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Inference for Generalized Gini Indices Using the Iterated-Bootstrap Method

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  • Xu, Kuan

Abstract

Inference using the iterated-bootstrap method proposed by Hall is appealing for cases in which the percentile method needs to be used but the nominal level of a confidence interval has to be adjusted. One natural application is for generalized Gini indices of income inequality. When applying these theoretical inequality measures directly to sample data for the purpose of statistical inference, economists must come up with some measure of sampling variation. This is particularly the case when the index estimates are compared over time to infer information on the changes of social welfare and inequality. Although there are difficulties in the existing inferential procedures, a more intuitive approach is to use the iterated-bootstrap method.

Suggested Citation

  • Xu, Kuan, 2000. "Inference for Generalized Gini Indices Using the Iterated-Bootstrap Method," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 223-227, April.
  • Handle: RePEc:bes:jnlbes:v:18:y:2000:i:2:p:223-27
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    Citations

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    Cited by:

    1. William Horrace & Joseph Marchand & Timothy Smeeding, 2008. "Ranking inequality: Applications of multivariate subset selection," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(1), pages 5-32, March.
    2. Khosravi Tanak, A. & Mohtashami Borzadaran, G.R. & Ahmadi, J., 2017. "Maximum Tsallis entropy with generalized Gini and Gini mean difference indices constraints," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 554-560.
    3. Stephen G. Donald & Yu‐Chin Hsu & Garry F. Barrett, 2012. "Incorporating covariates in the measurement of welfare and inequality: methods and applications," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 1-30, February.
    4. Stéphane Mussard & Pi Alperin María Noel, 2006. "Measuring Significance of Inequalities with Heterogeneous Groups and Income Sources," Cahiers de recherche 06-13, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
    5. Kuan Xu, 2003. "How Has the Literature on Gini's Index Evolved in the Past 80 Years?," Department of Economics at Dalhousie University working papers archive howgini, Dalhousie, Department of Economics.
    6. Yoonseok Lee & Donggyun Shin, 2013. "Measuring Social Unrest Based on Income Distribution," Center for Policy Research Working Papers 160, Center for Policy Research, Maxwell School, Syracuse University.
    7. Khosravi Tanak, A. & Mohtashami Borzadaran, G.R. & Ahmadi, J., 2015. "Entropy maximization under the constraints on the generalized Gini index and its application in modeling income distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 657-666.
    8. Joseph Gastwirth & Reza Modarres & Efstathia Bura, 2005. "The use of the Lorenz curve, Gini index and related measures of relative inequality and uniformity in securities law," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 451-469.
    9. El-Osta, Hisham S. & Morehart, Mitchell J., 2009. "Welfare Decomposition in the Context of the Life Cycle of Farm Operators: What Does a National Survey Reveal?," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 38(2), October.
    10. Yong Tao & Xiangjun Wu & Changshuai Li, 2014. "Rawls' Fairness, Income Distribution and Alarming Level of Gini Coefficient," Papers 1409.3979, arXiv.org.
    11. Timothy Moran, 2005. "Bootstrapping the LIS: Statistical Inference and Patterns of Inequality in the Global North," LIS Working papers 378, LIS Cross-National Data Center in Luxembourg.

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