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Contrasting the Gini and Zenga indices of economic inequality

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  • Francesca Greselin
  • Leo Pasquazzi
  • Ričardas Zitikis

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.

Suggested Citation

  • Francesca Greselin & Leo Pasquazzi & Ričardas Zitikis, 2013. "Contrasting the Gini and Zenga indices of economic inequality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 282-297, February.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:282-297
    DOI: 10.1080/02664763.2012.740627
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    Citations

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    Cited by:

    1. Francesca Greselin & Simone Pellegrino & Achille Vernizzi, 2021. "The Zenga Equality Curve: A New Approach to Measuring Tax Redistribution and Progressivity," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(4), pages 950-976, December.
    2. Alex Cobham & Lukas Schlögl & Andy Sumner, 2016. "Inequality and the Tails: the Palma Proposition and Ratio," Global Policy, London School of Economics and Political Science, vol. 7(1), pages 25-36, February.
    3. Alex Cobham & Andrew Sumner, 2013. "Is it all about the tails? The Palma measure of income inequality," Working Papers 308, ECINEQ, Society for the Study of Economic Inequality.
    4. Francesca Greselin & Simone Pellegrino & Achille Vernizzi, 2017. "Lorenz versus Zenga Inequality Curves: a New Approach to Measuring Tax Redistribution and Progressivity," Working papers 046, Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    5. Luigi Grossi & Mauro Mussini, 2017. "Inequality in Energy Intensity in the EU-28: Evidence from a New Decomposition Method," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    6. Pasquazzi Leo & Zenga Michele, 2018. "Components of Gini, Bonferroni, and Zenga Inequality Indexes for EU Income Data," Journal of Official Statistics, Sciendo, vol. 34(1), pages 149-180, March.
    7. Alex Cobham, Andy Sumner, 2013. "Is It All About the Tails? The Palma Measure of Income Inequality-Working Paper 343," Working Papers 343, Center for Global Development.
    8. Francesca Greselin & Simone Pellegrino & Achille Vernizzi, 2020. "The Social Welfare Implications of the Zenga Index," Papers 2006.12623, arXiv.org.
    9. Satya R. Chakravarty & Palash Sarkar, 2021. "An inequality paradox: relative versus absolute indices?," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 241-254, August.
    10. Monti Maria Giovanna & Pellegrino Simone & Vernizzi Achille, 2024. "The Zenga Index Reveals More Than the Gini and the Bonferroni Indexes. An Analysis of Distributional Changes and Social Welfare Levels," Working papers 084, Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    11. Alex Cobham & Luke Schlogl & Andy Sumner, 2015. "Inequality and the Tails: The Palma Proposition and Ratio Revisited," Working Papers 143, United Nations, Department of Economics and Social Affairs.
    12. Greselin, Francesca & Zitikis, Ricardas, 2015. "Measuring economic inequality and risk: a unifying approach based on personal gambles, societal preferences and references," MPRA Paper 65892, University Library of Munich, Germany.
    13. Youri Davydov & Francesca Greselin, 2020. "Comparisons Between Poorest and Richest to Measure Inequality," Sociological Methods & Research, , vol. 49(2), pages 526-561, May.
    14. Francesca Greselin & Ričardas Zitikis, 2018. "From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective," Econometrics, MDPI, vol. 6(1), pages 1-20, January.
    15. Małgorzata Ćwiek & Kamila Trzcińska, 2023. "Assessment of goodness of fit of income distribution in France and Germany based on the Zenga distribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4013-4027, October.
    16. Alina Jędrzejczak & Dorota Pekasiewicz, 2020. "Changes in Income Distribution for Different Family Types in Poland," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 26(2), pages 135-146, May.
    17. Michele Zenga, 2016. "On the decomposition by subpopulations of the point and synthetic Zenga (2007) inequality indexes," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 375-405, December.

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