For at least a century academics and governmental researchers have been developing measures that would aid them in understanding income distributions, their differences with respect to geographic regions, and changes over time periods. It is a fascinating area due to a number of reasons, one of them being the fact that different measures, or indices, are needed to reveal different features of income distributions. Keeping also in mind that the notions of `poor' and `rich' are relative to each other, Zenga (2007) proposed a new index of economic inequality. The index is remarkably insightful and useful, but deriving statistical inferential results has been a challenge. For example, unlike many other indices, Zenga's new index does not fall into the classes of L, U, and V-statistics. In this paper we derive desired statistical inferential results, explore their performance in a simulation study, and then use the results to analyze data from the Bank of Italy Survey on Household Income and Wealth (SHIW).

Greselin, F., Pasquazzi, L., & Zitikis, R. (2010). Zenga's new index of economic inequality, its estimation, and an analysis of incomes in Italy. JOURNAL OF PROBABILITY AND STATISTICS, 2010 [10.1155/2010/718905].

Zenga's new index of economic inequality, its estimation, and an analysis of incomes in Italy

GRESELIN, FRANCESCA;PASQUAZZI, LEO;
2010

Abstract

For at least a century academics and governmental researchers have been developing measures that would aid them in understanding income distributions, their differences with respect to geographic regions, and changes over time periods. It is a fascinating area due to a number of reasons, one of them being the fact that different measures, or indices, are needed to reveal different features of income distributions. Keeping also in mind that the notions of `poor' and `rich' are relative to each other, Zenga (2007) proposed a new index of economic inequality. The index is remarkably insightful and useful, but deriving statistical inferential results has been a challenge. For example, unlike many other indices, Zenga's new index does not fall into the classes of L, U, and V-statistics. In this paper we derive desired statistical inferential results, explore their performance in a simulation study, and then use the results to analyze data from the Bank of Italy Survey on Household Income and Wealth (SHIW).
Si
Articolo in rivista - Articolo scientifico
Scientifica
Zenga index, confidence interval, lower conditional expectation, upper conditional expectation, conditional tail expectation, Bonferroni curve, Lorenz curve, Vervaat process.
English
26
http://ideas.repec.org/p/pra/mprapa/17147.html
Special issue on Actuarial and Financial Risks: Models, Statistical Inference, and Case Studies
Greselin, F., Pasquazzi, L., & Zitikis, R. (2010). Zenga's new index of economic inequality, its estimation, and an analysis of incomes in Italy. JOURNAL OF PROBABILITY AND STATISTICS, 2010 [10.1155/2010/718905].
Greselin, F; Pasquazzi, L; Zitikis, R
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/9673
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