A Comparative Note on the Reliability of Inequality Indicator Estimates

The importance of accurate measurement of economic or social inequality is generally accepted. Estimators of its most popular measure, the Gini coefficient, are commonly criticised for becoming increasingly imprecise for increasingly skewed distributions and small to moderate samples, i.e., two phenomena that we face more frequently today. More robust inequality measures, typically based on quantile ratios, are well studied in theory, but still attract little attention in practice. We compare through simulations bias, mean squared error and sensitivity to outliers of several inequality estimators, namely, the Gini and quantile-based indicators, including the recently proposed quantile ratio index (QRI). Our results, based on Italian SILC and synthetic data, demonstrate that the QRI estimator offers superior precision and robustness, making it a reliable tool for monitoring inequality.