Three essays on technology policies

This dissertation is composed of 3 related papers: Chapter 2. One Size Fits All !?!--The Optimal Patent Policy for Different Technologies; Chapter 3. Optimal Patent Scope in the Presence of Network Externality; Chapter 4. Integrating Environmental Regulation and Technology Policy--to Promote Pollution Abatement Innovation and Diffusion. Although the theorists of the optimal patent policy assume that the optimal patent policy is the same for all types of innovations (one size fits all), the empirical evidence shows that the effectiveness of the patent system varies dramatically across innovation types. This empirical evidence suggests that the theoretical study of patent policy should consider the differences in the nature of technology. In this dissertation, I consider three aspects of the nature of technology--the degree of codifiability, the presence of network externalities and environmental externalities--and show how they influence the optimal choice of patent scope. Chapter 2 provides a general framework for studying both patent life and patent scope by considering imitation and licensing at the same time. Surprisingly, our model suggests that the socially optimal patent scope should follow the same principle (as narrow as possible) across technologies (again, one size fits all) but for different reasons. In Chapter 3, I argue that narrow patent scope is socially optimal when the effects of network externality are large. As the expected output is raised, the increase of consumer surplus can outweigh the increase of imitation cost and the enlarged market can accommodate more suppliers with the innovator being fairly rewarded. In contrast with Chapters 2 and 3, which study productive innovation, Chapter 4 is devoted to studying another type of innovation--environmental innovation. Environmental regulations can create demand for environmental innovation. Technology policies are required to foster the supply. With this integrated view, we suggest the optimal technology policies under different environmental regulation regimes. Although the model is formulated in the context of environmental issues, I show that it is really quite general by applying the same insight to the demand-side management of energy and spectrum auction issues