Minimum sample sizes in asymptotic confidence intervals for Gini’s inequality measure

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Minimum sample sizes in asymptotic confidence intervals for Gini’s inequality measure

Articolo

Rivista

STATISTICA & APPLICAZIONI

Sezione

Open Access

Titolo

Minimum sample sizes in asymptotic confidence intervals for Gini’s inequality measure

Autori

Francesca Greselin, Leo Pasquazzi

Editore

Vita e Pensiero

Formato

Articolo |

Pdf

Online da

02-2008

Issn

1824-6672 (stampa) | 2283-6659 (digitale)

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Descrizione Commenti dei lettori

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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