Causal Inference, High-Frequency Data, and the Recreational Value of Water Quality

Emerging datasets capture rich temporal variation in recreation behavior, but recreation demand analyses have traditionally used variation across sites, rather than across time, to value environmental amenities. We introduce a model and estimation procedure designed to exploit panel variation in recreation demand analyses by embedding panel data causal inference techniques within a travel cost random utility model. To demonstrate the method, we use “structural synthetic controls” to value the welfare losses caused by water-quality-induced beach closures in southeast Michigan. Losses tend to be larger on weekends and hotter days, and our results suggest that a stigma effect reduces visitation even after the beach reopens. Our method is particularly useful for valuing the recreational impacts of resource shocks, like harmful algal blooms or wildfires, and it can be applied broadly given the increasing availability of high-frequency recreation data.