Academic journal article Journal of Leisure Research

A Comparison of the Effect of Multiple Destination Trips on Recreation Benefits as Estimated by Travel Cost and Contingent Valuation Methods

Academic journal article Journal of Leisure Research

A Comparison of the Effect of Multiple Destination Trips on Recreation Benefits as Estimated by Travel Cost and Contingent Valuation Methods

Article excerpt


The Travel Cost Method (TCM) and Contingent Valuation Method (CVM) are commonly used methods to value publicly provided outdoor recreation opportunities. While there are several types of TCM models, traditional TCM models estimate a demand function for the number of trips using the cost of traveling to the site as a proxy for price. Economic benefits are derived from this demand curve by integrating under this demand curve between the current price and vertical intercept of the demand curve, i.e., the price that at which no one would visit. One purpose of this paper is to empirically demonstrate a solution to an empirical problem that arises when one of the key assumptions of the TCM demand model is violated: interpretation of travel costs as the price of an outdoor recreation trip. Specifically, if a person visits multiple site destinations on one trip from home, it would be incorrect to interpret the entire trip cost to any one of the sites the visitor might be sampled at as the price of a trip to that site (Haspel & Johnson, 1982). If these multiple destination observations are treated in the same way as single destination trips, Haspel & Johnson claim the TCM will yield a biased estimate of the recreation benefits of a site.

The second purpose is to investigate whether the multiple destination trip distinction influences benefit estimates derived from CVM. As a stated preference approach, it is plausible that the differing nature of single destination and multiple destination trips might be reflected in the benefit estimates reported by visitors.

One way of dealing with the multiple destination trip problem in TCM is to identify multiple destination trip taking individuals and drop them from the sample for the purposes of estimating the benefits per person (Smith & Kopp, 1980). However, this could lead to a biased estimate of total recreation site benefits if the multiple destination visitors have substantially different benefits than single destination visitors. This bias may result in a misallocation of budget and management effort at these sites as compared to sites visited primarily as a single destination.

Related to the multi-destination problem is the multi-purpose trip problem. Here, some proportion of a person's total trip travel cost and travel time are incurred for other trip purposes that may not be related to the natural resource based outdoor recreation activity the analyst is attempting to value. Examples of multiple purpose trips include trips taken to the area with the main reason to visit family, friends or on business. The other purposes may occur at basically the same destination or at destinations en route. If we are interested in estimating the economic value of the single recreation site, we may have a mis-specification problem as we observe only the overall multiple purpose trip demand function, not the site-specific trip demand function. That is, we observe the total trip price, but know little about the price for the individual site or activity we wish to value.

Whether the bias in the TCM estimate of benefits is statistically significant has not been evaluated in most previous papers on this topic because the authors did not have standard errors or confidence intervals for their benefit estimates (Smith & Kopp, 1980; Haspel & Johnson, 1982). However, Mendelsohn, Hof, Peterson and Johnson (1992) developed standard errors for their consumer surplus estimates and calculated a t-statistic of 1.94 for the test of equality of consumer surplus for single destination trips ($10) and multiple destination trips ($17). The t-statistic suggests no difference at the 5% level, but would imply statistical difference at the 10% level.

Mendelsohn et al. (1992) have also suggested treating multiple destinations as a distinct site bundle, and estimating a separate demand function for it as part of a system of demand functions. This works well if there are just a few combinations of sites frequently visited. …

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