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.

info:eu-repo/semantics/publishedVersion