A Dynamic Oligopoly Structural Model for the Prescription Drug Market After Patent Expiration
Bibliographic record
Abstract
Motivated by the slow diffusion of generic drugs and the increase in prices of brand-name drugs after generic entry, I incorporate consumer learning and consumer heterogeneity into an empirical dynamic oligopoly model. In the model, firms choose prices to maximize their expected total discounted profits. Moreover, generic firms make their entry decisions before patent expiration. The entry time of generics depends on the FDA random approval process. I apply this model to the market of clonidine. The demand side parameters are estimated in a previous paper (Ching (2003)). The supply side parameters are estimated and calibrated here. The model replicates the stylized facts fairly well. I confirm that consumer heterogeneity in price sensitivity plays an important role in explaining the brand-name pricing pattern. I also apply the model to examine the impact of a policy experiment, which shortens the expected approval time for generics. Although this experiment brings generics to the market sooner, it also reduces the number of generic entrants as the likelihood of entering a crowded market in the early periods increases. Given the change in the magnitude of the policy parameter, the experiment improves the rate of learning, and lowers the equilibrium generic prices throughout the period. However, it hardly raises the overall welfare
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".