Knowledge Spillovers Across Europe: Evidence from a Poisson Spatial Interaction Model with Spatial Effects

By following the paper trail left by patent citations between high-technology patents in Europe we use a Bayesian hierarchical Poisson spatial interaction modelling approach to identify and measure spatial separation effects to interregional knowledge flows, as captured by patent citations. The model introduced here is novel in that it allows for spatially structured origin and destination effects for the regions. Estimation of the model is carried out within a Bayesian framework using data augmentation and Markov Chain Monte Carlo (MCMC) methods, related to recent work in Frühwirth-Schnatter and Wagner (2004). This allows MCMC sampling from well-known distribution families and, thus, provides a substantial improvement over MCMC estimation based on Metropolis-Hastings sampling from non-standard conditional distributions. Estimation results from our model provides evidence that geography matters. First, geographical distance between origin and destination regions has a significant impact on knowledge spillovers, and this effect is substantial. Second, national border effects are important and dominate geographical distance effects. Third, the latent spatial effects exhibit weak spatial dependence. Not only geography, but also technological proximity matters. Interregional knowledge flows are industry specific and occur most often between regions located close to each other in technological space.