A Pairwise Strategic Network Formation Model with Group Heterogeneity: With an Application to International Travel

Abstract: In this study, we consider a pairwise network formation model in which each dyad of agents strategically determines the link status between them. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood (ML) method. First, we obtain consistent estimates for the heterogeneity parameters at individual level using the ML estimator. Second, we estimate the latent group structure using the binary segmentation algorithm based on the results obtained from the first step. Finally, based on the estimated group membership, we re-execute the ML estimation. Under certain regularity conditions, we show that the proposed estimator is asymptotically unbiased and distributed as normal at the parametric rate. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarity and a certain level of degree heterogeneity in the network formation behavior.