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Five Decades of Union Wages, Nonunion Wages, and Union Wage Gaps at Unionstats.com

Author

Listed:
  • Macpherson, David A.

    (Trinity University)

  • Hirsch, Barry

    (Georgia State University)

Abstract

Union, nonunion, and overall wages, plus regression-based union wage gap estimates, are provided annually, beginning in 1973 using the Current Population Surveys (CPS). The estimates are presented economy-wide by demographics and sectors (private/public, industries). Union wage gaps are higher in the private than in the public sector, higher for men than women, roughly similar for black and white men, and much higher for black than for white women. We estimate mean weekly earnings above CPS topcodes by gender and year, assuming a Pareto distribution in the right tail of the distribution. The database is online and will be updated annually.

Suggested Citation

  • Macpherson, David A. & Hirsch, Barry, 2021. "Five Decades of Union Wages, Nonunion Wages, and Union Wage Gaps at Unionstats.com," IZA Discussion Papers 14398, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp14398
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    References listed on IDEAS

    as
    1. Henry S Farber & Daniel Herbst & Ilyana Kuziemko & Suresh Naidu, 2021. "Unions and Inequality over the Twentieth Century: New Evidence from Survey Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1325-1385.
    2. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4, National Bureau of Economic Research, Inc.
    3. Richard B. Freeman, 1980. "Unionism and the Dispersion of Wages," ILR Review, Cornell University, ILR School, vol. 34(1), pages 3-23, October.
    4. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    5. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, March.
    6. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    7. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    8. Harry J. Holzer, 1982. "Unions and the Labor Market Status of White and Minority Youth," ILR Review, Cornell University, ILR School, vol. 35(3), pages 392-405, April.
    9. Barry T. Hirsch & David A. MacPherson, 2003. "Union Membership and Coverage Database from the Current Population Survey: Note," ILR Review, Cornell University, ILR School, vol. 56(2), pages 349-354, January.
    10. Stephen B. Jarrell & T. D. Stanley, 1990. "A Meta-Analysis of the Union-Nonunion Wage Gap," ILR Review, Cornell University, ILR School, vol. 44(1), pages 54-67, October.
    11. Freeman, Richard B & Medoff, James L, 1981. "The Impact of the Percentage Organized on Union and Nonunion Wages," The Review of Economics and Statistics, MIT Press, vol. 63(4), pages 561-572, November.
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    Cited by:

    1. Hofmann, Bastian & Schoonjans, Eline, 2023. "Timing is key: When does the market react to unionization efforts?," Finance Research Letters, Elsevier, vol. 54(C).

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    More about this item

    Keywords

    earnings; union wage gaps; CPS topcodes;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J51 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Trade Unions: Objectives, Structure, and Effects
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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