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

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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.

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