  Income inequality estimators are biased in small samples, leading generally
to an underestimation. This aspect deserves particular attention when
estimating inequality in small domains and performing small area estimation at
the area level. We propose a bias correction framework for a large class of
inequality measures comprising the Gini Index, the Generalized Entropy and the
Atkinson index families by accounting for complex survey designs. The proposed
methodology does not require any parametric assumption on income distribution,
being very flexible. Design-based performance evaluation of our proposal has
been carried out using EU-SILC data, their results show a noticeable bias
reduction for all the measures. Lastly, an illustrative example of application
in small area estimation confirms that ignoring ex-ante bias correction
determines model misspecification.
