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Missing Women in Tech: The Labor Market for Highly Skilled Software Engineers

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  • Raviv Murciano-Goroff

    (Strategy & Innovation Department, Questrom School of Business, Boston University, Boston, Massachusetts 02215)

Abstract

This paper examines the behavior of job seekers and recruiters in the labor market for software engineers. I obtained data from a recruiting platform where individuals can self-report their computer programming skills and recruiters can message individuals they wish to contact about job opportunities. I augment this data set with measures of each individual’s previous programming experience based on analysis of actual computer source code they wrote and shared within the open-source software community. This novel data set reveals that candidates’ self-reported technical skills are quantitatively important predictors of recruiter interest. Consistent with social psychology and behavioral economics studies, I also find female programmers with previous experience in a programming language are 11.07% less likely than their male counterparts to self-report knowledge of that programming language on their resume. Despite public pronouncements, however, recruiters do not appear more inclined toward recruiting female candidates who self-report knowing programming languages. Indeed, recruiters are predicted to be 6.47% less likely to express interest in a female candidate than a male candidate with comparable observable qualifications even if those qualifications are very strong. Ultimately, a gender gap in the self-reporting of skills on resumes exists; but recruiters do not appear to be adjusting their response to such signals in ways that could increase the representation of women among software engineering recruits.

Suggested Citation

  • Raviv Murciano-Goroff, 2022. "Missing Women in Tech: The Labor Market for Highly Skilled Software Engineers," Management Science, INFORMS, vol. 68(5), pages 3262-3281, May.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:5:p:3262-3281
    DOI: 10.1287/mnsc.2021.4077
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    Cited by:

    1. Mallory Avery & Andreas Leibbrandt & Joseph Vecci, 2023. "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech," Monash Economics Working Papers 2023-09, Monash University, Department of Economics.
    2. Ariel J. Binder & Amanda Eng & Kendall Houghton & Andrew Foote, 2024. "Is the Gender Pay Gap Largest at the Top?," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 248-253, May.
    3. Abraham, Lisa, 2023. "The gender gap in performance reviews," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 459-492.

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