Identifying Poverty Groups in Nairobi's Slum Settlements: A Latent Class Analysis Approach

Leontine Alkema, University of Washington
Ousmane Faye, Université de Liège
Michael Mutua, African Population and Health Research Center (APHRC)

This paper aims to contribute to knowledge on conceptualizing and measuring urban poverty by categorizing households according to their socio-economic status. We identify groups with similar profiles of socio-economic status using Latent Class Analysis (LCA). In LCA an unobserved, latent variable (poverty) explains the association between observed variables (indicators of socio-economic status). Compared to other methods for measuring poverty (such as Principal Component Analysis), in LCA the number and size of the poverty groups is determined by the data. This study uses data from the longitudinal Nairobi Urban Health Demographic Surveillance System to identify poverty groups in two slums in Nairobi; Korogocho and Viwandani. In Korogocho we identify three groups, the poorest group accounting for 19% of all households. In Viwandani we identify four groups, with 27% of the households in the poorest group.

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Presented in Session 96: New Approaches in Demographic Estimation and Modeling