A Structural Model of Correlated Learning and Late-Mover Advantages: The Case of Statins
构建了一个关联学习的结构模型，解释后发者如何从先发者的信息溢出中获益，并以加拿大他汀类药物市场为例，验证了关联学习与信息性、说服性推广共同解释了立普妥和可定两款后发药物的成功。
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.