Diffusion Processes with Private Network Information (Job Market Paper)
Abstract: Innovations, either products or ideas, often diffuse in the population via social ties. This paper studies the identification and estimation of diffusion processes in social and economic networks. Compared to the classic diffusion literature that assumes a continuous population with a stochastic network structure, we provide a new econometric framework to analyze diffusion processes in fixed networks where strategic players hold private information about their links. Extending Sadler (2020), we propose a model where Bayesian players observe their close neighbors. We demonstrate the existence of the equilibrium of the model, and characterize the unique symmetric equilibrium. Based on these theoretical findings, we propose a consistent and tractable two-step estimator for individual payoffs using feasible data from a single large networks. We evaluate the finite sample performance using Monte Carlo simulations.
Identification and Estimation of Many-to-Many Matching Games
Abstract: This paper studies a market with many-to-many contracts when the number of market participants grows large. Many-to-many contracts allow a seller to trade with multiple buyers and a buyer to trade with multiple sellers. We focus on investigating the identification of payoff parameters. In many-to-many matching markets, several issues have to be addressed: bias would arise since the outcomes are only observed when links are formed between two agents, and the maximum number of relationships an agent can enter into would possibly affect the set of stable outcomes. We show that under certain conditions, the number of firms (workers) that are willing to be matched to a specific worker (firm) grows at a rate regardless of the capacity of both sides. Furthermore, we show a correspondence between the stable matching outcomes in a many-to-many matching framework and that in a one-to-one matching framework.