Profiling multidimensional poverty and inequality in Kenya and Zambia at sub-national levels (updated, version 2)

Abstract

Persistent spatial disparities in poverty remain prevalent in most developing and transition economies. However, spatial analyses of poverty in poor countries are generally limited to rural-urban or provincial breakdowns. In addition, despite the fact that poverty is a multidimensional phenomenon, existing sub-national level poverty analyses mainly use money metric indicators of individual welfare. In this study, we use census data to estimate multidimensional poverty at lower levels of geographic disaggregation in Zambia and Kenya. Our results show that, in general, the extent of multidimensional poverty is significantly higher in rural areas than urban areas in both countries. However, although deprivation levels in access to basic services are relatively lower in large urban centres such as Nairobi and Mombasa in the case of Kenya, and Lusaka, Livingstone, and Ndola in Zambia, these urban centres are also areas where deprivation levels have increased significantly over time. These findings suggest that the extent of provision of basic services in urban centres do not match to the extent required to accommodate the rapid urban growth that has occurred over the last few decades in both countries. Furthermore, there are large differences in poverty within urban areas and even within cities. For instance, constituency level estimates show that within Nairobi city, the incidence of poverty varies from 20% in Westland constituency to 41% in Langata constituency. In the case of Zambia, within Lusaka city, the incidence of poverty ranges widely, from 17% in Kabwata constituency to 53-55% in Chawama and Kanyama constituencies. An examination of inequality, measured either with the Gini coefficient for income and asset-index and the variance for multiple deprivation levels, reveals important variations in intra-regional inequities across regions. This inequality picture cuts across the poverty status of regions. These results highlight the importance of sufficient level of geographic disaggregation in poverty analysis in order to identify disadvantaged areas within rural and urban regions of a country.