Econometric Society Meetings, ASSA Meetings, SOLE, and Canadian Public Economics Study Group
Bibliographic record
Abstract
Abstract: This paper estimates the effects of federal research funding on research outcomes at 71 research universities. We provide a new interpretation of the instrumental variable estimate of the coefficient of a regression of the output of an institution on an input. Absent parameter heterogeneity, it measures the total change in output when an institution obtains an additional unit of the input due to a change in the shadow price of the input. Our instrument for research funding is alumni representation on U.S. Congressional appropriations committees. The estimates show that an increase of $1 million in federal research funding (1993$) to a university results in 11-18 more articles and $353,000 more in total faculty salaries. The change in citations per article is small and imprecisely estimated. With respect to patents, the results are mixed. When the shadow price of federal research funding falls, as a first approximation, universities buy more federal research funding and produce more but not necessarily higher quality research output. We gratefully acknowledge financial assistance from the Andrew W. Mellon Foundation. We thank Hai
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".