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International comparison of the impact of digital transformation on employment

Author

Listed:
  • You, Jing
  • Xu, Xiangyu
  • Liao, Deng
  • Lin, Chen

Abstract

Theoretical analysis in this paper examines the impact of digital transformation on employment and its transmission mechanisms. It proposes that regional characteristics, such as market size, industry structure, and labor structure, are important factors influencing the employment effect of digital transformation. Empirically, this paper analyzes the employment effect of digital transformation using economic panel data from 68 countries spanning the years 2013–2019 and finds that: 1) In terms of employment, digital transformation is dominated by the substitution effect. In terms of wages, digital transformation presents wage-rising effects. 2) Large market size and advanced industry structure significantly mitigate the employment substitution effect and enhance the wage-rising effect. 3) The skillization of labor structure has no significant impact on the employment substitution effect of digital transformation, but it significantly enhances the wage-raising effect. 4) Developing countries experience a more pronounced employment substitution effect from digital transformation, while developed countries witness a more prominent wage-increasing effect. The robustness of these results has been confirmed after introducing a one-period lag in the explanatory variables and utilizing instrumental variables. These findings of this paper offer valuable insights for achieving a balance between equity and efficiency in the context of digital transformation.

Suggested Citation

  • You, Jing & Xu, Xiangyu & Liao, Deng & Lin, Chen, 2024. "International comparison of the impact of digital transformation on employment," Journal of Asian Economics, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:asieco:v:95:y:2024:i:c:s1049007824001155
    DOI: 10.1016/j.asieco.2024.101820
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    as
    1. Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2015. "The Next Generation of the Penn World Table," American Economic Review, American Economic Association, vol. 105(10), pages 3150-3182, October.
    2. Philippe Aghion & Céline Antonin & Simon Bunel, 2019. "Artificial Intelligence, Growth and Employment: The Role of Policy," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 149-164.
    3. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    4. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    5. Hashem Pesaran, M. & Yamagata, Takashi, 2008. "Testing slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 142(1), pages 50-93, January.
    6. James Bessen, 2018. "AI and Jobs: the role of demand," NBER Working Papers 24235, National Bureau of Economic Research, Inc.
    7. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    8. 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.
    9. Lee, Neil & Clarke, Stephen, 2019. "Do low-skilled workers gain from high-tech employment growth? High-technology multipliers, employment and wages in Britain," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    10. 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.
    11. Dimitrios Bakas & Theodore Panagiotidis & Gianluigi Pelloni, 2017. "Regional And Sectoral Evidence Of The Macroeconomic Effects Of Labor Reallocation: A Panel Data Analysis," Economic Inquiry, Western Economic Association International, vol. 55(1), pages 501-526, January.
    12. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    13. Lorraine Dearden & Javier Ferri & Costas Meghir, 2002. "The Effect Of School Quality On Educational Attainment And Wages," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 1-20, February.
    14. Fossen, Frank M. & Sorgner, Alina, 2022. "New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    15. Jonas Hjort & Jonas Poulsen, 2019. "The Arrival of Fast Internet and Employment in Africa," American Economic Review, American Economic Association, vol. 109(3), pages 1032-1079, March.
    16. Dauth, Wolfgang & Findeisen, Sebastian & Südekum, Jens & Wößner, Nicole, 2017. "German robots - the impact of industrial robots on workers," IAB-Discussion Paper 201730, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    17. Guy Michaels & Ashwini Natraj & John Van Reenen, 2010. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 Years," CEP Discussion Papers dp0987, Centre for Economic Performance, LSE.
    18. Daron Acemoglu & Pascual Restrepo, 2022. "Tasks, Automation, and the Rise in U.S. Wage Inequality," Econometrica, Econometric Society, vol. 90(5), pages 1973-2016, September.
    19. Südekum, Jens & Dauth, Wolfgang & Findeisen, Sebastian & Woessner, Nicole, 2017. "German Robots – The Impact of Industrial Robots on Workers," CEPR Discussion Papers 12306, C.E.P.R. Discussion Papers.
    20. 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.
    21. repec:nbr:nberch:14019 is not listed on IDEAS
    22. 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.
    23. George J. Borjas & Richard B. Freeman, 2019. "From Immigrants to Robots: The Changing Locus of Substitutes for Workers," NBER Working Papers 25438, National Bureau of Economic Research, Inc.
    24. Guy Michaels & Ashwini Natraj & John Van Reenen, 2014. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 60-77, March.
    25. repec:hal:spmain:info:hdl:2441/7n49nkmngd8448a5ts5gt5ade0 is not listed on IDEAS
    26. repec:spo:wpmain:info:hdl:2441/7n49nkmngd8448a5ts5gt5ade0 is not listed on IDEAS
    27. Osea Giuntella & Yi Lu & Tianyi Wang, 2022. "How do Workers and Households Adjust to Robots? Evidence from China," NBER Working Papers 30707, National Bureau of Economic Research, Inc.
    28. Ana L. Revenga, 1992. "Exporting Jobs?The Impact of Import Competition on Employment and Wages in U. S. Manufacturing," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(1), pages 255-284.
    Full references (including those not matched with items on IDEAS)

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