Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks

TL;DR

This study analyzes gender disparities in computer science faculty hiring, revealing that institutional prestige and candidate productivity are primary factors, with gender effects being indirect and influenced by other covariates.

Contribution

It introduces a network model of faculty hiring that accounts for institutional prestige and productivity, highlighting subtle gender effects and their influence on hiring patterns.

Findings

Hiring outcomes are mainly influenced by institutional prestige and candidate productivity.

Gender differences exist in productivity, postdoctoral training, and career mobility.

Higher-ranked departments tend to hire more women than expected, affecting lower-ranked departments.

Abstract

Women are dramatically underrepresented in computer science at all levels in academia and account for just 15% of tenure-track faculty. Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals. Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring and the non-independence of hires. Using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America, we investigate the multi-dimensional nature of gender inequality in computer science faculty hiring through a network model of the hiring process. Overall, we find that hiring outcomes are most directly affected by (i) the relative prestige between hiring and placing institutions and (ii) the scholarly productivity of the candidates. After including these, and other features, the addition of gender did not significantly reduce modeling error. However, gender differences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the effects of gender are indirectly incorporated into hiring decisions through gender's covariates. Furthermore, we find evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar efforts by lower ranked departments. These findings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.