# **1 Introduction**

The first Sustainable Development Goal (SDG) is to reduce global poverty.<sup>1</sup> As a custodian agency responsible for monitoring progress, the World Bank measures and monitors global poverty across countries based on income or consumption expenditures reported in household budget surveys and the international poverty lines (Ferreira et al., 2016; Ravallion et al., 1991; World Bank, 1990; World Bank, 2022). However, the current practice of measuring global poverty implicitly adopts official national definitions of urban areas, which have been shown to vary widely across countries (Dijkstra et al., 2021; Roberts et al., 2017; Satterthwaite, 2007; World Bank, 2009). This variation poses a challenge in disaggregating poverty by urban and rural areas in a globally comparable way. The lack of globally consistent information on urban poverty hinders efficient and effective resource allocation to achieve the SDGs. National governments may struggle to allocate resources optimally between urban and rural areas, as well as between different types of urban areas within their countries. Unfortunately, only a few attempts have been made to measure and analyze global poverty from an urban/rural perspective, with Ravallion et al. (2007) being an exceptional early example examining the urbanization of global poverty. At best, cross-country comparisons have been made based on poverty measures using national poverty lines (for example; Ferré et al., 2012).

Building on recent work by Combes et al. (2023), this paper aims to provide new evidence on poverty distributions within and between countries based on globally consistent urban and rural poverty measurements.<sup>2</sup> Combes et al. (2023) present a new effort to consistently delineate urban areas across countries using two different approaches: the Degree of Urbanization (DOU) and Dartboard (DB) approaches. The DOU approach classifies cells in a gridded population layer into different urban and rural categories by applying uniform population and population density thresholds to all countries (Dijkstra et al., 2021). Meanwhile, the DB approach also classifies gridded population cells but distinguishes different urban and rural categories based on each country’s own population density distribution (de Bellefon et al., 2021). In other words, the DOU is an absolute measure of urbanization, while the DB is a relative measure. These new measures suggest that official urban definitions tend to underestimate urban population shares in Sub-Saharan Africa (Figure 1). Moreover, the DOU and DB approaches distinguish multiple types of urban areas, providing a more nuanced understanding of urbanization beyond a simple urban-rural dichotomy (Cattaneo et al., 2022). For this study, a novel dataset was created by integrating these new urban classifications into the existing household budget surveys (HBS), covering 221,000 households and approximately 1 million individuals from 20 low- and middle-income countries across the world.

Based on this new international database of urban poverty, we primarily focus on 16 countries in Sub-Saharan Africa (SSA), where 60 percent of extreme poverty is concentrated (World Bank, 2022). We first examine a set of questions regarding poverty incidence across different geographic areas. One fundamental question is _whether and to what extent poverty incidence is lower in urban compared to rural areas_ . While productivity tends to be higher in denser areas due to agglomeration effects (Duranton and Puga, 2004; Duranton, 2015; Glaeser and Gottlieb, 2009; Gollin et al., 2002; Grover et al., 2021; Michaels et al., 2012) and the sorting of higher-ability individuals into these areas (Combes et al., 2008), monetary poverty is not necessarily lower in urban areas once the higher cost of living is considered<sup>3</sup> . Our newly created dataset confirms that for the 16 Sub-Saharan African countries we consider, the cost of living is higher in urban areas, particularly in denser urban areas. Urban poverty can be as prevalent and severe as rural poverty when the negative effects of density, such as crowding and congestion externalities, outweigh its benefits (Lucci et al., 2018; Marx et al., 2013). Additionally, it is an empirical

> 1Target 1.1 aims to ‘eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day’. The latest extreme poverty line is $2.15 per day in 2017 Purchasing Power Parity (PPP) terms (World Bank, 2022).

> 2The preliminary findings from 7 countries are reported in Combes et al. (2022).

> 3For example, African cities have a high cost of living relative to their country’s GDP level (Nakamura et al., 2019).

2