Mapping poverty at multiple geographical scales

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.

22 pages, 7 figures

References in corpus (4)

• Sparsity information and regularization in the horseshoe and other shrinkage priors
• Micro-Estimates of Wealth for all Low- and Middle-Income Countries
• Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach
• High-Resolution Poverty Maps in Sub-Saharan Africa