Bibliographic record
Abstract
This paper extends the well-known news-vendor model with pricing to the case of public interest goods (safety products, energy efficient appliances, health-related products, and the like). Since such goods have significant societal value, the process of their supply is frequently subject to government interventions. The goals of such interventions are to improve the affordability of the good (coordinate price), increase the accessibility to the good (coordinate quantity/service level), and maximize social welfare. The welfare in our model consists of the firm’s profit, consumer surplus, and externality benefit net the government cost. We consider three intervention mechanisms: rebates, subsidies, and buyback guarantees, and compare them with the decentralized and centralized cases with respect to these goals.We find that buybacks are unable to coordinate price and have a hard time coordinating quantity, and thus have only a minor impact on the welfare. Rebates can coordinate price and quantity and can improve welfare when the production cost is low, but they become completely ineffective when the production cost is high. Subsidies emerge as a clear winner in our analysis: they can coordinate price and quantity and result in a welfare loss that is within 10-20% of the centralized solution in most cases we considered. As we discuss, such superior performance of the subsidy mechanism is driven by its ability to directly impact price and significantly reduce the risk in the system, leading to the overall highest welfare, albeit with a large government.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".