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Measuring Inequality Using Censored Data: A Multiple Imputation Approach Author info | Abstract | Publisher info | Download info | Related research | Statistics Stephen P. Jenkins
Richard V. Burkhauser
Shuaizhang Feng
Jeff Larrimore
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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.
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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number
866.
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Length: II, 29 p.
Date of creation: 2009Date of revision:
Handle: RePEc:diw:diwwpp:dp866Contact details of provider: Postal: Mohrenstra�e 58, D-10117 Berlin Phone: xx49-30-89789-0 Fax: xx49-30-89789-200 Email: Web page: http://www.diw.de/en More information through EDIRC
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Keywords: Income inequality ; topcoding ; partially synthetic data ; CPS ; current population survey ; generalized beta of the second kind distribution ; Other versions of this item:
Paper Jenkins, Stephen P. & Burkhauser, Richard V. & Feng, Shuaizhang & Larrimore, Jeff, 2009.
"Measuring Inequality Using Censored Data: A Multiple Imputation Approach ,"
IZA Discussion Papers
4011, Institute for the Study of Labor (IZA).
[Downloadable!] Stephen Jenkins & Richard Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009.
"Measuring Inequality Using Censored Data: A Multiple Imputation Approach ,"
Working Papers
09-05, Center for Economic Studies, U.S. Census Bureau.
[Downloadable!] Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009.
"Measuring inequality using Censored data: A multiple imputation approach ,"
Working Papers
108, ECINEQ, Society for the Study of Economic Inequality.
[Downloadable!] Find related papers by JEL classification: D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Microeconomic Data
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Atkinson, Anthony B., 1970.
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Schluter, Christian & Trede, Mark, 2002.
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Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996.
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[Downloadable!] (restricted)
Di An & Roderick J. A. Little, 2007.
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Journal Of The Royal Statistical Society Series A ,
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[Downloadable!] (restricted)
Full
references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
Richard V. Burkhauser & Shuaizhang Feng & Stephen P. Jenkins & Jeff Larrimore, 2008.
"Estimating Trends in US Income Inequality Using the Current Population Survey: The Importance of Controlling for Censoring ,"
NBER Working Papers
14247, National Bureau of Economic Research, Inc.
[Downloadable!] (restricted)
Other versions:
Richard Burkhauser & Shuaizhang Feng & Stephen Jenkins & Jeff Larrimore, 2008.
"Estimating Trends in U.S. Income Inequality Using the Current Population Survey: The Importance of Controlling for Censoring ,"
Working Papers
08-25, Center for Economic Studies, U.S. Census Bureau.
[Downloadable!] Burkhauser, Richard V. & Feng, Shuaizhang & Jenkins, Stephen P. & Larrimore, Jeff, 2008.
"Estimating Trends in US Income Inequality Using the Current Population Survey: The Importance of Controlling for Censoring ,"
IZA Discussion Papers
3690, Institute for the Study of Labor (IZA).
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