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Who Is afraid of machines?

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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.

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  • Satiris Blanas & Gino Gancia & Sang Yoon (Tim) Lee, 2019. "Who Is afraid of machines?," Economics Working Papers 1661, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:1661
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    1. Luc Behaghel & Eve Caroli & Muriel Roger, 2014. "Age-biased Technical and Organizational Change, Training and Employment Prospects of Older Workers," Economica, London School of Economics and Political Science, vol. 81(322), pages 368-389, April.
    2. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    3. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    4. Aum, Sangmin & Lee, Sang Yoon (Tim) & Shin, Yongseok, 2018. "Computerizing industries and routinizing jobs: Explaining trends in aggregate productivity," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 1-21.
    5. Daron Acemoglu & Pascual Restrepo, 2022. "Demographics and Automation [Automation and Demographic Change]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 1-44.
    6. David M. Byrne & Carol Corrado & Daniel E. Sichel, 2017. "Own-Account IT Equipment Investment," FEDS Notes 2017-10-04-2, Board of Governors of the Federal Reserve System (U.S.).
    7. Daron Acemoglu & Pascual Restrepo, 2018. "Demographics and Automation," Boston University - Department of Economics - Working Papers Series dp-299, Boston University - Department of Economics.
    8. Beckmann, Michael & Schauenberg, Bernd, 2007. "Age-biased technological and organizational change: firm-level evidence and management implications," Working papers 2007/05, Faculty of Business and Economics - University of Basel.
    9. Patrick Aubert & Eve Caroli & Muriel Roger, 2006. "New technologies, organisation and age: firm-level evidence," Economic Journal, Royal Economic Society, vol. 116(509), pages 73-93, February.
    10. repec:nbr:nberch:14019 is not listed on IDEAS
    11. 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.
    12. repec:dau:papers:123456789/10051 is not listed on IDEAS
    13. Daron Acemoglu & Gino Gancia & Fabrizio Zilibotti, 2015. "Offshoring and Directed Technical Change," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(3), pages 84-122, July.
    14. repec:dau:papers:123456789/7243 is not listed on IDEAS
    15. Eli Berman & John Bound & Zvi Griliches, 1994. "Changes in the Demand for Skilled Labor within U. S. Manufacturing: Evidence from the Annual Survey of Manufactures," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 109(2), pages 367-397.
    16. Bartel, Ann P & Sicherman, Nachum, 1993. "Technological Change and Retirement Decisions of Older Workers," Journal of Labor Economics, University of Chicago Press, vol. 11(1), pages 162-183, January.
    17. Borghans, L. & ter Weel, B.J., 2002. "Do older workers have more trouble using a computer than younger workers?," ROA Research Memorandum 1E, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    18. 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.
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    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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