-making at the national and sub-national levels to monitor and improve water quality management measures.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable as no internationally estimated data is used to impute.

7. References and Documentation

URL: http://www.sdg6monitoring.org/indicators/target-63/indicators632/

References:

EU (European Parliament, Council of the European Union), 2000. Water Framework Directive (WFD) 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy, Official Journal L327, 1–72. Available at: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0060

UNECE, 1992. Convention on the Protection and Use of Transboundary Watercourses and International Lakes. Available at: http://www.unece.org/fileadmin/DAM/env/water/pdf/watercon.pdf

WMO, 2012. International Glossary of Hydrology. No. 385 World Meteorological Organization and United Nations Educational, Scientific and Cultural Organization. Available at: http://library.wmo.int/pmb_ged/wmo_385-2012.pdf

6.4.1

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity

0.c. Indicator

Indicator 6.4.1: Change in water-use efficiency over time

0.d. Series

Water Use Efficiency (United States dollars per cubic meter) (ER_H2O_WUEYST)

0.e. Metadata update

2023-05-15

0.f. Related indicators

This indicator needs to be combined with the water stress indicator 6.4.2 to provide adequate follow-up of the target 6.4.

Other indicators, specifically those for Targets 1.1, 1.2, 2.1, 2.2, 5.4, 5.a, 6.1, 6.2, 6.3, 6.5 will complement the information provided by this indicator.

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

Change in water use efficiency over time (CWUE): The change in the ratio of the value added to the volume of water use, over time.

Water Use Efficiency (WUE) is defined as the value added of a given major sector^{^[1]} divided by the volume of water used. Following the United Nations International Standard Industrial Classification of All Economic Activities ISIC 4 coding , sectors are defined as:

agriculture; forestry; fishing (ISIC A), hereinafter “agriculture”;
mining and quarrying; manufacturing; electricity, gas, steam and air conditioning supply; constructions (ISIC B, C, D and F), hereinafter “MIMEC”;
all the service sectors (ISIC E and ISIC G-T), hereinafter “services”.

Concepts:

Water use: water that is received by an industry or households from another industry or is directly abstracted. [SEEA-Water (ST/ESA/STAT/SER.F/100), par. 2.21]
Water abstraction: water removed from the environment by the economy. [SEEA-Water (ST/ESA/STAT/SER.F/100), par. 2.9]
Water use for irrigation (km³/year)
- Annual quantity of water used for irrigation purposes. It includes water from renewable freshwater resources, as well as water from over-abstraction of renewable groundwater or abstraction of fossil groundwater, direct use of agricultural drainage water, (treated) wastewater, and desalinated water. [AQUASTAT Glossary]
Water use for livestock (watering and cleaning) (km³/year)
- Annual quantity of water used for livestock purposes. It includes water from renewable freshwater resources, as well as water from over-abstraction of renewable groundwater or abstraction of fossil groundwater, direct use of agricultural drainage water, (treated) wastewater, and desalinated water. It includes livestock watering, sanitation, cleaning of stables, etc. If connected to the public water supply network, water used for livestock is included in the services water use. [AQUASTAT Glossary]
Water use for aquaculture (km³/year)
- Annual quantity of water used for aquaculture. It includes water from renewable freshwater resources, as well as water from over-abstraction of renewable groundwater or abstraction of fossil groundwater, direct use of agricultural drainage water, (treated) wastewater, and desalinated water. Aquaculture is the farming of aquatic organisms in inland and coastal areas, involving intervention in the rearing process to enhance production and the individual or corporate ownership of the stock being cultivated. [AQUASTAT Glossary]
Water use for the MIMEC sectors (km³/year)
- Annual quantity of water used for the MIMEC sector. It includes water from renewable freshwater resources, as well as over-abstraction of renewable groundwater or abstraction of fossil groundwater and use of desalinated water or direct use of (treated) wastewater. This sector refers to self-supplied industries not connected to the public distribution network. [AQUASTAT Glossary. To be noted that in AQUASTAT, the sectors included in the MIMEC group are referred to as “industry”]^[2]
Water use for the services sectors (km³/year)
- Annual quantity of water used primarily for the direct use by the population. It includes water from renewable freshwater resources, as well as over-abstraction of renewable groundwater or abstraction of fossil groundwater and the use of desalinated water or direct use of treated wastewater. It is usually computed as the total water used by the public distribution network. It can include that part of the industries, which is connected to the municipal network. [AQUASTAT Glossary. To be noted that in AQUASTAT, the sectors included in “services” are referred to as “municipal”]
Value added (gross)
- Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. [WB Databank, metadata glossary, modified]
Arable land
- Arable land is the land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for “Arable land” are not meant to indicate the amount of land that is potentially cultivable. [FAOSTAT]
Permanent crops
- Permanent crops are the land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under "forest"). Permanent meadows and pastures are excluded from land under permanent crops. [FAOSTAT]
Proportion of irrigated land on the total cultivated land
- Total harvested irrigated crop area, expressed in percentage. Area under double irrigated cropping (same area cultivated and irrigated twice a year) is counted twice.

In order to maintain consistency with the terminology used in SEEA-Water, the terms water use and water abstraction are utilized in this text. In particular, “water abstraction” must be considered synonym of “water withdrawal, as expressed in both AQUASTAT and the statement of the SDG target 6.4. ↑

In AQUASTAT, as well as in the World Bank databank and in other national and international datasets, the MIMEC sector is referred to as “Industry”. Also, SEEA-Water uses the term “industrial use” of water. ↑

2.b. Unit of measure

The unit of the indicator is expressed in Value/Volume, commonly USD/m³.

2.c. Classifications

System of Environmental-Economic Accounting for Water (SEEA-water)

SEEA-water is used to define the concept of “water use” in the context of this indicator, and to describe the water flows among users.

International Standard Industrial Classification of All Economic Activities, revision 4

ISIC-4 is used as the standard for the definition of the economic sectors.

3.a. Data sources

Data needed for the calculation of the indicator are administrative data collected at country level by the relevant institutions, either technical (for water and irrigation) or economic (for value added). Official counterparts at country level are the national statistics offices and/or the line Ministry for water resources and irrigation. More specifically, FAO requests countries to nominate a National Correspondent to act as the focal point for the data collection and communication. Data are mainly published within national statistical yearbooks, national water resources and irrigation master plans, and other reports (such as those from projects, international surveys or results and publications from national and international research centres).

3.b. Data collection method

Data collection is done through FAO's global information system on water and agriculture (AQUASTAT)

and the AQUASTAT questionnaire on water and agriculture. The data collection process relies on a network of National Correspondents, officially nominated by their respective countries, in charge of the provision of official national data to AQUASTAT. As of August 2020, 150 countries have nominated national correspondents, as well as alternate correspondents from different agencies. Countries submit data through the annual AQUASTAT questionnaire on water and agriculture, which contains- among others - the information required for the calculation of SDG indicator 6.4.1. Regarding the economic indicators Gross Value Added (GVA), FAO uses UNSD database and aggregates it following the revision 4 ISIC-4 is used as the standard for the definition of the economic sectors.

3.c. Data collection calendar

Data are collected every year through the AQUASTAT network of National Correspondents. FAO has dispatched the questionnaires to the National Correspondents in July 2021.

3.d. Data release calendar

Data are released every year, usually in February following the UNSD collection schedule.

3.e. Data providers

Data come from governmental sources. Data providers are different depending on the country. In many cases data collection at country level is coordinated by the National Statistics Office (NSO). Data not generated by a country is displayed with an appropriate qualifier.

3.f. Data compilers

Calculation rules are predefined and use data referring to the same year to generate aggregate values.

3.g. Institutional mandate

FAO has a mandate to “collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture”. (FAO Constitution, Article 1)

4.a. Rationale

The rationale behind this indicator consists in providing information on the efficiency of the economic and social usage of water resources, i.e., value added generated by the use of water in the main sectors of the economy, and distribution network losses.

The distribution efficiency of water systems is implicit within the calculations and could be made explicit if needed and where data are available.

This indicator addresses specifically the target component “substantially increase water-use efficiency across all sectors”, by measuring the output per unit of water from productive uses of water as well as losses in municipal water use. It does not aim at giving an exhaustive picture of the water utilization in a country. Other indicators, specifically those for Targets 1.1, 1.2, 2.1, 2.2, 5.4, 5.a, 6.1, 6.2, 6.3, 6.5 will complement the information provided by this indicator. In particular, the indicator needs to be combined with the water stress indicator 6.4.2 to provide adequate follow-up of the target 6.4.

Together, the three sectoral efficiencies provide a measure of overall water efficiency in a country. The indicator provides incentives to improve water use efficiency through all sectors, highlighting those sectors where water use efficiency is lagging behind.

The interpretation of the indicator would be enhanced by the utilization of supplementary indicators to be used at country level. Particularly important in this sense would be the indicator on efficiency of water for energy and the indicator on the efficiency of the municipality distribution networks.

4.b. Comment and limitations

The corrective coefficient, C_r, for the agricultural sector is needed in order to focus the indicator on the irrigated production. This is done for two main reasons:

To ensure that only runoff water and groundwater (so-called blue water) are considered in computing the indicator;
To eliminate a potential bias of the indicators, which otherwise would tend to decrease if rainfed cropland is converted to irrigated.

4.c. Method of computation

Computation Method:

Water use efficiency is computed as the sum of the three sectors listed above, weighted according to the proportion of water used by each sector over the total use. In formula:

Where:

WUE = Water use efficiency

A_we = Irrigated agriculture water use efficiency [USD/m³]

M_we = MIMEC water use efficiency [USD/m³]

S_we = Services water use efficiency [USD/m³]

P_A = Proportion of water used by the agricultural sector over the total use

P_M = Proportion of water used by the MIMEC sector over the total use

P_S = Proportion of water used by the service sector over the total use

The computing of each sector is described below.

Water use efficiency in irrigated agriculture is calculated as the agricultural value added per agricultural water use, expressed in USD/m³.

In formula:

Where:

A_we = Irrigated agriculture water use efficiency [USD/m³]

GVA_a= Gross value added by agriculture (excluding river and marine fisheries and forestry) [USD]

C_r = Proportion of agricultural GVA produced by rainfed agriculture

V_a = Volume of water used by the agricultural sector (including irrigation, livestock and aquaculture) [m³]

The volume of water used by the agricultural sectors (V) is collected at country level through national records and reported in questionnaires, in units of m³/year (see example in AQUASTAT http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls). Agricultural value added in national currency is obtained from national statistics, converted to USD and deflated to the baseline year.

C_r can be calculated from the proportion of irrigated land on the total Arable land and Permanent crops (hereinafter “cultivated land”, as follows:

Where:

A_i = proportion of irrigated land on the total cultivated land, in decimals

0.563 = generic default ratio between rainfed and irrigated yields

More detailed estimations are however possible and encouraged at country level.

Water efficiency of the MIMEC sectors (including power production): MIMEC value added per unit of water used for the MIMEC sector, expressed in USD/m³.

In formula:

Where:

M_we = Industrial water use efficiency [USD/m³]

GVA_m = Gross value added by MIMEC (including energy) [USD]

V_m = Volume of water used by MIMEC (including energy) [m³]

MIMEC water use (V_m) is collected at country level through national records and reported in questionnaires, in units of m³/year (see example in AQUASTAT http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls). MIMEC value added is obtained from national statistics, deflated to the baseline year.

Services water supply efficiency is calculated as the service sector value added (ISIC 36-39 and ISIC 45-98) divided by water used for distribution by the water collection, treatment and supply industry (ISIC 36), expressed in USD/m³.

In formula:

Where:

S_we = Services water use efficiency [USD/m³]

GVA_s = Gross value added by services [USD]

V_s = Volume of water used by the service sector [m³]

Data on volumes of used and distributed water are collected at country level from the municipal supply utilities records and reported in questionnaires, in units of km³/year or million m³/year (see example in AQUASTAT http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls). Services value added is obtained from national statistics, deflated to the baseline year.

Change in water use efficiency (CWUE) is computed as the ratio of water use efficiency (WUE) in time t minus water use efficiency in time t-1, divided by water use efficiency in time t-1 and multiplied by 100:

It must be noted that computing the indicator in an aggregated manner, i.e. total GDP over total water use, would lead to an overestimation of the indicator. That is due to the fact that, for the agricultural sector, only the value produced under irrigation has to be counted in calculating the indicator. Hence, the sum of the value added of the various sectors used in these formulas is not equivalent to the total GDP of the country.

4.d. Validation

Data validation is done in a number of steps.

The AQUASTAT questionnaire embeds automatic validation rules to allow National Correspondents to identify any data consistency errors while compiling the data.
Once the questionnaire is submitted, FAO thoroughly reviews the information reported, using the following tools:
- Manual cross-variable check. This includes cross-comparison with similar countries as well as historic data for the countries.
- Time-series coherency by running an R-script to compare reported data with those corresponding to previous years

Verification of the metadata, in particular the source of the proposed data. The critical analysis of the compiled data gives preference to national sources and expert knowledge.

After this verification, exchanges between the National Correspondents and FAO takes place to correct and confirm the collected data.
The last validation step is an automated validation routine included in the Statistical Working System (SWS), which uses almost 200 validation rules.

4.e. Adjustments

Since national level data is frequently tailored to be useful at national level and not for international comparisons, data may be manipulated in order to maximize international comparability. Adjusted data is displayed with an appropriate qualifier. Data is rounded according to a specific methodology http://www.fao.org/aquastat/en/databases/maindatabase/metadata/

Additionally, the Statistical Working System (SWS) has the correspondence among different international codes (FAOSTAT, UNSDM49, ISO2, ISO3) for geographic areas and is used to convert area codes in the external sources to UNSDM49 codes which is the standard used in the SWS.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

If scattered data (over time) are available, linear interpolation method takes place if there are at least two non-missing values in the time series. If not, the only possible way to impute it is through the carry-forward. Imputed data is displayed with an appropriate qualifier.

• At regional and global levels

If country data are missing, the value of the indicator will be considered in the average of the others in the same region. Imputed data is displayed with an appropriate qualifier.

4.g. Regional aggregations

The aggregation for global and regional estimations is done by summing up the values of the various parameters constituting the elements of the formula, i.e. value added by sector and water use by sector. The aggregated indicator is then calculated by applying the formula with those aggregated data, as if it were a single country.

An Excel sheet with the calculations exists, and can be shared with the IAEG if required.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

A set of tools is available to countries for the compilation of the indicator. Among them, a step-by-step methodological guide, an interpretation paper, and an e-learning course. All the tools are available on the FAO web pages, at: http://www.fao.org/sustainable-development-goals/indicators/641/en/
During 2020 and 2021, FAO has organized four virtual trainings for Asia, Latin-America and the Caribbean and Africa on SDG 6.4.
FAO’s AQUASTAT team provides continued guidance to the countries through the National Correspondents during the data collection time to ensure data is duly and timely compiled.

4.i. Quality management

The annual AQUASTAT questionnaire, used for collecting information on SDG indicator 6.4.1 has been endorsed by FAO’s Office of the Chief Statistician (OCS).
During the SDG reporting process, the OCS provides overall guidance, including metadata reporting, based on the Metadata Dissemination Standard approved by the FAO IDWG-Statistics Technical Task Force.
After revision and validation, SDG indicators are submitted to the OCS which also ensures the quality of the data and results.

4.j. Quality assurance

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: http://www.fao.org/docrep/019/i3664e/i3664e.pdf provides the necessary principles, guidelines, and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO’s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).

4.k. Quality assessment

Overall evaluation of data quality is based on standard quality criteria and follows FAO’s SQAF. It also includes:

A qualitative and quantitative manual cross-variable check after data is received. This consists of the verification that all the numbers are consistent based on the internal validation rules embedded in the questionnaire. Any issues identified are flagged and listed to be followed-up with the countries.
Time-series coherency check done by running an R-script to compare reported data with those corresponding to previous years. Based on this, a scattered diagram is also made by variable and country to allow for a visual verification of historical data. The critical analysis of the compiled data gives preference to national sources and expert knowledge, unless these greatly diverge from historic data or in the case of drastic changes in methodologies used by countries.
Verification of the metadata, in particular the source of the proposed data. When data sources are not provided, the questionnaire is added as the data source of a given value.

5. Data availability and disaggregation

Data availability:

The data needed for the indicator are collected through AQUASTAT and other databases (FAOSTAT, UNSD) for 168 countries worldwide

Time series:

1961-2019 (Discontinuous depending on the country. Data are interpolated to create timelines).

Disaggregation:

The indicator covers all the economic sectors according to the ISIC classification, providing the means for more detailed analysis of the water use efficiency for national planning and decision-making.

Although the subdivision into three major aggregated economic sectors is sufficient for the purpose of compiling the indicator, wherever possible it is advisable to further disaggregate the indicator, according to the following criteria:

Economically, a more refined subdivision of the economic sector can be done using ISIC Rev.4 by the following groups:
- Agriculture, Forestry and Fisheries (ISIC A);
- Mining and Quarrying (ISIC B);
- Manufacturing (ISIC C);
- Electricity, Gas, Steam and Air Conditioning Supply (ISIC D);
- Water Supply, Sewerage, Waste Management and Remediation Activities (ISIC E), by
- Water Collection, Treatment and Supply (ISIC 36)
  - Sewerage (ISIC 37)
  - Construction (ISIC F)
- Other industries (sum of remaining industries)
Geographically, computing the indicator by river basin, watershed or administrative units within a country.

These levels of disaggregation, or a combination of those, will give further insight on the dynamics of water use efficiency, providing information for remedial policies and actions.

Data are vertically interpolated in the presence of missing values to allow for a time series analysis.

6. Comparability/deviation from international standards

Geographical: Regional differences, especially in relation to irrigated agriculture and different climatic conditions (including variability) are to be considered in the interpretation of this indicator, especially in countries with substantial amounts of available water resources. Also for this reason, coupling this indicator with water stress (6.4.2) is important for the interpretation of the data.

Over-time: time series are comparable across time.

7. References and Documentation

AQUASTAT main page: http://www.fao.org/aquastat/en/
AQUASTAT glossary: http://www.fao.org/aquastat/en/databases/glossary/
AQUASTAT Main country database: http://www.fao.org/aquastat/statistics/query/index.html
AQUASTAT Water use: http://www.fao.org/aquastat/en/overview/methodology/water-use/
AQUASTAT Water resources: http://www.fao.org/aquastat/en/overview/methodology/water-resources/
AQUASTAT publications dealing with concepts, methodologies, definitions, terminologies, metadata, etc.: http://www.fao.org/aquastat/en/resources/
AQUASTAT methodology - quality Control: https://www.fao.org/aquastat/en/overview/methodology#main
AQUASTAT metadata http://www.fao.org/aquastat/en/databases/maindatabase/metadata/
AQUASTAT Statistical working system (SWS). Migration of the Statistical Processes into the SWS. https://sws-methodology.github.io/faoswsAquastat/index.html#welcome
FAOSTAT production database: http://faostat3.fao.org/download/Q/*/E
UNSD/UNEP Questionnaire on Environment Statistics – Water Section

http://unstats.un.org/unsd/environment/questionnaire.htm

http://unstats.un.org/unsd/environment/qindicators.htm

Framework for the Development of Environment Statistics (FDES 2013) (Chapter 3): http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf
International Recommendations for Water Statistics (IRWS) (2012): https://unstats.un.org/unsd/publication/seriesM/seriesm_91e.pdf
OECD/Eurostat Questionnaire on Environment Statistics – Water Section: http://ec.europa.eu/eurostat/web/environment/water
OECD National Accounts data files: http://www.oecd-ilibrary.org/economics/data/oecd-national-accounts-statistics_na-data-en
SEEA-Water: https://seea.un.org/sites/seea.un.org/files/seeawaterwebversion_final_en.pdf
SEEA Central Framework: https://seea.un.org/sites/seea.un.org/files/seea_cf_final_en.pdf
UNSD National Accounts Main Aggregates Database: http://unstats.un.org/unsd/snaama/selbasicFast.asp
World Bank Databank (World Economic Indicators) http://databank.worldbank.org/data/home.aspx
ISIC rev. 4:

https://unstats.un.org/unsd/classifications/Econ/Download/In%20Text/CPCprov_english.pdf

FAO e-learning course SDG Indicator 6.4.1 - Change in water-use efficiency over time: https://elearning.fao.org/course/view.php?id=475

6.4.2

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

0.c. Indicator

Indicator 6.4.2: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources

0.d. Series

Level of water stress: freshwater withdrawal as a proportion of available freshwater resources (%) (ER_H2O_STRESS)

0.e. Metadata update

2023-05-15

0.f. Related indicators

6.4.1: Change in water-use efficiency over time

6.1.1: Proportion of population using safely managed drinking water services

6.3.1: Proportion of wastewater safely treated

6.6.1: Change in the extent of water-related ecosystems over time

6.5.1: Degree of integrated water resources management implementation (0-100)

2.4.1: Proportion of agricultural area under productive and sustainable agriculture

15.3.1: Proportion of land that is degraded over total land area

1.5.1: Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

11.5.1: Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definition:

The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental flow requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity.

Concepts:

This indicator provides an estimate of pressure by all sectors on the country’s renewable freshwater resources. A low level of water stress indicates a situation where the combined withdrawal by all sectors is marginal in relation to the resources, and has therefore little potential impact on the sustainability of the resources or on the potential competition between users. A high level of water stress indicates a situation where the combined withdrawal by all sectors represents a substantial share of the total renewable freshwater resources, with potentially larger impacts on the sustainability of the resources and potential situations of conflicts and competition between users.

Total renewable freshwater resources (TRWR) are expressed as the sum of internal and external renewable water resources. The terms “water resources” and “water withdrawal” are understood here as freshwater resources and freshwater withdrawal.

Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation.

External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties.

Total freshwater withdrawal (TFWW) is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and services^[1]. It is estimated at the country level for the following three main sectors: agriculture, services (including domestic water withdrawal) and industries (including cooling of thermoelectric plants). Freshwater withdrawal includes fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water.

Environmental flow requirements (EFR) are defined as the quantity and timing of freshwater flows and levels necessary to sustain aquatic ecosystems, which, in turn, support human cultures, economies, sustainable livelihoods, and wellbeing. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of.^[2] Methods of computation of EFR are extremely variable and range from global estimates to comprehensive assessments for river reaches. For the purpose of the SDG indicator, water volumes can be expressed in the same units as the TFWW, and then as percentages of the available water resources.

In AQUASTAT, Services water withdrawal is reported as Municipal water withdrawal. ↑

They are indeed taken into account by other targets and indicators, such as 6.3.2, 6.5.1 and 6.6.1. ↑

2.b. Unit of measure

Percent (%)

2.c. Classifications

The System of Environmental-Economic Accounting for Water: SEEA-Water for water resources and withdrawals (Available at https://seea.un.org/content/seea-water
The World Census of Agriculture 2020: WCA (Volume 1), for irrigation definitions (Available at: http://www.fao.org/world-census-agriculture).

3.a. Data sources

Data for this indicator are usually collected by national ministries and institutions having water-related issues in their mandate, such as national statistic offices, ministries of water resources, agriculture or environment. Official counterparts at country level are the national statistics office and/or the line ministry for water resources and irrigation. More specifically, FAO requests countries to nominate a National Correspondent to act as the focal point for the data collection and communication. Data are mainly published within national statistical yearbooks, national water resources and irrigation master plans and other reports (such as those from projects, international surveys or results and publications from national and international research centres).

3.b. Data collection method

Data collection is done through FAO’s Global Information System on Water and Agriculture (AQUASTAT) and AQUASTAT questionnaire on water and agriculture. The data collection process relies on a network of National Correspondents, officially nominated by their respective countries, in charge of the provision of official national data to AQUASTAT. As at August 2020, 150 countries have nominated national correspondents as well as alternate correspondents from different agencies. Countries submit data through the annual AQUASTAT questionnaire on water and agriculture, which contains, among others, the information required for the calculation of SDG indicator 6.4.2.

3.c. Data collection calendar

Data are collected every year through the AQUASTAT network of National Correspondents. FAO has dispatched the questionnaires to the National Correspondents in July 2022.

3.d. Data release calendar

Data for the indicator are released every year, usually in February following the UNSD collection schedule.

3.e. Data providers

Data come from governmental sources. The institutions responsible for data collection at national level vary according to countries. However, in general data for this indicator are provided by the Ministry of Agriculture, Ministry of Water, Ministry of Environment and other line Ministries. In many cases, data collection at country level is coordinated by the National Statistics office (NSO).

3.f. Data compilers

Calculation rules are predefined and use data referring to the same year to general aggregate values.

3.g. Institutional mandate

FAO has, as part of its mandate, the function of “collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture”. (FAO Constitution, Article 1)

4.a. Rationale

The purpose of this indicator is to show the degree to which water resources are being exploited to meet the country's water demand. It measures a country's pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress regarding “withdrawals and supply of freshwater to address water scarcity”, i.e. the environmental component of target 6.4.

The indicator shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of the indicator indicate that water does not represent a particular challenge for economic development and sustainability.

However, extremely low values may indicate the inability of a country to use properly its water resources for the benefit of the population. In such cases, a moderate and controlled increase in the value of the indicator can be a sign of positive development.

The indicator is computed based on three components:

Total renewable freshwater resources (TRWR)

Total freshwater withdrawal (TFWW)

Environmental flow requirements (EFR)

4.b. Comment and limitations

Freshwater withdrawal as a percentage of renewable freshwater resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management.

Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, especially in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator. Although there is no universal consensus on such thresholds, a proposal is presented below.

Trends in freshwater withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other.

Estimation of water withdrawal by sector may represent a limitation to the computation of the indicator. Few countries publish water withdrawal data on a regular basis by sector.

There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress.

Other limitations that affect the interpretation of the water stress indicator include:

difficulty to obtain accurate, complete and up-to-date data;
potentially large variation of sub-national data;
lack of account of historical (e.g., due to climate change and population growth) and seasonal variations in water resources;
lack of consideration to the distribution among water uses;
lack of consideration of water quality and its suitability for use; and
the indicator can be higher than 100 percent when water withdrawal non-renewable freshwater (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when freshwater withdrawal includes part or all the water set aside for environmental flow requirements.

Some of these issues can be solved through disaggregation of the indicator at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count.

4.c. Method of computation

Computation Method:

Method of computation: The indicator is computed as the total freshwater withdrawn (TFWW) divided by the difference between the total renewable freshwater resources (TRWR) and the environmental flow requirements (EFR), multiplied by 100. All variables are expressed in km³/year (10⁹ m³/year).

$S t r e s s (%) = \frac{T F W W}{(T R W R - E F R)} \times 100$

Following the experience of the initial five years of application of the indicator, and consistent with the approach taken during the MDG program, the threshold of 25% has been identified as the upper limit for a full and unconditional safety of water stress as assessed by the indicator 6.4.2.

That means on one hand, that values below 25% can be considered safe in any instance (no stress); on the other, that values above 25% should be regarded as potentially and increasingly problematic, and should be qualified and/or reduced.

Above 25% of water stress, four classes have been identified to signal different levels of stress severity:

NO STRESS <25%
LOW 25% - 50%
MEDIUM 50% - 75%
HIGH 75-100%
CRITICAL >100%

4.d. Validation

Data validation is done in a few steps.

the AQUASTAT questionnaire embeds automatic validation rules to allow National Correspondents to identify any data consistency errors while compiling the data.
Once the questionnaire is submitted, FAO thoroughly reviews the information reported, using the following tools:
- Manual cross-variable check. This includes cross-comparison with similar countries as well as historic data for the countries.
- Time-series coherency by running an R-script to compare reported data with those corresponding to previous years
- Verification of the metadata, especially the source of the proposed data. The critical analysis of the compiled data gives preference to national sources and expert knowledge.
After this verification, exchanges between the National Correspondents and FAO take place to correct and confirm the collected data.
The last validation step is an automated validation routine included in the Statistical Working System (SWS), which uses almost 200 validation rules.

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Three types of imputation are made at country level to fill in missing years in the timeseries:

Linear imputation: between two available data-points.
Carry forward: after the last available data-points and up to 10 years.
Vertical imputation: in case of available total freshwater withdrawal but missing disaggregation by sources, and if existing disaggregation existed for previous years, the respective ratio by sources is applied to the available total.

• At regional and global levels

Thanks to the imputation methods at country level, data will be available for the whole time series (unless the latest official value was obtained more than 10 years ago). Imputed data is displayed with an appropriate qualifier.

4.g. Regional aggregations

Regional and global estimates will be done by summing up the national figures on renewable freshwater resources and total freshwater withdrawal, considering only the internal renewable water resources of each country to avoid double counting, and the external renewable freshwater resources of the region, if any. In case of regional aggregation without physical continuity (such as income groupings or Least Developed Countries group, etc.), total renewable water resources are summed up. The EFR at regional level is estimated as the average of the countries’ EFRs, in percentage, and applied to the regional water resources.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

A set of tools is available to countries for the compilation of the indicator. Among them, a step-by-step methodological guide, an interpretation paper, and an e-learning course. All the tools are available on the FAO web pages, at: http://www.fao.org/sustainable-development-goals/indicators/642/en/.
During 2020,2021 and 2022, FAO has organized regional virtual trainings for Asia, Latin-America and the Caribbean and Africa on SDG 6.4. and contributed to global workshops on SDG 6.
FAO’s AQUASTAT team provides continued guidance to the countries thought the National Correspondents during the data collection time to ensure data is duly and timely compiled.

4.i. Quality management

The AQUASTAT questionnaire on water and agriculture, used for collecting information on SDG indicator 6.4.2, was endorsed by FAO’s Office of the Chief Statistician (OCS).
During the reporting process, the OCS provides overall guidance, including on metadata reporting, based on the Metadata Dissemination Standard approved by the FAO IDWG-Statistics Technical Task Force.
Data on Environmental flow requirements is reupdated only when detailed methodology and metadata are provided and when consistency in the values is ensured.
After revision and validation, data are submitted to the OCS who also ensures the quality of the data and results.

4.j. Quality assurance

4.k. Quality assessment

Overall evaluation of data quality is based on standard quality criteria and follows FAO’s SQAF. It also includes:

A qualitative and quantitative manual cross-variable check after data is received. This consists of the verification that all the numbers are consistent based on the internal validation rules embedded in the questionnaire. Any issues identified are flagged and listed to be followed-up with the countries.
Time-series coherency check done by running an R-script to compare reported data with those corresponding to previous years. Based on this, a scattered diagram is also made by variable and country to allow for a visual verification of historical data. The critical analysis of the compiled data gives preference to national sources and expert knowledge, unless these greatly diverge from historic data or in the case of drastic changes in methodologies used by countries with significant influence on the results.
Verification of the metadata, especially the source of the proposed data. When data sources were not provided, the questionnaire was added as the data source of a given value. For the

5. Data availability and disaggregation

Data needed for the indicator are collected through AQUASTAT for 168 countries worldwide.

Time series:

1961-2019 (Discontinuous, depending on country. Data are interpolated to create timelines.)

Disaggregation:

Sectoral disaggregated data are provided to show the respective contribution of the different sectors to the water stress level, and therefore the relative importance of actions needed to contain water demand in the different sectors (agriculture, services and industry). The contribution of the different sectors to the water stress level is calculated as the proportion of sectoral withdrawals over total freshwater withdrawals, after taking into account the EFR. sectors are defined following the United Nations International Standard Industrial Classification of All Economic Activities ISIC 4 coding,

agriculture; forestry; fishing (ISIC A), hereinafter “agriculture”;
mining and quarrying; manufacturing; electricity, gas, steam and air conditioning supply; constructions (ISIC B, C, D and F), hereinafter “MIMEC”;
all the service sectors (ISIC E and ISIC G-T), hereinafter “services”.

At national level, water resources and withdrawals are estimated or measured at the level of appropriate hydrological units (river basins, aquifers). It is therefore possible to obtain a geographical distribution of water stress by hydrological unit, thus allowing for more targeted response in terms of water demand management.

6. Comparability/deviation from international standards

Geographical: For national estimates incoming freshwater is counted as being part of the country’s available freshwater resources, while global estimates can only be done by adding up the internal renewable freshwater resources (water generated within the country) of all countries in order to avoid double counting. Moreover, external freshwater resources are computed according to treaties, if present, which may lead to different values with respect to the actual freshwater resources assessed through hydrology.

Over-time: time series are comparable across time.

7. References and Documentation

URL:

http://www.fao.org/aquastat/en/

References:

Food and Agricultural Organization of the United Nations (FAO). AQUASTAT, FAO's Global Water Information System. Rome. Website http://www.fao.org/aquastat/en/.

The following resources of specific interest to this indicator are available on these sites:

AQUASTAT glossary (http://www.fao.org/aquastat/en/databases/glossary/).

AQUASTAT Main country database (http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en )

AQUASTAT Water use (http://www.fao.org/aquastat/en/overview/methodology/water-use/).

AQUASTAT Water resources (http://www.fao.org/aquastat/en/overview/methodology/water-resources/).

AQUASTAT publications dealing with concepts, methodologies, definitions, terminologies, metadata, etc. (http://www.fao.org/aquastat/en/resources/publications/reports/)

IWMI – Global environmental flows assessment
http://eflows.iwmi.org/

IWMI - Global Environmental Flow Information for the Sustainable Development Goals
http://www.iwmi.cgiar.org/Publications/IWMI_Research_Reports/PDF/pub168/rr168.pdf

UNSD/UNEP Questionnaire on Environment Statistics – Water Section

http://unstats.un.org/unsd/environment/qindicators.htm

Framework for the Development of Environment Statistics (FDES 2013) (Chapter 3) http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf

OECD/Eurostat Questionnaire on Environment Statistics – Water Section

https://ec.europa.eu/eurostat/documents/1798247/6664269/Data-Collection-Manual-for-OECD_Eurostat-Questionnaire-on-Inland-Waters.pdf/f5f60d49-e88c-4e3c-bc23-c1ec26a01b2a?t=1611245054001

Several documents exist that can be used to support countries in the computation of this indicator. Among them:

Understanding AQUASTAT - FAO's global water information system
This information note covers a twenty-year history of the collection and analysis of water-related data and its dissemination as an international public good, freely available to all. The process of collecting and checking the data has resulted in the establishment of a unique network of collaborators who provide data, use data from other countries for comparative purposes, and exchange views and experiences on how best to measure and account for water-related use. Users range from international private companies to non-governmental organizations, and virtually all significant reports related to water depend on the data provided by AQUASTAT.
http://www.fao.org/3/a-bc817e.pdf

Incorporating environmental flows into “water stress” indicator 6.4.2 - Guidelines for a minimum standard method for global reporting.

These guidelines are intended to assist countries to participate in the assessment of SDG 6.4.2 on water stress by contributing data and information on environmental flows (EF). They provide a minimum standard method, principally based on the Global Environmental Flows Information System (GEFIS), which is accessible via http://eflows.iwmi.org.

https://www.unwater.org/app/uploads/2019/01/SDG6_EF_LOW2.pdf

Renewable Water Resources Assessment - 2015 AQUASTAT methodology review
http://www.fao.org/3/a-bc818e.pdf

Global database on municipal wastewater production, collection, treatment, discharge and direct use in agriculture
This paper describes the rationale and method to setup and feed the AQUASTAT database on municipal wastewater production, collection, treatment, discharge or direct use in agriculture. The best available sources of information have been reviewed, including peer-reviewed papers, proceedings of workshops, conferences and expert meetings, global or regional databases, as well as country briefs, national reports and direct communications by country government officials and experts.
http://www.fao.org/3/a-bc823e.pdf

Cooling water for energy generation and its impact on national-level water statistics
This technical note, describing the issue of cooling water for energy generation and its impact on national-level water statistics, has two purposes: 1) to act as a general informational resource and 2) to encourage governmental agencies responsible for water usage to gather and report information disaggregated by sub-sector (keeping thermoelectric withdrawals separate from industrial and hydroelectric withdrawals), and to determine the point at which lower water withdrawal designs are more favourable, even if the required capital cost is higher.
http://www.fao.org/3/a-bc822e.pdf

Municipal and industrial water withdrawal modelling for the years 2000 and 2005 using statistical methods
This document describes the efforts to generate models that estimate the municipal and industrial water withdrawals for the years 2000 and 2005.
http://www.fao.org/3/a-bc821e.pdf

Disambiguation of water statistics
The nomenclature surrounding water information is often confusing and gives rise to different interpretations and thus confusion. When discussing the way in which renewable water resources are utilized, the terms water use, usage, withdrawal, consumption, abstraction, extraction, utilization, supply and demand are often used without clearly stating what is meant.
http://www.fao.org/3/a-bc816e.pdf

FAO-AQUASTAT questionnaire on water and agriculture
These annual Guidelines and questionnaires have been prepared specifically designed to collect the SDG 6.4. related water variables, and therefore to update the core variables in AQUASTAT database.
http://www.fao.org/aquastat/en/overview/methodology/

International Recommendations for Water Statistics
The International Recommendations for Water Statistics (IRWS) were developed to help strengthen national information systems for water in support of design and evaluation of Integrated Water Resources Management (IWRM) policies.
https://unstats.un.org/unsd/EconStatKB/KnowledgebaseArticle10209.aspx

UNSD/UNEP Questionnaire on Environment Statistics – Water Section
http://unstats.un.org/unsd/environment/questionnaire.htm
http://unstats.un.org/unsd/environment/qindicators.htm

UNSD ‘National Accounts Main Aggregates Database’
http://unstats.un.org/unsd/snaama/selbasicFast.asp

FAO e-learning course on SDG Indicator 6.4.2 - Level of water stress: https://elearning.fao.org/course/view.php?id=365

6.5.1

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.5: By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate

0.c. Indicator

Indicator 6.5.1: Degree of integrated water resources management

0.d. Series

Degree of integrated water resources management implementation (%) ER_H2O_IWRMD

Degree of integrated water resources management implementation, enabling environment (%) ER_H2O_IWRMD_EE

Degree of integrated water resources management implementation, financing (%) ER_H2O_IWRMD_FI

Degree of integrated water resources management implementation, institutions and participation (%) ER_H2O_IWRMD_IP

Degree of integrated water resources management implementation, management instruments (%) ER_H2O_IWRMD_MI

Proportion of countries by IWRM implementation category (%) ER_H2O_IWRMP

0.e. Metadata update

2022-12-16

0.f. Related indicators

1.4.1, 1.4.2, 2.3.1, 2.3.2, 4.7.1, 5.5.1, 5.5.2, 6.6.1, 6.a.1, 6.b.1, 7.1.1, 7.1.2, 8.5.1, 8.5.2, 10.2.1, 11.3.1, 11.3.2, 13.2.1, 13.2.2, 15.9.1, 16.3.1, 16.3.2, 16.5.1, 16.5.2, 16.7.1, 16.7.2

0.g. International organisations(s) responsible for global monitoring

United Nations Environment Programme (UNEP)

1.a. Organisation

United Nations Environment Programme (UNEP), implemented by the UNEP-DHI Centre on Water and Environment

2.a. Definition and concepts

Definition:

Indicator 6.5.1 is ‘degree of integrated water resources management implementation’. It measures the stages of development and implementation of Integrated Water Resources Management (IWRM), on a scale of 0 to 100, in six categories (see Rationale section). The indicator score is calculated from a country survey with 33 questions, with each question scored on the same scale of 0-100.

The definition of IWRM is based on an internationally agreed definition, and is universally applicable. IWRM was officially established in 1992 and is defined as “a process which promotes the coordinated development and management of water, land and related resources in order to maximise economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems.” (GWP 2010).

The method builds on official UN IWRM status reporting, from 2008 and 2012, of the Johannesburg Plan of Implementation from the UN World Summit for Sustainable Development (1992).

Concepts:

The concept of IWRM is measured in 4 main sections, each representing key dimension of IWRM:

Enabling environment: this includes the policies, laws, plans and strategies which create the ‘enabling environment’ for IWRM.
Institutions and participation: includes the range and roles of political, social, economic and administrative institutions that help to support the implementation of IWRM.
Management Instruments: The tools and activities that enable decision-makers and users to make rational and informed choices between alternative actions.
Financing: Budgeting and financing made available and used for water resources development and management from various sources.

The indicator is based on a national survey structured around these four main sections. Each section is split into two parts: questions concerning the ‘National level’ and ‘Other levels’ respectively. ‘Other levels’ includes sub-national (including provinces/states for federated countries), basin level, and the transboundary level as appropriate. These two parts address the wording of Target 6.5 ‘implement [IWRM] at all levels …’.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Classification of inland water bodies: https://unstats.un.org/unsd/classifications/Family/Detail/2002

Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)

3.a. Data sources

Monitoring progress on meeting SDG 6.5 is owned by and is the responsibility of the national government. The government assigns a ministry with the primary responsibility for overseeing this survey, which then takes on the responsibility of coordinating the national IWRM monitoring and reporting process. As water management issues cut across a wide number of sectors, often overseen by different ministries and other administrative bodies at national or other levels, the process should be inclusive. Major stakeholders should be involved in order to contribute to well informed and objective answers to the survey.

The ministry is invited to nominate a national “IWRM focal point”, who may or may not be a government official. The UN provides support where needed and possible. The following steps are suggested as guidance only, as it is up to countries to decide which process or processes would best serve their needs. It should also be noted that the following steps represent a ‘ladder’ approach, in that completing all the steps will generally lead to a more robust indicator. However, it may not be possible or necessary for all countries to complete all steps.

The responsible ministry or IWRM focal point contacts other relevant ministries/agencies to compile responses to the questionnaire. Each possible response option has a score which is used to calculate the overall indicator score.
The completed draft survey is reviewed by government stakeholders. These stakeholders could include those involved in water-relevant sectors, such as agriculture, energy, water supply and environment, as well as water management at different administrative levels. This process may be electronic (e.g. via email) and/or through workshops.
The revised draft survey is validated at a multi-stakeholder workshop. Apart from government representatives these stakeholders could include water user associations, private sector, interest groups concerned with e.g. environment, agriculture, poverty, and academia. The suggested process is through a workshop but alternative means of consultation e.g. email or online call for public submissions could be considered. Note that steps 2 and 3 could be combined if desired.
The responsible ministry or IWRM focal point discusses with relevant officials and consolidates the input into a final version. This version is the basis for calculating the degree of IWRM implementation (0-100) for global reporting.
The responsible ministry submits the final indicator score to the national statistics office responsible for compiling all national SDG target data.

Based on the national survey, UN-Water periodically prepares synthesis reports for regional and global levels to provide overall progress on meeting SDG target 6.5.

Temporal Coverage: A reporting cycle of three years is recommended.

3.b. Data collection method

Official counterparts at the country level oversee the validation and consultation process.

The survey has been designed so that the indicator is comparable between countries and time periods. No adjustments are foreseen.

3.c. Data collection calendar

Data is collected approximately every 3-4 years. The baseline dataset was collected in 2017, with the second data collection round in 2020. Subsequent data collection rounds are expected in 2023-24, 2026-27, and 2030. Each data collection round spans approximately 9-12 months.

3.d. Data release calendar

Data is released approximately 3 months after the close of each data collection round.

3.e. Data providers

The information required to complete the survey is expected to be held by government officials responsible for water resources management in the country, supported by official documentation. E.g. Ministry of Water in coordination with Ministry of Environment, Ministry of Finance, Ministry of Planning, Ministry of Lands and Agriculture, Ministry of Industry and Mining etc. See also ‘data sources’ section above. As a minimum, a small group of officials may be able to complete the survey. However, these government officials may belong to various government authorities, and coordination is required to determine and validate the responses to each question. Increased government and non-government stakeholder participation in validating the question scores will lead to a more robust indicator score and facilitate tracking progress over time.

3.f. Data compilers

United Nations Environment Programme (UNEP), implemented by the UNEP-DHI Centre on Water and Environment, and UN-Water partners, under the UN-Water Integrated Monitoring Initiative for SDG 6 (IMI-SDG6).

3.g. Institutional mandate

UNEP is the designated Custodian Agency for the indicator. Support on the collection, processing, and dissemination of statistics for this indicator is provided by the UNEP-DHI Centre on Water and Environment, the Global Water Partnership (GWP), and Cap-Net.

4.a. Rationale

The indicator provides a direct progress measurement of the first part of Target 6.5 “…implement integrated water resources management at all levels …”. The indicator score provides an easy and understandable way of measuring progress towards the target, with ‘0’ interpreted as no implementation of IWRM, and ‘100’ interpreted as IWRM being fully implemented.

To further aid interpretation and comparison, the indicator results can be categorized as follows:

Degree of implementation	Score range	General interpretation for overall IWRM score
Very high	91 - 100	Vast majority of IWRM elements are fully implemented, with objectives consistently achieved, and plans and programmes periodically assessed and revised.
High	71 - 90	IWRM objectives of plans and programmes are generally met, and geographic coverage and stakeholder engagement is generally good.
Medium-high	51 - 70	Capacity to implement elements of IWRM is generally adequate, and elements are generally being implemented under long-term programmes.
Medium-low	31 - 50	Elements of IWRM are generally institutionalized, and implementation is underway.
Low	11 - 30	Implementation of elements of IWRM has generally begun, but with limited uptake across the country, and potentially low engagement of stakeholder groups.
Very low	0 - 10	Development of elements of IWRM has generally not begun, or has stalled.

The concept of the survey is that it provides sufficient information to be of real value to the countries in determining their progress towards the target, and through this, various aspects of IWRM. A balance has been sought between providing sufficient information to cover the core principles of IWRM, and thus providing a robust indicator value, and not overburdening countries with unnecessary reporting requirements.

Countries are encouraged to provide additional information on each question, which may help to qualify their choice of score, and/or put that score into their national context.

Indicator 6.5.1 is supported by indicator 6.5.2 “Proportion of transboundary basin area with an operational arrangement for water cooperation”, which directly addresses the portion of Target 6.5 “…, including through transboundary cooperation as appropriate.”.

4.b. Comment and limitations

The challenge of subjectivity in responses associated with this type of survey is being addressed in a number of ways:

Draft responses are reviewed by a number of governmental and non-governmental stakeholders in an open, inclusive and transparent process.
Countries are encouraged to provide further information to qualify their responses and/or set them in the national context.
Guidelines are provided for each of the four main sections, each question, and each of the six thresholds for every single question, to ensure responses are as objective as possible, and are comparable both between countries, and between reporting periods.

To achieve robust indicator results requires a country process involving a wide range of stakeholders which requires a certain amount of time and resources. The advantage of this is that it puts in place a process that addresses the integrated and indivisible nature of the SDG targets, as well as stressing the importance of “leaving no one behind”.

4.c. Method of computation

The survey contains 33 questions divided into the four main sections described above.
Each question is given a score between 0 and 100, in increments of 10, guided by threshold descriptions for the following 6 categories:
- Very low (0)
- Low (20)
- Medium-low (40)
- Medium-high (60)
- High (80)
- Very high (100)
  
  Where question is not applicable, n/a can be selected as a reply, providing adequate explanation.
  
  Note that more question-specific guidance is provided for each threshold for each question, to ensure objective and comparable results.
The un-weighted average of the question scores within each of the four sections is calculated to give a score of 0 – 100 for each section, rounded to the nearest whole number. Questions with response n/a are omitted from calculation.
The section scores (rounded to the nearest whole number), are averaged (un-weighted), and rounded to the nearest whole number, to give the indicator score, expressed as a number between 0 and 100.

4.d. Validation

There is a dedicated SDG 6.5.1 Help Desk for ensuring the quality of the statistical results. Firstly, the data goes through any national quality assurance and approval processes, before being submitted to the Help Desk. The Help Desk then undertakes the Quality Assurance procedure described in section 4.j. All issues are discussed between the Help Desk and the Focal Point(s). Only when all issues are resolved, are the data finalised and entered into the Help Desk Database. The data is then submitted to the UNEP SDG focal point, who collates all indicator data for which UNEP is the Custodian Agency, where a further quality check is undertaken, prior to submission to the SDGs Indicator Database.

4.e. Adjustments

Once the validation process described above is complete, no further adjustments are made.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

The indicator and survey have been designed for all countries to be able to submit an indicator value, and the number of country responses under the SDG process is in excess of 90%. Estimates for countries not responding to the survey are therefore not made.

• At regional and global levels

As the number of country responses is in excess of 90%, this coverage of data is deemed to be representative of regional and global aggregates. Estimates for countries not responding to the survey are therefore not made.

4.g. Regional aggregations

Following the Agenda 2030 principle of “leaving no one behind”, regional and global values are based on simple, un-weighted averages of country scores. The country scores are presented as a whole number, and regional and global averages are also presented as a whole number. Global averages are based on country values, not regional averages.

Regional values may be assembled by regional bodies responsible for water resources in the region, such as the African Ministerial Council on Water (AMCOW), the European Environment Agency (EEA), and the United Nations Economic and Social Commission for West Asia (ESCWA).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

National focal points selected by each country.
National focal points are responsible for coordinating a national process to engage governmental and non-governmental stakeholders, as appropriate in the context of each country, to develop draft responses and finalise responses. This may be via email, workshops, and online notices.
The following guidance materials are available for national focal points in 7 languages (English, Spanish, French, Arabic, Russian, Chinese, Portuguese), at http://iwrmdataportal.unepdhi.org/about http://iwrmdataportal.unepdhi.org: the survey (MS Word); a detailed monitoring guide; and a PowerPoint presentation and video recording. In addition, focal points may access the following country-level materials at http://iwrmdataportal.unepdhi.org/countrydatabase: the 2017 and 2020 baseline surveys and 2-page results summaries (for 186 countries); and 2017 and 2020 workshop reports (for 36 countries) from https://www.gwp.org/en/sdg6support/sdgmap/.
In addition, an “SDG 6.5.1 Facilitator’s Training Course” is available online via https://www.gwp.org/en/sdg6support/consultations/where-we-are/stage-1-activities/ , through the SDG 6 IWRM Support Programme.
A more comprehensive “IWRM Stage 1 Support Package” is available for a limited number of countries (approximately 60 countries in 2020) see https://www.gwp.org/en/sdg6support/consultations/where-we-are/stage-1-activities/. The Support Package includes seed funding to engage a Facilitator to support the stakeholder consultation process.

Extensive explanations are provided in the monitoring guide and in the survey itself. The survey contains: an overall introduction and explanation; a glossary; an introduction and glossary in each of the four sections; threshold descriptions for six thresholds for each question; and a number of footnotes to explain aspects of questions or threshold descriptions. All materials can be downloaded from http://iwrmdataportal.unepdhi.org. In addition, a dedicated Help Desk is available to provide assistance at all times. The Help Desk is accessible via email iwrmsdg651@un.org.

4.i. Quality management

UNEP-DHI Centre, which manages the statistical reporting processes for UNEP on indicator 6.5.1, operates through a Business Management System that fulfils the requirements of ISO 9001 (quality management), which covers relevant areas such as consulting and capacity development and training courses.

4.j. Quality assurance

The following quality assurance guidelines are available to all individuals involved in quality assurance for 6.5.1.

Process:

Nominate person responsible for the quality assurance (QA) for a country response once it is submitted for the first time.
Acknowledge receipt and inform the country of the QA process.
Update the QA spreadsheet, indicating date of receipt and who submitted.
Upload draft survey (MS Word) to the Dropbox folder.
Undertake ALL checks described below.
If there are any discrepancies, revert to UNEP-DHI colleagues.
Once action is agreed, respond to the countries.
Complete all checks on each subsequent version of the questionnaire until all quality issues are resolved and questionnaire is marked ‘final’.

Checks:

Focal point: Confirm the person submitting is the formal national focal point. If not, any reply should also add the national focal point in Cc.
Cover sheet: check if cover sheet is correctly filled out. Cross-check if the person submitting is the formal national focal point. If not, any reply should include the national focal point in Cc.
Question scores and calculations: In the spreadsheet ‘Quality_Assurance_651_2020.xlsx’ on Dropbox, fill in the given responses in sheet “QA 2020 scores-status”. Make the following checks to scores:
1. All questions answered. The official guidance is that all questions should be answered (either with a score or n/a).
2. If there is confusion about whether to score or use ‘n/a’ for sub-national level questions, this list of administrative divisions by country may help in our understanding of the sub-national level(s) https://en.wikipedia.org/wiki/List_of_administrative_divisions_by_country
3. Scores are in range from 0-100, in increments of 10. If they only give ‘even’ scores (e.g. 0, 20, 40 etc), then they may not have understood that they can also give ‘odd’ scores (10, 30, 50 etc), if they feel their situation lies between two threshold descriptions.
4. Any differences between ‘given’ and ‘calculated’ section scores and overall score are given in columns C – G. If the difference is greater than +/- 0.5, the cells are automatically highlighted in red using conditional formatting. One must also fill in the date of last submission in column B, otherwise the differences will not be calculated.
5. Compare with baseline (2017). The QA ‘2017 Comparison’ spreadsheet automatically calculates differences. Note any negative changes (orange), or increases of more than 20 (yellow). If there are any significant/unexpected differences, the country should have given some explanation in the free text fields.
6. In the ‘given’ calculations (section 5 of the survey instrument), check that section averages and overall score are rounded to the nearest whole number. Rounding mistakes might occur.
7. Note: in the calculations, 0 scores are included, and N/A scores should be omitted. N/A scores should always have explanation (unless obvious – e.g. transboundary questions for island states).
8. Check if the final score is calculated as average of rounded section averages.
9. In the free text responses in columns (BE-BF) in the main “QA 2020-score status” tab, for assigning Low/ Medium/ High categories the following criteria should be followed: Low: Less than three quarters of questions have responses and/or responses are poor quality. Medium: At least three quarters of the questions have responses, and/or responses are varying quality. Each question and the points make sense and are useful. High: All questions have responses and most responses are high quality. NB: Quality responses mean ones that are useful/informative/detailed and can contribute to stakeholder understanding/discussions and planning.
Free text fields: Using the ‘text’ tabs:
Check that the free text make sense in the context of the score (and vice versa) (particularly in the case of (n/a or 100 responses).
Check that n/a (not applicable) is used appropriately. i.e. only if the question is not applicable to the country. In some cases, a score of zero should be given, and in others, perhaps they need more help to figure out how to answer the question.
Guidance for assigning Low/ Medium/ High categories: Low: Blank or not useful. Medium: Some text and details. High: Useful amount of text and detail than can contribute to stakeholder understanding/consensus and planning.

ANNEXES.

Annex B: Transboundary level:
Check the ‘transboundary basins’ table. A full list of transboundary basins can be found here: http://twap-rivers.org/indicators/Report.ashx?type=IndicatorResultsSummary. Go to the final worksheet/tab to see the countries in each basin. You can also check the maps here: http://twap-rivers.org/indicators/ to see if the basin is likely to be important for that country, or if there is only a small portion of the basin in their country (in which case they may not list it).
For transboundary aquifers, check: https://ggis.un-igrac.org/view/twap
In case any sub-basins are listed, check that the main basin name is included in brackets.
Check the transboundary questions: 1.2c; 2.2e; 3.2d; and 4.2c, and see if these make sense in the context of the country.
Island countries should give ‘n/a’ for all the questions that relate to transboundary waters.
Annex C: Barriers / enablers: Is this filled out? Low, moderate, or high level of information?
Guidance for assigning Low/ Medium/ High categories: Low: Less than 1 point for each question. Medium: At least one point for each question and the points make sense and are useful. High: Useful analysis that would contribute to future planning.
Annex D: Priorities: completed (Yes/No/Partially)? Any info in the ‘comments’ field (low, moderate, high level of info there?)
Guidance for assigning Low/ Medium/ High categories: Low: Blank to few words. Medium: A few useful points. High: A longer analysis/ commentary.
Annex E: Country process: Level of info in the free text field, the table, and in the ‘additional info’ field completed.
Guidance for assigning Low/ Medium/ High categories: Low: Blank to few words. Medium: Minimum info to be useful to understand transparency. High: More detailed description that gives good idea of robustness and transparency of the process.

All data is provided by each country and is therefore fully owned by the countries. Each country undertakes stakeholder consultation, to a level that is appropriate given resources and capacity available to them, to ensure that the data has adequate acceptance and ownership within the country. Guidance on consultation processes are provided in the monitoring guide and through the introductory PowerPoint and video for focal points (all materials available at http://iwrmdataportal.unepdhi.org).

4.k. Quality assessment

The quality management procedures in place are deemed sufficient to ensure the data submitted to the SDGs Indicator Database is of acceptable quality.

5. Data availability and disaggregation

Data availability:

Total number of countries: 185 (96% of UN Member States) (UNEP 2020)

The following covers the region (UNSD regional groupings): followed by the number of countries with data / total countries in region (as of 2020); followed by the percentage of countries with data.

Regional grouping	Number of countries with data / total countries in region (as of 2020)	Percentage of countries with data
Australia and New Zealand	2/2	100%
Central and Southern Asia	14/14	100%
Eastern and South-Eastern Asia	16/16	100%
Europe and Northern America	44/45	98%
Latin America and the Caribbean	32/33	97%
Northern Africa and Western Asia	23/23	100%
Oceania (excluding Australia and New Zealand)	9/12	75%
Sub-Saharan Africa	46/48	96%
World	185/193	96%

Time series:

Pre-SDGs: 2008, 2012 (UN-Water 2008, 2012).

SDG period: 2017, 2020.

All on IWRM Portal (http://iwrmdataportal.unepdhi.org)

Disaggregation:

The strength of the indicator lies in the potential for disaggregating the country score into the four main dimensions of IWRM, and further to the questions in the survey. This provides countries with a quick assessment of which aspects of IWRM are progressing well, and which aspects require increased efforts to reach the target.

The nature of the target, indicator and survey does not lend itself to disaggregation by sex, age group, income etc. However, social equality is an integral part of IWRM, and there are questions which directly address issues such as gender, vulnerable groups, geographic coverage and broad stakeholder participation in water resources development and management. These questions provide an indication of the national and sub-national situation regarding social equality.

6. Comparability/deviation from international standards

Sources of discrepancies:

Indicator is calculated by countries according to the internationally agreed methodology, and there are no deviations from international standards.

7. References and Documentation

URLs: http://iwrmdataportal.unepdhi.org . This contains the latest survey instrument, monitoring guide, and all supporting documentation.

https://www.gwp.org/en/sdg6support/ : SDG 6 IWRM Support Programme.

References:

- UNEP (2021). Progress on Integrated Water Resources Management. Tracking SDG 6 series: global indicator 6.5.1 updates and acceleration needs.

- GWP and UNEP-DHI (2021). Progress on Integrated Water Resources Management (IWRM) in the

Asia-Pacific Region 2021: Learning exchange on monitoring and implementation towards SDG

6.5.1

- GWP Centroamérica, mayo de 2021: Estado de la implementación de la Gestión Integrada de los Recursos Hídricos en Centroamérica y Republica Dominicana al 2020.

- AMCOW 2018: Status Report on the Implementation of Water Resources Management in Africa: a regional report for SDG indicator 6.5.1 on IWRM implementation. AMCOW 2018: Status Report on the Implementation of Water Resources Management in Africa: a regional report for SDG indicator 6.5.1 on IWRM implementation.

- UNEP 2018: Progress on integrated water resources management. Global baseline for SDG 6 Indicator 6.5.1: degree of IWRM implementation. http://iwrmdataportal.unepdhi.org/IWRMDataJsonService/Service1.svc/DownloadPublicationsReportDoc/English/report

- United Nations Economic and Social Commission for West Asia (2019). Status Report on the Implementation of Integrated Water Resources Management in the Arab Region: Progress on SDG indicator 6.5.1. http://iwrmdataportal.unepdhi.org/IWRMDataJsonService/Service1.svc/DownloadOnAboutPage/Full_Report/Arabic

- UN-Water initiative on integrated monitoring of SDG 6. http://sdg6data.org

- UN-Water, 2016: Water and Sanitation Interlinkages across the 2030 Agenda for Sustainable Development. Geneva. https://www.unwater.org/app/uploads/2016/08/Water-and-Sanitation-Interlinkages.pdf

6.5.2

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.5: By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate

0.c. Indicator

Indicator 6.5.2: Proportion of transboundary basin area with an operational arrangement for water cooperation

0.d. Series

The metadata applies to all series under indicator 6.5.2.

0.e. Metadata update

2023-07-18

0.f. Related indicators

SDG indicator 6.5.2 provides a complement to SDG indicator 6.5.1 which measures the advancement of Integrated Water Resources Management (IWRM) at all levels.

In addition, as the only indicator in the 2030 Agenda for Sustainable Development explicitly referring to transboundary cooperation, indicator 6.5.2 can play a catalytic role across multiple SDGs and targets including:

SDG 1 – No Poverty

- Indicator 1.1.1 Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)

- Indicator 1.4.1 Proportion of population living in households with access to basic services

SDG 2 – Zero Hunger

- Indicator 2.4.1 Proportion of agricultural area under productive and sustainable agriculture

SDG 3 – Good Health and Well-being

- Indicator 3.9.2 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)

SDG 4 – Quality Education

- Indicator 4.7.1 Extent to which (i) global citizenship education and (ii) education for sustainable development, including gender equality and human rights, are mainstreamed at all levels in: (a) national education policies, (b) curricula, (c) teacher education and (d) student assessment

SDG 5 – Gender Equality

- Indicator 5.5.2 Proportion of women in managerial positions

SDG 7 – Affordable and Clean Energy

- Indicator 7.1.2 Proportion of population with primary reliance on clean fuels and technology

- Indicator 7.2.1 Renewable energy share in the total final energy consumption

SDG 11 – Sustainable Cities and Communities

- Indicator 11.5.2 Direct disaster economic loss in relation to global GDP, including disaster damage to critical infrastructure and disruption of basic services

SDG 13 – Climate Action

- Indicator 13.3.2 Number of countries that have communicated the strengthening of institutional, systemic and individual capacity-building to implement adaptation, mitigation and technology transfer, and development actions

SDG 14 – Life below Water

- Indicator 14.1.1 Index of coastal eutrophication and floating plastic debris density

- Indicator 14.2.1 Proportion of national exclusive zones managed using ecosystem-based approaches

SDG 15 – Life on Land

- Indicator 15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type

- Indicator 15.2.1 Progress towards sustainable forest management

SDG 16 – Peace, Justice and Strong Institutions

- Indicator 16.1.2 Conflict-related deaths per 100,000 population, by sex, age and cause

- Indicator 16.7.2 Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population group

SDG 17 – Partnerships for the Goals

- Indicator 17.9.1 Dollar value of financial and technical assistance (including through North-South, South-South and triangular cooperation) committed to developing countries

- Indicator 17.14.1 Number of countries with mechanisms in place to enhance policy coherence of sustainable development

0.g. International organisations(s) responsible for global monitoring

Intergovernmental Hydrological Programme of United Nations Educational, Scientific and Cultural Organization (UNESCO-IHP)

United Nations Economic Commission for Europe (UNECE)

1.a. Organisation

Intergovernmental Hydrological Programme of United Nations Educational, Scientific and Cultural Organization (UNESCO-IHP)

United Nations Economic Commission for Europe (UNECE)

2.a. Definition and concepts

Definition:

The indicator monitors the “transboundary basin” area within a country covered by an “operational” “arrangement for water cooperation”.

A “transboundary basin” refers to a river or lake basin, or an aquifer system that marks, crosses or is located on boundaries between two or more states. A basin comprises the entire catchment area of a surface water body (river or lake), or for groundwater, the area of the aquifer, i.e. the entire permeable water-bearing geological formation. For the purpose of calculating the value of SDG indicator 6.5.2 the transboundary basin area is the extent of the catchment area (river or lake basin); or the extent of the aquifer.

“Arrangement for water cooperation” refers to a bilateral or multilateral treaty, convention, agreement or other formal arrangement, such as memorandum of understanding between countries sharing transboundary basins that provides a framework for cooperation on transboundary water management. Agreements or other kinds of formal arrangements may be interstate, intergovernmental, interministerial, interagency or between regional authorities.

“Operational” means that an agreement for cooperation between the countries sharing transboundary basins meets all the following four criteria:

- There is a joint body or mechanism (e.g. a river basin organization) for transboundary cooperation;

- There are regular, i.e., at least annual, formal communications between riparian countries in form of meetings (either at the political and/or technical level);

- There is a joint or coordinated water management plan(s), or joint objectives have been set;

- There is a regular, i.e., at least annual, exchange of data and information.

Concepts:

The monitoring has as its basis the spatial coverage of transboundary basins shared by each country, and focuses on monitoring whether these are covered by cooperation arrangements that are “operational”. The criteria to be met for the cooperation on a specific basin to be considered “operational” seek to capture whether the arrangement(s) provides the basic elements needed to allow that arrangement to implement cooperation in water management.

2.b. Unit of measure

Basin area, in km², covered by operational arrangements.

2.c. Classifications

Not applicable

3.a. Data sources

At the country level, ministries and agencies responsible for surface water and groundwater resources (depends on the country but commonly the ministry of the environment, water, natural resources, energy or agriculture; institutes of water resources, hydrology or geology, or geological surveys) typically have the spatial information about the location and extent of the surface water basin boundaries and aquifer delineations (as Geographical Information Systems (GIS) shapefiles). Information on existing arrangements and their operationality is also commonly available from the same institutions.

Some countries already report to regional organizations on the advancement of transboundary water cooperation, and similar arrangements could be strengthened and facilitated.

In the absence of available information at the national level, global datasets on transboundary basins as well as databases of agreements and organizations for transboundary cooperation are available, which could be used in the absence of more detailed information, in the short term in particular.

-Delineations of transboundary basins

In global databases, the delineations are available through the Global Environment Facility’s Transboundary Waters Assessment Programme (GEF TWAP) (see http://www.geftwap.org/). GEF TWAP covered 286 main transboundary rivers, 206 transboundary lakes and reservoirs and 199 transboundary aquifers. GEF TWAP groundwater component was prepared by UNESCO-IHP International Groundwater Resources Assessment Centre (IGRAC).

In 2021, the Oregon State University identified 310 international river basins (https://transboundarywaters.science.oregonstate.edu/sites/transboundarywaters.science.oregonstate.edu/files/Database/Data/register/McCracken_Wolf_2019.pdf) and IGRAC produced the Transboundary Aquifers of the World Map featuring 468 shared aquifers discovered so far (see https://ggis.un-igrac.org/). Relevant information has also been compiled for transboundary aquifers by the UNESCO Internationally Shared Aquifers Resources Management Programme (ISARM) (see http://www.isarm.org/)

-Cooperation arrangements

Existing agreements or other arrangements for transboundary water cooperation are available from the International Freshwater Treaties Database (IFTD), maintained by Oregon State University (OSU) (see https://transboundarywaters.science.oregonstate.edu/content/international-freshwater-treaties-database). This was updated to include freshwater-related arrangements up to 2008. A current update is ongoing to bring the database to 2019. The treaty database includes in total 686 international freshwater treaties.

-Organizations for transboundary water cooperation

OSU’s International River Basin Organization (RBO) Database provides detailed information about over 120 international river basin organizations, including bilateral commissions, around the world (see https://transboundarywaters.science.oregonstate.edu/content/international-river-basin-organization-rbo-database).

Regional assessments describing and inventorying agreements have been undertaken, contributing to the baseline globally. For example, the assessment by UNECE of transboundary water cooperation in the pan-European region; the inventory of Shared Water Resources in Western Asia by the United Nations Economic and Social Commission for Western Asia (UNESCWA); and regional inventories of transboundary aquifers under the UNESCO-IHP ISARM: ISARM Americas, ISARM Africa, ISARM South-East Europe and ISARM Asia.

3.b. Data collection method

Data on transboundary basins and their operational arrangements has not been traditionally included within the National Statistical Systems but the information needed to calculate the indicator is simple, does not require advanced monitoring capacities and is normally available to all countries.

Spatial information (“transboundary basin area”) is normally available in ministries in charge of water resources. Regarding the operationality of arrangement, the data needed for calculating the indicator can be directly obtained from information from administrative records (Member States have records of cooperation arrangements).

The limitations in terms of comparability of the results between countries are the same as the ones described in Section 4.b. However, a clear definition and consideration of the criteria as developed in the detailed methodology is available to countries to ensure a common reference for the countries.

Moreover, the elements of the indicator are based on the main principles of customary international water law, which are also contained in the two UN conventions – 1997 Convention on the Law of the Non-navigational Uses of International Watercourses (Watercourses Convention) and the 1992 Convention on the Protection and Use of Transboundary Watercourses and International Lakes (Water Convention) – as well as the draft Articles on The Law of Transboundary Aquifers (2008; UN General Assembly resolutions 63/124, 66/104, 68/118, 71/150, 74/193 and 77/112).

The mechanism of reporting under the Water Convention also allows for sub-components of the indicator to be reported by countries, which will ensure both more confidence on the final indicator value (validation) and increased comparability.

3.c. Data collection calendar

First reporting exercise, in 2017; and then at three yearly intervals.

3.d. Data release calendar

Early 2018; and then at three yearly intervals

3.e. Data providers

Data are not so far included in the National Statistical Systems but the information needed to calculate the indicator is simple, does not require advanced monitoring capacities and is normally available to all countries at the ministries or agencies responsible for water resources. Spatial information (“transboundary basin area”) is normally available in ministries in charge of water resources. The value of this component is relatively fixed although the precision may vary (especially on aquifers), and may require only limited update on the basis of improved knowledge. Regarding operationality of arrangement the data needed for calculating the indicator can be directly obtained from information from administrative records (Member States have records of cooperation arrangements).

3.f. Data compilers

UNECE and UNESCO-IHP gather the information needed from the 153 countries sharing transboundary basins for the calculation of the indicator, especially on the transboundary basins (rivers, lakes and aquifers) shared by countries, the applicable cooperative arrangements, and their operationality.

Since 2017, the Water Convention’s regular reporting on transboundary water cooperation, commits its Parties to collect information relevant to SDG indicator 6.5.2, as part of an established mandatory mechanism for Parties every 3 years. The reporting covers transboundary rivers, lakes and groundwaters. More than 130 countries participate in the Water Convention’s activities, as non-Parties are also invited. UNECE acts as Secretariat for the Water Convention.

Some countries also report to regional organizations (e.g. the European Union or the Southern African Development Community) on the advancement of transboundary water cooperation, and similar arrangements could be strengthened and facilitated.

3.g. Institutional mandate

Not applicable

4.a. Rationale

The majority of the world’s water resources are shared: 468 transboundary aquifers have been identified in 2021 and 310 transboundary lake and river basins cover nearly one half of the Earth’s land surface and account for an estimated 60% of global freshwater. Approximately 40% of the world’s population lives in river and lake basins shared by two or more countries and over 90% lives in countries that share basins. Development of water resources has impacts across transboundary basins, potentially on countries sharing transboundary basins, and use of surface water or groundwater may affect the other resource, which are often interlinked. Intensive water use, flow regulation or pollution risks going as far as compromising the development aspirations of countries sharing transboundary basins and therefore transboundary cooperation is required. However, cooperation is in many cases not advanced.

Specific agreements or other arrangements concluded between countries sharing transboundary basins are a key precondition to ensure long-term, sustainable cooperation. International customary water law (as reflected in the 1992 Water Convention, the 1997 Watercourses Convention, and the 2008 draft Articles on the Law of Transboundary Aquifers), as well as existing experience and good practices, all point to minimum requirements for operational cooperation. These minimum requirements are captured by the four criteria for operationality.

This is the basis for the explicit call for transboundary water cooperation in the wording of target 6.5 and the importance of monitoring this indicator to complement indicator 6.5.1 which measures the advancement of Integrated Water Resources Management (IWRM).

Progress by a particular country towards the cooperation aspect of target 6.5, reflected by the value of indicator 6.5.2, can be achieved either by establishing new operational cooperation arrangements, or making existing arrangements operational by developing and regularizing activities, or expanding the coverage of cooperation arrangements with the ultimate objective to cover all surface waters and groundwaters.

4.b. Comment and limitations

The spatial information on transboundary surface water basins’ boundaries and the extent of the catchment areas are commonly available and essentially static; consequently, once determined, no updating need is expected.

The information on the areal extent of transboundary aquifers may evolve over time as such information is generally more coarse but likely to improve because of the evolving knowledge on aquifers. Technical studies and exchange of information will improve the delineation and might also lead to the identification of additional transboundary aquifers.

In situations where more than two riparian countries share a basin, but only some of them have operational cooperation arrangements, the indicator value may mask the gap that a riparian country does not have cooperation arrangements with all its upstream and downstream neighbours. Such complementary information can be obtained by aggregating data at the level of the basins but not from the reporting at the national level.

The legal basis for cooperation develops slowly: conclusion of new agreements on transboundary waters is commonly a long process that takes many years.

The operationality of cooperation is more dynamic as it evolves with the expansion of cooperation. The operationality can be expected to evolve over shorter time frames, and in a year or two, progress could potentially be observed.

4.c. Method of computation

Step 1 Identify the transboundary surface waters and aquifers in the territory of the country

While the identification of transboundary surface water is relatively straightforward, the identification of transboundary aquifers often requires more considered investigations.

If there are no transboundary surface waters or groundwaters, reporting is not applicable.

Step 2 Calculate the surface area of each transboundary basin and the total sum

Commonly at least the basins of the rivers and lakes have been delineated through topographic maps and the basin area is known or easily measurable.

The total transboundary surface area in the country is the sum of the surface areas in the country of each of the transboundary basins and aquifers (expressed in km2). Transboundary areas for different types of systems (e.g. river and lakes basin and aquifers) or multiple aquifers may overlap. The area of transboundary aquifers, even if located within a transboundary river or lake basin, should be added to be able to track progress of cooperation on transboundary aquifers.

The calculations can most easily be carried with Geographical Information Systems (GIS). Once generated, with appropriate tools for spatial analysis, the shapes of the surface river and lake basins and the aquifers can be used to report both disaggregated (for the surface water basin or aquifer) and aggregated (agreement exists on either one).

Step 3 Review existing arrangements for transboundary water cooperation and verify which transboundary waters are covered

Some operational arrangements for transboundary water cooperation in place cover both surface waters and groundwaters (and their associated river and lakes basins and aquifers). In such cases, it should be clear that the geographical extent of both is used to calculate the indicator value. In other cases, the area of application may be limited to a border section of the river basin or sub-basin and in such cases only the corresponding area should be considered as potentially having an operational arrangement for calculating the indicator value. At the end of this step, it should be known which transboundary basins are covered by arrangements for transboundary water cooperation (and their respective areas).

Step 4 Check which of the existing arrangements for transboundary water cooperation are operational

The following check-list allows countries to determine whether the cooperation arrangement on a particular basin or in relation to a particular country is operational:

- does a joint body or mechanism for transboundary water cooperation exist?

- is there at least annual (on average) formal communication in form of meetings, either at the political and/or technical level?

- has a joint or coordinated water management plan(s), or joint objectives been adopted?

- is there at least annual (on average) exchange of information and data?

If any of the conditions are not met, the arrangement for transboundary water cooperation cannot be considered operational. This information is currently available in countries and can also be withdrawn from global, regional or basin databases.

Step 5 Calculate the indicator value

Calculate the indicator value, by adding up the total surface area in the country of the transboundary surface waters and aquifers that are covered by an operational cooperation arrangement and dividing it by the total summed up area in the country of all transboundary basins (including aquifers). The sum should then be multiplied by 100 to obtain a percentage.

4.d. Validation

Countries are requested to submit data on their transboundary basins covered by operational arrangements through the uses of a reporting template or questionnaire. The templates are submitted to the co-custodian agencies, UNECE and UNESCO for review. Countries are encouraged to submit drafts of their templates to the custodian agencies for feedback prior to the final submission. Once submitted, the custodian agencies review the national templates to assess firstly, whether sufficient and accurate information is provided in order to calculate the national SDG indicator value, and secondly, whether an official endorsement of the template is provided (in the form of a signature).

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

In the case of spatial data: For the basin delineations, Digital Elevation Model information can be used to delineate surface water basin boundaries. For aquifers, geological maps can provide a basis for approximating aquifer extent. In the case of groundwater, uncertainty about transboundary nature remains unless investigations of hydraulic properties have been made. In the absence of administrative records, gaps about the cooperation arrangements are difficult to fill, although such arrangements tend to be widely available.

• At regional and global levels

The indicator does not apply to countries without a terrestrial border, so notably island states will not report a value on this indicator.

International databases and inventories (as described in section 3.a) are available for reference in the absence of information reported by countries. Missing surface water basin extent can be extracted from Digital Elevation Models available globally. Global geological maps and maps of hydrogeology/groundwater potential also exist which could be used to approximate aquifer extent (surface area).

Concerning arrangements, consistency of information reported by countries sharing the same transboundary basins can be used to fill gaps in information about arrangements and their operationality.

4.g. Regional aggregations

Regional and global estimates are obtained by undertaking the average of individual country values at regional and global level.

However, baseline assessment from global databases can be performed at any desired geographical scale: sub-national, national, regional, basin scale, global, etc. However, data gaps can limit this possibility starting from regional level.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Through UN-Water, UNECE and UNESCO have developed a step-by-step methodology that countries can use to compile data at the national level on SDG indicator 6.5.2. The methodology, which was revised in January 2020 prior to the second reporting exercise, is available in English, French, Russian and Spanish through the UN-Water website - https://www.unwater.org/publications/step-step-methodology-monitoring-transboundary-cooperation-6-5-2/.

In addition, UNECE, through an expert group made up of both parties and non-parties to the Water Convention, developed a Guide to reporting under the Water Convention and as a contribution to SDG indicator 6.5.2 (see https://unece.org/environment-policy/publications/guide-reporting-under-water-convention-and-contribution-sdg) in January 2020. The guide, which is available in Arabic, English, French, Russian and Spanish, supports countries in the completion of the reporting template by explaining key terminology and providing examples of how particular questions might be addressed.

4.i. Quality management

Not applicable

4.j. Quality assurance

Not applicable

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

Before starting the SDG reporting process, data were not included in the National Statistical Systems but the information needed to calculate the indicator is simple, does not require advanced monitoring capacities and is normally available to all countries at the ministries or agencies responsible for water resources.

Data is available for the 153 countries having territorial borders in a number of existing databases.

Disaggregation:

Data would be most reliably collected at the national level. Basin level data can also be disaggregated to country level (for national reporting) and aggregated to regional and global level.

6. Comparability/deviation from international standards

Sources of discrepancies:

As the computation of the indicator is based on the spatial information (“transboundary basin area”) and operationality of arrangements as the two basic components, differences can arise in the computation of each of these components individually.

Regarding both components, countries have the most up-to-date information, which can be supplemented by the data from various international projects and inventories, which contribute also to establishing a baseline globally.

The difference on the value of transboundary basin area can arise from a different delineation of the transboundary water bodies, especially aquifers, or even the consideration of their transboundary nature as their identification and delineation can be based on different hydrogeological studies and can be updated, which is not necessarily reflected in international databases.

The difference in the consideration of the operationality of the arrangements may arise from not identifying the same arrangements or considering differently the four criteria that serve as the basis for the operationality classification:

- existence of a joint body or mechanism for transboundary cooperation

- regularity of formal communication in form of meetings

- existence of joint or coordinated water management plan(s), or of joint objectives

- regularity of the exchange of information and data

A different interpretation in the object of application (only surface water or both surface water and groundwater) may constitute another reason.

Collection of country input through validation mechanisms, has improved and will continue to improve, the consistency and accuracy of the information across the countries as the monitoring progresses.

7. References and Documentation

UNECE: https://unece.org/environmental-policy/water/transboundary_water_cooperation_reporting

UNESCO: https://www.unesco.org/en/ihp/sdg6-5-2 ;

UN-WATER SDG6 monitoring: www.sdg6monitoring.org/indicator-652

UN-WATER SDG6 data portal: www.sdg6data.org/indicator/6.5.2

Decision VII/2 establishing the reporting mechanism under the Water Convention: https://unece.org/DAM/env/documents/2015/WAT/11Nov_17-19_MOP7_Budapest/ece.mp.wat.49.add.2.eng.pdf

Additional documentation:

Global Environment Facility’s Transboundary Waters Assessment Programme: http://www.geftwap.org/

Internationally Shared Aquifer Resources Management Programme (UNESCO’s International Hydrological Programme): http://www.isarm.org/

Treaties on transboundary waters, Oregon State University: https://transboundarywaters.science.oregonstate.edu/content/international-freshwater-treaties-database

International River Basin Organisations database, Oregon State University: https://transboundarywaters.science.oregonstate.edu/content/international-river-basin-organization-rbo-database

Regional examples:

Assessment of transboundary water cooperation in the pan-European region: https://unece.org/environment-policy/publications/second-assessment-transboundary-rivers-lakes-and-groundwaters

Inventory of Shared Water Resources in Western Asia: https://www.unescwa.org/publications/inventory-shared-water-resources-western-asia

6.6.1a

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes

0.c. Indicator

Indicator 6.6.1: Change in the extent of water-related ecosystems over time

0.d. Series

Nationally derived quantity of groundwater (millions of cubic metres per annum) EN_WBE_NDQTGRW

Nationally derived quantity of rivers (million of cubic metres per annum) EN_WBE_NDQTRVR

Lakes and rivers permanent water area (square kilometres) EN_LKRV_PWAN

Lakes and rivers permanent water area (% of total land area) EN_LKRV_PWAP

Lakes and rivers seasonal water area (square kilometres) EN_LKRV_SWAN

Lakes and rivers seasonal water area (% of total land area) EN_LKRV_SWAP

Lakes and rivers permanent water area change (%) EN_LKRV_PWAC

Lakes and rivers seasonal water area change (%) EN_LKRV_SWAC

Reservoir minimum water area (square kilometres) EN_RSRV_MNWAN

Reservoir minimum water area (% of total land area) EN_RSRV_MNWAP

Reservoir maximum water area (square kilometres) EN_RSRV_MXWAN

Reservoir maximum water area (% of total land area) EN_RSRV_MXWAP

Wetlands area (square kilometres) EN_WBE_WTLN

Wetlands area (% of total land area) EN_WBE_WTLP

Lake water quality turbidity (%) EN_LKW_QLTRB

Lake water quality trophic state (%) EN_LKW_QLTRST

Mangrove area (square kilometres) EN_WBE_MANGN

Mangrove area baseline (square kilometres) EN_WBE_MANGBN

Mangrove area gain (square kilometres) EN_WBE_MANGGN

Mangrove area gain (%) EN_WBE_MANGGP

Mangrove area loss (square kilometres) EN_WBE_MANGLN

Mangrove area loss (%) EN_WBE_MANGLP

Mangrove total area change (%) EN_WBE_MANGC

0.e. Metadata update

2022-07-07

0.f. Related indicators

6.3.2, 6.4.1, 6.4.2, 6.5.1, 6.5.2, 15.3.1

0.g. International organisations(s) responsible for global monitoring

United Nations Environment Programme (UNEP)

1.a. Organisation

United Nations Environment Programme (UNEP)

2.a. Definition and concepts

Definition:

Indicator 6.6.1 tracks the extent to which different types of water-related ecosystems are changing in extent over time. The indicator is multifaceted capturing data on different types of freshwater ecosystems and to measure extent change the indicator considers spatial area changes, water quality and water quantity changes. The indicator uses satellite-based Earth observations to globally monitor different freshwater ecosystems types. Earth observation data series on surface area are available on permanent water, seasonal water, reservoirs, wetlands, mangroves; as well as generating data on water quality, using trophic state and turbidity of water bodies. Satellite images can be represented as numerical data, which in turn are aggregated into meaningful statistics of ecosystem change attributed to administrative areas such as national, sub-national (e.g. regions and provinces) and river basin boundaries. Global data products for river flows and groundwater level have not yet been produced at useful spatial and temporal resolutions to be incorporated into this SDG 6.6.1 methodology. Currently, these data should continue to be provided from modelling or from ground-based measurements and required from the countries.

Table 1: SDG indicator 6.6.1 data derived from Earth observations
*Ecosystem*	*Unit*	*Features*
Lakes & Rivers (permanent water area)	surface area	annual and multi-annual changes in permanent water area (1984-present) statistics for new and lost permanent water (2000-2020) statistics aggregated at national, sub-national & basin scales
Lakes & Rivers (seasonal water area)	surface area	annual and multi-annual changes in seasonal water area (1984-present) statistics for new and lost seasonal water (2000-2020) annual seasonality statistics for periods: 0-1, 3-6, 7-11 months statistics aggregated at national, sub-national & basin scales
Reservoirs	surface area water quality	annual and multi-annual changes in reservoir surface area (1984-present) statistics for new and lost reservoir area (2000-2020) statistics aggregated at national, sub-national & basin scales Monthly, annual and multi-annual measurements of trophic state and turbidity for 4,200 lakes and reservoirs globally (at 300m resolution)
Mangroves	surface area	annual and multi-annual changes in mangrove area (2000-2016) statistics aggregated at national, sub-national & basin scales
Wetlands	surface area	wetlands area (baseline area comprised of data btw 2016-2018) statistics aggregated at national, sub-national & basin scales wetlands area changes will be included starting in 2021/22
Lakes	water quality	Monthly, annual and multi-annual measurements of trophic state and turbidity for 4,200 lakes and reservoirs globally (at 300m resolution)
SDG indicator 6.6.1 data derived from national in-situ measurements
*Ecosystem*	*Unit*	*Features*
Rivers	flow	modelled natural runoff/streamflow, and/or in-situ stream/river flow measurements, aggregated over time, of all major rivers
Groundwater	level	Changes to volume measurements, over time, of all major groundwater aquifers

Concepts:

The concepts and definitions used in the methodology have been based on existing international frameworks and glossaries unless where indicated otherwise below.

Water-related ecosystems are a sub-set of all ecosystems. They contain the world’s freshwater resources and can be defined as “a dynamic complex of plant, animal, and micro-organism communities and the non-living environment dominated by the presence of flowing or still water, interacting as a functional unit.” (MEA, 2005; Dickens et al, 2019). The indicator is framed around the monitoring of different types of water-related ecosystems including lakes, rivers, wetlands, groundwater and artificial waterbodies such as reservoirs. These water-related ecosystems contain freshwater, except for mangroves which contain brackish water (i.e. a combination of fresh and saltwater), however, mangroves are still included within indicator 6.6.1. Reservoirs are also included as a category of water-related ecosystem within the indicator methodology; while it is recognized that reservoirs are not traditional water ecosystems which should necessarily warrant protection and restoration, in many countries they hold a noteworthy amount of freshwater and have thus been included. By including data on reservoirs, it is intended that countries can better understand changes occurring to artificial water bodies in conjunction with changes occurring to natural water bodies. Ecosystems that are not included under indicator 6.6.1 are: coral reefs and sea grass which are covered within Goal 14 (Oceans); and mountains, forests, and drylands which are covered within Goal 15 (Land). The extent to which each of the water-related ecosystems included under indicator 6.6.1 can be measured, uses one or more of the following physical parameters of change: spatial area, quantity (or volume) of water, and water quality. The full monitoring methodology for indicator 6.6.1 is available here. The extent to which each of the water-related ecosystems included under indicator 6.6.1 can be measured, uses one or more of the following physical parameters of change: spatial area, quantity (or volume) of water, and water quality.

Inland vegetated wetlands include areas of marshes, peatlands, swamps, bogs and fens, the vegetated parts of floodplains as well as rice paddies and flood recession agriculture. Inland vegetated wetlands do not include coastal mangroves. Data on mangroves which are produced separately to inland wetlands. This SDG indicator methodology is used for official reporting of SDG indicator 6.6.1 statistics. The SDG indicator 6.6.1 methodology does not apply the definition of wetlands defined by the Ramsar Convention on Wetlands, which is: “areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters”. The Ramsar definition of wetlands may be interpreted to mean all water within a country including the marine environment. The SDG indicator 6.6.1 definition refers to only a specific group of inland vegetated wetlands typologies.

Permanent and seasonal water. A permanent water surface is underwater throughout the year whilst a seasonal water surface is underwater for less than 12 months of the year. Some locations don’t have observations for all 12 months of the year (for reasons such as polar night). In these cases, water is considered as seasonal if the number of months where water is present is less than the number of months where valid observations were acquired.

A second consideration is lakes and rivers that freeze for part of the year. During the frozen period water is still present under the ice (true both for rivers/lakes and the sea). If water is present throughout the observation period (i.e. unfrozen period), the water body is considered as a permanent water surface. If the area of the water body contracts during the unfrozen period, then the pixels along the borders of the lake or river are no longer water, and those pixels will be considered as a seasonal water surface.

Reservoirs are artificial (or human-made) bodies of freshwater, as opposed to lakes which are naturally occurring. The reservoirs dataset represents surface area data on artificial water bodies including reservoirs formed by dams, flooded areas such as opencast mines and quarries, flood irrigation areas, and water bodies created by hydro-engineering projects such as waterway and harbour construction.

Turbidity is an indicator of water clarity, quantifying the haziness of the water and acting as an indicator of underwater light availability.

Trophic State refers to the degree at which organic matter accumulates in the water body and is most commonly used in relation to monitoring eutrophication.

Surface Water refers to any area of surface water unobstructed by aquatic vegetation. This includes the following 3 water-related ecosystem categories: rivers and estuaries, lakes, and artificial waterbodies.

Extent – has been expanded beyond spatial extent to capture additional basic parameters needed for the protection and restoration of water-related ecosystems. Extent includes three components: the spatial extent or surface area, the quality, and the quantity of water-related ecosystems.

Change means a shift from one condition of extent to another over time within a water-related ecosystem, measured against a point of reference.

Permanent and seasonal water concept definitions and data resolution

Data on the spatial and temporal dynamics of naturally occurring surface water has been generated for the entire globe. A Global Surface Water dataset (Pekel et al., 2016) has been produced by the European Commission's Joint Research Centre. The dataset documents different facets of the long term (since 1984 onward) water dynamics at 30x30 meter pixel resolution. The dataset documents permanent and seasonal surface water surfaces. All naturally occurring surface water larger in area than 30x30 meters has been mapped and at this 30-meter grid/pixel spatial resolution satellite imagery is predominantly capturing areas of lakes and wide rivers (ie.. rivers over 30meters wide). The data include land areas that are temporarily inundated. Smaller rivers and waterbodies are not captured as they are too narrow to detect or are masked by forest canopy. The data include individual full-resolution images acquired by the Landsat 5, 7 and 8 and Sentinel 1 satellites. These satellites capture images which are distributed publicly by the United States Geological Survey and by the European Union’s Copernicus space programme. Together they provide multispectral imagery at 30x30 meter resolution in six visible, near and shortwave infrared channels, plus thermal imagery at 60x60 meters.

The data includes land surfaces that are under water (e.g. a permanent water area) for all twelve months of a year. It also accounts for seasonal and climactic fluctuations of water, meaning lakes and rivers which freeze for part of the year are captured. Areas of permanent ice, such as glaciers and ice caps as well as permanently snow-covered land areas are not included. Areas of consistent cloud cover inhibit the observation of water surfaces in some areas and in these limited locations optical observations may not be available. A global shoreline mask has been applied to the data to prevent ocean water being included in the freshwater statistics and the methodology for this shoreline mask is published in the journal of operational oceanography, available here (Sayer et al. 2019).

The accuracy of the Global Surface Water map was determined using over 40,000 control points from around the world and across the 36 years. The full validation methodology and results have been published in the scientific journal Nature, available here, (Pekel et al., 2016). The validation results show that the water detection expert system produced less than 1% of false water detections, and that less than 5% of water surfaces were missed. The provided maps are derived from the analysis of over four million images collected over 36 years which have been individually processed using an accurate expert system classifier.

The SDG 6.6.1 data portal (www.sdg661.app) documents various water transitions relating to permanent and seasonal surface water - these are changes in water state between two points in time (e.g. 2000 - 2019). Data is available for various transitions including new permanent water surfaces (i.e. conversion of a no water place into a permanent water place.); lost permanent water surfaces (i.e. conversion of a permanent water place into a no water place) as well as new and lost seasonal water. These allow monthly water presence or absence data to be captured. It is possible to identify specific months/years in which conditions changed, e.g. the date of filing of a new dam, or the month/year in which a lake disappeared. In addition, data on seasonality are provided, capturing changes resulting from intra and inter-annual variability or resulting from appearance or disappearance of seasonal or permanent water surfaces. The data separates 'permanent' water bodies (those that are present throughout the period of observation) [nominally a year] from 'seasonal' (those that are present for only part of the year).

2.b. Unit of measure

Change in the spatial area/extent of freshwater: KM2, Percent (%)

Change in quality of freshwater: Percent (%)

Change in the quantity of freshwater: millions of cubic metres per annum

2.c. Classifications

Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)

3.a. Data sources

Surface water area data, acquired by the Landsat 5, 7 and 8 satellites at a 30 m resolution, has been generated for the entire globe from 2000-2019. From 2016 onwards (up to and including 2030), higher spatial and temporal resolution satellites, including both optical and radar satellites, are used.). Additional datasets are used refine open water spatial area data, including the Global Reservoir and Dam (GRanD) geospatial database. To generate spatial area of vegetated wetlands, a combination of imagery from Landsat 8 and Sentinel 1 and 2 are used Global Mangrove Watch data is derived from JAXA ALOS satellites and Landsat to generate mangrove extent. Water quality is derived from MERIS and European Sentinel satellites.

Lake water trophic state and TSS lake observations are obtained from combined Landsat and Sentinel satellites paired with instruments like OLCI, MODIS, and VIIRS. The sensor instruments used to detect TSS and trophic state determine the spatial resolution of water quality within lakes which can be detected. Some of the more accurate water quality sensors have 250-350-meter resolution, while less accurate sensors can detect TSS and trophic state changes to 100 m resolution.

The source of data for monitoring stream flow and groundwater quantity is from national in situ measurements of groundwater level within aquifers and stream flow quantity. However globally derived hydrological run off modelled data will soon be available and used to measure stream flow as part of indicator 661 replacing the need for In-situ stream flow measurements to be collected.

3.b. Data collection method

Each sub-indicator (including permanent lakes and river area; seasonal lakes and river area; reservoir minimum and maximum area and water quality; inland wetlands area; mangroves area; lake water quality) is computed separately and thus Indicator 6.6.1 is undertakes several sub-indicator specific computational methods. Globally derived data using spatial area measurements are computed in a comparable and consistent manner across the different ecosystem types e.g. surface water, wetland, mangroves. Globally derived data on water quality is computed using the parameters of turbidity and trophic state to infer a measure of water quality. National data on quantity of water in ecosystems is used to measure stream flow and groundwater volumes. Below are the method descriptions:

3.c. Data collection calendar

Data collection:

Annual estimation of globally derived satellite-based data released around May each year and uploaded onto the SDG 661 data portal www.sdg661.app. Every three/four years data is communicated to national focal points for validation.

3.d. Data release calendar

First reporting cycle: June 2018; Second reporting cycle: June 2020; Third reporting cycle: June 2023.

3.e. Data providers

Data on Permanent Water, Seasonal Water, and Reservoir Water - European Commission Joint Research Centre – Global Surface Water Explorer
Data on Water Turbidity and Trophic State- European Copernicus Land Service products
Data on Mangroves - Global Mangrove Watch
Data on Wetlands - DHI GRAS
Data on river flow - national institutions
Data on groundwater – national institutions

3.f. Data compilers

United Nations Environment Programme (UNEP)

3.g. Institutional mandate

UNEP was awarded the mandate of custodian agency for SDG indicator 6.6.1 by the Inter-agency and Expert Group on SDG Indicators. In its capacity as custodian, UNEP are responsible for the development of the internationally comparable monitoring methodology and metadata, with national data, and regional and global aggregations reported to the SDG global data base and these statistics included in the Secretary Generals SDG progress reports.

4.a. Rationale

Target 6.6 aims to “protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes” through Indicator 6.6.1 which aims to understand how and why these ecosystems are changing in extent over time. All of the different components of Indicator 6.6.1 are important to form a comprehensive picture that enables informed decisions towards the protection and restoration of water-related ecosystems. However, a lack of data within countries to support Indicator 6.6.1 has become clear through the 2017 pilot testing and thus a combination of national data and data based on satellite images is proposed. All data generated is processed using internationally recognized methodologies, with results assessed and approved by countries, resulting in high quality global datasets with extensive spatial and temporal scale.

4.b. Comment and limitations

To support countries in fulfilling monitoring and reporting requirements for SDG indicator 6.6.1, UNEP has worked with partner organisations to develop technically robust and internationally comparable global data series, thereby significantly contributing towards filling the global data gap on measuring changes in the extent of water-related ecosystems. The indicator methodology mobilizes the collection of available earth observation data on spatial area and water quality parameters. At the 7th IAEG-SDG meeting in April 2018 the indicator methodology was approved and classified as Tier II. Shortly afterwards, in November 2018, it was reclassified to a Tier I indicator methodology. The Tier I classification means that the indicator is conceptually clear, has an internationally established methodology and standards are available, and data are regularly produced by at least 50 per cent of countries and of the population in every region where the indicator is relevant. The full SDG indicator 6.6.1 monitoring methodology details specific limitations associated with the production of data for the different ecosystem types relevant to SDG indicator 6.6.1, including links to publications pertaining to the data production methodologies.

SDG indicator 6.6.1 is designed enable countries to understand the extent to which protecting and restoring different types of water-related ecosystem (e.g. lakes, rivers, reservoirs, wetlands, mangroves). It does not measure how many water-related ecosystems have been protected and restored. It is assumed that countries use the available data to actively make decisions, but these actions are not currently being measured. The data generated should be considered alongside other data, in particular land use change and demographic data, to better enable countries to understand the drivers of ecosystem change and put in place appropriate policy and legislative mechanisms that result in the protection and restoration water-related ecosystems.

UNEP periodically invites national contact persons to participate in consultations with the aim to validate estimated national values.

4.c. Method of computation

Computation Method:

Permanent and Seasonal Surface Water

Description of the method used to globally map all surface water

Data on the spatial and temporal dynamics of naturally occurring surface water has been generated for the entire globe. A Global Surface Water dataset (Pekel et al., 2016) has been produced by the European Commission's Joint Research Centre. The dataset documents different facets of the long term (since 1984 onward) water dynamics at 30x30 meter pixel resolution. The dataset documents permanent and seasonal surface water surfaces. All naturally occurring surface water larger in area than 30x30 meters has been mapped and at this 30-meter grid/pixel spatial resolution satellite imagery is predominantly capturing areas of lakes and wide rivers. The data include land areas that are temporarily inundated such as wetlands and paddy fields. Smaller rivers and waterbodies are not captured as they are too narrow to detect or are masked by forest canopy. The data include individual full-resolution images acquired by the Landsat 5, 7 and 8 and Sentinel 1 satellites. These satellites capture images which are distributed publicly by the United States Geological Survey and by the European Union’s Copernicus space programme. Together they provide multispectral imagery at 30x30 meter resolution in six visible, near and shortwave infrared channels, plus thermal imagery at 60x60 meters.

The accuracy of the Global Surface Water map was determined using over 40,000 control points from around the world and across the 36 years. The full validation methodology and results have been published in the scientific journal Nature (Pekel et al., 2016). The validation results show that the water detection expert system produced less than 1% of false water detections, and that less than 5% of water surfaces were missed. The provided maps are derived from the analysis of over four million images collected over 36 years which have been individually processed using an accurate expert system classifier.

The SDG 6.6.1 data portal documents various water transitions relating to permanent and seasonal surface water - these are changes in water state between two points in time (e.g. 2000 - 2019). Data is available for various transitions including new permanent water surfaces (i.e. conversion of a no water place into a permanent water place.); lost permanent water surfaces (i.e. conversion of a permanent water place into a no water place) as well as new and lost seasonal water. These allow monthly water presence or absence data to be captured. It is possible to identify specific months/years in which conditions changed, e.g. the date of filing of a new dam, or the month/year in which a lake disappeared. In addition, data on seasonality are provided, capturing changes resulting from intra and inter-annual variability or resulting from appearance or disappearance of seasonal or permanent water surfaces. The data separates 'permanent' water bodies (those that are present throughout the period of observation) [nominally a year] from 'seasonal' (those that are present for only part of the year).

Calculating the change in surface area of permanent and seasonal surface water

Data on surface water dynamics are available for a 38-year period, from 1984-onward. Every year new annual data is produced and added to this time series. To calculate percentage change in lake and river area using a 2000-2021 dataset, a baseline period is first defined against which to measure change. This methodology uses 2000-2004 as the 5-year baseline period and to be compared against any subsequent 5-year target period. For each 5-year period the water state (permanent, seasonal or no water) is decided by a majority rule, and the water transitions between the baseline and the target period is subsequently used to compute the percentage change (∆) in the spatial area of permanent and seasonal waters by equation 1:

$E q u a t i o n 1 : ∆ = \frac{(α - β) + (ρ - σ)}{ε + β + σ} \times 100$

And subject to the following for computing permanent surface water dynamics:

$∆$ – percentage change in spatial extent

$α$ – New permanent water (i.e. conversion of a no water place into a permanent water place)

$β$ – Lost permanent water (i.e. conversion of a permanent water place into a no water place)

$ρ$ – Seasonal to permanent (i.e. conversion of seasonal water into permanent water)

$σ$ – Permanent to seasonal (i.e. conversion of permanent water into seasonal water)

$ε$ – Permanent water surfaces (i.e. area where water is always observed)

While the following applies for computing seasonal water dynamics:

$∆$ – percentage change in spatial extent

$α$ – New seasonal water (i.e. conversion of a no water place into a seasonal water place)

$β$ – Lost seasonal water (i.e. conversion of a seasonal water place into a no water place)

$ρ$ – Permanent to seasonal (i.e. conversion of permanent water into seasonal water)

$σ$ – Seasonal to permanent (i.e. conversion of seasonal water into permanent water)

$ε$ – Seasonal water surfaces (i.e. area where seasonal water is always observed)

The nature of this formula yields percentage change values as either positive or negative, which helps to indicate how spatial area is changing. On the SDG661 data portal, statistics are displayed using both positive and negative symbols. For interpretation of the statistics, if the value is shown as positive, the statistics represent an area gain while if the value is shown as negative, it represents a loss in surface area.

The use of ‘positive’ and ‘negative’ terminology does not imply a positive or negative state of the water-related ecosystem being monitored. Gain or loss in surface water area can be beneficial or detrimental. The resulting impact of a gain or loss in surface area must be locally contextualized. The percentage change statistic produced represents how the total area of lakes, rivers within a given boundary (e.g. nationally) is changing over time. Percentage change statistics aggregated at a national scale should be interpreted with some degree of caution because these statistics reflect the areas of all the lakes and rivers within a country boundary. For this reason, sub-national statistics are also made available including at basin and sub-basin scales. The statistics produced at these smaller scales reflects area changes to a smaller number of lakes and rivers within a basin or sub-section of a basin, allowing for localized, water body specific, decision making to occur.

Reservoirs

Description of the method used to globally map changes to reservoir surface area

A global reservoir dynamics dataset has been produced by the European Commission's Joint Research Centre. The dataset documents the long term (since 1984 onward) spatial area dynamics of 8,869 reservoirs at 30x30 meter pixel resolution. The reservoirs dataset represents surface area data on artificial waterbodies including reservoirs formed by dams, flooded areas such as opencast mines and quarries, and water bodies created by hydro-engineering projects such as waterway and harbour construction. The map below shows the reservoirs at their maximum extent. The dataset will be progressively complemented and continuously updated to account for newly build reservoirs. Each reservoir is documented as separate object with a unique ID assigned. The reservoirs dataset is derived from the Global Surface Water Explorer (GSWE) dataset, onto which is applied an expert system classifier designed to separate natural and artificial water bodies. The expert systems classifier is non-parametric to account for uncertainty in data, incorporate image interpretation expertise into the classification process, and uses multiple data sources. The expert system has been developed to delineate natural and artificial water using an evidential reasoning approach; the geographic location and the temporal behaviour of each pixel; and fed with the following datasets:

Global Surface Water Explorer (Pekel et al., 2016): This dataset that maps the location and long term (since 1984 onward) temporal distribution of water surfaces at global scale. The maps show different facets of surface water dynamics and document where and when open water was present on the Earth's surface. The maps include natural (rivers, lakes, coastal margins and wetlands) and artificial water bodies (reservoirs formed by dams, flooded areas such as opencast mines and quarries, flood irrigation areas such as paddy fields, and water bodies created by hydro-engineering projects such as waterway and harbour construction). The complete history of any water surface can be accessed at the pixel scale as temporal profile. These profiles allow for identifying specific months or years during which conditions changed, e.g. the date on which a new dam was created, or the month or year in which a lake disappeared. The GSWE dataset is continuously updated providing consistent global monitoring of open water bodies.

Global Reservoir and Dam Database (Lehner et al, 2011): The Global Reservoir and Dam Database v1.3 is the output of an international effort to collate existing dam and reservoir datasets with the aim of providing a single, geographically explicit and reliable database for the scientific community. The initial version (v1.1) of GRanD contains 6,862 records of reservoirs. The latest version (v1.3) augments v1.1 with an additional 458 reservoirs and associated dams to bring the total number of records to 7320.

Global Digital Surface Model: ALOS World 3D - 30m is a global digital surface model (DSM) dataset with a horizontal resolution of approximately 30 meters (1 arcsec mesh). The dataset is based on the DSM dataset (5-meter mesh version) of the World 3D Topographic Data. More details are available in the dataset documentation here.

Digital Elevation Data (Farr et al, 2004): The Shuttle Radar Topography Mission (SRTM, see Farr et al. 2007) is a digital elevation dataset at 30 meters resolution provided by NASA JPL at a resolution of 1 arc-second.

Known limitations and scope for improvements

The current version of the Global Reservoir Dynamics dataset has the following known limitations:

- Some reservoirs built prior 1984 may be missing;

- Reservoirs smaller than 3 hectares (30 000 square meters) may be missing;

- Branches of reservoirs whose width is smaller than 30 meters may be missing.

Calculating the extent to which reservoir area is changing over time

Data on reservoir areas are available for a 38-year period, from 1984-onward. Every year new annual data is produced and added to this time series. To calculate percentage change in reservoir area using a 2000-2021 dataset, a baseline period is first defined against which to measure change. This methodology uses 2000-2004 as the 5-year baseline period and to be compared against any subsequent 5-year target period. For each 5-year period the water state (permanent, seasonal or no water) is decided by a majority rule, and the water transitions between the baseline and the target period is subsequently used to compute the percentage change (∆) in the spatial area of reservoirs.

The computation is based on water detection within water bodies designated as reservoirs.

The equation 1 is subject to the following parametrization for computing changes in minimum reservoir extent.

While equation 2 is applied for computing changes in maximum reservoir area:

$E q u a t i o n 2 :$ $∆ = \frac{(α - β) + (ρ - σ)}{(ε + β + ϑ) + (ϵ + σ + \partial)} \times 100$

Where:

$∆$ – percentage change in spatial extent

$α$ – New permanent water (i.e. conversion of a no water place into a permanent water place)

$β$ – Lost permanent water (i.e. conversion of a permanent water place into a no water place)

$ρ$ – New seasonal water (i.e. conversion of a no water place into a seasonal water place)

$σ$ – Lost seasonal water (i.e. conversion of a seasonal water place into a no water place)

$ϑ$ – Permanent to seasonal (i.e. conversion of permanent water into seasonal water)

$\partial$ – Seasonal to permanent (i.e. conversion of seasonal water into permanent water)

$ε$ – Permanent water surfaces (i.e. area where water is always observed)

$ϵ$ – Seasonal water surfaces (i.e. area where seasonal water is always observed)

Minimum water extent of reservoirs is the lowest observed (or minimum) surface area of reservoirs in a year (intra-annual measurement). This minimum extent varies from one year to another. The data shows the extent to which the annual minimum surface area of reservoirs has changed compared to a reference period. This change is calculated by comparing the minimum extent of the most recent five years against a five year reference period (2000-2004). Change is either gain or loss both shown in both percentage and km2 units.

Maximum water extent of reservoirs is an intra-annual measurement corresponding to the highest observed (or maximum) extent a reservoir reaches within a year. The data shows the extent to which the annual maximum surface area of reservoirs has changed compared to a reference period. This change is calculated by comparing the maximum area of the most recent five years against a five year reference period (2000-2004). Change is either gain or loss both shown in both percentage and km2 units.

Wetlands

Description of the method used to globally map wetlands

Inland vegetated wetlands are mapped according to the following definition: “Inland vegetated wetlands include areas of marshes, peatlands, swamps, bogs and fens, the vegetated parts of flood plains as well as rice paddies and flood recession agriculture”. This sub-indicator only measures inland vegetated wetlands and not coastal mangroves (see section 3.5 of this methodology on mangroves). This SDG indicator methodology is used for official reporting of SDG indicator 6.6.1 statistics. A high-resolution global geo-spatial mapping of inland vegetated wetlands has been produced detailing the spatial area of wetlands per country. The data on wetlands has been produced to support countries with monitoring their wetland ecosystems and bridge an existing global data gap. The data production method uses a consistent wetland monitoring mechanism based on satellite Earth Observation data and the global map includes the entire land surface of Earth except for Antarctica and a few small islands. As wetlands tend to be susceptible to high annual variations, multi-annual data was collected to even out potential annual biases and create a robust estimate of wetland area. Data was gathered from 2016, 2017 and 2018 and combined to produce a wetlands area baseline measurement (in km2).

Future annual updates will enable wetlands change statistics to be produced and these once available these will be displayed on the SDG 6.6.1 data portal. Predicting wetland area using Earth Observation data relies on four components: stratification, training data, machine learning, and post-processing. The approach uses all available data from the satellites Sentinel-1, Sentinel-2, and Landsat 8 to predict wetland probability. A Digital Elevation Model is used to qualify wetland predictions and a post-processing routine converts the wetland probability map into a map of wetland area. In addition, topographic information from satellite-derived Digital Elevation Models (DEMs) are used. Close to 4 million satellite images amounting to 2.8 petabyte of data were analysed and classified as wetland or non-wetland using an automated machine learning model. Users of the global wetland map should be aware that the map represents a first line rapid assessment of the global distribution of vegetated wetlands. The methodology applied identifies vegetated inland wetlands. This may generate underestimations compared to national statistics which may integrate metrics on surface water and coastal/marine wetlands.

Figure 1. Workflow for mapping global wetland area

Data accuracy for the available wetlands data is approximately 70% and wetland data with 100% accuracy is not feasible at this current time. While it is based on a scientifically sound and robust mapping approach, there will inevitably be inaccuracies in the wetland predictions both in terms of commission and omission errors. Notable commission errors are for instance high-intensive irrigated agriculture parcels being classified as wetlands because they resemble many of the inherent spectral characteristics of wetlands (i.e. high moisture and vegetation presence even in dry season). Omission errors will mainly be attributed to the large diversity of wetlands. Despite best effort to train the model across the widest range of wetlands possible, there will be types of wetlands and instances of wetland behaviour that will not be adequately captured in a global model. For instance, some ephemeral wetlands are rarely flooded or wet and therefore often missed by satellite datasets. In other cases, the wet part of a wetland may occur under a dense vegetation canopy, which is difficult to assess using Earth Observation data, where the presence of water/moist conditions is not easily detected. Other limitations of the data are:

Only regional stratification is applied including strata spanning several countries. Using a finer level of stratification will help improve local/national wetland predictions;
The accuracy of the wetlands map will improve further once cross referenced with more national wetland inventories and ground truthing;
Terrain information from satellite derived DEMs is key input for mapping wetlands globally. The current reference datasets are the 30-meter SRTM DEM which covers the globe from 60oNorth⁰ to 56oSouth⁰, while the region north of 60⁰ north relied on a lower resolution 90-meter DEM model was used. Options for 30-meter DEMs north of 60oN⁰ exists and should be considered in future updates;
Small islands and potentially even entire small island states fall outside the acquisition plan of the Sentinel satellites. As a result, no wetland prediction has been performed for these areas. It will be possible to develop separate models for these missing islands using alternative input satellite data (e.g. using Landsat alone).

Future updates and iterations of the wetlands map will address the above limitations, including a potential shift into a deep learning model to more explicitly reflect temporal and spatial aspects of wetland predictions. Despite limitations with the methodology the production of high-resolution wetland mapping for the entire globe is at the forefront of currently available technology and computing power. It represents a huge step forward towards reporting accurate, statistically robust wetland data.

Calculating the change in surface area of wetlands per country

No change in surface area has yet been calculated. However, a baseline surface area has been calculated per country. This methodology uses a 2017 baseline (based on input imagery data from 2016 to 2018 to even out potential annual biases). Going forward, updates to this wetland area datasets will be produced annually. Once the update is produced it will be possible to calculate change of wetland area from the baseline reference period. Using this baseline period, percentage change of spatial extent is calculated using equation 3:

$E q u a t i o n 3 : ∆ = \frac{γ - β }{β} \times 100$

Where:

$∆$ – percentage change in spatial extent;

$β$ – the spatial wetland area for the baseline reference period;

$γ$ – the spatial area for the reporting period.

Mangroves

Description of the method used to measure mangrove area

Global mangrove area maps were derived in two phases, initially producing a global map showing mangrove area (for 2010) and thereafter producing six additional annual data layers (for 1996, 2007, 2008, 2009, 2015 and 2016) (Bunting et al., 2018). The method uses a combination of radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data. Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (1 x 1 degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and sub-tropical coastlines in the Americas, Africa, Asia and Oceania. The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was generated based on geographical parameters such as latitude, elevation and distance from ocean water. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling some 38 million points using historical mangrove maps for the year 2000 (Giri et al., 2010; Spalding et al., 2010), water occurrence maps (Pekel et al, 2017), and Digital Elevation Model data (SRTM-30).

The maps for the other six epochs were derived by detection and classification of mangrove losses (defined as a decrease in radar backscatter intensity) and mangrove gains (defined and a backscatter increase) between the 2010 ALOS PALSAR data on one hand, and JERS-1 SAR (1996), ALOS PALSAR (2007, 2008 & 2009) and ALOS-2 PALSAR-2 (2015 & 2016) data on the other. The change pixels for each annual dataset were then added or removed from the 2010 baseline raster mask (buffered to allow detection of mangrove gains also immediately outside of the mask) to produce the yearly extent maps.

Classification accuracy of the 2010 baseline dataset was assessed with approximately 53,800 randomly sampled points across 20 randomly selected regions. The overall accuracy was estimated to 95.25 %, while User’s (commission error) and Producer’s (omission error) accuracies for the mangrove class were estimated at 97.5% and 94.0%, respectively. Classification accuracies of the changes were assessed with over 45,000 points, with an overall accuracy of 75.0 %. The User’s accuracies for the loss, gain and no-change classes respectively were estimated at 66.5%, 73.1% and 83.5%. The corresponding Producer’s accuracies for the three classes were estimated as 87.5%, 73.0% and 69.0%, respectively.

Calculating the area of mangrove per country

Data on mangroves area are available for 1996, 2007, 2008, 2009, 2010, 2015 and 2016. New annual data for 2017 and 2018 will be released in 2021, and annual data from 2019 and onwards planned for 2022. For the purpose of producing national statistics to monitor indicator 6.6.1, the year 2000 has been used as a proxy based on the 1996 annual dataset to align with this baseline with that of the surface water dataset. National mangrove extent for the year 2000 will be used as the baseline reference period. Annual mangrove extent is compared to this baseline year. Percentage change of spatial extent is calculated using equation 3.

Using equation 3 to calculate the percentage change in mangrove spatial extent, the following explanation is used:

$∆$ – percentage change in spatial extent;

$β$ – the national spatial extent from year 2000;

$γ$ – the national spatial extent of any other subsequent annual period.

Limitations of the mangrove data:

The mangroves map is a global dataset, and as such, it should not be expected to achieve the same high level of accuracy everywhere as a local scale map derived through ground surveys or the use of very high spatial resolution geospatial data. A global area mapping exercise using consistent data and methods – although supplemented with ground-based data for calibration and validation – for logistical reasons generally requires a trade-off in terms of local scale accuracy. Nonetheless, global maps can be improved locally (or nationally) by adding improved information (in-situ data and aerial or drone data) for training and re-classification.
Several different factors can affect the classification accuracy, including satellite data availability, mangrove species composition and level of degradation.
While the original pixel spacing of the satellite data used for the mapping is 25-30 metres, a minimum mapping unit of approximately 1 hectare is recommended due to the classification uncertainty of a single pixel. The classification errors (in particular omission errors) typically increase in regions of disturbance and fragmentation such as aquaculture ponds, as well as along riverine or coastal reef mangroves that form narrow shoreline fringes of a few pixels.
In general, the mangrove seaward border is more accurately defined than the landward side where distinction between mangrove and certain wetland or terrestrial vegetation species can be unclear.
Striping artefacts due to Landsat-7 scanline error are present in some areas, particularly West African regions due to lack of Landsat-5 data and persistent cloud cover.
Known data gaps in this version (v2.0) of the dataset: Aldabra island group (Seychelles); Andaman and Nicobar Islands (India); Bermuda (U.K.); Chagos Islands; Europa Island (France); Fiji (part east of Antemeridian); Guam and Saipan (U.S.); Kiribati; Maldives; Marshall Islands; Peru (south of latitude S4°), and Wallis and Futuna Islands (France).
As with wetland mapping the production of high-resolution mangrove data for the entire globe is at the forefront of currently available technology and computing power. It represents a huge step forward towards reporting accurate, statistically robust mangrove data which can be updated continuously.

Turbidity and Trophic state

Description of the method used to globally map reservoir area

The global dataset measures two lake water parameters: Turbidity (TUR) and an estimate of Trophic State Index (TSI). The products were produced by the Copernicus, the Earth Observation program of the European Union. For the two parameters the dataset documents monthly averages as well as multi-annual per-monthly averages for the periods 2006-2010 and 2017-2020. The products are mapped at a 300x300 meter pixel resolution capturing data for a total of 4265 lakes. Each lake has individual identification information allowing it to be related to other hydrological datasets. A list of all lake IDs and additional information (location, name – where known, area) is available. Turbidity is derived from suspended solids concentration estimates and the Trophic State Index is derived from phytoplankton biomass by proxy of chlorophyll-a.

Table 2: Trophic state index and related chlorophyll-a concentration classes

(according to Carlson (1977)

Trophic classification	Trophic State Index, Copernicus Global Land Service TSI values	Chlorophyll-a (µg/l) (upper limit)
Oligotrophic	0	0.04
	10	0.12
	20	0.34
	30	0.94
Mesotrophic	40	2.6
Mesotrophic	50	6.4
Eutrophic	60	20
Eutrophic	70	56
Hypereutrophic	80	154
	90	427
	100	1183

Products in the period 2006 - 2010 are based on observations from the MERIS sensor, whereas the product 2017-2020 is derived from OLCI sensors. Land/water buffer maps as well as ice maps were applied to improve the accuracy of the data. The products were tested against consistency (time series) and against in situ data, both for a selected set of lakes. A detailed technical methodology is available to download at the SDG661 data portal (SDG661.app).

Calculating Turbidity and Trophic State Index statistics

A baseline reference period has been produced comprising monthly averages across 5 years of observations for the period 2006-2010. From these five years of data, 12 monthly averages (one for each month of the year) for both trophic state and turbidity, were derived. A further set of observations are then used to calculate change against the baseline data. These monthly data comprise years 2017, 2018, 2019 and 2020. The 12 monthly averages for these three years have been derived.

Monthly deviation of the multiannual baseline is computed using equation 4:

$\frac{M o n t h l y a v e r a g e - M o n t h l y b a s e l i n e}{M o n t h l y b a s e l i n e} \times 100$

For each pixel, and for each month, the number of valid observations has been counted and the number of months where there were monthly deviations, falling in one of the following range of values: 0-25% (low), 25-50% (medium), 50-75% (high), 75-100% (extreme). An annual deviation synthesis is also produced.

The data represent the number of lakes impacted by a degradation of their environmental conditions (i.e. showing a deviation in turbidity and trophic state from the baseline) compared to the total number of lakes within a country. The values per pixel / per lake are calculated so as to account for different sized lakes. When viewing the summary dashboard of national statistics within on the 661SDG .. data platform, data on turbidity and/or trophic state is displayed as number of lakes ‘affected’ compared with the total number of lakes in the country. A lake is categorized as being ‘affected’ when the value for either turbidity or trophic state exceeds 50% when compared to the lakes baseline value. Once a turbidity or trophic state event passes this threshold, the event is then recorded within the national summary statistics to show the lake has been affected. Once an event is recorded it remains captured on the dashboard – meaning the number of affected lakes will either stay constant or accumulate over time. The number will not decrease. The data is not informing whether a lake is considered to be of good or bad quality, only that a lake water event has occurred and has been recorded. Each event is considered indicative of a degradation in water quality; however, it is important to note that the turbidity and trophic state are included in indicator 6.6.1 as indirect (or proxy) indicators for water quality. These two parameters are not a direct measurement of water quality; however, they perform a very successful proxy role. The proxy parameters are therefore used to alert countries to these events, encouraging countries to investigate why an event occurred and determine if any remedial action is required. You can trace when high and extreme events have event occurred within the advanced analysis of the data.

River Flow

Measuring or modelling river flow (discharge)

River and estuary discharge, or the volume of water moving downstream per unit of time, is an essential metric for understanding water quantity within an ecosystem and availability for human use. Countries should provide total annual discharge per major river in order to observe change in river discharge over time.

This section describes key considerations for monitoring discharge and provides criteria for discharge data generated to support Indicator 6.6.1.

Common in-situ monitoring methods: There are a variety of methods for monitoring discharge in situ and selection should be based on the size and type of the waterbody, terrain and velocity of water flow, the desired accuracy of measurement, as well as finances available. Two the most common and accessible approaches are gauging stations and using current meters. In many countries, gauging stations are the most prevalent means for measuring river discharge as they allow even for continuous and often real-time monitoring. These are fixed locations along a river or estuary where the change in water surface level (stage) is monitored at locations where a unique relationship exists between stage and flow and a so-called rating curve can be produced. Water surface height (stage) is captured frequently, and the discharge estimated, most often at monthly intervals but in many places, this is available at daily intervals or even continuously. Current meters and other instruments can be used to monitor flow and calculate discharge. For example, propeller, pygmy or electromagnetic current meters are often used to measure velocity and can be used in conjunction with cross-sectional area methods to obtain flow rates. Acoustic Doppler Current Profiler’s (ADCPs) are widely used for larger rivers/estuaries to accurately measure bed depth, velocity, and discharge. They are often attached to boats and dragged along a waterbody, but permanent installations can also be found, sending out acoustic waves and measuring acoustic reflectance. Meters and instruments like ADCPs are significantly more costly than other methods of measurement and require skilled operators and good maintenance programmes. However, in larger rivers they may be the most appropriate option, especially during high flow conditions.

Location of Monitoring: The chosen monitoring method may dictate where along a river or estuary the discharge is captured. For example, if fixed weirs are in place, monitoring will always take place here. Since in situ discharge monitoring can be time and cost-intensive, choosing strategic locations which represent a whole river or estuary is recommended. The minimum monitoring effort is to locate one flow measuring site within proximity to each basin’s exit (into another basin). In addition, monitoring at the exit point from all major tributaries adds a substantial level of information. Where there is a local impact on discharge due to human influence, then it is recommended to monitor flow upstream and downstream of these areas so that the overall situation can be managed.

Frequency of Monitoring: The quantity of water in a river or estuary can change rapidly in response to rainfall and weather patterns. The more data on discharge there is, the higher the accuracy is of that discharge data. However, again it is important to focus efforts and choose a strategic frequency for monitoring. Data on discharge should ideally be collected at a given location once a month at minimum (ideally at a daily frequency) and this data can then be used to determine annual and long-term trends. The quantity of water in estuaries may be significantly influenced by tidal inflows, thus this indicator is limited to the freshwater inflows to the estuary from the upstream river.

Modelling Discharge: In addition to in situ monitoring which always is impacted by all forms of flow moderation, storage or abstractions upstream, discharge may also be modelled from one of the many available models which use climatic and land-use data, amongst other data, to estimate both natural and present-day flows. Globally hydrological model applications are available and in some countries these or similar models have been developed for the local context and are calibrated using real measured data. It is recommended that modelled discharge data is complimented by measured in situ data wherever possible to ensure accuracy. Conceptual hydrological models for flow and discharge estimation are normally less amenable to detecting the flow impacts of minor land-cover changes over time as the models are calibrated on historical flow data and associated land-use conditions.

Groundwater

Measuring quantity of groundwater within aquifers

The changes to the quantity of groundwater within aquifers is important information for many countries that rely heavily on groundwater availability. For the purposes of Indicator 6.6.1 monitoring the changes to groundwater levels gives a good indication of changes to the water stored in an aquifer. Furthermore, only significant ground water aquifers, that can be seen as individual freshwater ecosystems will be included in the reporting.

Location of Monitoring: Measuring the level of groundwater within an aquifer is done through the use of boreholes. One of the challenges in setting up monitoring is choosing the location of boreholes which will adequately represent the total groundwater situation for an aquifer. The number of boreholes that need to be monitored cannot be prescribed because the distribution of groundwater can be variable depending on the location and characteristics of aquifers. It is recommended that sufficient boreholes to characterise the area should be monitored, with the capacity of the country being a factor in deciding how many would best represent the area. It is highly recommended that data should be taken from observation boreholes / monitoring boreholes (these are boreholes which are not equipped with pumps). Data from used (pumped) boreholes should be avoided. In case a pumped borehole needs to be used for measurements, then it is crucial to allow for a sufficiently long recovery period in which the borehole is not used so that the groundwater level in the borehole can stabilise prior to any measurement.

Frequency of Monitoring: Groundwater levels change as a result of changes in groundwater recharge (affected by climate conditions, and land use) and by anthropogenic removals from the system (groundwater abstraction). Seasonal and wet/dry cycle influences need to be understood and hence monthly monitoring is optimal, but collection at least twice per year, in the wet and dry seasons, is necessary.

Criteria for Indicator 6.6.1 Data

Groundwater quantity data provided to the custodian agency(s) will be quality checked to ensure data integrity. Collection of groundwater level data generates statistics that are a proxy to the quantity of groundwater in an aquifer over time. In order to examine this change over time, percentage change in groundwater level will be generated and validated between the custodian agency(s) and the country. Calculating percentage change at a national level requires the establishment of a common reference period for all aquifers, which can either be based on historical groundwater level data (preferred) or modelled data if available. In cases where these are unavailable, a more recent period can be adopted to represent the ‘baseline’ or reference period. Countries should provide the annual level of groundwater in order to observe change in aquifer volume over time. A data collection table is provided in the monitoring methodology as an annex.

4.d. Validation

All satellite-based Earth observation data on freshwater are updated annually and uploaded to the SDG indicator 661 data portal (www.sdg661.app) where is freely accessible and data are freely downloadable. Every 3-4 years, in alignment with the timeline of the SDG6 Integrated Monitoring Initiative coordinated by UN Water, national SDG indicator 6.6.1 data are shared with national indicator focal points (pre-confirmed SDG 661 indicator focal persons) for no-objection approval.

4.e. Adjustments

No adjustments are made.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Due to the use of satellite data for some sub-indicators, it is not expected to have missing data for these sub-indicators. For all other sub-indicators, missing values are not imputed.

At regional and global levels

Missing values are not imputed.

4.g. Regional aggregations

For the aggregation methods, please see:

https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

A full SDG indicator monitoring methodology is available in all UN languages here.

All documentation on methodologies, downloads, production partners are available at the Freshwater Ecosystem Explorer (www.sdg661.app)www.sdg661.app

4.i. Quality management

The production methodologies for each freshwater satellite data set comprises quality management procedures and processes integrated into the data production process to ensure a minimum and consistent quality standard is met.

4.j. Quality assurance

The data production processes for each freshwater satellite data set comprises quality assurance (mathematical formulas) as an integrated component of the data production process to ensure a minimum and consistent quality standard is met and guarantying statically robust and internationally comparable data across time and space produced for all countries. The data production processes are published, including through peer reviewed scientific journals. Quality assurance processes are additionally carried out by data production teams at the European Commission. Data is shared and approved by countries and quality management processes are conducted at the United Nations Environment Programme according to approved standard operating procedures on data handling, aggregation, and management, prior to indicator data submission to UNSD.

4.k. Quality assessment

Refer to 4.i and 4.j.

5. Data availability and disaggregation

Data availability:

All SDG 6.6.1 indicator data is freely available and downloadable at the Freshwater Ecosystem Explorer www.sdg661.app

Time series:

The reporting on this indicator will follow an annual cycle.

Disaggregation:

Indicator 6.6.1 can be disaggregated by ecosystem type (which enables decision at ecosystem level to be taken). The SDG 661 data can also be disaggregated at different spatial scales i.e. National, basin, sub-administrative level, lakes, and reservoirs.

6. Comparability/deviation from international standards

Sources of discrepancies:

Not applicable

7. References and Documentation

URL: http://www.sdg6monitoring.org/indicators/target-66/indicators661/

All documentation on methodologies, downloads, production partners are available at the Freshwater Ecosystem Explorer (www.sdg661.app).

In developing the methodology for indicator 6.6.1 UNEP set up a technical expert group. This group provided inputs into the development of the monitoring methodology. A first draft (Tier III) methodology was piloted in 2017 and sent to all UN Member States accompanied with relevant capacity support materials. A limited number of Member States (19 per cent) submitted data to UNEP after a period of 8 months. The data that was received was of poor quality and coverage. Countries cited a lack of data to report, and neither time nor resources to initiate new ecosystem monitoring.

Following on from the global piloting and testing phase, and to address a known global data gap for the indicator, the methodology was revised to incorporate data on water-related ecosystem derived from satellite-based Earth observations. UNEP engaged with a series of partners working with global data products considered relevant and suitable for the indicator. The assessment of global data sources considered data quality, resolution, frequency of measurements, global coverage, time series, and scalability (i.e. disaggregated data at national and sub-national levels). The result was a methodology that is statistically robust producing internationally comparable data without being too onerous for countries to report on. The technical expert group was consulted on the updated methodology before submission to the IAEG-SDG for approval.

At the 7th IAEG-SDG meeting in April 2018, the indicator methodology was approved and classified as Tier II. Shortly afterwards, in November 2018, it was reclassified to a Tier I indicator methodology. The Tier I classification means that the indicator is conceptually clear, has an internationally established methodology and standards are available, and data are regularly produced by at least 50 per cent of countries and of the population in every region where the indicator is relevant.

Throughout 2019, UNEP continued to work with its partners to improve the globally available datasets relevant to SDG indicator 6.6.1 and the measurement of changes occurring to different types of water-related ecosystem. As such, this methodology was updated in March 2020 to include more detailed information about the approach used to obtain satellite-based Earth observation data with regard to the sub-indicators.

References

Bunting P., Rosenqvist A., Lucas R M., Rebelo L. M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C. M. (2018). The Global Mangrove gmwatch – a new 2010 Global gbaseline of Mangrove mextent. Remote Sens.ing, 10,() 1669 .https://doi.org/10.3390/rs10101669.

Dickens et al, 2019 : Chris Dickins, Matthew McCartney: Water-related Ecosystems, International

Water Management Institute, Sri Lanka. https://doi.org/10.3390/su11020462

Farr et al, 2004 : Farr et al, 2004 - Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M.,Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin,

M., Burbank, D., and Alsdorf, D.E., 2007, The shuttle radar topography mission: Reviews of

Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.

Giri, C., Ochieng, E., Tieszen L. L., Zhu, Z., Singh, A., Loveland, T.R., Masek, J. & Duke, N. (2011). Status and distribution of mangrove forests ofthe world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154-159. Available at: https://doi.org/10.1111/j.1466-8238.2010.00584.x

Lehner et al, 2011: Lehner et al, 2011 - Lehner, B., C. Reidy Liermann, C. Revenga, C. Vörösmarty, B. Fekete, P. :Crouzet, P. Döll, M. Endejan, K. Frenken, J. Magome, C. Nilsson, J.C. Robertson, R. Rodel, N.

Sindorf, and D. Wisser. 2011. High-resolution mapping of the world’s reservoirs and dams for

sustainable river-flow management. Frontiers in Ecology and the Environment 9 (9): 494-502.

MEA, 2005: –Millennium Ecosystem Assessment (2005) Ecosystems and Human Well Being:

Wetlands and water synthesis. Island Press, Washington DC. https://www.millenniumassessment.org/documents/document.358.aspx.pdf

Pekel, JF. ., Cottam, A., Gorelick N., &Belward A,.S (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540) : https://doi.org/10.1038/nature20584.

Sayer et al. 2019 : Sayer et al, 2019 – Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins,

Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van

Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John

Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank MullerKarger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30

meter resolution global shoreline vector and associated global islands database for the development of

standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI:

10.1080/1755876X.2018.1529714

Spalding M., Kainuma, M. & Collins, L. (2010). World Atlas of Mangroves (v1.1). London, U.K.: Earthscan (Taylor & Francis). ISBN: 978-1-84407-657-4. Available at: https://data.unep-wcmc.org/datasets/5.

6.6.1b

0.a. Goal

Goal 6: Ensure availability and sustainable management of water and sanitation for all

0.b. Target

Target 6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes

0.c. Indicator

Indicator 6.6.1: Change in the extent of water-related ecosystems over time

0.d. Series

Extent of inland wetlands (square kilometres)

Extent of human made wetlands (square kilometres)

0.e. Metadata update

2022-03-31

0.f. Related indicators

15.1

0.g. International organisations(s) responsible for global monitoring

Secretariat of the Ramsar Convention on Wetlands

1.a. Organisation

Secretariat of the Ramsar Convention on Wetlands

2.a. Definition and concepts

Definition:

“extent of wetlands”

This term can be defined as the surface area of wetlands. It is measured in km²or hectares. It is expected that the surface reported by countries in 2018 correspond to that of 2017; if not, the reference year should be indicated.

“change in the extent of wetlands”

This term refers to the percentage change in area of wetlands from a baseline reference. For reporting such change, the previous extent, if known, and the period over which the change has taken place should be specified.

Concepts:

In order to provide a precise definition of the indicator, it is crucial to provide a definition of

“Water related ecosystems”. For this purpose, the definition of the Ramsar Convention on Wetlands is used.

the Ramsar definition of “wetlands”

The Ramsar definition is very broad, reflecting the purpose and global coverage of the Convention:

In accordance with Article 1.1 of the Convention,
“Wetlands are areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres”.

In addition, in accordance with Article 2.1, Ramsar Sites
“may incorporate riparian and coastal zones adjacent to the wetlands, and islands or bodies of marine water deeper than six metres at low tide lying within the wetlands”.

- the Ramsar system of classifying wetland types

Many national definitions and classifications of “wetlands” are in use. They have been developed in response to different national needs and take into account the main biophysical features (generally vegetation, landform and water regime, and sometimes also water chemistry such as salinity) and the variety and size of wetlands in the locality or region being considered.

The Ramsar Classification System for Wetland Types, adopted at COP4 in 1990, and amended at COP6 in 1996 (Resolution VI.5) and at COP7 in 1999 (Resolution VII.11) has value as a basic internationally applicable habitat description for sites designated for the Ramsar List of Wetlands of International Importance.

The System (see Annex 1) describes the types of wetland covered by each of the wetland type codes. Note that the wetland types are grouped in three major categories: marine/coastal, inland, and human-made wetlands. Within a single Ramsar Site or other wetland, there may be wetland types from two or more of these categories, particularly if the wetland is large.

For the purpose of the Target and Indicator, and based on the National Reports Parties report on the use of the three major categories. Countries also use Ramsar definition that has been internationally agreed under the Convention. The minimum information that should be provided is the total area of wetlands for each of these three categories with an emphasis on inland wetlands or freshwater ecosystems for purpose of indicator 6.6.1 (see table below, the explanations of each wetland type code is in Annex 1).

Table 1: Tabulations of Wetland Type characteristics, Inland Wetlands:

Fresh water	Flowing water	Permanent	Rivers, streams, creeks	M
			Deltas	L
			Springs, oases	Y
		Seasonal/intermittent	Rivers, streams, creeks	N
	Lakes and pools	Permanent	> 8 ha	O
			< 8 ha	Tp
		Seasonal/intermittent	> 8 ha	P
			< 8 ha	Ts
	Marshes on inorganic soils	Permanent	Herb-dominated	Tp
		Permanent/ Seasonal/intermittent	Shrub-dominated	W
			Tree-dominated	Xf
		Seasonal/intermittent	Herb-dominated	Ts
	Marshes on peat soils	Permanent	Non-forested	U
			Forested	Xp
	Marshes on inorganic or peat soils	High altitude (alpine)		Va
		Tundra		Vt
Saline, brackish or alkaline water	Lakes	Permanent		Q
		Seasonal/intermittent		R
	Marshes & pools	Permanent		Sp
		Seasonal/intermittent		Ss
Fresh, saline, brackish or alkaline water	Geothermal			Zg
	Subterranean			Zk(b)

2.b. Unit of measure

The extent of wetlands is measured in km²

2.c. Classifications

The international standard classification being used is the Ramsar Classification System for Wetland Types, adopted at COP4 in 1990, and amended at COP6 in 1996 (Resolution VI.5) and at COP7 in 1999 (Resolution VII.11) which is a basic internationally applicable habitat description for sites designated for the Ramsar List of Wetlands of International Importance and other wetlands. See item 7 Annex 1 for the full classification.

3.a. Data sources

The Ramsar Convention on Wetlands Secretariat has been collecting and analysing data on country implementation since 2000 including information about wetland inventories. This is done at intervals of 3 years, that is the cycle of Country reporting under the Convention

The 1999 review of the state of wetland inventory worldwide (Global review of wetland resources and priorities for wetland inventory - GRoWI), which was undertaken for the Ramsar Convention, identified not only the major gaps in the extent to which wetland inventory had been undertaken, but also found that for the inventories which had been made, it was frequently very hard to trace their existence, to identify their purpose, scope and coverage, and/or to access the information contained in them.

Another source of information is the update of the Wetland Extent Trends (WET) Index that was commissioned by the Ramsar Convention Secretariat to WCMC. The Wet Index is an updatable indicator of wetland area trends where there are still gaps of information. However, it is not applicable at national level and has not been used, as data are not available at national level. This will be fixed with national reports.

In the format for National Report for COP13 the Contracting Parties agreed the inclusion of an indicator on the extent of wetlands and change in the extent (indicator 6.6.1). For COP13, 44% of Contracting Parties have completed national wetlands inventories and 16% of Parties reported that their wetland inventories are in progress. Therefore, all data are provided to the Ramsar Secretariat by countries in the form of a country report following a standard format, which includes the original data and reference sources and descriptions of how these have been used to estimate the extent of wetlands.

3.b. Data collection method

All data are provided by Ramsar Administrative Authorities to the Ramsar Secretariat in the form of country reports of implementation of the Convention based on a standard format that it is been approved by the Standing Committee. The format includes indicators to estimate wetland extent with reference sources.

As indicated in the Quality Assurance section, for remaining countries where no information is provided, a report is prepared by the Ramsar Secretariat using existing information and a literature search. All country reports (including those prepared by the Ramsar Secretariat) are sent to the respective Administrative Authority for validation before finalization.

3.c. Data collection calendar

Data collection process for indicator 6.6.1 has started in 2018 and data collection will take place also in 2019.

3.d. Data release calendar

Updated data with time series and including year 2020 will be released late 2020.

3.e. Data providers

Ramsar Administrative Authorities prepare and submit to the Ramsar Secretariat their National Reports on implementation for each Conference of the Parties. Countries with dependent territories prepare more than one report. For the remaining countries where no information is provided, a report is prepared by the Ramsar Secretariat using existing information and a literature search that is validated by the concerned countries.

3.f. Data compilers

Secretariat of the Ramsar Convention on Wetlands: The Secretariat expects to work with UNEP as co-custodian of this indicator and other UN agencies and partners.

3.g. Institutional mandate

At the 52nd meeting of the Standing Committee (SC52) in 2016, Contracting Parties of the Convention on Wetlands approved the inclusion of an indicator on wetland extent in the National Report to COP13. Subsequently, the UN General Assembly in July 2017 adopted the global indicator framework (A/RES/71/313) that included Indicator 6.6.1 on change in the extent of water-related ecosystems over time. Given that Contracting Parties were reporting on extent as part of the National Reports, the Interagency Expert Group on SDGs in 2017 appointed the Convention on Wetlands as co-custodian of Indicator 6.6.1 using data coming from National Reports, which used wetland inventories as a main source.

As noted in Resolution XIII.7, enhancing the Convention’s visibility and synergies with other multilateral environmental agreements and other international institutions, the Convention on Wetlands is co-custodian with UNEP of SDG Indicator 6.6.1. The Convention contributes to monitoring progress with data from National Reports on extent of wetlands, based on the Convention’s definitions and requirements for reporting.

Paragraph 40 of Resolution XIII.7 “requests the Secretariat to continue working with Contracting Parties on the completion of national wetland inventories and wetland extent to report on SDG Indicator 6.6.1”.

The Standing Committee at its 54th and 57th meetings, through Decisions SC54-26 and
SC57-47, approved the allocation of funds to support Contracting Parties in the completion of wetland inventories and report on wetland extent under Indicator 6.6.1.

4.a. Rationale

The Ramsar Convention on Wetlands is the Intergovernmental treaty that provides the framework for the Conservation and wise use of wetlands and their resources. The Convention was adopted in 1971 and came into force in 1975. Since then, 170 Countries representing almost 90% on UN member states, from all the world´s geographic regions have acceded to become Contracting Parties under the Convention.

At its 52nd meeting, in 2016, the Standing Committee of the Ramsar Convention agreed that Parties would include in their national reports for the 13th meeting of the Conference of the Parties, which have been submitted in January 2018, data on the “extent” of wetlands. This requirement provides an intergovernmental mechanism to obtain verified data that clearly contribute to Indicator 6.6.1 on wetland extent, but also to collect information for Target 15.1 which consider other types of ecosystems.

The indicator provides a measure of the relative extent of inland wetlands in a country. It follows the rationale of the forest indicator (Indicator 15.1.1). The availability of accurate data on a country's wetland extent based on the country´s wetland inventory is crucial for decision making regarding policies, restoration of critical wetlands or designation under national or international management or protected area categories.

Changes in the wetland extent reflect wetland loss and degradation for land use changes or for other uses and may help identify unsustainable practices from different sectors.

4.b. Comment and limitations

The 1999 review of the state of wetland inventory worldwide (Global review of wetland resources and priorities for wetland inventory - GRoWI), which was undertaken for the Ramsar Convention, identified not only the major gaps in the extent to which wetland inventory had been undertaken, but also found that for the inventories which had been made, it was frequently very hard to trace their existence, to identify their purpose, scope and coverage, and/or to access the information contained in them.

In the light of these findings and to help address this lack of access by those who need to use wetland inventory for a wide range of Convention implementation purposes, the Convention’s Scientific & Technical Review Panel (STRP) developed a standard model for wetland inventory metadata (i.e., data about the characteristics of a wetland inventory, rather than the inventory data itself) in order to facilitate those who have inventories in making the existence and availability of these more publicly accessible.

In 2002, several limitations were identified (Ramsar COP8) in the use of EO for routinely deriving wetland information. These included the cost of the technology, the technical capacity needed to use the data, the unsuitability of the data available for some basic applications (in terms of spatial resolution), the lack of clear, robust and efficient user-oriented methods and guidelines for using the technology, and a lack of solid track record of successful case studies that could form a basis for operational activities.

Historical optical data is available from Landsat and Spot missions; however, persistent cloud cover in certain regions renders much of these data unusable. Distinguishing between permanent and temporary surface water and wetlands can therefore be difficult considering the available historical data. It is further noted that for complex environments with different wetland types, in situ data or local knowledge is critical to support the analysis of the EO data, and is sometimes the only way to obtain information on certain wetland types.

Another limitation is that some countries are in the process of updating or completing their national wetlands inventories. In others, there are still gaps or difficulty to access the available information.

Despite the above limitations, the use of the measure of extent of wetlands will respond to the indicator and will allow having a practical mechanism in the short term to track the status of water related ecosystems with robust data and foster action for the conservation of these important ecosystems.

4.c. Method of computation

Wetland area (Km² or ha, reference year)/Change in the extent of wetlands (water-related ecosystems over time) a baseline reference and year.

Based upon the national wetland inventory (complete or partial), countries provide a baseline figure in square kilometres for the extent of wetlands (according to the Ramsar definition) for the year 2017. The minimum information that should be provided is the total area of wetlands for each of the three major categories; “marine/coastal”, “inland” and “human-made.

If the information is available, countries indicate the % change in the extent of wetlands over the last three years. If the period of data covers more than three years, countries provide the available information, and indicate the period of the change. For reporting such change, the previous extent, if known, and the period over which the change has taken place should be specified.

This indicator can be aggregated to global or regional level by adding all country values globally or in a specific region.

4.d. Validation

The Convention contributes to monitoring progress of Indicator 6.6.1 with data from National Reports on extent of wetlands, based on the Convention’s definitions and requirements for reporting. State Parties to the Convention report to the Secretariat every three years that is the cycle of the Convention. The data submitted by the State Parties on their National Reports on Indicator 6.6.1 are review by the Secretariat and Focal Points of the State Parties are contacted in case clarifications are necessary. Once the clarifications are made, the data are submitted to the SDGs Indicators Database.

4.e. Adjustments

As indicated in item 2.c, the international standard classification being used is the Ramsar Classification System for Wetland Types, adopted at the Fourth Meeting of the Conference of the Parties to the Convention on Wetlands (COP4) in 1990.

When reporting on the SDGs data, we use the regional aggregates according to the “SDG regional groupings for compliance with SDG processes.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For countries where no information on wetland inventories was provided to the Ramsar Convention on Wetlands Secretariat as part of their National Reports to COP13 (16% of countries) a report is in preparation by the Ramsar Secretariat using existing information from previous assessments and literature search. The reports are shared with the concerned countries in order to comment and make any adjustment complementation to the data.

At regional and global levels

As indicated above

4.g. Regional aggregations

Since information is available for all countries, regional and global estimates are produced by summation.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries under the Ramsar Convention provide all data in the form of a country report following a standard format approved by the Standing Committee, which includes the original data and reference of wetland inventories as the main source of information.

Detailed methodology and guidance on how to provide the data on extent for indicator 6.6.1 in their National Reports and to use Ramsar definition and classification is found in the document “Guidance on information on national wetland extent, is provided in Target 8 National Wetlands Inventory of the Ramsar National Report for COP13 and COP14 ”.

The Ramsar Convention on Wetlands has taken many steps to ensure the wise use and conservation of wetlands globally. This has included the development and promotion of guidance and best practice tools for the inventory, assessment and monitoring of change in wetlands with emphasis in recent years on the application of an increasing number of satellite-based remote sensing approaches (Davidson & Finlayson 2007; Mackay et al. 2009; Ramsar Secretariat 2010a). This has become necessary as there is an increasing demand for information that can be readily used by wetland managers to help stem the ongoing loss and degradation of wetlands.

The utility of different remote sensing datasets for wetland inventory, monitoring and assessment is well established, through the provision of site based (Land Use Land Cover (LULC)) maps characterising an ecosystem, to the analysis of time series data (remote sensing datasets collected consistently over a particular time period) to determine changes.

The availability and accessibility of EO datasets suitable for addressing the information needs of the Ramsar Convention and wetland practitioners has increased dramatically in the recent past; increasing capabilities in terms of spatial, temporal and spectral resolution of the data have enabled more efficient and reliable monitoring of the environment over time at global, regional and local scales.

The Scientific and Technical Review Panel of the Convention has produced a Ramsar Technical Report on “Best practice guidelines for the use of Earth Observation for wetland inventory, assessment and monitoring: An information source for wetland managers provided by the Ramsar Convention for Wetlands”. The Ramsar Convention and EO based approaches build on those previously undertaken on the use of EO technologies for implementation of the Convention (Ramsar, 2002; Davidson & Finlayson, 2007; Mackay et al., 2009) and are placed within the conceptualisation of wetland inventory, assessment and monitoring that were incorporated into the IF-WIAM (Ramsar Secretariat, 2010b).

The purpose of the report is to provide an overview of the application of EO technologies to inform wetland managers and practitioners, and stakeholders, including those from related sectors, such as protected area managers and wetland education centre staff (Ramsar Convention, 2015) about “best practice” use of EO technologies, taking into account requirements and recommendations from the Convention.

EO provides an effective means for periodic mapping and monitoring over regional to global scales. It should, however, not be expected that global datasets, can achieve the same high level of accuracy everywhere as a local scale map derived through ground surveys and the use of finer resolution (aerial, drones) geospatial data.

Although mapping of land cover and land uses are one of the most common uses of EO data, there are still challenges in assessing the current status and changes in wetlands over time. Monitoring historical trends and changing patterns of wetlands is complicated by the lack of medium to high-resolution data in particular prior to 2000.

Despite the ever-expanding data archives, improving quality and increasing suitability of EO data for wetland inventory, monitoring and assessment, it is important to note that “ground-truthing” or field-based assessments and validation are still a vital component of any work involving EO data, whose occasional omission may still lead to problematic results.

Ramsar partners such as Jaxa and ESA have conducted pilot projects that provide geospatial information to provide changes to Ramsar, national wetland practitioners, decision makers, and NGOs.

Wetland inventory provides the basis for guiding the development of appropriate assessment and monitoring, and is used to collect information to describe the ecological character of wetlands including that used to support the listing of Ramsar sites, as recorded in the Ramsar Information Sheet (Ramsar Secretariat, 2012), assessment considers the pressures and associated risks of adverse change in ecological character; and monitoring, which can include both survey and surveillance, provides information on the extent of any change that occurs as a consequence of management actions.

Under the Convention, multiple guidelines have been developed to support countries to complete national wetland inventories (NWIs) including the use of metadata (Some of these guidelines are mentioned below). More recently in 2020, the Secretariat prepared a toolkit on wetlands inventory to assist Contracting Parties to implement or update a NWI. The aim of the toolkit is to provide practical guidance and examples of how to implement an NWI, including a step-by-step process and resources to support each recommendation. Good practices and examples on the areas of carrying out and updating NWIs, inventory methods, data collection, Earth observation and use of wetland inventories in decision-making are provided. Examples that illustrate how to solve the challenges faced by Contracting Parties are also included. The toolkit includes an introduction linking NWIs to SDG targets and expounding on the importance of an NWI for decision-making, including suggestions for building the case for supporting and protecting wetlands.

The Secretariat is using the toolkit as a central resource for the development of training materials, webinars and other training opportunities for Contracting Parties.

Ramsar Guidelines

A new toolkit for National Wetlands Inventories

https://www.ramsar.org/sites/default/files/documents/library/nwi_toolkit_2020_e.pdf

Handbook 15 Wetland Inventory. Ramsar Secretariat 2010a.

https://www.ramsar.org/sites/default/files/documents/pdf/lib/hbk4-15.pdf

Ramsar Handbooks: Handbook 13 Inventory, assessment and monitoring. Ramsar Secretariat 2010b https://www.ramsar.org/sites/default/files/documents/pdf/lib/hbk4-13.pdf

Ramsar Technical Report 2 Low-cost GIS software and data for wetland inventory, assessment & monitoring.

https://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr02.pdf

Ramsar Technical Report 4: A Framework for a wetland inventory metadatabase.

https://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr04.pdf

Ramsar (2002). The Ramsar Convention on Wetlands, The 8th Meeting of the Conference of the Parties to the Convention on Wetlands, Valencia, Spain, 18-26 November 2002, COP8 DOC. 35, The use of Earth Observation technology to support the implementation of the Ramsar Convention, http://www.ramsar.org/sites/default/files/documents/pdf/cop8/cop8_doc_35_e.pdf

Resolution VIII.6 A Ramsar Framework for Wetland Inventory http://www.ramsar.org/document/resolution-viii6-a-ramsar-framework-for-wetland-inventory

Resolution VI.12 National Wetland Inventories and candidate sites for listing http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_vi.12e.pdf

Resolution VII.20 Priorities for wetland inventory http://www.ramsar.org/sites/default/files/documents/library/key_res_vii.20e.pdf

Resolution IX.1 Additional scientific and technical guidance for implementing the Ramsar wise use concept Annex E. An Integrated Framework for wetland inventory assessment and monitoring http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_ix_01_annexe_e.pdf

Resolution X.15 Describing the ecological character of wetlands and data needs and formats for core inventory: harmonized scientific and technical guidance http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_x_15_e.pdf

Ramsar Technical Report 10: The use of Earth Observation for wetland inventory, assessment and monitoring | Ramsar

The Ramsar Convention on Wetlands. (2011). The 11th Meeting of the Conference of the Parties to the Convention on Wetlands, Bucharest, Romania, 6-13 July, 2012. Resolution XI.8, Annex 2: Strategic Framework and guidelines for the future development of the List of Wetlands of International Importance of the Convention on Wetlands (Ramsar, Iran, 1971) – 2012 revision.

https://www.ramsar.org/sites/default/files/documents/library/cop11-res08-e-anx2_revcop13.pdf

Davidson, N.C. & Finlayson, C.M. (2007). Earth Observation for wetland inventory, assessment and monitoring. Aquatic Conservation: Marine and Freshwater Ecosystems, 17, 219-228.

Earth Observation for wetland inventory, assessment and monitoring | N.C. Davidson; C.M. Finlayson | download (booksc.org)

MacKay, H., Finlayson, C.M., Fernández-Prieto, D., Davidson, N., Pritchard, D. & Rebelo, L.-M. (2009). The role of Earth Observation (EO) technologies in supporting implementation of the Ramsar Convention on Wetlands. Journal of Environmental Monitoring 90(7), 2234-2242.

The role of Earth Observation (EO) technologies in supporting implementation of the Ramsar Convention on Wetlands | H. MacKay; C.M. Finlayson; D. Fernández-Prieto; N. Davidson; D. Pritchard; L.-M. Rebelo | download (booksc.org)

4.i. Quality management

At the 52nd meeting of the Standing Committee (SC52) in 2016, Contracting Parties of the Convention on Wetlands approved the inclusion of an indicator on wetland extent in the National Report to COP13. The Secretariat provides guidance and training to Contracting Parties for the submission of National Reports to COP13/COP14 and developed a toolkit and training on wetlands inventories to enable them to provide data that could be used for SDG Indicator 6.6.1 reporting. The Secretariat also works with Parties to complete and refine information on extent that has been submitted to the Secretariat and to identify information that is available in existing inventories referred in National Reports, that has not been used to report on wetland extent. Through this mechanism, national validated data using accepted international definitions of wetlands are provided to measure the extent of water-related ecosystems under SDG 6.

4.j. Quality assurance

Once received, the country reports undergo a rigorous review process to ensure correct use of definitions and methodology as well as internal consistency. A comparison is made with past information and other existing data sources. Regular contacts between national correspondents and Ramsar Staff by e-mail and webinars/regional/sub-regional review workshops form part of this review process in order to support country capacities in particular for monitoring purposes.

Missing reports prepared by the Ramsar Secretariat for Indictor 6.6.1 are sent to the respective Ramsar Administrative Authority for validation before finalization and publishing of data. The data are then aggregated at sub-regional, regional and global levels by the Ramsar Secretariat team.

4.k. Quality assessment

Refinement of data includes reporting on wetland type using the two main categories in the Ramsar classification: inland and human-made wetlands. Through this mechanism, national validated data using accepted international definitions of wetlands under the Convention are provided to measure the extent of water-related ecosystems under SDG 6.

5. Data availability and disaggregation

Data availability:

Data are available for all countries (143) that submitted National Reports for COP13 as well as for previous COPs as indicated below. The data collected include information on wetland inventories and extent. For the missing country data (16%) as indicated in the “Quality assurance section”, the Secretariat will prepare in 2018 reports with the available source of information for Indictor 6.6.1 that will be sent to the respective Ramsar Administrative Authorities for validation. The gaps of information will be addressed during 2018 and 2019 to fully report in late 2020.

Time series:

The Secretariat holds National Report information from COP8 (2002), COP9 (2005), COP10 (2008), COP11 (2012), COP12 (2015) and COP13 (2018) National Reports, in databases which permit an analysis of trends in implementation over time, from the 2002-2005 triennium to 2012-2015 that includes specific indicators such as wetland inventories. However, for wetland extent, the data collection has started in 2018. Contracting Parties report in two main categories in the Ramsar classification: inland and human-made wetlands.

Disaggregation:

No further disaggregation of this indicator

6. Comparability/deviation from international standards

The national figures are reported by the countries themselves following standardized format for the National Reports for the COPs that included definitions and reporting years, thus eliminating any discrepancies between global and national figures. The reporting format ensures that countries provide the full reference for original data sources as well as national definitions and terminology.

7. References and Documentation

References and links are provided in the section of methods and guidance available to countries for the compilation of the data at the national level.

Annex 1 Ramsar Wetland Classification

The codes are based upon the Ramsar Classification System for Wetland Types, as approved by the Conference of the Contracting Parties in Recommendation 4.7 and amended by Resolutions VI.5 and VII.11.

To assist in identification of the correct Wetland Types, the Secretariat has provided below tabulations of some of the characteristics of each Wetland Type, for Marine/Coastal Wetlands and Inland Wetlands.

Marine/Coastal Wetlands

A -- Permanent shallow marine waters in most cases less than six metres deep at low tide; includes sea bays and straits.

B -- Marine subtidal aquatic beds; includes kelp beds, sea-grass beds, tropical marine meadows.

C -- Coral reefs.

D -- Rocky marine shores; includes rocky offshore islands, sea cliffs.

E -- Sand, shingle or pebble shores; includes sand bars, spits and sandy islets; includes dune systems and humid dune slacks.

F -- Estuarine waters; permanent water of estuaries and estuarine systems of deltas.

G -- Intertidal mud, sand or salt flats.

H -- Intertidal marshes; includes salt marshes, salt meadows, saltings, raised salt marshes; includes tidal brackish and freshwater marshes.

I -- Intertidal forested wetlands; includes mangrove swamps, nipah swamps and tidal freshwater swamp forests.

J -- Coastal brackish/saline lagoons; brackish to saline lagoons with at least one relatively narrow connection to the sea.

K -- Coastal freshwater lagoons; includes freshwater delta lagoons.

Zk(a) – Karst and other subterranean hydrological systems, marine/coastal

Table 2: Tabulations of Wetland Type characteristics, Marine / Coastal Wetlands:

Saline water	Permanent	< 6 m deep	A
		Underwater vegetation	B
		Coral reefs	C
	Shores	Rocky	D
	Shores	Sand, shingle or pebble	E
Saline or brackish water	Intertidal	Flats (mud, sand or salt)	G
		Marshes	H
		Forested	I
	Lagoons		J
	Estuarine waters		F
Saline, brackish or fresh water	Subterranean		Zk(a)
Fresh water	Lagoons		K

Inland Wetlands

L -- Permanent inland deltas.

M -- Permanent rivers/streams/creeks; includes waterfalls.

N -- Seasonal/intermittent/irregular rivers/streams/creeks.

O -- Permanent freshwater lakes (over 8 ha); includes large oxbow lakes.

P -- Seasonal/intermittent freshwater lakes (over 8 ha); includes floodplain lakes.

Q -- Permanent saline/brackish/alkaline lakes.

R -- Seasonal/intermittent saline/brackish/alkaline lakes and flats.

Sp -- Permanent saline/brackish/alkaline marshes/pools.

Ss -- Seasonal/intermittent saline/brackish/alkaline marshes/pools.

Tp -- Permanent freshwater marshes/pools; ponds (below 8 ha), marshes and swamps on inorganic soils; with emergent vegetation water-logged for at least most of the growing season.

Ts -- Seasonal/intermittent freshwater marshes/pools on inorganic soils; includes sloughs, potholes, seasonally flooded meadows, sedge marshes.

U -- Non-forested peatlands; includes shrub or open bogs, swamps, fens.

Va -- Alpine wetlands; includes alpine meadows, temporary waters from snowmelt.

Vt -- Tundra wetlands; includes tundra pools, temporary waters from snowmelt.

W -- Shrub-dominated wetlands; includes shrub swamps, shrub-dominated freshwater marshes, shrub carr, alder thicket on inorganic soils.

Xf -- Freshwater, tree-dominated wetlands; includes freshwater swamp forests, seasonally flooded forests, wooded swamps on inorganic soils.

Xp -- Forested peatlands; peatswamp forests.

Y -- Freshwater springs; oases.

Zg -- Geothermal wetlands.

Zk(b) – Karst and other subterranean hydrological systems, inland.

Note: “floodplain” is a broad term used to refer to one or more wetland types, which may include examples from the R, Ss, Ts, W, Xf, Xp, or other wetland types. Some examples of floodplain wetlands are seasonally inundated grassland (including natural wet meadows), shrublands, woodlands and forests. Floodplain wetlands are not listed as a specific wetland type herein.

Table 3: Tabulations of Wetland Type characteristics, Inland Wetlands:

Fresh water	Flowing water	Permanent	Rivers, streams, creeks	M
			Deltas	L
			Springs, oases	Y
		Seasonal/intermittent	Rivers, streams, creeks	N
	Lakes and pools	Permanent	> 8 ha	O
			< 8 ha	Tp
		Seasonal/intermittent	> 8 ha	P
			< 8 ha	Ts
	Marshes on inorganic soils	Permanent	Herb-dominated	Tp
		Permanent/ Seasonal/intermittent	Shrub-dominated	W
			Tree-dominated	Xf
		Seasonal/intermittent	Herb-dominated	Ts
	Marshes on peat soils	Permanent	Non-forested	U
			Forested	Xp
	Marshes on inorganic or peat soils	High altitude (alpine)		Va
		Tundra		Vt
Saline, brackish or alkaline water	Lakes	Permanent		Q
		Seasonal/intermittent		R
	Marshes & pools	Permanent		Sp
		Seasonal/intermittent		Ss
Fresh, saline, brackish or alkaline water	Geothermal			Zg
	Subterranean			Zk(b)

Human-made wetlands

1 -- Aquaculture (e.g. fish/shrimp) ponds.

2 -- Ponds; includes farm ponds, stock ponds, small tanks (generally below 8 ha).

3 -- Irrigated land; includes irrigation channels and rice fields.

4 -- Seasonally flooded agricultural land (including intensively managed or grazed wet meadow or pasture).

5 -- Salt exploitation sites; salt pans, salines, etc.

6 -- Water storage areas; reservoirs/barrages/dams/impoundments (generally over 8 ha).

7 -- Excavations; gravel/brick/clay pits; borrow pits, mining pools.

8 -- Wastewater treatment areas; sewage farms, settling ponds, oxidation basins, etc.

9 --Canals and drainage channels, ditches.

Zk(c) – Karst and other subterranean hydrological systems, human-made

7.a.1

0.a. Goal

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

0.b. Target

Target 7.a: By 2030, enhance international cooperation to facilitate access to clean energy research and technology, including renewable energy, energy efficiency and advanced and cleaner fossil-fuel technology, and promote investment in energy infrastructure and clean energy technology

0.c. Indicator

Indicator 7.a.1: International financial flows to developing countries in support of clean energy research and development and renewable energy production, including in hybrid systems

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

Organisation for Economic Co-operation and Development (OECD) and International Renewable Energy Agency (IRENA)

1.a. Organisation

Organisation for Economic Co-operation and Development (OECD) and International Renewable Energy Agency (IRENA)

2.a. Definition and concepts

Definition:

The flows are covered through two complementary sources.

OECD: The flows covered by the OECD are defined as all official loans, grants and equity investments received by countries on the DAC List of ODA Recipients from foreign governments and multilateral agencies, for the purpose of clean energy research and development and renewable energy production, including in hybrid systems extracted from the OECD/DAC Creditor Reporting System (CRS) with the following sector codes:

• 23210 Energy generation, renewable sources – multiple technologies - Renewable energy generation programmes that cannot be attributed to one single technology (codes 23220 through 23280 below). Fuelwood/charcoal production should be included under forestry 31261.

• 23220 Hydro-electric power plants - Including energy generating river barges.

• 23230 Solar energy for centralised grids

• 23231 Solar energy for isolated grids and standalone systems

• 23232 Solar energy – thermal applications

• 23240 Wind energy - Wind energy for water lifting and electric power generation.

• 23250 Marine energy - Including ocean thermal energy conversion, tidal and wave power.

• 23260 Geothermal energy - Use of geothermal energy for generating electric power or directly as heat for agriculture, etc.

• 23270- Biofuel-fired power plants Use of solids and liquids produced from biomass for direct power generation. Also includes biogases from anaerobic fermentation (e.g. landfill gas, sewage sludge gas, fermentation of energy crops and manure) and thermal processes (also known as syngas); waste fired power plants making use of biodegradable municipal waste (household waste and waste from companies and public services that resembles household waste, collected at installations specifically designed for their disposal with recovery of combustible liquids, gases or heat). See code 23360 for non-renewable waste-fired power plants.

• 23410 Hybrid energy electric power plants

• 23631 Electric power transmission and distribution (isolated mini-grids)

Research and development of energy efficiency technologies and measures is captured under CRS sector code 23182 on Energy research. The above flows also include technical assistance provided to support production, research and development as defined above.

IRENA: The flows covered by IRENA are defined as all additional loans, grants and equity investments received by developing countries (defined as countries in developing regions, as listed in the UN M49 composition of regions) from all foreign governments, multilateral agencies and additional development finance institutions (including export credits, where available) for the purpose of clean energy research and development and renewable energy production, including in hybrid systems. These additional flows cover the same technologies and other activities (research and development, technical assistance, etc.) as listed above and exclude all flows extracted from the OECD/DAC CRS.

2.b. Unit of measure

Million United States Dollars (USD) at constant prices for a base year. The base year for the constant prices and exchange rates is updated every year and it usually has a two-year lag behind the publication cycle. (e.g., the 2020 cycle would report 2018 constant prices)

2.c. Classifications

The definition and classification of renewable technologies complies with the UN Standard International Energy Product Classification (SIEC). Definitions of other concepts are given above.

3.a. Data sources

The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc).

IRENA’s data on financial flows from public sources in support of renewable energy are available in IRENA’s Public Renewable Energy Investment Database. IRENA collects these data from a wide range of publicly available sources, including the databases and annual reports of all of the main development finance institutions and 20 other bilateral and multilateral agencies investing in renewable energy. The database is updated annually and (at end-2021) covers public renewable energy investment flowing to 41 developed countries and 109 developing countries, for the period 2000-2020. As new publicly-funded financial institutions start investing in renewable energy, the IRENA database will expand to include these new investors over time.

3.b. Data collection method

See above

3.c. Data collection calendar

Data for a year is collected during the following year.

3.d. Data release calendar

OECD DAC data is updated four times a year, with complete and detailed data published at year-end (covering the previous year). IRENA investment data is available at year-end (covering the previous year).

3.e. Data providers

See above

3.f. Data compilers

Organisation for Economic Co-operation and Development (OECD) and International Renewable Energy Agency (IRENA).

3.g. Institutional mandate

The OECD DAC Mandate states that the overarching objective of the DAC for the period 2018-2022 is to promote development co-operation and other relevant policies to contribute to implementation of the 2030 Agenda for Sustainable Development, including sustained, inclusive and sustainable economic growth, poverty eradication, improvement of living standards in developing countries, and to a future in which no country will depend on aid.

In order to achieve this overarching objective, the Committee shall:

monitor, assess, report, and promote the provision of resources that support sustainable development by collecting and analysing data and information on ODA and other official and private flows, in a transparent way.

With a mandate from countries around the world, IRENA encourages governments to adopt enabling policies for renewable energy investments, provides practical tools and policy advice to accelerate renewable energy deployment, and facilitates knowledge sharing and technology transfer to provide clean, sustainable energy for the world’s growing population. Collecting official statistics (including international public finance flows) is in line with these aims.

4.a. Rationale

Total Official Development Assistance (ODA) and Other Official flows (OOF) to developing countries quantify the public financial effort (excluding export credits) that donors provide to developing countries for renewable energies. The additional flows (from the IRENA database) capture the flows to non-ODA Recipients in developing regions, flows from countries and institutions not currently reporting to the DAC and certain other types of flows, such as export credits.

Energy access is a major development constraint in many developing countries and, while starting from a relatively low base, energy demand is expected to grow very rapidly in many of these countries in the future. This presents an opportunity for developing countries to utilize clean and renewable technologies to meet their future energy needs if they can gain access to the appropriate technologies and expertise. This indicator provides a suitable measure of the international support given to developing countries to access these technologies.

4.b. Comment and limitations

Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements. At present, flows to clean energy research and development are only partially covered by the database and a few other areas (e.g. off-grid electricity supply, investments in improved cookstove projects) may be covered only partially.

The IRENA database currently only covers financial institutions that have invested a total of USD 400 million or more in renewable energy. The process of continuous improvement of the database includes verifying the data against data produced by the multilateral development banks for climate finance reporting and by comparing the data with other independent reporting by international development finance agencies.

4.c. Method of computation

The OECD flows are calculated by taking the total official flows (ODA and OOF) from DAC member countries, multilateral organisations and other providers of development assistance to the sectors listed above. The IRENA (additional) flows are calculated by taking the total public investment flows from IRENA’s Public Renewable Energy Investment Database and excluding: domestic financial flows; international flows to countries outside developing regions; international flows to multilateral recipients not elsewhere specified; and flows reported by OECD (as described above). The flows are commitments measured in current United States Dollars (USD).

Flows are tracked by individual commitment or activity level. When there are duplicate commitments between the OECD and IRENA databases, these are excluded from the IRENA database.

The flows are converted to constant USD at a base year that normally has a two-year lag behind the publication year. The computation uses the DAC deflator methodology explained by the OECD on their web site.

4.d. Validation

For OECD, see: http://www.oecd.org/dac/stats/methodology.htm

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Not applicable - there is no imputation of missing values.

• At regional and global levels

Not applicable - there is no imputation of missing values to obtain regional or global totals.

4.g. Regional aggregations

Regional and global totals are calculated by summing all available data from countries.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable.

4.i. Quality management

IRENA validates this indicator for regional, technological, donor, and time aggregations. Any values that are not properly categorised are reviewed at the project level, and manually categorised under the appropriate technology, country, year or instrument type.

4.j. Quality assurance

OECD/DAC data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). IRENA data are compiled from national sources following the United Nations Fundamental Principles of Official Statistics: https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx.

4.k. Quality assessment

This indicator is considered in good order when all the international financial flows of the database are correctly allocated to a country, year, technology, instrument type, and any other category respective to the requirements for the Global SDG database, and as required by the UNSD. Furthermore, the flows are in good order when properly deflated to account for inflation and exchange rate changes.

5. Data availability and disaggregation

Data availability:

The CRS contains flows to all DAC recipient countries. Global and regional figures are based on the sum of ODA and OOF flows to the renewable energy projects.

IRENA currently includes data about renewable energy projects in 41 developed countries and 109 developing countries (150 countries overall).

Time series:

OECD: annual data from 1960 onwards (see above).

IRENA: annual data from 2000 onwards.

Disaggregation:

Data in the CRS contain markers which reflect whether a policy objective is attained through the activity. Measuring gender equality is included in the CRS. Data from the CRS are reported at the project level and can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid (project, agriculture sub-sector, etc.).

Data in IRENA are stored by country (source and recipient) at the project-level, allowing disaggregation of the data in several dimensions. For example, financial flows can be divided by technologies (i.e. bioenergy, geothermal energy, hydropower, marine energy, solar energy, and wind energy) and sub-technologies (e.g. onshore and offshore wind), by geography (both at the country and regional level), by financial instrument and by type of recipient.

6. Comparability/deviation from international standards

Sources of discrepancies:

Neither OECD nor IRENA make estimates of these figures. The data all come from national sources reported to OECD or, in the case of IRENA, from officially published statistics.

7. References and Documentation

CRS: See all links here: http://www.oecd.org/dac/stats/methodology.htm

IRENA Renewable Energy Finance Flows: http://resourceirena.irena.org/gateway/dashboard/?topic=6&subTopic=8

7.b.1

0.a. Goal

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

0.b. Target

Target 7.b: By 2030, expand infrastructure and upgrade technology for supplying modern and sustainable energy services for all in developing countries, in particular least developed countries, small island developing States and landlocked developing countries, in accordance with their respective programmes of support

0.c. Indicator

Indicator 7.b.1: Installed renewable energy-generating capacity in developing countries (in watts per capita)

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

This indicator is also used as indicator 12.a.1

0.g. International organisations(s) responsible for global monitoring

International Renewable Energy Agency (IRENA)

1.a. Organisation

International Renewable Energy Agency (IRENA)

2.a. Definition and concepts

Definition:

The indicator is defined as the installed capacity of power plants that generate electricity from renewable energy sources divided by the total population of a country. Capacity is defined as the net maximum electrical capacity installed at the year-end and renewable energy sources are as defined in the IRENA Statute (see concepts below).

Concepts:

Electricity capacity is defined in the International Recommendations for Energy Statistics or IRES (UN, 2018) as the maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e., after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity).

The IRENA Statute defines renewable energy to include energy from the following sources: hydropower; marine energy (ocean, tidal and wave energy); wind energy; solar energy (photovoltaic and thermal energy); bioenergy; and geothermal energy.

2.b. Unit of measure

Watts per capita

2.c. Classifications

Electricity capacity classifications follow the International Recommendations for Energy Statistics or IRES

3.a. Data sources

IRENA’s electricity capacity database contains information about the electricity generating capacity installed at the year-end, measured in megawatt (MW). The dataset covers all countries and areas from the year 2000 onwards. The dataset also records whether the capacity is on-grid or off-grid and is split into 36 different renewable energy types that can be aggregated into the six main sources of renewable energy.

Population data:

For the population part of this indicator, IRENA uses population data from the United Nations World Population Prospects. The population data reflects the residents in a country or area regardless of legal status or citizenship. The values are midyear estimates.

The United Nations Department of Economic and Social Affairs published information about its methodology on the link below:

https://population.un.org/wpp/Methodology/

3.b. Data collection method

The capacity data are collected as part of IRENA’s annual questionnaire cycle. Questionnaires are sent to countries at the start of a year asking for renewable energy data for two years previously (i.e. at the start of 2019, questionnaires ask for data for the year 2017). The data are then validated and checked with countries and published in the IRENA Renewable Energy Statistics Yearbook at the end of June. To minimise reporting burden, the questionnaires for some countries are pre-filled with data collected by other agencies (e.g. Eurostat) and are sent to countries for them to complete any additional details requested by IRENA.

At the same time as this, preliminary estimates of capacity for the previous year are also collected from official sources where available (e.g. national statistics, data from electricity grid operators) and from other unofficial sources (mostly industry associations for the different renewable energy sectors). These are published at the end of March.

3.c. Data collection calendar

Capacity data are recorded as a year-end figure. The data are collected in the first six months of every year.

3.d. Data release calendar

Estimates of generating capacity for a year are published at the end of March in the following year. Final figures for the previous year are published at the end of June.

3.e. Data providers

Renewable energy generating capacity:

National Statistical Offices and National Energy Agencies of Ministries (the authority to collect this data varies between countries). Data for preliminary estimates may also be collected from industry associations, national utility companies or grid operators.

Population:

United Nations Population Division- World Population Prospects.

3.f. Data compilers

International Renewable Energy Agency (IRENA).

3.g. Institutional mandate

With a mandate from countries around the world, IRENA encourages governments to adopt enabling policies for renewable energy investments, provides practical tools and policy advice to accelerate renewable energy deployment, and facilitates knowledge sharing and technology transfer to provide clean, sustainable energy for the world’s growing population. Renewable energy capacity statistics are in line with these aims.

4.a. Rationale

The infrastructure and technologies required to supply modern and sustainable energy services cover a wide range of equipment and devices that are used across numerous economic sectors. There is no readily available mechanism to collect, aggregate and measure the contribution of this disparate group of products to the delivery of modern and sustainable energy services. However, one major part of the energy supply chain that can be readily measured is the infrastructure used to produce electricity.

Renewables are considered a sustainable form of energy supply, as their current use does not usually deplete their availability to be used in the future. The focus of this indicator on electricity reflects the emphasis of the target on modern sources of energy and is particularly relevant for developing countries where the demand for electricity is often high and its availability is constrained. Furthermore, the focus on renewables reflects the fact that the technologies used to produce renewable electricity are generally modern and more sustainable than non-renewables, particularly in the fastest growing sub-sectors of electricity generation from wind and solar energy.

The division of renewable electricity capacity by population (to produce a measure of Watts per capita) is proposing to scale the capacity data to account for the large variation in needs between countries. It uses population rather than GDP to scale the data, because this is the most basic indicator of the demand for modern and sustainable energy services in a country.

This indicator should also complement indicators 7.1.1 and 7.2.1. With respect to electricity access, it will provide additional information to the proportion of people with electricity access by showing how much infrastructure is available to deliver that access (in terms of the amount of capacity per person). The focus on renewable capacity will also add value to the existing renewables indicator (7.2.1) by showing how much renewable energy is contributing to the need for improved electricity access.

4.b. Comment and limitations

At present, electricity only accounts for about one-quarter of total energy use in the World and an even lower share of energy use in most developing countries. The focus of this indicator on electricity capacity does not capture any trends in the modernisation of technologies used to produce heat or provide energy for transport.

However, with the growing trend towards electrification of energy end-uses, the focus here on electricity may become less of a weakness in the future and may also serve as a general indicator of the progress towards greater electrification in developing counties. That, in itself, should be seen as a shift towards the use of more modern technology to deliver sustainable energy services.

Furthermore, as reflected in many national policies, plans and targets, increasing the production of electricity and, in particular, renewable electricity, is seen by many countries as a first priority in their transition to the delivery of more modern and sustainable energy services. Thus, this indicator is a useful first-step towards measuring overall progress on this target that reflects country priorities and can be used until other additional or better indicators can be developed.

4.c. Method of computation

For each country and year, the renewable electricity generating capacity at the end of the year is divided by the total population of the country as of mid-year (July 1st).

4.d. Validation

All countries are invited to provide their capacity data or at least review the data that IRENA has compiled (from other official and unofficial sources) through an annual process of data collection using the IRENA Renewable Energy Questionnaire. This process is reinforced through IRENA’s renewable energy statistics training workshops, which are held twice a year in different (rotating) regions. To date, over 200 energy statisticians have participated in these workshops, many of whom provide renewable energy data to IRENA. In addition, IRENA’s statistics are presented each year to member countries at one of IRENA’s three governing body meetings, where discrepancies or other data issues can be discussed with country representatives.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level:

At the country level, electricity capacity data are sometimes missing for two reasons:

Delays in responding to IRENA questionnaires or publication of official data. In such cases, estimates are made so that global and regional totals can be calculated. The most basic treatment is to repeat the value of capacity from the previous year. However, IRENA also checks unofficial data sources and collects data about investment projects (see Indicator 7.a.1). These other sources can be used to identify if any new power plants have been commissioned in a year and are used where available to update the capacity value at the end of a year. Any such estimates are eventually replaced by official or questionnaire data when that becomes available.
Off-grid capacity data are frequently missing from national energy statistics or is presented in non-standard units (e.g. numbers of mini-hydro plants in a country rather than their capacity in MW). Where official data are not available, off-grid capacity figures are collected by IRENA from a wide variety of other official and unofficial sources in countries (e.g. development agencies, government departments, NGOs, project developers and industry associations) and this information is added to the capacity database to give a more complete picture of developments in the renewable energy sector in a country. These data are peer reviewed each year through an extensive network of national correspondents (the REN21 Network) and is checked with IRENA country focal points when they attend IRENA meetings and training workshops.

When capacity data are missing, mostly in non-state territories, these are excluded from the dataset.

At regional and global levels:

See above. Regional and global totals are only estimated to the extent that figures for some countries may be estimated in each year. (See also data availability below).

4.g. Regional aggregations

Regional and global averages are calculated by summing the renewable generating capacity for a region or the World and dividing that by the corresponding figure for the total population. The indicator is for developing countries only, so these regional aggregates (averages) also reflect only the average for the developing countries in each region.

This calculation excludes the population of those countries and/or territories that have missing capacity data. As such, the regional and global population values used in the calculation might differ from those reported in the UN World Population Prospects.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Guidance for the collection of electricity capacity data are provided by the International Recommendations for Energy Statistics. IRENA also produces methodological guidance for countries, specifically about how to measure renewable energy and collect renewable energy data. This is supported by a comprehensive programme of regional renewable energy statistics training workshops and ongoing communications with countries as part of the annual questionnaire cycle.

4.i. Quality management

Data for renewable energy capacity is validated by technology, year and country during the IRENA statistics cycle.

4.j. Quality assurance

IRENA data are compiled from national sources following the United Nations Fundamental Principles of Official Statistics: https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx.

4.k. Quality assessment

The quality of the data are verified by automated validation routines for aggregates. Furthermore, official questionnaires guarantee the validity for each data point, where applicable.

5. Data availability and disaggregation

Data availability:

The total number of capacity records in the database (all developing countries/areas, all years since 2000, all technologies) is 11,000. In terms of numbers of records, 3,120 (28%) are estimates and 740 (7%) are from unofficial sources. The remaining records (65%) are all from returned questionnaires or official data sources.

However, in terms of the amount of capacity covered in the database, the shares of data from estimated and unofficial sources is only 5% and 1% respectively. The large difference between these measures is due to the inclusion of off-grid capacity figures in the database. The amount of off-grid generating capacity in a country is frequently estimated by IRENA, but the amounts of off-grid capacity recorded in each case is often relatively small.

Time series:

Renewable generating capacity data are available from 2000 onwards.

Disaggregation:

IRENA’s renewable capacity data are available for every country and area in the world from the year 2000 onwards. These figures can also be disaggregated by technology (solar, hydro, wind, etc.) and by on-grid and off-grid capacity.

6. Comparability/deviation from international standards

Sources of discrepancies:

The main source of discrepancies between different sources of electricity capacity data are likely to be due to the under-reporting or non-reporting of off-grid capacity data (see above) or slight variations in the definition of installed capacity. IRENA uses the IRES definition of capacity agreed by the Oslo Group on Energy Statistics, while some countries and institutions may use slightly different definitions of capacity to reflect local circumstances (e.g. the reporting of derated rather than maximum net installed capacity or the reporting of built rather than commissioned capacity at year-end).

7. References and Documentation

UN, 2018. International Recommendations for Energy Statistics (IRES). New York City: United Nations. Retrieved from https://unstats.un.org/unsd/energystats/methodology/documents/IRES-web.pdf

IRENA Statistical Yearbooks: https://www.irena.org/Statistics

7.1.1

0.a. Goal

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

0.b. Target

Target 7.1: By 2030, ensure universal access to affordable, reliable and modern energy services

0.c. Indicator

Indicator 7.1.1: Proportion of population with access to electricity

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

World Bank Group

1.a. Organisation

World Bank Group

2.a. Definition and concepts

Definition:

Proportion of population with access to electricity is the percentage of population with access to electricity.

SDG7 ensures access to affordable, reliable, sustainable and modern energy for all. Specifically, Indicator 7.1.1 refers to the proportion of population with access to electricity. This is expressed in percentage figures and is disaggregated by total, urban and rural access rates per country, as well as by UN regional and global classifications.

Concepts:

Electricity access in this scenario refers to the proportion of population in the considered area (country, region, and global context) that has access to consistent sources of electricity.

The World Bank’s Global Electrification Database compiles nationally representative household survey data as well as census data since 1990. It also incorporates data from the Socio-Economic Database for Latin America and the Caribbean, the Middle East and North Africa Poverty Database, and the Europe and Central Asia Poverty Database, all of which are based on similar surveys.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Regional and global classifications refer to the list of standard country or area codes for statistical use (M49) provided by the United Nations Statistics Division
Country classification by income group is based on the World Bank Country and Lending Groups.
Country population data are extracted from the World Development Indicators.

3.a. Data sources

Data for access to electricity are collected from household surveys and censuses, tapping into a wide number of different household survey types including: Multi-tier Framework (MTF), Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, including those by various government agencies (for example, ministries of energy and utilities).

The World Bank is the agency that has taken responsibility for compiling a meta-database of statistics on electricity access harvested from the full global body of household surveys. The World Bank Electrification Database covers more than 219 countries for the period from 1990 and is updated regularly.

For more information on compiling access to energy data see Global Tracking Framework report (2013) (Chapter 2, Annex 2, page 127-129).

Reports produced by international agencies such as the UN, World Bank, USAID, National Statistics Offices, as well as country censuses are used to collect data. Though some of the reports might not directly focus on energy access, they tend to include questions regarding access to electricity. Also, for the sake of consistency in methodology across countries, government and utility data are not considered.

3.b. Data collection method

If data sources have any information on electricity access, it is collected and analysed in line with the previous trends and future projections of each country. Data validation is conducted by checking that the figures are reflective of the ground level scenario as well as are in line with country populations, income levels and electrification programs.

3.c. Data collection calendar

The database collected from household surveys and censuses is updated annually for the second half of the year.

3.d. Data release calendar

The annual release of new data for SDG7.1.1 is usually in early June.

3.e. Data providers

It varies according to the country and its context. Data are collected from national statistics agencies as well as international agencies such as the UN and World Bank.

3.f. Data compilers

World Bank Group

3.g. Institutional mandate

Along with the SDG 7 custodian agencies, including the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), and the World Health Organization (WHO), the World Bank is designated by the UN Statistical Commission to collect, process, and disseminate data with regional, and global aggregates, in relation to the progress in achieving the SDG 7 goal. During the process of updating and disseminating the electrification database, as a consultation organization, the World Bank is responsible for acting in consultation with internal stakeholders, national statistics agencies, and the UN regional commissions.

4.a. Rationale

Access to electricity addresses major critical issues in all the dimensions of sustainable development. The target has a wide range of social and economic impacts, including facilitating development of income generating activities and lightening the burden of household tasks.

Under the global target of equal access to energy, SDG7.1.1 focuses specifically on electricity access available to the global population. In order to gain a clear picture, access rates are only considered if the primary source of lighting is the local electricity provider, solar systems, mini-grids and stand-alone systems. Sources such as generators, candles, batteries, etc., are not considered due to their limited working capacities and since they are usually kept as backup sources for lighting.

4.b. Comment and limitations

The World Bank aims to estimate demand side access rates in order to better understand the access levels experienced by the population. This is different from the supply side access rates usually provided by governments, ministries, etc. The data are primarily compiled from national household surveys and censuses. But since these are carried out infrequently, it is difficult to understand the ground level trends for short term periods. Collecting data for rural areas as well as last-mile connectivity problems also cause errors in data collection that could skew results.

While the existing global household survey evidence base provides a good starting point for tracking household energy access, it also presents several limitations that will need to be addressed over time. In many parts of the world, the presence of an electricity connection in the household does not necessarily guarantee that the energy supplied is adequate in quality and reliability or affordable in cost and it would be desirable to have fuller information about these critical attributes of the service, which have been highlighted in SDG7.

Substantial progress has already been made toward developing and piloting a new methodology known as the Multi-Tier Framework for Measuring Energy Access (World Bank) which is able to capture these broader dimensions of service quality and would make it possible to go beyond a simple yes/no measure of energy access to a more refined approach that recognizes different levels of energy access, and also takes into account the affordability and reliability of energy access explicitly referenced in the language of SDG7. The methodology for the Multi-Tier Framework for Measuring Energy Access has already been published based on a broad consultative exercise and represents a consensus view across numerous international agencies working in the field. Discussions are also progressing with the World Bank’s Household Survey Technical Working Group regarding the mainstreaming of this methodology into the standardized household questionnaire design that will be applied every three years in all low-income countries between 2015 and 2030 as part of the broader SDG monitoring exercise.

The adoption of this methodology will allow – over time – the more refined measurement of energy access, making it possible to report more disaggregated information regarding the type of electricity supply (grid or off-grid), the capacity of electricity supply provided (in Watts), the duration of service (daily hours and evening hours), the reliability of service (in terms of number and length of unplanned service interruptions), the quality of service (in terms of voltage fluctuations), as well as affordability and legality of service.

Another advantage of this approach is that they can be applied not only to measuring energy access at the household level, but also its availability to support enterprises and deliver critical community services, such as health and education.

Methodological challenges associated with the measurement of energy access are more fully described in the Global Tracking Framework (2013) (Chapter 2, Section 1, page 75-82), and in the ESMAP (2015) Report “Beyond Connections: Energy Access Redefined” both of which are referenced below.

4.c. Method of computation

To estimate values, a multilevel nonparametric modelling approach—developed by the World Health Organization to estimate clean fuel usage—was adapted to predict electricity access and used to fill in the missing data points for the time period from 1990 onwards. Where data is available, access estimates are weighted by population. Multilevel nonparametric modelling considers the hierarchical structure of data (country and regional levels), using the regional classification of the United Nations.

The model is applied for all countries with at least one data point. In order to use as much real data as possible, results based on real survey data are reported in their original form for all years available. The statistical model is used to fill in data only for years where they are missing and to conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The difference between real data points and estimated values is clearly identified in the database.

Countries classified as “High Income” based on the World Bank Country and Lending Groups are assumed to reach universal access from the first year the country joined the category.

In the present report, to avoid having electrification trends from 1990 to 2010 overshadow electrification efforts since 2010, the model was run twice:

With survey data and assumptions from 1990 to the latest year for model estimates from 1990 to the latest year
With survey data and assumptions from 2010 to the latest year for model estimates from 2010 to the latest year

Given the low frequency and the regional distribution of some surveys, several countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modelling approach was adopted to fill in the missing data points. This modelling approach allowed the estimation of electrification rates for 219 countries over the time periods. The SE4ALL Global Tracking Framework Report (2013) referenced below provides more details on the suggested methodology for tracking access to energy (Chapter 2, Section 1, page 82-87).

4.d. Validation

After completing data compilation, the World Bank initially contacted each energy team for highly strategic countries or some countries with data discrepancy issues. Following the initial round, the World Bank coordinates with internal stakeholders and the UN regional commissions to validate the accuracy of the data. In this process, the World Bank is in charge of responding to any inquiries and comments.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Given the low frequency and regional distribution of some surveys, many countries have gaps in data availability. A simple modelling approach was adopted to fill in the missing data points, in order to develop the historical evolution and starting point of the electrification rates. The estimation is conducted using a model with region, country and time variables. The model keeps the original observation if data is available. The statistical model is used to fill in data only for years where they are missing and to help conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The estimated values are clearly identified (“Estimate”) in the database. In the meantime, if a country value indicates a high discrepancy compared with either IEA data or data from the past publication, the country is considered as an outlier and not affected by the regional trends. As a result, such countries only have their country effects in model estimates.

At regional and global levels

Values for regional and global levels are calculated by incorporating all survey data along with model-estimated values substituting missing values. Regional and global classifications are based on the UN M49 series for statistical use.

4.g. Regional aggregations

Regional and global data are population-weighted by summing up all available values across countries listed in the UN regional classification.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Countries generally use internationally accepted methods of conducting censuses and national surveys. There is some level of disparity between countries and regional methodologies, but the efforts to harmonize data is improving. The Multi-Tier Framework (MTF) by the World Bank is one such method being used to increase accuracy of data collection.

4.i. Quality management

A non-parametric model is consistently used to obtain a complete set of annual trends of electricity access rates by fulfilling the data gaps with model estimates. The model draws from solid fuel use modelling used in Bonjour et al (2013). The model closely follows empirical data without being influenced by large fluctuations in survey estimates. In general, regional trends are borrowed for the absence of survey data. However, some countries, which have significant discrepancies with IEA data, are considered as an outlier, not reflecting the regional trends, but just relying on their country effects.

4.j. Quality assurance

A multi-level review process in collaboration with industry experts, national statistical offices, country and regional experts as well as partnering international agencies and UN bodies is conducted before finalizing the data.

Before finalizing electricity access data, the World Bank team contacts the relevant national statistical offices and the UN regional commissions asking for reviews and suggestions for the prepared figures. The database also goes through multiple rounds of vetting process internally through departments. The relevant links are provided below under References.

4.k. Quality assessment

Good quality data of electricity access should be generally aligned with the trends from the past data at country level. Also, the World Bank’s data results would not have high discrepancies about more than 5 percentage points with IEA data, although the World Bank (based on standardized household surveys and censuses) and IEA (based on government-reported values) maintain separate database of global electricity access rates. Meanwhile, given the consultation with internal stakeholders and the UN regional commissions, data points of some countries are adjusted to reflect their certain circumstances, such as national conflict. Therefore, for these countries, the access rate is not linearly increased.

5. Data availability and disaggregation

Data availability:

Data have been collected from 1990 through the latest year on an annual basis.

Time series:

Data for countries have been compiled from 1990 to the latest year, but there are gaps in accurate data availability.

Disaggregation:

Electricity access rates are disaggregated by geographic location into total, urban and rural rates. Countries that are classified as “High Income” are assumed to reach universal access from the first year it was added to the category. Disaggregation of access to electricity by rural or urban place of residence is available at country, regional and global levels.

6. Comparability/deviation from international standards

Sources of discrepancies:

The World Bank database compiles electricity usage data, while many international agencies and national ministries report electricity production data. This is the main cause for data discrepancies. The quality and accuracy of population data can also lead to differences in assessing electrification.

7. References and Documentation

URL:

https://databank.worldbank.org/source/world-development-indicators

https://trackingsdg7.esmap.org/

References:

Bonjour, S., Adair-Rohani, H., Wolf, J., Bruce, N. G., Mehta, S., Prüss-Ustün, A., Lahiff, M., Rehfuess, E. A., Mishra, V. & Smith, K. R. (2013). Solid fuel use for household cooking: country and regional estimates for 1980–2010. Environmental health perspectives, 121(7), 784-790.
Global Tracking Framework Report (2013). http://trackingenergy4all.worldbank.org
Global Tracking Framework Report (2015). http://trackingenergy4all.worldbank.org/
International Energy Agency’s World Energy Outlook. https://www.iea.org/topics/world-energy-outlook
Multi-Tier Framework for Measuring Energy Access. https://www.esmap.org/node/55526
UNSD Standard country or area codes for statistical use (M49). https://unstats.un.org/unsd/methodology/m49/
World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

7.1.2

0.a. Goal

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

0.b. Target

Target 7.1: By 2030, ensure universal access to affordable, reliable and modern energy services

0.c. Indicator

Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

3.9.1: Mortality rate attributed to household and ambient air pollution

0.g. International organisations(s) responsible for global monitoring

World Health Organization (WHO)

1.a. Organisation

World Health Organization (WHO)

2.a. Definition and concepts

Definition:

Proportion of population with primary reliance on clean fuels and technology is calculated as the number of people using clean fuels and technologies for cooking, heating and lighting divided by total population reporting that any cooking, heating or lighting, expressed as percentage. “Clean” is defined by the emission rate targets and specific fuel recommendations (i.e. against unprocessed coal and kerosene) included in the normative guidance WHO guidelines for indoor air quality: household fuel combustion.

Concepts:

Current global data collection focuses on the primary fuel used for cooking, categorized as solid or non-solid fuels, where solid fuels are considered polluting and non-modern, while non-solid fuels are considered clean. This single measure captures a good part of the lack of access to clean cooking fuels but fails to collect data on type of device or technology used for cooking, and fails to capture other polluting forms of energy use in the home such as those used for lighting and heating.

New evidence-based normative guidance from the WHO (i.e. WHO Guidelines for indoor air quality guidelines: household fuel combustion), highlights the importance of addressing both fuel and the technology for adequately protecting public health. These guidelines provide technical recommendations in the form of emissions targets for as to what fuels and technology (stove, lamp, and so on) combinations in the home are clean. These guidelines also recommend against the use of unprocessed coal and discourage the use of kerosene (a non-solid but highly polluting fuel) in the home. They also recommend that all major household energy end uses (e.g. cooking, space heating, lighting) use efficient fuels and technology combinations to ensure health benefits.

For this reason, the technical recommendations in the WHO guidelines, access to modern cooking solution in the home will be defined as “access to clean fuels and technologies” rather than “access to non-solid fuels.” This shift will help ensure that health and other “nexus” benefits are better counted, and thus realized.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Regional and global classifications refer to the list of standard country or area codes for statistical use (M49) provided by the United Nations Statistics Division

3.a. Data sources

Primary household fuels and technologies, particularly for cooking, is routinely collected at the national levels in most countries using censuses and surveys. Household surveys used include: United States Agency for International Development (USAID)-supported Demographic and Health Surveys (DHS); United Nations Children’s Fund (UNICEF)-supported Multiple Indicator Cluster Surveys (MICS); WHO-supported World Health Surveys (WHS); and other reliable and nationally representative country surveys.

The World Health Organization is the agency that has taken responsibility for compiling a database of statistics on access to clean and polluting fuels and technologies harvested from the full global body of household surveys for cooking, heating and lighting. Currently, the WHO Database covers cooking energy for 171 countries and one territory for the period 1960-2020 and is updated regularly and publicly available. For lighting, the WHO database includes data for 125 countries for the period 1963-2020. For heating, the WHO database includes data for 71 countries for the period 1977-2020.

Presently WHO is working with national surveying agencies, country statistical offices and other stakeholders (e.g. researchers) to enhance multipurpose household survey instruments to gather data on the fuels and technologies used for heating and lighting.

In 2020, as a result of a survey enhancement process, data collection for the cooking database included main cooking fuel, exhaust systems (chimney or fan), cooking technology and cooking location. Lighting data collection focused on main lighting fuel. Data collection for the heating database included main heating fuel as well as heating technology.

3.b. Data collection method

Surveys collected are nationally representative and contain data at household or population level.

Typical cooking survey questions include: “Major fuel used for cooking”, “What is the main source of cooking fuel in your household?”, “What type of fuel does your household mainly use for cooking?”, “Which is the main source of energy for cooking?”, “In your household, what type of cookstove is mainly used for cooking?”.

Typical heating survey questions include: “Main fuel used for heating”, “What type of fuel and energy source is used in the heater?”, “What does your household mainly use for space heating when needed?”

Typical lighting survey questions include: “Main fuel use for lighting”, “At night, what does your household mainly use to light the household?”.

3.c. Data collection calendar

The next round of data collection is planned for the second half of 2022.

3.d. Data release calendar

The annual release of new data for SDG7.1.1 is usually in May.

3.e. Data providers

National Statistical Offices or any national providers of household surveys and censuses.

3.f. Data compilers

WHO, Environment, Climate Change and Health Department (ECH).

3.g. Institutional mandate

Along with the SDG 7 custodian agencies, including the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), and the World Bank (WB), the World Health Organisation is designated by the UN Statistical Commission to collect, process, and disseminate data with regional, and global aggregates, in relation to the progress in achieving the SDG 7 goals. During the process of updating and disseminating the clean cooking estimates, the WHO is responsible for acting in consultation with SDG 7 custodian agencies, national statistics agencies, and the UN regional commissions.

4.a. Rationale

Cooking, lighting and heating represent a large share of household energy use across the low- and middle-income countries. For cooking and heating, households typically rely on solid fuels (such as wood, charcoal, biomass) or kerosene paired with inefficient technologies (e.g. open fires, stoves, space heaters or lamps). It is well known that reliance on such inefficient energy for cooking, heating and lighting is associated with high levels of household (indoor) air pollution. The use of inefficient fuels for cooking alone is estimated to cause over 4 million deaths annually, mainly among women and children. This is more than Tuberculosis, Human Immuno-deficiency Virus and malaria combined. These adverse health impacts can be avoided by adopting clean fuels and technologies for all main household energy end-or in some circumstances by adopting advanced combustion cook stoves (i.e. those which achieve the emission rates targets provided by the WHO guidelines) and adopting strict protocols for their safe use. Given the importance of clean and safe household energy use as a human development issue, universal access to energy among the technical practitioner community is currently taken to mean access to both electricity and clean fuels and technologies for cooking, heating and lighting. For this reason, clean cooking forms part of the universal access objective under the UN Secretary General’s Sustainable Energy for All initiative.

4.b. Comment and limitations

The indicator uses the type of primary fuels and technologies used for cooking, heating, and lighting as a practical surrogate for estimating human exposure to household (indoor) air pollution and its related disease burden, as it is not currently possible to obtain nationally representative samples of indoor concentrations of criteria pollutants, such as fine particulate matter and carbon monoxide. However, epidemiological studies provide a science-based evidence for establishing those estimates using these surrogates.

The indicator is based on the main type of fuel and technology used for cooking as cooking occupies the largest share of overall household energy needs. However, many households use more than one type of fuel and stove for cooking and, depending on climatic and geographical conditions, heating with polluting fuels can also be a contributor to household (indoor) air pollution levels. In addition, lighting with kerosene, a very polluting and hazardous fuel is also often used, and in some countries is the main fuel used for cooking.

While the existing global household survey evidence base provides a good starting point for tracking household energy access for cooking fuel, it also presents limitations that will need to be addressed over time. Currently there is a limited amount of available data capturing the type of fuel and devices used in the home for heating and lighting. Accordingly, WHO in cooperation with World Bank, and the Global Alliance for Clean Cook stoves, led a survey enhancement process with representatives from country statistical offices and national household surveying agencies (e.g. Demographic and Health Survey, Multiple Indicator Cluster Survey, Living Standards Measurement Survey) to better gather efficiently and harmoniously information on the fuels and technologies for cooking, heating and lighting. The efforts concluded in the creation of 6 new questions that will replace and slightly expand the current set of questions commonly used on national multipurpose surveys to assess household energy.

Substantial progress has already been made toward developing and piloting a new methodology known as the Multi-Tier Framework for Measuring Energy Access (World Bank) which is able to capture the affordability and reliability of energy access explicitly referenced in the language of SDG7 and harnesses the normative guidance in the WHO guidelines to benchmark tiers of energy access. The methodology for the Multi-Tier Framework for Measuring Energy Access has already been published based on a broad consultative exercise and represents a consensus view across numerous international agencies working in the field. The 2020 estimates provided include data extracted from these surveys.

4.c. Method of computation

The indicator is modelled with household survey data compiled by WHO. The information on cooking fuel use and cooking practices comes from about 1440 nationally representative survey and censuses. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), and other nationally developed and implemented surveys.

Estimates of primary cooking energy for the total, urban and rural population for a given country and year are obtained together using a single multivariate hierarchical model. Using household survey data as inputs, the model jointly estimates primary reliance on 6 specific fuel types:

unprocessed biomass (e.g. wood),
charcoal,
coal,
kerosene,
gaseous fuels (e.g. LPG), and
electricity; and a final category including other clean fuels (e.g. alcohol).

Estimates of the proportion of the population with primary reliance on clean fuels and technology (SDG indicator 7.1.2) are then derived by aggregating the estimates for primary reliance on clean fuel types from the model. Details on the model are published in Stoner et al. (2020).

Only survey data with less than 15% of the population reporting “missing” and “no cooking” and “other fuels” were included in the analysis. Surveys were also discarded if the sum of all mutually exclusive categories reported was not within 97.9-102.1%. Fuel use values were uniformly scaled (divided) by the sum of all mutually exclusive categories excluding “missing”, “no cooking” and “other fuels”.

Countries classified as high-income according to the World Bank country classification (80 countries) in the 2020 fiscal year were assumed to have fully transitioned to clean household energy and therefore are reported as >95% access to clean technologies.

No estimates were reported for low- and middle-income countries without data (Bulgaria, Lebanon and Libya). Modelled specific fuel estimates were derived for 128 low- and middle-income countries and 2 countries with no World Bank income classification (Cook Islands and Niue). Estimates of overall clean fuel use were reported for 190 countries.

4.d. Validation

Countries are consulted annually on the national data collected for the 7.1.2 SDG indicator.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Missing values for individual fuels within a survey are automatically imputed by the model (Stoner et al., 2020). For surveys where fuel use is only reported for the whole population (i.e. with no urban or rural disaggregation), the urban and rural values are automatically imputed by the model (Stoner et al., 2020).

No estimates are reported for low- and middle-income countries with no data (Bulgaria, Lebanon, Libya). All central estimates are reported alongside measures of uncertainty. Where countries have very limited survey data (e.g. only one survey suitable for modelling within 1990-2020), the measures of uncertainty are naturally wider. High income countries are assumed to have transitioned to clean fuels and technologies, and are reported as >95% of their population using clean fuels and technologies.

• At regional and global levels

Low- and middle-income countries with no data were excluded from regional and global aggregations, and values of 100% clean fuel and technology use were used for High income countries for regional and global calculations.

4.g. Regional aggregations

Regional and global estimates are population-weighted; within a region, the country values are multiplied by the corresponding country populations to obtain weighted fuel values. These values are then summed and divided by the sum of the population of the countries included.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable

4.i. Quality management

The input data for single multivariate hierarchical model used to estimate the access to clean cooking presents some challenges that are related to inconsistencies in both the quality and the quantity of information that is available from the surveys:

inconsistency in survey design and collection, together with missing values, which can lead to highly unstable time series for some individual fuels in some countries,
for surveys where the number of respondents is not available, only the proportions using each fuel are given and the original counts (the number of respondents using each fuel) are non-recoverable.
information on trends in the use of specific fuels is required for both urban and rural areas but, in many cases, surveys provide data for only the overall population (Stoner et al., 2020).

Therefore, several adjustments are included in the model in order to tackle the observed challenges coming from the source data (for more on this see Stoner et al., 2020).

4.j. Quality assurance

Before finalizing clean cooking estimates, the WHO team contacts the UN regional commissions asking for reviews and suggestions for the prepared figures. The data also goes through multiple rounds of internal consultations with SDG 7 custodian agencies.

4.k. Quality assessment

Following the consultations with internal stakeholders and the UN regional commissions, data estimates of some countries may undergo additional revisions.

5. Data availability and disaggregation

Data availability:

For cooking fuels, coverage of 171 countries is available through the WHO Global Household Energy Database.

For lighting fuels, the WHO database includes data for 125 countries.

For heating fuels, the WHO database includes data for 71 countries.

Time series:

From 1960 to 2020

Disaggregation:

Disaggregated estimates for different end-uses (i.e. cooking, heating and lighting; with expected improvements in household surveys, this will be possible for heating and lighting for all countries.

Disaggregation of access to clean fuel and technologies for cooking by rural or urban place of residence is possible for all countries with survey data.

Gender disaggregation by main user (i.e. cook) of cooking energy will be available with expected improvements in household surveys.

Gender disaggregation of head of household for cooking, lighting and heating is available

Energy is a service provided at the household, rather than individual level.

Nonetheless, it is used differentially by men and women and has different impacts on their health and well-being. What will be possible, in principle, is to report energy access disaggregated by the main user of cooking energy.

In addition, WHO's Household energy database includes country data from thirty countries on the time spent by children collecting fuelwood and water disaggregated by sex. With the improvements in data collection via the below mentioned survey harmonization process, data will be available on reporting time spent exclusively on fuel collection rather than in combination with water collection.

6. Comparability/deviation from international standards

Sources of discrepancies:

There may be discrepancies between internationally reported and nationally reported figures. The reasons are the following:

Modelled estimates versus survey data point.
Use of different definitions of polluting (or previously solid) fuels (wood only or wood and any other biomass, e.g. dung residues; kerosene included or not as polluting fuels).
Use of different total population estimate.
Estimates are expressed as percentage of population using polluting (or solid) fuels (as per SDG indicator) as compared to percentage of household using polluting (or solid) fuels (as assessed by surveys such as DHS or MICS).
In the estimates presented here, values above 95% polluting fuel use are reported as “>95”, and values below 5% as “<5”.

Changes in modelling methodology:

Prior to 2018, estimates of the proportion of the population primarily relying on solid fuels were obtained from a multilevel model with region and nonparametric functions of time as the only covariates (Bonjour et al., 2013). For tracking SDG7 in 2018 and 2019 this model was used to estimate polluting and clean fuel use, though this time it was implemented in the Bayesian framework for increased robustness and more reliable quantification of uncertainty. For 2020, the model has been expanded to allow estimates for individual fuels, and extra flexibility has been added to the functions of time to better capture nonlinear trends in some countries (Stoner et al. 2020). These refinements have been introduced alongside an ever-expanding collection of data, which underwent a major quality-control effort. Due to the increased data availability, borrowing of information across regions is no longer essential, hence time is now the only covariate.

On both occasions where the model changed, the WHO conducted a thorough sensitivity analysis, including full country-by-country comparisons of estimates between the existing model and the candidate model. In most cases, estimates of the proportion using clean fuels exhibited little change, see annex below. Where larger discrepancies were identified, they were carefully investigated to determine the likely cause. Many of these were in fact the result of the new model better capturing nonlinear trends.

The same model is used for the 2022 revision, with updated data inputs as described in previous sections.

7. References and Documentation

URL:

https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution

References:

Global Tracking Framework report (2013)

http://trackingenergy4all.worldbank.org/

Global Tracking Framework Report (2015)

http://trackingenergy4all.worldbank.org/

Global Tracking Framework database (2015)

http://data.worldbank.org/data-catalog/sustainable-energy-for-all

Multi-Tier Framework for Measuring Energy Access,

https://www.esmap.org/node/55526

WHO Guidelines for indoor air quality: Household Fuel Combustion, WHO (2014)

https://www.who.int/publications/i/item/9789241548885

Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G., & Adair-Rohani H. (2020) Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. Journal of the Royal Statistical Society: Series C (Applied Statistics) 69(4), 815-839. DOI: 10.1111/rssc.12428

Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, and Smith KR (2013). Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980–2010. Environmental Health Perspectives, https://doi.org/10.1289/ehp.1205987

Population using solid fuels meta-data, WHO

http://apps.who.int/gho/indicatorregistry/App_Main/view_indicator.aspx?iid=318

Annex

A comparison plot is provided to illustrate the differences between existing model and the candidate model. Estimated values for each of the WHO regions are plotted, showing consistency between the existing model and the candidate model.

7.2.1

0.a. Goal

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

0.b. Target

Target 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix

0.c. Indicator

Indicator 7.2.1: Renewable energy share in the total final energy consumption

0.d. Series

Not applicable

0.e. Metadata update

2022-03-31

0.f. Related indicators

Indicator 9.4.1: CO₂ emission per unit of value added

Indicator 13.2.2: Total greenhouse gas emissions per year

0.g. International organisations(s) responsible for global monitoring

International Energy Agency (IEA)

United Nations Statistics Division (UNSD)

International Renewable Energy Agency (IRENA)

1.a. Organisation

International Energy Agency (IEA)

United Nations Statistics Division (UNSD)

International Renewable Energy Agency (IRENA)

2.a. Definition and concepts

Definition:

The renewable energy share in total final consumption is the percentage of final consumption of energy that is derived from renewable resources.

Concepts:

Renewable energy consumption includes consumption of energy derived from: hydro, wind, solar, solid biofuels, liquid biofuels, biogas, geothermal, marine and renewable waste. Total final energy consumption is calculated from balances as total final consumption minus non-energy use.

Comments regarding specific renewable energy sources:

Solar energy includes solar PV and solar thermal.
Liquid biofuels include biogasoline, biodiesels and other liquid biofuels.
Solid biofuels include fuelwood, animal waste, vegetable waste, black liquor, bagasse and charcoal.
Renewable waste energy covers energy from renewable municipal waste.

2.b. Unit of measure

Percent (%)

2.c. Classifications

The “International Recommendations for Energy Statistics” (IRES), adopted by the UN Statistical Commission, is the globally recognized standard used to develop the energy statistics underlying the calculation of the indicator.

This standard is available at: unstats.un.org/unsd/energystats/methodology/ires.

3.a. Data sources

Data on renewable energy consumption are available through national energy balances compiled based on data collected by the International Energy Agency (for around 150 countries) and the United Nations Statistics Division (UNSD) for all countries. The energy balances make it possible to trace all the different sources and uses of energy at the national level.

Some technical assistance may be needed to improve these statistics, particularly in the case of renewable energy sources. Specialized industry surveys (e.g. on bioenergy use) or household surveys (in combination with the measurement of other indicators) would be feasible approaches to filling in data gaps (e.g. for use of firewood, off-grid solar energy).

3.b. Data collection method

UNSD also collects energy statistics from countries according to the same harmonised methodology.

3.c. Data collection calendar

Data are collected on an annual basis.

3.d. Data release calendar

The IEA World Energy Balances are published in February, April and July with progressively broader geographical coverage (publishing full information for two calendar years prior and selected information for one year prior). The UN Energy Statistics Database is made available towards the end of the calendar year with full geographical coverage (publishing information for two calendar years prior).

3.e. Data providers

National administrations, as described in documentation on sources for IEA and UNSD:

http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf

unstats.un.org/unsd/energystats/data

3.f. Data compilers

The International Energy Agency (IEA) and the United Nations Statistics Division (UNSD)

The IEA and UNSD are the primary compilers of energy statistics across countries and develop internationally comparable energy balances based on internationally agreed methodologies. Aggregates are based on analysis merging of IEA and UNSD data.

3.g. Institutional mandate

IEA as one of the custodian agencies responsible for monitoring progress towards the SDG 7.2 target, leverage on their national data efforts and add value by promoting coherent standards, definitions and methodologies for both raw data and the derived indicators with the ultimate goal of producing internationally comparable datasets.

The UNSD mission in the area of energy statistics is to strengthen national statistical systems in order to assist countries to produce high quality energy statistics and balances. The mission is realized through four workstreams: Data collection (since 1950); Development of methodological guidelines and standards in energy statistics (e.g., IRES, ESCM); Capacity building (to disseminate such methodology and to assist countries to strengthen their energy statistical systems); and International cooperation and coordination. UNSD was selected as one of the custodians of indicator 7.2.1 because it collects for all countries the underlying data necessary to calculate the indicator.

4.a. Rationale

The target “By 2030, increase substantially the share of renewable energy in the global energy mix” impacts all three dimensions of sustainable development. Renewable energy technologies represent a major element in strategies for greening economies everywhere in the world and for tackling the critical global problem of climate change. A number of definitions of renewable energy exist; what they have in common is highlighting as renewable all forms of energy that their consumption does not deplete their availability in the future. These include solar, wind, ocean, hydropower, geothermal sources, and bioenergy (in the case of bioenergy, which can be depleted, sources of bioenergy can be replaced within a short to medium-term frame). Importantly, this indicator focuses on the amount of renewable energy actually consumed rather than the capacity for renewable energy production, which cannot always be fully utilized. By focusing on consumption by the end user, it avoids the distortions caused by the fact that conventional energy sources are subject to significant energy losses along the production chain.

4.b. Comment and limitations

A limitation with existing renewable energy statistics is that they are not able to distinguish whether renewable energy is being sustainably produced. For example, a substantial share of today’s renewable energy consumption comes from the use of wood and charcoal by households in the developing world, which sometimes may be associated with unsustainable forestry practices. There are efforts underway to improve the ability to measure the sustainability of bio-energy, although this remains a significant challenge.
Off-grid renewables data are limited and not sufficiently captured in national and international energy statistics.
The method of allocation of renewable energy consumption from electricity and heat output assumes that the share of transmission and distribution losses are the same among all technologies. However, this is not always true; for example when renewables are usually located in more remote areas and may incur larger losses.
Likewise, imports and exports of electricity and heat are assumed to follow the renewable share of electricity and heat generation, respectively. This is a simplification that in many cases will not affect the indicator too much, but that might do so in some cases, for example, when a country only generates electricity from fossil fuels but imports a great share of the electricity it uses from a neighboring country’s hydroelectric power plant.
Methodological challenges associated with defining and measuring renewable energy are more fully described in the Global Tracking Framework (IEA and World Bank, 2013) Chapter 4, Section 1, pages 194-200. Data for traditional use of solid biofuels are generally scarce globally, and developing capacity in tracking such energy use, including developing national-level surveys, is essential for sound global energy tracking.

4.c. Method of computation

This indicator is based on the development of comprehensive energy statistics across supply and demand for all energy sources – statistics used to produce the energy balance. Internationally agreed methodologies for energy statistics are described in the “International Recommendations for Energy Statistics” (IRES), adopted by the UN Statistical Commission, available at: unstats.un.org/unsd/energystats/methodology/ires.

Once an energy balance is developed, the indicator can be calculated by dividing final energy consumption from all renewable sources by total final energy consumption. Renewable energy consumption is derived as the sum of direct final consumption of renewable sources plus the components of electricity and heat consumption estimated to be derived from renewable sources based on generation shares. The indicator is calculated based on the following formula:

${T F E C}_{R E S} = \frac{{T F E C}_{R E S} + ({T F E C}_{E L E} \times \frac{{E L E}_{R E S}}{{E L E}_{T O T A L}}) + ({T F E C}_{H E A T} \times \frac{{H E A T}_{R E S}}{{H E A T}_{T O T A L}})}{{T F E C}_{T O T A L}}$

Where:

$T F E C$ : Total final energy consumption is the sum of final energy consumption in the transport, industry and other sectors (also equivalent to the total final consumption minus the non-energy use).

$E L E$ : Gross electricity production

$H E A T$ : Gross heat production

$R E S$ : Renewable energy sources which include hydropower, wind, solar photovoltaic, solar thermal, geothermal, tide/wave/ocean, renewable municipal waste, solid biofuels, liquid biofuels, and biogases.

The denominator is the total final energy consumption of all energy products, while the numerator includes the direct consumption of renewable energy sources plus the final consumption of gross electricity and heat that is estimated to have come from renewable sources. This estimation allocates the amount of electricity and heat consumption to renewable sources based on the share of renewables in gross production in order to perform the calculation at the final energy level. For instance, if total final consumption is 150 TJ for biogas energy, while total final consumption of electricity is 400 TJ and heat 100 TJ, and the share of biogas is 10 percent in electricity output and 5 percent in heat output, the total reported number for biogas consumption will be 195 TJ (150 TJ+400TJ*10%+100TJ*5%).

The Global Tracking Framework Report (IEA and World Bank, 2013) provides more details on the suggested methodology for defining and measuring renewable energy (Chapter 4, Section 1, page 201-202).

4.d. Validation

The IEA has several internal procedures in place for energy data validation. This includes energy balance checks, time series analysis and reconciling differences in statistical classifications and definitions. UNSD also has a number of internal validation procedures to ensure internal data consistency, for instance through energy balance checks, and trend consistency, e.g. by way of time series analysis.

4.e. Adjustments

The country specific commodity balances underlying the IEA energy data are based on national energy data of heterogeneous nature converted and adapted to fit the IEA format and methodology. Considerable effort has been made to ensure that the data adhere to the IEA definitions based on the guidelines provided by IRES. Nevertheless, energy statistics at the national level are often collected using criteria and definitions which differ, sometimes considerably, from those of international organisations. This is especially true for non-OECD countries, which are submitting data to the IEA on a voluntary basis. The IEA has identified most of these differences and, where possible, adjusted the data to meet international definitions. For details on recognized country specific anomalies and the corresponding adjustments, please refer to country specific notes included in the IEA World energy balances documentation file available at: wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf

Likewise, UNSD also needs to adjust certain data to fit the international methodology set by IRES, thus ensuring data comparability across countries. Data from all countries are submitted voluntarily to UNSD, sometimes via non-standard formats or through sharing of national publications. The identification of such deviations from the standard is an ongoing task, and UNSD has started publishing some of this information in a supplement to the Energy Statistics Database named “Notes on sources”, available at: unstats.un.org/unsd/energystats/pubs/yearbook/, with the goal of increasing transparency and providing more and more information with time.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

The IEA has attempted to provide all the elements of energy balances down to the level of final consumption, for over 150 countries. Providing all the elements of supply, as well as all inputs and outputs of the main transformation activities and final consumption has often required estimations. Estimations have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.

Likewise, UNSD attempts to provide full energy balances for the 225 countries and areas it covers, including the 75 or so it covers for SDG reporting. This may require searching for national official publications, data from other international organizations and expert estimation based on reputable sources and other publicly available information. Generally speaking, data on the supply side is more widely available than transformation activities and final consumption.

• At regional and global levels

In addition to estimates at a country level, adjustments addressing differences in definitions alongside estimations for informal and/or confidential trade, production or consumption of energy products are sometimes required to complete major aggregates, when key statistics are missing. Such estimations and adjustments implemented by IEA have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.

4.g. Regional aggregations

Aggregates are calculated, whether by region or global, using final energy consumption as weights.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The IEA data corresponding to OECD countries are derived based on information provided in

the five fuel-specific annual IEA/Eurostat joint questionnaires completed by the national administrations. These questionnaires are available online at: iea.org/areas-of-work/data-and-statistics/questionnaires

The IEA commodity balances for all other countries are based on national energy data of heterogeneous nature, converted and adapted to fit the IEA format and methodology based on IRES recommendations.

In addition to IRES, UNSD has published the Energy Statistics Compilers Manual (ESCM - unstats.un.org/unsd/energystats/methodology/escm/) as a practical companion to assist countries in the compilation of data according to the international methodology. UNSD sends countries its own questionnaire (unstats.un.org/unsd/energystats/questionnaire/), except to the countries which are mandated to submit the IEA/Eurostat joint questionnaires. In the latter case, UNSD obtains data from the IEA.

4.i. Quality management

The IEA, in co-operation with the Statistical Office of the European Communities (Eurostat), has published an Energy Statistics Manual. This Manual helps the energy statisticians have a better grasp of definitions, units and methodologies. Moreover, IEA has established a quality management framework based on the internationally recognized guidelines recommended by IRES to ensure quality of statistical products.

ESCM contains a full chapter on the Generic Statistical Business Process Model applied to energy statistics, helping countries manage energy data quality. Inside UNSD, processes are established to ensure the quality of its products, and such processes are reviewed periodically.

4.j. Quality assurance

The IEA follows the guidelines recommended by the IRES to ensure relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity as well as coherence and comparability of the data.

UNSD coordinated input from international organizations and countries to publish IRES and its practical companion, the ESCM. Each of both contains a chapter on quality assurance and metadata to help guide all countries ensure good energy data quality.

4.k. Quality assessment

The IEA has an extensive data quality validation process through exchange with national data providers worldwide. It also convenes its Energy Statistics Development Group meeting to discuss energy statistics developments with its Members, and cooperates with partners worldwide to ensure coherence of data and methods.

UNSD assesses many quality aspects of the data by means of internal checks, exchanges with national data providers, and comparison with alternative sources.

5. Data availability and disaggregation

Data availability:

Between the various existing data sources, primarily the IEA World Energy Balances and the UN Energy Statistics Database, annual total and renewable energy consumption for every country and area can be collected. The Tracking SDG7: The Energy Progress Report (formerly Sustainable Energy for All Global Tracking Framework) is reporting this indicator at a global level between 1990 and 2030.

Time series:

2000 – present

Disaggregation:

Disaggregation of the data on consumption of renewable energy, e.g. by resource and end-use sector, could provide insights into other dimensions of the goal, such as affordability and reliability. For solar energy, it may also be of interest to disaggregate between on-grid and off-grid capacity.

6. Comparability/deviation from international standards

Sources of discrepancies:

The IEA World energy balances and the UN Energy Statistics Database, which provide the underlying data for calculating this indicator, are global databases obtained following harmonised definitions and comparable methodologies across countries. However, they do not represent an official source for national submissions of the indicator 7.2.1 on renewable energy. Due to possible deviations from IRES in national methodologies, national indicators may differ from the internationally comparable ones.

Difference may arise due to different sources of official energy data, dissimilarities in the underlying methodologies, adjustments and estimations.

7. References and Documentation

URL:

iea.org; unstats.un.org/unsd/energystats

References:

IEA Energy Balances and Statistics

http://www.iea.org/statistics/

UN Energy Statistics Database

unstats.un.org/unsd/energystats/data (description) and data.un.org/Explorer.aspx?d=EDATA (data). Downloadable though API (https://data.un.org/ws). Browse contents on https://data.un.org/SdmxBrowser/start.

IEA SDG 7 webpage: iea.org/reports/sdg7-data-and-projections

United Nations. 2018. “International Recommendations for Energy Statistics”. unstats.un.org/unsd/energystats/methodology/ires

International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO). 2019. “Tracking SDG7: The Energy Progress Report 2019”. trackingsdg7.esmap.org/

International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO). 2018. “Tracking SDG7: The Energy Progress Report 2018”. trackingsdg7.esmap.org/

International Energy Agency (IEA) and the World Bank. 2017. “Global Tracking Framework 2017—Progress toward Sustainable Energy”. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO. seforall.org/sites/default/files/eegp17-01_gtf_full_report_final_for_web_posting_0402.pdf

International Energy Agency (IEA) and the World Bank. 2015. “Global Tracking Framework 2015—Progress Toward Sustainable Energy”, World Bank, Washington, DC. Doi: 10.1596/978-1-4648 -0690-2 License: Creative Commons Attribution CC BY 3.0 IGO. seforall.org/sites/default/files/GTF-2105-Full-Report.pdf

International Energy Agency (IEA) and the World Bank. 2013. “Global Tracking Framework 2013”. webstore.iea.org/global-tracking-framework-2013

IRENA Renewable Energy Database

https://www.irena.org/statistics.

United Nations. 2016. “Energy Statistics Compilers Manual”

unstats.un.org/unsd/energystats/methodology/escm/

7.3.1

0.a. Goal

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

0.b. Target

Target 7.3: By 2030, double the global rate of improvement in energy efficiency

0.c. Indicator

Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP

0.d. Series

Energy intensity level of primary energy (megajoules per constant 2017 purchasing power parity GDP)

0.e. Metadata update

2022-03-31

0.f. Related indicators

Indicator 9.4.1: CO₂ emission per unit of value added

Indicator 13.2.2: Total greenhouse gas emissions per year

0.g. International organisations(s) responsible for global monitoring

International Energy Agency (IEA)

United Nations Statistics Division (UNSD)

1.a. Organisation

International Energy Agency (IEA)

United Nations Statistics Division (UNSD)

2.a. Definition and concepts

Definition:

Energy intensity is defined as the energy supplied to the economy per unit value of economic output.

Concepts:

Total energy supply, as defined by the International Recommendations for Energy Statistics (IRES), is made up of production plus net imports minus international marine and aviation bunkers plus-stock changes. Gross Domestic Product (GDP) is the measure of economic output. For international comparison purposes, GDP is measured in constant terms at purchasing power parity.

2.b. Unit of measure

Energy intensity is expressed in megajoules per unit of purchasing power parity GDP in constant 2017 USD figures.

2.c. Classifications

This standard is available at: unstats.un.org/unsd/energystats/methodology/ires.

3.a. Data sources

Total energy supply is typically calculated in the making of energy balances. Energy balances are compiled based on data collected for around 150 economies from the International Energy Agency (IEA) and for all countries in the world from the United Nations Statistics Division (UNSD). GDP data are taken mainly from the World Bank – World Development Indicator database.

3.b. Data collection method

The IEA collects energy data at the national level according to harmonised international definitions and questionnaires, as described in the UN International Recommendations for Energy Statistics available at : (unstats.un.org/unsd/energystats/methodology/ires). UNSD also collects energy statistics from countries according to the same harmonised methodology.

The most recent GDP estimates published by the World Bank with reference year of 2017 have been used when calculating this indicator. Additionally, missing years for countries with at least one data point for GDP reported by the World Bank have been estimated using National Accounts – Analysis of Main Aggregates (AMA) growth rates.

3.c. Data collection calendar

Data are collected on an annual basis.

3.d. Data release calendar

The IEA World Energy Balances are published in February, April and July with progressively broader geographical coverage (publishing full information for two calendar years prior and selected information for one year prior). The UN Energy Balances are made available towards the end of the calendar year with full geographical coverage (publishing information for two calendar years prior).

3.e. Data providers

National administrations, as described in documentation on sources for IEA and UNSD:

wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf

unstats.un.org/unsd/energystats/data/

3.f. Data compilers

The International Energy Agency (IEA) and the United Nations Statistics Division (UNSD)

The IEA and UNSD are the primary compilers of energy statistics from across countries and develop internationally comparable energy balances based on internationally agreed methodologies. Aggregates are based on a merging between IEA and UNSD data.

3.g. Institutional mandate

IEA as one of the custodian agencies responsible for monitoring progress towards the SDG 7.3 target, leverage on their national data efforts and add value by promoting coherent standards, definitions and methodologies for both raw data and the derived indicators with the ultimate goal of producing internationally comparable datasets.

The UNSD mission in the area of energy statistics is to strengthen national statistical systems in order to assist them in producing high quality energy statistics and balances. The mission is realized through four workstreams: Data collection (since 1950); Development of methodological guidelines and standards in energy statistics (e.g., IRES, ESCM); Capacity building (to disseminate such methodology and to assist countries to strengthen their energy statistical systems); and International cooperation and coordination. UNSD was selected as one of the custodians of indicator 7.3.1 because it collects for all countries the underlying data necessary to calculate the denominator of this indicator.

4.a. Rationale

Energy intensity is an indication of how much energy is used to produce one unit of economic output. It is an inverse proxy of the efficiency with which an economy is able to use energy to produce economic output. A lower ratio indicates that less energy is used to produce one unit of output, so decreasing trends indicate progress.

4.b. Comment and limitations

Energy intensity is only an imperfect proxy for energy efficiency. It can be affected by a number of factors, such as climate, structure of the economy, nature of economic activities etc. that are not necessarily linked to pure efficiency. For better assessment of energy efficiency progress, more disaggregated data are needed, such as those at the sectoral and end-use level.

4.c. Method of computation

Once the energy balance is developed, the indicator can be obtained by dividing total energy supply over GDP.

4.d. Validation

UNSD also has a number of internal validation procedures to ensure internal data consistency, for instance through energy balance checks, and trend consistency, e.g. by way of time series analysis.

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

The IEA has attempted to provide all the elements of energy balances, for over 150 countries. Providing all the elements of energy supply, has often required estimations. Estimations have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.

• At regional and global levels

In addition to estimates at a country level, adjustments addressing differences in definitions alongside estimations for informal and/or confidential trade, production or stock changes of energy products are sometimes required to complete major aggregates, when key statistics are missing. Such estimations and adjustments implemented by IEA have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.

4.g. Regional aggregations

Aggregates are calculated, whether by region or globally, by summing both total energy supply and gross domestic products over the group of relevant countries.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The IEA data corresponding to OECD countries are derived based on information provided in the five fuel specific annual OECD questionnaires completed by the national administrations. These questionnaires are available online at: iea.org/areas-of-work/data-and-statistics/questionnaires

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

UNSD assesses many quality aspects of the data by means of internal checks, exchanges with national data providers, and comparison with alternative sources.

5. Data availability and disaggregation

Data availability:

IEA and UN Energy Balances combined provide total energy supply data for all countries on an annual basis. GDP data are available for most countries on an annual basis.

Time series:

2000 – present

Disaggregation:

Disaggregation of energy intensity, e.g. by final consumption sectors or end-uses, could provide further insights into progress towards energy efficiency. At present it is only feasible to calculate such sector disaggregation for the following sectors – industry, residential, transport, agriculture, households – as reported in the Tracking SDG7: The Energy Progress Report (formerly Sustainable Energy for All Global Tracking Framework). It would be desirable, over time, to develop more refined sectoral level energy intensity indicators that make it possible to look at energy intensity by industry (e.g. cement, steel) or by type of vehicle (e.g. cars, trucks), for example. Doing so will not be possible without further statistical data collection, also including collaboration with relevant institutions and energy consumers. Full methodological explanations are provided in the IEA Energy Efficiency Indicators: Fundamentals on Statistics manual available at: iea.org/reports/energy-efficiency-indicators-fundamentals-on-statistics

Decomposition analysis of energy intensity trends seeks to filter out factors that affect energy demand, such as economy wide scale and structural shifts, from more narrowly defined energy intensity shifts. This analysis is also reported in the Tracking SDG7: The Energy Progress Report or in the IEA Energy Efficiency Indicators Highlights available at: iea.org/reports/energy-efficiency-indicators

6. Comparability/deviation from international standards

Sources of discrepancies:

The IEA World energy balances and the UN Energy Statistics Database, which provide the underlying data for calculating this indicator, are global databases obtained following harmonised definitions and comparable methodologies across countries. However, they do not represent an official source for national submissions of the indicator 7.3.1 on energy efficiency. Due to possible deviations from IRES in national methodologies, national indicators may differ from the internationally comparable ones.

Difference may arise due to different sources of official energy data, dissimilarities in the underlying methodologies, adjustments and estimations.

7. References and Documentation

URL:

www.iea.org/; unstats.un.org/unsd/energystats

References:

IEA Energy Balances and Statistics

http://www.iea.org/statistics/

UN Energy Statistics Database

unstats.un.org/unsd/energystats/data (description) and data.un.org/Explorer.aspx?d=EDATA

Downloadable though API (https://data.un.org/ws). Browse contents on https://data.un.org/SdmxBrowser/start.

IEA SDG 7 webpage: iea.org/reports/sdg7-data-and-projections

IEA Energy Efficiency Indicators Highlights

iea.org/reports/energy-efficiency-indicators

IEA Energy Efficiency Indicators Overview

https://www.iea.org/reports/energy-efficiency-indicators-overview

United Nations (2018). “International Recommendations for Energy Statistics (IRES)”.unstats.un.org/unsd/energystats/methodology/ires

International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO) (2019). “Tracking SDG7: The Energy Progress Report 2019”. trackingsdg7.esmap.org/

International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO) (2018). “Tracking SDG7: The Energy Progress Report 2018”. trackingsdg7.esmap.org/

International Energy Agency (IEA) and the World Bank (2017). “Global Tracking Framework 2017—Progress toward Sustainable Energy”. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO. seforall.org/sites/default/files/eegp17-01_gtf_full_report_final_for_web_posting_0402.pdf

International Energy Agency (IEA) and the World Bank (2015). “Global Tracking Framework 2015—Progress Toward Sustainable Energy”, World Bank, Washington, DC. Doi: 10.1596/978-1-4648 -0690-2 License: Creative Commons Attribution CC BY 3.0 IGO. seforall.org/sites/default/files/GTF-2105-Full-Report.pdf

International Energy Agency (IEA) and the World Bank (2013). “Global Tracking Framework 2013”.

webstore.iea.org/global-tracking-framework-2013

United Nations (2016). “Energy Statistics Compilers Manual (ESCM)”

https://unstats.un.org/unsd/energystats/methodology/escm/

8.a.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.a: Increase Aid for Trade support for developing countries, in particular least developed countries, including through the Enhanced Integrated Framework for Trade-related Technical Assistance to Least Developed Countries

0.c. Indicator

Indicator 8.a.1: Aid for Trade commitments and disbursements

0.e. Metadata update

2016-07-19

0.g. International organisations(s) responsible for global monitoring

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definition:

Aid for Trade commitments and disbursements is the gross disbursements and commitments of total Official Development Assistance (ODA) from all donors for aid for trade.

Concepts:

The DAC defines Official Development Assistance (ODA) as “those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are i) provided by official agencies, including state and local governments, or by their executive agencies; and ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)

Other official flows (OOF),excluding officially supported export credits, are defined as transactions by the official sector which do not meet the conditions for eligibility as ODA, either because they are not primarily aimed at development, or because they are not sufficiently concessional. See http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf, Para 24.

Aid for Trade is captured in the CRS through sector codes in the 331 series and the aid for trade marker. see here: http://www.oecd.org/dac/stats/purposecodessectorclassification.htm.

‘All donors’ refers to DAC donors, non-DAC donors and multilateral organisations.

3.a. Data sources

The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.b. Data collection method

3.c. Data collection calendar

Data are published on an annual basis in December for flows in the previous year. Detailed 2015 flows will be published in December 2016.

3.d. Data release calendar

December 2016.

3.e. Data providers

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.f. Data compilers

OECD

4.a. Rationale

Total Official Development Assistance (ODA) and Other Official Flows (OOF) to developing countries quantify the public effort that donors provide to developing countries for aid for trade.

4.b. Comment and limitations

Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete from 1995 for commitments at an activity level and 2002 for disbursements.

4.c. Method of computation

The sum of ODA and OOF flows from all donors to developing countries for aid for trade.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Due to high quality of reporting, no estimates are produced for missing data.

At regional and global levels

Not applicable.

4.g. Regional aggregations

Global and regional figures are based on the sum of ODA and OOF flows for aid for trade activities.

5. Data availability and disaggregation

Data availability:

On a donor basis for all DAC countries and many non-DAC providers (bilateral and multilateral) that report to the DAC on aid for scholarships.

On a recipient basis for all developing countries eligible for ODA.

Disaggregation:

This indicator can be disaggregated by donor, recipient country, type of finance, type of aid, trade policy and regulations and trade related adjustment sub-sectors, etc..

6. Comparability/deviation from international standards

Sources of discrepancies:

DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.

7. References and Documentation

URL:

www.oecd.org/dac/stats

References:

See all links here: http://www.oecd.org/dac/stats/methodology.htm

8.b.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.b: By 2020, develop and operationalize a global strategy for youth employment and implement the Global Jobs Pact of the International Labour Organization

0.c. Indicator

Indicator 8.b.1: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy

0.e. Metadata update

2023-03-31

0.f. Related indicators

8.5.2; 8.6.1; 8.7.1

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

The proposed methodology draws on:

Global policy instruments, notably:

Resolution on The youth employment crisis: A call for action^[1], adopted at the 101^st session of the International Labour Conference (ILC) in June 2012. In calling for vigorous, collective action to address an aggravated youth employment crisis, this resolution advocates for a multi-pronged approach with policy measures that are context-specific and integrated, entailing strategies which bring together in a coherent manner a variety of instruments to increase the demand, enhance the supply and improve matching in youth labour markets.
Recovering from the crisis: A Global Jobs Pact^[2] adopted by the ILC at its June 2009 session. Based on the ILO’s Decent Work Agenda, the Global Jobs Pact presents an integrated portfolio of policies that puts employment and social protection at the centre of crisis response, recognising the critical role of participation and social dialogue.

ILO databases:

International monitoring of youth employment policies was carried out over the period 2010-2012 by the Youth Employment Network (YEN) – a partnership between the ILO, United Nations and World Bank – utilising a questionnaire sent to national authorities. This evolved into YouthPOL^[3], an inventory of youth employment policies and programmes maintained by the ILO (65 countries covered to date).
The ILO also maintains EmPol, a dataset of broader national employment policies (143 countries covered).

Available online at: https://www.ilo.org/ilc/ILCSessions/101stSession/texts-adopted/WCMS_185950/lang--en/index.htm ↑

https://www.ilo.org/ilc/ILCSessions/98thSession/texts/WCMS_115076/lang--en/index.htm ↑

http://www.ilo.org/dyn/youthpol/en/f?p=30850:1001:0::NO::: ↑

2.b. Unit of measure

Categorical variable with values possible values of 0, 1, 2 or 3.

2.c. Classifications

Not applicable

3.a. Data sources

Global survey for data collection: Requesting responsible national entities to provide relevant information and support documents; a survey questionnaire is developed and administered by the ILO with biennial frequency to assess progress.This will be complemented by regular information and updates from ILO country offices on development, adoption and implementation of youth employment policies in countries covered by these offices, every year.

Data compilation: by the ILO; disseminated through ILOSTAT, a new repository dedicated to Indicator 8.b.1 and the active use of YouthPOL, EmPol and other databases (e.g. NATLEX – the ILO database of national labour, social security and related human rights legislation), as appropriate.

Data validation: Regular quality checks are conducted on all data, in particular when: (i) an already available document has not been directly provided by the government itself; (ii) it is unclear if the strategy and related action plan have been officially adopted; or (iii) there are doubts regarding the implementation of the strategy.

3.b. Data collection method

See section 3.a.

3.c. Data collection calendar

Proposed methodology to the ICLS: October 2018
Refinement of survey questionnaire and technical guidelines: October – November 2018
Final testing: November 2018 - February 2019
Regular administration of the survey: started early 2019

3.d. Data release calendar

Annual

3.e. Data providers

National entities (ministries or other government agencies) responsible for development, employment and youth policies. The ILO maintains a roster of national actors involved in the monitoring process.

3.f. Data compilers

ILO

3.g. Institutional mandate

The Department of Statistics (STATISTICS) works to provide relevant, timely and reliable labour statistics, to develop international standards for better measurement of labour issues and enhanced international comparability, and to help member States develop and improve their labour statistics.

The Employment Policy Department (EMPLOYMENT) is responsible for promoting full and productive employment by developing integrated employment, development and skills policies (ILO, 2012) that are inclusive, gender sensitive and sustainable. The department is mandated to coordinate ILO efforts to promote decent job opportunities for young women and men; over the years, it has supported the formulation, implementation and review of national youth employment strategies and action plans in different countries and regions (ILO, 2008; ILO, 2015). This type of targeted action and related achievements have been included in the ILO programming framework and performance system.

The ILO supports its constituents and other development stakeholders through knowledge and capacity building as well as through policy advocacy and advice. The list of references at the end of this note offers examples of recent major ILO contributions to knowledge building on youth employment and youth employment policy (ILO, 2017).

4.a. Rationale

The purpose of SDG indicator 8.b.1 is to provide an indication of the progress of countries in addressing youth employment issues. In this respect, it is assumed that having officially adopted what can be recognised as a structured strategy for youth employment would mean larger attention given by a country to youth labour market challenges, compared to countries with no strategy. In fact, the development of such a strategy usually entails broad participation of and consultation/coordination among different stakeholders.

4.b. Comment and limitations

Governments may have de facto national strategies for youth employment, but lack an officially adopted de jure document. For SDG 8.b.1 monitoring purposes only what emerges from de jure documents is considered.

4.c. Method of computation

The information and documents provided by national authorities will be analysed by the ILO to classify countries according to this grid:

Value	Description
Missing value	No information available to assess the existence of a national strategy for youth employment.
0	The country has not developed any national strategy for youth employment or taken steps to develop or adopt one.
1	The country is in the process of developing a national strategy for youth employment.
2	The country has developed and adopted a national strategy for youth employment
3	The country has operationalised a national strategy for youth employment.

In all cases, the grid refers to a national strategy for youth employment as a distinct strategy or as part of a national employment strategy.

Missing values (i.e. no response/unknown) are noted as such. They are omitted from the final global and regional breakdown: proportions are only calculated on the basis of received responses. However, the global and regional response rates will be indicated.

The possible development of metadata notes complementing the grid is being considered. Among other aspects, these notes may refer to the measures and provisions in place, and would also consider the involvement of national constituents in the development and operationalization of the strategies.

The ILO may also envisage to conduct a more detailed analysis of selected country documents for purposes which go beyond the scope of SDG monitoring, in order to gather insights on institutional and operational matters in national efforts for youth employment.

The following steps are followed in developing the indicator methodology:

Examination of relevant policy instruments, including the above-mentioned Call for action and Global Jobs Pact. Adopted by ILO tripartite constituents, these documents provide a sound framework for defining SDG indicator 8.b.1.
Review of ILO databases on employment and youth employment policies (EmPOL and YouthPOL), maintained by the Employment Policy Department.
A methodology for defining, measuring and validating this indicator (the present document).
A survey instrument (questionnaire) to collect national-level information on youth employment policies from national entities. The information is used to determine if countries have developed and operationalized a national strategy for youth employment as a stand-alone strategy or as part of a national employment or sectoral strategy, in line with the above-mentioned ILC resolutions.
Technical guidelines for data providers and compilers, along with the above-mentioned questionnaire and detailed notes.

Consultations with pertinent ministries and social partners’ representatives are held throughout the process.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

For countries that have not responded in the current survey round, the last country responses to the ILO survey are reported. The underlying assumption is that policy changes are unlikely to occur each year and therefore recent responses to the ILO survey remain valid.

4.g. Regional aggregations

None

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable

5. Data availability and disaggregation

Data availability:

The methodology is mainly based on a methodology used for the ILO youth employment policies database (YouthPOL) that covers 145 countries in 5 regions, including: Colombia, Mexico, Jordan, Australia, Cambodia, China, Republic of Korea, Philippines, Germany, Kazakhstan, Russian Federation, Italy, Spain, and Ukraine. The data can be accessed in this link. The methodology is based on a simplified version of the questionnaires used in this database.

Time series: This submission covers data from 2019 to 2020.

6. Comparability/deviation from international standards

Not applicable

7. References and Documentation

International Conference of Labour Statisticians, 20^th. Session. Resolution III www.ilo.org/20thicls

International Labour Office (ILO). 2008. Guide for the preparation of National action Plans on Youth Employment. (Geneva, ILO)

_. 2012. Guide for the formulation of national employment policies. (Geneva).

_. 2015. Comparative Analysis of Policies for Youth Employment in Asia and the Pacific. (Geneva).

_. 2017. Global employment trends for youth 2017: paths to a better working future (Geneva)

O’higgins, N. 2017. Rising to the youth employment challenge: new evidence on key policy issues (Geneva, ILO).

8.1.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countries

0.c. Indicator

Indicator 8.1.1: Annual growth rate of real GDP per capita

0.d. Series

Annual growth rate of real GDP per capita (%)

0.e. Metadata update

2023-03-31

0.f. Related indicators

Any economic statistics related SDG indicator

0.g. International organisations(s) responsible for global monitoring

United Nations Statistics Division (UNSD)

1.a. Organisation

United Nations Statistics Division (UNSD)

2.a. Definition and concepts

Annual growth rate of real Gross Domestic Product (GDP) per capita is calculated as the percentage

change in the real GDP per capita between two consecutive years. Real GDP per capita is calculated by

dividing GDP at constant prices by the population of a country or area. The data for real GDP are

measured in constant US dollars to facilitate the calculation of country growth rates and aggregation of

the country data.

2.b. Unit of measure

Annual growth rate of real GDP per capita: Percent (%)

GDP: US dollars

Population: Number

2.c. Classifications

Different versions of The System of National Accounts (1968, 1993 and 2008 SNA)

International Standard Industrial Classification (ISIC 3) of all Economic Activities

3.a. Data sources

The underlying annual GDP estimates in domestic currency are collected from countries or areas annually through a national accounts questionnaire (NAQ), while the underlying population estimates are obtained from the UN Population Division on https://population.un.org/wpp/Download/Standard/Population/

3.b. Data collection method

Each year, the national accounts section of the UNSD sends a pre-filled NAQ to countries or areas to collect the latest data on official annual national accounts in domestic currency. In order to lighten the reporting burden of countries to different international and regional organizations, the UNSD receives data from the Organisation for Economic Co-operation and Development (OECD), the United Nations Economic Commission for Europe (ECE) and the Caribbean Community (CARICOM) on behalf of their constituents.

3.c. Data collection calendar

The exercise to collect official annual national accounts estimates from countries or areas using the national accounts questionnaire starts in February of each year for the data available up to the end of the previous year.

3.d. Data release calendar

December of each year

3.e. Data providers

National statistics offices, central banks or national agencies responsible for compiling official national accounts estimates for a country or area.

3.f. Data compilers

United Nations Statistics Division (UNSD)

3.g. Institutional mandate

The National Accounts Section of the United Nations Statistics Division:

Contributes to the international coordinated development and updating of the System of National Accounts (SNA); and undertakes methodological research on issues on the research agenda of the SNA in collaboration with the Intersecretariat Working Group on National Accounts (ISWGNA).

Supports the implementation programme of the SNA by developing and updating supporting normative standards, training material and compilation guidance for the implementation of national accounts and supporting economic statistics and maintaining a knowledge base on economic statistics.

Delivers a statistical capacity building programme for the implementation of the 2008 SNA and supporting statistics through a series of regional and interregional workshops and seminars in collaboration with the regional commissions and regional agencies and through a limited number of individual country technical assistance missions.

Collects and disseminates annual national accounts statistics from countries and provides substantive service to the Committee on Contributions of the Fifth Committee of the United Nations on technical aspects of the elements of scale methodology for assessing the contributions to the United Nations by Member States.

Publishes the outputs of the Section in various publications of UNSD.

4.a. Rationale

Real Gross Domestic Product (GDP) per capita is a proxy for the average standard of living of residents in

a country or area.

A positive percentage change in annual real GDP per capita can be interpreted as an increase in the average standard of living of the residents in a country or area.

4.b. Comment and limitations

Although countries or areas calculate GDP using the common principles and recommendations in the United Nations System of National Accounts (SNA), there are still problems in international comparability of GDP estimates. These include:

Different versions of the SNA (for example, 1968, 1993 or 2008) countries or areas use in calculating their GDP estimates.
Different degree of coverage of informal and non-observed economic activities in the GDP estimates.

Further, as a necessary condition to being a key economic performance indicator of sustainable development, one of the often-cited limitations of GDP is that it does not account for the social and environmental costs of production. It is designed as a measure of the level of overall well-being. For example, growth in real GDP per capita reveals nothing concerning energy and material interactions with the environment.

4.c. Method of computation

The annual growth rate of real Gross Domestic Product (GDP) per capita is calculated as follows:

Convert annual real GDP in domestic currency at 2015 prices for a country or area to US dollars at 2015 prices using the 2015 exchange rates.
Divide the result by the population of the country or area to obtain annual real GDP per capita in constant US dollars at 2015 prices.
Calculate the annual growth rate of real GDP per capita in year t+1 using the following formula: $\frac{G_{t + 1} - G_{t}}{G_{t}} \times 100$ , where G_t+1 is the real GDP per capita in 2015 US dollars in year t+1 and G_t is the real GDP per capita in 2015 US dollars in year t.

4.d. Validation

The official national accounts data in domestic currency are validated to check for errors. The validation procedure involves ensuring that aggregates are equal to the sum of their components and that data series which are provided in multiple tables are represented consistently.

4.e. Adjustments

The current and constant price GDP series are converted into US dollars by applying the corresponding market exchange rates as reported by the International Monetary Fund (IMF). When these conversion rates are not available, other IMF rates are used (official rates or principal rates). For countries whose exchange rates are not reported by the IMF, the annual average of United Nations operational rates of exchange (UNOPs) is applied. The UNOPs are conversion rates that are applied in official transactions of the United Nations with these countries. These exchange rates are based on official, commercial and/or tourist rates of exchange.

In cases where a country experiences considerable distortion in the conversion rates, the UNSD uses price-adjusted rates of exchange (PARE) as an alternative to the exchange rates reported by the IMF or UN operational rates of exchange. The conversion based on PARE corrects the distorting effects of uneven price changes that are not well reflected in the other conversion rates. Consequently, unrealistic levels in GDP and other national accounts aggregates expressed in US Dollars may have been adjusted for certain time periods to improve the economic analysis at national, regional and local levels.

The constant-price GDP series for each country is then divided by its population to obtain its real GDP per capita.

More information on the methodology to estimate the data is available on https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

When a full set of official annual GDP data is not available, estimation procedures are employed to obtain estimates for the entire time series. When full data are not available, a hierarchy of other data sources is used to gather information on the national accounts of a country or area. The data gathered are then either used directly or estimation procedures are applied to obtain the annual GDP data.

If official data are not available, the selection of data sources is based on following hierarchy:

1. Official publications and websites of national statistical offices, central banks or relevant government ministries;
2. Official statistics disseminated by Eurostat, European Central Bank and the Organization for Economic Cooperation and Development (OECD) for their members;
3. Information provided by Permanent Missions to the United Nations;
4. Economic surveys and estimates prepared by United Nations’ Regional Economic Commissions (i.e. UNECE, ECLAC, ESCAP, UNECA and ESCWA);
5. Publications of international organizations with a strong focus on statistical data collection (including regional development banks). The most common sources used for their respective countries are listed below: Asia: Asian Development Bank, ASEAN, Arab Monetary Fund, Secretariat of the Pacific Community (SPC) Africa: African Development Bank, Afristat, Banque des États de l’Afrique Centrale (BEAC), Union Économique Monétaire Ouest Africain (UEMOA) Americas: CARICOM, Caribbean Development Bank, Eastern Caribbean Central Bank (ECCB) Other: OECD for non-member countries Statistical Committee of the Commonwealth of Independent States.
6. Estimates and indicators from other international organizations. The most common sources used are: the International Monetary Fund (IMF) and the World Bank;
7. Publications or websites of specialized groups, the most common sources used are: the Gulf Cooperation Council, the Asia-Pacific Economic Cooperation (APEC), the Committee of Central Bank Governors in SADC; the Islamic Development Bank, and the Statistical Training Centre for Islamic Countries;
8. Economic data from commercial providers and other sources, the most common sources used are: the Economic Intelligence Unit and the United States Central Intelligence Agency;
9. Information from neighbouring countries where no alternative source is available (Switzerland for Liechtenstein; France for Monaco; Italy for San Marino; Spain for Andorra; and some Pacific Islands for other Pacific Islands);

The estimation methods involved in preparing the GDP estimates using sources other than official data include trend extrapolation, using appropriate indices for inflating or deflating relevant data series, and share distribution of GDP. A hierarchical assessment is followed to determine which method should be used. Effort is made to keep data estimation methods consistent from year to year.

At regional and global levels

After the missing real GDP country or area data are imputed using the methods as described above, they are summed up to derive the respective regional or global aggregates and then divided by the corresponding population data to obtain the regional or global real GDP per capita. After that, annual growth rates in regional or global real GDP per capita are calculated using the formula described above.

4.g. Regional aggregations

For each year, the real GDP and population estimates for each country or area are summed up to derive the regional and global aggregates. The regional and global aggregates are then divided by the corresponding population to derive the regional and global real GDP per capita estimates. These estimates are then used to calculate the annual growth rates in regional and global real GDP per capita using the formula described above.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

GDP: National Accounts Statistics: Main Aggregates and Detailed Tables, 2021 See https://unstats.un.org/unsd/nationalaccount/pubsDB.asp?pType=3

Population: United Nations Demographic Yearbook

See: https://unstats.un.org/unsd/demographic-social/products/dyb/dybsets/2021.pdf

GDP: 2008 SNA

See https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf

Population: Principles and Recommendations for Population and Housing Censuses See https://unstats.un.org/unsd/publication/seriesM/Series_M67rev3en.pdf

4.i. Quality management

All official data received by the United Nations Statistics Division are checked for errors prior to incorporation in the United Nations official data database. The checking involves ensuring that aggregate indicators are equal to the sum of their components and that indicators which are provided in multiple tables are represented consistently. Footnotes are added to the data when necessary.

Similarly, the estimated data are checked for consistency by ensuring that aggregate indicators are equal to the sum of their components and that indicators which are represented in multiple tables are represented consistently. The estimates derived for each year are compared to previous years to ensure that estimates are prepared consistently from year to year. Additionally, the growth rate from year to year is analyzed to identify anomalies in the data.

4.j. Quality assurance

Data are validated in accordance with the international statistical standards. Discrepancies are resolved through written communication with countries.

4.k. Quality assessment

The estimates derived for each year are compared to previous years to ensure that estimates are prepared consistently from year to year. Additionally, the growth rate from year to year is analysed to identify anomalies in the data.

5. Data availability and disaggregation

Data availability:

National statistics offices, central banks or national agencies responsible for compiling official national accounts estimates for a country or area.

Time series:

Annual data from 1970 to 2021 are available.

Disaggregation:

It is possible to disaggregate the country data by region, if countries can make available the underlying

regional data which are consistent with the national accounts data to perform the disaggregation.

6. Comparability/deviation from international standards

Sources of discrepancies:

The differences with country data include the following:

Official country data are typically available in domestic currency only. The data estimates for this indicator are in US dollars.

Countries or areas may not have a full set of official GDP data. The GDP data estimated by UNSD include imputations using various estimation procedures as described above to obtain estimates for the entire time series.

Official country data are often reported as multiple sets of time series versions, with each version representing a unique methodology used to compile the national accounts data (for example, a difference between two time series versions could reflect a change in currency, a switch from 1968 SNA to 1993 SNA, a change in the office responsible for compiling national accounts, etc.). These time series versions may not be comparable, especially when a country has shifted from the 1968 SNA to 1993 SNA or 2008 SNA.

When a single version of a time series does not exist for the entire period (1970 to t-1), backcasting procedures are used to link the most recently reported time series version with the previous series. Note that if there is a change of fiscal year between two official data time series, the older series are converted to the fiscal year type of the most recent time series prior to backcasting.

Backcasting procedures are also used when constant price time series versions include multiple base years or when constant price time series versions are reported as constant prices of the previous year (CPPY). CPPY data are backcasted by using the officially reported current price data and the officially reported constant price data. The data are backcasted into a single series with a fixed base year.

The population estimates from the United Nations Population Division may be different from country-produce estimates as the former include analysis carried out to take into account deficiencies such as incompleteness of coverage, lack of timeliness and errors in the reporting or coding of the basic information and to establish past population trends by resolving the inconsistencies affecting the basic data.

7. References and Documentation

URL:

https://unstats.un.org/unsd/snaama/Index

References:

http://unstats.un.org/unsd/nationalaccount/sna.asp http://unstats.un.org/unsd/nationalaccount/data.asp

https://unstats.un.org/unsd/snaama/Index

http://data.un.org/Explorer.aspx?d=SNAAMA

https://population.un.org/wpp/

8.2.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high value added and labour-intensive sectors

0.c. Indicator

Indicator 8.2.1: Annual growth rate of real GDP per employed person

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

1.1.1, 8.3.1, 8.5.2, 10.4.1

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

The annual growth rate of real Gross Domestic Product (GDP) per employed person conveys the annual percentage change in real GDP per employed person.

Concepts:

GDP: It is the main measure of national output, representing the total value of all final goods and services within the System of National Accounts (SNA) production boundary produced in a particular economy (that is, the dollar value of all goods and services within the SNA production boundary produced within a country’s borders in a given year). According to the SNA, “GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output … GDP is also equal to the sum of the final uses of goods and services (all uses except intermediate consumption) measured at purchasers’ prices, less the value of imports of goods and services GDP is also equal to the sum of primary incomes distributed by resident producer units.”

Real GDP: Real GDP refers to GDP calculated at constant prices, that is, the volume level of GDP, excluding the effect of inflation and favouring comparisons of quantities beyond price changes. Constant price estimates of GDP are calculated by expressing values in terms of a base period. In theory, the price and quantity components of a value are identified and the price in the base period is substituted for that in the current period.

Employment: All persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Data are based on the SNA.

3.a. Data sources

Description:

Output measures used in the numerator of this indicator Gross Domestic Product (GDP) are best obtained from the production side of national accounts and represent, as much as possible, GDP at market prices for the aggregate economy (adjusted for inflation, in constant prices).

Employment data used in the denominator are preferably derived from labour force or other household surveys with an employment module. In the absence of a household survey, establishment surveys, administrative records or official estimates based on reliable sources can be used as well as population censuses. It is however important to note that employment data from establishment surveys will capture the number of jobs and not the number of persons employed as preferred for the denominator. Also, establishment surveys cover, in many cases, the formal sector and employers and employees only, not accounting for the whole economy.

When calculating this indicator, it is important to ensure that the coverage of the employment data is consistent with that of the national accounts.

3.b. Data collection method

For the purposes of international reporting on the SDG indicators, the ILO uses country-level estimates of GDP in constant 2015 US$ from the World Bank’s World Development Indicators database and country-level estimates on employment from household surveys or derived from the ILO modelled estimates to calculate levels and growth rates of labour productivity at the country, regional and global levels.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

ILO estimates of labour productivity are part of the ILO modelled estimates series, analysed in the ILO's World Employment and Social Outlook reports. The ILO estimates are released once per year alongside these reports.

3.e. Data providers

Input GDP and employment data are provided by national statistical offices, and in some cases labour ministries or other related agencies.

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets, and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

Real GDP per employed person being a measure of labour productivity, this indicator represents a measure of labour productivity growth, thus providing information on the evolution, efficiency and quality of human capital in the production process.

Economic growth in a country can be ascribed to many factors, including increased employment and more effective work by those who are employed. This indicator casts light on the latter effect, therefore being a key measure of economic performance. Labour productivity (and growth) estimates can support the formulation of labour market policies and monitor their effects. They can also contribute to the understanding of how labour market performance affects living standards.

4.b. Comment and limitations

Output measures are obtained from national accounts and represent, as much as possible, GDP at market prices for the aggregate economy. However, despite common principles that are mostly based on the United Nations System of National Accounts, there are still significant problems in international consistency of national accounts estimates, based on factors such as differences in the treatment of output in services sectors, differences in methods used to correct output measures for price changes (in particular, the use of different weighting systems to obtain deflators) and differences in the degree of coverage of informal economic activities.

Data on employment used in the denominator of this indicator refer, as much as possible, to the average number of persons with one or more paid jobs during the year. Employment data are based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS).

4.c. Method of computation

$R e a l G D P p e r e m p l o y e d p e r s o n = \frac{G D P a t c o n s t a n t p r i c e s }{T o t a l e m p l o y m e n t}$

The numerator and denominator of the equation above should refer to the same reference period, for example, the same calendar year.

If we call the real GDP per employed person “LabProd”, then the annual growth rate of real GDP per employed person is calculated as follows:

$A n n u a l g r o w t h r a t e o f r e a l G D P p e r e m p l o y e d p e r s o n = \frac{(L a b P r o d i n y e a r n) - (L a b P r o d i n y e a r n - 1)}{(L a b P r o d i n y e a r n - 1)} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Multivariate regression techniques are used to impute missing employment values at the country level.

For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://www.ilo.org/ilostat-files/Documents/TEM.pdf.

At regional and global levels

Regional and global figures are aggregates of the country-level figures including the imputed values.

4.g. Regional aggregations

To address the problem of missing data, the ILO designed several econometric models which are used to produce estimates of labour market indicators in the countries and years for which real data are not available. The employment data derived from the ILO modelled estimates are used to produce estimates on labour productivity. These models use multivariate regression techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://www.ilo.org/ilostat-files/Documents/TEM.pdf.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

See:

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm)
ILO Manual – Decent Work Indicators, Concepts and Definitions – Chapter 1, Economic and social context for decent work http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm (second version, page 214)
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the 13^th International Conference of Labour Statisticians (October 1982), available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
Resolution concerning statistics of work, employment and labour underutilization http://www.ilo.ch/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
System of National Accounts 2008 http://unstats.un.org/unsd/nationalaccount/sna2008.asp
Trends Econometric Models: A Review of Methodology http://www.ilo.org/empelm/pubs/WCMS_120382/lang--en/index.htm
ILOSTAT Database (https://ilostat.ilo.org/)
ILOSTAT Database – Topics - Labour productivity (https://ilostat.ilo.org/topics/labour-productivity/).

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

5. Data availability and disaggregation

Data Availability:

Data for this indicator is available for 188 countries and territories.

Time series:

Data for this indicator is available as of 2000 in the SDG Indicators Global Database, but time series going back to 1991 and including estimates up to 2022 are available in ILOSTAT.

Disaggregation:

No disaggregation required for this indicator.

6. Comparability/deviation from international standards

Sources of discrepancies:

The main limitations of the use of labour productivity as a global indicator arise from problems in the international comparability of data, more specifically from methodological differences across countries. Even though national output measures, in particular GDP estimates, are derived mainly from national accounts which should be based on internationally agreed principles consolidated in the United Nations SNA, there are still significant obstacles to the international consistency of national accounts estimates. These range from differences in the treatment of the output of service sectors to adjustments for price changes and variations in the coverage of informal activities and the underground economy.

Employment or labour input figures also suffer from comparability issues, especially in terms of differences in age coverage, the definition of employment, geographical and institutional coverage, the treatment of special groups and the coverage of informal employment.

In cases where the contribution to GDP of forms of work other than employment are expected to be significant, such as in the case of own-use production of goods (subsistence agriculture and fishing) or volunteer work, the exclusion of participation and time-spent in these productive activities can be an important source of bias in the resulting indicators.

7. References and Documentation

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm)
Estimates and projections of labour market indicators (http://www.ilo.org/empelm/projects/WCMS_114246/lang--en/index.htm)
ILO Manual – Decent Work Indicators, Concepts and Definitions – Chapter 1, Economic and social context for decent work http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm (second version, page 2149)
Resolution concerning statistics of work, employment and labour underutilization http://www.ilo.ch/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
System of National Accounts 2008 http://unstats.un.org/unsd/nationalaccount/sna2008.asp
Trends Econometric Models: A Review of Methodology http://www.ilo.org/empelm/pubs/WCMS_120382/lang--en/index.htm
ILOSTAT Database (https://ilostat.ilo.org/)
ILOSTAT Database – Topics - Labour productivity (https://ilostat.ilo.org/topics/labour-productivity/).
Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases, available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf

8.3.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro-, small- and medium-sized enterprises, including through access to financial services

0.c. Indicator

Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex

0.d. Series

Proportion of informal employment, by sector and sex (13^th ICLS)

Proportion of informal employment, by sector and sex (19^th ICLS)

0.e. Metadata update

2023-03-31

0.f. Related indicators

1.1.1, 1.3.1, 8.5.2

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

This indicator presents the share of employment which is classified as informal employment in the total economy, and separately in agriculture and in non-agriculture.

Concepts:

Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the two series for a given country is the operational criteria used to define employment, with one series based on the statistical standards from the 13^th International Conference of Labour Statisticians (ICLS) and the other series based on 19^th ICLS standards. In the 19^th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.

Informal employment comprises persons who in their main or secondary jobs were in one of the following categories:

- Own-account workers, employers and members of producers’ cooperatives employed in their own informal sector enterprises (the characteristics of the enterprise determine the informal nature of their jobs)

- Own-account workers engaged in the production of goods exclusively for own final use by their household (e.g. subsistence farming)

- Contributing family workers, regardless of whether they work in formal or informal sector enterprises (they usually do not have explicit, written contracts of employment, and are not subject to labour legislation, social security regulations, collective agreements, etc., which determines the informal nature of their jobs)

- Employees holding informal jobs, whether employed by formal sector enterprises, informal sector enterprises, or as paid domestic workers by households (employees are considered to have informal jobs if their employment relationship is, in law or in practice, not subject to national labour legislation, income taxation, social protection or entitlement to certain employment benefits)

For the purpose of classifying persons into formal or informal employment for this indicator, only the characteristics of the main job are considered.

An enterprise belongs to the informal sector if it fulfils the three following conditions:

- It is an unincorporated enterprise (it is not constituted as a legal entity separate from its owners, and it is owned and controlled by one or more members of one or more households, and it is not a quasi-corporation: it does not have a complete set of accounts, including balance sheets)

- It is a market enterprise (it sells at least some of the goods or services it produces);

- The enterprise is not registered or the employees of the enterprise are not registered or the number of persons engaged on a continuous basis is below a threshold determined by the country

2.b. Unit of measure

Percent (%)

2.c. Classifications

The breakdown by sector is based on the International Standard Industrial Classification of All Economic Activities (ISIC). Agriculture corresponds to ISIC Rev. 4 section A, Rev. 3 sections A and B, and Rev.2 section 1 and non-agriculture corresponds to Rev. 4 sections B-U, Rev. 3 sections C-Q, and Rev. 2 sections 2-9.

3.a. Data sources

The preferred source of data for this indicator is a labour force survey, with sufficient questions to determine the informal nature of jobs and whether the establishment where the person works in belongs to the formal or the informal sector.

3.b. Data collection method

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

National Statistical Offices

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets, and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

In contexts where social protection coverage is limited, social security benefits (such as unemployment insurance) are insufficient or even inexistent, and/or where wages and pensions are low, individuals may have to take up informal employment to ensure their livelihood. In these situations, indicators such as the unemployment rate would provide a very incomplete picture of the labour market situation, overlooking major deficits in the quality of employment. Statistics on informality are key to assessing the quality of employment in an economy and are relevant to developing and developed countries alike (ILOSTAT indicator description for informality, available at https://ilostat.ilo.org/resources/concepts-and-definitions/description-informality/).

4.b. Comment and limitations

The considerable heterogeneity of definitions and operational criteria used by countries to measure informal employment greatly hinders the international comparability of statistics on informality.

In order to counter this challenge, for the purpose of SDG global reporting and monitoring, the series is solely based on harmonized data produced by the ILO using the same operational process for all countries. Although some differences in criteria and definitions remain across countries, the process is designed to produce data that are as internationally comparable as possible given the underlying data sources.

4.c. Method of computation

$P r o p o r t i o n o f i n f o r m a l e m p l o y m e n t i n t o t a l e m p l o y m e n t = \frac{I n f o r m a l e m p l o y m e n t }{T o t a l e m p l o y m e n t} \times 100$

$P r o p o r t i o n o f i n f o r m a l e m p l o y m e n t i n a g r i c u l t u r e = \frac{I n f o r m a l e m p l o y m e n t i n a g r i c u l t u r a l a c t i v i t i e s}{T o t a l e m p l o y m e n t i n a g r i c u l t u r e} \times 100$

$P r o p o r t i o n o f i n f o r m a l e m p l o y m e n t i n n o n a g r i c u l t u r a l e m p l o y m e n t = \frac{I n f o r m a l e m p l o y m e n t i n n o n a g r i c u l t u r a l a c t i v i t i e s}{T o t a l e m p l o y m e n t i n n o n a g r i c u l t u r a l a c t i v i t i e s} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Not applicable

• At regional and global levels

See below

4.g. Regional aggregations

The ILO produces global and regional estimates of informal employment by sex based on available national estimates reflecting the 13^th ICLS standards. Global and regional estimates do not include the breakdown by sector (agriculture, non-agriculture). The estimates range from 2004 to 2022. Input data for informality is available for at least one year of the series in 75 per cent of the countries in the target sample. Benchmark employment data are derived from the ILO modelled estimates series.

Missing observations are imputed using a series of models that establish statistical relationships between the observed incidence of informal employment and explanatory variables. The explanatory variables used include economic and demographic variables, such as GDP per capita and urbanisation. Panel data regression and cross-validation techniques are used to establish the statistical relationships necessary for the imputation. The global and regional proportions of informal employment are obtained by first adding up, across countries, the numerator and denominator of the formula that defines the proportion of workers in informal employment outlined above.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm)
Resolution concerning statistics of employment in the informal sector, adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_087484.pdf
Guidelines concerning a statistical definition of informal employment, adopted by the Seventeenth International Conference of Labour Statisticians (November-December 2003) available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_087622.pdf
ILO manual Measuring informality: A statistical manual on the informal sector and informal employment available at http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_222979.pdf

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Data for this indicator is available for 136 countries and territories in the 13^th ICLS series and 77 countries and territories in the 19^th ICLS series.

Both country-reported estimates and ILO harmonized estimates of informal employment are available in ILOSTAT (https://ilostat.ilo.org/).

Time series:

The submission covers global and regional data for 2004 to 2022 and country data from 2000 to 2022.

Disaggregation:

Data on this indicator is requested disaggregated by sector and sex.

Here, sector refers to the breakdown by agriculture/non-agriculture. Where necessary and possible, the disaggregation by sector could go into a more detailed breakdown by economic activity. For the purpose of global and regional monitoring, no breakdowns of agriculture and non-agriculture are used.

In order to produce this indicator, employment statistics disaggregated by formal/informal employment and by economic activity (agriculture/non-agriculture) are needed.

6. Comparability/deviation from international standards

Sources of discrepancies:

Although some international standards do exist for the compilation of informal employment statistics, the relevant concepts and definitions have been left relatively flexible so as to accommodate national contexts and needs. This means that, in practice, the operational criteria used by countries to compile data at the national level vary significantly from country to country, hindering the international comparability of statistics. The comparability of informal employment statistics is also highly sensitive to differences in the geographical areas covered, the economic activities covered and the treatment of special groups of workers.

7. References and Documentation

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm)
Resolution concerning statistics of employment in the informal sector, adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_087484.pdf
Guidelines concerning a statistical definition of informal employment, adopted by the Seventeenth International Conference of Labour Statisticians (November-December 2003) available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_087622.pdf
ILO manual Measuring informality: A statistical manual on the informal sector and informal employment, available at http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---

publ/documents/publication/wcms_222979.pdf

ILOSTAT database, available at https://ilostat.ilo.org
Resolution concerning statistics of work, employment and labour underutilization adopted by the Nineteenth International Conference of Labour Statisticians (October 2013), available at https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the 13^th International Conference of Labour Statisticians (October 1982), available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdfQuick guide to understanding the impact of the new statistical standards on ILOSTAT databases, available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf
International Standard Industrial Classification of All Economic Activities https://unstats.un.org/unsd/classifications/Econ/isic

8.4.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the lead

0.c. Indicator

Indicator 8.4.1: Material Footprint, material footprint per capita, and material footprint per GDP

0.d. Series

Material footprint per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollar) EN_MAT_FTPRPG

Material footprint per capita, by type of raw material (tonnes) EN_MAT_FTPRPC

Material footprint, by type of raw material (tonnes) EN_MAT_FTPRTN

0.e. Metadata update

2022-08-12

0.f. Related indicators

12.2.1, 8.4.2, 12.2.2

0.g. International organisations(s) responsible for global monitoring

United Nations Environment Programme (UNEP)

1.a. Organisation

United Nations Environment Programme (UNEP)

2.a. Definition and concepts

Definitions:

Material Footprint (MF) is the attribution of global material extraction to domestic final demand of a country. The total material footprint is the sum of the material footprint for biomass, fossil fuels, metal ores and non-metallic minerals.

Concepts:

Domestic Material Consumption (DMC) and MF need to be looked at in combination, as they cover the two aspects of the economy, production and consumption. The DMC reports the actual amount of material in an economy, MF the virtual amount required across the whole supply chain to service final demand. A country can, for instance, have a very high DMC because it has a large primary production sector for export or a very low DMC because it has outsourced most of the material intensive industrial process to other countries. The material footprint corrects for both phenomena.

2.b. Unit of measure

Tonnes;

Kilograms per constant United States dollar;

Tonnes per capita.

2.c. Classifications

Material categories accordance to the global EW-MFA guide “UNEP (2021). The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting” (https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&isAllowed=y);
Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)

3.a. Data sources

The global estimation for MF is based on data available from different national and international datasets in the domain of material flow accounts, agriculture, forestry, fisheries, mining and energy statistics. International statistical sources for MF include the International Energy Agency, the United Nations Statistical Division, the United States Geological Survey, the Food and Agriculture Organization and COMTRADE databases.

3.b. Data collection method

For global estimation, the International Resource Panel (IRP) Global Material Flows and Resource Productivity working group compiles the data from national and international databases.

At the same time, country-provided indicators are collected through the QUESTIONNAIRE ON ECONOMY WIDE MATERIAL FLOW ACCOUNTS for the SDG indicators 8.4.1/12.2.1 and 8.4.2/12.2.2.

3.c. Data collection calendar

First data collection in 2022 and every 2 to 3 years after.

3.d. Data release calendar

First data release in 2017, the second in 2021 (fully estimated data). Then, in 2022 and every 2 to 3 years after (both globally estimated and country data).

3.e. Data providers

National Statistical Offices

3.f. Data compilers

United Nations Environment Programme (UNEP), Organization for Economic Co-operation and Development (OECD) and EUROSTAT

3.g. Institutional mandate

UNEP was mandated as a Custodian Agency for indicator 8.4.1 / 12.2.1 by the Inter-agency and Expert Group on SDG Indicators. UNEP IRP is the mechanism within UNEP supporting all work aspect in relation to Material Flow Accounting.

4.a. Rationale

Material footprint of consumption reports the amount of primary materials required to serve final demand of a country and can be interpreted as an indicator of the material standard of living/level of capitalization of an economy. Per-capita MF describes the average material use for final demand.

4.b. Comment and limitations

A footprint calculation uses the global Multi-Regional Input Output (MRIO) analysis, which compiles information from many countries national statistics to create a global multi-regional input-output table. This process requires a high level of computing capacity by supercomputers. Therefore, a limited number of countries can do the analysis on its own.

4.c. Method of computation

Material footprint by type of raw material (tonnes) is calculated as:

$M F = {D E + R M E}_{I M} - {R M E}_{E X }$

Where:

$M F$ – material footprint;

$D E$ – domestic extraction of materials;

${R M E}_{I M}$ – raw material equivalent of imports;

${R M E}_{E X}$ – raw material equivalents of exports.

For the attribution of the primary material needs of final demand a global, multi-regional input-output (MRIO) framework is employed. The attribution method based on I-O analytical tools is described in detail in Wiedmann et al. 2015. It is based on the Eora MRIO framework developed by the University of Sydney, Australia (Lenzen et al. 2013) which is an internationally well-established and the most detailed and reliable MRIO framework available to date.

Material footprint per capita, by type of raw material (tonnes), is calculated as:

$M F p e r c a p i t a = \frac{M F}{A n n u a l a v e r a g e p o p u l a t i o n}$

Material footprint per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollar), is calculated as:

$M F p e r G D P = \frac{M F}{G D P i n c o n s t a n t 2015 U n i t e d S t a t e s D o l l a r s}$

4.d. Validation

United Nations Environment Programme (UNEP) sends a prefilled questionnaire with estimated data to the National Statistical Office (NSO) Focal Points (FP) with a request to validate globally estimated data for this indicator and replace the data if needed/possible. The FPs coordinate data validation with stakeholders within their countries and report back the data to UNEP. For countries with no national data collected for this indicator, UNEP asks to agree on publishing and releasing the estimated data on UNEP’s World Environment Situation Room and UNSD SDG Global database.

4.e. Adjustments

UNEP replaces globally estimated data by national data if requested by the country.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

A zero is imputed when no positive real value was officially recorded, in the base data sets used, for any of the underlying components which make up this aggregated total. Thus “0.0” can represent either NA, or a genuine 0.0, or (crucially) a combination of both, which is a common situation. This allows for values to be easily aggregated further; however, it should be thus noted that due to imputing missing values as “0.0”, the aggregations may represent a lower value than the actual situation.

• At regional and global levels:

Similarly, missing values are imputed as zero in the regional and global aggregations. However, in the case where no data is available at all for a particular country, then the per capita and per GDP estimates are weighted averages of the available data.

4.g. Regional aggregations

The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf

4.h. Methods and guidance available to countries for the compilation of the data at the national level

United Nations Environment Programme (UNEP) jointly with the International Resource Panel (IRP), United Nations Statistics Division (UNSD), the Statistical Office of the European Union (Eurostat) and the Organisation for Economic Co-operation and Development (OECD) have developed a global manual on Economy-Wide Material Flow Accounting (EW-MFA) which brings in the European guidelines but provides a modular approach for countries looking to develop EW-MFA for the first time and it addresses specific issues related to resource extractive based economies. UNEP (2021). The use of natural resources in the economy - A Global Manual on Economy Wide Material Flow Accounting: https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&isAllowed=y
EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006

4.i. Quality management

Quality management is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy-Wide Material Flow Accounting (UNEP, 2021).

4.j. Quality assurance

Quality assurance is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy Wide Material Flow Accounting (UNEP, 2021).

4.k. Quality assessment

Quality assessment is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), in consultation with countries (nominated Focal Points) after receiving their feedback on the globally estimated indicators.

5. Data availability and disaggregation

Data availability:

The data covers about 160 countries (either globally estimated or country data).

Time series:

The data set presented in the SDG database covers a time period of 20 years (2000-2019).

The International Resource Panel (IRP) publishes estimated data series for 1970-2019 on its website.

Disaggregation:

The Material Footprint indicator is disaggregated into four main material categories (biomass, fossil fuels, metal ores and non-metallic minerals).

6. Comparability/deviation from international standards

Material Footprint is calculated coherent with international standards, recommendations, and classifications such as the System of National Accounts 2008, the System of Environmental-Economic Accounting – Central Framework 2012, the Balance of Payments and International Investment Position, the International Standard Industrial Classification of All Economic Activities (ISIC), the Central Product Classification (CPC) and the Framework for the Development of Environment Statistics.

Sources of discrepancies:

Not applicable

7. References and Documentation

URL:

UNEP (2021), The use of National Resources in the Economy: a Global Manual on Economy Wide Material Flow Accounting. https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&isAllowed=y

References:

EUROSTAT (2013). Economy-Wide Material Flow Accounts. Compilation guide 2013: https://ec.europa.eu/eurostat/documents/1798247/6191533/2013-EW-MFA-Guide-10Sep2013.pdf/54087dfb-1fb0-40f2-b1e4-64ed22ae3f4c

EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006

Wiedmann, T., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, K. Kanemoto, (2013) The Material Footprint of Nations, Proc. Nat. Acad. Sci. Online before print.

Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research, 25:1, 20-49.

8.4.2

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

0.c. Indicator

Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP

0.d. Series

Domestic material consumption, by type of raw material (tonnes) EN_MAT_DOMCMPT

Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars) EN_MAT_DOMCMPG

Domestic material consumption per capita, by type of raw material (tonnes) EN_MAT_DOMCMPC

0.e. Metadata update

2022-08-12

0.f. Related indicators

8.4.1, 12.2.1, 12.2.2

0.g. International organisations(s) responsible for global monitoring

United Nations Environment Programme (UNEP)

1.a. Organisation

United Nations Environment Programme (UNEP)

2.a. Definition and concepts

Definitions:

Domestic Material Consumption (DMC) is a standard material flow accounting (MFA) indicator and reports the apparent consumption of materials in a national economy.

DMC measures the total amount of material (biomass, fossil fuels, metal ores and non-metallic minerals) directly used in an economy and based on accounts of direct material flows, i.e., domestic material extraction and physical imports and exports.

Concepts:

DMC and Material Footprint (MF) need to be looked at in combination, as they cover the two aspects of the economy, production and consumption. The DMC reports the actual amount of material in an economy, MF the virtual amount required across the whole supply chain to service final demand. A country can, for instance, have a very high DMC because it has a large primary production sector for export or a very low DMC because it has outsourced most of the material intensive industrial process to other countries. The material footprint corrects for both phenomena.

2.b. Unit of measure

Tonnes;

Kilograms per constant United States dollar;

Tonnes per capita.

2.c. Classifications

Material categories accordance to the global EW-MFA guide “UNEP (2021). The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting” (https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&isAllowed=y);
Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)

3.a. Data sources

The global estimation of DMC is based on data available from different national and international datasets in the domain of agriculture, forestry, fisheries, mining and energy statistics. International statistical sources for DMC include the International Energy Agency, the United Nations Statistical Division, the United States Geological Survey, the Food and Agriculture Organisation and COMTRADE databases.

3.b. Data collection method

For global estimation, the International Resource Panel (IRP) Global Material Flows and Resource Productivity working group compiles the data from national and international databases.

At the same time, country-provided indicators are collected through the QUESTIONNAIRE ON ECONOMY WIDE MATERIAL FLOW ACCOUNTS for the SDG indicators 8.4.1/12.2.1 and 8.4.2/12.2.2.

3.c. Data collection calendar

First data collection in 2022 and every 2 to 3 years after.

3.d. Data release calendar

First data release in 2017, the second in 2021 (fully estimated data). Then, in 2022 and every 2 to 3 years after (both globally estimated and country data).

3.e. Data providers

National Statistical Offices

3.f. Data compilers

United Nations Environment Programme (UNEP), Organization for Economic Co-operation and Development (OECD) and EUROSTAT

3.g. Institutional mandate

UNEP was mandated as Custodian Agency for indicator 8.4.2 / 12.2.2 by the Inter-agency and Expert Group on SDG Indicators. UNEP IRP is the mechanism within UNEP supporting all work aspect in relation to Material Flow Accounting.

4.a. Rationale

Domestic Material Consumption (DMC) reports the amount of materials that are used in a national economy. It is a territorial (production side) indicator. DMC also presents the amount of material that needs to be handled within an economy, which is either added to material stocks of buildings and transport infrastructure or used to fuel the economy as material throughput. It describes the physical dimension of economic processes and interactions. It can also be interpreted as long-term waste equivalent. Per-capita DMC describes the average level of material use in an economy – an environmental pressure indicator – and is also referred to as metabolic profile.

4.b. Comment and limitations

Domestic Material Consumption cannot be disaggregated to economic sectors which limits its potential to become a satellite account to the System of National Accounts (SNA).

4.c. Method of computation

Domestic Material Consumption (DMC) is a standard material flow accounting (MFA) indicator. MFAs below to environmental-economic accounts and apply the accounting concepts, structures, rules and principles of the System of Environmental-Economic Accounting 2012 - Central Framework. It should be used in conjunction with reading the global EW-MFA guide The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting (https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&isAllowed=y).

Domestic Material Consumption (DMC), by type of raw material (tonnes) is calculated as:

$D M C = D E + I M - E X,$

Where:

$D M C$ – domestic material consumption;

$D E$ – domestic extraction of materials;

$I M$ – direct imports;

$E X$ – direct exports.

DMC measure the amount of materials that are used in economic processes. It does not include materials that are mobilized for the process of domestic extraction but do not enter the economic process.

Domestic material consumption per capita, by type of raw material (tonnes), is calculated as:

$D M C p e r c a p i t a = \frac{D M C}{A n n u a l a v e r a g e p o p u l a t i o n}$

Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars), is calculated as:

$D M C p e r G D P = \frac{D M C}{G D P i n c o n s t a n t 2015 U n i t e d S t a t e s D o l l a r s}$

4.d. Validation

4.e. Adjustments

UNEP replaces globally estimated data by national data if requested by the country.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

• At regional and global levels:

Similarly, missing values are imputed as zero in the regional and global aggregations. However, in the case where no data is available at all for a particular country, the per capita and per GDP estimates are weighted averages of the available data.

4.g. Regional aggregations

The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf

4.h. Methods and guidance available to countries for the compilation of the data at the national level

United Nations Environment Programme (UNEP), jointly with the International Resource Panel (IRP) and United Nations Statistics Division (UNSD), the Statistical Office of the European Union (Eurostat) and the Organisation for Economic Co-operation and Development (OECD) have developed a global manual on Economy-Wide Material Flow Accounting (EW-MFA) which brings in the European guidelines, but provides a modular approach for countries looking to develop EW-MFA for the first time and it addresses specific issues related to resource extractive based economies.

UNEP (2021). The use of natural resources in the economy - A Global Manual on Economy Wide Material Flow Accounting: https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&isAllowed=y
EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018:https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

The data covers 193 countries (either globally estimated or country data).

Time series:

The data set presented in the SDG database covers a time period of 20 years (2000-2019).

The International Resource Panel (IRP) publishes estimated data series for 1970-2019 on its website.

Disaggregation:

The Domestic Material Consumption (DMC) indicator is disaggregated by main material categories (biomass, fossil fuels, metal ores and non-metallic minerals).

6. Comparability/deviation from international standards

Domestic Material Consumption is calculated coherent with international standards, recommendations, and classifications such as the System of National Accounts 2008, the System of Environmental-Economic Accounting – Central Framework 2012, the Balance of Payments and International Investment Position, the International Standard Industrial Classification of All Economic Activities (ISIC), the Central Product Classification (CPC) and the Framework for the Development of Environment Statistics.

Sources of discrepancies:

Not applicable

7. References and Documentation

URL:

References:

EUROSTAT (2013). Economy-Wide Material Flow Accounts. Compilation Guide 2013: https://ec.europa.eu/eurostat/documents/1798247/6191533/2013-EW-MFA-Guide-10Sep2013.pdf/54087dfb-1fb0-40f2-b1e4-64ed22ae3f4c

EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006

Wiedmann, T., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, K. Kanemoto, (2013) The Material Footprint of Nations, Proc. Nat. Acad. Sci. Online before print.

Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research, 25:1, 20-49.

8.5.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value

0.c. Indicator

Indicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilities

0.d. Series

Average hourly earnings of employees by sex and occupation (local currency)

0.e. Metadata update

2023-03-31

0.f. Related indicators

1.1.1, 5.5.2, 8.2.1, 10.4.1

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

This indicator provides information on the mean hourly earnings from paid employment of employees by sex, occupation, age, and disability status.

Concepts:

Earnings refer to the gross remuneration in cash or in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. Earnings exclude employers’ contributions in respect of their employees paid to social security and pension schemes and also the benefits received by employees under these schemes. Earnings also exclude severance and termination pay.

For international comparability purposes, statistics of earnings used relate to employees’ gross remuneration, i.e. the total before any deductions are made by the employer in respect of taxes, contributions of employees to social security and pension schemes, life insurance premiums, union dues and other obligations of employees. As stated in the indicator title, data on earnings should be presented on the basis of the arithmetic average of the hourly earnings of all employees.

2.b. Unit of measure

Current local currency

2.c. Classifications

The breakdown by occupation is based on the latest version of the International Standard Classification of Occupation (ISCO).

3.a. Data sources

There are a variety of possible sources of data on employees’ earnings.

Establishment surveys are usually the most reliable source, given the high accuracy of earnings figures derived from them (the information typically comes from the payroll, so is precise). However, the scope of these statistics is limited to the coverage of the establishment survey in question (usually excluding small establishments, agricultural establishments and/or informal sector establishments).

Household surveys (and especially labour force surveys) can provide earnings statistics covering all economic activities, and all establishment types and sizes, but the quality of the data is highly dependent on the accuracy of respondents’ answers.

Data on earnings could also be derived from a variety of administrative records.

3.b. Data collection method

The ILO Department of Statistics processes national household survey micro datasets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

At the national level, the agency responsible for producing data on earnings is usually the national statistical office.

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

Earnings are a key aspect of quality of employment and living conditions. Information on hourly earnings disaggregated by various classifications (sex, age, occupation, disability status) provide some indication of the extent to which pay equality is respected or achieved.

4.b. Comment and limitations

The variety of possible sources for statistics on earnings greatly hinders international comparability, as each type of source has its own coverage, scope, and characteristics. It would not be fully accurate to compare, for example, hourly earnings from a labour force survey for one country with hourly earnings from an establishment survey for another. The use of non-standard definitions and the heterogeneity of operational criteria applied further hamper cross-country comparisons.

4.c. Method of computation

Computation Method:

The method of calculation used to obtain the average hourly earnings of employees depends on the source of data used and the type of information it provides. For instance, where there is information available on each worker’s hourly earnings and hours worked, the average is a weighted average calculated by summing up the product of each worker’s hourly earnings times the hours worked and dividing it by the total number of hours worked by all workers. In other words:

$A v e r a g e h o u r l y e a r n i n g s = \frac{\sum (h o u r l y e a r n i n g s o f e a c h e m p l o y e e x h o u r s w o r k e d b y e a c h e m p l o y e e)}{T o t a l n u m b e r o f h o u r s w o r k e d b y a l l e m p l o y e e s}$

Statistics on average hourly earnings by sex can be used to calculate the gender pay gap, as follows:

$G e n d e r p a y g a p = \frac{(A v e r a g e h o u r l y e a r n i n g s_{m e n}) - (A v e r a g e h o u r l y e a r n i n g s_{w o m e n})}{(A v e r a g e h o u r l y e a r n i n g s_{m e n})} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

Not applicable

4.g. Regional aggregations

Not applicable

4.h. Methods and guidance available to countries for the compilation of the data at the national level

- Resolution concerning the measurement of employment-related income, adopted by the Sixteenth International Conference of Labour Statisticians (January 1998), available at http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087490/lang--en/index.htm

- Resolution concerning the International Classification of Status in Employment (ICSE), adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087562/lang--en/index.htm

- Resolution concerning an integrated system of wages statistics, adopted by the Twelfth International Conference of Labour Statisticians (January 1973), available at http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087496/lang--en/index.htm

- ILO manual: An integrated system of wages statistics, available at http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_315657.pdf

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Data for this indicator is available for 123 countries and territories.

Time series: The submission covers data from 2000 to 2022.

Disaggregation:

This indicator should be disaggregated by sex, occupation, age, and disability status.

6. Comparability/deviation from international standards

Sources of discrepancies:

Earnings statistics present a number of complications in terms of their international comparability, most of which arise from the variety of possible sources of data. The various sources available -- establishment surveys, household surveys and administrative records -- differ in their methods, objectives and scope, which influences the results obtained. The coverage of the source may vary in terms of the geographical areas covered, the workers covered (for example, part-time workers or informal workers may be excluded) and the establishments covered (for example, establishments below a certain size or of a certain sector may be excluded). In cases where the earnings of workers excluded from the coverage of the source are significantly different than those of workers included, the statistics would not be representative of the country as a whole and would not be strictly comparable to those of countries using a more comprehensive source.

When using household surveys as a source of earnings statistics, there are a number of issues related to the accuracy of the earnings information reported by the respondents. They may over declare or under declare their earnings for various reasons, or they may report gross or net wages while including or excluding bonuses and benefits, without distinction. This naturally affects the reliability of the results.

7. References and Documentation

Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, available at https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm

Resolution concerning the measurement of employment-related income, adopted by the Sixteenth International Conference of Labour Statisticians (January 1998), available at http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087490/lang--en/index.htm

Resolution concerning the International Classification of Status in Employment (ICSE), adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087562/lang--en/index.htm

Resolution concerning an integrated system of wages statistics, adopted by the Twelfth International Conference of Labour Statisticians (January 1973), available at http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087496/lang--en/index.htm

ILO manual: An integrated system of wages statistics, available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_315657.pdf

ILOSTAT database, available at https://ilostat.ilo.org

ILOSTAT’s indicator description on earnings and labour cost, at https://ilostat.ilo.org/resources/concepts-and-definitions/description-earnings-and-labour-cost/

International Standard Classification of Occupations (ISCO) https://ilostat.ilo.org/resources/concepts-and-definitions/classification-occupation/

8.5.2

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value

0.c. Indicator

Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilities

0.d. Series

Unemployment rate, by sex and age (13^th ICLS)

Unemployment rate, by sex and disability (13^th ICLS)

Unemployment rate, by sex and age (19^th ICLS)

Unemployment rate, by sex and disability (19^th ICLS)

0.e. Metadata update

2023-03-31

0.f. Related indicators

1.1.1, 8.2.1, 8.3.1, 8.6.1, 10.4.1

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

The unemployment rate conveys the percentage of persons in the labour force who are unemployed.

Concepts:

Unemployed persons are defined as all those of working age (usually aged 15 and above) who were not in employment, carried out activities to seek employment during a specified recent period and were currently available to take up employment given a job opportunity, where: (a) “not in employment” is assessed with respect to the short reference period for the measurement of employment; (b) to “seek employment” refers to any activity when carried out, during a specified recent period comprising the last four weeks or one month, for the purpose of finding a job or setting up a business or agricultural undertaking; (c) the point when the enterprise starts to exist should be used to distinguish between search activities aimed at setting up a business and the work activity itself, as evidenced by the enterprise’s registration to operate or by when financial resources become available, the necessary infrastructure or materials are in place or the first client or order is received, depending on the context; (d) “currently available” serves as a test of readiness to start a job in the present, assessed with respect to a short reference period comprising that used to measure employment (depending on national circumstances, the reference period may be extended to include a short subsequent period not exceeding two weeks in total, so as to ensure adequate coverage of unemployment situations among different population groups).

Persons in employment are defined as all those of working age (usually aged 15 and above) who, during a short reference period such as one week or one day, were engaged in any activity to produce goods or provide services for pay or profit. The difference between the 13th and 19th ICLS series for a given country is the operational criteria used to define employment, with two series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other two series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.

The labour force corresponds to the sum of persons in employment and in unemployment.

For more information on the definitions of employment and unemployment refer to the Resolution concerning statistics of work, employment and labour underutilization Adopted by the 19^th International Conference of Labour Statisticians.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Disability status is based on the WHO International Classification of Functioning, Disability and Health (ICF), according to which disability covers impairments (problems in body function or structure such as a significant deviation or loss), activity limitations (difficulties in executing activities) and participation restrictions (problems in involvement in life situations). For measurement purposes, the ICF defines a person with disability as a person who is limited in the kind or amount of activities that he or she can do because of ongoing difficulties due to a long-term physical condition, mental condition or health problem.

3.a. Data sources

The preferred official national data source for this indicator is a household-based labour force survey.

In the absence of a labour force survey, a population census and/or other type of household surveys with an appropriate employment module may also be used to obtain the required data. It is important to note that unemployment data derived from employment office records or unemployment registers would not refer to unemployment (as defined for the purposes of this indicator, using the three-criteria of being without a job, seeking employment and available for employment) but to registered unemployment, and thus, it would not be comparable with indicator 8.5.2.

3.b. Data collection method

The ILO Department of Statistics processes national household survey microdata sets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

Mainly national statistical offices, and in some cases labour ministries or other related agencies, at the country-level. In some cases, regional or international statistical offices can also act as data providers.

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

The unemployment rate is a useful measure of the underutilization of the labour supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labour force and of the performance of the labour market. Short-term time series of the unemployment rate can be used to signal changes in the business cycle; upward movements in the indicator often coincide with recessionary periods or in some cases with the beginning of an expansionary period as persons previously not in the labour market begin to test conditions through an active job search.

4.b. Comment and limitations

Even though in most developed countries the unemployment rate is useful as an indicator of labour market performance, and specifically, as a key measure of labour underutilization, in many developing countries, the significance and meaning of the unemployment rate could be questioned. In the absence of unemployment insurance systems or social safety nets, persons of working age must avoid unemployment, resorting to engaging in some form of economic activity, however insignificant or inadequate. Thus, in this context, other measures should supplement the unemployment rate to comprehensively assess labour underutilization.

4.c. Method of computation

The computation is identical for both series:

$U n e m p l o y m e n t r a t e = \frac{T o t a l u n e m p l o y m e n t}{T o t a l l a b o u r f o r c e} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Multivariate regression techniques are used to impute missing values at the country level. However, the imputed missing country values are only used to calculate the global and regional estimates; they are not used for international reporting on the SDG indicators by the ILO.

For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://www.ilo.org/ilostat-files/Documents/TEM.pdf.

At regional and global levels

Regional and global figures are aggregates of the country-level figures including the imputed values.

4.g. Regional aggregations

To address the problem of missing data, the ILO designed several econometric models which are used to produce estimates of labour market indicators based on the 13^th ICLS standards in the countries and years for which real data are not available. The unemployment estimates derived from the ILO modelled estimates are used to produce global and regional estimates on unemployment rates. These models use multivariate regression techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://www.ilo.org/ilostat-files/Documents/TEM.pdf.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

To calculate this indicator (according to the ILO definitions of unemployment and unemployment rate), data is needed on both the labour force and the unemployed, by sex and age (and eventually disability status). This data is collected at the national level mainly through labour force surveys (or other types of household surveys with an employment module). For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question.

For further information, see section 7 with references and documentation.

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Data disaggregated by sex and age for this indicator is available for 217 countries and territories in the 13^th ICLS series and 126 countries and territories in the 19^th ICLS series.

The indicator is widely available based on real observations provided by countries and derived from national labour force surveys, other types of household surveys or population census.

However, the disaggregation by disability is not as widely available and this submission only includes 109 countries and territories in the 13^th ICLS series and 73 countries and territories in the 19^th ICLS series.

Time series:
Data for disaggregation by sex and age for this indicator is available as of 2000 for countries in the SDG Indicators Global Database, but time series going back further are available in ILOSTAT. Global and regional aggregates disaggregated by sex and age are available through 2022.

Data for disaggregation by disability status is available for the period from 2003 to 2022 at the country level.

Disaggregation:

This indicator should, ideally, be disaggregated by sex, age group and disability status.

6. Comparability/deviation from international standards

Sources of discrepancies:

Differences in the questionnaires used in the household surveys as the basic measurement tool may entail differences in specific definitions of employment and unemployment, differences in the treatment of specific groups or differences in the operational criteria used to determine the individual’s labour force status.

The unemployment rate is dependent on the geographical coverage of the survey since urban and rural areas tend to have significant differences in the incidence of unemployment. It is important to note that unemployment indicators do not convey any information on the characteristics of the unemployed (their education level, ethnic origin, socio-economic background, work experience, duration of unemployment, etc.), which is crucial to cast light on labour market failures.

7. References and Documentation

Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, available at https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm
ILOSTAT (https://ilostat.ilo.org/)
ILOSTAT’s topic page on Unemployment and Labour Underutilization (https://ilostat.ilo.org/topics/unemployment-and-labour-underutilization/).
Decent Work Indicators Manual: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---

stat/documents/publication/wcms_223121.pdf

Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19^th ICLS in 2013: https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases (https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf)
Trends Econometric Models: A Review of Methodology: http://www.ilo.org/wcmsp5/groups/public/---ed_emp/---emp_elm/---trends/documents/publication/wcms_120382.pdf
ILO Manual – Decent Work Indicators, Concepts and Definitions – Chapter 1, Employment opportunities http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm (second version, pages 34 and 49)

8.6.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.6: By 2020, substantially reduce the proportion of youth not in employment, education or training

0.c. Indicator

Indicator 8.6.1: Proportion of youth (aged 15–24 years) not in education, employment or training

0.d. Series

Proportion of youth (aged 15-24 years) not in education, employment or training, by sex (13^th ICLS)

Proportion of youth (aged 15-24 years) not in education, employment or training, by sex (19^th ICLS)

0.e. Metadata update

2023-03-31

0.f. Related indicators

8.5.2

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

This indicator conveys the proportion of youth (aged 15-24 years) not in education, employment or training (also known as "the youth NEET rate").

Concepts:

For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive). According to the International Standard Classification of Education (ISCED), education is defined as organized and sustained communication designed to bring about learning. Formal education is defined in ISCED as education that is institutionalized, intentional, and planned through public organizations and recognized private bodies and, in their totality, make up the formal education system of a country.

Non-formal education, like formal education is defined in ISCED as education that is institutionalized, intentional and planned by an education provider but is considered an addition, alternative and/or a complement to formal education. It may be short in duration and/or low in intensity and it is typically provided in the form of short courses, workshops, or seminars. Informal learning is defined in ISCED as forms of learning that are intentional or deliberate, but not institutionalized. It is thus less organized and less structured than either formal or non-formal education. Informal learning may include learning activities that occur in the family, in the workplace, in the local community, and in daily life, on a self-directed, family-directed or socially directed basis. For the purposes of this indicator, persons will be considered in education if they are in formal or non-formal education, as described above, but excluding informal learning.

Employment is defined as all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the two series for a given country is the operational criteria used to define employment, with one series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.

For the purpose of this indicator, persons are considered to be in training if they are in a non-academic learning activity through which they acquire specific skills intended for vocational or technical jobs.

Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation, or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Youth are defined as persons aged 15 to 24 (inclusive) for the purpose of this indicator.

3.a. Data sources

The preferred official national data source for this indicator is a household-based labour force survey.

In the absence of a labour force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.

3.b. Data collection method

The ILO Department of Statistics processes national household survey microdata sets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets, and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

The share of youth not in employment, education or training (youth NEET rate) provides a measure of youth who are outside the educational system, not in training and not in employment, and thus serves as a broader measure of potential youth labour market entrants than youth unemployment. It includes discouraged worker youth as well as those who are outside the labour force due to disability or engagement in household chores, among other reasons. Youth NEET is also a better measure of the current universe of potential youth labour market entrants as compared with the youth inactivity rate, as the latter includes those youth who are outside the labour force and are in education, and thus are furthering their skills and qualifications.

4.b. Comment and limitations

The calculation of this indicator requires having reliable information on both the labour market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers.

In terms of the analysis of the indicator, in order to avoid misinterpreting it, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labour force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analysing the NEET rate.

4.c. Method of computation

$Y o u t h N E E T r a t e = \frac{ Y o u t h - (Y o u t h i n e m p l o y m e n t + Y o u t h n o t i n e m p l o y m e n t b u t i n e d u c a t i o n o r t r a i n i n g)}{Y o u t h} \times 100$

It is important to note here that youth simultaneously in employment and education or training should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: $Y o u t h N E E T r a t e = \frac{(U n e m p l o y e d y o u t h + Y o u t h o u t s i d e t h e l a b o u r f o r c e) - (U n e m p l o y e d y o u t h i n e d u c a t i o n o r t r a i n i n g + Y o u t h o u t s i d e t h e l a b o u r f o r c e i n e d u c a t i o n o r t r a i n i n g) }{Y o u t h} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/

At regional and global levels

Regional and global figures are aggregates of the country-level figures including the imputed values.

4.g. Regional aggregations

The NEET aggregates are derived from the ILO modelled estimates that are used to produce global and regional estimates of, amongst others, rates of youth not in employment, with employment based on the 13^th ICLS standards. These models use multivariate regression and cross-validation techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. The regional and global NEET rates are obtained by first adding up, across countries, the numerator and denominator of the formula that defines NEET rates as outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to produce the NEET rate for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

To calculate this indicator, reliable data are needed on both the labour market situation and the participation in the educational system of the youth. These data are collected at the national level mainly through labour force surveys (or other types of household surveys with an employment module). For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question.

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Data for this indicator is available for 173 countries and territories in the 13^th ICLS series and 102 countries and territories in the 19^th ICLS series.

Time series:

Country data for this indicator is available as of 2000 in the SDG Indicators Global Database, but longer time series are available in ILOSTAT. Global and regional data in this submission covers a period of 2005 to 2022.

Disaggregation:

No disaggregation is specifically required for this indicator, although having it disaggregated by sex is desirable, as is disaggregation by detailed age groups within the youth age band.

6. Comparability/deviation from international standards

A number of factors can limit the comparability of statistics on the youth NEET rate between countries or over time. When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.).

Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13th or 19th ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series.

7. References and Documentation

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm )
Decent Work Indicators Manual: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf
Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19^th ICLS in 2013: https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf
International Standard Classification of Education (ISCED) developed by UNESCO: http://uis.unesco.org/en/topic/international-standard-classification-education-isced
What does NEETs mean and why is the concept so easily misinterpreted? (ILO, W4Y, Technical brief n°1): http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/publication/wcms_343153.pdf
ILOSTAT (https://ilostat.ilo.org/).
ILO Manual – Decent Work Indicators, Concepts and Definitions – Chapter 1, Employment opportunities http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm (second version, page 38).

8.7.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.7: Take immediate and effective measures to eradicate forced labour, end modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labour, including recruitment and use of child soldiers, and by 2025 end child labour in all its forms

0.c. Indicator

Indicator 8.7.1: Proportion and number of children aged 5–17 years engaged in child labour, by sex and age

0.d. Series

Proportion of children engaged in economic activity, by sex and age (%)

Proportion of children engaged in economic activity and household chores, by sex and age (%)

0.e. Metadata update

2022-03-31

0.f. Related indicators

Many other SDG indicators have links and are relevant to child labour.

0.g. International organisations(s) responsible for global monitoring

United Nations Children's Fund (UNICEF)

International Labour Organization (ILO)

1.a. Organisation

United Nations Children's Fund (UNICEF)

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

The number of children engaged in child labour corresponds to the number of children reported to be in child labour during the reference period (usually the week prior to the survey). The proportion of children in child labour is calculated as the number of children in child labour divided by the total number of children in the population. For the purposes of this indicator, children include all persons aged 5 to 17.

Concepts:

Three principal international legal instruments – ILO Convention No. 138 (Minimum Age) (C138), United Nations Convention on the Rights of the Child (CRC), ILO Convention No. 182 (Worst Forms) (C182) together set the legal boundaries for child labour, and provide the legal basis for national and international actions against it. In accordance with these instruments, child labour is work that children should not be doing because (a) they are too young or (b) is likely to harm their health, safety or morals, due to its nature or the conditions in which it is carried out.

The resolutions adopted by the International Conference of Labour Statisticians (ICLS), the world’s acknowledged standard-setting body in the area of labour statistics, provide the basis for translating the legal standards governing the concept of child labour into statistical terms for the purpose of child labour measurement.

In accordance with the ICLS resolutions^[1], child labour can be measured on the basis of the production boundary set by the United Nations System of National Accounts (UN SNA) or on the basis of the general production boundary. The former limits the frame of reference to economic activity, while the latter extends it to include both economic activity and unpaid household services, that is, the production of domestic and personal services by a household member for consumption within their own household, commonly called “household chores”.

Following from this, two indicators are used for measuring child labour for the purpose of SDG reporting, the first based on the production boundary set by the UN SNA and the second based on the general production boundary.

Indicator 1: Proportion and number of children aged 5-17 years engaged in economic activities at or above age-specific hourly thresholds (SNA production boundary basis)

Child labour for the 5 to 11 age range: children working for 1 hour or more per week in economic activity;

Child labour for the 12 to 14 age range: children working for 14 hours or more per week in economic activity;

Child labour for the 15 to 17 age range: children working for 43 hours or more per week in economic activity.

Indicator 2: Proportion and number of children aged 5-17 years engaged in economic activities and household chores at or above age-specific hourly thresholds (general production boundary basis):

Child labour for the 5 to 11 age range: children working for 1 hour or more per week in economic activity and/or involved in unpaid household services for 21 hours or more per week;

Child labour for the 12 to 14 age range: children working for 14 hours or more per week in economic activity and/or involved in unpaid household services for 21 hours or more per week;

Child labour for the 15 to 17 age range: children working for 43 hours or more per week in economic activity.^[2]

The concept of child labour also includes the worst forms of child labour other than hazardous (18^th ICLS paragraphs 33 to 34) as well as hazardous work (18^th ICLS paragraphs 21 to 32). The worst forms of child labour include all forms of slavery or similar practices such as trafficking and the recruitment and use of child soldiers, the use or procurement of children for prostitution or other illicit activities, and other work that is likely to harm children’s health, safety or well-being.

20th International Conference of Labour Statisticians. Resolution to amend the 18th ICLS Resolution concerning statistics of child labour. ILO. Geneva, October 2019. ↑

No hourly threshold is set for household chores for ages 15-17. ↑

2.b. Unit of measure

Percent (%)

2.c. Classifications

The definition of child labour is in line with the standard set by the latest 20^th International Conference of Labour Statisticians. Resolution to amend the 18th ICLS Resolution concerning statistics of child labour. ILO. Geneva, October 2019

3.a. Data sources

Household surveys such as National Labour Force Surveys, National Multipurpose Household Surveys, UNICEF-supported Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS), ILO-supported Statistical Information and Monitoring Programme on Child Labour (SIMPOC), and World Bank Living Standard Measurement surveys (LSMS) are among the most important instruments for generating information on child labour in developing countries. Estimates of child labour generated by these survey instruments are increasingly relied on by countries to monitor progress towards national and global child labour elimination targets. Many countries also produce national labour estimates and reports that often include data on child labour and/or employment among children.

3.b. Data collection method

UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).

As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators it is custodian or co-custodian to, to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicited feedback directly from National Statistical Offices (NSOs), as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed and discussed with ILO. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

3.c. Data collection calendar

3.d. Data release calendar

Updated data on 8.7.1 will be available in the SDG reporting period every February/March.

3.e. Data providers

National Statistical Offices (for the most part) and line ministries/other government agencies and International agencies that have conducted labour force surveys or other household surveys through which data on child labour were collected.

3.f. Data compilers

United Nations Children's Fund (UNICEF) and International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets, and provides technical assistance and training to ILO member States to support their efforts to produce high quality labour market data, including child labour data.

UNICEF provides technical and financial assistance to Member States to support their efforts to collect high quality data on child labour, including through UNICEF-supported MICS household survey programme. UNICEF also compiles child labour statistics with the goal of making internationally comparable datasets publicly available, and it analyzes child labour statistics, which are included in relevant data-driven publications, including in its flagship publication, The State of the World’s Children.

4.a. Rationale

Far too many children in the world remain trapped in child labour, compromising their individual future and our collective futures. According to the latest ILO global estimates, about 152 million children worldwide – 64 million girls and 88 million boys - are child labourers, accounting for almost 10 percent of the child population. These stark figures underscore the need for accelerated progress against child labour in the lead up to the 2025 target date for ending child labour in all its forms, and the accompanying need for child labour statistics to monitor and guide efforts in this regard. Reliable, comprehensive and timely data on the nature and extent of child labour provide a basis for determining priorities for national global action against child labour. Statistical information on child labour, and more broadly on all working children, also provide a basis for increasing public awareness of the situation of working children and for the development of appropriate regulatory frameworks and policies.

4.b. Comment and limitations

While the concept of child labour includes working in activities that are hazardous in nature, to ensure comparability of estimates over time and to minimize data quality issues, work beyond age-specific hourly thresholds are used as a proxy for hazardous work for the purpose of reporting on SDG indicator 8.7.1. Further methodological work is needed to validate questions specifically aimed at identifying children in hazardous working conditions.

Similarly, while the worst forms of child labour other than hazardous also form part of the concept of child labour more broadly, data on the worst forms of child labour are not currently captured in regular household surveys given difficulties with accurately and reliably measuring it. Therefore, this element of child labour is not captured by the indicators used for reporting on SDG 8.7.1.

In addition, ‘own use production of goods’, including activities such as fetching water and collecting firewood, falls within the production boundary set by the United Nations System of National Accounts (SNA). However, for the purpose of SDG reporting of indicator 8.7.1, and with the goal of facilitating international comparability, fetching water and collecting firewood have been classified as unpaid household services (i.e., household chores), a form of production that lies outside the SNA production boundary.

More broadly, child labour estimates based on the statistical standards set out in the ICLS resolution represent useful benchmarks for international comparative purposes but are not necessarily consistent with estimates based on national child labour legislation. ILO Convention No. 138 contains a number of flexibility clauses left to the discretion of the competent national authority in consultation (where relevant) with workers’ and employers’ organizations (e.g., minimum ages, scope of application).^[3] This means that there is no single legal definition of child labour across countries, and thus, no single statistical measure of child labour consistent with national legislation across countries.

Principal areas of flexibility in the Convention include: (a)minimum ages: Members whose economy and educational facilities are insufficiently developed may specify a lower general minimum age of 14 years (Art. 2.4) and a lower age range for light work of 12 to 14 years (Art 7.4); and (b) scope of application: Members may exclude from the application of the Convention limited (non-hazardous) categories of employment or work in respect of which special and substantial problems of application arise (Art. 4.1). Members whose economy and administrative facilities are insufficiently developed may also initially limit the scope of application of the Convention (Art. 5.1) beyond a core group of economic activities or undertakings (Art. 5.3). ↑

4.c. Method of computation

Children aged 5-17: Number of children aged 5-17 reported in child labour during the week prior to the survey divided by the total number of children aged 5-17 in the population, multiplied by 100.

Children aged 5-14: Number of children aged 5-14 reported in child labour during the week prior to the survey divided by the total number of children aged 5-14 in the population, multiplied by 100.

Children aged 15-17: Number of children aged 15-17 reported child labour during the week prior to the survey divided by the total number of children aged 15-17 in the population, multiplied by 100.

4.d. Validation

A wide consultative process is undertaken to compile, assess and validate data from national sources.

The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed and discussed between the co-custodian agencies, UNICEF and ILO. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why.

4.e. Adjustments

While the concept of child labour includes working in activities that are hazardous in nature, to ensure

comparability of estimates over time and to minimize data quality issues, work beyond age-specific hourly thresholds are used as a proxy for hazardous work for the purpose of reporting on SDG indicator 8.7.1. Similarly, while the worst forms of child labour other than hazardous also form part of the concept of child labour more broadly, data on the worst forms of child labour are not currently captured in regular household surveys given difficulties with accurately and reliably measuring it. Therefore, this element of child labour is not captured by the indicators used for reporting on SDG 8.7.1. In addition, ‘own use production of goods’, including activities such as fetching water and collecting firewood,

falls within the production boundary set by the United Nations System of National Accounts (SNA). However, for the purpose of SDG reporting of indicator 8.7.1, and with the goal of facilitating international comparability, fetching water and collecting firewood have been classified as unpaid household services (i.e., household chores), a form of production that lies outside the SNA production boundary.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Country data are not published when data for a country are entirely missing.

At regional and global levels

For details on the methodology for treatment of missing data in the calculation of regional and global aggregates see, Child Labour: Global estimates 2020, trends and the road forward

4.g. Regional aggregations

For details on the methodology for calculation of regional aggregates, see Child Labour: Global estimates 2020, trends and the road forward

4.h. Methods and guidance available to countries for the compilation of the data at the national level

See Section 3.a.

4.i. Quality management

The process behind the production of reliable statistics on child labour is well established within UNICEF and the ILO. The quality and process leading to the production of the SDG indicator 8.7.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.

4.j. Quality assurance

UNICEF and ILO maintain the global database on child labour that is used for official SDG reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF and ILO to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible, based on trends and consistency with previously published/reported estimates for the indicator.

4.k. Quality assessment

Data consistency and quality checks are regularly conducted for validation of the data before dissemination.

5. Data availability and disaggregation

Data availability:

Nationally representative and comparable data are currently available for around 100 low-and middle-income countries.

Time series:

Not available.

Disaggregation:

Sex.

6. Comparability/deviation from international standards

Sources of discrepancies:

The country estimates compiled and presented in the global SDG database have been re-analyzed by UNICEF and ILO in accordance with the definitions and criteria detailed above (see ‘Concepts’). This means that the country data values included in the global SDG database will differ from those published and presented in national survey reports.

7. References and Documentation

UNICEF statistics on child labour: https://data.unicef.org/topic/child-protection/child-labour/

ILO statistics on child labour: http://www.ilo.org/ipec/ChildlabourstatisticsSIMPOC/Questionnairessurveysandreports/lang--en/index.htm

Child Labour: Global estimates 2020, trends and the road forward: https://data.unicef.org/resources/child-labour-2020-global-estimates-trends-and-the-road-forward/

8.8.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment

0.c. Indicator

Indicator 8.8.1: Fatal and non-fatal occupational injuries per 100,000 workers, by sex and migrant status

0.d. Series

Non-fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)

Fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)

0.e. Metadata update

2023-03-31

0.f. Related indicators

1.3.1, 8.8.2

0.g. International organisations(s) responsible for global monitoring

International Labour Organisation (ILO)

1.a. Organisation

International Labour Organisation (ILO)

2.a. Definition and concepts

Definition:

This indicator provides information on the number of fatal and non-fatal occupational injuries per 100,000 workers in the reference group during the reference period. It is a measure of the personal likelihood or risk of having a fatal or a non-fatal occupational injury for each worker in the reference group.

The number of occupational injuries expressed per a given number of workers in the reference group is also known as the incidence rate of occupational injuries.

Concepts:

Definitions of the main concepts presented below are derived from the Resolution concerning statistics of occupational injuries (resulting from occupational accidents), adopted by the 16th International Conference of Labour Statisticians (ICLS) in 1998

(https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm).

Occupational accident: an unexpected and unplanned occurrence, including acts of violence, arising out of or in connection with work which results in one or more workers incurring a personal injury, disease or death. Occupational accidents are to be considered travel, transport or road traffic accidents in which workers are injured and which arise out of or in the course of work; that is, while engaged in an economic activity, or at work, or carrying out the business of the employer.

Occupational injury: any personal injury, disease or death resulting from an occupational accident. An occupational injury is different from an occupational disease, which comes as a result of an exposure over a period of time to risk factors linked to the work activity. Diseases are included only in cases where the disease arose as a direct result of an accident. An occupational injury can be fatal or non-fatal (and non-fatal injuries could entail the loss of workdays).

Fatal occupational injury: an occupational injury leading to death within one year of the day of the occupational accident.

Case of occupational injury: the case of one worker incurring one or more occupational injuries as a result of one occupational accident.

Workers in the reference group: workers in the reference group refer to the average number of workers in the particular group under consideration and who are covered by the source of the statistics on occupational injuries (for example, those of a specific sex or in a specific economic activity, occupation, region, age group, or any combination of these, or those covered by a particular insurance scheme, accident notification systems, or household or establishment survey).

2.b. Unit of measure

Ratio of cases per 100,000 workers

2.c. Classifications

Migrant status is determined according to country of birth (native-born or foreign-born) or country of citizenship (citizen or non-citizen).

3.a. Data sources

The recommended data sources are different types of administrative records, such as records of national systems for the notification of occupational injuries (labour inspection records and annual reports; insurance and compensation records, death registers), supplemented by household surveys (especially in order to cover informal sector enterprises and the self-employed) and/or establishment surveys.

The metadata should clearly specify (i) whether the statistics relate to cases of occupational injury which have been reported (to an accident notification system or to an accident compensation scheme), compensated (by an accident insurance scheme) or identified in some other way (for example through a survey of households or establishments) and (ii) whether cases of occupational disease and cases of injury due to commuting accidents are excluded from the statistics, as recommended.

3.b. Data collection method

The ILO Department of Statistics processes national household survey microdata sets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

Labour ministries, labour inspection, national insurance, and/or national statistical offices

3.f. Data compilers

International Labour Organisation (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

This indicator provides valuable information that could be used to formulate policies and programmes for the prevention of occupational injuries, diseases and deaths. It could also be used to monitor the implementation of these programmes and to signal particular areas of increasing risk such as a particular occupation, industry or location. Although the principal objective of this indicator is to provide information for prevention purposes, it may be used for a number of other purposes, such as to identify the occupations and economic activities with the highest risk of occupational injuries; to detect changes in the pattern and occurrence of occupational injuries, so as to monitor improvements in safety and reveal any new areas of risk; to inform employers, employers’ organizations, workers and workers’ organizations of the risks associated with their work and workplaces, so that they can take an active part in their own safety; to evaluate the effectiveness of preventive measures; to estimate the consequences of occupational injuries, particularly in terms of days lost or costs; and to provide a basis for policymaking aimed at encouraging employers, employers’ organizations, workers and workers’ organizations to introduce accident prevention measures.

4.b. Comment and limitations

There may be problems of underreporting of occupational injuries, and proper systems should be put in place to ensure the best reporting and data quality. Underreporting is thought to be present in countries at all levels of development but may be particularly problematic in some developing countries. Data users should be aware of this issue when analysing the data. Double counting of cases of occupational injury may also happen in cases where data from several registries (records kept by different agencies, for example) are consolidated to have more comprehensive statistics.

Because data quality issues may be present, it may be more relevant to analyse indicator trends rather than levels. When measured over a period of time, the data can reveal progress or deterioration in occupational safety and health, and thus point to the effectiveness of prevention measures. This indicator is volatile and strong annual fluctuations may occur due to unexpected but significant accidents or national calamities. The underlying trend should therefore be analysed.

4.c. Method of computation

The incidence rates of fatal and non-fatal occupational injuries will be calculated separately, since statistics on fatal injuries tend to come from a different source than those on non-fatal injuries, which would make their sum into total occupational accidents inaccurate.

The fatal occupational injury incidence rate is expressed per 100,000 workers in the reference group, and thus, is calculated as follows:

$F a t a l o c c u p a t i o n a l i n j u r y i n c i d e n c e r a t e = \frac{N e w c a s e s o f f a t a l i n j u r y d u r i n g t h e r e f e r e n c e y e a r}{W o r k e r s i n t h e r e f e r e n c e g r o u p d u r i n g t h e r e f e r e n c e y e a r} \times 100, 000$

Similarly, the non-fatal occupational injury incidence rate is calculated as follows:

$N o n f a t a l o c c u p a t i o n a l i n j u r y i n c i d e n c e r a t e = \frac{N e w c a s e s o f n o n f a t a l i n j u r y d u r i n g t h e r e f e r e n c e y e a r}{W o r k e r s i n t h e r e f e r e n c e g r o u p d u r i n g t h e r e f e r e n c e y e a r} \times 100, 000$

In calculating the average number of workers, the number of part-time workers should be converted to full-time equivalents. For the calculation of rates, the numerator and the denominator should have the same coverage. For example, if self-employed persons are not covered by the source of statistics on fatal occupational injuries, they should also be taken out of the denominator.

4.d. Validation

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

Not applicable

4.g. Regional aggregations

Not applicable

4.h. Methods and guidance available to countries for the compilation of the data at the national level

This indicator could come from a variety of sources at the national level, including various kinds of administrative records (insurance records, labour inspection records, etc.), household surveys and establishment surveys.

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm)
ILO Manual – Decent Work Indicators, Concepts and Definitions – Chapter 8, Safe work environment https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm (second version, page 156)
Resolution concerning statistics of occupational injuries (resulting from occupational accidents) https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm
Global database on occupational safety and health legislation – LEGOSH http://www.ilo.org/safework/info/publications/WCMS_217849/lang--en/index.htm
Occupational injuries statistics from household surveys and establishment surveys https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_173153.pdf
ILOSTAT (https://ilostat.ilo.org)
ILOSTAT Metadata – Indicator descriptions (https://ilostat.ilo.org/resources/concepts-and-definitions/description-occupational-injuries/)

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

5. Data availability and disaggregation

Data availability:

Data on fatal injuries per 100,000 workers is available for 97 countries and territories. Data on non-fatal injuries per 100,000 workers is available for 95 countries and territories.

Time series:

The submission covers data from 2000 to 2021.

Disaggregation:

This indicator should be disaggregated by both sex and migrant status.

Wherever possible, it would also be useful to have information disaggregated by economic activity and occupation.

6. Comparability/deviation from international standards

Sources of discrepancies:

The variety of possible sources of data on occupational injuries hinders the comparability of data across countries since each type of source provides information on different specific concepts. Data derived from administrative records are not strictly comparable since they include numerous types of records that follow different rules and are maintained by different agencies. Two main sources of data are records of notifications by employers to the competent authority and insurance records of the authority compensating the victims. These two would clearly yield different results, since it is possible that not all injuries that were compensated to workers were reported by the employer and vice versa. It is also possible that these records have a different geographical coverage or that they cover different economic activities.

When statistics come from an establishment survey, the results would be closer to those from records of notifications made by employers since it is also the employer who provides the establishment survey information. However, establishment surveys tend not to cover the informal sector, establishments of a very small size and sometimes the agricultural sector.

When statistics come from a household survey, their reliability depends heavily on the accuracy of the respondents, who may be subjective in the information given.

7. References and Documentation

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm)
ILOSTAT database: https://ilostat.ilo.org
ILOSTAT Metadata – Indicator Descriptions (https://ilostat.ilo.org/resources/concepts-and-definitions/description-occupational-injuries/)
Decent Work Indicators Manual: https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm
Resolution concerning statistics of occupational injuries (resulting from occupational accidents) adopted by the 16th ICLS in 1998: https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm

8.8.2

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment

0.c. Indicator

Indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant status

0.d. Series

Not applicable

0.e. Metadata update

2023-07-10

0.f. Related indicators

This indicator links with 8.8.1 & 8.b.1; 16.2.2; 16.10.1; 16.a.1; 16.b.1

0.g. International organisations(s) responsible for global monitoring

International Labour Organization (ILO)

1.a. Organisation

International Labour Organization (ILO)

2.a. Definition and concepts

Definition:

The indicator measures the level of national compliance with fundamental rights at work (freedom of association and collective bargaining, FACB) for all ILO member states based on six international ILO supervisory body textual sources and also on national legislation. It is based on the coding of textual sources against a list of evaluation criteria and then converting the coding into indicators.

Concepts:

Freedom of association and collective bargaining rights and their supervision

The principles of freedom of association and collective bargaining (FACB) are and have long been at the core of the ILO’s normative foundations. These foundations have been established in the ILO’s Constitution (1919), the ILO Declaration of Philadelphia (1944), in two key ILO Conventions (namely the Freedom of Association and Protection of the Right to Organise Convention, 1948 (No. 87) and the Right to Organise and Collective Bargaining Convention, 1949 (No. 98)) and the ILO Declaration on Fundamental Principles and Rights at Work (1998). They are also rights proclaimed in the Universal Declaration of Human Rights (1948) and other international and regional human rights instruments. With the adoption of the 1998 ILO Declaration, the promotion and realization of these fundamental principles and rights also became a constitutional obligation of all ILO member States.

FACB rights are considered as ‘enabling rights’, the realisation of which is necessary to promote and realise other rights at work. They provide an essential foundation for social dialogue, effective labour market governance and realization of decent work. They are vital in enabling employers and workers to associate and efficiently negotiate work relations, to ensure that both employers and workers have an equal voice in negotiations, and that the outcome is fair and equitable. As such they play a crucial role in the elaboration of economic and social policies that take on board the interests and needs of all actors in the economy. FACB rights are also salient because they are indispensable pillars of democracy as well as the process of democratization.

FACB rights, together with other international labour standards, are backed by the ILO’s unique supervisory system. The ILO regularly examines the application of standards in member States and highlights areas where those standards are violated and where they could be better applied. The ILO’s supervisory system includes two kinds of supervisory mechanisms: the regular system of supervision and the special procedures. The prior entails the examination of periodic reports submitted by member States on the measures taken to implement the provisions of ILO Conventions ratified by them. The special procedures, that is, representations, complaints and the special procedure for complaints regarding freedom of association through the Freedom of Association Committee, allow for the examination of violations on the basis of a submission of a representation or a complaint.

2.b. Unit of measure

The unit of measurement is the number of coded evaluation criteria (see Tables 1-2 of https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf).

2.c. Classifications

Not applicable

3.a. Data sources

The method makes use of six ILO textual sources:

Reports of the Committee of Experts on the Application of Conventions and Recommendations;
Reports of the Conference Committee on the Application of Standards;
Country Baselines Under the ILO Declaration Annual Review;
Representations under Article 24 of the ILO Constitution;
Complaints under Article 26 of the ILO Constitution; and
Report on the Committee on Freedom of Association.

For non-ratifying countries, the method also codes relevant national legislation with the goal to offset information asymmetries between ratifying and non-ratifying countries as regards FACB rights in law. Ratifying countries are defined as those that have ratified both Conventions 87 and 98, in which case its national legislation is not coded. Non-ratifying countries, on the other hand, fall into two categories, those that have ratified neither 87 nor 98 and those that have ratified only one of these Conventions. If a country has ratified only 87, its national legislation is coded for violations pertaining to 98, as violations under 87 fall under the remit of the ILO’s Committee of Experts as well as Committee on the Application of Standards. Similarly, if a country has ratified only 98, its national legislation is coded for violations pertaining to 87. Note that for federal states, only federal-level legislation is coded.

The coding of national legislation is carried out in close collaboration with the International Labour Office to ensure that it is done in a manner consistent with the ILO’s supervisory system. In addition, countries may also make available information on national legislation when reporting on this indicator through Voluntary National Reports or national reporting platforms or any other national reports.

3.b. Data collection method

Given that the statistical foundation of the indicator are the ILO textual sources (see above) and that those sources are themselves based on information provided by the Governments, workers’ and employers’ organizations, the data collection is carried out by the ILO.

The data collection is based on the coding of the relevant textual sources (see above) against a list of evaluation criteria and then converting the coding into indicators.

3.c. Data collection calendar

Not applicable

3.d. Data release calendar

Data are released in February of each year.

3.e. Data providers

Given that the statistical foundation of the indicator are the ILO textual sources (see below) and that those sources are themselves based on information provided by the Governments, workers’ and employers’ organizations, the data is provided by the ILO.

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

In 2018, the 20th International Conference of Labour Statisticians (ICLS) adopted a ‘Resolution concerning the methodology of the SDG indicator 8.8.2 on labour rights’. Point (b) of the resolution recommends that the International Labour Office communicate on behalf of the ICLS, the confirmation that the ILO should be the custodian agency for indicator 8.8.2, given that ILO textual sources are its statistical foundation.^[1]

See at: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf ↑

4.a. Rationale

The indicator measures the level of national compliance with fundamental rights at work (freedom of association and collective bargaining, FACB) for all ILO member states based on the coding of six ILO supervisory body textual sources and also on national legislation against a list of evaluation criteria and then converting the coding into indicators.

4.b. Comment and limitations

Based on the consultation with the ILO’s tripartite constituents (i.e., representatives of government, employers’, and workers’ organizations), it was decided to prominently present the following chapeau text in the reporting of SDG indicator 8.8.2:

“SDG indicator 8.8.2 seeks to measure the level of national compliance with fundamental labour rights (freedom of association and collective bargaining). It is based on six International Labour Organization (ILO) supervisory body textual sources and also on national legislation. National law is not enacted for the purpose of generating a statistical indicator of compliance with fundamental rights, nor were any of the ILO textual sources created for this purpose. Indicator 8.8.2 is compiled from these sources and its use does not constitute a waiver of the respective ILO Constituents’ divergent points of view on the sources’ conclusions.”^[2]

To highlight the difference between ratifying and non-ratifying countries, the following additional clarification is provided:

“SDG indicator 8.8.2 is not intended as a tool to compare compliance among ILO member States. It should specifically be noted that reporting obligations of an ILO member State to the ILO’s supervisory system and thus ILO textual sources are different for ratifying and non‐ratifying ILO member States.”^[3]

Based on the decisions adopted by the tripartite technical committee set up to further address refinements to the methodology^[4], for countries where the score should be treated with care due to the possibility of insufficient information in the textual sources, the following note will be added:

“The score should be treated with care due to the possibility of insufficient information in the textual sources, based on comparison with an externally produced indicator (see Metadata, point 4.f.).”^[5]

Idem. P. 17 ↑

Idem. P. 18 ↑

⁴

Idem. PP. 1-2 of “Amendment: Refinements to the methodology for SDG indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on ILO textual sources and national legislation, by sex and migrant status” ↑

⁵

Idem. P. 1 of “Amendment: Refinements to the methodology for SDG indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on ILO textual sources and national legislation, by sex and migrant status” ↑

4.c. Method of computation

The method is based on the coding of textual sources (see above) against a list of evaluation criteria and then converting the coding into indicators. For the list of evaluation criteria, see Table 1 and 2 (pp. 6-12.) at: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf

The indicator has a range from 0 to 10, with 0 being the best possible score (indicating higher levels of compliance with FACB rights) and 10 the worst (indicating lower levels of compliance with FACB rights). For the purpose of computation, in the first step, the coding of textual sources is transformed into a binary coding, with 1 assigned to observed non-compliance and 0 to no observed non-compliance (unweighted raw scores). The binary coding is then multiplied by the weights as derived from the Delphi method (weighted raw scores). The final scores are the weighted raw scores normalized in a range from 0 to 10.

Using the Delphi Method to Construct Evaluation Criteria Weights

The weights were constructed with the use of the Delphi method. The application of the Delphi method involved two rounds of surveys conducted via email of internationally-recognized experts in labour law having knowledge of the ILO’s supervisory system and particular knowledge of FACB rights as defined by the ILO. Regional representation was another consideration. Experts remained anonymous with respect to each other throughout the process.

Applying the weights, normalization and default scores

The raw coding uses the letters “a” through “g” (with each letter corresponding to one of the seven textual sources) to represent coded violations of FACB rights for each evaluation criteria, yielding a column of 180 cells for any given country and year. In order to apply the weights, any cell containing one or more letters is assigned a value of 1 and any blank cell for which there are no coded violations is assigned a value of 0, creating a binary coding column. The number of letters in a cell does not affect the construction of the binary coding column, in order to avoid double-counting given that the textual sources commonly reference each other. The cells of the column of weights are then multiplied by corresponding cells of the binary coding column and summing across the cells of the resultant column yields a weighted non-normalized score for any given country and year.

To normalize the indicators over time, 95 is assigned as the maximum weighted non-normalized score for the indicator. This roughly equals to the maximum weighted non-normalized score of one-half of the countries having the most coded violations of FACB rights of workers and their organizations for the years 2000, 2005, 2009 and 2012. The highest weighted non-normalized score for several countries hovered around 80. On this basis, the non-normalized score for any given country and year is normalized to range in value from 0 to 10, the best and worst possible scores respectively. In the future, if any country should receive a non-normalized score of greater than 95, this will be capped at 95, yielding a normalized score of 10.^{^[6]}

In addition, the method applies the notion that general prohibitions in law imply general prohibitions in practice (though not vice versa). In terms of coding, this means that – both for workers and employers -the direct coding of “General prohibition of the right to establish and join organizations” in law automatically triggers the coding of “General prohibition of the development of independent organizations” in practice; the direct coding of “General prohibition of the right to collective bargaining” in law automatically triggers the coding of the “General prohibition of collective bargaining” in practice ; and, finally, for workers, the direct coding of “General prohibition of the right to strike” in law automatically triggers the coding of the “General prohibition of strikes” in practice .

Based on the decisions adopted by the tripartite technical committee set up to further address refinements to the methodology, in addition to the above normalization rules, a “load” of 3.5 will be added to the normalized score of the country in cases of all-encompassing violations of FACB rights, that is, for “General prohibition of the right to establish and join organizations” in law, “General prohibition of the development of independent organizations” in practice, “General prohibition of the right to collective bargaining” in law, and “General prohibition of collective bargaining” in practice.

Table 1. Hypothetical Example of Coding and Indicator Construction (for a Single Country and Year)

Evaluation Criteria		Textual coding	Binary coding	Weights	Binary coding x Weights
	Ia. Fundamental civil liberties in law
2	Infringements of trade unionists' basic freedoms	a	1	1,93	1,93
	Ib. Fundamental civil liberties in practice
6	Killing or disappearance of trade unionists in relation to their trade union activities	af	1	2,00	2,00
9	Other violent actions against trade unionists in relation to their trade union activities	af	1	1,82	1,82
12	Arrest, detention, imprisonment, charging and fining of trade unionists in relation to their trade union activities	af	1	1,95	1,95
	IIa. Right of workers to establish and join organizations in law
24	Exclusion of workers from the right to establish and join organizations	a	1	1,86	1,86
30	Lack of adequate legal guarantees against anti-union discriminatory measures	a	1	1,75	1,75
33	Infringements of the right to establish and join federations/confederations/international organizations	abf	1	1,73	1,73
	IIb. Right of workers to establish and join organizations in practice
37	Previous authorization requirements	af	1	1,70	1,70
42	Committed against trade union officials re violation no. 41	f	1	1,89	1,89
43	Lack of guarantee of due process and/or justice re violation no. 41	f	1	1,80	1,80
	IIIa. Other union activities in law
49	Infringements of the right to freely elect representatives	a	1	1,80	1,80
50	Infringements of the right to freely organize and control financial administration	ab	1	1,59	1,59
52	Prohibition of all political activities	ab	1	1,73	1,73
	IVa. Right to collective bargaining in law
66	Acts of interference in collective bargaining	a	1	1,66	1,66
	IVb. Right to collective bargaining in practice
72	Exclusion of workers from the right to collective bargaining	a	1	1,84	1,84

	Sum (non-normalized score)		15		27,05
	Normalized score (0 = best, 10 = worst)¹				2,85

¹ The formula used is: (x*10/95), where x = the weighted non-normalized score for a given country and year and is capped at 95.

⁶

The formula is thus: (x*10/95), where x = the weighted non-normalized score for a given country and year and is capped at 95. ↑

4.d. Validation

The indicator is based on three key premises: (i) definitional validity – the extent to which the evaluation criteria and their corresponding definitions accurately reflect the phenomena they are meant to measure; (ii) transparency – how readily a coded violation can be traced back to any given textual source; and (iii) inter-coder reliability – the extent to which different evaluators working independently are able to consistently arrive at the same results.

Definitional validity. As these are meant to be indicators of international FACB rights, the evaluation criteria and their corresponding definitions are directly based on the ILO Constitution, ILO Conventions No. 87 and 98 and the related body of comments of the ILO supervisory bodies.^[7] Given that the ILO supervisory system is also guided by these definitions, this facilitates the coding itself given the heavy reliance on ILO textual sources produced by the supervisory system.

Transparency. A key rationale for the large number of evaluation criteria is to eliminate catchall evaluation criteria for violations of FACB rights not elsewhere coded, that is, violations for which there are no explicit evaluation criteria. This level of detail also facilitates the transparency of the method, in that very specific violations can be readily traced back to individual textual sources. This is made possible by the coding itself, in which violations are coded with the letters “a” through “g,” with each letter standing for one of the seven textual sources coded (see Table 1.).

Inter-coder reliability. The method is based on clear and comprehensive coding rules as well as definitions for each of the evaluation criteria with the aim of making the indicators reproducible. Inter-coder reliability was assessed in the process of training teams of lawyers (sequentially and independently of each other) to do the coding and in double-checking their coding, which resulted in a number of clarifications and refinements to the coding rules and definitions. This process led to the conclusion that the inter-coder reliability of the method depends first and foremost on the coders being sufficiently well-trained and in particular being sufficiently well-versed in the coding rules and definitions to be able to apply them consistently.

⁷

The related body of comments of the ILO supervisory bodies are: Digest of Decisions and Principles of the Freedom of Association Committee of the Governing Body of the ILO (ILO, 2006); Freedom of Association and Collective Bargaining: General Survey of the Reports on the Freedom of Association and the Right to Organise Convention (No. 87), 1948, and the Right to Organise and Collective Bargaining Convention (No. 98) (ILO, 1994); General Survey on the Fundamental Conventions Concerning Rights at Work in Light of the ILO Declaration on Social Justice for a Fair Globalization, 2008 (ILO, 2012). ↑

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

There is no treatment of missing values at country level. The indicator will be reported for countries where, based on comparison with an externally produced indicator, the score should be treated with care due to the possibility of insufficient information in the textual sources. For these countries, based on the decisions adopted by the tripartite technical committee set up to further address refinements to the methodology, the indicator will be reported with the following note: “The score should be treated with care due to the possibility of insufficient information in the textual sources, based on comparison with an externally produced indicator (see Metadata, point 4.f.).”

At regional and global levels

For the computation of the regional aggregates, treatment of missing values (i.e. scores that are recommended to be dropped) is based on the following rules: (1). If scores are missing for all years, the country is dropped from the sample; (2). If scores are available for a single year, the available score is used for all other years; (3). If scores are available for multiple but not all years, the missing value is computed as the average of available scores.

4.g. Regional aggregations

The regional and global aggregates are weighted averages (with weights derived from ILO labour force estimates).

A country’s weight is the share of its labour force in the global labour force for a given time period, where the labour force is derived from the latest edition of the ILO modelled estimates (for further information on the estimates, please refer to the ILO modelled estimates methodological description, available at https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Not applicable

4.i. Quality management

4.j. Quality assurance

Not applicable

4.k. Quality assessment

Not applicable

5. Data availability and disaggregation

Data availability:

The data is available for all ILO member states. This submission covers country, regional and global data from 2015 to 2021.

Disaggregation:

The disaggregation by sex and migrant status is not currently available.

6. Comparability/deviation from international standards

Not applicable

7. References and Documentation

International Conference of Labour Statisticians (2018) 20^th Session, www.ilo.org/20thicls

8.9.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products

0.c. Indicator

Indicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rate

0.d. Series

Not applicable

0.e. Metadata update

2023-03-31

0.f. Related indicators

Target 14.7: By 2030, increase the economic benefits to small island developing States and least developed countries from the sustainable use of marine resources, including through sustainable management of fisheries, aquaculture and tourism.

Target 12.b: Develop and implement tools to monitor sustainable development impacts for sustainable tourism that creates jobs and promotes local culture and products

Indicator 12.b.1 Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability

0.g. International organisations(s) responsible for global monitoring

World Tourism Organization (UNWTO)

1.a. Organisation

World Tourism Organization (UNWTO)

2.a. Definition and concepts

Definition:

Tourism Direct GDP (TDGDP) is defined as the sum of the part of gross value added (at basic prices) generated by all industries in response to internal tourism consumption plus the amount of net taxes on products and imports included within the value of this expenditure at purchasers’ prices. The indicator relies on the Tourism Satellite Account: Recommended Methodological Framework 2008, an international standard adopted by the UN Statistical Commission and led by UNWTO, Organisation for Economic Co-operation and Development (OECD) and EUROSTAT.

Concepts:

Tourism direct gross value added (TDGVA) is the part of gross value added generated by tourism industries and other industries of the economy that directly serve visitors in response to internal tourism consumption.

Gross Domestic Product (GDP): It is the main measure of national output, representing the total value of all final goods and services within the System of National Accounts (SNA) production boundary produced in a particular economy (that is, the dollar value of all goods and services within the SNA production boundary produced within a country’s borders in a given year). According to the SNA, “GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output. GDP is also equal to the sum of the final uses of goods and services (all uses except intermediate consumption) measured at purchasers’ prices, less the value of imports of goods and services. GDP is also equal to the sum of primary incomes distributed by resident producer units.”

2.b. Unit of measure

Percent (%)

2.c. Classifications

The methodology for the calculation of Tourism Direct GDP is in line with the Tourism Satellite Account: Recommended Methodological Framework (TSA:RMF 2008) and the International Recommendations for Tourism Statistics 2008 (IRTS 2008) which defines the tourism characteristic industries (i.e. tourism industries) and provides a list of tourism industries for international comparability purposes based on the International Standard Industrial Classification of All Economic Activities (ISIC Rev. 4)

3.a. Data sources

The indicator is sourced from countries’ Tourism Satellite Account (TSA), which is a satellite account to the National Accounts.

3.b. Data collection method

UNWTO sends a pre-filled excel questionnaire (including data from official publications and official websites) to countries to collect the latest data on TDGDP. To lighten the reporting burden on countries, UNWTO cooperates with the Organisation for Economic Co-operation and Development (OECD) which provides to UNWTO the data collected from its member and partner countries^[1]. UNWTO then integrates the data received from OECD with the data it collects directly from non-OECD countries. This exercise is being carried out on a yearly basis since 2019.

OECD list of member countries is available at: https://www.oecd.org/about/members-and-partners/ ↑

3.c. Data collection calendar

The questionnaire is sent out to countries in September and data collection is closed in February of the following year.

3.d. Data release calendar

The data is released twice a year in the UNWTO’s Tourism Statistics Database, the first update is done in November and the second in January

3.e. Data providers

Only official country entities, usually National Statistics Offices and/or National Tourism Administrations.

3.f. Data compilers

World Tourism Organization (UNWTO)

3.g. Institutional mandate

As per the article 13 of the agreement between the United Nations and the World Tourism Organization: “the United Nations recognizes the World Tourism Organization as the appropriate organization to collect, to analyse, to publish, to standardize and to improve the statistics of tourism, and to promote the integration of these statistics within the sphere of the United Nations system.” The World Tourism Organization is the custodian agency for SDG indicator 8.9.1.

4.a. Rationale

Target 8.9 has several dimensions and indicator 8.9.1 caters to the core intention of the target which calls to “promote sustainable tourism”. While sustainable tourism is multidimensional in itself (with economic, social and environmental aspects), the economic contribution of tourism captured by this indicator, and (relative) increases or decreases in it, indicates the degree to which tourism is being successfully promoted. Ideally, this indicator needs to be complemented with additional indicators on the social (e.g. employment, etc.) and environmental (energy use, GHG emissions, etc.) aspects of tourism that can be disaggregated to provide a more complete picture of the promotion of sustainable tourism and thus the monitoring of this target.

This indicator is useful for policy on tourism at international, national level and the level of sub-national regions as it provides a measure of the economic contribution of tourism which can be compared over time, across countries, to total GDP and to the GDP contributions of other economic activities. Tourism Direct GDP includes the contributions from all forms of tourism—inbound tourism, domestic tourism and outbound tourism—in line with the International Recommendations for Tourism Statistics 2008 (IRTS 2008). The indicator has been found especially useful in raising awareness of the economic importance of tourism and making the case for a more proactive, sustainable management of a sector that is often overlooked in policy agendas at all levels.

4.b. Comment and limitations

Given that a significant number of countries already have or are working to implement Tourism Satellite Accounts (TSA), data on the suggested indicators could become available in more countries in the near future.

The data demands for implementing TSA (detailed input-output or supply and use tables, among others); means that it is often not possible or cost effective to realize frequent updating of the TSA. Therefore, some countries produce estimates of TSA aggregates, in between reference years and/or nowcast estimates, to have more current data and to produce a time series.

In the absence of important shocks to the economy and to tourism, historically TDGDP/GDP tended to not show very large variations from one year to the next, however the effects of the Covid-19 pandemic on tourism are quite evident through this indicator in many countries. Considering also that variations may stem from the numerator and/or denominator, it is often useful from an analytical perspective to consider the indicator in different forms and adaptations: absolute value, % change in constant price, and TDGDP per visitor or per employed person.

Related economic aggregates on tourism like Tourism Direct Gross Value Added and the Gross Value Added of the Tourism Industries (in aggregate form and disaggregated by tourism industry) are also important and may be used as approximations to indicator 8.9.1 for analytical purposes.

4.c. Method of computation

Tourism direct GDP as a proportion of total GDP (in%):

$(\frac{T D G D P}{G D P}) \times 100$

Tourism direct GDP in growth rate

$[(\frac{{T D G D P}_{t}}{{T D G D P}_{t - 1}}) - 1] \times 100$

4.d. Validation

Every year historical data is requested. If there are differences in the newly reported data for the country with respect to the data available previously, countries are consulted. Similarly, if other inconsistencies are found, there is ongoing follow up with countries.

UNWTO is also custodian for indicator 12.b.1 and the data collected there serves as a valuable validation step for the data provided for indicator 8.9.1. For example, since Table 6 of TSA is necessary for the compilation of TDGDP, the data reported by countries are cross validated with the availability of this table ( data reported for SDG indicator 12.b.1).

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

When a country does not measure the Tourism direct GDP but measures the Tourism Direct Gross Value Added (TDGVA), the indicator Tourism Direct Gross Value Added as a proportion of total Gross Value Added (in %) is used as a proxy. When it is the case, a footnote is included in the data.

Tourism Direct Gross Value added (TDGVA) as a proportion of total Gross Value Added (GVA), in %:

$(\frac{T D G V A}{G V A}) \times 100$

4.g. Regional aggregations

Aggregates are computed for the SDG regions and at the global level.

For every year, estimates for countries with missing data are computed as follows:

For countries without any reported data
A multivariate linear regression model is used with as explanatory variables the number of hotel rooms in the country and inbound tourism expenditure (computed from Balance of Payments data provided by the International Monetary Fund (IMF)), both available via UNWTO´s statistical database.
For countries with reported data for years other than the year of reference
A simple linear model based on inbound tourism expenditure (computed from the IMF Balance of Payments data) as explanatory variable is used to estimate the nominal percentage change in TDGDP. These values are used to retro- and extrapolate the values reported by the country, using these as benchmark.

For reference years between years with reported data, a linear trend between reported years is used.

Special cases
Some data reported by countries that do not correspond to Tourism Direct GDP or GVA and are therefore not published, may still be used in the calculation of aggregates.

For each year, countries without reported data for which the methodology yields negative estimates or for which no data to feed the linear models are available are discarded. Regional (and global) aggregates are then obtained by computing weighted averages of TDGDP, using total GDP as the weight, for countries within the region of interest for which data or estimates are available.

GDP coverage for each aggregate is obtained by calculating the percentage of total regional GDP that is represented by countries for which data is reported or for which an estimate is available. If this coverage is relatively low (below 60 percent), estimates are published with a cautionary footnote.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The methodology is described in the Tourism Satellite Account: Recommended Methodological Framework 2008.

For the purposes of SDG reporting, UNWTO suggests an experimental approach that might be used by countries with limited data to compile estimates of TDGDP using the conceptual framing of the TSA and the most commonly available data but not requiring the full compilation of TSA. In this regard, the approach is intended to provide a starting point for countries with limited data that can then move towards the compilation of TSA and the more complete measurement of TDGDP. For more information, see Proposals for estimating Tourism Direct GDP with limited data.

4.i. Quality management

Recommendations on quality management for the underlying tourism data needed to compile a TSA are available in the International Recommendations for Tourism Statistics 2008 (IRTS 2008), the UN ratified methodological framework for measuring tourism.

4.j. Quality assurance

Any discrepancies are resolved through written communication with countries.

4.k. Quality assessment

The data should comply with the recommendations on concepts, definitions and classifications provided in the international standards: the Tourism Satellite Account: Recommended Methodological Framework 2008.

5. Data availability and disaggregation

Data availability:

As of March 2022, more than 70 countries have data available for this indicator in 2020. The number of countries with a TSA exercise is monitored by SDG indicator 12.b.1. According to data reported by countries for the SDG indicator 12.b.1, more than 89 countries have conducted a TSA exercise in the period between 2017 and 2021.

Time series:

Annual data from 2008 onwards are available.

Disaggregation:

TDGDP is derived from the productive activities that cater directly to tourism and so it could be possible to disaggregate by tourism industries (e.g. accommodation for visitors, the different kinds of passenger transportation, etc.).

Sub-national disaggregation/estimates of Tourism Direct GDP are possible and there are a number of countries’ subnational regions that have information on this. However, there is no consensus on a methodology for doing this in a standardized way, compromising international comparability. In any case, it seems that collection of data would be warranted only for those regions where tourism is considered a significant (economic) activity and/or a policy priority.

6. Comparability/deviation from international standards

Sources of discrepancies:

Discrepancies might arise from different degrees of adherence to Tourism Satellite Account: Recommended Methodological Framework 2008 and different TSA reference years.

7. References and Documentation

International Recommendations for Tourism Statistics 2008 (IRTS 2008)

Tourism Satellite Account: Recommended Methodological Framework 2008 (TSA: RMF 2008)

8.10.1

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all

0.c. Indicator

Indicator 8.10.1: (a) Number of commercial bank branches per 100,000 adults and (b) Number of automated teller machines (ATMs) per 100,000 adults

0.d. Series

Applies to all series

0.e. Metadata update

2023-03-31

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

International Monetary Fund (STAFI - Financial Access Survey Team)

1.a. Organisation

International Monetary Fund (STAFI - Financial Access Survey Team)

2.a. Definition and concepts

Definition:

The number of commercial bank branches per 100,000 adults

The number of automated teller machines (ATMs) per 100,000 adults

Adult population refers to the total population in the reporting jurisdiction of individuals 15 years old and above

Concepts:

The number of commercial bank branches refers to the total number of commercial bank branches in the country reported annually by the central bank or the main financial regulator of the country to the Financial Access Survey (FAS). To make the indicator meaningful for cross-country comparison, the number of commercial bank branches is scaled per 100,000 adults.

The number of automated teller machines (ATMs), refers to the number of ATMs in the country for all types of financial institutions such as: commercial banks, non-deposit taking microfinance institutions, deposit taking micro finance institutions, credit unions and credit cooperatives, and other deposit takers. This information is reported annually by the central bank or the main financial regulator of the country to the FAS. To make the indicator meaningful for cross-country comparison, the number of ATMs is scaled per 100,000 adults.

2.b. Unit of measure

Per 100,000 adults

2.c. Classifications

Not applicable

3.a. Data sources

The indicators in the Financial Access Survey (FAS) database are collected on an annual basis since 2009, covering the period since 2004. Information is collected from central banks or other main financial regulators for 189 jurisdictions.

All data and metadata are available free of charge to the public on the IMF’s FAS website, along with other key documents.

3.b. Data collection method

Every year, the Financial Access Survey (FAS) Team reaches out to FAS respondents to initiate the annual survey process. Data are compiled by countries and sent to the IMF through the Integrated Collection System (ICS) or National Summary Data Page (NSDP), which allows for a secure submission of country information.

3.c. Data collection calendar

The data collection round is launched around end-March every year; collection occurs on an annual basis.

3.d. Data release calendar

The Financial Access Survey (FAS) data are publicly disseminated on a rolling basis as soon as submissions are reviewed and validated by the FAS Team, with complete dissemination at end-September each year. Submissions that have passed through the validation process are made available on the FAS website on the following Monday after successful validation.

3.e. Data providers

Central banks or other financial regulators.

3.f. Data compilers

International Monetary Fund.

3.g. Institutional mandate

Not applicable

4.a. Rationale

Access to and use of formal financial services is essential. Services such as savings, insurance, payments, credit and remittances allow people to manage their lives, plan and pay expenses, grow their businesses and improve their overall welfare. As banks remain one of the key institutions for access to formal financial services, having an accessible bank branch is an important initial point of access to financial services and therefore use of them. Bank branches are complemented by other important points of access such as automated teller machines of all formal financial institutions, which can extend financial services to remote locations.

4.b. Comment and limitations

Since 2009, the Financial Access Survey (FAS) collects information from administrative sources on an annual basis. The central bank or the main financial regulator reports yearly information including the two indicators that are part of the SDGs. Since its launch, 189 economies have contributed to the FAS, which now contains more than 100 series on financial inclusion covering the period since 2004.

4.c. Method of computation

The indicators are calculated based on data collected directly from the central bank or the main financial regulator in the country. The formula to obtain these indicators are:

$T h e n u m b e r o f c o m m e r c i a l b a n k b r a n c h e s p e r 100, 000 {a d u l t s}_{i t} = \frac{N u m b e r o f c o m m e r c i a l b a n k {b r a n c h e s}_{i t} }{\frac{A d u l t {p o p u l a t i o n}_{i t}}{100, 000}}$

$T h e n u m b e r o f a u t o m a t e d t e l l e r m a c h i n e s (A T M s) p e r 100, 000 {a d u l t s}_{i t} = \frac{N u m b e r o f a u t o m a t e d t e l l e r m a c h i n e s {(A T M s)}_{i t} }{ \frac{A d u l t {p o p u l a t i o n}_{i t}}{100, 000}}$

Where “i” indicates the country and “t” indicates the year. The source of information for the number of commercial bank branches and the number of ATMs is the Financial Access Survey (FAS), while the source of information for the adult population is the World Development Indicators or the World Factbook.

4.d. Validation

The Financial Access Survey (FAS) questionnaire has built-in consistency checks to help data reporters spot inconsistencies in data reporting. Once the data is reported to the FAS, it undergoes a round of automated validation checks. If any inconsistency is detected, the FAS Team engages with the country authorities for clarifications or adjustments to the data provided. In case a country needs to add additional relevant information pertinent to the data reported, they can do so through the metadata portal in Integrated Collection System (ICS).

Every year, submissions are disseminated on the FAS website (data.imf.org/fas) on a rolling basis as soon as they are reviewed and validated.

4.e. Adjustments

Data are taken from the World Bank's World Development Indicators database and the World Factbook. In cases where data for the most recent period are not available, data for the previous period is used.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Missing values are registered as empty. “n/a” are used when the country indicates that those services or institutions do not exist in the country, or alternatively, do not fall under the supervisory scope of a regulatory agency.

At regional and global levels

“n/a” are used when the country indicates that those services or institutions do not exist in the country, or alternatively, do not fall under the supervisory scope of a regulatory agency. Trend extrapolation is used for countries that have not reported the latest data.

4.g. Regional aggregations

Country level: information provided by the authorities, recalculated as number of access points per 100,000 adults. For regional values, the Financial Access Survey (FAS) aggregates information of all countries and uses country’s adult population as weights.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Information collected by the Financial Access Survey (FAS) relies on the “FAS Guidelines and Manual”, which is published every year in English, Spanish and French. To foster the use of a common methodology, the definitions of financial institutional units and instruments covered in the FAS are primarily based on the IMF’s Monetary and Financial Statistics Manual and Compilation Guide (https://www.imf.org/-/media/Files/Data/Guides/mfsmcg-final.ashx). The FAS also publishes a Glossary for FAS indicators.

All these documents can be found in FAS website - documents.

4.i. Quality management

The Financial Access Survey (FAS) questionnaire has built-in consistency checks to help data reporters spot inconsistencies in data reporting. Once the data is reported to the FAS, it undergoes a round of automated validation checks and careful review by the FAS team. The analytical work on the reported data also aids spotting and correcting inconsistencies in the data, if any.

4.j. Quality assurance

The Financial Access Survey (FAS) data are collected through the Integrated Collection System (ICS) or the National Summary Data Page (NSDP), which allows for a secure submission of country information. Data submitted by countries are received internally in a system that facilitates the validation process conducted by the FAS Team.

Each submission is carefully reviewed, and when necessary, the FAS Team engages with the country authorities for clarifications or adjustments to the data provided. In case a country needs to add additional relevant information pertinent to the data reported, they can do so through the metadata portal in ICS.

4.k. Quality assessment

The Financial Access Survey (FAS) is a supply-side database with data reported from central banks and other financial regulators sourced from administrative data. Supply-side data tend to be more accurate than demand-side surveys. Furthermore, any deviations from the FAS methodology or fluctuations are reported by the country in the metadata, which is available on the FAS data portal.

5. Data availability and disaggregation

Data availability:

Covering 189 economies, the Financial Access Survey (FAS) provides a unique set of high-quality global supply side data. It contains 121 times series and 70 indicators that are expressed as ratios to GDP, land area, or adult population to facilitate cross-country comparisons.

Time series:

Since 2004; on an annual basis.

Disaggregation:

Data are provided at country level, by year. Aggregates are compiled by region in accordance with UN suggested regional aggregations.

6. Comparability/deviation from international standards

The Financial Access Survey (FAS) is a supply-side database based on administrative data from central banks or other main financial regulators. The data collection is centralized at the regulatory agency, which sources data from financial institutions and financial services providers for series for which data are available. The regulatory agency reports aggregates for the total economy to the FAS. The FAS provides country-level metadata that explain the institutional coverage of each reporting economy. Data from the FAS may differ from household-based surveys because of possible difference in coverage, scope, or concept definitions.

7. References and Documentation

URL:

http://data.imf.org/fas

8.10.2

0.a. Goal

Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

0.b. Target

Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all

0.c. Indicator

Indicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider

0.d. Series

Applies to all series.

0.e. Metadata update

2023-03-31

0.f. Related indicators

Not applicable

0.g. International organisations(s) responsible for global monitoring

World Bank

1.a. Organisation

World Bank

2.a. Definition and concepts

Definition:

The percentage of adults (ages 15+) who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months.

Concepts:

Financial institution accounts (excluding mobile money) denote the percentage of respondents who report having an account (by themselves or together with someone else) at a bank, credit union, microfinance institution, or post office that falls under prudential regulation by a government body.

Data on adults with a financial institution account include respondents who reported having an account at a bank or at another type of financial institution, such as a credit union, a microfinance institution, a cooperative, or the post office (if applicable). The data also include an additional 3 percent of respondents in 2021 who reported receiving wages, government transfers, a public sector pension, or payments for agricultural products into a financial institution account in the past year; paying utility bills or school fees from a financial institution account in the past year; or receiving wages, government transfers, or agricultural payments into a card in the past year. The definition does not include non-bank financial institutions such as pension funds, retirement accounts, insurance companies, or equity holdings such as stocks.

Mobile money accounts denote the percentage of respondents who report personally using a mobile money service to make payments, buy things, or to send or receive money in the past year. Data on adults with a mobile money account include respondents who reported personally using services included in the GSM Association’s Mobile Money for the Unbanked (GSMA MMU) database to pay bills or to send or receive money in the past year. The data also include an additional 2 percent of respondents in 2021 who reported receiving wages, government transfers, a public sector pension, or payments for agricultural products through a mobile phone in the past year. Unlike the definition of account at a financial institution, the definition of mobile money account does not include the payment of utility bills or school fees through a mobile phone. The reason is that the phrasing of the possible answers leaves it open as to whether those payments were made using a mobile money account or an over-the-counter service.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Data is also aggregated using the World Bank classifications for World, low- and middle-income economies, income groups (low-income, lower-middle-income, and upper-middle-income economies), and developing regions.

3.a. Data sources

The indicators in the ~~2021~~ Global Findex Database 2021 ~~Financial Inclusion (Global Findex) database~~ are drawn from survey data covering almost 128,000 people in 123 economies—representing 91 percent of the world’s population. The survey was carried out over the 2021 calendar year, and now marks the fourth round of Global Findex data since 2011. Typically, the survey captures data from more than 140 economies, but surveying was postponed in a handful of countries in 2021 due to COVID-19. These countries were surveyed in 2022, and data on these additional 17 countries will be available in 2023.

The surveying is undertaken by Gallup, Inc. as part of its Gallup World Poll, , which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. ~~Data on additional 17 countries will be available in 2023.~~

Full report, including methodology and interview procedures, data preparation, margin of error and notes by country are all available under Methodology Table A.1 here:

https://www.worldbank.org/en/publication/globalfindex/Report

3.b. Data collection method

In most developing economies, Global Findex data have traditionally been collected through face-to-face

interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, during 2021, due to ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies.

Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership.

All samples are probability-based and nationally representative of the resident adult population.

3.c. Data collection calendar

Four rounds of data collection are completed, for years: 2011, 2014, 2017, and 2022.

Surveying for 2020 was delayed to 2021 due to COVID-19 restrictions.

3.d. Data release calendar

Data are collected every three years.

3.e. Data providers

Collected by Gallup, Inc. through the Gallup World Poll and compiled by the World Bank

3.f. Data compilers

World Bank

3.g. Institutional mandate

Development Research Group (DECRG) is the World Bank's principal research department under the Development Economics Vice Presidency. Under the guidance of the World Bank’s Chief Economist, DECRG provides high quality economic information, data, analysis, research, and training to the Bank Group and its partners. DECRG's research programs generate and disseminate knowledge about development policies essential for the achievement of the World Bank's ultimate mandate of poverty reduction and shared prosperity. Within DECRG, the “Findex team” manages the extensive Global Findex Database that provides in-depth data on how people, especially women and the poor, save, borrow, make payments and manage risk.

The Global Findex Database is collected in partnership with Gallup, Inc., as a part of its Gallup World Poll. For more information on their mandate and methodology , please see here: https://www.gallup.com/178667/gallup-world-poll-work.aspx.

4.a. Rationale

Financial inclusion means that households have access to and can effectively use appropriate financial services that are provided responsibly and sustainably in a well-regulated environment. Studies show that when people participate in the financial system, they are better able to start and expand businesses, invest in education, manage risk and absorb financial shocks. Measurement is key to understanding financial inclusion and identifying opportunities to remove barriers that may be preventing people from using financial services. The Global Findex Database 2021 is the first global, comparable database of demand-side financial inclusion indicators, capturing insights into how adults around the world save, borrow, make payments and manage risk.

4.b. Comment and limitations

Global Findex Database—only measures the ‘perception’ people have about their account ownership and usage by providing individual-level survey data on the demographic characteristics of users of financial services. This demand-side data collects information on the percent of adults who think of themselves as ‘banked’ and having access to an account. The database complements but does replace existing supply-side data and other household surveys.

4.c. Method of computation

The indicator is based on data collected through individual level surveys in each country with representative samples. Data weighting is used to ensure a nationally representative sample for each economy. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the poststratification weight, which corrects for sampling and nonresponse error. Poststratification weights use economy-level population statistics on gender and age and, where reliable data are available, education or socioeconomic status. Regional population weights are used to calculate regional aggregates.

Full report, including methodology and interview procedures, data preparation, margin of error and notes by country are all available under Methodology Table A.1 here: https://www.worldbank.org/en/publication/globalfindex/Report

4.d. Validation

There is a thorough review process of the Global Findex surveys to ensure its quality and integrity. Gallup, Inc, the survey vendor for the Global Findex database, follows the highest standards for sampling and collecting the data and thoroughly vets the data before sharing it with the Global Findex team. Once the data is received by the Findex team, the team vets the data by comparing headline indicators from the database against data from previous rounds of the database.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

At country level

Data for “don’t know” and “refused to answer” form 0-1% of the total responses and are counted as “no” instead of missing. Any estimates based on fewer than 10% of survey sample observations are considered economically insignificant and therefore suppressed.

At regional and global levels

Not applicable

4.g. Regional aggregations

Regional estimates are calculated by aggregating individual level surveys for each country in the developing regions. Appropriate regional population-based weights are applied. Information on developing regional classification(s) is taken from the World Bank and the population data is taken from the World Development Indicators (WDI).

4.i. Quality management

The Findex team oversees the quality management of the Global Findex Database.

4.j. Quality assurance

For quality assurance, the Findex team has additional processes in place after it receives the data from Gallup Inc. The Findex team compares the data against (the limited number of) demand-side survey data from external sources for countries whenever available such as from the Financial Inclusion Insights Survey, FinScope, or from Central Banks. The team also consults with key practitioners both within the World Bank and at other key organizations to confirm the credibility of headline numbers on account ownership.

4.k. Quality assessment

All the source code for the headline indicator is reviewed by an independent and external statistics department within the World Bank.

5. Data availability and disaggregation

Data availability:

There are 162 countries with regional and World aggregates that have at least 1 data point after 2011 for this indicator.

Time series:

Triennial (2011,2014, 2017 and 2021)

Disaggregation:

Disaggregation are available by: Income (Adults in the poorest 40 percent of households vs. Adults in the richest 60 percent of households); Participation in labour force (In labor force vs. Out of labor force); Age (Ages 15-25 vs. Age 25+); Education level (Primary and below vs. Primary and above); Urbanicity of residence (Rural vs. Urban ); Gender (Female vs. Male)

6. Comparability/deviation from international standards

Sources of discrepancies:

Global Findex Database is an individual level survey, measuring an individual’s perception of ownership of accounts. It assumes financial inclusion is an individual-level concept which may create two potential discrepancies. First, the data may deviate from supply side data which counts the number of accounts and may overstate the level of financial inclusion in a country if a significant number of adults have an account they do not use and did not formally close. Second, not all demand-side data is the same and that the data from different demand-side surveys cannot necessarily be compared if the survey method is different. In particular, surveys of individuals cannot be compared directly with surveys of household heads, since the use of financial services can differ considerably between different household members.

7. References and Documentation

URL:

https://www.worldbank.org/en/publication/globalfindex

References:

Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, and Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

9.a.1

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.a: Facilitate sustainable and resilient infrastructure development in developing countries through enhanced financial, technological and technical support to African countries, least developed countries, landlocked developing countries and small island developing States

0.c. Indicator

Indicator 9.a.1: Total official international support (official development assistance plus other official flows) to infrastructure

0.e. Metadata update

2017-07-09

0.g. International organisations(s) responsible for global monitoring

Organisation for Economic Co-operation and Development (OECD)

1.a. Organisation

Organisation for Economic Co-operation and Development (OECD)

2.a. Definition and concepts

Definitions:

Gross disbursements of total ODA and other official flows from all donors in support of infrastructure.

Concepts:

ODA: The DAC defines ODA as “those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are

provided by official agencies, including state and local governments, or by their executive agencies; and
each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and

is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent).

(See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)

(See http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf, Para 24).

Support to infrastructure includes all CRS sector codes in the 200 series (see here: http://www.oecd.org/dac/stats/purposecodessectorclassification.htm)

3.a. Data sources

The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.b. Data collection method

3.c. Data collection calendar

Data are published on an annual basis in December for flows in the previous year.

Detailed 2015 flows was published in December 2016.

3.e. Data providers

Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.

3.f. Data compilers

OECD

4.a. Rationale

Total ODA and OOF flows to developing countries quantify the public effort (excluding export credits) that donors provide to developing countries for infrastructure.

4.b. Comment and limitations

Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements.

4.c. Method of computation

The sum of ODA and OOF flows from all donors to developing countries for infrastructure.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Due to high quality of reporting, no estimates are produced for missing data.

• At regional and global levels

Not applicable.

4.g. Regional aggregations

Global and regional figures are based on the sum of ODA and OOF flows to the agriculture sector.

5. Data availability and disaggregation

Data availability:

On a recipient basis for all developing countries eligible for ODA.

Disaggregation:

This indicator can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid, sub-sector, etc.

6. Comparability/deviation from international standards

Sources of discrepancies:

DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.

7. References and Documentation

URL:

www.oecd.org/dac/stats

References:

See all links here: http://www.oecd.org/dac/stats/methodology.htm

9.b.1

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.b: Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities

0.c. Indicator

Indicator 9.b.1: Proportion of medium and high-tech industry value added in total value added

0.d. Series

Proportion of medium and high-tech manufacturing value added in total value added (%)

0.e. Metadata update

2022-03-31

0.f. Related indicators

4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skill

9.2.1: Manufacturing value added as a proportion of GDP and per capita

0.g. International organisations(s) responsible for global monitoring

United Nations Industrial Development Organization (UNIDO)

1.a. Organisation

United Nations Industrial Development Organization (UNIDO)

2.a. Definition and concepts

Definitions:

The proportion of medium-high and high-tech industry (MHT hereafter) value added in total value added of manufacturing (MVA hereafter) is a ratio value between the value added of MHT industry and MVA.

Concepts:

The value added of an industry (industry value added) is a survey concept that refers to the given industry’s net output derived from the difference of gross output and intermediate consumption. Manufacturing sector is defined according to the International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3 (1990) or Revision 4 (2008). It refers to industries belonging to sector D in revision 3 or sector C in Revision 4.

Technology classification is based on research and development (R&D) expenditure relative to value added otherwise referred as R&D intensity. Data for R&D intensity are presented in a report (Galindo-Rueda and Verger, 2016) published by the OECD in 2016, which also proposes a taxonomy for industry groups with different ranges of R&D expenditure relative to their gross value added. MHT industries have traditionally been defined exclusively to manufacturing industries. However, there have been recent efforts (Galindo-Rueda and Verger, 2016) to extend the definition to non-manufacturing industries as well. Nevertheless, medium-high and high technology sectors are primarily represented by manufacturing industries.

The following table includes the classification of MHT industries by ISIC Rev. 3 and ISIC Rev. 4.

ISIC Rev.4	Description	ISIC Rev.3	Description
20	Manufacture of chemicals and chemical products	24	Manufacture of chemicals and chemical products
21	Manufacture of basic pharmaceutical products and pharmaceutical preparations	29	Manufacture of machinery and equipment n.e.c.
252	Manufacture of weapons and ammunition	30	Manufacture of office, accounting and computing machinery
26	Manufacture of computer, electronic and optical products	31	Manufacture of electrical machinery and apparatus n.e.c.
27	Manufacture of electrical equipment	32	Manufacture of radio, television and communication equipment and apparatus
28	Manufacture of machinery and equipment n.e.c.	33	Manufacture of medical, precision and optical instruments, watches and clocks
29	Manufacture of motor vehicles, trailers and semi-trailers	34	Manufacture of motor vehicles, trailers and semi-trailers
30^*	Manufacture of other transport equipment	35^**	Manufacture of other transport equipment
325	Manufacture of medical and dental instruments and supplies

* Excluding 301 (Building of ships and boats)

** Excluding 351 (Building and repairing of ships and boats)

MVA is the value added of manufacturing industry, which is Section C of ISIC Rev.4, and Section D of ISIC Rev.3.

2.b. Unit of measure

Percent (%)

2.c. Classifications

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3

3.a. Data sources

Data can be found in UNIDO INDSTAT4 Database by ISIC Revision 3 and ISIC Revision 4 respectively.

3.b. Data collection method

Data are collected using General Industrial Statistics Questionnaire which is filled by National Statistical Offices (NSOs) and submitted to UNIDO annually. Data for OECD countries are obtained directly from OECD. Country data are also collected from official publications and official websites.

3.c. Data collection calendar

Data are collected annually from NSOs and OECD.

3.d. Data release calendar

UNIDO INDSTAT database is updated between March and April every year.

3.e. Data providers

National statistical offices (NSOs) in non-OECD countries, and OECD countries by OECD.

3.f. Data compilers

United Nations Industrial Development Organization (UNIDO)

3.g. Institutional mandate

UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO’s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.

4.a. Rationale

Industrial development generally entails a structural transition from resource-based and low technology activities to MHT activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labour productivity. Increasing the share of MHT sectors also reflects the impact of innovation.

4.b. Comment and limitations

Value added by economic activity should be reported at least at 3-digit ISIC for compiling MHT values.

4.c. Method of computation

The indicator is calculated as the share of the sum of the value added from MHT economic activities to MVA.

$\frac{S u m o f v a l u e a d d e d i n M H T e c o n o m i c a c t i v i t i e s}{M V A} * \times 100$

4.d. Validation

UNIDO engages with countries in regular consultations during the data collection process to ensure the data quality and international comparability.

4.e. Adjustments

Data are collected through the UNIDO General Industrial Statistics Questionnaire to receive information on differences in concept, scope, coverage and classification used. The final data are adjusted to follow ISIC and facilitate international comparability.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level:

Missing values are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2016).

• At regional and global levels:

Imputation applied at country level.

4.g. Regional aggregations

Regional and global aggregates are calculated as a weighted average of countries’ MHT shares in a group. Weights are taken based on the MVA share in a group (UNIDO MVA Database).

4.h. Methods and guidance available to countries for the compilation of the data at the national level

International Recommendations for Industrial Statistics (IRIS) 2008

https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf

International Standard Industrial Classification of All Economic Activities (ISIC)

https://unstats.un.org/unsd/classifications/Econ/isic

4.i. Quality management

UNIDO published a handbook for statisticians involved in the regular industrial statistics programmes of NSOs or line ministries (Industrial Statistics - Guidelines and Methodology). It describes the statistical methods related to the major stages of industrial statistics operation. Moreover, UNIDO has established a quality management framework based on the internationally recognized guidelines recommended by IRIS to ensure quality of statistical products.

4.j. Quality assurance

The UNIDO Quality Assurance Framework is followed to ensure that the statistical activities of UNIDO are relevant and the data compiled and disseminated are accurate, complete within the defined scope and coverage, timely, comparable in terms of internationally recommended methods and classification standards and internally coherent to variables included in the datasets. While these generally accepted, broad dimensions of quality of statistical data may be defined in each NSO's own quality assurance framework. UNIDO makes maximum effort that data produced from the statistical operation undertaken with the UNIDO technical cooperation are accurate, internationally comparable and coherent.

4.k. Quality assessment

UNIDO employs a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics.

5. Data availability and disaggregation

Data availability:

More than 150 economies

Time series:

Data for this indicator are available from 2000 in the UN Global SDG Database, but longer time series are available in the CIP database.

Disaggregation:

No disaggregation available.

6. Comparability/deviation from international standards

Sources of discrepancies:

Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures.

7. References and Documentation

URL:

http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx

References:

Competitive Industrial Performance (CIP) report 2018. https://www.unido.org/sites/default/files/files/2019-05/CIP_Report_2019.pdf

International Standard Industrial Classification of All Economic Activities 2008. https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf

Galindo-Rueda, F. and F. Verger (2016). OECD Taxonomy of Economic Activities Based on R&D Intensity, OECD Science, Technology and Industry Working Papers, 2016/04, OECD Publishing, Paris. Available at:

http://dx.doi.org/10.1787/5jlv73sqqp8r-en

UNIDO (2009). UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740

UNIDO (2010). Industrial Statistics - Guidelines and Methodology https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf

UNIDO (2013). The Industrial Competitiveness of Nations 2013. https://www.unido.org/sites/default/files/2013-07/Competitive_Industrial_Performance_Report_UNIDO_2012_2013_0.PDF

9.c.1

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.c: Significantly increase access to information and communications technology and strive to provide universal and affordable access to the Internet in least developed countries by 2020

0.c. Indicator

Indicator 9.c.1: Proportion of population covered by a mobile network, by technology

0.e. Metadata update

2023-03-31

0.g. International organisations(s) responsible for global monitoring

International Telecommunication Union (ITU)

1.a. Organisation

International Telecommunication Union (ITU)

2.a. Definition and concepts

Definitions:

Proportion of population covered by a mobile network, broken down by technology, refers to the percentage of inhabitants living within range of a mobile-cellular signal, irrespective of whether or not they are mobile phone subscribers or users. This is calculated by dividing the number of inhabitants within range of a mobile-cellular signal by the total population and multiplying by 100.

Concepts:

The indicator is based on where the population lives, and not where they work or go to school, etc. When there are multiple operators offering the service, the maximum population number covered should be reported. Coverage should refer to LTE and above (4G), broadband (3G) and any technology (2G) mobile-cellular technologies and include:

- 2G mobile population coverage: refers to the percentage of inhabitants that are within range of at least a 2G mobile-cellular signal, irrespective of whether or not they are subscribers. This includes mobile-cellular technologies such as GPRS, CDMA2000 1x and most EDGE implementations. The indicator refers to the theoretical ability of subscribers to use non-broadband speed mobile data services, rather than the number of active users of such services.

- 3G population coverage: refers to the percentage of inhabitants that are within range of at least a 3G mobile-cellular signal, irrespective of whether or not they are subscribers. This is calculated by dividing the number of inhabitants that are covered by at least a 3G mobile-cellular signal by the total population and multiplying by 100. It excludes people covered only by GPRS, EDGE or CDMA 1xRTT.

- LTE population coverage: Refers to the percentage of inhabitants that live within range of LTE/LTE-Advanced, mobile WiMAX/WirelessMAN or other more advanced mobile-cellular networks, irrespective of whether or not they are subscribers. This is calculated by dividing the number of inhabitants that are covered by the previously mentioned mobile-cellular technologies by the total population and multiplying by 100. It excludes people covered only by HSPA, UMTS, EV-DO and previous 3G technologies, and also excludes fixed WiMAX coverage.

As technologies evolve and as more and more countries will deploy and commercialize more advanced mobile-broadband networks (5G etc.), the indicator will include further breakdowns.

2.b. Unit of measure

Percent (%)

2.c. Classifications

Technologies as defined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020.

3.a. Data sources

This indicator is based on an internationally agreed definition and methodology, which have been developed under the coordination of the International Telecommunication Union (ITU), through its Expert Groups and following an extensive consultation process with countries. It is also a core indicator of the Partnership on Measuring ICT for Development's Core List of Indicators, which has been endorsed by the UN Statistical Commission (last time in 2014).

ITU collects data for this indicator through an annual questionnaire from national regulatory authorities or Information and Communication Technology Ministries, who collect the data from Internet service providers.

3.b. Data collection method

The International Telecommunication Union (ITU) collects data for this indicator through a questionnaire from national regulatory authorities or Information and Communication Technology Ministries, who collect the data from Internet service providers.

3.c. Data collection calendar

The International Telecommunication Union (ITU) collects data twice a year from Member States, in 1^st quarter and in 3^rd quarter.

3.d. Data release calendar

Data are released twice a year, In July and December, in the World Telecommunication/ICT Indicators Database and in the ITU DataHub, see https://datahub.itu.int/

3.e. Data providers

Telecommunication/ Information and Communication Technology (ICT) regulatory authority, or Ministry of ICTs.

3.f. Data compilers

International Telecommunication Union (ITU)

3.g. Institutional mandate

As the UN specialized agency for Information and Communication Technology (ICTs), the International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States, see resolution 131 of the ITU Plenipotentiary Conference, https://www.itu.int/pub/S-CONF-ACTF-2022 .

4.a. Rationale

The percentage of the population covered by a mobile cellular network can be considered as a minimum indicator for Information and Communication Technology (ICT) access since it provides people with the possibility to subscribe to and use mobile-cellular services to communicate. Over the last decade, mobile-cellular networks have expanded rapidly and helped overcome very basic infrastructure barriers that existed when fixed-telephone networks – often limited to urban and highly populated areas - were the dominant telecommunication infrastructure.

While 2G (narrowband) mobile-cellular networks offer limited (and mainly voice-based) services, higher-speed networks (3G and LTE) provide increasingly high-speed, reliable and high-quality access to the Internet and its increasing amount of information, content, services, and applications. Mobile networks are therefore essential to overcoming infrastructure barriers, helping people join the information society and benefit from the potential of ICTs, in particular in least developed countries.

The indicator highlights the importance of mobile networks in providing basic, as well as advanced communication services and will help design targeted policies to overcome remaining infrastructure barriers, and address the digital divide. Many governments track this indicator and have set specific targets in terms of the mobile population coverage (by technology) that operators must achieve.

4.b. Comment and limitations

Some countries have difficulty calculating overall mobile-cellular population coverage. In some cases, data refer only to the operator with the largest coverage, and this may understate the true coverage.

4.c. Method of computation

The indicator percentage of the population covered by a mobile network, broken down by technology, refers to the percentage of inhabitants living within range of a mobile-cellular signal, irrespective of whether or not they are mobile phone subscribers or users. This is calculated by dividing the number of inhabitants within range of a mobile-cellular signal by the total population and multiplying by 100.

$(N r . i n h a b i t a n t s c o v e r e d b y a n y m o b i l e - c e l l u l a r s i g n a l) / (T o t a l p o p u l a t i o n) \times 100$

4.d. Validation

Data are submitted by Member States to the International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Missing values are estimated using data published by mobile cellular operators that have the largest market share.

• At regional and global levels

Missing values are estimated using data published by mobile cellular operators that have the largest market share.

4.g. Regional aggregations

Global and regional estimates are produced using weighted country-level data. First, the missing country-level data are estimated using data of the dominant mobile operator. Once all the country-level percentages are available, the number of people covered by the mobile signal is calculated by multiplying the percentage of population covered by the signal to the population of the country. The regional and world total population covered by a signal were calculated by summing the country-level data. The aggregate percentages were calculated by dividing the regional totals by the population of respective groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020: https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx

4.i. Quality management

Data are checked and validated by the ICT Data and Analytics (IDA) Division of the International Telecommunication Union (ITU). Countries are contacted to clarify and correct their submissions.

4.j. Quality assurance

The guidelines of the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020 are followed.

4.k. Quality assessment

The guidelines of the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020 are followed.

5. Data availability and disaggregation

Data availability:

Data for this indicator exist for more than 160 economies.

Time series:

1997 onwards for 2G

2007 onwards for 3G

2012 onwards for LTE

Disaggregation:

Based on the data for the percentage of the population covered by a mobile network, broken down by technology, and on rural population figures, countries can produce estimates on rural and urban population coverage. International Telecommunication Union (ITU) produces global estimates for the rural population coverage, by technology.

6. Comparability/deviation from international standards

Sources of discrepancies:

None. International Telecommunication Union (ITU) uses the data provided by countries, including the in-scope population that is used to calculate the percentages.

7. References and Documentation

URL:

References:

ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020: https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx

9.1.1

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all

0.c. Indicator

Indicator 9.1.1: Proportion of the rural population who live within 2 km of an all-season road

0.e. Metadata update

2020-09-01

0.f. Related indicators

None

0.g. International organisations(s) responsible for global monitoring

World Bank

1.a. Organisation

World Bank

2.a. Definition and concepts

Definitions:

The indicator (commonly known as the Rural Access Index or RAI) measures the share of a country’s rural population that lives within 2 km of an all-season road.

Concepts:

The indicator is measured by combining three sets of geospatial data: where people live, the spatial distribution of the road network, and road passability. The use of spatial data has various advantages. It can help ensure consistency across countries. The level of spatial resolution is broadly the same regardless of the size of the country or subnational boundaries. Any given norm of connectivity (for example, 2 km distance from a road) is uniquely and unambiguously applied for all countries.

Population Distribution - Quality population distribution data are essential for correct measurement of rural access. In some countries, census data is available in a geospatially detailed, reliable format. For other countries, population distribution data sets have been developed by the international research community, interpreting subnational census data through various modelling techniques. For the RAI, WorldPop data has been found to provide a reliable estimate. That estimate can also be refined through engagement between the national statistics offices and WorldPop to reconcile data at the level of enumeration areas.

Rural-Urban Definition – Related to population distribution data, an important challenge facing the index is the need for a consistent and reliable urban and rural definition to exclude urban areas from the calculation. The inclusion of urban areas would create a substantial upward bias in the RAI, because most urban residents have “access to roads,” no matter how it is defined. Ideally, spatial data determining urban-rural boundaries are needed at a similar level of resolution as the population. As such data may rely on different definitions in different countries, globally produced urban extents may be used, such as the Global Urban Rural Mapping Project v1 Urban Extent Polygons.

Road Network Data – Data on road locations may come from a number of sources. Ideally government data are used, as they are consistent with the road network for which road agencies are responsible and are relatively easily merged with other operational databases. In countries where the road location data may not be detailed enough or entirely missing or where there is a large unclassified network, alternative data sources may be available, such as the open source OpenStreetMap.

Road Condition Data – The principle of the “all-season” road network remains central to the original concept of measuring the RAI. An “all-season road” is defined as a road that is motorable all year round by the prevailing means of rural transport (often a pick-up or a truck which does not have four-wheel-drive). Predictable interruptions of short duration during inclement weather (e.g. heavy rainfall) are accepted, particularly on low volume roads. A road that it is likely to be impassable to the prevailing means of rural transport for a total of 7 days or more per year is not regarded as all-season. Note that some roads agencies use the term “all-weather” to describe their roads, however “all-weather” typically means “paved” and should not be confused with “all-season” which can include unpaved roads too.

It is important to determine whether access to facilities and services is available all year round, and hence the possibility of the road throughout the year is an essential factor in this aspect of contributing to poverty reduction. Information on the condition of the road network is frequently maintained by road agencies as part of their operational responsibilities.

The traditional road inventory survey can collect data on road condition, including the International Roughness Index (IRI), at a high level of information quality, to determine whether a road is “all-season”. For the purpose of the RAI, the road condition threshold is generally set at an IRI of less than 6 meters/km for paved roads, and an IRI of less than 13 meters/km for unpaved roads. When IRI is not available, other types of condition assessment may be used if comparable. The use of smartphones with GPS are being investigated in order to accurately map local transport services routes, and identify which rural roads are open all year and hence are all-season roads. These condition thresholds should only be used, however, where there is reliable road condition data available. The parameters should be calibrated to the local conditions, i.e. checks should be made to determine that paved roads in poor condition are largely not all-season, and that unpaved roads in fair or poor condition are largely not all-season. The parameters can be adjusted accordingly to the local conditions, based on a systematic and documented study.

In the event that accurate road condition data is not available, then accessibility factors provide an alternative means to road condition for identifying “all-season” roads. Such factors do not require ground measurements of road condition to be made. Accessibility factors are those which determine the likelihood of a road being all-season, or the risk of a road being inaccessible.

3.a. Data sources

Data on population distribution are typically sourced from WorldPop or national census results, depending on the reliability and spatial granularity of country systems. Road location and quality data are provided by the national road agencies responsible for their upkeep. Accessibility factors are defined by national roads agencies in collaboration with national statistics offices and other agencies as appropriate.

3.b. Data collection method

A partnership between NSOs, national road agencies, and the World Bank as custodian agency is necessary to effectively generate RAI results. In some countries, World Bank transport staff work closely with national agencies, with data generation and calculation of the RAI built into a broader engagement. In other countries, NSOs and road agencies provide RAI results directly to the World Bank as custodian.

3.c. Data collection calendar

Source collection is ongoing by the Transport Global Practice of the World Bank in coordination with NSOs and national road agencies.

3.d. Data release calendar

The World Bank Group is committed to releasing available RAI updates on a yearly basis.

3.e. Data providers

The World Bank typically receives data from national road agencies and NSOs directly. As the underlying calculation relies primarily on road agency data, such agencies are generally the primary counterpart for RAI data.

3.f. Data compilers

Within the World Bank, the Transport Global Practice is in charge of the collection and validation of RAI data and results. The Global Practice archives the datasets obtained from NSOs and road agencies and then harmonizes them, applying common methodologies. Where NSOs and road agencies calculate the RAI using their own data and methodologies, the Transport Global Practice is responsible for reviewing the underlying data and assumptions and validating the results for inclusion in the global SDG dataset. The objective is to ensure that the data generated, curated, and disseminated by the World Bank are up to date, meet high-quality standards, and are well documented and consistent across dissemination channels. World Bank country staff works in close collaboration with national statistical authorities on the data collection and dissemination process.

4.a. Rationale

Among other factors, transport connectivity is an essential part of the enabling environment for inclusive and sustained growth. In developing countries, particularly in Africa, the vast majority of agricultural production remains smallholder farming with limited access to local, regional, or global markets. Isolated manufacturing and other local businesses (except for those related to mining) often lag behind in the global market. Limited transport connectivity is also a critical constraint to accessing social and administrative services, especially in rural areas where the majority of the poor live.

Rural access is key to unleashing untapped economic potential and eradicating poverty in many developing countries. In the short term, transport costs and travel time can be reduced by improved road conditions. Over the longer term, agricultural productivity will be increased, and firms will become more profitable with the creation of more jobs, eventually helping to alleviate poverty.

To make good investments, quality data are required. Since resources are limited, it is essential to understand where the most critical unmet needs exist, and monitor efforts made over time. In the transport sector, there are few global indicators. The quality of roads is often unknown and a matter of concern in developing countries. In Africa, the Road Management Initiative, started by the Africa Transport Policy Program in the late 1990s, developed a road sector database, which includes road network condition data such as the share of roads in good or bad condition. But this database is largely outdated and insufficient.

The Rural Access Index (RAI), originally developed by the World Bank in 2006, is among the most important global development indicators in the transport sector, providing a strong, clearly understandable and conceptually consistent indicator across countries. It measures the proportion of people living in rural areas who have access to an all-season road within a walking distance of approximately 2 kilometres (km). Although the underlying methodology has been updated to leverage additional sources of data, the RAI remains the most widely accepted metric for tracking access to transport in rural areas.

The RAI has four primary benefits: sustainability due to its reliance on already existing data, consistency in methodology across countries and time, simplicity in understanding, and operational relevance for the government agencies responsible for generating and aggregating the underlying data.

4.b. Comment and limitations

The Indicator relies substantially on data collected by road agencies and national statistics offices for their operational work. As such, its update is dependent on the frequency of update of the road condition surveys and national census. When these data sets are not from the same year, the basic principle to be followed is that a more stable data set should be used with more flexibility. For instance, a national rural roads program could dramatically improve the quality of roads in a certain locality in a relatively short term, while population data are fairly stable over five years. In such a case, the road quality data would be considered as an anchor, with the closest or adjusted population data applied.

The Indicator depends heavily on the quality and extent of the underlying spatial data. The extent of the road network data, and how well it reflects the reality on the ground, can be a particular issue. Verification against open source data and satellite data where possible is recommended. More data are always better. Efforts should also be made to collect detailed road data, including on tertiary or feeder roads, which may not be covered in the existing spatial road network data regardless of whether government or open data sources are used. If condition data is not available, then use of accessibility factors can be considered.

The 2 km norm of access may not be as applicable in all areas. In extremely mountainous countries, there has been significant research into walking times and preparation of accessibility maps that take into account mountainous terrain, locations of rivers and footbridges. However, for global consistency purposes and comparability across countries, the 2 km distance threshold has been maintained (equivalent to a 20-30 minute walk in most regions).

While the RAI provides an objective benchmark for assessing access to transport in rural areas, “universal” road access of 100% should not be set as a target. First mile or last mile connectivity is not intended to imply all-season road access. Connectivity can be a system of engineered trails and footbridges as in Nepal, or designated river navigation channels and jetties as in Bangladesh, or a system of solar lit beacons and marked desert trails in Sudan. There are many more such examples: most rural settlements in the Amazon, Orinoco, Congo and Upper Nile River basins, have no or limited hinterland road access. The outer islands of the archipelagos of Indonesia and Philippines and South Pacific Islands rely heavily on coastal shipping. Similarly, vast regions of Siberia, the Russian steppes and Mongolia depend on rail. The deltas of Mekong, the Ganges-Brahmaputra, Indus rely on water transport. It is simply not possible, nor desirable, to address last mile connectivity by all-season rural roads in many situations. In addition, in South Asia and growingly in Africa, motorcycles and autorickshaws are the mainstay of personal mobility and account for a growing share of rural commerce. “All-season” for motorcycles and autorickshaws is not the same as “all-season” for 4-wheeled vehicles. And in the not too distant future, self-driving all-terrain vehicles, or drones, could provide an important transport service. As a global benchmark, however, the RAI should be considered as a starting point to begin discussions of all season access.

4.c. Method of computation

The indicator is calculated by overlying three basic geospatial datasets: population distribution, road location, and road passability. The RAI is calculated as the rural population within a 2 km buffer of a good road divided by the total rural population of the country.

First, the spatial distribution of the rural population needs to be determined. This involves obtaining the population dataset for the country, either from country sources or global datasets such as WorldPop.

Next, the road network should be merged with road condition assessments, either in terms of IRI if available, or visual assessment. Those roads with a quality not meeting the threshold of the RAI (not providing “all-season” access) should be excluded. In general, the RAI adopts a road condition threshold is generally set at an IRI of less than 6 meters/km for paved roads and an IRI of less than 13 meters/km for unpaved roads. If IRI is unavailable, alternative assessments of road condition may be used, if comparable. If road condition data is not available, then accessibility factors can be defined to identify those roads at highest risk of impassability. A 2 km buffer should be generated around the road network meeting the condition threshold or highest risk. Urban areas should be removed from both the road data and the population data.

Finally, the rural population living within the 2 km buffer should be calculated. The final RAI is determined by dividing this portion of the rural population with the total rural population.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

No gap filling is done to report national numbers.

• At regional and global levels

This is a country specific indicator and no aggregation is currently planned.

4.g. Regional aggregations

This is a country specific indicator and no aggregation is currently planned. As additional country level data becomes available aggregation may be possible at a supranational level.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

The World Bank, as custodian agency, with support from the UK Department for International Development (DFID) and the Research for Community Access Partnership (ReCAP), has developed and published a full methodological document for the RAI, including detailed descriptions of various data sources, variations on the standard methodology, and a step-by-step guide. In addition, a GIS tool has been developed to calculate the RAI from provided data sets. These resources and others are being collected into an online portal for the Rural Access Index.

4.j. Quality assurance

5. Data availability and disaggregation

Data availability:

As of 2019, data is readily available for more than 30 countries, with consultations ongoing for a number more. While data is available for some Asian and Latin American countries as well, Africa accounts for the largest share of the available information. Consultations are underway to engage with additional countries.

Time series:

Due to the long update cycle of national road condition surveys, the RAI is not expected to be updated on an annual basis, but instead aligned with national systems. This implies a likely 3-5 year time frame for update. Current data spans the period from 2009-2019, with 1-2 data points per country.

Disaggregation:

Due to its nature as a geospatially derived indicator, the RAI can be calculated at subnational levels down to the level of granularity of the underlying datasets. While the World Bank will only report country level results for SDG monitoring, subnational results can be calculated for country use.

6. Comparability/deviation from international standards

Sources of discrepancies:

Relying heavily on national data, differences in national systems undoubtedly are reflected in the top level indicator (including road quality classification, national census methodologies, etc.). Use of globally derived datasets such as WorldPop may result in somewhat different results from national data if the NSO has not engaged with WorldPop. However, an assessment of sample countries indicates that these discrepancies are likely limited in their impact of the overall result.

7. References and Documentation

The guiding methodology for the RAI can be found at:

World Bank. 2016. Measuring rural access: using new technologies (English). Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/367391472117815229/Measuring-rural-access-using-new-technologies

More information on the RAI, including Supplemental Guidelines on the use of accessibility factors prepared in collaboration with ReCAP, correlations with poverty and other development indicators, and the latest data sets can be accessed on the World Bank’s RAI data catalogue entry: https://datacatalog.worldbank.org/dataset/rural-access-index-rai

The Sustainable Mobility for All initiative provides input and leverages the RAI in its global tracking framework. More information here: http://sum4all.org/

9.1.2

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all

0.c. Indicator

Indicator 9.1.2: Passenger and freight volumes, by mode of transport

0.e. Metadata update

2023-07-18

0.g. International organisations(s) responsible for global monitoring

International Civil Aviation Organization (ICAO); International Transport Forum (ITF); United Nations Conference on Trade and Development (UNCTAD).

1.a. Organisation

International Civil Aviation Organization (ICAO); International Transport Forum (ITF); United Nations Conference on Trade and Development (UNCTAD).

2.a. Definition and concepts

Definitions:

Passenger volumes are measured in passenger-kilometres while freight volumes are measured in tonne-kilometres, and broken down by mode of transport. For the purposes of monitoring this indicator, passenger-km data are split between aviation, road (broken down between passenger cars, buses and motorcycles) and rail, and tonne-km are split between aviation, road, rail and inland waterways. Maritime freight is measured in metric tons and container port traffic is measured in twenty-foot equivalent unit (TEU).

Concepts:

Aviation:

The International Civil Aviation Organization (ICAO) through its Statistics Division has established standard methodologies and definitions to collect and report traffic (passenger and freight volume) data related to air transport. These standards and methodologies have been adopted by the 193 Member States of ICAO and also by the Industry stakeholders i.e. air carriers and airports. The data of ICAO is used by States and also the World Bank for its development indicators. ICAO uses Air Transport Reporting Forms A, AS, B and C to arrive at the passenger and freight volumes for air transport.

Precise definition of all different concepts and metadata related to Air Transport Reporting Forms A, AS, B and C to arrive at the passenger and freight volumes for air transport, as approved by the ICAO Statistics Division and Member States can be found at the ICAO website given below -

http://www.icao.int/sustainability/pages/eap-sta-excel.aspx/.

Martime

Definitions:

International maritime freight is an indicator reflecting (1) the sum of international freight volumes loaded (exports) and unloaded (imports) at ports worldwide and measured in metric tonnes, and (2) container port traffic at world ports measured in twenty-foot equivalent unit (TEU).

Data is collected by the UNCTAD secretariat from various sources, including industry, government and specialised maritime transport data providers and consultancies. Volumes are expressed in metric tonnes and twenty-foot equivalent unit (TEU).

As data on international maritime freight volumes are not widely available, only the data in tonnes (rather than tonne-km) and at the regional level are reported.

Data at country level are available for container port traffic measured in twenty-foot equivalent unit (TEU).

Concepts:

The UNCTAD secretariat collects and compiles the data from various websites and reports, including, by port and industry associations and authorities, national statistics offices, UN Monthly Bulletin of Statistics, governments, specialised agencies such as the International Energy Agency (IEA), the US Energy Information Administration (EIA), the Organization of the Petroleum Exporting Countries (OPEC), and British Petroleum (BP). Data is also collected from reports issued by maritime specialised sources such as Drewry Maritime Research (DMR), Clarksons Research Services (CRS), Dynamar, and Lloyd’s List Intelligence (LLI).

Road, Rail, Inland waterways

For definitions of all relevant terms, the UNECE/ITF/Eurostat Glossary for Transport Statistics can be consulted. The 5^th edition of this publication is available at https://unece.org/DAM/trans/main/wp6/pdfdocs/Glossary_for_Transport_Statistics_EN.pdf

2.b. Unit of measure

Aviaiton: Revenue Passenger-Kilometres (RPK) and Freight Tonne-Kilometres (FTK)

Martime: Metric tonnes and twenty-foot equivalent unit (TEU).

Road, Rail:

Passenger-Kilometres (Pkm) and Tonne-Kilometres (Tkm)

Inland Waterways: Tonne-Kilometres (Tkm)

2.c. Classifications

Maritime:

Regional and sub-regional level data based on UNSD classification.

3.a. Data sources

Aviation

ICAO Air Transport Reporting Forms approved by the Statistics Division of ICAO and its Member States has been used to define standards, methodologies and to collect aviation data since the 1950's. ICAO definitions and metadata is also used by the Aviation Industry as the basis of collecting data and conducting analysis.

Maritime:

The UNCTAD secretariat collects and compiles the data from various websites and reports, including, by port and industry associations and authorities, national statistics offices, UN Monthly Bulletin of Statistics, governments, specialised agencies such as the International Energy Agency (IEA), the US Energy Information Administration (EIA), the Organization of the Petroleum Exporting Countries (OPEC), and, British Petroleum (BP). Data is also collected from reports issued by maritime specialised sources such as Drewry Maritime Research (DMR), Clarksons Research Services (CRS), Dynamar, and Lloyd’s List Intelligence (LLI).

Road, Rail, Inland waterways:

The ITF runs transport models that are used to provide transport information for all regions.

3.b. Data collection method

Aviation:

Official aviation statistics are reported on a regular basis by Member States to ICAO through Air Transport Reporting Forms.

Maritime:

Data are not based on a systematic reporting by countries and relies mainly on secondary sources that may vary over time. Official reporting by countries is very limited. Some data is only available at regional or sub-regional level.

The UNCTAD secretariat is currently collaborating with a specialized data provider and UN-DESA to elaborate a standard methodology that is based on UN Comtrade data to generate annual data on maritime freight flows, at country level and for all UN member countries.

Note: Maritime cargo movements are counted only once regardless of whether the transhipment port is located within the same country or not

Road, Rail, Inland waterways:

Data come from the ITF Global Models.

ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris

3.c. Data collection calendar

Aviation:

Every year by the fall data for the previous year is available to ICAO Member States at a country level.

Road/Rail/Inland waterways:

There is no compilation of data submitted from the countries. Data comes from the ITF Global Models which are updated every two years. In the last iteration of the ITF Global Models, data are available for 2015, 2019, 2020 and 2022. 2021 data are an interpolation of 2020 and 2022 data.

ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris

3.d. Data release calendar

Aviation:

Data are collected on a regular basis and a high level of coverage is expected to be available by the fall following the reference year.

Maritime:

Data are collected for the reference year on-ongoing process. Data are published annually on-line on UNCTADstat and in the annual Review of Maritime Transport in November of each year.

Road, Rail, Inland waterways:

Data come from the ITF Global Models which are updated every two years.

ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris

3.e. Data providers

Name:

ICAO, ITF, UNCTAD

Aviation :

International Civil Aviation organisation (ICAO).

Maritime:

Name: United Nations Conference on Trade and Development (UNCTAD)

Description: Data collected by UNCTAD from various sources, including government, industry and specialized maritime data sources and providers.

Road, Rail, Inland waterway:

Data are from ITF Global Model estimation.

3.f. Data compilers

International Civil Aviation organisation (ICAO)

International Transport Forum (ITF)

3.g. Institutional mandate

ICAO:

ICAO is funded and directed by 193 national governments to support their diplomacy and cooperation in air transport as signatory states to the Chicago Convention (1944). Its core function is to maintain an administrative and expert bureaucracy (the ICAO Secretariat) supporting these diplomatic interactions, and to research new air transport policy and standardization innovations as directed and endorsed by governments through the ICAO Assembly, or by the ICAO Council which the assembly elects.

https://www.icao.int/about-icao/Pages/default.aspx

UNCTAD:

Established in 1964, the United Nations Conference on Trade and Development (UNCTAD), published its annual Review of Maritime Transport for the first time in 1968. The publication is part of UNCTAD's research and analytical work in the field of maritime transport aimed at helping developing countries maximize their trade and investment opportunities and increase their participation in the world economy. It has been regularly reconfirmed in the quadrennial Ministerial Conferences, most recently by UNCTAD XIII in Doha (2012) and UNCTAD XIV in Nairobi (2016). The mandates emanating from these conferences have emphasized sustainable and resilient transport as priority action areas and established “Sustainable and Climate Resilient Maritime Transport” as an important thematic area n UNCTAD’s work programme and the Review of Maritime Transport.

ITF:

The International Transport Forum (ITF) was created by Ministerial Declaration in Dublin in 2006 on the legal basis of the European Conference of Ministers of Transport (ECMT), itself established as an international organisation by treaty (Protocol) signed in Brussels on 17 October 1953. The objectives of the ITF are to serve as a global platform for discussion and prenegotiation of transport policy issues across all modes. Unique in its global and modal scope, the ITF works to foster a deeper understanding of the role of transport in economic growth, environmental sustainability and social inclusion. It aspires to raise the public profile of transport policy.

4.a. Rationale

Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all. Trans-border infrastructure development is best captured by passenger and freight volumes moved by Member States and Regions. A growth in passenger and freight volumes shows a robust infrastructure development happening in States and Regions along with the resultant socio-economic benefit. Air Transport is particularly important not only for the economic and job benefits but also because it is one of the only mode of transport that can be relied on during emergencies and disease outbreaks to reach food, medicines, medical personnel, vaccines and other supplies speedily to the affected persons in the affected areas. In addition, tracking how the non-road share of freight volumes, and the public transport share of passenger volumes, changes over time allows insights into the overall sustainability of the global transport system.

Aviation:

Informed decision-making is the foundation upon which successful businesses are built. In a fast-growing industry like aviation, planners and investors require the most comprehensive, up-to-date, and reliable data. ICAO’s aviation data/statistics programme is to provide accurate, reliable and consistent aviation data so that States, international organizations, aviation industry, tourism and other stakeholders can make better projections. The UN recognized ICAO as the central agency responsible for the collection, analysis, publication, standardization, improvement and dissemination of statistics pertaining to civil aviation.

Maritime:

The volume of international maritime freight and container port traffic movements provide an overall indication of the importance of port infrastructure for trade and development and may be relied upon to infer the quality and adequacy of seaports and their hinterland connections. Maritime transport is the dominant mode of international freight transport when flows are measured in volume terms. Behind the global and regional headline estimates, individual contributions vary by region and type of cargo, reflecting, among other factors, differences in countries’ economic structures, composition of trade, urbanization, levels of development, extent of integration into global trading networks, degree of participation in global supply chains, and the quality of transport infrastructure.

World container port traffic reflects the importance of containerized trade and countries’ participation in global liner shipping networks and globalized manufacturing production processes.

Road, Rail, Inland waterways:

The International Transport Forum has developed a set of modelling tools to build its own forward-looking scenarios of transport activity. Covering all modes of transport, freight and passenger, the tools are unified under a single framework.

For passenger volumes, the following models are used to generate the data: the urban passenger transport model and the non-urban passenger transport model.

The urban passenger transport model is a strategic tool to test the impacts of policies and technology trends on urban travel demand, related CO2 emissions and accessibility indicators.

The non-urban passenger transport model is a strategic tool that tests the impacts of multiple policies and trends on the non-urban passenger sector.

For freight volumes, the non-urban freight transport model is used to generate the data. The non-urban freight transport model assesses and provides scenario forecasts for freight flows around the globe. It is a network model that assigns freight flows of all major transport modes to specific routes, modes, and network links.

The ITF Modelling Framework is available at The ITF Modelling Framework.

4.b. Comment and limitations

Aviation:

Coverage for aviation is for all ICAO 193 Member States.

Maritime:

Coverage for international maritime freight volumes at regional and sub-regional level.

Road, Rail, Inland waterways:

Coverage at regional and sub-regional level.

4.c. Method of computation

Aviation

The aviation passenger and freight volumes are reported for the air carriers through ICAO Air Transport Reporting Forms and grouped by Member States of ICAO.

Road/Rail/Inland waterways

Urban passenger transport model

The model is designed as a systems dynamic model (stock and flow model) to evaluate the development of urban mobility in all cities over 50 000 inhabitants around the world. It combines data from various sources that form one of the most extensive databases on global city mobility to account for fifteen transport modes. These range from the conventional private car and public transport to new alternative modes such as shared mobility.

Non-urban passenger transport model

The model provides scenario forecasts for non-urban transport activity and its related CO2 emissions up to 2050. The model estimates activity between urban areas (intercity travel) and passenger activity happening locally in non-urban areas (intra-regional travel). The latter includes travel in peri-urban and rural areas. The model is developed to assess the impact of transport, economic and environmental policy measures (air liberalisation, carbon pricing, etc.), as well as the impact of technological developments and breakthroughs (electric aviation, autonomous vehicles, etc.).

Non-urban freight transport model

The most recent version of the ITF freight model integrates the (previously distinct) surface and international freight models. International and domestic freight flows are calibrated on data on national freight transport activity (in tonnes-kilometres, tkm) as reported by ITF member countries. Reported data is also used to validate the route assignment of freight flows. Trade projections in value terms stem from the OECD trade model and converted into cargo weight (tonnes). These weight movements are then assigned to an intermodal freight network that develops over time in line with scenario settings. These define infrastructure availability, available services and related costs.

The model uses 2015 as its baseline year and provides estimation values for 2015, 2019, 2020, 2022, and 2025, then with computations done in five-year intervals. Therefore, the data for 2021 is derived through interpolation of the simulated values for 2020 and 2022.

The ITF Modelling Framework is available at The ITF Modelling Framework.

Maritime:

The indicator is calculated through a sum of international maritime freight volumes and container port traffic as collected by UNCTAD secretariat from websites and reports by various industry, government and specialised maritime transport data providers and consultancies. Data on international maritime freight excludes transhipments and domestic maritime freight volumes.

Cargo flows originating in or destined to landlocked countries are attributed to the ports of neighbouring coastal transit countries. The mode of transport “maritime” is assigned to an international trade transaction when the goods arrived at the country’s external border (the seaport) transported by ship.

Data on container port traffic include full and empty containers as well as transhipment traffic.

Data is collected and compiled from various websites and reports, including, by port and industry associations and authorities, national statistics offices, UN Monthly Bulletin of Statistics, governments, specialised agencies such as the International Energy Agency (IEA), the US Energy Information Administration (EIA), the Organization of the Petroleum Exporting Countries (OPEC), and British Petroleum (BP). Data is also collected from reports issued by maritime specialised sources such as Drewry Maritime Research (DMR), Clarksons Research Services (CRS), Dynamar, and Lloyd’s List Intelligence (LLI).

4.d. Validation

Aviation:

ICAO Statistics Programme has put in place a series of robust data quality control functions to automate all the necessary calculations and producing a report for each reporting form. These quality control processes were divided into two main activities: verification and validation.

Maritime:

UNCTAD secretariat monitors, collects, and compiles the data at the country level as well as at regional/sub-regional level. It continuously updates the data as new data and information becomes available. Some commercial providers of maritime statistics publish global data that is derived, for example, from shipping contracts, and UNCTAD compares its own data with those published by commercial providers.

Road/Rail/Inland waterways:

There is no compilation of data submitted from the countries. Data comes from the ITF Global Models.

ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris

4.e. Adjustments

Road, rail, inland waterways:

In order to provide a worldwide regional coverage, data from the ITF transport models are used (see point 4.f).

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Aviation data are broadly complete.

For inland transport statistics: In case of missing data for a country for which at least one data point is available since 2000, we calculate estimates based on the expected growth rate for the country. The growth rates are computed from other socio-economic variables, such as Gross Domestic Product (GDP), population or urbanization.

For road, rail, and inland waterways:

Not applicable

Maritime:

International maritime freight: In case of missing data for a country or a sub-region for which a data point is available since 2006, UNCTAD makes an estimate based on the expected growth rate of the volume of merchandise trade. If not available, use is made of the latest year for which data was available.

Container port traffic: In case of missing data, UNCTAD makes an estimate by extrapolating from the liner shipping connectivity and ship capacity deployment data, which has shown to be highly correlated with container port traffic. Container ship deployment data are available for all container ships of the world, which thus allows for estimates on container port traffic to be generated even if no national data is available. In other cases, UNCTAD makes an estimate based on the expected growth rate of the volume of merchandise trade.

4.g. Regional aggregations

Aggregation by region based on UN classification of country groupings, including by geography and development status.

Road/Rail/Inland waterways: The model estimations are at a country level but the analysis is only possible at the regional groupings using simple summation from country level.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Aviation:

States refer to the ICAO Reference Manual on the Statistics Programme (Doc 9060) to compile and file traffic reports at a national level.

Road/Rail/Inland waterways

ITF only provides model results to be public at the regional level.

Maritime:

Countries do not systematically collect or report data on international maritime freight and container port traffic. UNCTAD relies on data published by industry and information published by specialized sources.

4.i. Quality management

Aviation:

ICAO applies the recommendations of the Committee for the Coordination of Statistical Activities (CCSA), including the Principles Governing International Statistical Activities.

Maritime:

UNCTAD systematically applies the recommendations of the Committee for the Coordination of Statistical Activities (CCSA), including the Principles Governing International Statistical Activities. UNCTAD participates in the work of the Chief Statisticians or coordinators of statistical activities of United Nations agencies and international and supranational organizations assembled in the Committee for the Coordination of Statistical Activities and ensures the implementation of their principles. https://unstats.un.org/unsd/ccsa/principles_stat_activities/

Road/Rail/Inland waterways

This is not a statistical product resulting of data collection. Data are generated from a modelling exercise.

ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris

4.j. Quality assurance

Aviation:

ICAO applies the United Nations Statistics Division (UNSD) fundamental principles and good practices of official statistics, and particularly the generic national quality assurance framework (NQAF). The complete version of the guidelines of NQAF is available at: http://unstats.un.org/unsd/dnss/qualityNQAF/nqaf.aspx.

Maritime:

UNCTAD conducts annual checks of collected data by updating the data with latest data available and comparing the data for internal consistency, against previous years, or similar data published or produced by other sources, including commercial sources specialized maritime transport data providers and research entities. Correspondence is undertaken with countries when necessary to collect, compare or confirm relevant data.

Road/Rail/Inland waterways:

Not Applicable

5. Data availability and disaggregation

Data availability:

Aviation

Data already provided for all 193 Member States that have air transport activities

Road/Rail/Inland waterways

2015,2019,2020,2021

Time series:

Aviation

From 1970's

Road/Rail/Inland waterways

2015,2019,2020,2021

Disaggregation:

Aviation

The indicator can be dis-aggregated by -Country, Country pair, City Pair, Region, Segment (International and domestic)

Road/Rail/Inland waterways

The indicator can be disaggregated by mode of transport.

Maritime:

Data availability: International maritime freight data at regional and sub-regional level; 2006-2019

Container port traffic data cover 176 countries: 2010-2019

Disaggregation: International maritime freight: global, regional and subregional levels.

Container port traffic: global, regional and country levels

6. Comparability/deviation from international standards

Maritime:

Sources of discrepancies:

Data based on varied and mixed sources. This entails differences in computational systems and methods which may result in discrepancies.

Data on container port traffic for some countries are based on estimates by UNCTAD while extrapolating from the liner shipping connectivity and ship capacity deployment data. These remain proxies and may not capture the actual volumes handled by the ports in these countries.

7. References and Documentation

URL:

www.icao.int

https://www.itf-oecd.org/itf-modelling-framework-1

https://w3.unece.org/PXWeb/en

https://unctadstat.unctad.org/EN/

UNCTAD. Review of Maritime Transport Series: https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime-transport

UNCTAD statistics (UNCTADstat): http://stats.unctad.org/maritime

9.2.1

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.2: Promote inclusive and sustainable industrialization and, by 2030, significantly raise industry’s share of employment and gross domestic product, in line with national circumstances, and double its share in least developed countries

0.c. Indicator

Indicator 9.2.1: Manufacturing value added as a proportion of GDP and per capita

0.d. Series

Manufacturing value added as a proportion of GDP (in constant 2015 USD)

Manufacturing value added as a proportion of GDP (in current USD)

Manufacturing value added per capita

0.e. Metadata update

2022-03-31

0.f. Related indicators

1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)

8.1.1: Annual growth rate of real GDP per capita

10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilities

10.4.1: Labour share of GDP

0.g. International organisations(s) responsible for global monitoring

United Nations Industrial Development Organization (UNIDO)

1.a. Organisation

United Nations Industrial Development Organization (UNIDO)

2.a. Definition and concepts

Definitions:

Manufacturing value added (MVA) as a proportion of gross domestic product (GDP) is a ratio between MVA and GDP, both reported in constant 2015 USD.

MVA per capita is calculated by dividing MVA in constant 2015 USD by population of a country or area.

Concepts:

The gross value added measures the contribution to the economy of each individual producer, industry or sector in a country. The gross value added generated by any unit engaged in production activity can be calculated as the residual of the units’ total output less intermediate consumption, goods and services used up in the process of producing the output, or as the sum of the factor incomes generated by the production process (System of National Accounts 2008). Manufacturing refers to industries belonging to the section C defined by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 4, or D defined by ISIC Revision 3.

GDP represents the sum of gross value added from all institutional units resident in the economy. For the purpose on comparability over time and across countries MVA and GDP are estimated in terms of constant prices in USD. The current series are given at constant prices of 2015.

2.b. Unit of measure

MVA as a proportion of GDP: Percent (%)

MVA per capita: constant 2015 USD

2.c. Classifications

System of National Accounts 2008

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3

3.a. Data sources

UNIDO maintains the MVA database. Figures for updates are obtained from national account estimates produced by UN Statistics Division (UNSD) and from official publications.

3.b. Data collection method

The MVA and GDP country data are collected through a national accounts questionnaire (NAQ) sent by UNSD. More information on the methodology is available on

https://unstats.un.org/unsd/snaama/methodology.pdf

Missing or inconsistent values are verified with national sources and World Development Indicators (WDI). The preference is given to the data from national sources.

Population data are obtained from UN DESA Population Division. More information on the methodology is available on

https://population.un.org/wpp/Publications/Files/WPP2019_Methodology.pdf

3.c. Data collection calendar

Data collection is carried out by receiving data electronically throughout the year.

3.d. Data release calendar

UNIDO MVA database is updated between March and April every year.

3.e. Data providers

United Nations Statistics Division (UNSD) and official publications

UNSD from National Statistical Offices (NSOs)

3.f. Data compilers

United Nations Industrial Development Organization (UNIDO)

3.g. Institutional mandate

4.a. Rationale

MVA is a well-recognized and widely used indicator by researchers and policy makers to assess the level of industrialization of a country. The share of MVA in GDP reflects the role of manufacturing in the economy and a country’s national development in general. MVA per capita is the basic indicator of a country’s level of industrialization adjusted for the size of the economy. One of the statistical uses of MVA per capita is classifying country groups according to the stage of industrial development.

4.b. Comment and limitations

Differences may appear due to different versions of System of National Accounts (SNA) or ISIC revisions used by countries.

4.c. Method of computation

$M V A a s a p r o p o r t i o n i n G D P = \frac{M V A}{G D P} \times 100$

$M V A p e r c a p i t a = \frac{M V A}{p o p u l a t i o n}$

4.d. Validation

UNIDO engages with countries in regular consultations during the data collection process to ensure the data quality and international comparability.

4.e. Adjustments

UNSD collects national accounts data through a regular consultation with countries and areas by sending the UN NAQ to obtain important information about differences in concept, scope, coverage and classification used. The final estimates are provided to facilitate international comparability. More detailed information on estimation methods is available here:

https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf

The MVA data are nowcasted by UNIDO to enhance a timely analysis of manufacturing trends.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Methodology for the National Accounts Main Aggregates Database

Because of a time-gap of at least one year between the latest year, UNIDO applies nowcasting methods to fill in the missing data up to the current year (Boudt et al., 2009).

• At regional and global levels

No imputation used.

4.g. Regional aggregations

Regional, global aggregation of direct summation of country values within the country groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

International Recommendations for Industrial Statistics (IRIS) 2008

https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf

System of National Accounts 2008

https://unstats.un.org/unsd/publication/seriesf/SeriesF_2Rev5e.pdf

International Standard Industrial Classification of All Economic Activities (ISIC)

https://unstats.un.org/unsd/classifications/Econ/isic

4.i. Quality management

The National Accounts Section of the UNSD supports the implementation programme of the SNA by developing and updating supporting normative standards, training material and compilation guidance for the implementation of national accounts and supporting economic statistics and maintaining a knowledge base on economic statistics. Moreover, UNSD provides substantive service to the Committee on Contributions of the Fifth Committee of the United Nations on technical aspects of the elements of scale methodology for assessing the contributions to the United Nations by Member States. UNIDO collects and disseminates National Accounts statistics in consultation with UNSD.

4.j. Quality assurance

4.k. Quality assessment

The National Accounts Section of the UNSD and UNIDO employ a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics.

5. Data availability and disaggregation

Data availability:

For more than 200 economies

Time series:

Data for this indicator are available as of 2000 in the UN Global SDG Database, but longer time series are available in the UNIDO MVA database.

Disaggregation:

No disaggregation available.

6. Comparability/deviation from international standards

Sources of discrepancies: Minor differences may arise due to 1) exchange rates for conversion to USD 2) different base years used for constant price data 3) methods for recent period estimation and 4) different versions of SNA and ISIC revisions used by countries.

7. References and Documentation

URL:

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4

https://unstats.un.org/unsd/snaama/methodology.pdf
https://population.un.org/wpp/Publications/Files/WPP2019_Methodology.pdf

References:

Boudt, Todorov, Upadhyaya (2009): Nowcasting manufacturing value added for cross-country comparison; Statistical Journal of IAOS

International Recommendations for Industrial Statistics 2008. https://unstats.un.org/unsd/industry/Docs/IRIS_2008_En.pdf

International Yearbook of Industrial Statistics; UNIDO, https://www.unido.org/resources-publications-flagship-publications/international-yearbook-industrial-statistics

International Standard Industrial Classification of All Economic Activities 2008. https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf

System of National Accounts 2008. https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf

UNIDO (2009), UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740

9.2.2

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

0.c. Indicator

Indicator 9.2.2: Manufacturing employment as a proportion of total employment

0.d. Series

Manufacturing employment as a proportion of total employment (13th ICLS)

Manufacturing employment as a proportion of total employment (19th ICLS)

0.e. Metadata update

2023-03-31

0.f. Related indicators

8.2.1 Annual growth rate of real GDP per employed person

0.g. International organisations(s) responsible for global monitoring

United Nations Industrial Development Organization (UNIDO)

(with the collaboration of the International Labour Organization – ILO)

1.a. Organisation

United Nations Industrial Development Organization (UNIDO)

(with the collaboration of the International Labour Organization – ILO)

2.a. Definition and concepts

Definitions:

This indicator presents the share of manufacturing employment in total employment.

Concepts:

Employment comprises all persons of working age who during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the two series for a given country is the operational criteria used to define employment, with one series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.

No distinction is made between persons employed full time and those working less than full time.

The manufacturing sector is defined according to the International Standard Industrial Classification of all Economic Activities (ISIC) revision 4 (2008, the latest) or revision 3 (1990). It refers to industries belonging to sector C in revision 4 or sector D in revision 3.

2.b. Unit of measure

Percent (%)

2.c. Classifications

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3

3.a. Data sources

The preferred official national data source for this indicator is a household-based labour force survey.

In the absence of a labour force survey, a population census and/or other type of household survey with an appropriate employment module may also be used to obtain the required data.

Where no household survey exists, establishment surveys or some types of administrative records may be used to derive the required data, keeping into account the limitations of these sources in their coverage. Specifically, these sources may exclude some types of establishments, establishments of certain sizes, some economic activities or some geographical areas.

3.b. Data collection method

UNIDO employment data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually.

3.c. Data collection calendar

Continuous

3.d. Data release calendar

Continuous

3.e. Data providers

Mainly national statistical offices, and in some cases labour ministries or other related agencies, at the country-level. In other cases, regional or international statistical offices can also act as data providers.

3.f. Data compilers

International Labour Organization (ILO)

3.g. Institutional mandate

The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.

4.a. Rationale

This indicator conveys the contribution of manufacturing in total employment. It measures the ability of the manufacturing sector to absorb surplus labour from agricultural and other traditional sectors. However, in developed countries an opposite trend is expected where emphasis has shifted to reduction in labor in manufacturing as part of cost-cutting measures, to promote more capital-intensive industries.

4.b. Comment and limitations

The characteristics of the data source impact the international comparability of the data, especially in cases where the coverage of the source is less than comprehensive (either in terms of country territory or economic activities). In the absence of a labour force survey (the preferred source of data for this indicator), some countries may use an establishment survey to derive this indicator, but these usually have a minimum establishment size cut-off point and small units which are not officially registered (whether in manufacturing or not) would thus not be included in the survey. Consequently, employment data may be underestimated. Discrepancies can also be caused by differences in the definition of employment or the working–age population.

4.c. Method of computation

Computation Method:

$\frac{T o t a l e m p l o y m e n t i n m a n u f a c t u r i n g a c t i v i t i e s }{T o t a l e m p l o y m e n t i n a l l e c o n o m i c a c t i v i t i e s} \times 100$

4.d. Validation

4.e. Adjustments

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

Multivariate regression and cross-validation techniques are used to impute missing values at the country level. The additional variables used for the imputation include a range of indicators, including labour market and economic data. However, the imputed missing country values are only used to calculate the global and regional estimates; they are not used for international reporting on the SDG indicators by the ILO. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/.

• At regional and global levels

The aggregates are derived from the ILO modelled estimates that are used to produce global and regional estimates of, amongst others, employment by economic activity, with employment based on the 13th ICLS standards. These models use multivariate regression and cross-validation techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. The regional and global shares of employment in manufacturing are obtained by first adding up, across countries, the numerator and denominator of the formula that defines the manufacturing employment as a proportion of total employment - outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to compute the share for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/.

4.g. Regional aggregations

The global and regional aggregates are calculated after direct summation of country values within country groups.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, (https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm).
Decent Work Indicators Manual: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf
Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19^th ICLS in 2013: https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
International Standard Industrial Classification of All Economic Activities 2008. (https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf).

4.i. Quality management

4.j. Quality assurance

Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.

4.k. Quality assessment

The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data. If the issues cannot be clarified, the respective information is not published.

5. Data availability and disaggregation

Data availability:

Data is available in ILOSTAT for 203 countries and territories in the 13^th ICLS series and 117 countries and territories in the 19^th ICLS series.

Time series:

Data for this indicator is available from 2000 in the UN Global SDG Database, but longer time series are available in ILOSTAT.

Disaggregation:

This indicator can be disaggregated by sex, occupation, age, region and others.

6. Comparability/deviation from international standards

Sources of discrepancies:

Other differences may arise due to: a) discrepancies in data sources; b) ISIC Revision used by a country; c) informal employment; d) coverage of data source (geographical coverage, economic activities covered, types of establishments covered, etc.); e) working-age population definition.

7. References and Documentation

URL:

https://ilostat.ilo.org/

https://ilostat.ilo.org/resources/concepts-and-definitions/

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4

References:

ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, (https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm).
Decent Work Indicators Manual: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf
Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19^th ICLS in 2013: https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf
Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases (https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf)
ILOSTAT database: https://ilostat.ilo.org
International Standard Industrial Classification of All Economic Activities 2008. (https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf).

9.3.1

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

Target 9.3: Increase the access of small-scale industrial and other enterprises, in particular in developing countries, to financial services, including affordable credit, and their integration into value chains and markets

0.c. Indicator

Indicator 9.3.1: Proportion of small-scale industries in total industry value added

0.d. Series

Proportion of small-scale manufacturing industries in total manufacturing value added^[1]

In March 2023, the series description was updated from “Proportion of small-scale industries in total industry value added” to “Proportion of small-scale manufacturing industries in total manufacturing value added” for clarity; content in the series is the same. ↑

0.e. Metadata update

2023-03-31

0.f. Related indicators

9.3.2: Proportion of small-scale industries with a loan or line of credit

0.g. International organisations(s) responsible for global monitoring

United Nations Industrial Development Organization (UNIDO)

1.a. Organisation

United Nations Industrial Development Organization (UNIDO)

2.a. Definition and concepts

Definitions:

Small-scale industrial enterprises, in the SDG framework also called “small-scale industries”, defined here for the purpose of statistical data collection and compilation refer to statistical units, generally enterprises, engaged in production of goods and services for market below a designated size class.

Proportion of “small-scale industries” in total industry value added represents an indicator calculating the share of manufacturing value added of small-scale manufacturing enterprises in the total manufacturing value added.

Concepts:

International recommendations for industrial statistics 2008 (IRIS 2008) (United Nations, 2011) define an enterprise as the smallest legal unit that constitutes an organizational unit producing goods or services. The enterprise is the basic statistical unit at which all information relating to its production activities and transactions, including financial and balance-sheet accounts, are maintained. It is also used for institutional sector classification in the 2008 System of National Accounts.

An establishment is defined as an enterprise or part of an enterprise that is situated in a single location and in which only a single productive activity is carried out or in which the principal productive activity accounts for most of the value added. An establishment can be defined ideally as an economic unit that engages, under single ownership or control, that is, under a single legal entity, in one, or predominantly one, kind of economic activity at a single physical location. Mines, factories and workshops are examples. This ideal concept of an establishment is applicable to many of the situations encountered in industrial inquiries, particularly in manufacturing.

Although the definition of an establishment allows for the possibility that there may be one or more secondary activities carried out in it, their magnitude should be small compared with that of the principal activity. If a secondary activity within an establishment is as important, or nearly as important, as the principal activity, then the unit is more like a local unit. It should be subdivided so that the secondary activity is treated as taking place within an establishment separate from the establishment in which the principal activity takes place.

In the case of most small-sized businesses, the enterprise and the establishment will be identical. Some enterprises are large and complex with different kinds of economic activities undertaken at different locations. Such enterprises should be broken down into one or more establishments, provided that smaller and more homogeneous production units can be identified for which production data may be meaningfully compiled.

As introduced in IRIS 2008 (United Nations, 2011), an economic activity is understood as referring to a process, that is, the combination of actions carried out by a certain entity that uses labor, capital, goods and services to produce specific products (goods and services). In general, industrial statistics reflect the characteristics and economic activities of units engaged in a class of industrial activities that are defined in terms of the International Standard Industrial Classification of All Economic Activities, Revision 4 (ISIC Rev.4) (United Nations, 2008) or International Standard Industrial Classification of All Economic Activities, Revision 3.1 (ISIC Rev. 3) (United Nations, 2002).

Total numbers of persons employed is defined as the total number of persons who work in or for the statistical unit, whether full-time or part-time, including:

Working proprietors
Active business partners
Unpaid family workers
Paid employees (for more details see United Nations, 2011).

The size of a statistical unit based on employment should be defined primarily in terms of the average number of persons employed in that unit during the reference period. If the average number of persons employed is not available, the total number of persons employed in a single period may be used as the size criterion. The size classification should consist of the following classes of the average number of persons employed: 1-9, 10-19, 20-49, 50-249, 250 and more. This should be considered a minimum division of the overall range; more detailed classifications, where required, should be developed within this framework.

Value added cannot be directly observed from the accounting records of the units. It is derived as the difference between gross output or census output and intermediate consumption or census input (United Nations, 2011). The value added at basic prices is calculated as the difference between the gross output at basic prices and the intermediate consumption at purchasers’ prices. The valuation of value added closely corresponds to the valuation of gross output. If the output is valued at basic prices, then the valuation of value added is also at basic prices (the valuation of intermediate consumption is always at purchasers’ prices).

All above mentioned terms are introduced to be in line with IRIS 2008 (United Nations, 2011).

2.b. Unit of measure

Percent (%)

2.c. Classifications

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3

3.a. Data sources

National statistical offices (NSOs)

3.b. Data collection method

Countries were contacted to provide information on data availability for monitoring small-scale manufacturing enterprises. The data come mostly from annual industrial surveys, where value added is disaggregated by size classes given in terms of number of employees and from surveys focusing particularly on small enterprises, or small and medium enterprises in general.

3.c. Data collection calendar

Data are collected annually from NSOs, OECD and EUROSTAT.

3.d. Data release calendar

UNIDO SDG-9 database is updated between March and April every year including the 9.3.1 indicator.

3.e. Data providers

Data are collected primary from national sources, from official publications and official websites, and from OECD (Structural and Demographic Business Statistics) and EUROSTAT (Structural Business Statistics database).

3.f. Data compilers

United Nations Industrial Development Organization (UNIDO)

3.g. Institutional mandate

4.a. Rationale

Industrial enterprises are classified to small compared to large or medium for their distinct nature of economic organization, production capability, scale of investment and other economic characteristics. “Small-scale industries” can be run with a small amount of capital, relatively unskilled labor and using local materials. Despite their small contribution to total industrial output, their role in job creation, especially in developing countries is recognized to be significant where the scope of absorbing surplus labor force from traditional sectors such as agriculture or fishery is very high. “Small-scale industries” are capable of meeting domestic demand of basic consumer goods such as food, clothes, furniture, etc.

4.b. Comment and limitations

The main limitation of existing national data is varying size classes by country indicating that data are obtained from different target populations. Data of one country are not comparable to another.

The definition of size class in many countries is tied up with the legal and policy framework of the country. It has implications on registration procedure, taxation and different waivers aimed to promote “small-scale industries”. Therefore, countries may agree on a common size class for compilation purposes. In this context, UNIDO proposes that all countries compile the employment and value-added data by a size class of “small-scale industries” as with less than 20 persons employed. From such data, an internationally comparable data on the share of “small-scale industries” in total could be derived.

4.c. Method of computation

The proportion of “small-scale industries” in total value added is an indicator calculated as a share of value added for small-scale manufacturing enterprises in total manufacturing value added:

$\frac{M a n u f a c t u r i n g v a l u e a d d e d o f " s m a l l - s c a l e i n d u s t r i e s "}{T o t a l m a n u f a c t u r i n g v a l u e a d d e d} * \times 100$

Manufacturing sector is defined according to the International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3 (1990) or Revision 4 (2008). It refers to industries belonging to sector D in revision 3 or sector C in Revision 4.

4.d. Validation

UNIDO engages with countries in regular consultations during the data collection process to ensure the data quality and international comparability.

4.e. Adjustments

Data are collected through the UNIDO Small Industrial Enterprises Questionnaire to receive information on differences in concept, scope, coverage and classification used. The final data are adjusted to follow ISIC and facilitate international comparability.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

No treatment of missing values is applied at country level.

• At regional and global levels

No treatment of missing values is applied at regional and global levels.

4.g. Regional aggregations

Regional and global aggregates are currently not provided due to a limited geographical coverage and regional representativeness. The 2021 edition of 9.3.1 data series covers only 67 economies, mostly classified as developed economies.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

International Recommendations for Industrial Statistics (IRIS) 2008

https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf

International Standard Industrial Classification of All Economic Activities (ISIC)

https://unstats.un.org/unsd/classifications/Econ/isic

4.i. Quality management

4.j. Quality assurance

4.k. Quality assessment

UNIDO employs a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics.

5. Data availability and disaggregation

Data availability:

Data for around 70 economies were collected.

Time series:

Data are provided on a very irregular basis. Data available from annual industrial surveys show yearly frequency, while surveys on small and medium enterprises are conducted either irregularly or with a given time lag (for instance once in five years).

Disaggregation:

Data can be disaggregated by manufacturing sub-sectors.

6. Comparability/deviation from international standards

Sources of discrepancies:

Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures.

7. References and Documentation

URL:

https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf

References:

United Nations. (2002). International Standard Industrial Classification of All Economic Activities (ISIC Revision 4). New York: United Nations.

United Nations. (2008). International Standard Industrial Classification of All Economic Activities (ISIC Revision 3.1). New York: United Nations.

https://unstats.un.org/unsd/publication/SeriesM/seriesm_4rev3_1e.pdf

United Nations. (2011). International Recommendations for Industrial Statistics 2008 (IRIS 2008), New York: United Nations. http://dx.doi.org/10.18356/677c08dd-en

OECD. (2019). Structural and Demographic Business Statistics (SDBS). Paris: OECD.

http://www.oecd.org/std/business-stats/structuralanddemographicbusinessstatisticssdbsoecd.htm

UNIDO (2009). UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740

UNIDO (2010). Industrial Statistics - Guidelines and Methodology https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf

9.3.2

0.a. Goal

Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

0.b. Target

0.c. Indicator

Indicator 9.3.2: Proportion of small-scale industries with a loan or line of credit

0.d. Series

Proportion of small-scale industries with a loan or line of credit (%)

0.e. Metadata update

2022-03-31

0.f. Related indicators

9.3.1: Proportion of small-scale industries in total industry value added

0.g. International organisations(s) responsible for global monitoring

United Nations Industrial Development Organization (UNIDO)

World Bank

1.a. Organisation

United Nations Industrial Development Organization (UNIDO)

World Bank

2.a. Definition and concepts

Definitions^{^[1]}:

This indicator shows the number of “small-scale industries” with an active line of credit or a loan from a financial institution in the reference year in percentage to the total number of such enterprises.

Concepts:

As introduced in IRIS 2008 (United Nations, 2011), an economic activity is understood as referring to a process, that is , the combination of actions carried out by a certain entity that uses labor, capital, goods and services to produce specific products (goods and services). In general, industrial statistics reflect the characteristics and economic activities of units engaged in a class of industrial activities that are defined in terms of the International Standard Industrial Classification of All Economic Activities, Revision 4 (ISIC Rev.4) (United Nations, 2008) or International Standard Industrial Classification of All Economic Activities, Revision 3.1 (ISIC Rev. 3) (United Nations, 2002).

Total numbers of persons employed is defined as the total number of persons who work in or for the statistical unit, whether full-time or part-time, including:

Working proprietors
Active business partners
Unpaid family workers
Paid employees (for more details see United Nations, 2011).

A loan is a financial instrument that is created when a creditor lends funds directly to a debtor and receives a non-negotiable document as evidence of the asset. This category includes overdrafts, mortgage loans, loans to finance trade credit and advances, repurchase agreements, financial assets and liabilities created by financial leases, and claims on or liabilities to the International Monetary Fund (IMF) in the form of loans. Trade credit and advances and similar accounts payable/receivable are not loans. Loans that have become marketable in secondary markets should be reclassified under debt securities. However, if only traded occasionally, the loan is not reclassified under debt securities (IMF, 2011).

Lines of credit and loan commitments provide a guarantee that undrawn funds will be available in the future, but no financial liability/asset exists until such funds are provided. Undrawn lines of credit and undisbursed loan commitments are contingent liabilities of the issuing institutions— generally, banks (IMF, 2011). A loan or line of credit refers to regulated financial institutions only.

Some of the text on concepts and definition may be identical to Metadata submitted for Indicators 9.3.1. ↑

2.b. Unit of measure

Percent (%)

2.c. Classifications

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4

International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3

3.a. Data sources

Data were collected from the World Bank Enterprise Surveys as a pilot study on this indicator, however the preferable source of data are national statistical offices.

3.b. Data collection method

One of the main sources of data for this indicator currently available is the Enterprise Survey conducted by the World Bank (www.enterprisesurveys.org), which covers the formal sector and contains data for small and medium enterprises only (with 5 or more employees). In some countries, additional surveys, including Informal Surveys of unregistered enterprises and/or Micro Surveys for registered firms with less than five employees, are conducted and available at country level.

The Enterprise Survey is based on a representative sample of enterprises run by the private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. Since 2002, the World Bank has collected these data from face-to-face interviews with top managers and business owners in over 174,000 companies in 151 economies.

The surveys have been conducted since 2002 by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Data from 2006 onward is comparable across countries. The raw individual country datasets, aggregated datasets (across countries and years), panel datasets, and all relevant survey documentation are publicly available on the Enterprise Surveys web site.

The indicator uses a simple weighted percentage formula, where the weights are the sampling weights. The strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Enterprise Surveys provide indicators covering manufacturing and services activities. Proportion of “small-scale industries” with a loan or line of credit for manufacturing only can be extracted from the micro data.

Enterprises are classified as small, medium or large based on the number of employees as follows:

Size of enterprise	Number of employees
Small	5 to 19
Medium	20 to 99
Large	more than 99

The survey also defines an enterprise with female ownership as an enterprise having at least one female owner, and female-managed is measured by whether the top manager is a woman.

3.c. Data collection calendar

Data are collected through the World Bank Enterprise Surveys conducted in countries.

3.d. Data release calendar

The data are regularly updated on the World Bank Enterprise Surveys website. The Enterprise Surveys are implemented every year in around 20 countries. Data frequency for each country is around 4 years.

The UNIDO SDG-9 database is updated between March and April every year including the 9.3.2 indicator.

3.e. Data providers

World Bank Enterprise Surveys

3.f. Data compilers

United Nations Industrial Development Organization (UNIDO)

World Bank Enterprise Surveys

3.g. Institutional mandate

4.a. Rationale

Thus “small-scale industries” play an important role in the economy. However, it has quite limited access to financial services, especially in developing countries. In order to improve the skill of workers and technology for production, small-scale industrial enterprises require financial support in the form of preferential loan, credit etc. This indicator shows how widely financial institutions are serving the “small-scale industries”. Together with the indicator SDG 9.3.1, this indicator reflects the main message of the target 9.3 which seeks to increase the access of “small-scale industries” to financial services.

4.b. Comment and limitations

The definition of size class in many countries is tied up with the legal and policy framework of the country. It has implications on registration procedure, taxation and different waivers aimed to promote “small-scale industries”. Therefore, countries may agree on a common size class for compilation purposes. In this context, UNIDO proposes that all countries compile the data by a size class of “small-scale industries” as with less than 20 persons employed. From such data, an internationally comparable data on the share of “small-scale industries” in total could be derived.

4.c. Method of computation

The proportion of “small-scale industries” with a loan or line of credit is calculated as the number of “small-scale industries” with an active line of credit or a loan from a financial institution in the reference year in percentage to the total number of such enterprises:

$\frac{t h e n u m b e r o f " s m a l l - s c a l e i n d u s t r i e s " w i t h l o a n o r l i n e o f c r e d i t}{T o t a l n u m b e r o f " s m a l l - s c a l e i n d u s t r i e s "} \times 100$

The indicator is calculated as a share of small-scale manufacturing enterprises with a loan or line of credit in the total number of small-scale manufacturing enterprises. Calculation of the indicator can be extended for other economic activities.

4.d. Validation

This indicator is computed using data collected from the World Bank’s Enterprise Surveys. A detailed manual and guide on the Enterprise Surveys implementation is found here (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf). Section 4.4 “Data Collection Cycle” of this document describes the processes in place used to validate or check the survey data which is collected to ensure quality.

4.e. Adjustments

For any given survey, during the quality checks outlined in the Enterprise Surveys manual and guide (section 4.4), if inconsistencies or mistakes are found in the data, the World Bank transmits this feedback to the fieldwork team that is conducting the survey in the first place. The fieldwork team should make sure that any data mistakes are corrected (or if the data is indeed correct, provide the justification to the World Bank) when submitting the final survey dataset.

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

No treatment of missing values is applied at country level.

• At regional and global levels

No treatment of missing values is applied at regional and global levels.

4.g. Regional aggregations

The Enterprise Surveys are implemented every year in around 20 countries. Data frequency is limited for each country around 4 years. Regional and global averages are thus computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys adhering to the Enterprise Surveys Global Methodology are used to compute these regional and global aggregates.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

International Recommendations for Industrial Statistics. (2008).

https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf

International Standard Industrial Classification of All Economic Activities (ISIC).

https://unstats.un.org/unsd/classifications/Econ/isic

International Monetary Fund. (2011). Public Sector Debt Statistics: Guide for Compilers and Users. Washington, DC: International Monetary Fund.

https://www.elibrary.imf.org/view/IMF069/11874-9781616351564/11874-9781616351564/front.xml?language=en&redirect=true

World Bank Enterprise Surveys methodology.

https://www.enterprisesurveys.org/en/methodology

4.i. Quality management

A detailed manual and guide on the Enterprise Surveys implementation is found here (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf). This manual provides a comprehensive overview of the quality management of the Enterprise Surveys.

4.j. Quality assurance

The process of quality assurance includes the review of survey questionnaires/documentations/metadata, examination of reliability of data, and making sure they comply with international standards (e.g. workforce concepts in the survey questions correspond to ILO standards), and examining the consistency and coherence within the data set as well as with the time series of data and the resulting indicators.

The UNIDO quality assurance framework is followed to check data quality and consistency before data dissemination.

UNIDO (2009). UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740

4.k. Quality assessment

For any given survey, quality checks outlined in the Enterprise Surveys manual and guide (section 4.4), are implemented during data collection (survey fieldwork), and the World Bank transmits the resulting feedback to the fieldwork team that is conducting the survey in the first place.

5. Data availability and disaggregation

Data availability:

Data for around 150 economies were collected.

Time series:

Surveys are implemented every year in around 20 countries. Data frequency for each country is around 4 years.

Disaggregation:

No disaggregation available.

6. Comparability/deviation from international standards

Sources of discrepancies:

Discrepancies might arise due to the natural evolution of questionnaire design and survey methodology over time.

7. References and Documentation

URL:

https://www.enterprisesurveys.org/