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Measuring inequality using Censored data: A multiple imputation approach

Stephen Jenkins (), Richard Burkhauser (), Shuaizhang Feng and Jeff Larrimore ()
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Shuaizhang Feng: Shanghai University of Finance and Economics and Princeton University

No 108, Working Papers from ECINEQ, Society for the Study of Economic Inequality

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: D31 C46 C81 (search for similar items in EconPapers)
Date: 2009
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Working Paper: Measuring Inequality Using Censored Data: A Multiple Imputation Approach (2009) Downloads
Working Paper: Measuring Inequality Using Censored Data: A Multiple Imputation Approach (2009) Downloads
Working Paper: Measuring inequality using censored data: a multiple imputation approach (2009) Downloads
Working Paper: Measuring Inequality Using Censored Data: A Multiple Imputation Approach (2009) Downloads
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