Mapping poverty at multiple geographical scales
Poverty mapping is a powerful tool to study the geography of poverty.
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.
This is an output from the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme.
Citation
De Nicolò, S., Fabrizi, E., & Gardini, A. Mapping poverty at multiple geographical scales (preprint), ResearchGate, June 2023