IDEAS home Printed from https://ideas.repec.org/a/sae/ecdequ/v37y2023i2p183-197.html
   My bibliography  Save this article

COVID-19-Induced Automation: An Exploratory Study of Critical Occupations

Author

Listed:
  • Chun Song
  • Lionel J. Beaulieu
  • Indraneel Kumar
  • Roberto Gallardo

Abstract

The COVID-19 pandemic may have spurred automation, especially in critical occupations. This article explores the potential of each detailed Standard Occupational Classification System (SOC) occupation being automated due to COVID-19. The authors explore two key elements of each occupation: its exposure to diseases such as COVID-19 and the probability of that occupation being automated. The results reveal that food preparation, service, and cleaning-related occupations have a higher chance of pandemic-induced automation. Using monthly U.S. job postings from 2016 to 2021, the estimates show that the potential pandemic-induced automation is associated with a statistically significant decrease in job postings. A higher Automation Index is associated with fewer job postings since the pandemic. Such trends remain robust after accounting for posting duration and excluding health-related occupations. These findings contribute to the early assessment of the impact of COVID-19 on the potential integration of automation in the labor force and offer insights into building a resilient and labor-centric post-pandemic labor market.

Suggested Citation

  • Chun Song & Lionel J. Beaulieu & Indraneel Kumar & Roberto Gallardo, 2023. "COVID-19-Induced Automation: An Exploratory Study of Critical Occupations," Economic Development Quarterly, , vol. 37(2), pages 183-197, May.
    • Handle: RePEc:sae:ecdequ:v:37:y:2023:i:2:p:183-197
    DOI: 10.1177/08912424231163151
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/08912424231163151
    Download Restriction: no
    File URL: https://libkey.io/10.1177/08912424231163151?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
    ---><---

    References listed on IDEAS

    as
    1. Coibion, Olivier & Gorodnichenko, Yuriy & Weber, Michael, 2020. "Labor Markets During the Covid-19 Crisis: A Preliminary View," Department of Economics, Working Paper Series qt7rx7t91p, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    2. Nir Jaimovich & Henry E. Siu, 2020. "Job Polarization and Jobless Recoveries," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 129-147, March.
    3. Forsythe, Eliza & Kahn, Lisa B. & Lange, Fabian & Wiczer, David, 2020. "Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims," Journal of Public Economics, Elsevier, vol. 189(C).
    4. David Autor & Anna Salomons, 2018. "Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share," NBER Working Papers 24871, National Bureau of Economic Research, Inc.
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    6. Alex Chernoff & Casey Warman, 2023. "COVID-19 and implications for automation," Applied Economics, Taylor & Francis Journals, vol. 55(17), pages 1939-1957, April.
    7. Paolo E. Giordani & Francesco Rullani, 2020. "The Digital Revolution and COVID-19," Working Papers 06, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    8. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    9. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    10. Joshua Bernstein & Alexander W. Richter & Nathaniel A. Throckmorton, 2020. "COVID-19: A View from the Labor Market," Working Papers 2010, Federal Reserve Bank of Dallas.
    11. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    12. Ernst Ekkehardt & Merola Rossana & Samaan Daniel, 2019. "Economics of Artificial Intelligence: Implications for the Future of Work," IZA Journal of Labor Policy, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 9(1), pages 1-35, June.
    13. Coombs, Crispin, 2020. "Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research," International Journal of Information Management, Elsevier, vol. 55(C).
    14. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2020. "Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment," Finance and Economics Discussion Series 2020-030, Board of Governors of the Federal Reserve System (U.S.).
    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. Youngsoon Kwon & Myungkyu Shim & Hee-Seung Yang, 2024. "Pandemic Work Mode and Automation: Evidence from South Korea," Working papers 2024rwp-236, Yonsei University, Yonsei Economics Research Institute.
    2. Nataliia Dotsenko & Igor Chumachenko & Andrii Galkin & Heorhii Kuchuk & Dmytro Chumachenko, 2023. "Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment," Sustainability, MDPI, vol. 15(19), pages 1-13, September.

    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. Blanas, Sotiris & Oikonomou, Rigas, 2023. "COVID-induced economic uncertainty, tasks and occupational demand," Labour Economics, Elsevier, vol. 81(C).
    2. Matthias Firgo & Peter Mayerhofer & Michael Peneder & Philipp Piribauer & Peter Reschenhofer, 2018. "Beschäftigungseffekte der Digitalisierung in den Bundesländern sowie in Stadt und Land," WIFO Studies, WIFO, number 61633, October.
    3. Egana-delSol, Pablo & Micco, Alejandro, 2024. "The Role of Technological Change in the Evolution of the Employment to Output Elasticity," IZA Discussion Papers 17003, Institute of Labor Economics (IZA).
    4. Shreya Roy & Sugata Marjit & Bibek Ray Chaudhuri, 2022. "Role of Artificial Intelligence in Intra-Sectoral Wage Inequality in an Open Economy: A Finite Change Approach," CESifo Working Paper Series 9862, CESifo.
    5. Fossen, Frank M. & Sorgner, Alina, 2022. "New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Shimizu, Ryosuke & Momoda, Shohei, 2023. "Does automation technology increase wage?," Journal of Macroeconomics, Elsevier, vol. 77(C).
    7. Ryosuke Shimizu & Shohei Momoda, 2020. "Does Automation Technology increase Wage?," KIER Working Papers 1039, Kyoto University, Institute of Economic Research.
    8. Heluo, Yuxi & Fabel, Oliver, 2024. "Job computerization, occupational employment and wages: A comparative study of the United States, Germany, and Japan," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    9. Jacobs, Julian, 2024. "The artificial intelligence shock and socio-political polarization," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    10. Anna-Maria Kanzola, 2024. "The Knowledge Content of the Greek Production Structure in the Aftermath of the Greek Crisis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 936-957, March.
    11. repec:eur:ejesjr:364 is not listed on IDEAS
    12. Lu, Jing & Xiao, Qinglan & Wang, Taoxuan, 2023. "Does the digital economy generate a gender dividend for female employment? Evidence from China," Telecommunications Policy, Elsevier, vol. 47(6).
    13. Henrik Schwabe & Fulvio Castellacci, 2020. "Automation, workers’ skills and job satisfaction," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-26, November.
    14. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    15. repec:eur:ejesjr:361 is not listed on IDEAS
    16. Ünveren, Burak & Durmaz, Tunç & Sunal, Seçkin, 2023. "AI revolution and coordination failure: Theory and evidence," Journal of Macroeconomics, Elsevier, vol. 78(C).
    17. 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.
    18. Mutascu, Mihai, 2021. "Artificial intelligence and unemployment: New insights," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 653-667.
    19. Barbieri, Laura & Mussida, Chiara & Piva, Mariacristina & Vivarelli, Marco, 2019. "Testing the employment and skill impact of new technologies: A survey and some methodological issues," MERIT Working Papers 2019-032, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    20. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    21. Ugur, Mehmet, 2024. "Innovation, market power and the labour share: Evidence from OECD industries," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    22. Huajie Jiang & Qiguo Gong, 2022. "Does Skill Polarization Affect Wage Polarization? U.S. Evidence 2009–2021," Sustainability, MDPI, vol. 14(21), pages 1-17, October.

    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:sae:ecdequ:v:37:y:2023:i:2:p:183-197. 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: SAGE Publications (email available below). General contact details of provider: .

    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.