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Income inequality estimators are biased in small samples, leading generallyto an underestimation. This aspect deserves particular attention whenestimating inequality in small domains and performing small area estimation atthe area level. We propose a bias correction framework for a large class ofinequality measures comprising the Gini Index, the Generalized Entropy and theAtkinson index families by accounting for complex survey designs. The proposedmethodology does not require any parametric assumption on income distribution,being very flexible. Design-based performance evaluation of our proposal hasbeen carried out using EU-SILC data, their results show a noticeable biasreduction for all the measures. Lastly, an illustrative example of applicationin small area estimation confirms that ignoring ex-ante bias correctiondetermines model misspecification.

Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples