Bias analysis of a linear order-statistic inequality index estimator: Unbiasedness under gamma populations

TL;DR

This paper analyzes a class of rank-based inequality measures, introducing an estimator with proven unbiasedness under gamma distributions and connecting it to well-known indices.

Contribution

It unifies various inequality indices within a common framework and establishes exact unbiasedness of the estimator for gamma populations, extending prior results.

Findings

Estimator is asymptotically unbiased under broad conditions.

Exact unbiasedness proven for gamma populations for any sample size.

Monte Carlo simulations confirm theoretical unbiasedness.

Abstract

This paper studies a class of rank-based inequality measures built from linear combinations of expected order statistics. The proposed framework unifies several well-known indices, including the classical Gini coefficient, the $m$th Gini index, the extended $m$th Gini index and particular cases of the $S$-Gini index, and also connects to spectral inequality measures through an integral representation. We investigate the finite-sample behavior of a natural U-statistic-type estimator that averages weighted order-statistic contrasts over all subsamples of fixed size and normalizes by the sample mean. A general bias decomposition is derived in terms of components that isolate the effect of random normalization on each rank level, yielding analytical expressions that can be evaluated under broad non-negative distributions via Laplace-transform methods. Under mild moment conditions, the estimator is shown to be asymptotically unbiased. Moreover, we prove exact unbiasedness under gamma populations for any sample size, extending earlier unbiasedness results for Gini-type estimators. A Monte Carlo study is performed to numerically check that the theoretical {unbiasedness} under gamma populations. Finally, a data set on GDP per capita across $34$ countries in the Americas is analyzes to illustrate the proposed methodology.