Measuring inequality using censored data: a multiple-imputation approach to estimation and inference

Stephen Jenkins, Richard Burkhauser, Shuaizhang Feng and Jeff Larrimore

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: To measure income inequality with right-censored (top-coded) data, we propose multiple-imputation methods for estimation and inference. Censored observations are multiply imputed using draws from a flexible parametric model fitted to the censored distribution, yielding a partially synthetic data set from which point and variance estimates can be derived using complete-data methods and appropriate combination formulae. The methods are illustrated using US Current Population Survey data and the generalized beta of the second kind distribution as the imputation model. With Current Population Survey internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using Current Population Survey public use data with cell mean imputations may lead to incorrect inferences. Multiply-imputed public use data provide an intermediate solution.

Keywords: censored data; Current Population Survey; generalized beta of the second kind distribution; income inequality; multiple imputation; top coding (search for similar items in EconPapers)
JEL-codes: C1 N0 (search for similar items in EconPapers)
Date: 2011-01
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Citations: View citations in EconPapers (58)

Published in Journal of the Royal Statistical Society. Series A: Statistics in Society, January, 2011, 174(1), pp. 63-81. ISSN: 0964-1998

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Journal Article: Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference (2011)
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