Measuring the Production of Scientific Human Capital: New Data, Methods, and Evidence on the U.S. Scientific Training Ecosystem

Using newly-collected data on the near-population of U.S. STEM PhD graduates since 1950, we develop a dissertation-based methodology for measuring PhD populations and use it to present new evidence on who funds PhD training, how many graduates are trained in areas of strategic national importance, and the effects of public investment in PhD training on the scientific workforce. The U.S. federal 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 information technology, 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 roughly one-for-one. To support further research, we provide public datasets at multiple levels of aggregation, reporting PhD graduates by (i) critical technology area and (ii) source of support. These data and methods complement existing data collection efforts by national statistics agencies, producing information which is otherwise costly to collect or not systematically observed, and can be extended forward in time and to other countries.