Frequentist inference in spatial discrete choice models with endogenous congestion effects and club-correlated random effects

Agents may consider information and other signals from their peers (especially close peers) when making their spatial site choices. However, the presence of other agents in a spatial location may generate congestion or agglomeration effects. Disentangling the potential peer effects with issues of congestion is difficult since it is hard to ascertain whether the observed congestion effects are a result of observing others behavior or the influence of peer effects within the same network encouraging a fisherman to visit a site even in the presence of congestion. The research develops an empirical framework to decompose both motivations in a spatial discrete choice model in an effort to synthesize the congestion/agglomeration literature with the peer effects literature. Using Monte Carlo analysis we investigate the robustness of our proposed estimation routine to the conventional random utility model (RUM) that ignores both peer and congestion/agglomeration effects as well as the spatial sorting equilibrium model that ignore peer effects. Our results indicate that both the RUM and sorting equilibrium models can be used to successfully investigate the presence of a peer effects. However, the estimates of congestion effects are poor because of ignored correlated random effects. Recent literature has largely used Bayesian methods for this hard problem. We also explore the use of Fixed Effects Multinomial Logit estimates to first estimate the base model, and then extract generalized residuals to estimate the peer effects.