Robustness Properties of Inequality Measures

Abstract

Inequality measures are often used to summarize information about empirical income distributions. However the resulting picture of the distribution and of changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general depend upon the underlying properties of the inequality measure. We investigate this issue theoretically using a technique based on the influence function, and illustrate the magnitude of the effect using a simulation. We consider both direct nonparametric estimation from the sample, and indirect estimation using a parametric model; in the latter case we demonstrate the application of a robust estimation procedure. We apply our results to two micro-data examples.