# **6 Discussion and conclusion**

This study presents a new analysis of internationally comparable urban and rural poverty statistics, utilizing a novel dataset created for 16 SSA countries. By integrating consistent urban delineation approaches into the framework of global poverty measurement, we provide valuable insights into the spatial dimensions of poverty.

Primarily focusing on 16 SSA countries, our analysis highlights the urgent need to address urban poverty in order to accelerate global poverty reduction efforts. Based on internationally consistent urban and poverty measures, we find that urban poverty rates are consistently lower than rural poverty rates in all the studied countries. Notably, poverty incidence in dense urban areas is particularly low, while poverty rates in low-density urban areas are closer to those in rural areas. The patterns hold even when accounting for country fixed effects and controlling for observed household characteristics. The use of the DOU and DB approaches, instead of relying solely on each country’s official urban definition, reveals higher urban poverty rates in most countries. However, the choice between the DOU or DB approaches does not significantly impact the results. In terms of the spatial distribution of poverty, our findings challenge previous assumptions by revealing that a larger share of the poor population resides in urban areas than previously thought. While the incidence of poverty may be lower in urban areas, the sheer number of people living in these areas compensates for this, resulting in a significant concentration of poverty. In many of the studied countries, more than half of the poor populations live in DOU/DB urban areas, with dense urban areas accommodating a substantial share of these poor populations. Furthermore, our analysis demonstrates that the choice between the DOU and DB approaches has a critical impact on the spatial distribution of poverty. The DB approach, which is specific to each country and captures local variations in population density, better reflects the concentration of poverty in urban areas. This is particularly evident in countries with either high or low average population density.

From a methodological perspective, our study carries important implications. Our approach allows for the disaggregation of official global poverty statistics into globally consistent urban and rural areas, as well as more nuanced geographic categories along the urban-rural continuum. This is the first study to attempt such an endeavor. We do not exclusively recommend one over the other, as both the DOU and DB approaches have advantages depending on the purpose of the analyses. Additionally, our results highlight the importance of understanding the spatial distribution of poverty at a global scale in order to allocate resources more efficiently and effectively for poverty reduction and the achievement of the SDGs.

While our approach is innovative, it is important to acknowledge its limitations. Firstly, our data is cross-sectional in nature, which means that we cannot fully control for unobserved heterogeneity, such as sorting based on individual abilities, by adding individual fixed effects. Although it is theoretically possible to add more time points to our dataset using GHSPOP and WorldPop data from other years, the comparability of these datasets and the consistency of consumption and poverty measures in HBS over time need to be carefully assessed, which is beyond the scope of this study. Another methodological limitation is the availability of data in the HBS. Our approach can only analyze countries where the location information of geographically disaggregated units is available in the HBS. This data availability in the HBS is crucial to the approach. Furthermore, there is inconsistency in the spatial deflation approach within the current global poverty monitoring system. Many countries measure global poverty without adjusting for subnational cost of living differences. While we apply spatial deflators to such countries, the spatial deflation approaches are not consistent across countries. The quality of the underlying population layers and HBS can also impact our results. The lack of availability of a recently conducted population census, which is common in low-income countries, particularly poses a threat to the quality of gridded population datasets.<sup>30</sup>

> 30For example, Ethiopia's latest population census was conducted in 2007.

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