Mapping poverty at multiple geographical scales

2023/06/22 by Silvia De Nicolò, De Nicolò, Silvia, Enrico Fabrizi +3
Medicine · Economics, Econometrics and Finance · Computer Science · #Data-Driven Disease Surveillance #Spatial and Panel Data Analysis #Bayesian Methods and Mixture Models

paper · pdf · doi:10.48550/arxiv.2306.12674

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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