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

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. This paper was accepted by Yan Chen, behavioral economics and decision analysis.