Abstract Accurately estimating the welfare impacts of environmental changes in recreation demand modeling requires robust causal inference methods. However, a persistent challenge remains as the zero market share issue restricts the causal inference application to broader regions and longer periods. To address this, we integrate empirical Bayes posterior mean estimation into a two-step random coefficient logit model, ensuring that sites with low or zero visitation are properly incorporated without distorting demand estimates. We apply this framework to the 2021 Huntington Beach oil spill, using high-frequency cellphone data to track changes in beach visits. By combining the Synthetic Difference-in-Differences (SDID) approach with empirical Bayes-adjusted market shares, we examine the causal effects of temporary beach closures on visitor welfare. Our findings reveal that Huntington Beach experienced the largest and most prolonged welfare loss, with an estimated aggregate loss of 1. 0 million and weekly losses of 83 thousand persisting beyond the initial closure. In contrast, Newport Beach and Laguna Beach exhibited faster recoveries. This study advances recreation demand modeling by refining demand estimation for low-visit sites and strengthening causal inference techniques for environmental disruptions, ultimately providing a more reliable framework for assessing the economic costs of beach closures and other environmental policy interventions.
Cheng et al. (Fri,) studied this question.