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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

Author

Listed:
  • Stephen P. Jenkins

    (ISER, University of Essex)

  • Shuaizhang Feng

    (Shanghai University of Finance and Economics)

  • Richard V. Burkhauser

    (Cornell University)

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.

Suggested Citation

  • Stephen P. Jenkins & Shuaizhang Feng & Richard V. Burkhauser, 2007. "Using the P90/P10 Index to Measure US Inequality Trends with Current Population Survey Data: A View from Inside the Census Bureau Vaults," Working Papers 72, ECINEQ, Society for the Study of Economic Inequality.
  • Handle: RePEc:inq:inqwps:ecineq2007-72
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    More about this item

    Keywords

    inequality; income; earnings; Current Population Survey; decile ratio; Gini coefficient;
    All these keywords.

    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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