Formulary Pricing with Optional Participation: Reconciling Static and Dynamic Efficiency
Bibliographic record
Abstract
Given the rapid growth in health care spending that is often attributed to technological change, a variety of price/cost control mechanisms are being used by many public and private institutions to curb that growth. While many of these mechanisms implicitly concern maximizing consumer surplus, i.e., maximizing static efficiency after an innovation has been developed, there is a concern on how they influence the R&D investments that make new technologies available in the first place, i.e., dynamic efficiency which concerns aligning the social costs and benefits of R&D. It is well known that dynamic efficiency is determined by how much of the social surplus from the innovation is appropriated as producer surplus. This paper presents a conceptual economic framework in which listing and pricing aspects of a national drug formulary can be examined. In order to strike a balance between static and dynamic efficiency we assume that the primary objective of providing public drug coverage is to assist patients to have better access to drug treatments, and ultimately improve their welfare. However, the drug plan's buying power should not be used to weaken the incentives for innovation provided by other public policies. The drug plan cannot force firms to accept non-negotiated terms for their products. Participation in the drug formulary is therefore optional, and producers are allowed to market their drugs outside the formulary and set prices freely. Using a spatial-differentiation model with heterogeneous consumer preferences, we first show that if drug coverage is provided without effective price negotiation, firms may be able to capture all the value generated by the drug subsidy by charging higher prices. Thus a proper price setting mechanism is required in order for the drug plan to achieve its primary objective. By exercising its buying power, a drug plan can usually lower prices to a level below the market prices, while firms still make more profit than they would do when drugs are not subsidized. Also interestingly, although drugs with greater therapeutic values are generally priced higher under market pricing, this could be reversed if the drug plan's objective were to maximize consumer surplus subject to having firms' participation in the formulary. This result contrasts with arguments that better products should always be priced higher even under public drug plans.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".