IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v193y2020ics0165176520301919.html
   My bibliography  Save this article

Testing the automation revolution hypothesis

Author

Listed:
  • Scholl, Keller
  • Hanson, Robin

Abstract

Wages and employment predict automation in 832 U.S. jobs, 1999 to 2019, but add little to top 25 O*NET job features, whose best predictive model did not change over this period. Automation changes predict changes in neither wages nor employment.

Suggested Citation

  • Scholl, Keller & Hanson, Robin, 2020. "Testing the automation revolution hypothesis," Economics Letters, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:ecolet:v:193:y:2020:i:c:s0165176520301919
    DOI: 10.1016/j.econlet.2020.109287
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176520301919
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2020.109287?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    2. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Florent Bordot & Andre Lorentz, 2021. "Automation and labor market polarization in an evolutionary model with heterogeneous workers," LEM Papers Series 2021/32, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Kerstin Hotte & Melline Somers & Angelos Theodorakopoulos, 2022. "Technology and jobs: A systematic literature review," Papers 2204.01296, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    2. Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2022. "The empirics of technology, employment and occupations: lessons learned and challenges ahead," DISCE - Quaderni del Dipartimento di Politica Economica dipe0028, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    3. Hensvik, Lena & Skans, Oskar Nordström, 2023. "The skill-specific impact of past and projected occupational decline," Labour Economics, Elsevier, vol. 81(C).
    4. Fossen, Frank M. & Sorgner, Alina, 2021. "Digitalization of work and entry into entrepreneurship," Journal of Business Research, Elsevier, vol. 125(C), pages 548-563.
    5. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2022. "Patenting in 4IR technologies and firm performance [Robots and jobs: evidence from US labor markets]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 112-136.
    6. Seamus McGuinness & Konstantinos Pouliakas & Paul Redmond, 2023. "Skills-displacing technological change and its impact on jobs: challenging technological alarmism?," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 32(3), pages 370-392, April.
    7. Su, Chi-Wei & Yuan, Xi & Umar, Muhammad & Lobonţ, Oana-Ramona, 2022. "Does technological innovation bring destruction or creation to the labor market?," Technology in Society, Elsevier, vol. 68(C).
    8. Songul Tolan & Annarosa Pesole & Fernando Martinez-Plumed & Enrique Fernandez-Macias & José Hernandez-Orallo & Emilia Gomez, 2020. "Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks," JRC Working Papers on Labour, Education and Technology 2020-02, Joint Research Centre.
    9. Zuazu-Bermejo, Izaskun, 2022. "Robots and women in manufacturing employment," ifso working paper series 19, University of Duisburg-Essen, Institute for Socioeconomics (ifso).
    10. Gruetzemacher, Ross & Paradice, David & Lee, Kang Bok, 2020. "Forecasting extreme labor displacement: A survey of AI practitioners," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    11. Genz, Sabrina & Gregory, Terry & Janser, Markus & Lehmer, Florian & Matthes, Britta, 2021. "How do workers adjust when firms adopt new technologies?," ZEW Discussion Papers 21-073, ZEW - Leibniz Centre for European Economic Research.
    12. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    13. Christenko, Aleksandr, 2022. "Automation and occupational mobility: A task and knowledge-based approach," Technology in Society, Elsevier, vol. 70(C).
    14. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    15. Fossen, Frank M. & Sorgner, Alina, 2019. "New Digital Technologies and Heterogeneous Employment and Wage Dynamics in the United States: Evidence from Individual-Level Data," IZA Discussion Papers 12242, Institute of Labor Economics (IZA).
    16. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    17. Bertin Martens & Songul Tolan, 2018. "Will this time be different? A review of the literature on the Impact of Artificial Intelligence on Employment, Incomes and Growth," JRC Working Papers on Digital Economy 2018-08, Joint Research Centre.
    18. Nikolova, Milena & Cnossen, Femke & Nikolaev, Boris, 2024. "Robots, meaning, and self-determination," Research Policy, Elsevier, vol. 53(5).
    19. Gardberg, Malin & Heyman, Fredrik & Norbäck, Pehr-Johan & Persson, Lars, 2020. "Digitization-based automation and occupational dynamics," Economics Letters, Elsevier, vol. 189(C).
    20. Albanesi, Stefania & Dias da Silva, Antonio & Jimeno, Juan Francisco & Lamo, Ana & Wabitsch, Alena, 2023. "New Technologies and Jobs in Europe," CEPR Discussion Papers 18220, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Automation; Wages; Employment; Occupations; Artificial intelligence; Technology;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • 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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:193:y:2020:i:c:s0165176520301919. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.