Pharmaceutical M&A Activity: Effects on Prices, Innovation, and Competition
Bibliographic record
Abstract
The rise of blockbuster pharmaceutical acquisitions has prompted fears that unprecedented market concentration will weaken competition. Two of the most prominent concerns focus on the upstream and downstream ends of the pharmaceutical industry: (1) the concern that these mergers will concentrate the market for discovery and will therefore lead to fewer discoveries; and (2) the concern that merging large marketing, sales, and distribution forces will strengthen the hands of select pharmaceutical manufacturers and weaken downstream competition. Having considered potential dynamic effects in the industry and conducted a series of preliminary interviews with knowledgeable observers, though, this Article argues that neither of these common fears is systematically warranted. There are, however, potential dangers in market concentration at an intermediate stage during the discovery-to-development path: the stage for regulatory approval. These preliminary findings are a product of dramatic changes that are currently reshaping the structure of the pharmaceutical industry. This Article discusses how these structural changes contribute to the current merger wave, how dynamic responses by industry players in response to the merger wave mitigate the potential harm from competition, and how the political arena might still offer threats to market concentration.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".