Model-assisted estimation of small area poverty measures: an application within the Valencia Region in Spain

Abstract

This paper introduces small area estimators of poverty indexes, with special attention to the poverty rate (or Head Count Index), and studies the sampling design consistency and the asymptotic normality of these estimators. The estimators are assisted by nested error regression models and are model-assisted counterparts of model-based empirical best predictors. Simulation studies show that these estimators present a good balance between sampling bias and mean squared error. Data from the 2013 Spanish living conditions survey with respect to the region of Valencia are used to determine the performance of this new method for estimating the poverty rate.