Collaboration in Bipartite Networks

This paper studies the impact of collaboration on research output. First, we build a micro-founded model for scientific knowledge production, where collaboration between researchers is represented by a bipartite network. The Nash equilibrium of the game incorporates both the complementarity effect between collaborating researchers and the substitutability effect between concurrent projects of the same researcher. Next, we propose a Bayesian MCMC procedure to estimate the structural parameters, taking into account the endogenous participation of researchers in projects. Finally, we illustrate the empirical relevance of the model by analyzing the coauthorship network of economists registered in the RePEc Author Service. The estimated complementarity and substitutability effects are both positive and significant when the endogenous matching between researchers and projects is controlled for, and are downward biased otherwise. To show the importance of correctly estimating the structural model in policy evaluation, we conduct a counterfactual analysis of research incentives. We find that the effectiveness of research incentives tends to be understated when the complementarity effect is ignored and overstated when the substitutability effect is ignored.