Indicator: 9.3.2
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 9.3.2: 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
2022-03-310.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]:
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.
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:
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.
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
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 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.
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 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 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:
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.
World Bank Enterprise Surveys 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/
References:
International Monetary Fund. (2011). Public Sector Debt Statistics: Guide for Compilers and Users. Washington, DC: International Monetary Fund.
United Nations. (2002). International Standard Industrial Classification of All Economic Activities (ISIC Revision 4). New York: United Nations.
https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf
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
World Bank Enterprise Surveys. 2020. Methodology. http://www.enterprisesurveys.org/methodology