Crowding In with Impure Altruism: Theory and Evidence from Volunteerism in National Parks
Bibliographic record
Abstract
This paper makes three contributions to the literature on private provision of public goods. First, we identify limitations of the frequently used specification test that distinguishes between the standard models of pure and impure altruism based on the extent of crowding out. While the literature takes as given the result that crowding out should be less with impure altruism compared with pure altruism, we show that, in general, it can be either more or less. Second, we propose a more general test based on the presence of crowding in, rather than the extent of crowding out. Third, we provide empirical evidence. Using a unique panel data set on volunteerism in U.S. National Parks, we estimate the causal effect of changes in public funding within parks on the amount of within-park volunteerism. The overall finding is that each additional dollar of public expenditure crowds in 27 cents worth of volunteerism on average. We show how the estimates of crowding in, along with heterogeneity based on park and volunteer hour types, are theoretically consistent with the mainstay model of impure altruism.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".