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Publicação
Multidimensional poverty in Benin
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Orientador(es)
Resumo(s)
We examine an individual-level poverty measure for Benin using cross-sectional
data. Since our measure is defined within the interval [0,1], we combine fractional
regression models and machine learning models for fractions to examine the factors influencing multidimensional poverty measures and to predict poverty levels.
Our approach illustrates the potential of combining parametric models, that inform
on the statistical significance and variable interactions, with SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE) plots obtained from a
random forest. Results highlight the importance of addressing gender inequalities
in education, particularly by increasing access to female education, to effectively
reduce poverty. Furthermore, natural conditions arising from agroecological zones
are significant determinants of multidimensional poverty, which underscores the
need for climate change policies to address poverty in the long term, especially in
countries heavily reliant on agriculture. Other significant determinants of welfare
include household size, employment sector, and access to financial accounts.
Descrição
Palavras-chave
Multidimensional Poverty Benin Fractional regression model Machine learning SHAP values ALE plots
Contexto Educativo
Citação
Arranhado, Esmeralda, Lágida Barbosa e João A. Bastos (2024). "Multidimensional poverty in Benin". REM Working paper series, nº 0343/2024
Editora
ISEG – REM (Research in Economics and Mathematics)