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arXiv · 2306.12674

Mapping poverty at multiple geographical scales

Silvia De Nicolò·Enrico Fabrizi·Aldo Gardini
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Abstract

Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their heterogeneity at finer levels. We introduce a small area multi-scale approach integrating survey and remote sensing data that leverages information at different spatial resolutions and accounts for hierarchical dependencies, preserving estimates coherence. We map poverty rates by proposing a Bayesian Beta-based model equipped with a new benchmarking algorithm that accounts for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.

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Silvia De Nicolò, Enrico Fabrizi, Aldo Gardini. 2023-06-22. Mapping poverty at multiple geographical scales. https://doi.org/10.1093/jrsssa%2Fqnae023

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