This dissertation studies the welfare generated by outdoor recreation. Chapters 1 and 2 study a high-profile form of recreation: U.S. national park visitation. Chapter 3 uses innovative data to study a classic topic: valuing water quality benefits. All three chapters apply a two-stage estimation procedure that exploits panel variation in visitation and resource quality within a random utility maximization (RUM) travel cost model, the field’s “workhorse” model.In Chapter 1, I conduct the most comprehensive analysis of demand for the U.S. National Park System to date. I create a versatile and unified framework to analyze demand for 140 national parks throughout the contiguous United States. Combining nationally representative surveys, park-level visitor counts, and a statistical atlas of park attributes, I estimate a RUM model of visitation from 2005 through 2019. The model produces estimates of park awesomeness and explains awesomeness using detailed park attribute data. Iconic parks like Glacier, Yellowstone, and Grand Canyon all rank among the most awesome parks. Visitors prefer parks with charismatic wildlife, like bison, and bald eagles, wide-ranging elevation, and coastline.My second chapter applies the data infrastructure and model from Chapter 1 to analyze how climate change will impact the welfare generated by national park visitation. I estimate visitor preferences for long-run average temperatures and short-run temperature deviations, and I use these preferences to simulate visitor welfare under future climate conditions. Visitors prefer temperatures between 70F and 85F, and on average, they dislike cold more than they dislike extreme heat. Assuming limited changes to park resources, I find climate change will likely increase the welfare generated by national park visitation. The overall gains are driven by large benefits in cooler seasons that outweigh the losses from extreme heat in the summer.Chapter 3, co-authored with Hyunjung Kim, blends the modeling and estimation techniques from Chapters 1 and 2 with a high-frequency, administrative park visitation dataset. We quantify the losses from water quality-induced beach closures at Lake St. Clair Metropark in southeast Michigan. Our park visitation data include the residential ZIP code and exact minute of park entry for the universe of visits to the Huron-Clinton Metropark system. Our preferred model estimates a daily panel of park fixed eects, and regresses the fixed eects on a beach closure indicator in a second stage. We estimate that the 2022 beach closures caused welfare losses of around $70,000.
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