## approaches, we utilize both the DOU and DB classifications.

To begin, we apply the DOU and DB classifications to the WorldPop and GHSPOP gridded population layers, which are then overlaid with geo-located HBS data. When GPS information is available in a country’s HBS, the overlay process is straightforward. However, for countries lacking GPS information, we overlay the data at the lowest possible geographic unit level. The population shares of each DOU/DB category are calculated for each geographic unit, by generating aggregated statistics, such as poverty rates at the national and sub-national levels with an application of sampling weights. To obtain a binary indicator for households in each type of DOU and DB area in a country’s HBS, we employ a “threshold” approach. This involves determining the number of people residing in grid cells of a particular type for each geographic area. If the majority of the population in an area lives in rural locations, the area is classified as rural. Conversely, areas are classified as urban under the DOU approach if the majority of the population resides in urban centers or clusters. Similarly, for DB indicators, areas are classified as core areas, suburbs, or towns based on the share of the population in each respective category.

It is worth noting that Henderson et al. (2019) also utilize one of the urban definitions employed in our study (the DOU approach) and gridded population data (GHSPOP). While both studies adopt a microdata approach, Henderson et al. (2019) primarily rely on the Demographic and Health Survey (DHS), whereas our study utilizes official household budget surveys. While the DHS provides valuable insights into a range of demographic and health outcomes, often more detailed than what is included in HBS data, the HBS data also encompasses a consumption (or income) variable to measure poverty, which is absent in the DHS data.

# **4.2 Integrating new urban classifications into global poverty measurement**

To improve the accuracy and consistency of poverty analysis, we apply the DOU and DB classifications instead of the previously used, but inconsistently defined, urban/rural classifications found in HBS data. This integration involves several steps.

First, we update the spatial deflators in HBS data using the DOU and DB classifications to convert nominal consumption expenditures to real ones (equation [1] and equation [2]). By incorporating spatial deflators, we are able to account for price variations across subnational areas. While constructing new spatial deflators for all countries based on the same methodology and price data would be ideal, it is not feasible due to variations in price data availability. Instead, we update the spatial deflators used for the official global poverty estimation.<sup>21</sup> The resulting changes in poverty rates are solely attributed to the reallocation of households between urban and rural areas based on the transition from official to consistent urban definitions.

Second, we modify the existing spatial deflators by recalculating them based on the new DOU/DB classification. For example, in the case of Tanzania, the official spatial deflator is a Paasche price index constructed using food unit values for 26 provinces without any urban and rural distinction. However, we utilize alternative geographic divisions, such as urban centers, urban clusters, and rural areas for six regions based on the DOU classification, and cores, suburbs, towns, and other rural areas for six regions based on the DB classification.<sup>22</sup> In most countries, we replicate the steps involved in calculating spatial deflators by adjusting their geographic divisions.<sup>23</sup>

> 21However, for countries where global poverty is estimated using nominal consumption—Bangladesh, Egypt, Ethiopia, Ghana, and Tanzania—we use the spatial deflator used for poverty estimation with the national poverty lines.

> 22For Tanzania, we group 26 provinces into six regions because it is impossible to distinguish DOU/DB categories within each province due to the limited HBS sample size. Also, we classify all households in Dar es Salaam as urban centers (DOU) or cores (DB) when constructing spatial deflators due to only a limited number of households living in other DOU/DB categories in Dar es Salaam.

> 23 Constructing a poverty line for each subnational region is a common practice. Poverty is measured based on nominal consumption aggregate—instead of real consumption aggregate—with regional poverty lines that take account of the cost-of-living differences across regions. Bangladesh and Egypt employ such a regional poverty line approach. We reconstruct regional poverty lines for those countries by changing the geographic divisions. For example, Bangladesh’s official poverty lines are constructed for 16 geographic domains (City Corporation, urban,

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