The impact of pharmaceutical marketing on market access, treatment coverage, pricing, and social welfare
Bibliographic record
Abstract
Pharmaceutical spending in the United States, Canada, and the EU is growing. Public payers cover a large portion of these costs and have responded by instituting various pricing and access policies to limit their expenditure. One challenge that public payers face is additional demand induced by a manufacturer's marketing effort. We use a game theoretic approach to study the impact of pharmaceutical marketing on six practical pricing and access policies: negotiated pricing, open pricing, controlled pricing, a listing process, a risk-sharing arrangement, and a value-based pricing with risk-sharing arrangement. We find that all non-value-based policies result in either restricted access or suboptimal treatment coverage. We find that marketing is the highest in the first-best setting where all decisions are made by a social planner. We also find that the value-based pricing with risk-sharing arrangement is preferred by the manufacturer and from a societal perspective whereas no policy is universally preferred by a health care payer. A value-based pricing with risk-sharing arrangement always results in zero net monetary benefit for a health care payer. Therefore, considering non-value-based arrangements, we find that a negotiated pricing policy, a controlled pricing policy, or a risk-sharing arrangement may be socially preferred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".