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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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; both teacher heads agree on what is shown here.
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".