Designing Auctions in R&D: Optimal Licensing of an Innovation

Abstract

Abstract We study an R&D game in which a research unit undertakes a (non-observable) research effort and, if an innovation is obtained, auctions licenses to a pool of producers. Each producer has a private valuation for the license and suffers a negative externality when a competitor becomes a licensee. We compare the optimal rule for the allocation of licenses and the level of research effort implemented by the innovator in two scenarios: free licensing by the innovator vs. optimal regulation. As long as the cost of public intervention is sufficiently low, free licensing induces two different types of inefficiencies: an excessively high price for licenses and a suboptimal dissemination of knowledge, and an excessively low research effort. This indicates that public intervention should combine the following measures: (i) an antitrust agency which limits the royalties that innovators can ask for a license, and (ii) a direct subsidy to research activity.