Reliable inference for the GINI Index

Russell Davidson

Working Papers from HAL

Abstract: Although attention has been given to obtaining reliable standard errors for the plugin estimator of the Gini index, all standard errors suggested until now are either complicated or quite unreliable. An approximation is derived for the estimator by which it is expressed as a sum of IID random variables. This approximation allows us to develop a reliable standard error that is simple to compute. A simple but effective bias correction is also derived. The quality of inference based on the approximation is checked in a number of simulation experiments, and is found to be very good unless the tail of the underlying distribution is heavy. Bootstrap methods are presented which alleviate this problem except in cases in which the variance is very large or fails to exist. Similar methods can be used to find reliable standard errors of other indices which are not simply linear functionals of the distribution function, such as Sen's poverty index and its modification known as the Sen-Shorrocks-Thon index.

Keywords: Gini index; delta method; asymptotic inference; jackknife; bootstrap (search for similar items in EconPapers)
Date: 2009-12-30
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00443553
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Citations: View citations in EconPapers (54)

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Related works:
Journal Article: Reliable inference for the Gini index (2009)
Working Paper: RELIABLE INFERENCE FOR THE GINI INDEX (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:halshs-00443553

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