Household Poverty Determinants in Kenya: A Demographic and Health Survey Wealth Index Approach

(ProQuest: . denotes formulae omitted.)IntroductionPoverty is unjust. The World Bank estimates that roughly 46% of Kenyans are poor at national poverty lines. Analysis of poverty in Kenya has largely depended on traditional measures such as household incomes, expenditures and consumption levels. However, these choices of measurement variables are inherently inaccurate in a developing country setup as opposed to the case in a developed one. Data on accurate incomes, expenditures or levels of consumption are difficult to collect. The use of indices is highly instrumental in this setup where households are ranked by scores on common variables capturing their asset endowments (Miriam et al., 2004). The calculated wealth index in the DHS is such one of these indices that are used to categorize households in respect to their economic welfare.The aim of this paper is to try and find out what determines whether a given household is firstly poor or rich while using the binomial logit and secondly to probe into what determines a household's probability to be poorest (hard core poverty). Inherent in themselves, indices are richer in the fact that they less likely suffer from measurement errors as is the case when measuring incomes, expenditures or consumption data. Secondly, they are constructed from a set of common household asset endowments that are observable and respondents do not find them cumbersome to report (Booysen et al., 2008). Thirdly, indices can show long run welfare effects as opposed to expenditure or consumption data (Filmer and Pritchett, 2001) in that, a household that reports ownership of tillable land can arguably be said to have better long run welfare effects than just having reported high expenditure in the past month or so. We therefore sought to answer these questions;(i) What are the likely determinants of household welfare? (ii) What is the probability associated with households being considered poorest? Following the famous works of (Filmer and Pritchett, 2001), the use of DHS has been wider where studies have attempted to unpack welfare relationships and other economic variables such as health care. While showing the relationship between enrolment in schools and household welfare, (Filmer and Pritchett, 2001) use asset index from DHS data to show that in the absence of expenditure data, relationships can still be derived. In some of their earlier works they categorically show that there exist deviations in education attainment among households with different asset rankings in four countries examined: Pakistan, Nepal, Indonesia and India. In analysing poverty in nine countries in Africa, (Sahn and Stifel, 2000) employ the DHS data by constructing an asset index using the principal component analysis approach. Among other users of DHS data is (Booysen, 2002) in the study on poverty analyses done in South Africa. The study uses an asset index to compare welfare among households in South Africa. Where unemployment is rampant, it is likely that income or expenditure data may not fully represent long run welfare effects for households. Insurmountable evidence is in the empirical domain that asset indices can be used largely to avert the problems confounded in income, expenditure or even consumption data. This index is handy even in discrete choice modelling where latent variables can be studied to aid in revealing probabilities of what would drive units of analyses to either one extreme of poverty or the other. In Kenya studies have largely relied on expenditure and consumption data. (Mwabu et al., 2002) estimated poverty determinants using a linear regression model to identify that the household's economic activity, household size, household location (rural vs urban), education level of household head and livestock endowment explained significantly, the economic welfare of households. Using a discrete choice model (Nyaboke, 2000) estimated poverty in Kenya employing the Welfare Monitoring Survey data of 1994. …