Working Paper: CEPR ID: DP13911
Authors: Michael Knig; Chihsheng Hsieh; Xiaodong Liu
Abstract: This paper introduces a structural model for the coevolution of networks and behavior. The microfoundation of our model is a network game where agents adjust actions and network links in a stochastic best-response dynamics with a utility function allowing for both strategic externalities and unobserved heterogeneity. We show the network game admits a potential function and the coevolution process converges to a unique stationary distribution characterized by a Gibbs measure. To bypass the evaluation of the intractable normalizing constant in the Gibbs measure, we adopt the Double Metropolis-Hastings algorithm to sample from the posterior distribution of the structural parameters. To illustrate the empirical relevance of our structural model, we apply it to study R&D investment and collaboration decisions in the chemicals and pharmaceutical industry and find a positive knowledge spillover effect. Finally, our structural model provides a tractable framework for a long-run key player analysis.
Keywords: Strategic network formation; Network interactions; Stochastic best-response dynamics; Unobserved heterogeneity; Double Metropolis-Hastings algorithm; R&D collaboration networks; Key players
JEL Codes: C11; C31; C63; C73; L22
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Collaborations (O36) | R&D investments (O32) |
| Unobserved heterogeneity (C21) | Knowledge spillover effect on R&D investments (O36) |
| Same subsector (L68) | R&D collaborations (O36) |
| Different productivities (O49) | R&D collaborations (O36) |
| Common collaboration partner (D26) | R&D collaborations (O36) |
| Number of partners (J12) | Marginal benefit of collaborations (D26) |