Table 2: List of Analyzed Countries

|Region|Country|
|---|---|
|Sub-Saharan Africa (16)|Angola, Burkina Faso, Chad, Côte d’Ivoire, Ethiopia<br>Gabon, Ghana, Guinea, Guinea-Bissau, Lesotho,<br>Mauritania,<br>Malawi,<br>Niger,<br>Senegal,<br>Tanzania,<br>Uganda|
|Additional countries||
|North Africa and the Middle East (1)|Egypt, Arab Rep.*|
|South Asia (1)|Bangladesh|
|East Asia and Pacifc (1)|Vietnam|
|Latin America and the Caribbean (1)|Colombia|

Note: *The Arab Republic of Egypt only has information about poverty status and urban classifications at the individual level, other information such as consumption expenditures, age, sex, and education is not currently available.

Africa (Arab Republic of Egypt, Bangladesh, Vietnam, and Colombia) in Appendix C. For six of the 16 SSA countries in our study, we were able to access household-level GPS coordinates, specifically for Ethiopia, Gabon, Lesotho, Mauritania, Malawi, and Tanzania. In the remaining countries, GPS coordinates are available for enumeration areas (EA) or administrative units.

Table 3 summarizes the household-level data pooled across 16 Sub-Saharan African countries.<sup>18</sup> Each HBS dataset contains valuable information on household per capita consumption expenditures, which serves as a crucial factor in estimating poverty rates.<sup>19</sup> The spatially deflated per capita consumption expenditures exhibited a right-skewed distribution, with a mean of $3.99 (in 2017 PPP terms) and a standard deviation of 8.13. To ensure consistency and comparability, we utilized the consumption expenditure measures prepared by the World Bank for global poverty monitoring. However, we made certain adjustments through the reclassification of urban areas and the update of spatial deflators, as explained in Section 4. In specific cases, such as Ghana, Tanzania, and Egypt, we further modified the consumption expenditures by incorporating housing rents. This modification was made due to the concern of missing housing rents in consumption aggregates, particularly given our study’s focus on urban versus rural areas. It is important to note that these adjustments resulted in lower global poverty rates for these countries compared to World Bank reports.<sup>20</sup> In addition to consumption expenditures, our dataset includes various variables related to demographics, such as household size, household head’s age, sex, and marital status. Furthermore, we have information on education, including literacy rates and the highest education level achieved by the household head. Additionally, employment-related variables, such as employment status and economic sector of the household heads, have been included. Lastly, our dataset encompasses access to basic services, such as improved water, improved sanitation, and electricity.

> 18The Arab Republic of Egypt has only information about poverty status and urban classifications at the individual level, lacking other information such as real consumption expenditures, age, sex, and education. Country-level summary information is presented in Table A1 in Appendix A.

> 19As explained in Section 2.1, consumption expenditures are first aggregated for each household and then divided by the number of members to obtain per capita expenditures. It is assumed that individuals in the same household have the same welfare level and poverty status.

> 20It is case-by-case whether the modification results in an increase in poverty or a decrease in poverty, as the addition of housing rents to consumption aggregates can be offset by updated spatial deflators that adjust for housing price variations across sub-national areas.

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