Measuring Inequality Using Censored Data: A Multiple Imputation Approach

Stephen Jenkins, Richard Burkhauser, Shuaizhang Feng and Jeff Larrimore

No 4011, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter’s (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.

Keywords: topcoding; income inequality; CPS; Current Population Survey; partially synthetic data; Generalized Beta of the Second Kind distribution (search for similar items in EconPapers)
JEL-codes: C46 C81 D31 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2009-02
New Economics Papers: this item is included in nep-hap and nep-ltv
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Citations: View citations in EconPapers (8)

Published - revised version published in: Journal of the Royal Statistical Society, Series A (Statistics in Society), 2011, 174 (1), 63 - 81

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Related works:
Working Paper: Measuring Inequality Using Censored Data: A Multiple Imputation Approach (2009)
Working Paper: Measuring Inequality Using Censored Data: A Multiple Imputation Approach (2009)
Working Paper: Measuring inequality using censored data: a multiple imputation approach (2009)
Working Paper: Measuring inequality using Censored data: A multiple imputation approach (2009)
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