Minimum sample sizes in asymptotic confidence intervals for Gini’s inequality measure
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Statistical inference for inequality measures has been of considerable interest in recent years. Income
studies often deal with very large samples, hence precision would not seem a serious issue.
Yet, in many empirical studies large standard errors are observed (Maasoumi, 1997). Therefore, it
is important to provide methodologies to assess whether differences in estimates are statistically significant.
This paper presents an analysis of the performance of asymptotic confidence intervals for
Gini’s index, virtually the most widely used inequality index. To determine minimum sample sizes
assuring a given accuracy in confidence intervals, an extensive simulation study has been carried
out. A wide set of underlying distributions has been considered, choosing from specific models for
income data. As expected, the minimum sample sizes are seriously affected by some population
characteristics as tail heaviness and asymmetry. However, in a wide range of cases, it turns out that
they are smaller than sample sizes actually used in social sciences.
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