IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/29866.html

Computerization of White Collar Jobs

Author

Listed:
  • Marcus Dillender
  • Eliza Forsythe

Abstract

We investigate the impact of computerization of white-collar jobs on wages and employment. Using online job postings from 2007 and 2010--2016 for office and administrative support (OAS) jobs, we show that when firms adopt new software at the job-title level they increase the skills required of job applicants. Furthermore, firms change the task content of such jobs, broadening them to include tasks associated with higher-skill office functions. We aggregate these patterns to the local labor-market level, instrumenting for local technology adoption with national measures. We find that a 1 standard deviation increase in OAS technology usage reduces employment in OAS occupations by about 1 percentage point and increases wages for college graduates in OAS jobs by over 3 percent. We find negative wage spillovers, with wages falling for both workers with and without a college degree. These results are consistent with technological adoption inducing a realignment in task assignment across occupations, leading office support occupations to become higher skill. We argue relative wage gains for OAS workers indicates that factor-augmenting features of OAS technological change dominate task-substituting features. In addition, while we find that total employment increases, these gains primarily accrue to college-educated women.

Suggested Citation

  • Marcus Dillender & Eliza Forsythe, 2022. "Computerization of White Collar Jobs," NBER Working Papers 29866, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29866
    Note: LS PR
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w29866.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Marin, Giovanni & Vona, Francesco, 2023. "Finance and the reallocation of scientific, engineering and mathematical talent," Research Policy, Elsevier, vol. 52(5).
    2. Ma, Bianjing & Chen, Lei & Wang, Xiaohui & Ding, Song, 2024. "Who benefits more from the digital economy: (Non-)Cognitive ability and the labor income premium," International Review of Economics & Finance, Elsevier, vol. 96(PB).
    3. Chuan, Amanda & Zhang, Weilong, 2023. "Non-college Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," IZA Discussion Papers 16089, Institute of Labor Economics (IZA).
    4. Barth, Erling & Davis, James C. & Freeman, Richard B. & McElheran, Kristina, 2023. "Twisting the demand curve: Digitalization and the older workforce," Journal of Econometrics, Elsevier, vol. 233(2), pages 443-467.
    5. Eliza Forsythe, 2020. "Automation and Technological Change: The Outlook for Workers and Economies," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 21(03), pages 27-30, September.
    6. Dupuy, Arnaud & Raux, Morgan & Signorelli, Sara, 2024. "Digitalization, Change in Skill Distance between Occupations and Worker Mobility: A Gravity Model Approach," IZA Discussion Papers 17535, Institute of Labor Economics (IZA).
    7. Jiang, Wei & Tang, Yuehua & Xiao, Rachel J. & Yao, Vincent, 2025. "Surviving the fintech disruption," Journal of Financial Economics, Elsevier, vol. 171(C).
    8. David J Deming & Kadeem Noray, 2020. "Earnings Dynamics, Changing Job Skills, and STEM Careers," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(4), pages 1965-2005.
    9. Ma, Wenting & Ouimet, Paige & Simintzi, Elena, 2025. "Mergers and acquisitions, technological change, and inequality," Journal of Financial Economics, Elsevier, vol. 172(C).
    10. Chuan, A. & Zhang, W., 2021. "Non-College Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," Cambridge Working Papers in Economics 2177, Faculty of Economics, University of Cambridge.

    More about this item

    JEL classification:

    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:29866. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.