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Inference on inequality measures: A Monte Carlo experiment

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  • Philippe Kerm

Abstract

Two broad types of method tend to be used to estimate the sampling distribution of inequality measure estimators: analytical asymptotic approximations and resampling-based procedures (e.g. thebootstrap). The present paper attempts to check the coverage performance, in large samples, of a series of standard estimators of both types so as to provide a yardstick to choose among competing alternatives. Two sampling schemes are considered: simple random sampling and clustered sampling. The comparison is made using a Monte Carlo experiment and an application to Belgian data. It turns out that neither basic bootstrap procedures nor asymptotic approximations significantly outperform its competitors. Both yield acceptable estimates (especially in random samples) provided that sampling design is taken into account. Copyright Springer-Verlag 2002
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Suggested Citation

  • Philippe Kerm, 2002. "Inference on inequality measures: A Monte Carlo experiment," Journal of Economics, Springer, vol. 9(1), pages 283-306, December.
  • Handle: RePEc:kap:jeczfn:v:9:y:2002:i:1:p:283-306
    DOI: 10.1007/BF03052508
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    References listed on IDEAS

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    1. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
    2. Cowell, Frank & Schluter, Christian, 1998. "Measuring income mobility with dirty data," LSE Research Online Documents on Economics 2079, London School of Economics and Political Science, LSE Library.
    3. Mills, Jeffrey A & Zandvakili, Sourushe, 1997. "Statistical Inference via Bootstrapping for Measures of Inequality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 133-150, March-Apr.
    4. Christian Schluter & Mark Trede, 2002. "Statistical Inference for Inequality and Poverty Measurement with Dependent Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 493-508, May.
    5. Cowell, Frank A., 1989. "Sampling variance and decomposable inequality measures," Journal of Econometrics, Elsevier, vol. 42(1), pages 27-41, September.
    6. Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
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    Cited by:

    1. Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June.
    2. Juan Ramón García, "undated". "La desigualdad salarial en España. Efectos de un diseño muestral complejo," Working Papers 2003-26, FEDEA.
    3. 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.
    4. Carsten Schröder, 2011. "Cowell, F.: Measuring Inequality. London School of Economics Perspectives in Economic Analysis," Journal of Economics, Springer, vol. 104(3), pages 281-285, November.
    5. Tim Goedemé, 2013. "How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(1), pages 89-110, January.

    More about this item

    Keywords

    Inequality Measures; Inference; Clustered Sampling; D31; D63; I32;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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