# **Appendix B: Data Description**

# **B1. Poverty data**

To implement the analysis, we assemble the most comprehensive data on poverty taken from the Global Subnational Atlas of Poverty (GSAP), produced by the Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in the Development Economics Data Group (DECDG). All the teams are at the World Bank.

For each survey data, the geographical area choice is based on the survey representativeness based on the sampling and sample design and survey documentation when available. For most of the database, surveys are representative at the first administrative level (ADM1) or statistical regions (areas) for the purpose of survey. On average, there are 14 subnational areas for a given country and year observation. For 18 small countries (13 percent), there is no subnational data available from the surveys, thus the national level data is used.

Subnational poverty rates are calculated using official household or income surveys for the purpose of global poverty monitoring. Poverty rates are provided at the subnational level that is representative for the associated household or income survey used. Overall, cross-sectional poverty statistics are shown for about 5,500 subnational areas based on survey representativeness and availability of matched spatial geographic boundaries.

Geographic boundaries must match the subnational regions in these surveys. In many cases, there is a one-to-one association between the regions in a household survey and the areas defined at an administrative level in the country. In cases where there is not a one-to-one association, geographic boundaries are altered to fit the representativeness of the surveys. In some cases, the geographic representation is at the level of “urban”, or “rural”. In these cases, subnational areas in the household survey are aggregated to levels that can be appropriately represented by boundaries. Several sources of geospatial files were leveraged to construct the GSAP: GADM, GAUL, NUTS, and customized spatial files. The choice of spatial files is based on more disaggregated availability and geographic alignment with household surveys. For example, NUTS spatial files are used prominently for the European countries in GSAP, since these files are developed and regulated by the EU.

Building on the cross-sectional GSAP database, we construct a new database on poverty statistics based on almost 500 available household income/expenditure survey data in the Global Monitoring Database (GMD)<sup>13</sup> for 139 economies, with more than 90 percent of the survey data ranging from 2010 to 2019. This database consists of panel data that are representative at 1,650 subnational areas. The number of countries across regions and over time are presented in Figures B1 and B2.

As both country boundary and survey representativeness can change over time, constructing a panel data of poverty at area-level is not a simple task. When there is a change

> 13 The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports.

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