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Automation and productivity—a cross-country, cross-industry comparison

Author

Listed:
  • Lene Kromann
  • Nikolaj Malchow-Møller
  • Jan Rose Skaksen
  • Anders Sørensen

Abstract

We investigate the effects of automation on total factor productivity (TFP). Using industry-level panel data for nine countries, we find that more intensive use of industrial robots has a significantly positive effect on TFP. Specifically, an increase of one standard deviation in the robot intensity is associated with more than 6% higher TFP. Moreover, we find that the robot intensity increases with Chinese import competition and that automation is associated with higher wages and unchanged or higher employment.

Suggested Citation

  • Lene Kromann & Nikolaj Malchow-Møller & Jan Rose Skaksen & Anders Sørensen, 2020. "Automation and productivity—a cross-country, cross-industry comparison," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 29(2), pages 265-287.
    • Handle: RePEc:oup:indcch:v:29:y:2020:i:2:p:265-287.
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    File URL: http://hdl.handle.net/10.1093/icc/dtz039
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    Citations

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    Cited by:

    1. Kariem Soliman, 2021. "Are Industrial Robots a new GPT? A Panel Study of Nine European Countries with Capital and Quality-adjusted Industrial Robots as Drivers of Labour Productivity Growth," EIIW Discussion paper disbei307, Universitätsbibliothek Wuppertal, University Library.
    2. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    3. Andre Jungmittag, 2021. "Robotisation of the manufacturing industries in the EU: Convergence or divergence?," The Journal of Technology Transfer, Springer, vol. 46(5), pages 1269-1290, October.
    4. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    5. Kanit Sangsubhan & Kumpon Pornpattanapaisankul & Pisacha Kambuya, 2023. "Automation and Productivity: Evidence from Thai Manufacturing Firms," PIER Discussion Papers 199, Puey Ungphakorn Institute for Economic Research.
    6. Gan, Jiawu & Liu, Lihua & Qiao, Gang & Zhang, Qin, 2023. "The role of robot adoption in green innovation: Evidence from China," Economic Modelling, Elsevier, vol. 119(C).
    7. Simone d’alessandro & Tiziano Distefano & Guilherme Spinato Morlin & Davide Villani, 2023. "Policy Responses to Labour-Saving Technologies: Basic Income, Job Guarantee, and Working Time Reduction," JRC Working Papers on Social Classes in the Digital Age 2023-09, Joint Research Centre.
    8. Borsato, Andrea & Lorentz, André, 2023. "The Kaldor–Verdoorn law at the age of robots and AI," Research Policy, Elsevier, vol. 52(10).
    9. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    10. Yuan, Sai & Zhou, Ran & Li, Mengna & Lv, Chengchao, 2023. "Investigating the influence of digital technology application on employee compensation," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    11. Yin, Zi Hui & Zeng, Wei Ping, 2023. "The effects of industrial intelligence on China's energy intensity: The role of technology absorptive capacity," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    12. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    13. repec:gdk:wpaper:67 is not listed on IDEAS
    14. Filippi, Emilia & Bannò, Mariasole & Trento, Sandro, 2023. "Automation technologies and their impact on employment: A review, synthesis and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    15. Yining Zhang & Zhong Wu, 2021. "Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data," Sustainability, MDPI, vol. 13(9), pages 1-16, April.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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