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Income distribution and inequality measurement: the problem of extreme values

Author

Listed:
  • Frank A. Cowell

    (STICERD, London School of Economics)

  • Emmanuel Flachaire

    (EUREQua)

Abstract

We examine the statistical performance of inequality indices in the presence of extreme values in the data and show that these indices are very sensitive to the properties of the income distribution. Estimation and inference can be dramatically affected, especially when the tail of the income distribution is heavy, even when standard bootstrap methods are employed. However, use of appropriate methods for modelling the upper tail can greatly improve the performance of even those inequality indices that are normally considered particularly sensitive to extreme values

Suggested Citation

  • Frank A. Cowell & Emmanuel Flachaire, 2004. "Income distribution and inequality measurement: the problem of extreme values," Cahiers de la Maison des Sciences Economiques v04101, Université Panthéon-Sorbonne (Paris 1).
  • Handle: RePEc:mse:wpsorb:v04101
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    References listed on IDEAS

    as
    1. Braulke, Michael, 1983. "An approximation to the Gini coefficient for a population based on sparse information for sub-groups," Journal of Development Economics, Elsevier, vol. 12(1-2), pages 75-81.
    2. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    3. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
    4. Davidson, Russell & Flachaire, Emmanuel, 2007. "Asymptotic and bootstrap inference for inequality and poverty measures," Journal of Econometrics, Elsevier, vol. 141(1), pages 141-166, November.
    5. Anthony F. Shorrocks & James E. Foster, 1987. "Transfer Sensitive Inequality Measures," Review of Economic Studies, Oxford University Press, vol. 54(3), pages 485-497.
    6. Frank A Cowell & Maria-Pia Victoria-Feser, 2001. "Robust Lorenz Curves: A Semiparametric Approach," STICERD - Distributional Analysis Research Programme Papers 50, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Hall, Peter & Yao, Qiwei, 2003. "Inference in ARCH and GARCH models with heavy-tailed errors," LSE Research Online Documents on Economics 5875, London School of Economics and Political Science, LSE Library.
    8. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
    9. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-970, September.
    10. Schluter, Christian & Trede, Mark, 2002. "Tails of Lorenz curves," Journal of Econometrics, Elsevier, vol. 109(1), pages 151-166, July.
    11. Cowell, Frank A., 1989. "Sampling variance and decomposable inequality measures," Journal of Econometrics, Elsevier, vol. 42(1), pages 27-41, September.
    12. Peter Hall & Qiwei Yao, 2003. "Inference in Arch and Garch Models with Heavy--Tailed Errors," Econometrica, Econometric Society, vol. 71(1), pages 285-317, January.
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    More about this item

    Keywords

    Inequality measures; statistical performance; robustness;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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