We analyze a model of R&D alliance networks where firms are engaged in R&D collaborations that lower their production costs while competing on the product market. We provide a complete characterization of the Nash equilibrium and determine the optimal R&D subsidy program that maximizes total welfare. We then structurally estimate this model using a unique panel of R&D collaborations and annual company reports. We use our estimates to study the impact of targeted versus nondiscriminatory R&D subsidy policies and empirically rank firms according to the welfare-maximizing subsidies they should receive.

This paper introduces a structural model for the coevolution of networks and behavior. We characterize the equilibrium of the underlying game and adopt the Bayesian Double Metropolis-Hastings algorithm to estimate the model. We further extend the model to incorporate unobserved heterogeneity and show that ignoring this heterogeneity can lead to biased estimates in simulation experiments. We apply the model to study R&D investment and collaboration decisions in the chemical and pharmaceutical industry and find a positive knowledge spillover effect. Our model also provides a tractable framework for a long-run key player analysis.