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Who Is Afraid of Machines?

Author

Listed:
  • Sotiris Blanas

    (National Bank of Belgium)

  • Gino Gancia

    (Queen Mary University of London, CREI and CEPR)

  • Sang Yoon (Tim) Lee

    (Queen Mary University of London and CEPR)

Abstract

We study how various types of machines, namely, information and communication technologies, software, and especially industrial robots, affect the demand for workers of different education, age, and gender. We do so by exploiting differences in the composition of workers across countries, industries and time. Our dataset comprises 10 high-income countries and 30 industries, which span roughly their entire economies, with annual observations over the period 1982 - 2005. The results suggest that software and robots reduced the demand for low and medium-skill workers, the young, and women|especially in manufacturing industries; but raised the demand for high-skill workers, older workers and men - especially in service industries. These findings are consistent with the hypothesis that automation technologies, contrary to other types of capital, replace humans performing routine tasks. We also find evidence for some types of workers, especially women, having shifted away from such tasks.

Suggested Citation

  • Sotiris Blanas & Gino Gancia & Sang Yoon (Tim) Lee, 2019. "Who Is Afraid of Machines?," Working Papers 889, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:889
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    References listed on IDEAS

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    Cited by:

    1. 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 Études Économiques (INSEE), issue 510-511-5, pages 149-164.
    2. Alessandra Bonfiglioli & Rosario Crinò & Harald Fadinger & Gino Gancia, 2020. "Robot Imports and Firm-Level Outcomes," CRC TR 224 Discussion Paper Series crctr224_2020_243, University of Bonn and University of Mannheim, Germany.
    3. Sotiris Blanas, 2019. "The distinct effects of information technologies and communication technologies on the age-skill composition of labour demand," Working Paper Research 365, National Bank of Belgium.
    4. Dechezlepretre, Antoine & Hemous, David & Olsen, Morten & Zanella, Carlo, 2020. "Automating labor: evidence from firm-level patent data," LSE Research Online Documents on Economics 108420, London School of Economics and Political Science, LSE Library.
    5. Gravina, Antonio Francesco & Foster-McGregor, Neil, 2020. "Automation, globalisation and relative wages: An empirical analysis of winners and losers," MERIT Working Papers 2020-040, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. Jaan Masso & Priit Vahter, 2020. "Innovation As A Firm-Level Factor Of The Gender Wage Gap," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 128, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    7. Dechezleprêtre, Antoine & Hémous, David & olsen, morten & Zanella, carlo, 2019. "Automating Labor: Evidence from Firm-level Patent Data," CEPR Discussion Papers 14249, C.E.P.R. Discussion Papers.

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    More about this item

    Keywords

    Automation; Robots; Employment; Labor Demand; Labor Income Share;
    All these keywords.

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • 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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