Poverty and inequality indicators are often reported at the country level. However, these averages mostly hide large differences within countries themselves, leading to substantial disparities at a subnational level. For example, poverty rates in the poorest subnational region within low- and middle-income economies are often more than 30 percentage points higher than in the least poor subnational region (Figure 1). In the capital region of Chad, only 3 percent of the population lives on less than $2.15, while the poverty rate of the whole country is 31 percent, as reported in our 2024 Poverty, Prosperity, and Planet Report. Progress in reducing poverty may also be uneven within countries, as people in different locations face different opportunities and setbacks.
Understanding where these spatial inequalities exist and how they evolve over time is crucial to fighting global poverty. The World Bank’s Geospatial Poverty Portal, a part of Poverty and Inequality Platform, helps to fill this knowledge gap by providing subnational poverty, inequality, and multi-dimensional poverty indicators over time. This blog highlights recent major updates to the Portal.
The updated Subnational Poverty & Inequality Database (SPID) provides poverty and inequality indicators based on household surveys representing 2,045 unique subnational regions in 133 countries. The indicators cover a period of up to 20 years, allowing users to explore long-run changes in poverty and inequality across time and space.
The SPID includes measures of monetary poverty, multidimensional poverty, prosperity gap and inequality. This allows for direct comparison between different welfare metrics within a country. As shown below, Figure 2 illustrates the relationship between extreme poverty ($2.15 per person per day) and the Multidimensional Poverty Measure (MPM) at the subnational level. Subnational regions with similar rates of monetary poverty can have very different levels of multidimensional poverty, even within the same country.
Since 2024, many more survey years have been added to SPID. The database now has indicators from all subnational representative household surveys in our global database. This adds up to more than 13,000 subnational unit-years, with more than 10 survey years for some countries. The time series data allows users to explore trends in poverty and inequality within countries.
Also, subnational indicators for population subgroups are now available in the SPID. These allow users to explore, for example, how poverty rates differ between children and adults within countries over time. Figure 3 shows that children are more likely to live in poor households in most subnational regions. Users can generate and download charts comparing poverty rates between the two population groups directly on the platform.
The World Bank’s new Prosperity Gap indicator is included in the SPID. The indicator is a measure of the average shortfall from a prosperity standard of $25 per day, and it is made available at subnational level for the first time. SPID users can analyze this new measure of shared prosperity within countries over time. For example, Figure 4 shows how subnational prosperity gaps have evolved over time in Indonesia.
The Global Subnational Atlas of Poverty (GSAP) has been updated with more lineup years and regions. GSAP provides poverty rates using several lines that have been “lined-up” to a common reference year (2010, 2019, 2021). The method to lineup poverty estimates is the same as used for global poverty monitoring on the Poverty and Inequality Platform (PIP).
Merging subnational poverty and inequality with spatial data will enable more robust analyses. Boundaries defining all subnational regions in SPID and GSAP are available to download. This allows the subnational indicators to be merged with other datasets to study the relationship between welfare and policies or events affecting particular areas of a country, such as major infrastructure projects or weather shocks. The new World Bank Group Scorecard Vision Indicator “Percentage of people at high risk from climate-related hazards” is one example. It merges estimates of the population exposed to cyclones, droughts, floods and heatwaves derived from spatial data with survey-based indicators of vulnerability from SPID and other sources to calculate the number of people at high risk from these events.
We encourage you to take full advantage of access to these updated and extended datasets and look forward to seeing your work.
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