Gender, Prestige, and Productivity in Academic Hiring Networks and Career Trajectories

BIDS Data Science Lecture Series | March 17, 2017 | 1:10-2:30 p.m. | 190 Doe Library, UC Berkeley

Women are dramatically under-represented in many STEM fields at all levels in academia and account for just 15% of tenure-track faculty in computer science. 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 2,659 tenure-track faculty across 205 PhD-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. We find systematic inequalities organized around gender, prestige, and productivity, but their relationships with hiring have been changing over time. However, these relationships between individual and department characteristics do not stop at hiring. We also use the publication histories of tenure track professors to challenge the conventional narrative that productivity peaks early in a career and is followed by a slow decline. In fact, by introducing a simple model of individual career productivity trajectories, we analyze the timing of more than 200,000 publications to show that careers are far more diverse and varied than average statistics would suggest. Together, these results paint a complicated picture of academic hiring and productivity with implications for how and where future changes might occur.