Abstract
This chapter provides an introduction to Travel Cost Models used to estimate recreation demand and value recreational uses of the environment such as fishing, rock climbing, hunting, boating, etc. It includes a brief history, covers single-site and random-utility-based models, and discusses current issues and topics. The chapter is laid out in a step-by-step primer fashion. It is suitable for first-timers learning about travel cost modeling as well as seasoned analysts looking for a refresher on current approaches. The chapter includes an application of the random-utility-based model to beach use on the east coast of the USA along with measures of welfare loss for beach closures and changes in beach width.
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Notes
- 1.
A third group is the Kuhn-Tucker model, which combines features from the single-site and RUM models. It is not covered in this chapter. It is used less frequently and is more advanced than needed in this textbook. Phaneuf and Siderelis (2003) provide an excellent primer-like introduction to the Kuhn-Tucker model.
- 2.
For an interesting discussion of using zero inflated Poisson models see Paul Allison’s commentary at www.statisticalhorizons.com/zero-inflated-models.
- 3.
The single-site model can also be used to value quality change without stated-preference data by “pooling” or “stacking” many separate single-site models (Smith and Desvousges 1985). However, this approach does not account for substitution among sites and has largely fallen out of favor.
- 4.
The coefficient \(\alpha\) in Eqs. (6.8) and (6.9) is a measure of the marginal utility of income because it describes how site utility changes with a decrease in income (less money to spend on other things) if a trip is taken. Because trip cost “takes away from income,” \(\alpha\) is the marginal effect of taking away income (\(\alpha \; < \;0\)), and \(- \alpha\) is a measure of adding to income or the marginal utility of income (\(- \alpha > 0\)).
- 5.
- 6.
Following convention, I have specified \(\alpha\), the coefficient on trip cost, as fixed. Because \(\alpha\) is used in the denominator of Eq. (6.13) for valuation, values tend to be extremely sensitive to variation created by mixing. This is a practical fix and an area where more research is needed.
- 7.
The steps in estimating the single-site model are essentially the same. Site definition (Step 3) is obviously only for one site, and site characteristic data (Step 6) are typically not gathered. In instances where several single-site models are being “stacked” in estimation, analysts will often gather site characteristic data to allow for shifts in demand across sites.
- 8.
- 9.
Another way of accounting for preference heterogeneity is to estimate a Latent Class model, wherein people are sorted into a finite set of TCMs, each with its own set of parameters usually sorted by individual characteristics (Boxall and Adamowicz 2002).
- 10.
- 11.
Expenses like food and souvenirs are typically excluded because they are not necessary to make the recreation trip possible.
- 12.
Several studies have considered endogenous trip costs. Parsons (1991) analyzes endogenous residence choice and Baerenklau (2010) has a nice follow-up with some contrasting results. Bell and Strand (2003) let choice of route be endogenous. McConnell (1992), Berman and Kim (1999), Offenback and Goodwin (1994) analyze endogenous on-site time.
- 13.
Even if some trips are multiple-purpose, Parsons and Wilson (1997) show that multiple-purpose trips where all other purposes are incidental, can be treated as single-purpose trips. If people say trips are primarily for the purpose of recreation, one may be able to safely assume all other purposes are incidental.
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Parsons, G.R. (2017). Travel Cost Models. In: Champ, P., Boyle, K., Brown, T. (eds) A Primer on Nonmarket Valuation. The Economics of Non-Market Goods and Resources, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7104-8_6
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DOI: https://doi.org/10.1007/978-94-007-7104-8_6
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