Estimation of Poverty Rates for the Italian Population classified by Household Type and Administrative Region

The aim of the work is to provide estimates of some poverty rates for domains defined by cross-classifying the Italian population by household typology and administrative region, on the basis of data collected for Italy by the new “European Union – Statistics on Income and Living Conditions” survey (EU-SILC). This survey is designed to provide reliable estimates for large areas within countries much bigger than the sub-populations of our interest. To solve this problem, we suggest small area estimators derived from multivariate area level models, that improve the reliability of estimates “borrowing strength” over areas and by exploiting the correlation between the considered indicators. The unemployment rate calculated by household typology within administrative regions is used as auxiliary information to improve the precision of model based estimators. As estimation method we use a Hierarchical Bayesian approach implemented by means of MCMC computation methods. Among the different models being compared, the Multivariate Normal-Logistic model is found out to be the best performer.