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Automation and unemployment: Does collective bargaining moderate their association?

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  • Leibrecht, Markus
  • Scharler, Johann
  • Zhoufu, Yan

Abstract

The stock of robots used in industrial production in the OECD more than doubled over the last two decades. Empirically, the direction of the association between automation and (un-)employment varies across countries. Which factors explain this cross-country variation? We argue that differences in collective bargaining systems play a role. We structure the collective bargaining systems of 37 OECD and EU countries by the degree of coordination of their collective bargaining on the one hand, and by the strength of labor unions on the other hand. These results in four types of collective bargaining systems: highly coordinated with strong unions; highly coordinated with weak unions; weakly coordinated with strong unions and weakly coordinated with weak unions. We use a dynamic panel data approach to investigate whether the association between increased automation and the unemployment rates of different societal groups differs across collective bargaining systems. Our findings are consistent with the view that increased automation is positively associated with unemployment in countries where collective bargaining is weak. In coordinated systems the association is muted, notably for workers with medium skill levels, that is, for the group of workers which is frequently seen to be especially prone to be “automated away”. We cannot unveil indications of insider-outsider behavior of labor unions.

Suggested Citation

  • Leibrecht, Markus & Scharler, Johann & Zhoufu, Yan, 2023. "Automation and unemployment: Does collective bargaining moderate their association?," Structural Change and Economic Dynamics, Elsevier, vol. 67(C), pages 264-276.
  • Handle: RePEc:eee:streco:v:67:y:2023:i:c:p:264-276
    DOI: 10.1016/j.strueco.2023.08.006
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    as
    1. Qingyang Wu, 2023. "Sustainable growth through industrial robot diffusion: Quasi‐experimental evidence from a Bartik shift‐share design," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(4), pages 1107-1133, October.
    2. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    3. Assar Lindbeck & Dennis J. Snower, 2001. "Insiders versus Outsiders," Journal of Economic Perspectives, American Economic Association, vol. 15(1), pages 165-188, Winter.
    4. Daron Acemoglu, 2003. "Cross-Country Inequality Trends," Economic Journal, Royal Economic Society, vol. 113(485), pages 121-149, February.
    5. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    6. Francesco Bogliacino & Matteo Lucchese, 2016. "Endogenous skill biased technical change: testing for demand pull effect," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 25(2), pages 227-243.
    7. Sebastian Kripfganz & Jörg Breitung, 2022. "Bias-corrected estimation of linear dynamic panel data models," London Stata Conference 2022 05, Stata Users Group.
    8. Florent Bordot, 2022. "Artificial Intelligence, Robots and Unemployment: Evidence from OECD Countries," Journal of Innovation Economics, De Boeck Université, vol. 0(1), pages 117-138.
    9. Stefan Jestl, 2022. "Industrial Robots, and Information and Communication Technology: The Employment Effects in EU Labour Markets," wiiw Working Papers 215, The Vienna Institute for International Economic Studies, wiiw.
    10. Matthias Oschinski & Rosalie Wyonch, 2017. "Future Shock? The Impact of Automation on Canada’s Labour Market," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 472, March.
    11. Manudeep Bhuller & Karl Ove Moene & Magne Mogstad & Ola L. Vestad, 2022. "Facts and Fantasies about Wage Setting and Collective Bargaining," Journal of Economic Perspectives, American Economic Association, vol. 36(4), pages 29-52, Fall.
    12. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    13. van der Velde, Lucas, 2022. "Phasing out: Routine tasks and retirement," Journal of Comparative Economics, Elsevier, vol. 50(3), pages 784-803.
    14. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    15. Barbieri, Laura & Mussida, Chiara & Piva, Mariacristina & Vivarelli, Marco, 2019. "Testing the Employment Impact of Automation, Robots and AI: A Survey and Some Methodological Issues," IZA Discussion Papers 12612, Institute of Labor Economics (IZA).
    16. Pajarinen, Mika & Rouvinen, Petri & Ekeland, Anders, 2015. "Computerization Threatens One-Third of Finnish and Norwegian Employment," ETLA Brief 34, The Research Institute of the Finnish Economy.
    17. Francesco Bogliacino, 2014. "Innovation and employment: A firm level analysis with European R&D Scoreboard data," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 15(2), pages 141-154.
    18. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    19. Michael Lewis-Beck & Mary Stegmaier, 2013. "The VP-function revisited: a survey of the literature on vote and popularity functions after over 40 years," Public Choice, Springer, vol. 157(3), pages 367-385, December.
    20. Cirillo, Valeria & Evangelista, Rinaldo & Guarascio, Dario & Sostero, Matteo, 2021. "Digitalization, routineness and employment: An exploration on Italian task-based data," Research Policy, Elsevier, vol. 50(7).
    21. Maarten Goos & Alan Manning & Anna Salomons, 2009. "Job Polarization in Europe," American Economic Review, American Economic Association, vol. 99(2), pages 58-63, May.
    22. Bogliacino, Francesco & Piva, Mariacristina & Vivarelli, Marco, 2012. "R&D and employment: An application of the LSDVC estimator using European microdata," Economics Letters, Elsevier, vol. 116(1), pages 56-59.
    23. Pengyu Chen & Yiannis Karavias & Elias Tzavalis, 2022. "Panel unit-root tests with structural breaks," Stata Journal, StataCorp LP, vol. 22(3), pages 664-678, September.
    24. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    25. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    26. Maarten Goos & Alan Manning, 2007. "Lousy and Lovely Jobs: The Rising Polarization of Work in Britain," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 118-133, February.
    27. John T. Addison, 2016. "Collective bargaining systems and macroeconomic and microeconomic flexibility: the quest for appropriate institutional forms in advanced economies," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-53, December.
    28. Vivarelli, Marco, 2012. "Innovation, Employment and Skills in Advanced and Developing Countries: A Survey of the Literature," IZA Discussion Papers 6291, Institute of Labor Economics (IZA).
    29. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    30. Das, Mitali & Hilgenstock, Benjamin, 2022. "The exposure to routinization: Labor market implications for developed and developing economies," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 99-113.
    31. Andrea Garnero, 2021. "The impact of collective bargaining on employment and wage inequality: Evidence from a new taxonomy of bargaining systems," European Journal of Industrial Relations, , vol. 27(2), pages 185-202, June.
    32. Benjamin David, 2017. "Computer technology and probable job destructions in Japan: An evaluation," Post-Print hal-01549790, HAL.
    33. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    34. Maarten Goos & Alan Manning, 2007. "Lousy and Lovely Jobs: The Rising Polarization of Work in Britain," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 118-133, February.
    35. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    36. Sebastian Diessner & Niccolo Durazzi & David Hope, 2022. "Skill-Biased Liberalization: Germany’s Transition to the Knowledge Economy," Politics & Society, , vol. 50(1), pages 117-155, March.
    37. Bogliacino, Francesco & Pianta, Mario, 2010. "Innovation and Employment: a Reinvestigation using Revised Pavitt classes," Research Policy, Elsevier, vol. 39(6), pages 799-809, July.
    38. Breitung, Jörg & Kripfganz, Sebastian & Hayakawa, Kazuhiko, 2022. "Bias-corrected method of moments estimators for dynamic panel data models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 116-132.
    39. Nguyen, Quoc Phu & Vo, Duc Hong, 2022. "Artificial intelligence and unemployment:An international evidence," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 40-55.
    40. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    41. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    42. Fernández-Macías, Enrique & Klenert, David & Antón, José-Ignacio, 2021. "Not so disruptive yet? Characteristics, distribution and determinants of robots in Europe," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 76-89.
    43. Robert J. Flanagan, 1999. "Macroeconomic Performance and Collective Bargaining: An International Perspective," Journal of Economic Literature, American Economic Association, vol. 37(3), pages 1150-1175, September.
    44. Wolfgang Dauth & Sebastian Findeisen & Jens Suedekum & Nicole Woessner, 2021. "The Adjustment of Labor Markets to Robots [“Skills, Tasks and Technologies: Implications for Employment and Earnings]," Journal of the European Economic Association, European Economic Association, vol. 19(6), pages 3104-3153.
    45. David, Benjamin, 2017. "Computer technology and probable job destructions in Japan: An evaluation," Journal of the Japanese and International Economies, Elsevier, vol. 43(C), pages 77-87.
    46. Karavias, Yiannis & Tzavalis, Elias, 2014. "Testing for unit roots in short panels allowing for a structural break," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 391-407.
    47. Haapanala, Henri & Marx, Ive & Parolin, Zachary, 2022. "Robots and Unions: The Moderating Effect of Organised Labour on Technological Unemployment," IZA Discussion Papers 15080, Institute of Labor Economics (IZA).
    48. Reinhard Schunck, 2013. "Within and between estimates in random-effects models: Advantages and drawbacks of correlated random effects and hybrid models," Stata Journal, StataCorp LP, vol. 13(1), pages 65-76, March.
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    More about this item

    Keywords

    OECD; EU; Automation; Unemployment; Union density; Collective bargaining; Coordination; Labor market institutions;
    All these keywords.

    JEL classification:

    • J50 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - General
    • J51 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Trade Unions: Objectives, Structure, and Effects
    • J52 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Dispute Resolution: Strikes, Arbitration, and Mediation
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

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