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 accounting for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.
De Nicolò, S., Fabrizi, E., Gardini, A. (In stampa/Attività in corso). Mapping non-monetary poverty at multiple geographical scales. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY, N/A, 1-24 [10.1093/jrsssa/qnae023].
Mapping non-monetary poverty at multiple geographical scales
De Nicolò, S.;Gardini, A.
In corso di stampa
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 accounting for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
