<p>The dissertation consists of three chapters relating to pricing strategies. Chapter 1 studies coupons for prescription drugs and their impacts on consumer welfare, firm profits, and insurance payments. Chapter 2 examines consumer brand loyalty and learning in pharmaceutical demand and discusses implications for marketing and health care policy. Chapter 3 develops a framework for estimating demand and supply in an online market with many competing sellers and frequent price changes and proposes optimal pricing strategies for a large seller.</p><p>The first chapter studies an innovative price strategy in pharmaceuticals. Branded drug manufacturers have recently started to issue copay coupons as part of their strategy to compete with generics when their branded drugs are coming off patent. To explore the welfare implications of copay coupons, I estimate a model of demand and supply using pharmaceutical data on sales, prices, advertising, and copayments for cholesterol-lowering drugs and perform a counterfactual analysis where a branded manufacturer introduces coupons. The model allows flexible substitution patterns and consumer heterogeneity in price sensitivities and preferences for branded drugs. The counterfactuals quantify the effects of copay coupons for different assumptions about the take-up of coupons and the ability of the branded manufacturer to direct them to the most price-sensitive types of consumers. The results show that the agency problem between insurers and patients gives a branded manufacturer a strong incentive to issue copay coupons. Introducing copay coupons benefits the coupon issuer and consumers but raises insurance payments. In equilibrium, insurer spending can increase by as much as 25% even when just 5% of consumers have a coupon, with social welfare falling significantly.</p><p>Medicines for chronic conditions like high cholesterol, heart disease, and diabetes are repeatedly used for a long period of time. Consumer dynamics thus plays an important role in the demand for those drugs. In the second chapter, I estimate a demand model with brand loyalty and learning using micro-level data from cholesterol lowering drug markets in the United States. The estimates suggest high switching costs and strong learning effects at the molecule level in the markets. Switching costs raise the predicted probability of choosing the same drugs in a row and learning largely increases patient stickiness to a molecule in the long run. I discuss pricing implications of the estimation results for drug manufacturers, insurance companies, and policy makers. </p><p>The last chapter, coauthored with Dr. Andrew Sweeting and Dr. James W. Roberts, looks at pricing in a different context. We estimate a model of entry, exit and pricing decisions in an online market for event tickets where there are many competing sellers and prices change frequently. We use the estimates from our model to analyze the optimality of the pricing policy used by the largest seller (broker) in the market. We show that the broker's pricing policies substantially affect the prices set by his competitors. When we compare the broker's pricing policy with the prices that our model predicts are optimal we find that the broker sets approximately correct prices close to the game, when his pricing problem resembles a static one, but that he might be able to gain from using different pricing rules and updating prices more frequently further from the game.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/8763 |
| Date | January 2014 |
| Creators | LEE, CHUNG-YING |
| Contributors | Sweeting, Andrew |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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