The consumption gains of urban households, compared to rural households, can be attributed to differences in endowments rather than differences in returns to those endowments. The disparity in consumption levels between urban and rural households can be decomposed into two components based on the Oaxaca-Blinder decomposition (Blinder, 1973; Oaxaca, 1973): the difference in endowments (i.e., observed household characteristics, such as educational attainment and access to basic services) and the returns to those endowments. We conducted this decomposition analysis for each country. The results of the decompositions, as shown in Figure A18, indicate that the differences in endowments account for a significant portion of the consumption gaps between urban and rural areas. Furthermore, the disparity in endowments also plays a dominant role when analyzing the consumption gaps between densely populated and less densely populated urban areas.

# _Non-monetary poverty_

We can also analyze non-monetary poverty indicators, such as access to improved water sources, sanitation facilities, and electricity, using our dataset. To do this, we conducted regression analyses by replacing the outcome variable in equations (7) and (8) with dummy indicators representing access to each type of service. The results, presented in Table A5 in Appendix A, indicate that households residing in less densely populated areas are generally less likely to have access to improved water sources, sanitation facilities, and electricity. There is one exception, however, as observed in column 6 of Panel B under the DB delineation, where there is a slightly improved access to sanitation in suburbs compared to cores. This result may be driven by the presence of densely populated, yet underdeveloped slum areas, where infrastructure development is constrained due to a lack of available space. In other cases, households living in less dense areas have significantly lower access to basic amenities. This pattern holds across different types of amenities and is robust across delineation methods and the inclusion of various controls. Furthermore, when controlling for income (columns 2, 5, 8), the pattern remains consistent, indicating that that lower access to water, sanitation and electricity in less densely populated areas cannot be fully explained by higher incomes in more densely populated areas. Additionally, the results do not significantly change when controlling for demographic characteristics, education, and employment. In summary, our findings provide evidence that residents of less densely populated areas not only experience higher rates of monetary poverty but are also more susceptible to non-monetary forms of deprivation.

# _Employment across locations_

We also examine the employment patterns across different locations, considering the potential connection between spatial transformation (from rural to urban) and structural transformation (from agricultural to non-agricultural jobs). To investigate this, we estimated household-level regression models, using the employment shares in the agriculture and service sectors as the dependent variables. The results, which are presented in Table A6 in Appendix A, indicate that areas with lower population density tend to have a higher proportion of agricultural workers, while more densely populated areas tend to offer more employment opportunities in the service sector. This result remains consistent even after controlling for a set of observed household characteristics. Although this analysis is exploratory in nature, it suggests the significance of population density in the generation of productive jobs.

# _Robustness checks_

We replicate the analyses presented above based on WorldPop for 16 SSA countries by changing the gridded population layers to GHSPOP and by adding 4 non-SSA countries (Bangladesh, Colombia, Egypt, and Vietnam). The results in Appendix B and Appendix C, respectively, show that key messages from the main analysis hold.

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