A Distributional Analysis of Uni-and Multidimensional Poverty and Inequalities in Ethiopia

Abstract

This study analyzes uni-and multidimensional poverty and inequalities in rural and small towns in Ethiopia. Unlike the unidimensional measure, the multidimensional measure of poverty shows all the channels through which poverty may manifest itself; it also shows the extent of deprivation. The analysis uses 6 dimensions with 14 indicators to construct a multidimensional index of poverty and inequalities using Ethiopian Households’ Socioeconomic Survey dataset. The study also uses multiple correspondence analyses for determining relative weights in computing a multidimensional index and conducts a stochastic dominance analysis of distribution of poverty for different population segments. The paper sheds light on the degree of inequalities in consumption expenditure and multidimensional deprivations. In addition, it also compares the degree of poverty using the conventional measure of poverty and the multidimensional approach. It also examines the determinants of household poverty status using both unidimensional and multidimensional measures using the logit model. The results show that the intensity, severity, and depth of poverty varies substantially across the 2 measures. The unidimensional measure of poverty shows that 36 percent of the households were poor as compared to 46 percent multidimensionally poor households. Moreover, demographic, regional, and household heads’ characteristics also affect households’ poverty status.

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“… 15 , 19 , 37 It could be a proxy for wealth because it reflects increased access to foods purchased outside the household. 38 We also found that child dietary diversity is significantly associated with maternal education. Similar findings have been reported by other studies.…”

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“…This vast literature on the determinants of multidimensional poverty has been used with greater emphasis to examine poverty levels in L&MICs, such as Ethiopia (Bersisa & Heshmati, 2021), Nigeria (Olarinde et al, 2020), Cameroon (Gautam et al, 2021), Brazil (Medeiros et al, 2021), and Bangladesh (Lázár et al, 2020). For example, Bersisa and Heshmati (2021) showed that demographic and regional characteristics influence household poverty levels in Ethiopia. In Bangladesh, Lázár et al (2020) found that long-term reductions in structural poverty are caused mainly by the stability and performance of livelihoods, while the households that remain in poverty are those that experience high seasonality in their income and are involved in small-scale activities.…”