Robust, distribution-free inference for income share ratios under complex sampling

The quintile share ratio of disposable income is the primary inequality indicator of the European Union. As an inequality indicator, it must be sensitive to extreme large observations. Therefore, outliers have a strong impact on the bias and the variance of the classical quintile share ratio estimator. This may mislead the interpretation of income inequality. A class of estimators which are robust against outliers is introduced. They have a bounded influence function, they may reduce the bias incurred by the robustification and they reduce variability. Based on an asymptotic framework which respects the design-based, non-parametric approach, inference for these robust estimators is developed. A large simulation study with close to reality universes derived from the Statistics of Living Conditions Surveys of the EU allows to study the performance of the proposed estimators. Copyright Springer-Verlag Berlin Heidelberg 2014