isabel.molina@uc3m.es

(joint work with J.N.K. Rao)

 

Poverty mapping is crucial in order to find which are the critical regions to which policies aimed at reducing poverty should be targeted and then allocate the corresponding funds in a rational way. Unfortunately, when detailed maps are required for small regions, often official surveys do not have enough sample data within all target regions to provide reliable regional estimates. Those regions that are not well covered by the sample are called “small areas". For those areas, direct estimators, which use solely the data from the corresponding area, do not have enough precision. “Small area estimation" is the field that studies methods for improving the efficiency of direct estimators. This is achieved by means of implicit or explicit models that link all the areas. Small area estimation of poverty indicators is a challenge because most of poverty indicators are non linear with complex shapes. The basic procedures for small area estimation of general non linear parameters will be reviewed. More recent contributions that try to extend the basic methods to a wider range of situations will be also described. The goodness of these methods will be illustrated by the results of simulation studies. Poverty maps obtained in an application with Spanish data from the Survey on Income and Living Conditions will be also shown.

Keywords: empirical Bayes; hierarchical Bayes; linear mixed models; poverty indicators; small area estimation.