Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks
Women are dramatically underrepresented in computer science at all levels in academia and ac- count 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 nd that hiring outcomes are most directly a ected 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 signi cantly reduce modeling error. However, gender di er- ences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the e ects of gender are indirectly incorporated into hiring decisions through gender's covariates. Furthermore, we nd evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar e orts by lower ranked departments. These ndings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.