5Does Government Funding Change Behavior? An Empirical Analysis of Crowd‐Out
Bibliographic record
Abstract
When governments introduce programs or funding for initiatives that are partially provided by lower levels of governments or in the private or third sectors, should the government be concerned about whether its efforts are crowded out by changes in behavior by individuals and institutions participating in the provision of this good or service? The bulk of the theoretical literature suggests that crowd‐out is an issue. The (historic) bulk of the empirical literature, however, has failed to find a measurable crowd‐out effect. With better data and more sophisticated empirical techniques, there is a burgeoning literature that shows that crowd‐out exists. The purpose of this paper is to examine the recent literature that studies the issue of crowd‐out across a variety of venues to understand better the empirical estimation issues as well as the institutional details that can lead to a better understanding of the effects of government programs on individuals and organizations.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".