|Classifcation|Defnition|
|---|---|
|Towns|Sets of contiguous grid cells with population density > 95<sup>th </sup>percentile|
||of the counterfactual with no core.|
|Other rural|Areas not classifed as cities or towns.|
|areas||

Note: See Combes et al. (2023) for details. Towns are classified as urban in the stricter version of definition and as rural in the looser version of definition in Combes et al. (2023)

# **3 Data**

For this study, we have prepared a new dataset called the International Urban Poverty Database by integrating two types of data: high-resolution gridded population data for constructing urban classifications, and detailed household-level data for constructing poverty measures.

## **3.1 Gridded population layers**

We have utilized two gridded population datasets for the year 2015: GHSPOP and WorldPop, with a resolution of 1km and 250m, respectively. Both GHSPOP, created by the European Commission, and WorldPop, produced by the University of Southampton, are open-source datasets that cover most countries globally for multiple years.<sup>15</sup> The preparation of both datasets involves allocating each country’s census-based population across gridded cells within a given administrative area. For GHSPOP, the population is evenly distributed across grid cells that contain built-up areas (Florczyk et al., 2019). In contrast, WorldPop utilizes a machine learning approach, specifically the random forest method, to calculate weights for each grid cell. These weights are based on various spatial input layers, including land cover, nighttime lights, and (social) infrastructure data. The population is then distributed across grid cells using these weights. Constrained WorldPop further constrains the distribution of the population to grid cells that contain built-up areas (Stevens et al., 2015).

In this study, our primary choice of input population layer is WorldPop, while GHSPOP serves as an alternative data source. Although GHSPOP was used by the European Commission to develop the DOU method, WorldPop, particularly constrained WorldPop, appears to better reflect the true spatial distributions of populations. This is because a detailed map of all building footprints was used for the development of constrained WorldPop layers for Sub-Saharan Africa.<sup>16</sup> When selecting the resolution for the datasets, we need to consider two opposing factors: computational speed and precision of the constructed urban classifications. We have chosen a 1km resolution for GHSPOP, as it is associated with the development of the DOU method. For WorldPop, we have opted for a resolution of 250 meters, which is four times more precise while still being computationally feasible.<sup>17</sup> The results of our analyses based on GHSPOP are reported in Appendix B.

## **3.2 Household budget surveys**

In this study, we utilized the HBS data collected around 2015, which aligns with the years of WorldPop and GHSPOP. A key data requirement for this study is the information about the location identifiers of individual households, enumeration areas (EAs), or other geographic units within the HBS. Our analysis primarily focuses on HBS data with the required information from 16 SSA countries, including Angola, Burkina Faso, Chad, Côte d’Ivoire, Ethiopia, Gabon, Ghana, Guinea, Guinea-Bissau, Lesotho, Mauritania, Malawi, Niger, Senegal, Tanzania, and Uganda (Table 2). Additionally, we report results based on a few extra countries outside Sub-Saharan

> 15GHSPOP data is available on the following European Commission website: https://ghsl.jrc.ec.europa.eu/ghs_ pop2019.php. WorldPop data is available here: https://www.worldpop.org/project/categories?id=3.

> 16See Combes et al. (2023) for details.

> 17The results based on 1km data are available upon request.

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