Estimation of poverty indicators at sub-national level using multivariate small area models

In recent years, the measure of regional disparities on poverty and social exclusion has received increasing attention by policy makers and this has produced a growing demand of sub-national statistical information on income parameters. Taking into account the multidimensionality of this phenomenon, in the Laeken European Council (Eurostat, 2003) a set of financial poverty indicators were suggested to monitor the progress in fighting inequality. In the European Union regional statistical information on income can be obtained from the European Community Household Panel, a survey designed to provide reliable estimates for large regions in the countries. The aim of this work is to estimate at sub-national level some of the income indicators suggested in the Laeken council. We propose Bayesian small area estimators based on multivariate area level models exploiting the correlation between different indicators. The tendency of model based estimates to over-shrink towards the synthetic component can be a draw-back for policy makers interested in capturing regional disparities in financial poverty. To preserve the relationship between different indicators and disparities over areas, we adopt a multivariate constrained Bayes estimator. The comparison between results based on different models allows us to select the estimator realizing the best compromise between gain in efficiency and reduction of the over-shrinkage.