Measuring Inequality Using Censored Data: A Multiple Imputation Approach

Stephen Jenkins, Richard Burkhauser, Shuaizhang Feng and Jeff Larrimore

No 866, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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: Income inequality; topcoding; partially synthetic data; CPS; current population survey; 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: II, 29 p.
Date: 2009
New Economics Papers: this item is included in nep-ltv
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Citations: View citations in EconPapers (10)

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https://www.diw.de/documents/publikationen/73/diw_01.c.95918.de/dp866.pdf (application/pdf)

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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