  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.
