University faculty play a special role in shaping scientific innovation, both directly through their own research and indirectly through their training of students. Despite the cultural importance of this scientific workforce, its composition and dynamics are not well understood, and inequalities are pervasive, both at the individual and organizational levels. In this talk, I will describe the results of using data-driven techniques to understand the quantitative network structure of who hires whose graduates as faculty, and the resulting implications for individuals, institutions, and science as a whole.
Using comprehensive data on nearly 19,000 regular faculty in three scholastically distinct disciplines, we first extract an institutional prestige ranking that best explains the observed network of faculty hires among institutions. The inferred hierarchy reveals enormous differences in faculty production and placement, and allows us to quantify how increased institutional prestige leads to increased faculty production, better faculty placement, and a more influential position within a discipline. Then, using a subset of data on faculty in Computer Science we investigate whether men and women place differently into faculty positions, how gender covaries with scholarly productivity, and when gender parity in hiring may be achieved. I'll close with some forward-looking thoughts on using data-driven approaches to better understand and model the scientific workforce and its dynamics.
This is joint work with Samuel F. Way and Daniel B. Larremore.