Production, Migration, & Differentiation: Analyzing the Co-Evolution of Careers & Knowledge Production

Northwestern University, Evanston IL

Investigators

Abstract

Doctoral recipients represent a vital role in the economy, in the conduct of science and innovation; the transfer of knowledge between industry, government, and academe; and the education of the next generation of scientists and engineers. Several studies have sought to examine the production of innovation, commercialization, and publication of science. Less understood, however, are the conditions for the production of scientists and engineers. This project examines the conditions of doctoral training and how these conditions are related to career trajectories and future research and innovation of these students. The project will provide essential knowledge on the contextual factors which contribute to a robust scientific environment. These results will be of use to STEM departments and programs as they hire faculty, seek industry partnerships, and construct policies for doctoral training. This project examines doctoral training in the context of materials science and engineering utilizing a multi-level comparative research design at the individual, lab, and university levels with three interrelated goals. First, this project develops a novel conceptual model for characterizing how labs manage and organize research. It combines interviews with graduate students, faculty, and research staff with data that are localized and readily available to universities (such as sponsored projects data, disclosures, and annual reports) to analyze how different models of lab management work on-the-ground. Second, it examines the relationship between different lab models and the employment outcomes and research productivity of individual doctoral recipients, sourcing data for the latter from publicly available databases. To estimate the causal effect of the factors in the lab where students train and address issues of endogeneity, this project uses a quasi-experimental sample design and a model that accounts for sample selection. Third, taking advantage of advances in machine learning and computational text analysis, this project compares the outcomes of individual graduates within and across labs and describes how graduates' career choices impact the kind and content of research they produce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.