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Abstract
The current financial turbulence in Europe inspires and perhaps requires researchers to rethink how to measure incomes, wealth, and other parameters of interest to policy-makers and others. The noticeable increase in disparities between less and more fortunate individuals suggests that measures based upon comparing the incomes of less fortunate with the mean of the entire population may not be adequate. The classical Gini and related indices of economic inequality, however, are based exactly on such comparisons. It is because of this reason that in this paper we explore and contrast the classical Gini index with a new Zenga index, the latter being based on comparisons of the means of less and more fortunate sub-populations, irrespectively of the threshold that might be used to delineate the two sub-populations. The empirical part of the paper is based on the 2001 wave of the European Community Household Panel data set provided by EuroStat. Even though sample sizes appear to be large, we supplement the estimated Gini and Zenga indices with measures of variability in the form of normal, t-bootstrap, and bootstrap bias-corrected and accelerated confidence intervals.
Acknowledgements
Our sincere thanks are due to four anonymous referees for constructive criticism, questions, queries, and suggestions that helped us when revising the paper. Our thanks are also due to Abdelhakim Necir and Nariankadu D. Shyamalkumar for clarifying several techniques behind the statistical inferential theory employed in this paper. We also gratefully acknowledge the hospitality of the University of Milano-Bicocca and the University of Western Ontario during our respective research visits at these institutions. Thanks are due to the International Council for Canadian Studies and the Canadian Embassy in Rome (ICCS file number: 615 2 006) as well as the Research Development and Services at the University of Western Ontario (ROLA Proposal ID 28864) for grants that have played pivotal roles in accomplishing this project. The research has also been supported by the 2009 FAR at the University of Milano-Bicocca, Italy, and the Natural Sciences and Engineering Research Council (NSERC) of Canada.