non-monetary dimensions leads to a larger bias when evaluating the _incidence_ of low well-being in the south zones than in the north zones. For instance, the two zones with the largest fraction of omitted poor are in the south. This finding may be surprising given that (1) zones in the south have a smaller fraction of other-dimensions poor than zones in the north and (2) other-dimensions poverty has a similar correlation with monetary poverty in the north as in the south, as suggested by the coefficient of overlap. A key reason for this larger bias in south regions is that other-dimensions poverty is less heterogeneously distributed across space than monetary poverty. Another fact worth highlighting in Table 2 is that consistent poverty is the type of poverty most heterogeneously distributed across space. Consistently poor individuals are highly concentrated in north zones. Clearly, this follows from the fact that north zones both have higher monetary poverty rates and higher other-dimensions poverty rates. This potentially suggests that chronic monetary poverty might be largely concentrated in the north zones (Bolch et al. 2022).

_Table 2: Prevalence of different types of multidimensionally poor individuals, per zone, baseline scenario_

|||||**Zo**<br>|**nes**<br>|||**Coefficient of**|
|---|---|---|---|---|---|---|---|---|
||**National**|**South**<br>**West**|**South**<br>**South**|**South**<br>**East**|**North**<br>**Central**|**North**<br>**West**|**North**<br>**East**|**variation across**<br>**states**|
|Multidimensionally|||||||||
|<br>poor|51.9%|23.3%|36.1%|45.1%|54.2%|71.7%|80.5%|0.44|
|Monetary poor|40.5%|9.7%|20.8%|38.2%|43.1%|61.8%|69.9%|0.61|
|Other- dimensions<br>poor|27.9%|16.2%|20.9%|15.4%|30.0%|37.0%|45.6%|0.47|
|Omittedpoor|11.4%|13.5%|15.3%|6.9%|11.1%|10.0%|10.6%|0.52|
|Consistently poor|16.5%|2.6%|5.6%|8.4%|18.9%|27.0%|34.9%|0.79|
|Coefficient of overlap|2.1|1.8|1.4|2.0|2.2|1.7|1.4|0.30|
|Life expectancyat birth|49|52|50|50|49|49|45|0.06|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table shows different poverty rates: multidimensional poverty, monetary poverty, and non-monetary poverty at the national level and within Nigeria's six zones. The table also shows the level of overlap between monetary poverty and other dimensions poverty, materialized by the overlap coefficient and the life expectancy at birth. The variability of each of the above measures is compared using the coefficient of variation across stated presented in the last column.

# **Impact of assumptions on the fraction of poor**

We further study the sources of (multidimensional) poverty by using another method. We slightly change the assumptions of our design to study the impact of these assumptions.

First, we look at the respective impact of the three non-monetary dimensions of our baseline design (health, housing and security). More precisely, we contrast the fraction of other-dimensions poor and omitted poor when removing one of these three dimensions from our design. Table 3 provides the results. The results reveal that the importance of one dimension not only depends on the fraction of deprived individuals but also on the correlation between that dimension and monetary poverty. Indeed, for our baseline design, housing has the largest incidence of deprivation and removing it has the largest impact on the fraction of other-dimensions poor. However, housing is much more correlated with monetary poverty than health. As a result, health has the largest impact on the fraction of omitted poor. Table 3 23