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Using the P90/P10 Index to Measure US Inequality Trends with Current Population Survey Data: A View from Inside the Census Bureau Vaults

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Author Info
Stephen P. Jenkins () (ISER, University of Essex)
Shuaizhang Feng (Shanghai University of Finance and Economics)
Richard V. Burkhauser (Cornell University)

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Abstract

The March Current Population Survey (CPS) is the primary data source for estimation of levels and trends in labor earnings and income inequality in the USA. Time-inconsistency problems related to top coding in theses data have led many researchers to use the ratio of the 90th and 10th percentiles of these distributions (P90/P10) rather than a more traditional summary measure of inequality. With access to public use and restricted-access internal CPS data, and bounding methods, we show that using P90/P10 does not completely obviate time-inconsistency problems, especially for household income inequality trends. Using internal data, we create consistent cell mean values for all top-coded public use values that, when used with public use data, closely track inequality trends in labor earnings and household income using internal data. But estimates of longer-term inequality trends with these corrected data based on P90/P10 differ from those based on the Gini coefficient. The choice of inequality measure matters.

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Paper provided by ECINEQ, Society for the Study of Economic Inequality in its series Working Papers with number 72.

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Length: 49 pages
Date of creation: 2007
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Handle: RePEc:inq:inqwps:ecineq2007-72

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Keywords: inequality income earnings Current Population Survey decile ratio Gini coefficient

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Find related papers by JEL classification:
D3 - Microeconomics - - Distribution
J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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  4. John Pencavel, 2006. "A Life Cycle Perspective on Changes in Earnings Inequality among Married Men and Women," The Review of Economics and Statistics, MIT Press, vol. 88(2), pages 232-242, 08. [Downloadable!] (restricted)
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  6. Francine D. Blau & Lawrence M. Kahn, 2005. "Do Cognitive Test Scores Explain Higher U.S. Wage Inequality?," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 184-193, December. [Downloadable!] (restricted)
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  7. Feng, Shuaizhang & Burkhauser, Richard V. & Butler, J.S., 2006. "Levels and Long-Term Trends in Earnings Inequality: Overcoming Current Population Survey Censoring Problems Using the GB2 Distribution," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 57-62, January. [Downloadable!] (restricted)
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  11. Trejo, Stephen J, 1997. "Why Do Mexican Americans Earn Low Wages?," Journal of Political Economy, University of Chicago Press, vol. 105(6), pages 1235-68, December.
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  12. Juhn, Chinhui & Murphy, Kevin M & Pierce, Brooks, 1993. "Wage Inequality and the Rise in Returns to Skill," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 410-42, June. [Downloadable!] (restricted)
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