Dynamic equilibrium in the United States prescription drug market after patent expiration

The goal of this thesis is to study competition among brand-name originals and generic drugs in the U.S. pharmaceutical industry. Two interesting observations on this industry during 80s are: (i) there has been a slow diffusion of generic drugs into the market, though generics typically cost 50 to 75 percent less than the brand-name originals, (ii) many brand-name originators keep increasing their prices after generic entry. To explain these facts, I formulate and estimate an empirical dynamic oligopoly model that incorporates consumer learning, consumer heterogeneity and forward-looking firms. I also develop a practical method to estimate the parameters of the model that does not require solving the equilibrium model. Using this new method and a data set detailing the evolution of prices and market shares for 25 chemical entities from 1984–1990, I estimate the distribution of consumer preferences that determine how consumers evaluate risks, perceived attribute levels, and prices when choosing among brand-name originals and generics.;I also design and program a backward induction algorithm to numerically solve dynamic equilibrium model, in which the brand-name firm acts as a leader and generic firms act as followers. The computer program, together with the estimated preference parameter values and calibrated cost parameter values, is used to simulate data and see how well the model can explain the actual data. I find that learning plays an important role in explaining the initial slow increase in market share for generic drugs. I also demonstrate that consumer heterogeneity has the potential to explain the pricing pattern that brand-name prices increase in response to generic entry.