Targeting Rural Poverty: A Generalized Ordered Logit Model Analysis of Multidimensional Deprivation in Ethiopia’s Bilate River Basin

Understanding the complex and multidimensional nature of poverty is essential for designing effective and targeted policy interventions in rural Ethiopia. This study examined the determinants of multidimensional poverty in Bilate River Basin in South Ethiopia, employing cross-sectional household survey data collected in 2024. A total of 359 households were selected using a multistage sampling technique, ensuring representation across agro-ecological and socio-economic zones. The analysis applied the Generalized Ordered Logit (GOLOGIT) model to categorize households into four mutually exclusive poverty statuses: non-poor, vulnerable, poor, and extremely poor. The results reveal that age, dependency ratio, education level, livestock and ox ownership, access to information and credit, health status, and grazing land access significantly influence poverty status. Higher dependency ratios and poor health substantially increase the likelihood of extreme poverty, while livestock ownership and access to grazing land reduce it. Notably, credit use and access to information typically considered poverty reducing were associated with increased extreme poverty risks, likely due to poor financial literacy and exposure to misinformation. These findings underscored the multidimensional and dynamic nature of poverty, driven by both structural and behavioral factors. Policy implications point to the importance of integrated interventions that promote education, health, financial literacy, and access to productive assets to ensure sustainable poverty reduction and improved rural livelihoods in Ethiopia.

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Publication Details

Journal
Economies
Published
2025-06-24
DOI
https://doi.org/10.3390/economies13070181
Citations
3
Primary Topic
Agricultural risk and resilience
Type
article
Field-Weighted Citation Impact
3.56
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article

Targeting Rural Poverty: A Generalized Ordered Logit Model Analysis of Multidimensional Deprivation in Ethiopia’s Bilate River Basin

Tekle Leza, Yishak Gecho, Frew Moges
3 citations
Economies
Agricultural risk and resilience
3.56
article

Targeting Rural Poverty: A Generalized Ordered Logit Model Analysis of Multidimensional Deprivation in Ethiopia’s Bilate River Basin

Tekle Leza, Yishak Gecho, Frew Moges
article en
3 citations

Abstract

Understanding the complex and multidimensional nature of poverty is essential for designing effective and targeted policy interventions in rural Ethiopia. This study examined the determinants of multidimensional poverty in Bilate River Basin in South Ethiopia, employing cross-sectional household survey data collected in 2024. A total of 359 households were selected using a multistage sampling technique, ensuring representation across agro-ecological and socio-economic zones. The analysis applied the Generalized Ordered Logit (GOLOGIT) model to categorize households into four mutually exclusive poverty statuses: non-poor, vulnerable, poor, and extremely poor. The results reveal that age, dependency ratio, education level, livestock and ox ownership, access to information and credit, health status, and grazing land access significantly influence poverty status. Higher dependency ratios and poor health substantially increase the likelihood of extreme poverty, while livestock ownership and access to grazing land reduce it. Notably, credit use and access to information typically considered poverty reducing were associated with increased extreme poverty risks, likely due to poor financial literacy and exposure to misinformation. These findings underscored the multidimensional and dynamic nature of poverty, driven by both structural and behavioral factors. Policy implications point to the importance of integrated interventions that promote education, health, financial literacy, and access to productive assets to ensure sustainable poverty reduction and improved rural livelihoods in Ethiopia.

EconomiesVol. 13(7)
Wolaita Sodo University (ET)
Openalex Percentile: Top 8%
Agricultural risk and resilience
3.56
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