Vandercasteelen, 2019). Poverty density, on the other hand, is seen to drop dramatically with distance from the urban center, consistent with the concentration of the rural population in the peri-urban areas reported in the URCA global database. Overlaying information from the Demographic and Health Surveys, the Afrobarometer, and pollution data with population density data from the Gridded Population of the World Version 4 (GPWv4) for Sub-Saharan Africa, Gollin and co-authors (2021) further show the existence of rising gradients by population density in a series of poverty correlates such as private wealth and consumption, housing quality, access to public goods and amenities, and child health. They do not find any decline in air quality nor a noticeable increase in crime.

Yet, the spatial information base underpinning these findings is often crude or limited to a few case countries<sup>7</sup> and the importance of different economic forces (agglomeration, skill sorting) affecting these outcomes, the strength of which differs along the urban hierarchy, and by extension, the surrounding hinterlands, remains poorly understood. Just like all urban areas are not the same, neither are all rural places made equal: not all rural societies are constituted by dispersed and relatively isolated villages, with little access to services and living off agriculture only and other primary activities. In fact, less than 1 percent of the global population lives in the rural hinterland (Cattaneo, Nelson, & McMenomy, 2021). On the other hand, in Sub-Saharan Africa, only 12 percent lives within 1 hour from a large city, while 41 percent of the rural population lives within 1 hour from a small city or town. As a result, it may be that strengthening bonds between smaller cities and surrounding rural areas has a greater potential for economic growth and poverty reduction than a focus on large cities, which are further away from where the poor live. For their case region Kagera, in Tanzania, De Weerdt and coauthors (2021) thus find that the deterring effect of distance by far outweighs the attraction of greater earning opportunity at the urban destination, with both effects reinforced for lesser educated and poorer populations. It explains why many rural–urban migrants end up in towns rather than cities.

Ultimately, these are empirical questions which require understanding how rural populations “gravitate” around different city types and, subsequently, integrating this information with spatial information on poverty. Regression-based poverty mapping using census data already represented a paradigm shift in the level of detail with which geographical patterns of poor populations could be mapped and understood. Combined with the URCA database, the link between a country’s urban hierarchy, with its rising gradient in earning opportunities and amenities, and poverty, mostly concentrated in the rural areas, can now be better understood and poverty reducing efforts better targeted across different URCAs.

To illustrate this, Figure 5 compares for Nigeria, the new disaggregated poverty map developed by the World Bank (2014) with the URCAs (Figure 5) and examines how this can support improved geographical targeting of poverty alleviation programs in the country. At the national scale, the broad pattern of poverty distribution is of predominately lower poverty rates in southwestern regions, with a general progression to higher poverty rates to the north and east. Almost a fifth of the population lives in large cities, and more than 40 percent in intermediate and small cities, or towns. The remainder mostly live 1-hour away from an urban center.

> 7 Gollin, Kirchberger and Lagakos (2021) are a notable exception.

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