  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.
