Bibliographic record
Abstract
It is very common that after patent expirations of original pharmaceutical products (brand products), other pharmaceutical firms enter the market with generic drugs which has the same molecule as the original drugs. For example, in any pharmacies in North America, acetaminophen or ibuprofen, the generic version of Tylenol or Advil, respectively, can be easily found. The generic entry (introduction of chemically equivalent drug) of a brand product significantly lowers the average price of the drugs with the same molecule and thus, it is commonly believed that the total market size for the molecule increases significantly. However, after the generic entry, brand firms tend to stop spending on detailing, the most common marketing activity of pharmaceutical firms, which is sending their representatives to physicians and explaining the efficacy of drugs, and investments for clinical trials because generic products can free-ride on the marketing efforts of the brand firms. Also, firms producing the competing drugs with similar efficacy, e.g., me-too drugs, can adjust their detailing efforts after the generic entry. Therefore, it is unclear whether the generic entries actually expand the total market for the focal molecule after controlling for the detailing activities of focal firms and competing firms. In this research we try to answer this question by using the Canadian data on drugs in statin class, which is a very popular class of anti-cholesterol drugs. Our estimation results provide policy makers with valuable implications as follows. When a drug is strong, the generic entry seems to increase social welfare so the policy maker should encourage the generic entry. However, when a drug is weak, the generic entry does not seem to have any significant impact on social welfare so the policy maker seems to have less incentive to encourage the generic entry. In addition, by observing the generic drug market in Canada, where generic drug market is mature, the generic drug companies and policy makers in Asia, where generic drug market is still growing, can learn the dynamics of generic market development.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 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".