Title: Bias calibration for robust estimation of inequality indices in small areas
Authors: Setareh Ranjbar - HEC Lausanne (Switzerland) [presenting]
Elvezio Ronchetti - University of Geneva (Switzerland)
Abstract: Today the availability of rich sample surveys provides a ground for researchers and policy makers to pursue more ambitious objectives. This information in line with auxiliary data coming through administrative channels is used for a better prediction/estimation of social and economic indices, e.g. inequality or poverty measures, that can help to determine more precisely their target domains. The domains for which the sample size is not large enough to provide an acceptable direct estimate, are referred to as small areas. The existence of outliers in the sample data can significantly harm the estimation for areas in which they occur, especially where the domain-sample size is small. Robust estimation of finite population total and mean in the presence of outliers has been discussed in the literature and the results has been extended to the cumulative distribution time ago. Based on a robust EBLUP estimation, we propose two new approaches to calibrate for the bias of nonlinear functionals, such as the Gini index and when the so-called representative outliers come from a skewed heavy tail distribution. The method is also used to impute missing income values, a common occurrence in labour force surveys.