Confidence Interval Estimation for Inequality Indices of the Gini Family

In this paper we present some nonparametric bootstrap methods to constructdistribution-free confidence intervals for inequality indices belonging to theGini family. These methods have a coverage accuracy better than that obtainedwith the asymptotic distribution of their natural estimators, typically thestandard normal. The coverage performances of these methods are evaluated forthe index R by Gini with a Monte Carlo experiment on samples simulated fromthe Dagum income model (Type I), which is usually used to describe the incomedistribution.