Work

Measuring the production of scientific human capital: New data, methods, and evidence on the U.S. scientific training ecosystem

Year: 2026

Type: article

Abstract: We develop a dissertation-based methodology for measuring PhD populations and use it to present new evidence on who funds STEM PhD training in the United States, how many graduates are trained in areas of strategic national importance, and the effects of public investment in PhD training on PhD production. The U.S. government is by far the largest source of financial and in-kind support for STEM PhD training in America. We identify universities and fields where PhD training has high rates of government, industry, or philanthropic support, and the organizations and universities that fund and train the most PhDs in critical technology areas such as AI, quantum science, and biotechnology. Leveraging variation in government support across agencies and over time, we provide evidence suggesting that increasing government-funded PhD trainees increases PhD production additively or with modest crowd-in. To support further research, we provide public data at multiple levels of aggregation. These data and methods complement existing data collection efforts by national statistics agencies, producing information which is otherwise hard to collect or not systematically observed, and can be extended forward in time and to other countries.

Source: Research Policy

DOI: 10.1016/j.respol.2026.105605

Authors: Dror Shvadron, Hansen Zhang, Lee Fleming, Daniel P. Gross

Institutions: University of Toronto, Duke University, University of California, Berkeley, National Bureau of Economic Research

Language: English

Topic: Research, Science, and Academia

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $4,280

Funders: National Science Foundation, Alfred P. Sloan Foundation, Duke University