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Robots and skill-biased development in employment structure: Evidence from China

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  • Tang, Chengjian
  • Huang, Keqi
  • Liu, Qiren

Abstract

We use inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) difference-in-differences (DID) to identify the causal relation between industrial robot adoption and employment structure in China. We find that robot adoption will significantly encourage firms to hire more highly skilled and highly educated workers, introducing skill-biased development into firms’ employment structure.

Suggested Citation

  • Tang, Chengjian & Huang, Keqi & Liu, Qiren, 2021. "Robots and skill-biased development in employment structure: Evidence from China," Economics Letters, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:ecolet:v:205:y:2021:i:c:s0165176521002378
    DOI: 10.1016/j.econlet.2021.109960
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    References listed on IDEAS

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    1. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
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    5. Daron Acemoglu, 1998. "Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1055-1089.
    6. 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.
    7. Fan, Haichao & Hu, Yichuan & Tang, Lixin, 2021. "Labor costs and the adoption of robots in China," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 608-631.
    8. David H. Autor & Lawrence F. Katz & Alan B. Krueger, 1998. "Computing Inequality: Have Computers Changed the Labor Market?," The Quarterly Journal of Economics, Oxford University Press, vol. 113(4), pages 1169-1213.
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    Citations

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    Cited by:

    1. Dario Guarascio & Alessandro Piccirillo & Jelena Reljic, 2024. "Will robot replace workers? Assessing the impact of robots on employment and wages with meta-analysis," LEM Papers Series 2024/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. 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.
    3. 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).
    4. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    5. Yang, Siying & Wang, Wenzhi & Ding, Tao, 2023. "Intelligent transformation and sustainable innovation capability: Evidence from China," Finance Research Letters, Elsevier, vol. 55(PB).

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    More about this item

    Keywords

    Industrial robots; Skill-biased employment; IPTW-DID; PSM-DID;
    All these keywords.

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • 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

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