Crowding out Both Sides of the Philanthropy Market: Evidence from a Panel of Charities
Bibliographic record
Abstract
Abstract: When the government gives a grant to a private charitable organization, do the donors to that organization give less? If they do, is it because the grants crowd out donors who feel they gave through taxes (classic crowd out), or is it because the grant crowds out the fund-raising of the charities who, after getting the grant, reduce efforts of fund-raising (fund-raising crowd out)? This is the first paper to separate these two effects. Using a panel of almost 3100 charities, we find that crowding out is significant, at about 56 percent. Most of this crowding out is due to reduced fund-raising. We estimate that 68 percent of crowd out is from reduced fund-raising, and only 32 percent from the classic crowd out. Such a finding could have important consequences for how governments structure grants to non-profits. Our results indicate, for example, that requirements that charities match a fraction of government grants with increases in private donations might be a feasible policy that could reduce the detrimental effects of crowding out.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".