Policy Incentives for Dangerous (But Necessary) Operations
Bibliographic record
Abstract
In industries where firms perform dangerous (but necessary) operations, liability costs—due to potential harm to third parties—can be significant. Firms may therefore find it optimal to exit the market, and this may lead to an inefficiently low number of incumbents. A social planner can discourage exit by offering appropriately designed subsidies. Ex ante subsidies defray the costs associated with making operations safer (e.g., funds to subsidize the purchase of safety equipment). Ex post subsidies mitigate the financial damages caused by an accident (e.g., funds to defray the cost of cleaning up a toxic spill). We consider a model where (i) firms have private information about their ability to improve reliability and (ii) reliability investments are unobservable. We demonstrate that when the social value of reliability outweighs the benefit of increased competition, it is optimal to offer ex ante subsidies alone (i.e., to subsidize the cost of making operations safer). Conversely, when the benefits of competition outweigh the benefits of reliability, a combination of ex ante and ex post subsidies is optimal (i.e., not only to subsidize safer operations, but also to share the costs of a potential accident).
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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".