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Estimating Trends in US Income Inequality Using the Current Population Survey: The Importance of Controlling for Censoring

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  • Richard V. Burkhauser
  • Shuaizhang Feng
  • Stephen P. Jenkins
  • Jeff Larrimore

Abstract

Using internal and public use March Current Population Survey (CPS) data, we analyze trends in US income inequality (1975-2004). We find that the upward trend in income inequality prior to 1993 significantly slowed thereafter once we control for top coding in the public use data and censoring in the internal data. Because both series do not capture trends at the very top of the income distribution, we use a multiple imputation approach in which values for censored observations are imputed using draws from a Generalized Beta distribution of the Second Kind (GB2) fitted to internal data. Doing so, we find income inequality trends similar to those derived from unadjusted internal data. Our trend results are generally robust to the choice of inequality index, whether Gini coefficient or other commonly-used indices. When we compare our best estimates of the income shares held by the richest tenth with those reported by Piketty and Saez (2003), our trends fairly closely match their trends, except for the top 1 percent of the distribution. Thus, we argue that if United States income inequality has been substantially increasing since 1993, such increases are confined to this very high income group.

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  • 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.
  • Handle: RePEc:nbr:nberwo:14247
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    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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