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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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File URL: http://hdl.handle.net/10.1007/BF03052508
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Bibliographic Info

Article provided by Springer in its journal Journal of Economics.

Volume (Year): 9 (2002)
Issue (Month): 1 (December)
Pages: 283-306

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Handle: RePEc:kap:jeczfn:v:9:y:2002:i:1:p:283-306

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Web page: http://www.springerlink.com/link.asp?id=108909

Related research

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

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References

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  1. 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.
  2. 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.
  3. Cowell, Frank A., 1989. "Sampling variance and decomposable inequality measures," Journal of Econometrics, Elsevier, vol. 42(1), pages 27-41, September.
  4. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
  5. 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 133-50, March-Apr.
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Cited by:
  1. 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.
  2. Juan Ramón García, . "La desigualdad salarial en España. Efectos de un diseño muestral complejo," Working Papers 2003-26, FEDEA.
  3. 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, Springer, vol. 110(1), pages 89-110, January.

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