A Logit Model Approach in Investigating the Impact of Education on Poverty Alleviation in Urban Bulawayo in Zimbabwe

Japhet Mulate and Lawrence Dumisani Nyathi
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Japhet Mulate: Economist, Homelink Pvt Limited (Subsidiary of Reserve Bank of Zimbabwe)
Lawrence Dumisani Nyathi: Lecturer, Department of Banking & Economic Sciences, National University of Science and Technology

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 11, 737-750

Abstract: The study investigates the impact of education on poverty in urban Bulawayo using a multinomial logistic regression model. The primary objective is to assess how different levels of education affect the likelihood of households falling into various poverty categories—non-poor, poor, and extremely poor. Additionally, the research examines the role of other factors, including household size, gender, marital status, and the number of dependents, in determining urban poverty status. Data for the study were collected using a combination of primary sources (questionnaires and interviews) and secondary sources (the 2022 Zimbabwe Population Census Report). The sampling involved a multistage probability approach, which ensured a representative distribution of households across different poverty levels. The multinomial logistic regression model was chosen because it allows for an analysis of multiple categorical outcomes, making it suitable for modeling the poverty status with respect to various explanatory variables. The findings reveal that education significantly and positively impacts poverty levels. education showed a positive relationship with poverty for certain urban households in Bulawayo, indicating that higher educational levels may correspond with higher poverty levels in certain cases. the findings are aligned with studies in economically constrained regions, where limited job absorption, skill mismatch, and high education costs can weaken education’s protective effect against poverty. The study also finds that household size and the number of dependents is significant predictors of poverty, with larger households experiencing higher poverty risks. Marital status and gender further demonstrate significant relationships with poverty; for instance, female-headed households have a higher probability of being poor. The study concludes that investment in quality education is crucial for poverty reduction in Bulawayo. It recommends enhancing educational quality and skill-based training to improve employability. This targeted approach could reduce poverty by equipping individuals with the competencies needed for the labor market.

Date: 2024
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