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Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach

Author

Listed:
  • Gert Bijnens

    (National Bank of Belgium
    KU Leuven, VIVES)

  • Shyngys Karimov

    (Amazon UK Services Ltd.)

  • Jozef Konings

    (Nazarbayev University
    KU Leuven, VIVES)

Abstract

This paper analyzes the impact of automatic wage indexation on employment. To boost competitiveness and increase employment, Belgium suspended its automatic wage indexation system in 2015. This resulted in a 2% fall in real wages for all workers. In the absence of a suitable control group, we use machine learning for the counterfactual analysis. We artificially construct the control group for a difference-in-difference analysis based on the pre-treatment evolution of treated firms. We find a positive impact on employment of 1.2%, which corresponds to a labor demand elasticity of − 0.6. This effect is more pronounced for manufacturing firms, where the elasticity reaches − 1. These results show that a suspension of the automatic wage indexation mechanism can be effective in preserving employment.

Suggested Citation

  • Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  • Handle: RePEc:kap:decono:v:171:y:2023:i:1:d:10.1007_s10645-023-09418-y
    DOI: 10.1007/s10645-023-09418-y
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    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    2. Emmanuel Saez & Benjamin Schoefer & David Seim, 2019. "Payroll Taxes, Firm Behavior, and Rent Sharing: Evidence from a Young Workers' Tax Cut in Sweden," American Economic Review, American Economic Association, vol. 109(5), pages 1717-1763, May.
    3. Gert Bijnens & Jozef Konings, 2020. "Declining business dynamism in Belgium," Small Business Economics, Springer, vol. 54(4), pages 1201-1239, April.
    4. Lichter, Andreas & Peichl, Andreas & Siegloch, Sebastian, 2015. "The own-wage elasticity of labor demand: A meta-regression analysis," European Economic Review, Elsevier, vol. 80(C), pages 94-119.
    5. Resce, Giuliano, 2022. "The impact of political and non-political officials on the financial management of local governments," Journal of Policy Modeling, Elsevier, vol. 44(5), pages 943-962.
    6. Pierre Cahuc & Stéphane Carcillo & Thomas Le Barbanchon, 2019. "The Effectiveness of Hiring Credits," SciencePo Working papers Main hal-03391893, HAL.
    7. Benjamin Wild Pugsley & Ay’egul ahin, 2019. "Grown-up Business Cycles," The Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 1102-1147.
    8. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    9. Pierre Cahuc & Stéphane Carcillo & Thomas Le Barbanchon, 2019. "The Effectiveness of Hiring Credits," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(2), pages 593-626.
    10. Bennmarker, Helge & Mellander, Erik & Öckert, Björn, 2009. "Do regional payroll tax reductions boost employment?," Labour Economics, Elsevier, vol. 16(5), pages 480-489, October.
    11. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    12. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    13. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    14. Mirko Draca & Stephen Machin & John Van Reenen, 2011. "Minimum Wages and Firm Profitability," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 129-151, January.
    15. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    16. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    17. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    18. Guoyi Zhang & Yan Lu, 2012. "Bias-corrected random forests in regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 151-160, March.
    19. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
    20. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Local mortality estimates during the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1189-1217, October.
    21. Egebark, Johan & Kaunitz, Niklas, 2018. "Payroll taxes and youth labor demand," Labour Economics, Elsevier, vol. 55(C), pages 163-177.
    22. Jan Abrell & Mirjam Kosch & Sebastian Rausch, 2019. "How Effective Was the UK Carbon Tax? — A Machine Learning Approach to Policy Evaluation," CER-ETH Economics working paper series 19/317, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    23. David Neumark & Diego Grijalva, 2017. "The Employment Effects of State Hiring Credits," ILR Review, Cornell University, ILR School, vol. 70(5), pages 1111-1145, October.
    24. Peter Harasztosi & Attila Lindner, 2019. "Who Pays for the Minimum Wage?," American Economic Review, American Economic Association, vol. 109(8), pages 2693-2727, August.
    25. Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020. "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(6), pages 1181-1217.
    26. Susan Athey & Mohsen Bayati & Guido Imbens & Zhaonan Qu, 2019. "Ensemble Methods for Causal Effects in Panel Data Settings," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 65-70, May.
    27. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    28. Pierre Cahuc & Stéphane Carcillo & Thomas Le Barbanchon, 2019. "The Effectiveness of Hiring Credits," SciencePo Working papers hal-03391893, HAL.
    29. Paul Beaudry & David A. Green & Ben M. Sand, 2018. "In Search of Labor Demand," American Economic Review, American Economic Association, vol. 108(9), pages 2714-2757, September.
    30. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    31. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    32. Ku, Hyejin & Schönberg, Uta & Schreiner, Ragnhild C., 2020. "Do place-based tax incentives create jobs?," Journal of Public Economics, Elsevier, vol. 191(C).
    33. Shyngys Karimov & Jozef Konings, 2021. "The start-up gap and jobs," Small Business Economics, Springer, vol. 57(4), pages 2067-2084, December.
    34. Geurts, Karen & Van Biesebroeck, Johannes, 2016. "Firm creation and post-entry dynamics of de novo entrants," International Journal of Industrial Organization, Elsevier, vol. 49(C), pages 59-104.
    35. Andrew C. Johnston, 2021. "Unemployment Insurance Taxes and Labor Demand: Quasi-Experimental Evidence from Administrative Data," American Economic Journal: Economic Policy, American Economic Association, vol. 13(1), pages 266-293, February.
    36. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    37. repec:hal:spmain:info:hdl:2441/2rcfhie1t29t8ri11cvv60qku0 is not listed on IDEAS
    38. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
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    1. Bart Cockx & Sam Desiere, 2023. "Labour costs and the decision to hire the first employee," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 23/1071, Ghent University, Faculty of Economics and Business Administration.

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    More about this item

    Keywords

    Labor demand; Wage elasticity; Counterfactual analysis; Machine learning;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy

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