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Inference by linearization for Zenga’s new inequality index: a comparison with the Gini index

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  • Matti Langel
  • Yves Tillé

Abstract

Zenga’s new inequality curve and index are two recent tools for measuring inequality. Proposed in 2007, they should thus not be mistaken for anterior measures suggested by the same author. This paper focuses on the new measures only, which are hereafter referred to simply as the Zenga curve and Zenga index. The Zenga curve Z(α) involves the ratio of the mean income of the 100α % poorest to that of the 100(1−α)% richest. The Zenga index can also be expressed by means of the Lorenz Curve and some of its properties make it an interesting alternative to the Gini index. Like most other inequality measures, inference on the Zenga index is not straightforward. Some research on its properties and on estimation has already been conducted but inference in the sampling framework is still needed. In this paper, we propose an estimator and variance estimator for the Zenga index when estimated from a complex sampling design. The proposed variance estimator is based on linearization techniques and more specifically on the direct approach presented by Demnati and Rao. The quality of the resulting estimators are evaluated in Monte Carlo simulation studies on real sets of income data. Finally, the advantages of the Zenga index relative to the Gini index are discussed. Copyright Springer-Verlag 2012

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  • Matti Langel & Yves Tillé, 2012. "Inference by linearization for Zenga’s new inequality index: a comparison with the Gini index," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1093-1110, November.
    • Handle: RePEc:spr:metrik:v:75:y:2012:i:8:p:1093-1110
    DOI: 10.1007/s00184-011-0369-1
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    References listed on IDEAS

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    6. Frank Cowell & Maria-Pia Victoria-Feser, 2003. "Distribution-Free Inference for Welfare Indices under Complete and Incomplete Information," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 1(3), pages 191-219, December.
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    Cited by:

    1. Greselin Francesca, 2014. "More Equal and Poorer, or Richer but More Unequal?," Stochastics and Quality Control, De Gruyter, vol. 29(2), pages 99-117, December.
    2. 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).
    3. 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, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    4. Lucio Barabesi & Giancarlo Diana & Pier Perri, 2015. "Gini index estimation in randomized response surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 45-62, January.
    5. 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.
    6. Ziqing Dong & Yves Tille & Giovanni Maria Giorgi & Alessio Guandalini, 2024. "Generalised Income Inequality Index," International Statistical Review, International Statistical Institute, vol. 92(1), pages 87-105, April.

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