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Using sample surveys as a cost effective tool to provide estimates forcharacteristics of interest at population and sub-populations (area/domain)level has a long tradition in "small area estimation". However, the existenceof outliers in the sample data can significantly affect the estimation forareas in which they occur, especially where the domain-sample size is small.Based on existing robust estimators for small area estimation we propose twonovel approaches for bias calibration. A series of simulations shows that ourmethods lead to more efficient estimators in comparison with other existingbias-calibration methods. As a real data example we apply our estimators toobtain \textit{Gini} coefficients in labour market areas of the Tuscany regionof Italy, where our sources of information are the EU-SILC survey and theItalian census. This analysis shows that the new methods reveal a differentpicture than existing methods. We extend our ideas to predictions fornon-sampled areas.

New Bias Calibration for Robust Estimation in Small Areas