Figure 3: Share of household heads working in agriculture across urban versus rural areas <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of household heads working in agriculture (%) Share of household heads working in agriculture (%) Share of household heads working in agriculture (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the share of household heads working in agriculture over different geographic areas in 16 SSA countries. WorldPop 250m is used for the DOU and DB methods.

poverty line considered ($2.15 or $3.65) or the method of urban delineation (official, DOU or DB) used. When using the extreme poverty line of $2.15 (Panel A), urban poverty rates are generally lower than rural poverty rates when urban areas are defined based on each country’s official definition. With the DOU approach, poverty rates are lowest in urban centers, followed by urban clusters and rural areas. In the case of the DB approach, poverty rates are lowest in urban cores, followed by suburbs, towns, and other rural areas. These patterns hold even when a higher poverty line is used (Panel B). This negative correlation between density and poverty is observed in almost all countries, as shown in Figure A4 in Appendix A.

It is interesting to note that poverty rates in denser urban areas, such as urban centers in the DOU approach and urban cores in the DB approach, are significantly lower than in other types of area. On the other hand, less dense urban areas, like urban clusters in the DOU approach and suburbs in the DB approach, tend to have poverty rates relatively close to rural areas.

The adoption of DOU and DB approaches leads to an increase in urban poverty rates as more rural households are reclassified as urban households. This finding is consistent with the study by Combes et al. (2023), which suggests that official national definitions of urban areas tend to underestimate levels of urbanization in African countries. Figure 5 compares urban poverty rates based on the official urban definitions (x-axis) and DOU or DB definitions (y-axis). Countries closer to the 45-degree line have similar poverty rates for both definitions. Several countries have higher urban poverty rates in the DOU or DB approaches than in the official urban definition, as they appear above the 45-degree line. For example, the urban poverty rate in Malawi (MWI) is more than 50 percent in the DOU and DB approaches, a significant increase from 20 percent with the official urban definition. Similar substantial increases are observed in a few other countries, such as Niger (NER) and Uganda (UGA). The comparison of urban poverty rates in the DOU and DB approaches in Panel C show that most countries have similar poverty rates between the two approaches, with a handful of countries (such as Malawi, Niger, Chad [TCD], etc.) having higher poverty rates with the DB approach.

While the negative cross-country correlation between poverty rates and GDP per capita is well known (see Panel A in Figure A1 in Appendix A), it is unknown whether such a correlation is observed for urban and rural poverty rates. Figure A8 demonstrates a linear relationship between urban and rural poverty rates observed across countries: countries with lower urban poverty rates also tend to have lower rural poverty rates. As a result, when urban areas are defined by the DOU or DB methods, urban and rural poverty rates tend to be lower in countries with higher per capita GDP levels (Figure A9 in Appendix A).

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