Interventions with Sticky Social Norms: A Critique
Bibliographic record
Abstract
We study the consequences of policy interventions when social norms are endogenous but costly to change. In our environment, a group faces a negative externality that it partially mitigates through incentives in the form of punishments. In this setting, policy interventions can have unexpected consequences. The most striking is that when the cost of bargaining is high, introducing a Pigouvian tax can increase output-yet in doing so increase welfare. An observer who saw that an increase in a Pigouvian tax raised output might wrongly conclude that this harmed welfare and that a larger tax increase would also raise output. This counter-intuitive impact on output is demonstrated theoretically for a general model and found in case studies for public goods subsidies and cartels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".