QUALITY
2.3 Revealed Preference Methods
2.3.2 Travel Cost Method For Estimating Recreation Demand And Benefits
2.3.2.2 Basic Assumptions of the Simple Travel Cost Method Like any measurement method, whether in economics or biology, the
2.3.2.2 Basic Assumptions of the Simple Travel Cost Method
is usually not a problem for rural recreation areas. They are located close to a few small towns and at varying distances from many cities and counties.
2.3.2.2.3 No Benefits from Travel
The third assumption is there are no benefits from travel itself. That is, we assume the trip cost is incurred to gain access to the benefits of the recreation site. If people enjoy the travel en route, then part of the travel cost is paid for the en route sightseeing and is not solely a sacrifice made to gain the benefits of the recreation site. If people do enjoy the driving en route, then our estimate of recreation site benefits will be overstated. The empirical evidence on this assumption is mixed. For short drives to infrequently visited sites in scenic areas, such as Colorado or the Pacific Northwest, there does seem to be some sightseeing value during the first hour of the drive. After the first hour or so, the disutility of travel time begins to dominate. In hot and arid climates such as New Mexico, even the first hour of travel seems to have a disutility. Of course, when recreationists make frequent trips up the same roads to visit the same sites, it is reasonable to assume there is probably little sightseeing value.
Nonetheless, this is an assumption that, with properly worded questionnaires, can be empirically tested for the recreation site in question.
When the assumptions of the TCM are met, the method is expected to yield valid estimates of visitors' net willingness to pay. When the assumptions of the TCM are not met, the method should either not be applied, or much more complex approaches are needed to address these complications.
2.3.2.2.4 Two Ways the Travel Cost Method Demand Curves Reflect Presence of Substitute Recreation Sites/Opportunities
The actual number of trips taken by campers to this particular state forest in our example reflects the presence of other substitute campgrounds.
If those other campgrounds were not present and this was the only campground, more campers would visit this state forest. Thus the horizontal intercept of the demand curve would be further to the right if no other substitute sites existed. The vertical intercept of our demand curve is shifted downward due to the availability of substitute campgrounds as well. This means the maximum amount a camper would pay to visit this particular campground is capped at the trip costs to similar quality (as perceived by the visitor) campground. In the travel cost method, this downward shift in the demand curve is reflected by a substitute index variable. If this index rises when the number of other campground accessible to a given city increases, then the variable will have a negative sign in the demand equation.
The net result is that the consumer surplus calculated from this demand curve reflects the presence of substitutes. In essence the consumer surplus obtained from a TCM demand curve that includes a substitute variable is the net willingness to pay for this preferred site over and above the next best substitute. That is, the benefits are the additional personal profit realized by having access to this site as compared to a less preferred or more expensive site.
These examples reflect one of the key advantages of the TCM: its ability to generate values that are based on actual observed recreation behavior. In addition, another advantage of TCM is that it can often be performed using existing data such as campground fee receipts, recreation permits for hunting, fishing, wilderness, and rafting, as well as simple license plate surveys (to identify how far visitors came to see the site). As such, the TCM is a relatively inexpensive method to calculate site specific recreation values.
2.3.2.2.5 Multi-Site Travel Cost Models to Incorporate Environmental Quality as a Demand Shifter
The single site TCM demand estimating model is quite useful if the analyst simply needs to estimate the value of existing recreation. This is the relevant value if there is a mutually exclusive use of the site that would completely displace the current recreation. For example, if our state forest was clear-cut or leased to a surface mining company, the consumer surplus estimate would show the economic loss to consumers from losing this campground. In the case of river recreation, if the river would be completely inundated by a dam, it would be important to compare the value of lost river recreation with the gains in water supply and reservoir recreation, as well as cost of the dam, to determine if this is an economically efficient change.
However, many policy analyses involve estimating the value of a change in quality of recreation. For example, relicensing of a hydropower dam often focuses on how much additional instream flow should be required to be released during the recreation season. Thus the current recreation will increase in value; the increase in value must be calculated and then compared to the opportunity cost of reduced hydropower. Many proposals to reduce non-point source pollution will improve water quality for fishing and water contact sports. Again, the key issue is measuring the gain in value of existing trips and additional trips that would result from the improvement in water quality.
With a single site TCM demand estimating model, it is difficult to observe how visitation would change with an improvement in water quality, since visitors from all the different cities receive the same water quality at
the site. That is, water quality is a public good, which is at the identical level for all visitors to this site. What we need is to observe how visitation changes with changes in water quality.
There are two ways we might observe how visitation changes with environmental quality. If environmental quality varies over time in a predictable fashion at this site, it may be possible to estimate the TCM demand model on a weekly or monthly basis, to reflect how visitation changes with this quality. While this is technically possible, it is rare to have such predictable changes in environmental quality that are known to visitors ahead of time, and to have visitation data on such a small time scale as a week.
The most common approach to incorporating environmental quality into a TCM demand estimating model is to pool visitation data across recreation sites that have markedly different levels of environmental quality. It is quite common for fish catch rates, water quality, instream flow, wildlife populations, and scenic quality to vary from one area to another based on differences in geology, source of water, weather conditions, and management activities. The analyst can take advantage of these natural experiments to observe how visitation responds to these changes in environmental quality. By pooling visitation data across sites (essentially stacking each site’s city of origin and site destination data vertically), the following quality augmented TCM demand equation could be estimated:
where
is now Trips from City i to Site j.
Travel is now Travel Cost from City i to Site j.
Environmental is the level of environmental quality at site j.
The size of will indicate how much the demand curve shifts to the right or left with a one unit change in environmental quality. For example, if environmental quality is water clarity measured in feet, then would indicate how much trips per capita would increase with a 1-foot increase in water clarity. The area between the pre-policy and post-policy higher demand curve would be a measure of the economic value of this gain in water clarity. These annual benefits could be compared to the annual costs of increasing water clarity by one additional foot to determine if it is economic efficient action.
2.3.2.2.6 Instream Flow TCM demand example.
The following example illustrates how fishing visitation data were pooled across rivers in Montana to observe how the number of fishing trips changed with fish catch rates. Since fish catch rates are influenced by water quantity and water quality, we essentially have a simple bio-economic model. This model can be applied to estimate the benefits of increased instream flows or improved water quality from reducing non-point source pollution from agriculture or timber harvesting, heavy metal discharge from mine sites, or any other factor influencing fish populations via water quality or quantity changes.
The data used with the Travel Cost Method to estimate willingness to pay for recreational fishing were collected from two surveys of Montana anglers in 1985. The model reflects pooling of visitation data to several rivers in western Montana. The TCM demand equation is:
where:
LTRIPCAP, the dependent variable = log of trips per capita from county of angler origin i to river j
LRTDIST = log of round trip distance from county i to river j LSTROUTC = log of sum of trout catch at river j,
LYRSFISH = log of average years fished of anglers in county i LEDUC = log of average years of education of anglers in county i.
LSBUSTRTC = log of substitute site fishing index. Index is based on trout catch per mile at alternative river k with higher fish catch per mile than the study river j.
LSOTHRC = log of sum of other sport fish catch at river j (mostly whitefish).
The R-squared is quite high, indicating that nearly 82% of the variation in trips per capita is explained by the set of independent variables. The equation also contains statistically significant variables for the influence of substitute rivers and fish catch. The individual coefficients are significant at the 95% level or greater. The coefficient on distance (the price variable) is highly significant.
Consumer surplus estimates for improving water quantity and water quality can be estimated by working with biologists to translate the changes
in water parameters into changes in trout populations (this is no easy task, as any biologist will tell you). Once the linkage between water quality or quantity and trout populations is established, the next step is to use the resulting change in the fish catch variable in the TCM demand equations estimated above to calculate the corresponding change in benefits. A river's total consumer surplus with an improvement in water quality is estimated with the site's existing trout catch; then catch is increased by the gain in catchable trout expected under the improved water quality scenario. The gain in the catchable trout variable in the demand equation shifts the Travel Cost Method demand curve outward to High Fish, as illustrated in Figure 9-5. The area between the D Low Fish and High Fish can be thought of as the gain in fishing benefits with the water quality improvement.
This shifting demand curve process is repeated for each decade as the fish population increases in response to improved water quality. The present value of the change in fishing benefits is calculated as the present value of the area between the two demand curves in Figure 9-5 over the affected river over some time period, often 50 to 100 years. The present value of these benefits would be compared to the present value of foregone timber or mining, or whatever the policy action is to bring about the change in water quality.
For more details on these multi-site Travel Cost Method models see Ward and Loomis, 1986; or Loomis and Walsh, 1997.
2.3.2.2.7 Recent Variations on Travel Cost Models to Better Incorporate Environmental Quality
In the last several years there have been several variants of TCM developed. These include site choice models, frequently referred to as Random Utility Models. These models are quite similar to the pooled multi- site models described above. The key difference is that the choice problem is facing the visitor is broken up into several stages: first, which site to visit; and next, the number of times to visit. The travel cost and the relative site quality affect both the probability of choosing a given site and the number of times the individual will visit each site. The site choice problem uses a more complex form of multiple regression, such as multinomial logit models common in the field of transportation planning. See Herriges and Kling, 1999 for a discussion of the several types of Random Utility Models.
Finally, there are also hedonic travel cost models that reverse the viewpoint of our original TCM models. In these models, the visitor is viewed as determining how much more travel cost to spend to "purchase"
recreation areas with higher quality levels.
All of these models allow for valuation of the incremental change in environmental quality and hence are quite useful in policy analysis.
However, these models often require quite detailed survey data and sophisticated statistical analysis. For evaluation of regulations with multi- million dollar costs, such an analysis effort is more than justified. But for small policy analysis, the cost of estimating these more elaborate models can quickly absorb the entire project analysis budget. For small projects we discuss the benefit transfer approach at the end of this chapter.