A New measure of income inequality

TL;DR

This paper introduces a novel income inequality measure focusing on the heavy tail of income distribution, with proven estimator consistency, asymptotic normality, and practical application to Indian states.

Contribution

It proposes a new inequality measure, develops consistent estimators, and demonstrates their properties through simulations and real-world data analysis.

Findings

Estimators are consistent and asymptotically normal.

JEL confidence intervals are effective in finite samples.

Application reveals differences in income inequality across Indian states.

Abstract

A new measure of income inequality that captures the heavy tail behavior of the income distribution is proposed. We discuss two different approaches to find the estimators of the proposed measure. We show that these estimators are consistent and have an asymptotically normal distribution. We also obtain a jackknife empirical likelihood (JEL) confidence interval of the income inequality measure. A Monte Carlo simulation study is conducted to evaluate the finite sample properties of the estimators and JEL-based confidence inerval. Finally, we use our measure to study the income inequality of three states in India.