Inference on inequality measures: A Monte Carlo experiment
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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- Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
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- 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.
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- 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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