Table 5: Estimation results of regressions on log expenditures with DOU classifications

||||Log of per|capita nominal|consumption exp|enditures|||
|---|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>All|(3)<br>All|(4)<br>Urban|(5)<br>All|(6)<br>All|(7)<br>All|(8)<br>Urban|
|Urban center|0_._809<sup>_∗∗∗_</sup>|0_._504<sup>_∗∗∗_</sup>|0_._379<sup>_∗∗∗_</sup>|0_._367<sup>_∗∗∗_</sup>|0_._659<sup>_∗∗∗_</sup>|0_._405<sup>_∗∗∗_</sup>|0_._296<sup>_∗∗∗_</sup>|0_._296<sup>_∗∗∗_</sup>|
||(0_._009)|(0_._009)|(0_._010)|(0_._010)|(0_._012)|(0_._011)|(0_._011)|(0_._011)|
|Urban cluster|0_._203<sup>_∗∗∗_</sup>|0_._105<sup>_∗∗∗_</sup>|0_._060<sup>_∗∗∗_</sup>||0_._146<sup>_∗∗∗_</sup>|0_._068<sup>_∗∗∗_</sup>|0_._028<sup>_∗∗∗_</sup>||
||(0_._009)|(0_._008)|(0_._008)||(0_._010)|(0_._009)|(0_._008)||
|Spatial defator|||||0_._690<sup>_∗∗∗_</sup>|0_._475<sup>_∗∗∗_</sup>|0_._412<sup>_∗∗∗_</sup>|0_._588<sup>_∗∗∗_</sup>|
||||||(0_._038)|(0_._032)|(0_._031)|(0_._052)|
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Demographic|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Education|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Employment|_No_|_No_|_Y es_|_No_|_No_|_No_|_Y es_|_No_|
|Adjusted R2|0_._548|0_._663|0_._673|0_._626|0_._553|0_._665|0_._674|0_._630|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|134548|134548|134548|75505|134548|134548|134548|75505|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DOU method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Urban center” or “Urban cluster”) are included in specifications (4) and (8). The baseline category is “Rural” in all specifications, except (4) and (8), where it is “Urban cluster”. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

with high-density areas (urban centers and urban cores) having a higher premium. The density effects are particularly large in some countries, including Malawi, Niger, Uganda, and Angola. In these countries, urban centers and urban cores have a substantially higher premium than low-density urban areas. In the case of Tanzania, there is no premium observed in low-density urban areas. As regards the DB approach, towns, which are classified as rural, are poorer than cores and suburbs for most countries, and even poorer than fully rural places in Niger, Angola, and Tanzania.

# **5.3 Spatial distribution of poverty**

## _Where is the mass of poverty concentrated?_

In this section, we examine the spatial distribution of poverty and explore where the majority of poor households is concentrated. When using the official urban definitions, extreme poverty is primarily concentrated in rural areas across the 16 SSA countries studied (Figure 6). In fact, more than 80 percent of the poor populations reside in rural areas in 13 out of 16 countries. However, when we instead use the DOU/DB definitions, the share of rural areas declines, reducing the number of countries with more than 80 percent of poor populations living in rural areas from 13 to 8/2 countries. The median share of the poor in urban areas increases from 12.6 percent in the original urban definition to 20.7 percent in the DOU definition. See Figure A6 and Figure A7 in Appendix A for each country’s urban/rural shares.

The reasons behind the concentration of poverty in rural areas in many countries can be attributed to several factors. Firstly, a significant portion of the population still resides in rural areas (e.g., Chad) (Figure A13 in Appendix A). Secondly, poverty incidence is substantially higher in rural areas (e.g., Niger). Interestingly, there is no correlation between the share of poor populations in urban areas and the level of per capita GDP (Figure A12 in Appendix A). While there is a loose correlation observed with the DOU results, the DB results show no correlation. This is because higher GDP per capita is associated with higher urban population shares but lower urban poverty rates, making it difficult to determine the relationship between a country’s level of development and the share of its poor population residing in urban areas.

When using the DB approach (Figure 6), the spatial distribution of poor populations shows 18