Estimation of Poverty Rate and Quintile Share Ratio for Domains and Small Areas
In the article, we consider the estimation of indicators on poverty and social exclusion for population subgroups or domains and small areas. For at risk of poverty rate, we discuss indirect design based estimators including model assisted logistic generalized regression estimators and model calibration estimators. Logistic mixed models are used in these methods. For quintile share ratio, indirect model based percentile adjusted predictor methods using linear mixed models are considered. Unit level auxiliary data are incorporated in the estimation procedures. For quintile share ratio, we present a method called frequency calibration or n calibration to be used in cases where aggregate level auxiliary data only are available. Design based direct estimators that do not use auxiliary data and models are used as reference methods. Design bias and accuracy of estimators are evaluated with design based simulation experiments using real register data maintained by Statistics Finland and semi synthetic data generated from the EU SILC survey.