Abstract
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.
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Lehtonen, R., Veijanen, A. (2016). Estimation of Poverty Rate and Quintile Share Ratio for Domains and Small Areas. In: Alleva, G., Giommi, A. (eds) Topics in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-27274-0_14
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