Cross-National Evidence on Generic Pharmaceuticals: Pharmacy vs. Physician-Driven Markets
Bibliographic record
Abstract
This paper examines the role of regulation and competition in generic markets.Generics offer large potential savings to payers and consumers of pharmaceuticals.Whether the potential savings are realized depends on the extent of generic entry and uptake and the level of generic prices.In the U.S., the regulatory, legal and incentive structures encourage prompt entry, aggressive price competition and patient switching to generics.Key features are that pharmacists are authorized and incentivized to switch patients to cheap generics.By contrast, in many other high and middle income countries, generics traditionally competed on brand rather than price because physicians rather than pharmacies are the decision-makers.Physician-driven generic markets tend to have higher generic prices and may have lower generic uptake, depending on regulations and incentives.Using IMS data to analyze generic markets in the U.S., Canada, France, Germany, U.K., Italy, Spain, Japan, Australia, Mexico, Chile, Brazil over the period 1998-2009, we estimate a three-equation model for number of generic entrants, generic prices and generic volume shares.We find little effect of originator defense strategies, significant differences between unbranded and unbranded generics, variation across countries in volume response to prices.Policy changes adopted to stimulate generic uptake and reduce generic prices have been successful in some E.U. countries.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".