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Using Post-Regularization Distribution Regression to Measure the Effects of a Minimum Wage on Hourly Wages, Hours Worked and Monthly Earnings

Author

Listed:
  • Biewen, Martin

    (University of Tuebingen)

  • Erhardt, Pascal

    (University of Tübingen)

Abstract

We evaluate the distributional effects of a minimum wage introduction based on a data set with a moderate sample size but a large number of potential covariates. Therefore, the selection of relevant control variables at each distributional threshold is crucial to test hypotheses about the impact of the treatment. To this end, we use the post-double selection logistic distribution regression approach proposed by Belloni et al. (2018a), which allows for uniformly valid inference about the target coefficients of our low-dimensional treatment variables across the entire outcome distribution. Our empirical results show that the minimum wage crowded out hourly wages below the minimum threshold, benefitted monthly wages in the lower middle but not the lowest part of the distribution, and did not significantly affect the distribution of hours worked.

Suggested Citation

  • Biewen, Martin & Erhardt, Pascal, 2024. "Using Post-Regularization Distribution Regression to Measure the Effects of a Minimum Wage on Hourly Wages, Hours Worked and Monthly Earnings," IZA Discussion Papers 16894, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp16894
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    References listed on IDEAS

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    1. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    2. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    3. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    4. Caliendo Marco & Wittbrodt Linda & Schröder Carsten, 2019. "The Causal Effects of the Minimum Wage Introduction in Germany – An Overview," German Economic Review, De Gruyter, vol. 20(3), pages 257-292, August.
    5. Burauel Patrick & Grabka Markus M. & Schröder Carsten & Caliendo Marco & Obst Cosima & Preuss Malte, 2020. "The Impact of the Minimum Wage on Working Hours," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(2-3), pages 233-267, April.
    6. Schröder, Carsten & König, Johannes & Fedorets, Alexandra & Goebel, Jan & Grabka, Markus M. & Lüthen, Holger & Metzing, Maria & Schikora, Felicitas & Liebig, Stefan, 2020. "The economic research potentials of the German Socio-Economic Panel study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 21(3), pages 335-371.
    7. Mario Bossler & Thorsten Schank, 2023. "Wage Inequality in Germany after the Minimum Wage Introduction," Journal of Labor Economics, University of Chicago Press, vol. 41(3), pages 813-857.
    8. Jonathan Roth, 2022. "Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends," American Economic Review: Insights, American Economic Association, vol. 4(3), pages 305-322, September.
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    More about this item

    Keywords

    wage structure; automatic specification search; double machine learning;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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