Bibliographic record
Abstract
This paper examines how the use of ‘authorized-generics’ (AGs) influences Canadian prescription drug prices. An authorized-generic is the actual brand name drug product, manufactured by the brand firm, but sold as a generic by a licensee or subsidiary of the brand, competing with independent generics (IGs), which operate independently from the brand firm. In theory, AGs have offsetting effects on drug prices. On the one hand, AGs compete against IGs and increases in the number of generic competitors should lower prices. On the other hand, the threat of AG entry into a therapeutic market might deter entry by IGs and this would lessen competition. Moreover, brand firms might increase prices of their brand drugs to increase demand for their AG. I find that when AGs are first to enter a drug market, average drug prices drop by about 12%; average prices drop by smaller amounts, the larger the AG share of the generic market. I could not directly assess whether the threat of AG entry into a market might deter entry by IGs. IG executives, however, state that the threat of AG entry has decreased their incentive to challenge ‘marginal’ drug markets. In particular the threat of AG entry has increased from $5m to $10m the threshold market size – the value of brand drug sales in the 10th year that it has been on the market, below which the IG firm will not attempt to enter. IG executives also stated that AGs have seriously reduced IG retained earnings. The reduction in retained earnings has hampered their ability to challenge brand drugs with annual sales well above $10m, but which have particularly high entry costs. Finally, the IG executives claimed that brand firms have attempted to use the threat of AG entry to negotiate agreements with the IG to delay entry (or not enter at all). A comprehensive evaluation of the competitive effects of AGs would need to verify and quantify these costs and compare these to the benefits of AG competition.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".