Project Description#
Often in development, we look to national policies and comparisons between them to find examples of effective policy, whether this is in economic growth, job creation, or public service delivery. However, this focus on national comparisons limits our perspective to one scale of analysis. Within most countries, there is both more than one level of governance, and therefore policy and public investment, but there are also uneven initial endowments and resources, levels of private investment and capital flows, and other unique factors of place. Exploring these differences at various geographic scales can shed light on why development policy can be more effective in one region than another. For reasons of comparability, cost-effectiveness, and the mandates of data collectors, this spatially disaggregated data is often not available from one country to the next, and often not comparable.
Over the next four years we will invest in the development and publication of newly derived and disaggregated datasets as well as open-source models and tools used to develop them. The team will innovate to develop, test, and publish useful methods, tools, and derivative data sets to fill priority data gaps. Specifically, this will focus on the assembly, quality assessment, and publication of multiple relevant economic, environmental, social, and governance data disaggregated at several scales: official subnational units, degree of urbanization, city extent, and metropolitan area. Besides the publication of these data at pre-established geographies, the project team will enhance the processes and tools for adding these geographic variables to existing survey data. Finally, we will make improvements in the discovery and use of existing data by developing improved open-source tools and guidance for users to discover, create, and repurpose open, contextual data in underserved communities.
Geographic variables and indicators will be prioritized based on their relevance to economic geography outside high-growth cities across all income categories. The goal is to provide more insight into neglected places, especially second-tier cities and rural areas. This will provide a more current/accurate understanding of the continuum of urbanization, and the relationship to growth, energy consumption, greenhouse gas (GHG) emissions, industrialization, and public and private investment.
Where appropriate, the project team will develop a suite of new data products to facilitate this line of work. Out prior experience in the development and deployment of Survey Solutions, NADA plus, The Global Electrification Platform, the renewable energy zoning tool, the Light Every Night database, and the GOSTnets mobility toolset, as well as support to multiple Urbanization reviews and Flagship publications make it the right place in the World Bank to implement the desired program around spatial data disaggregation and survey microdata enhancement. We will rely on the forthcoming Chief Statistician’s Data Quality Assurance Framework for data quality checking and metadata standards.