Microlevel structural poverty estimates for southern and eastern Africa

Elizabeth Tennant (), Yating Ru, Peizan Sheng, David S. Matteson and Christopher B. Barrett ()
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Elizabeth Tennant: a Department of Economics , Cornell University , Ithaca , NY 14853
Yating Ru: c Department of City and Regional Planning , Cornell University , Ithaca , NY 14853
Peizan Sheng: d Harris School of Public Policy , University of Chicago , Chicago , IL 60637
David S. Matteson: e Department of Statistics and Data Science , Cornell University , Ithaca , NY 14853
Christopher B. Barrett: g Cornell Jeb E. Brooks School of Public Policy , Cornell University , Ithaca , NY 14853

Proceedings of the National Academy of Sciences, 2025, vol. 122, issue 6, e2410350122

Abstract:

For many countries in the Global South traditional poverty estimates are available only infrequently and at coarse spatial resolutions, if at all. This limits decision-makers’ and analysts’ ability to target humanitarian and development interventions and makes it difficult to study relationships between poverty and other natural and human phenomena at finer spatial scales. Advances in Earth observation and machine learning-based methods have proven capable of generating more granular estimates of relative asset wealth indices. They have been less successful in predicting the consumption-based poverty measures most commonly used by decision-makers, those tied to national and international poverty lines. For a study area including four countries in southern and eastern Africa, we pilot a two-step approach that combines Earth observation, accessible machine learning methods, and asset-based structural poverty measurement to address this gap. This structural poverty approach to machine learning-based poverty estimation preserves the interpretability and policy-relevance of consumption-based poverty measures, while allowing us to explain 72 to 78% of cluster-level variation in a pooled model and 40 to 54% even when predicting out-of-country.

Keywords: assets; expenditures; machine learning; poverty maps; small area estimates (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:122:y:2025:p:e2410350122

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