Measuring the Effect of Federal Research Funding on Private Donations at Research Universities: Is Federal Research Funding More than a Substitute for Private Donations?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,048 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,016 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».