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