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Quality of a prescription drug is uncertain to patients, physicians and even the manufacturer of the drug. Because this uncertainty can deter physicians from prescribing the drug, it is important to investigate how various marketing communication activities help reveal the true quality of its product. In particular, this study investigates publicity and advertising under quality uncertainty. Chapter 1 studies the effect of publicity on consumer demand with a reduced form approach. Chapter 2 structurally investigates the roles of detailing and publicity when the information spill-over is present. Both chapters study the market of anti-cholesterol drugs (statins). Chapter 1 investigates the effects of publicity (media coverage) on consumer demand. The main obstacle to measuring the impact of publicity is that data on media coverage are difficult to interpret. To overcome this obstacle, we propose a new way to code information presented in news articles, mapping the information to a multi-dimensional attribute space. We combine our publicity data with data on sales, detailing, medical journal advertising, direct-to-consumer advertising (DTCA) and landmark clinical trial outcomes, and estimate a demand model. Our results suggest that not all forms of publicity are equal. In chapter 2, we study consumer learning about scientific evidence and its impact on demand for pharmaceutical products by using the Bayesian learning model. Unlike previous literature, our learning model allows consumer’s prior quality perceptions to be correlated across brands. This unique feature of the model allows us to investigate information spill-over effects across brands. The information spill-over allows late entrants to free-ride on first movers’ investment in clinical trials and marketing activities and to gain late mover advantage. In addition to using product level market share data, we supplement them with switching rates and discontinuing rates. The switching rate data are particularly useful for taking the presence of switching costs into consideration, which has been ignored in the literature using product-level data. Our estimated structural model has implications for managers in allocating resources to various types of marketing activities more efficiently and helps forecast returns of clinical trials that are sponsored by pharmaceutical firms.

Quality of a prescription drug is uncertain to patients, physicians and even the manufacturer of the drug. Because this uncertainty can deter physicians from prescribing the drug, it is important to investigate how various marketing communication activities help reveal the true quality of its product. In particular, this study investigates publicity and advertising under quality uncertainty. Chapter 1 studies the effect of publicity on consumer demand with a reduced form approach. Chapter 2 structurally investigates the roles of detailing and publicity when the information spill-over is present. Both chapters study the market of anti-cholesterol drugs (statins). Chapter 1 investigates the effects of publicity (media coverage) on consumer demand. The main obstacle to measuring the impact of publicity is that data on media coverage are difficult to interpret. To overcome this obstacle, we propose a new way to code information presented in news articles, mapping the information to a multi-dimensional attribute space. We combine our publicity data with data on sales, detailing, medical journal advertising, direct-to-consumer advertising (DTCA) and landmark clinical trial outcomes, and estimate a demand model. Our results suggest that not all forms of publicity are equal. In chapter 2, we study consumer learning about scientific evidence and its impact on demand for pharmaceutical products by using the Bayesian learning model. Unlike previous literature, our learning model allows consumer’s prior quality perceptions to be correlated across brands. This unique feature of the model allows us to investigate information spill-over effects across brands. The information spill-over allows late entrants to free-ride on first movers’ investment in clinical trials and marketing activities and to gain late mover advantage. In addition to using product level market share data, we supplement them with switching rates and discontinuing rates. The switching rate data are particularly useful for taking the presence of switching costs into consideration, which has been ignored in the literature using product-level data. Our estimated structural model has implications for managers in allocating resources to various types of marketing activities more efficiently and helps forecast returns of clinical trials that are sponsored by pharmaceutical firms.