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

TL;DR

This paper introduces a flexible bias correction framework for inequality estimators in small samples, improving accuracy in small domain and area-level analyses without relying on parametric income distribution assumptions.

Contribution

It proposes a novel, non-parametric bias correction method for various inequality measures that accounts for complex survey designs, enhancing estimation accuracy in small samples.

Findings

Noticeable bias reduction in inequality measures using EU-SILC data

Bias correction improves small area estimation accuracy

Ignoring bias correction leads to model misspecification

Abstract

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.