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Algorithmic Writing Assistance on Jobseekers’ Resumes Increases Hires

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  • Emma Wiles
  • Zanele T. Munyikwa
  • John J. Horton

Abstract

There is a strong association between writing quality in resumes for new labor market entrants and whether they are ultimately hired. We show this relationship is, at least partially, causal: in a field experiment in an online labor market with nearly half a million jobseekers, treated jobseekers received algorithmic writing assistance on their resumes. Treated jobseekers were hired 8% more often. Contrary to concerns that the assistance takes away a valuable signal, we find no evidence that employers were less satisfied. We present a model where better writing does not signal ability but helps employers ascertain ability, rationalizing our findings.

Suggested Citation

  • Emma Wiles & Zanele T. Munyikwa & John J. Horton, 2023. "Algorithmic Writing Assistance on Jobseekers’ Resumes Increases Hires," NBER Working Papers 30886, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30886
    Note: LS
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    Cited by:

    1. Xiang Hui & Oren Reshef & Luofeng Zhou, 2023. "The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market," CESifo Working Paper Series 10601, CESifo.
    2. Timm Teubner & Christoph M. Flath & Christof Weinhardt & Wil Aalst & Oliver Hinz, 2023. "Welcome to the Era of ChatGPT et al," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 95-101, April.

    More about this item

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics
    • J0 - Labor and Demographic Economics - - General
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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