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Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes

Author

Listed:
  • Doruk Cengiz
  • Arindrajit Dube
  • Attila S. Lindner
  • David Zentler-Munro

Abstract

We assess the effect of the minimum wage on labor market outcomes such as employment, unemployment, and labor force participation for most workers affected by the policy. We apply modern machine learning tools to construct demographically-based treatment groups capturing around 75% of all minimum wage workers—a major improvement over the literature which has focused on fairly narrow subgroups where the policy has a large bite (e.g., teens). By exploiting 172 prominent minimum wages between 1979 and 2019 we find that there is a very clear increase in average wages of workers in these groups following a minimum wage increase, while there is little evidence of employment loss. Furthermore, we find no indication that minimum wage has a negative effect on the unemployment rate, on the labor force participation, or on the labor market transitions. Furthermore, we detect no employment or participation responses even for sub-groups that are likely to have a high extensive margin labor supply elasticity—such as teens, older workers, or single mothers. Overall, these findings provide little evidence for changing search effort in response to a minimum wage increase.

Suggested Citation

  • Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28399
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    References listed on IDEAS

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    1. Sylvia Allegretto & Arindrajit Dube & Michael Reich & Ben Zipperer, 2017. "Credible Research Designs for Minimum Wage Studies," ILR Review, Cornell University, ILR School, vol. 70(3), pages 559-592, May.
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    4. Jeffrey Clemens & Michael R. Strain, 2017. "Estimating the Employment Effects of Recent Minimum Wage Changes: Early Evidence, an Interpretative Framework, and a Pre-Commitment to Future Analysis," NBER Working Papers 23084, National Bureau of Economic Research, Inc.
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    7. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    8. Laws, A., 2018. "Do minimum wages increase search effort?," Cambridge Working Papers in Economics 1857, Faculty of Economics, University of Cambridge.
    9. Christian Dustmann & Attila Lindner & Uta Schönberg & Matthias Umkehrer & Philipp vom Berge, 2022. "Reallocation Effects of the Minimum Wage," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 267-328.
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    Cited by:

    1. Balázs Égert & Jarmila Botev & Dave Turner & Balazs Egert, 2024. "Minimum Wages at a Turning Point?," CESifo Working Paper Series 11586, CESifo.
    2. Renada M. Goldberg, 2024. "Precarious Work Patterns on Workers’ Perceptions of Family-Level Resources, Cohesion, and Flexibility," Journal of Family and Economic Issues, Springer, vol. 45(1), pages 184-199, March.
    3. Dami'an Vergara, 2022. "Minimum Wages and Optimal Redistribution," Papers 2202.00839, arXiv.org, revised Dec 2022.
    4. Clemens, Jeffrey & Strain, Michael R., 2021. "The Heterogeneous Effects of Large and Small Minimum Wage Changes: Evidence over the Short and Medium Run Using a Pre-analysis Plan," IZA Discussion Papers 14747, Institute of Labor Economics (IZA).
    5. Filip Premik, 2021. "Estimating the effects of universal transfers: new ML approach and application to labor supply reaction to child benefits," GRAPE Working Papers 54, GRAPE Group for Research in Applied Economics.
    6. Halim,Daniel Zefanya & Seetahul,Suneha, 2023. "Why Do People Move ? A Data-Driven Approach to Identifying and PredictingGender-Specific Aspirations to Migrate," Policy Research Working Paper Series 10396, The World Bank.
    7. Ernest Boffy-Ramirez, 2022. "Push or Pull? Measuring the labor supply response to the minimum wage using an individual-level panel," Applied Economics, Taylor & Francis Journals, vol. 54(35), pages 4043-4059, July.
    8. Escudero, Veronica & Liepmann, Hannah & Vergara, Damian, 2024. "Directed Search, Wages, and Non-wage Amenities: Evidence from an Online Job Board," IZA Discussion Papers 17211, Institute of Labor Economics (IZA).
    9. Lennon, Conor & Teltser, Keith F. & Fernandez, Jose & Gohmann, Stephan, 2023. "How morality and efficiency shape public support for minimum wages," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 618-637.
    10. Wursten, Jesse & Reich, Michael, 2023. "Racial inequality in frictional labor markets: Evidence from minimum wages," Labour Economics, Elsevier, vol. 82(C).
    11. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.

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    More about this item

    JEL classification:

    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy
    • J8 - Labor and Demographic Economics - - Labor Standards
    • J88 - Labor and Demographic Economics - - Labor Standards - - - Public Policy

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