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Using The Pareto Distribution To Improve Estimates Of Topcoded Earnings

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  • Philip Armour
  • Richard V. Burkhauser
  • Jeff Larrimore

Abstract

Inconsistent censoring in the public-use March Current Population Survey (CPS) limits its usefulness in measuring labor earnings trends. Using Pareto estimation methods with less-censored internal CPS data, we create an enhanced cell-mean series to capture top earnings in the public-use CPS. We find that previous approaches for imputing topcoded earnings systematically understate top earnings. Annual earnings inequality trends since 1963 using our series closely approximate those found by Kopczuk, Saez, & Song (2010) using Social Security Administration data for commerce and industry workers. However, when we consider all workers, earnings inequality levels are higher but earnings growth is more modest
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  • Philip Armour & Richard V. Burkhauser & Jeff Larrimore, 2016. "Using The Pareto Distribution To Improve Estimates Of Topcoded Earnings," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1263-1273, April.
  • Handle: RePEc:bla:ecinqu:v:54:y:2016:i:2:p:1263-1273
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    Cited by:

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    2. Vladimir Hlasny & Paolo Verme, 2022. "The Impact of Top Incomes Biases on the Measurement of Inequality in the United States," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 749-788, August.
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    4. Felix Bierbrauer & Pierre Boyer & Andreas Peichl & Daniel Weishaar, 2023. "The Taxation of Couples," Rationality and Competition Discussion Paper Series 405, CRC TRR 190 Rationality and Competition.
    5. Sieuwerd Gaastra, 2020. "Personal Income Taxation and College Major Choice: A Case Study of the 1986 Tax Reform Act," Public Finance Review, , vol. 48(1), pages 3-42, January.
    6. Veronica Guerrieri & Alessandra Fogli, 2017. "The End of the American Dream? Inequality and Segregation in US cities," 2017 Meeting Papers 1309, Society for Economic Dynamics.
    7. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    8. Ramón E. López & Eugenio Figueroa B. & Pablo Gutiérrez C., 2016. "Fundamental accrued capital gains and the measurement of top incomes: an application to Chile," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(4), pages 379-394, December.
    9. J. Adam Cobb & Ken-Hou Lin, 2017. "Growing Apart: The Changing Firm-Size Wage Premium and Its Inequality Consequences," Organization Science, INFORMS, vol. 28(3), pages 429-446, June.
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    11. Süß Philipp, 2020. "Regional Market Income Inequality and its Impact on Crime in Germany: A Spatial Panel Data Approach with Local Spillovers," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(4), pages 387-415, August.
    12. Rauh, Christopher, 2017. "Voting, education, and the Great Gatsby Curve," Journal of Public Economics, Elsevier, vol. 146(C), pages 1-14.
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    14. Porro Francesco, 2014. "How We Can Evaluate the Inequality in Flint," Stochastics and Quality Control, De Gruyter, vol. 29(2), pages 119-128, December.
    15. Greselin Francesca, 2014. "More Equal and Poorer, or Richer but More Unequal?," Stochastics and Quality Control, De Gruyter, vol. 29(2), pages 99-117, December.
    16. João Nicolau & Pedro Raposo & Paulo M. M. Rodrigues, 2023. "Measuring wage inequality under right censoring," Economic Inquiry, Western Economic Association International, vol. 61(2), pages 377-401, April.
    17. Li Tan, 2021. "Imputing Top‐Coded Income Data in Longitudinal Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 66-87, February.
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    21. Ellis Scharfenaker & Markus P. A. Schneider, 2023. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Papers 23-41, Center for Economic Studies, U.S. Census Bureau.

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    More about this item

    JEL classification:

    • 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
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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