The paper targets the estimation of the poverty rate at the Upazila level in Bangladesh through the use of Demographic and Health Survey (DHS) data. Upazilas are administrative regions equivalent to counties or boroughs whose sample sizes are not large enough to provide reliable estimates or are even absent. We tackle this issue by proposing a small area estimation model complementing survey data with remote sensing information at the area level. We specify an Extended Beta mixed regression model within the Bayesian framework, allowing it to accommodate the peculiarities of sample data and to predict out-of-sample rates. In particular, it enables to include estimates equal to either 0 or 1 and to model the strong intra-cluster correlation. We aim at proposing a method that can be implemented by statistical offices as a routine. In this spirit, we consider a regularizing prior for coefficients rather than a model selection approach, to deal with a large number of auxiliary variables. We compare our methods with existing alternatives using a design-based simulation exercise and illustrate its potential with the motivating application

This is an output from the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme