Measuring the Effect of Federal Research Funding on Private Donations at Research Universities: Is Federal Research Funding More than a Substitute for Private Donations?
Bibliographic record
Abstract
The nature of federal research funding has changed in the United States over the last 30 years. In part, federal research funding has changed in the distribution of funding across disciplines and across universities. Federal funding to universities with historically low levels of funding has also experienced greater growth than those universities with historically high levels of funding. In addition, universities have become more involved in the political process with respect to the allocation of funding for higher education. As the nature of government funding changes, this paper questions its effect on private donations to research and non-research universities. The general presumption of much of the existing theoretical work is that government and private funding for charitable goods are substitutes. Limited evidence exists to suggest, in some circumstances, there may be a positive correlation between these two sources of funding. Potentially, because the government undertakes the expense to gather information about the research universities, and engages in such activities as peer-review of research proposals, the government through its grant awards may provide a signal of quality of research or other information to donors that is less noisy than that available to private donors. Similarly, there may be other types of spillover effects from research funding to private donations. In this case, a change in government grants has both a positive and negative effect on private donations, suggesting a positive correlation between private and public donations if the effect from the dissemination of information is greater than the substitution effect of government grants. I examine data for private and public universities in the United States to measure the relationship between private and public donations under a fixed-effects OLS regression. I explore issues of bias from endogeneity or omitted variables and report the results from a two stage least squares regression in which I use a set of measures that affect federal research funding but not private donations. Regardless of the specification, the results suggest private and public donations are positively correlated for research universities and negatively correlated for non-research institutions. On average, increasing federal research funding by one dollar increases private donations by 65 cents at research universities, decreases private donations by 9 cents at universities whose highest degree granted is a masters, and decreases private donations by 45 cents at liberal arts colleges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".