A Structural Model of Correlated Learning and Late-Mover Advantages: The Case of Statins
Bibliographic record
Abstract
We propose a structural model of correlated learning with indirect inference to explain late-mover advantages. Our model focuses on a class of products with the following two features: (i) products that build on a common fundamental technology (e.g., computer processor, car, smartphone, etc.) and (ii) that consumers can observe some product attributes of a product (e.g., CPU clock speed, horsepower of a car engine, screen size of a smartphone, etc.), but when making their purchase decisions, consumers are not sure how efficiently the product can translate its observed attributes to performing tasks that they care about. For products that base on a similar technology, it is plausible that consumers use the information signals of one product’s technological efficiency to help them update their belief about another product’s technological efficiency within the same product category. As a result, a late entrant could benefit from the information spillover generated by an early entrant. We apply our framework to the statin market in Canada, where drugs rely on a similar mechanism to reduce the cholesterol level. In our model, patients/doctors can observe a statin’s efficacy in reducing the cholesterol level, but they are uncertain about how effectively it can convert its cholesterol-reducing ability to reducing heart disease risks. Our estimation results show that the combination of correlated learning and informative and persuasive detailing explain the success of the two late entrants in the statin market: Lipitor and Crestor. This paper was accepted by Matthew Shum, marketing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".