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The heterogeneous effects of the minimum wage on employment across states

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  • Wang, Wuyi
  • Phillips, Peter C.B.
  • Su, Liangjun

Abstract

This paper studies the relationship between the minimum wage and the employment rate in the US using the framework of a panel structure model. The approach allows the minimum wage, along with some other controls, to have heterogeneous effects on employment across states which are classified into a group structure. The effects on employment are the same within each group but differ across different groups. The number of groups and the group membership of each state are both unknown a priori. The approach employs the C-Lasso technique, a recently developed classification method that consistently estimates group structure and leads to oracle-efficient estimation of the coefficients. Empirical application of C-Lasso to a US restaurant industry panel over the period 1990–2006 leads to the identification of four separate groups at the state level. The findings reveal substantial heterogeneity in the impact of the minimum wage on employment across groups, with both positive and negative effects and geographical patterns manifesting in the data. The results provide some new perspectives on the prolonged debate on the impact of minimum wage on employment.

Suggested Citation

  • Wang, Wuyi & Phillips, Peter C.B. & Su, Liangjun, 2019. "The heterogeneous effects of the minimum wage on employment across states," Economics Letters, Elsevier, vol. 174(C), pages 179-185.
  • Handle: RePEc:eee:ecolet:v:174:y:2019:i:c:p:179-185
    DOI: 10.1016/j.econlet.2018.11.002
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    Cited by:

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    2. Subir K. Chakrabarti & Srikant Devaraj & Pankaj C. Patel, 2021. "Minimum wage and restaurant hygiene violations: Evidence from Seattle," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(1), pages 85-99, January.
    3. Francesco Fallucchi & Andrea Mercatanti & Jan Niederreiter, 2021. "Identifying types in contest experiments," International Journal of Game Theory, Springer;Game Theory Society, vol. 50(1), pages 39-61, March.
    4. Yiren Wang & Peter C B Phillips & Liangjun Su, 2023. "Panel Data Models with Time-Varying Latent Group Structures," Papers 2307.15863, arXiv.org.
    5. Loukas Karabarbounis & Jeremy Lise & Anusha Nath, 2022. "Minimum Wages and Labor Markets in the Twin Cities," NBER Working Papers 30239, National Bureau of Economic Research, Inc.
    6. McGuinness, Seamus & Redmond, Paul & Delaney, Judith, 2019. "The prevalence and effect on hours worked of the minimum wage in Ireland: A sectoral and regional analysis," Research Series, Economic and Social Research Institute (ESRI), number RS93, June.
    7. Sophie-Charlotte Klose, 2020. "Identifying Latent Structures in Maternal Employment: Evidence on the German Parental Benefit Reform," Papers 2011.03541, arXiv.org.
    8. Cristian Valeriu Paun & Radu Nechita & Alexandru Patruti & Mihai Vladimir Topan, 2021. "The Impact of the Minimum Wage on Employment: An EU Panel Data Analysis," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    9. Wang, Wuyi & Su, Liangjun, 2021. "Identifying latent group structures in nonlinear panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 272-295.
    10. Aleksandra Majchrowska & Paweł Strawiński, 2022. "Heterogeneous employment effects of minimum wage policies," Working Papers 2022-18, Faculty of Economic Sciences, University of Warsaw.
    11. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    12. Baek, Jisun & Lee, Changkeun & Park, WooRam, 2021. "The impact of the minimum wage on the characteristics of new establishments: Evidence from South Korea," Labour Economics, Elsevier, vol. 72(C).
    13. Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
    14. Gmeiner, Michael & Gmeiner, Robert, 2023. "Estimating the employment effect of the minimum wage through variation in compliance: evidence from five US states," LSE Research Online Documents on Economics 121277, London School of Economics and Political Science, LSE Library.
    15. Alessandrini, Diana & Milla, Joniada, 2021. "Minimum Wage Effects on Human Capital Accumulation: Evidence from Canadian Data," IZA Discussion Papers 14178, Institute of Labor Economics (IZA).
    16. Luis E. Arango & Sergio A. Rivera, 2020. "“Disemployment” effects of the minimum wage in the Colombian manufacturing sector," Borradores de Economia 1107, Banco de la Republica de Colombia.
    17. Arango, Luis E. & Rivera, Sergio A., 2022. "Moderate wage increases and flexible labour contracts to protect employment in Colombian manufacturing," Journal of Policy Modeling, Elsevier, vol. 44(3), pages 578-598.
    18. David Neumark & Peter Shirley, 2021. "Myth or Measurement: What Does the New Minimum Wage Research Say about Minimum Wages and Job Loss in the United States?," NBER Working Papers 28388, National Bureau of Economic Research, Inc.
    19. Aleksandra Majchrowska & Paweł Strawiński, 2021. "Minimum wage and local employment: A spatial panel approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(5), pages 1581-1602, October.

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    More about this item

    Keywords

    Classification; C-Lasso; Latent group structures; Minimum wage; Unemployment;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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