THE DETERMINANTS OF URBAN POVERTY IN ETHIOPIA; IN THE CASE OF GIRAWA TOWN
Date
2019-06
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Publisher
WOLKITE UNIVERSITY
Abstract
Poverty is multi-dimensional phenomenon that extends beyond the economic arena to encompass factor such as the in ability to participate in social and political life. This study aims identifying the determinants of urban poverty in Grawa town with specific objectives of measuring the extent of poverty in the study area and identifies variables that affect the probability of being poor statistically significantly. Four urban kebeles were selected based on multi stage sampling techniques. The data is collected from both primary and secondary sources. The primary data for this study is collected from 105 household heads through application of appropriate statistical procedures. To analyse the collected data, Stata software was used. Logistic regression model was also used to analyse factors that determine urban poverty with the probability of a household being poor as a dependent variable and a set of demographic and socioeconomic variables as the explanatory parameters. By using Food Energy Intake (FEI) approach the surveyed households are identified as the poor and non-poor. Based on this, out of the 105 surveyed household heads, 65(53.85%) of them were found poor, the head count, poverty gap, and severity index of the survey obtained as 0.5385, 0.23 and 0.05 respectively. The variables that are positively correlated with the probability of being poor are family size,and marital status. The variables that are negatively correlated with the probability of being poor are age, sex, education, income, water access, household’s asset, and home ownership. Variables which affected significantly the incidence of poverty in the town are: age, family size, education level, monthly income of households, home ownership and households’ asset. Variables which are statistically insignificant indicators of poverty are; marital status, sex, and water access.
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Keywords
Urban,, Grawa,, Poverty, Food Energy Intake,, logistic regression model