Carson Reeling and I recently published a paper describing a new method to measure the value of outdoor recreation areas to visitors. The method works off a model that identifies trip patterns using aggregate data, such as visitor counts and census populations. This contrasts with traditional approaches that use individual trip information collected via surveys, which tend to be costly and time-consuming. Either way, it is this trip information (and the cost thereof) that conveys the value of recreation experiences.
The paper shows the proposed and traditional approaches produce similar estimates in a couple of applications with real-world data. One of these applications compares publicly available hunting data, available here, to individual trips numbering in the 100,000s (which is proprietary data from the Indiana DNR). While there are some differences, there are more similarities, and we find the our proposed approach produces even more similar results when we account for the fact that the residential location of hunters is more rural than the (census) population as a whole. So, overall, this suggests the method provides a practical, cost-effective and accessible alternative to measuring the economic value of recreation sites.
Our work builds on a large amount of earlier research. The travel cost method originated in a letter written to the U.S. National Park Service by Harold Hotelling in 1947. This letter described measuring the economic value of a visit in a linear demand framework using data on travel costs and per capita visitation rates from concentric zones around a park. Over time, economists and other researchers moved away from this simple design toward more utility-theoretic methods. One thing I liked about this project is learning how to use utility theory to infer individual from aggregate behavior (although this paper is far from the first to do that; see here).