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<p>Defining and measuring poverty influence the identification of individuals in poverty and the effectiveness of efforts to eradicate it. This paper examines the degree of matches and mismatches of various perspectives on poverty using data from the Ethiopian Socio-economic Survey. A fixed-effects multinomial logit model examined the <i>key</i> drivers behind overlap and mismatch in poverty profiling. It finds that monetary measures have declined more steadily than other measures, with poverty often persisting through temporary escapes and ongoing deprivation. Different measurement approaches reveal low static correlation and dynamic mismatches, suggesting that a one-size-fits-all strategy is ineffective. Regression results indicate that factors such as demographic variables, indebtedness, land renting, risk exposure, non-farm activities, literacy, participation in safety net programs, and wage labor were correlated with the discrepancies in poverty measurements in Ethiopia. Relying on a single measure risks misrepresenting the complex nature of poverty and may lead policymakers astray. Therefore, a combined index is necessary for a more comprehensive understanding of poverty.</p>

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Scrutinizing whether a one size fits all approach suffices to profile poverty in rural Ethiopia

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  • 发表日期：

    2026年5月18日

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  • Dereje Haile

摘要

Defining and measuring poverty influence the identification of individuals in poverty and the effectiveness of efforts to eradicate it. This paper examines the degree of matches and mismatches of various perspectives on poverty using data from the Ethiopian Socio-economic Survey. A fixed-effects multinomial logit model examined the key drivers behind overlap and mismatch in poverty profiling. It finds that monetary measures have declined more steadily than other measures, with poverty often persisting through temporary escapes and ongoing deprivation. Different measurement approaches reveal low static correlation and dynamic mismatches, suggesting that a one-size-fits-all strategy is ineffective. Regression results indicate that factors such as demographic variables, indebtedness, land renting, risk exposure, non-farm activities, literacy, participation in safety net programs, and wage labor were correlated with the discrepancies in poverty measurements in Ethiopia. Relying on a single measure risks misrepresenting the complex nature of poverty and may lead policymakers astray. Therefore, a combined index is necessary for a more comprehensive understanding of poverty.