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

Jenkins, Stephen P., Burkhauser, Richard V., Feng, Shuaizhang and Larrimore, Jeff (2011) Measuring inequality using censored data: a multiple-imputation approach to estimation and inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174 (1). pp. 63-81. ISSN 0964-1998

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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.

Item Type: Article
Official URL: http://www.wiley.com/bw/journal.asp?ref=0964-1998
Additional Information: © 2011 Royal Statistical Society
Library of Congress subject classification: H Social Sciences > HA Statistics
H Social Sciences > HC Economic History and Conditions
Sets: Departments > Social Policy
Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
Collections > Economists Online
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 02 Feb 2011 14:04
URL: http://eprints.lse.ac.uk/32013/

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