• location of the visitor¡¦s home ¡¦ how far they traveled to the site
  • how many times they visited the site in the past year or season
  • the length of the trip
  • the amount of time spent at the site
  • travel expenses
  • the person¡¦s income or other information on the value of their time
  • other socioeconomic characteristics of the visitor
  • other locations visited during the same trip, and amount of time spent at each
  • other reasons for the trip (is the trip only to visit the site, or for several purposes)
  • fishing success at the site (how many fish caught on each trip)
  • perceptions of environmental quality or quality of fishing at the site
  • substitute sites that the person might visit instead of this site
  • The value of improvements in water quality was only shown to increase the value of current beach use.  However, improved water quality can also be expected to increase overall beach use. 
  • Estimates ignore visitors from outside the Baltimore-Washington statistical metropolitan sampling area. 
  • The population and incomes in origin zones near the Chesapeake Bay beach areas are increasing,  which is likely to increase visitor-days and thus total willingness to pay.
  • changes in access costs for a recreational site
  • elimination of an existing recreational site
  • addition of a new recreational site
  • changes in environmental quality at a recreational site
  • number of visits from each origin zone (usually defined by zipcode)
  • demographic information about people from each zone
  • round-trip mileage from each zone
  • travel costs per mile
  • the value of time spent traveling, or the opportunity cost of travel time
  • exact distance that each individual traveled to the site
  • exact travel expenses
  • substitute sites that the person might visit instead of this site, and the travel distance to each
  • quality of the recreational experience at the site, and at other similar sites (e.g., fishing success)
  • perceptions of environmental quality at the site
  • characteristics of the site and other, substitute, sites
  • The travel cost method closely mimics the more conventional empirical techniques used by economists to estimate economic values based on market prices.
  • The method is based on actual behavior¡¦what people actually do¡¦rather than stated willingness to pay¡¦what people say they would do in a hypothetical situation.
  • The method is relatively inexpensive to apply.
  • On-site surveys provide opportunities for large sample sizes, as visitors tend to be interested in participating.
  • The results are relatively easy to interpret and explain.
  • The travel cost method assumes that people perceive and respond to changes in travel costs the same way that they would respond to changes in admission price.
  • The most simple models assume that individuals take a trip for a single purpose ¡¦ to visit a specific recreational site. Thus, if a trip has more than one purpose, the value of the site may be overestimated. It can be difficult to apportion the travel costs among the various purposes. 
  • Defining and measuring the opportunity cost of time, or the value of time spent traveling, can be problematic. Because the time spent traveling could have been used in other ways, it has an "opportunity cost." This should be added to the travel cost, or the value of the site will be underestimated. However, there is no strong consensus on the appropriate measure¡¦the person¡¦s wage rate, or some fraction of the wage rate¡¦and the value chosen can have a large effect on benefit estimates. In addition, if people enjoy the travel itself, then travel time becomes a benefit, not a cost, and the value of the site will be overestimated. 
  • The availability of substitute sites will affect values. For example, if two people travel the same distance, they are assumed to have the same value. However, if one person has several substitutes available but travels to this site because it is preferred, this person¡¦s value is actually higher. Some of the more complicated models account for the availability of substitutes.
  • Those who value certain sites may choose to live nearby. If this is the case, they will have low travel costs, but high values for the site that are not captured by the method.
  • Interviewing visitors on site can introduce sampling biases to the analysis.
  • Measuring recreational quality, and relating recreational quality to environmental quality can be difficult.
  • Standard travel cost approaches provides information about current conditions, but not about gains or losses from anticipated changes in resource conditions.
  • In order to estimate the demand function, there needs to be enough difference between distances traveled to affect travel costs and for differences in travel costs to affect the number of trips made. Thus, it is not well suited for sites near major population centers where many visitations may be from "origin zones" that are quite close to one another.
  • The travel cost method is limited in its scope of application because it requires user participation. It cannot be used to assign values to on-site environmental features and functions that users of the site do not find valuable. It cannot be used to value off-site values supported by the site. Most importantly, it cannot be used to measure nonuse values. Thus, sites that have unique qualities that are valued by non-users will be undervalued.
  • As in all statistical methods, certain statistical problems can affect the results. These include choice of the functional form used to estimate the demand curve, choice of the estimating method, and choice of variables included in the model.
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Travel cost method

This article deals with the Travel Cost Method, which is often used in evaluating the economic value of recreational sites. This is particularly important in the coastal zone because of the level of use and the potential values that can be attached to the natural coastal and marine environment.

The Travel Cost Method (TCM) is one of the most frequently used approaches to estimating the use values of recreational sites. The TCM was initially suggested by Hotelling [1] and subsequently developed by Clawson [2] in order to estimate the benefits from recreation at natural sites. The method is based on the premise that the recreational benefits at a specific site can be derived from the demand function that relates observed users’ behaviour (i.e., the number of trips to the site) to the cost of a visit. One of the most important issues in the TCM is the choice of the costs to be taken into account. The literature usually suggests considering direct variable costs and the opportunity cost of time spent travelling to and at the site. The classical model derived from the economic theory of consumer behaviour postulates that a consumer’s choice is based on all the sacrifices made to obtain the benefits generated by a good or service. If the price ( [math]p[/math] ) is the only sacrifice made by a consumer, the demand function for a good with no substitutes is [math]x=f(p)[/math] , given income and preferences. However, the consumer often incurs other costs ( [math]c[/math] ) in addition to the out-of-pocket price, such as travel expenses, and loss of time and stress from congestion. In this case, the demand function is expressed as [math]x = f(p, c)[/math] . In other words, the price is an imperfect measure of the full cost incurred by the purchaser. Under these conditions, the utility maximising consumer’s behaviour should be reformulated in order to take such costs into account. Given two goods or services [math]x_1, x_2[/math] , their prices [math]p_1, p_2[/math] , the access costs [math]c_1, c_2[/math] and income [math]R[/math] , the utility maximising choice of the consumer is:

[math]max \, U = u(x_1,x_2) \quad subject \, to \quad (p_1+c_1)x_1+(p_2+c_2)x_2=R . \qquad (1)[/math]

Now, let [math]x_1[/math] denote the aggregate of priced goods and services, [math]x_2[/math] the number of annual visits to a recreational site, and assume for the sake of simplicity that the cost of access to the market goods is negligible ( [math]c_1 \approx 0[/math] ) and that the recreational site is free ( [math]p_2=0[/math] ). Under these assumptions, equation (1) can be written as:

[math]max \, U = u(x_1,x_2) \quad subject \, to \quad p_1x_1+c_2x_2=R . \qquad (2)[/math]

Under these conditions, the utility maximising behaviour of the consumer depends on:

The TCM is based on the assumption that changes in the costs of access to the recreational site [math]c_2[/math] have the same effect as a change in price: the number of visits to a site decreases as the cost per visit increases. Under this assumption, the demand function for visits to the recreational site is [math]x_2=f(c_2)[/math] and can be estimated using the number of annual visits as long as it is possible to observe different costs per visit. The basic TCM model is completed by the weak complementarity assumption, which states that trips are a non-decreasing function of the quality of the site, and that the individual forgoes trips to the recreational site when the quality is the lowest possible [3] , [4] . There are two basic approaches to the TCM: the Zonal approach (ZTCM) and the Individual approach (ITCM). The two approaches share the same theoretical premises, but differ from the operational point of view. The original ZTCM takes into account the visitation rate of users coming from different zones with increasing travel costs. By contrast, ITCM, developed by Brown and Nawas [5] and Gum and Martin [6] , estimates the consumer surplus by analysing the individual visitors’ behaviour and the cost sustained for the recreational activity. These are used to estimate the relationship between the number of individual visits in a given time period, usually a year, the cost per visit and other relevant socio-economic variables. The ITCM approach can be considered a refinement or a generalisation of ZTCM [7] .

Demand function.jpg

[math]x_2 = g(c_2) . \qquad (3)[/math]

The demand function can also be estimated for non-homogeneous sub-samples introducing among the independent variables income and socio-economic variables representing individual characteristics [8] . Therefore, if an individual incurs [math]c_2^e[/math] per visit, he chooses to do [math]x_2^e[/math] visits a year, while if the cost per visit increases to [math]c_2^p[/math] the number of visits will decrease to [math]x_2^p[/math] . The cost [math]cp[/math] is the choke price, that is the cost per visit that results in zero visits. The annual user surplus (the use value of the recreational site) is easily obtained by integrating the demand function from zero to the current number of annual visits, and subtracting the total expenditures on visits.

Related articles

  • ↑ Hotelling, H. (1949), Letter, In: An Economic Study of the Monetary Evaluation of Recreation in the National Parks , Washington, DC: National Park Service.
  • ↑ Clawson, M. (1959), Method for Measuring the Demand for, and Value of, Outdoor Recreation . Resources for the Future, 10, Washington, DC.
  • ↑ Freeman, A.M. III. (1993). The Measurement of Environmental and Resource Values: Theory and Method , Washington, DC: Resources for the Future.
  • ↑ Herriges, J.A., C. Kling and D.J. Phaneuf (2004), 'What’s the Use? Welfare Estimates from Revealed Preference Models when Weak Complementarity Does Not Hold', Journal of Environmental Economics and Management , 47 (1), pp. 53-68.
  • ↑ Brown, W.G. and F. Nawas (1973), 'Impact of Aggregation on the Estimation of Outdoor Recreation Demand Functions', American Journal of Agricultural Economics , 55, 246-249.
  • ↑ Gum, R.L. and W.E.Martin (1974), 'Problems and Solutions in Estimating the Demand for and Value of Rural Outdoor Recreation', American Journal of Agricultural Economics , 56, 558-566.
  • ↑ Ward, F.A. and D. Beal (2000), Valuing Nature with Travel Cost Method: A Manual , Northampton: Edward Elgar.
  • ↑ Hanley, N. and C.L. Spash (1993), Cost Benefit Analysis and the Environment , Aldershot, UK: Edward Elgar.
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Book cover

A Primer on Nonmarket Valuation pp 187–233 Cite as

Travel Cost Models

  • George R. Parsons 5  
  • First Online: 11 February 2017

3744 Accesses

46 Citations

Part of the The Economics of Non-Market Goods and Resources book series (ENGO,volume 13)

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.

  • Travel cost model
  • Recreation demand
  • Welfare analysis
  • Random utility
  • Per-trip loss

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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.

For an interesting discussion of using zero inflated Poisson models see Paul Allison’s commentary at www.statisticalhorizons.com/zero-inflated-models .

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.

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 ( \(- \alpha > 0\) ).

In some cases, researchers will consider site utilities that are nonlinear in trip cost, allowing for nonconstant marginal utility of income and empirical forms of Eq. ( 6.15 ) that are not closed-form. See Herriges and Kling ( 1999 ) for a discussion and example.

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.

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.

Phaneuf ( 2002 ), for example, considers a variety of water quality measures, including pH, dissolved oxygen, phosphorous, ammonia, and an index defined by the U.S. Environmental Protection Agency. Lupi et al. ( 2003 ) use a catch rate of fish as a measure of quality.

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 ).

For an example applied to fish catch, see McConnell et al. ( 1995 ). For a debate on the validity of this strategy, see Morey and Waldman ( 1998 , 2000 ), and Train et al. ( 2000 ).

Expenses like food and souvenirs are typically excluded because they are not necessary to make the recreation trip possible.

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.

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.

Adamowicz, W. L. (1994). Habit formation and variety seeking in a discrete choice model of recreation demand. Journal of Agricultural and Resource Economics, 19, 19-31.

Google Scholar  

Adamowicz, W. L., Swait, J., Boxall, P., Louviere, J. & Williams, M. (1997). Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. Journal of Environmental Economics and Management, 32, 65-84.

Andrews, T. (1996). A discrete choice model of recreational trout angler benefits in Pennsylvania. Unpublished manuscript, Department of Economics, West Chester University, West Chester, PA.

Awondo, S. N., Egan, K. J. & Dwyer, D. F. (2011). Increasing beach recreation benefits by using wetlands to reduce contamination. Marine Resource Economics, 26, 1-15.

Baerenklau, K. A. (2010). A latent class approach to modeling endogenous spatial sorting in zonal recreation demand models. Land Economics, 86, 800-816.

Bell, K. P. & Strand, I. E. (2003). Reconciling models of recreational route and site choices. Land Economics, 79, 440-454.

Ben-Akiva, M. & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. In Marvin Manheim (Series Ed.), MIT Press Series: Vol. 9. Transportation studies. Cambridge, MA: MIT Press.

Berman, M. D. & Kim, H. J. (1999). Endogenous on-site time in the recreation demand model. Land Economics, 75, 603-619.

Bin, O., Landry, C. E., Ellis, C. L. & Vogelsong, H. (2005). Some consumer surplus estimates for North Carolina beaches. Marine Resource Economics, 20 (2), 145-161.

Bockstael, N. E., Hanemann, W. M. & Kling, C. L. (1987). Estimating the value of water quality improvements in a recreational demand framework. Water Resources Research, 23, 951-960.

Bockstael, N. E., Hanemann, W. M. & Strand, I. E. (1984). Measuring the benefits of water quality improvements using recreation demand models. Report presented to the U.S. Environmental Protection Agency. College Park: University of Maryland.

Bockstael, N. E., McConnell, K. E. & Strand, I. E. (1988). Benefits from improvements in Chesapeake Bay water quality. Report presented to the U.S. Environmental Protection Agency. College Park: University of Maryland.

Boxall, P. C. & Adamowicz, W. L. (2002). Understanding heterogeneous preferences in random utility models: A latent class approach. Environmental and Resource Economics, 23, 421-446.

Boxall, P., Hauer, G. & Adamowicz, W. (2001). Modeling congestion as a form of interdependence in random utility models (Staff paper 05-01). Department of Rural Economy, University of Alberta, Edmonton, Alberta, Canada.

Boxall, P., Rollins, K. & Englin, J. (2003). Heterogeneous preferences for congestion during a wilderness experience. Resource and Energy Economics, 25, 177-195.

Breffle, W. S. & Morey, E. R. (2000). Investigating preference heterogeneity in a repeated discrete-choice recreation demand model of Atlantic salmon fishing. Marine Resource Economics, 15, 1-20.

Brown, G. Jr. & Mendelsohn, R. (1984). The hedonic travel cost method. Review of Economics and Statistics, 66, 427-433.

Brown, W. G. & Nawas, F. (1973). Impact of aggregation on the estimation of outdoor recreation demand functions. American Journal of Agricultural Economics, 55, 246-249.

Burt, O. R. & Brewer, D. (1971). Estimation of net social benefits from outdoor recreation. Econometrica, 39, 813-827.

Cameron, A. C. & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. New York: Cambridge University Press.

Carson, R. T., Hanemann, W. M. & Wegge, T. C. (1987). Southcentral Alaska Sport Fishing Study. Report prepared by Jones and Stokes Associates for the Alaska Department of Fish and Game. Anchorage, AK.

Carson, R. T., Hanemann, W. M. & Wegge, T. C. (2009). A nested logit model of recreational fishing demand in Alaska. Marine Resource Economics, 24, 101-129.

Cicchetti, C. J., Fisher, A. C. & Smith, V. K. (1976). An econometric evaluation of a generalized consumer surplus measure: The Mineral King controversy. Econometrica, 44, 1259-1276.

Clawson, M. & Knetsch, J. L. (1969). Economics of outdoor recreation. Baltimore, MD: Johns Hopkins University.

Creel, M. D. & Loomis, J. B. (1990). Theoretical and empirical advantages of truncated count data estimators for analysis of deer hunting in California. American Journal of Agricultural Economics, 72, 434-441.

Cutter, W. B., Pendleton, L. & DeShazo, J. R. (2007). Activities in models of recreational demand. Land Economics, 83, 370-381.

Day, B. (2000). A recreational demand model of wildlife-viewing visits to the game reserves of the Kwazulu-Natal Province of South Africa (GEC-2000-08). Working paper, Centre for Social and Economic Research on the Global Environment.

Deaton, A. (1997). The analysis of household surveys: a microeconomic approach to development policy. Baltimore MD: The Johns Hopkins University Press.

Edwards, P. E. T., Parsons, G. R. & Myers, K. H. (2011). The economic value of viewing migratory shorebirds on the Delaware Bay: An application of the single site travel cost model using on-site data. Human Dimensions of Wildlife, 16, 435-444.

Egan, K. & Herriges, J. (2006). Multivariate count data regression models with individual panel data from an on-site sample. Journal of Environmental Economics and Management, 52, 567-581.

Egan, K. J., Herriges, J. A., Kling, C. L. & Downing, J. A. (2009). Valuing water quality as a function of water quality measures. American Journal of Agricultural Economics, 91, 70-86.

Englin, J., Boxall, P. & Watson, D. (1998). Modeling recreation demand in a Poisson system of equations: An analysis of the impact of international exchange rates. American Journal of Agricultural Economics, 80, 255-263.

Englin, J. & Cameron, T. A. (1996). Augmenting travel cost models with contingent behavior data. Environmental and Resource Economics, 7, 133-147.

Englin, J. & Shonkwiler, J. S. (1995). Modeling recreation demand in the presence of unobservable travel costs: Toward a travel price model. Journal of Environmental Economics and Management, 29, 368-377.

Feather, P. M. (1994). Sampling and aggregation issues in random utility model estimation. American Journal of Agricultural Economics, 76, 772-780.

Feather, P. & Hellerstein, D. (1997). Calibrating benefit function transfer to assess the conservation reserve program. American Journal of Agricultural Economics 79, 151-162.

Feather, P. & Shaw, W. D. (1999). Estimating the cost of leisure time for recreation demand models. Journal of Environmental Economics and Management, 38, 49-65.

Fisher, A. & Krutilla, J. V. (1972). Determination of optimal capacity of resource-based recreation facilities. Natural Resources Journal, 12, 417-444.

Fisher, W. L., Grambsch, A. E., Eisenhower, D. L., & Morganstein, D. R. (1991). Length of recall period and accuracy of estimates from the National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. American Fisheries Society Symposium, 12, 367-374.

Freeman III, A. M. (2003). The measurement of environmental and resource values: Theory and methods (2nd ed.). Washington, DC: RFF Press.

Greene, W. H. (2007). Econometric analysis (6th ed). Upper Saddle River, NJ: Pearson Prentice Hall.

Grijalva, T. C., Berrens, R. P., Bohara, A. K., Jakus, P. M. & Shaw, W. D. (2002). Valuing the loss of rock climbing access in wilderness areas: A national-level, random-utility model. Land Economics, 78, 103-120.

Gurmu, S. & Trivedi, P. K. (1996). Excess zeros in count models for recreational trips. Journal of Business and Economic Statistics, 14, 469-477.

Haab, T. C. & McConnell, K. E. (1996). Count data models and the problem of zeros in recreation demand analysis. American Journal of Agricultural Economics, 78, 89-102.

Haab, T. C. & McConnell, K. E. (2002). Valuing environmental and natural resources: The econometrics of non-market valuation. Cheltenham, United Kingdom: Edward Elgar.

Haener, M. K., Boxall, P. C., Adamowicz, W. L. & Kuhnke, D. H. (2004). Aggregation bias in recreation site choice models: Resolving the resolution problem. Land Economics, 80, 561-574.

Hang, D., McFadden, D., Train, K. & Wise, K. (2016). Is vehicle depreciation a component of marginal travel cost?: A literature review and empirical analysis. Journal of Transport Economics and Policy. 50(2):1-19.

Hanley, N., Bell, D. & Alvarez-Farizo, B. (2003a). Valuing the benefits of coastal water quality improvements using contingent and real behaviour. Environmental and Resource Economics, 24, 273-285.

Hanley, N., Shaw, W. D. & Wright, R. E. (2003b). The new economics of outdoor recreation. Cheltenham, United Kingdom: Edward Elgar.

Hauber, A. B. & Parsons, G. R. (2000). The effect of nesting structure specification on welfare estimation in a random utility model of recreation demand: An application to the demand for recreational fishing. American Journal of Agricultural Economics, 82, 501-514.

Hausman, J. A., Leonard, G. K. & McFadden, D. (1995). A utility-consistent, combined discrete choice and count data model assessing recreational use losses due to natural resource damage. Journal of Public Economics, 56, 1-30.

Hellerstein, D. M. (1991). Using count data models in travel cost analysis with aggregate data. American Journal of Agricultural Economics, 73, 860-866.

Hellerstein, D. M. (1992). The treatment of nonparticipants in travel cost analysis and other demand models. Water Resources Research 28, 1999-2004.

Hellerstein, D. M. & Mendelsohn, R. (1993). A theoretical foundation for count data models. American Journal of Agricultural Economics, 75, 604-611.

Herriges, J. A. & Kling, C. L. (Eds.). (1999). Valuing recreation and the environment: Revealed preference methods in theory and practice. Cheltenham, United Kingdom: Edward Elgar.

Herriges, J. A. & Kling, C. L. (Eds.). (2008). Revealed preference approaches to environmental valuation (Vol. 1). Aldershot, United Kingdom: Ashgate.

Herriges, J. A., Kling, C. L. & Phaneuf, D. J. (1999). Corner solution models of recreation demand: A comparison of competing frameworks. In J. A. Herriges & C. L. Kling (Eds.), Valuing recreation and the environment: Revealed preference methods in theory and practice. Cheltenham, United Kingdom: Edward Elgar.

Herriges, J. A. & Phaneuf, D. J. (2002). Inducing patterns of correlation and substitution in repeated logit models of recreation demand. American Journal of Agricultural Economics, 84, 1076-1090.

Hicks, R. L. & Strand, I. E. (2000). The extent of information: Its relevance for random utility models. Land Economics, 76, 374-385.

Hindsley, P., Landry, C. E. & Gentner, B. (2011). Addressing onsite sampling in recreation site choice models. Journal of Environmental Economics and Management, 62, 95-110.

Hoehn, J. P., Tomasi, T., Lupi, F. & Chen, H. Z. (1996). An economic model for valuing recreational angling resources in Michigan. Report presented to the Michigan Department of Environmental Quality. East Lansing: Michigan State University.

Hof, J. G. & King, D. A. (1982). On the necessity of simultaneous recreation demand equation estimation. Land Economics, 58, 547-552.

Hotelling, H. (1949). An economic study of the monetary valuation of recreation in the National Parks. Washington, DC: U.S. Department of the Interior, National Park Service and Recreation Planning Division.

Hunt, L. M., Boxall, P. C. & Boots, B. N. (2007). Accommodating complex substitution patterns in a random utility model of recreational fishing. Marine Resource Economics, 22, 155-172.

Hynes, S., Hanley, N. & Garvey, E. (2007). Up the proverbial creek without a paddle: Accounting for variable participant skill levels in recreational demand modelling. Environmental and Resource Economics, 36, 413-426.

Jakus, P. M., Bergstrom, J. C., Phillips, M. & Maloney, K. (2011). Modeling behavioral response to changes in reservoir operations in the Tennessee Valley region. In J. Whitehead, T. Haab & J.-C. Huang (Eds.), Preference data for environmental valuation: Combining revealed and stated approaches (pp. 253-272). New York: Routledge.

Joen, Y., Herriges, J. A., Kling, C. L. & Downing, J. (2011). The role of water quality perceptions in modelling lake recreation demand. In J. Bennett (Ed.), The international handbook on non-market environmental valuation (pp. 74-101). Cheltenham, United Kingdom: Edward Elgar.

Kaoru, Y. (1995). Measuring marine recreation benefits of water quality improvements by the nested random utility model. Resource and Energy Economics, 17, 119-136.

Kinnell, J. C., Bingham, M. F., Mohamed, A. F., Desvousges, W. H., Kiler, T. B., Hastings, E. K. & Kuhns, K. T. (2006). Estimating site choice decisions for urban recreators. Land Economics, 82, 257-272.

Laitila, T. (1999). Estimation of combined site-choice and trip-frequency models of recreational demand using choice-based and on-site samples. Economic Letters, 64, 17-23.

Landry, C. E. & Liu, H. (2009). A semi-parametric estimator for revealed and stated preference data—An application to recreational beach visitation. Journal of Environmental Economics and Management, 57, 205-218.

Landry, C. E. & Liu, H. (2011). Econometric models for joint estimation of revealed and stated preference site-frequency recreation demand models. In J. Whitehead, T. Haab & J.-C. Huang (Eds.), Preference data for environmental valuation: Combining revealed and stated approaches (pp. 87-100). New York: Routledge.

Leggett, C. G., Scherer, N., Haab, T. C., Bailey, R., Landrum, J. P. & Domanski, A. (2015). Assessing the economic benefits of reductions in marine debris at Southern California beaches: a random utility travel cost model. Industrial Economics Inc. Manuscript.

Lew, D. K., NOAA Fisheries & Larson, D. M. (2008). Valuing a beach day with a repeated nested logit model of participation, site choice, and stochastic time value. Marine Resource Economics, 23 (3), 233-252.

Lupi, F. & Feather, P. M. (1998). Using partial site aggregation to reduce bias in random utility travel cost models. Water Resources Research, 34, 3595-3603.

Lupi, F., Hoehn, J. P., and Christie, G. C. (2003). Using an economic model of recreational fishing to evaluate the benefits of sea lamprey ( Petromyzon marinus ) control on the St. Marys River. Journal of Great Lakes Research 29 (Supplement 1), 742-754.

Martinez-Espiñeira, R. & Amoako-Tuffour, J. (2008). Recreation demand analysis under truncation, overdispersion, and endogenous stratification: An application to Gros Morne National Park. Journal of Environmental Management, 88, 1320-1332.

Massey, D. M., Newbold, S. C. & Gentner, B. (2006). Valuing water quality changes using a bioeconomic model of a coastal recreational fishery. Journal of Environmental Economics and Management, 52 (1), 482-500.

McConnell, K. E. (1977). Congestion and willingness to pay: A study of beach use. Land Economics, 53, 185-195.

McConnell, K. E. (1992). On-site time in the demand for recreation. American Journal of Agricultural Economics, 74, 918-925.

McConnell, K. E. & Strand, I. (1981). Measuring the cost of time in recreation demand analysis: An application to sportfishing. American Journal of Agricultural Economics, 63, 153-156.

McConnell, K. E. & Strand, I. E. (1994). The economic value of mid and south Atlantic sportfishing. Report presented to the University of Maryland, U. S. Environmental Protection Agency, U.S. National Marine Fisheries Service, and the National Oceanic and Atmospheric Administration. College Park: University of Maryland.

McConnell, K. E., Strand Jr., I. E. & Blake-Hedges, L. (1995). Random utility models of recreational fishing: Catching fish using a Poisson process. Marine Resource Economics, 10, 247-261.

McConnell, K. E., Strand, I. E. & Bockstael, N. E. (1990). Habit formation and the demand for recreation: Issues and a case study. In V. K. Smith, A. D. Witte & A. N. Link (Eds.), Advances in applied microeconomics 5 (pp. 217-235). New York: Jai Press.

McFadden, D. (2001). Economic choices. American Economic Review, 91, 351-378.

Mendelsohn, R., Hof, J., Peterson, G. & Johnson, R. (1992). Measuring recreation values with multiple destination trips. American Journal of Agricultural Economics, 74, 926-933.

Moeltner, K. (2003). Addressing aggregation bias in zonal recreation models. Journal of Environmental Economics and Management, 45, 128-144.

Moeltner, K. & Englin, J. (2004). Choice behavior under time-variant quality: State dependence versus ‘play-it-by-ear’ in selecting ski resorts. Journal of Business and Economic Statistics, 22, 214-224.

Moeltner, K. & Shonkwiler, J. S. (2005). Correcting for on-site sampling in random utility models. American Journal of Agricultural Economics, 87, 327-339.

Morey, E. R. (1981). The demand for site-specific recreational activities: A characteristics approach. Journal of Environmental Economics and Management, 8, 345-371.

Morey, E. R. (1999). Two RUMS uncloaked: Nested-logit models of site choice and nested-logit models of participation and site choice. In J. A. Herriges & C. L. Kling (Eds.), Valuing recreation and the environment: Revealed preference methods in theory and practice (pp. 65-120). Cheltenham, United Kingdom: Edward Elgar.

Morey, E. R., Shaw, W. D. & Rowe, R. D. (1991). A discrete-choice model of recreational participation, site choice, and activity valuation when complete trip data are not available. Journal of Environmental Economics and Management, 20, 181-201.

Morey, E. R. & Waldman, D. M. (1998). Measurement error in recreation demand models: The joint estimation of participation, site choice, and site characteristics. Journal of Environmental Economics and Management, 35, 262-276.

Morey, E. R. & Waldman, D. M. (2000). Joint estimation of catch and other travel-cost parameters—Some further thoughts. Journal of Environmental Economics and Management, 40, 82-85.

Morgan, O. A. & Huth, W. L. (2011). Using revealed and stated preference data to estimate the scope and access benefits associated with cave diving. Resource and Energy Economics, 33, 107-118.

Murdock, J. (2006). Handling unobserved site characteristics in random utility models of recreation demand. Journal of Environmental Economics and Management, 51, 1-25.

Murray, C., Sohngen, B. & Pendelton, L. (2001). Valuing water quality advisories and beach amenities in the Great Lakes. Water Resources Research, 37, 2583-2590.

Offenbach, L. A. & Goodwin, B. K. (1994). A travel-cost analysis of the demand for hunting trips in Kansas. Review of Agricultural Economics, 16, 55-61.

Parsons, G. R. (1991). A note on choice of residential location in travel cost demand models. Land Economics, 67, 360-364.

Parsons, G. R. (2013). The travel cost model. In J. Shogren (Ed.), Encyclopedia of energy, natural resource, and environmental economics (Vol. 3: Environment, pp. 349-358). Amsterdam: Elsevier.

Parsons, G. R., Chen, Z., Hidrue, M., Lilley, J., Standing, N. & Lilley, J. (2013). Valuing beach width for recreational use: Combining revealed and stated preference data. Marine Resource Economics, 28, 221-241.

Parsons, G. R., Jakus, P. M. & Tomasi, T. (1999a). A comparison of welfare estimates from four models for linking seasonal recreational trips to multinomial logit models of site choice. Journal of Environmental Economics and Management, 38, 143-157.

Parsons, G. R., Kang, A. K., Leggett, C. G. & Boyle, K. J. (2009). Valuing beach closures on the Padre Island National Seashore. Marine Resource Economics, 24, 213-235.

Parsons, G. R. & Kealy, M. J. (1992). Randomly drawn opportunity sets in a random utility model of lake recreation. Land Economics, 68, 93-106.

Parsons, G. R. & Massey, D. M. (2003). A random utility model of beach recreation. In N. Hanley, W. D. Shaw & R. E. Wright (Eds.), The new economics of outdoor recreation (pp. 241-267). Cheltenham, United Kingdom: Edward Elgar.

Parsons, G. R., Massey, D. M. & Tomasi, T. (1999b). Familiar and favorite sites in a random utility model of beach recreation. Marine Resource Economics, 14, 299-315.

Parsons, G. R. & Needelman, M. S. (1992). Site aggregation in a random utility model of recreation. Land Economics, 68, 418-433.

Parsons, G. R., Plantinga, A. J. & Boyle, K. J. (2000). Narrow choice sets in a random utility model of recreation demand. Land Economics, 76, 86-99.

Parsons, G. R. & Stefanova, S. (2011). Gauging the value of short-time site closures in a travel-cost random utility model of recreation demand with a little help from stated preference data. In J. Whitehead, T. Haab & J.-C. Huang (Eds.), Preference data for environmental valuation: Combining revealed and stated approaches (pp. 239-252). New York: Routledge.

Parsons, G. R. & Wilson, A. J. (1997). Incidental and joint consumption in recreation demand. Agricultural and Resource Economics Review, 26, 1-6.

Peters, T., Adamowicz, W. L. & Boxall, P. C. (1995). Influence of choice set considerations in modeling the benefits from improved water quality. Water Resources Research, 31, 1781-1787.

Phaneuf, D. J. (2002). A random utility model for total maximum daily loads: Estimating the benefits of watershed-based ambient water quality improvements. Water Resources Research, 38, 1254-1264.

Phaneuf, D. J., Kling, C. L. & Herriges, J. A. (2000). Estimation and welfare calculations in a generalized corner solution model with an application to recreation demand. Review of Economics and Statistics, 82, 83-92.

Phaneuf, D. J. & Siderelis, C. (2003). An application of the Kuhn-Tucker model to the demand for water trail trips in North Carolina. Marine Resource Economics, 18 (1), 1-14.

Phaneuf, D. J. & Smith, V. K. (2005). Recreation demand models. In K.-G. Mäler & J. R. Vincent (Eds.), Handbook of environmental economics (pp. 671-761). Amsterdam: Elsevier.

Provencher, B. & Bishop, R. C. (1997). An estimable dynamic model of recreation behavior with an application to Great Lakes angling. Journal of Environmental Economics and Management, 33, 107-127.

Provencher, B. & Moore, R. (2006). A discussion of “using angler characteristics and attitudinal data to identify environmental preference classes: A latent-class model.” Environmental and Resource Economics, 34, 117-124.

Riera, P., McConnell, K. E., Giergiczny, M. & Mahieu, P. (2011). Applying the travel cost method to Minorca beaches: Some policy results. In J. Bennett (Ed.), The international handbook on non-market environmental valuation (pp. 60-73). Cheltenham, United Kingdom: Edward Elgar.

Scarpa, R., Thiene, M. & Train, K. (2008). Utility in willingness to pay space: A tool to address confounding random scale effects in destination choice to the Alps. American Journal of Agricultural Economics, 90, 994-1010.

Shaw, D. (1988). On-site samples’ regression: Problems of non-negative integers, truncation, and endogenous stratification. Journal of Econometrics, 37, 211-223.

Shaw, W. D. & Jakus, P. M. (1996). Travel cost models of the demand for rock climbing. Agricultural and Resource Economics Review, 25, 133-142.

Shaw, W. D. & Ozog, M. T. (1999). Modeling overnight recreation trip choice: Application of a repeated nested multinomial logit model. Environmental and Resource Economics, 13, 397-414.

Shonkwiler, J. S. & Shaw, W. D. (1996). Hurdle count-data models in recreation demand analysis. Journal of Agricultural and Resource Economics, 21(2), 210-219.

Siderelis, C., Brothers, G. & Rea, P. (1995). A boating choice model for the valuation of lake access. Journal of Leisure Research, 27, 264-282.

Small, K. A. & Rosen, H. S. (1981). Applied welfare economics with discrete choice models. Econometrica, 49, 105-130.

Smith, V. K. & Desvousges, W. H. (1985). The generalized travel cost model and water quality benefits: A reconsideration. Southern Economics Journal, 52, 371-381.

Smith, V. K., Desvousges, W. H. & McGivney, M. P. (1983). The opportunity cost of travel time in recreation demand models. Land Economics, 59, 259-278.

Sohngen, B. (2000). The value of day trips to Lake Erie beaches. Unpublished report, Department of Agricultural, Environmental and Development Economics, Ohio State University, Columbus.

Stefanova, S. (2009). Measuring the recreational value of changes in beach access, beach width, and vehicle access in the Mid-Atlantic region: Application of random utility models. (Doctoral dissertation). University of Delaware, Newark.

Swait, J., Adamowicz, W. & van Bueren, M. (2004). Choice and temporal welfare impacts: Incorporating history into discrete choice models. Journal of Environmental Economics and Management, 47, 94-116.

Tay, R., McCarthy, P. S. & Fletcher, J. J. (1996). A portfolio choice model of the demand for recreational trips. Transportation Research Part B: Methodological, 30, 325-337.

Thiene, M. & Scarpa, R. (2009). Deriving and testing efficient estimates of WTP distributions in destination choice models. Environmental and Resource Economics, 44, 379-395.

Timmins, C. & Murdock, J. (2007). A revealed preference approach to the measurement of congestion in travel cost models. Journal of Environmental Economics and Management, 53, 230-249.

Train, K. (1986). Qualitative choice analysis: Theory, econometrics, and an application to automobile demand. London: MIT Press.

Train, K. E. (1998). Recreation demand models with taste differences over people. Land Economics, 74, 230-239.

Train, K. (2009). Discrete choice methods with simulation (2nd ed.). New York: Cambridge University Press.

Train, K., McFadden, D. & Johnson, R. (2000). Discussion of Morey and Waldman’s “Measurement error in recreation demand models.” Journal of Environmental Economics and Management, 40, 76-81.

Trice, A. H. & Wood, S. E. (1958). Measurement of recreation benefits. Land Economics, 34, 195-207.

Von Haefen, R. H. (2008). Latent consideration sets and continuous demand systems. Environmental and Resource Economics, 41, 363-379.

Whitehead, J. C., Dumas, C. F., Herstine, J., Hill, J. & Buerger, R. (2008). Valuing beach access and width with revealed and stated preference data. Marine Resource Economics, 23, 119-135.

Whitehead, J. C., Haab, T. C. & Huang, J.-C. (2000). Measuring recreation benefits of quality improvements with revealed and stated behavior data. Resource and Energy Economics, 22, 339-354.

Whitehead, J., Haab, T. & Huang, J.-C. (Eds.). (2011). Preference data for environmental valuation: Combining revealed and stated approaches. New York: Routledge.

Yeh, C.-Y., Haab, T. C. & Sohngen, B. L. (2006). Modeling multiple-objective recreation trips with choices over trip duration and alternative sites. Environmental and Resource Economics, 34, 189-209.

Whitehead, J. C,, Phaneuf, D. J., Dumas, C. F., Herstine, J., Hill, J. & Buerger, B. (2010). Convergent validity of revealed and stated recreation behavior with quality change: a comparison of muliple and single site demands. Environmental and Resource Economics, 45, 91-112.

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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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Environmental Valuation: The Travel Cost Method

Profile image of Philip Graves

In previous chapters we have discussed approaches to environmental valuation that directly construct markets (contingent valuation and other stated preference approaches) or indirectly reveal values via observed willingness to pay for related goods (e.g., sum of specific damages or hedonic methods). Travel cost is a third method that indirectly values environmental goods by observing willingness to pay for related goods. The travel cost method estimates the economic value of recreational sites or other concentrated environmental amenities (e.g. wildlife observation) by looking at the full travel costs (time, out-of-pocket, and any applicable fees) of visiting the sites. In existence since a letter written in 1949 from Harold Hotelling to the Director of the National Park Service, a very large literature, well over one hundred articles, has sprung forth to value a host of site amenities. Parsons (2003) provides a quite complete exposition of the technical details of the various...

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In this study we estimate selected visitors&#39; demand and value for recreational trips to settings such as developed vs. undeveloped sites in U.S. national forests in the Southern United States using the travel cost method. The setting-based approach allows for valuation of multi-activity trips to particular settings. The results from an adjusted Poisson lognormal estimator corrected for truncation and endogenous stratification reveal that economic value per trip estimates are higher for wilderness compared to day-use developed settings, overnight-use developed settings, and general forest areas. Estimates of these economic values are important to resource managers because their management decisions and actions typically control recreational settings. For example, managers control developed campground capacity in a national forest, but typically not the number of campers below the capacity constraint and the number and types of activities visitors engage in during a multi-activity...

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Travel Cost Method (TCM)

Some amenities do not have a direct cost associated with them. For example, recreational sites may be free to enter. In order to apply a value to these types of amenities a value is often derived from a good or service which is complementary to the consumption of the free amenity. One method of estimating a value is therefore to collect data on the travel costs associated with accessing the amenity or recreational site. This is commonly referred to as the travel cost method of estimating the value of an amenity.

The travel cost method involves collecting data on the costs incurred by each individual in travelling to the recreational site or amenity. This ‘price’ paid by visitors is unique to each individual, and is calculated by summing the travel costs from each individuals original location to the amenity. By aggregating the observed travel costs associated with a number of individuals accessing the amenity a demand curve can be estimated, and as such a price can be obtained for the non-price amenity.

We can show this method of using observed or revealed preferences using a diagram as shown below.

D(Visits) shows the overall trend between travel costs and visit rates at a particular asset or site. To generate an estimate of the recreational value for the site, estimates are needed for the average visitors (V1) total recreational value for the site. This is then multiplied by the total number of visitors per annum giving the total annual recreational value of the site or asset. For a more complete explanation continue to the flash example .

Zonal Travel Cost Method Example

Using the zonal travel cost method a researcher can estimate the value of an asset by exploring the actual visitors or users of a site or asset, rather than potential visitors or users. The level of analysis focuses on the zones in which people live compared to the location of the asset. The researcher is required to specify the zones from which the site users travel to the asset.

Consider an example of valuing a country park. In this example four zones have been designated by the researcher. Zone A has an average travel time of 1 hour, and a distance of 25km. Zone B has an average travel time of 1.5 hours and a distance of 40km. Zone C has an average travel time of approximately 2 hours and a distance of 80km. Finally, zone D has an average travel time of 4 hours and a distance of 120km. The admission cost for all users is the same, and is equal to £10. The number of visits per year has been observed by the researcher for each zone. Zone A has an average of 10,000 visits per year. Zone B has an average of 12,000 visits per year, zone C has 8,000 visits, and zone D has 4,000 visits. This information is shown in the table below.

To calculate the value of the asset (V) for a single visit the researcher now uses the simple equation as follows:

V = ((T x w ) + (D x v ) + Ca) x Va

T = travel time (in hours) w = average wage rate (£/hour) D = distance (in km) v = marginal vehicle operating costs Ca = cost of Admission to asset Va = average number of visits per year

Using the country park example, the value of the asset can be calculated using this formula. It is important that the researcher provides an accurate measure for the average hourly wage rate, and also for the marginal vehicle operating costs. A common value for the operating costs is around £0.16p per km. This is the equivalent of around £0.40p per mile, a standard value given for vehicle operating costs in calculating expenses claims within firms and organisations.

In the example the average hourly wage rate is £10, and marginal vehicle operating costs are calculated at £0.16p per km. The researcher can now calculate the value of the country park for each zone to get an overall value for the asset. This is shown in the table below.

Limitations of the TCM

There are a number of limitations associated with the travel cost method of value estimation. These are as follows:

(1) Difficulties in measuring the cost of visiting a site.

It may actually be quite difficult to measure the cost of accessing a site or amenity. This is because of the opportunity cost associated with the travel time. If the opportunity cost of all individuals is the same then the estimated price will be accurate. If, however, the opportunity cost of individuals accessing the site varies, which is more likely, then the measure will be inaccurate.

For example, one individual’s opportunity cost of the travel time spent accessing a recreational site is equivalent to one hours wage equalling £35. However, another individual’s opportunity cost for an hours wage is only £8. This is problematic to the TCM as if individual’s opportunity costs differ including the costs of time spent at the site, this would change the price faced by different individuals by different amounts.

(2) The estimation of willingness to pay used in the TCM is for entire site access rather than specific features.

As the TCM only provides a price or value relating to the cost of accessing the amenity or recreational site, it does so for the whole site. It may, however, be the case that we wish to value a certain aspect of the site in our project appraisal. For example, we do not wish to value a whole park, but instead the fishing ponds within it.

(3) The exclusion of the marginal cost of other complementary goods.

The travel cost method does not account for the costs involved in purchasing complementary goods which may be required in order to enjoy accessing the amenity. For example, individuals accessing a park area may take a football with them, or a picnic. Alternatively, individuals accessing a recreational site may take walking equipment and tents with them. The marginal costs of using this equipment should be included in the price estimated.

(4) Multi-purpose or multi-activity journeys.

Individuals may visit an amenity or recreational site in the morning, but then visit another site or enjoy some other activity in the afternoon. The travel endured to access the amenity was also undertaken to enable access to the afternoon activity. In this case the cost incurred in travelling to the amenity does not represent the value the individuals place on the amenity, but that which they place on both the amenity they visited in the morning and the one which they visited in the afternoon.

(5) Journey value.

It may be the case that the journey itself has a value to the individual. If this is true then some of the cost incurred in travelling to the amenity should not actually be applied to the individual accessing the amenity, and as such should be removed from the estimation of the amenities value.

(6) Assumed responses to changes in price.

The TCM method assumes that individuals respond to changes in price regardless of its composition. For example, TCM assumes that individuals will react consistently to a £10 increase in travel cost as they would to a £10 increase in admission costs.

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COMMENTS

  1. Travel cost analysis

    The travel cost method of economic valuation, travel cost analysis, or Clawson method is a revealed preference method of economic valuation used in cost-benefit analysis to calculate the value of something that cannot be obtained through market prices (i.e. national parks, beaches, ecosystems). The aim of the method is to calculate ...

  2. PDF TRAVEL COST METHOD (TCM)

    Where tc is the full price of a visit given by tc = c + pw ( t1 + t2 ) = f + pd.d + pw (t1 + t2 ) (5) Four components of tc: admission fee monetary cost of travel time cost of travel time cost at site Maximizing (1) s.t. Constraint (4) leads to an indvidual‟sdemand or trip generation function (TGF) for visit v=f(M,tc,q)

  3. Travel Cost Method

    The travel cost method is used to estimate economic use values associated with ecosystems or sites that are used for recreation. The method can be used to estimate the economic benefits or costs resulting from: changes in access costs for a recreational site. elimination of an existing recreational site. addition of a new recreational site.

  4. Chapter 15: Environmental Valuation: The Travel Cost Method

    purchased in a given time period will increase at a lower price and dec rease at a higher ... The advantage of the CVM over some other approaches (e.g., Travel Cost Method, Hedonic Price Method ...

  5. Travel cost method

    The Travel Cost Method (TCM) is one of the most frequently used approaches to estimating the use values of recreational sites. The TCM was initially suggested by Hotelling [1] and subsequently developed by Clawson [2] in order to estimate the benefits from recreation at natural sites. The method is based on the premise that the recreational ...

  6. The Individual Travel Cost Method with Consumer-Specific Values of

    The treatment of the opportunity cost of travel time in travel cost models has been an area of research interest for many decades. Our analysis develops a methodology to combine the travel distance and travel time data with respondent-specific estimates of the value of travel time savings (VTTS). The individual VTTS are elicited with the use of discrete choice stated preference methods. The ...

  7. (PDF) ECONOMIC VALUATION USING TRAVEL COST METHOD

    The Travel Cost Method (TCM) has been employed to derive the demand model, whilst the concept of consumer surplus was used for value determination and comparison. The findings showed that the ...

  8. (PDF) The Travel Cost Model

    The travel cost method, based on welfare estimates typically from preferences revealed in survey responses, is the most well-established and commonly used method for the valuation of recreational ...

  9. The Travel Cost Model

    The travel cost model is used to value recreational uses of the environment. For example, it may be used to value the recreation loss associated with a beach closure due to an oil spill or to value the recreation gain associated with improved water quality on a river. The model is commonly applied in benefit-cost analyses and in natural ...

  10. Travel Cost Models

    Given the growth in population and income and the decline in transportation cost over time, recreation sites see more use. ... K. E., Giergiczny, M. & Mahieu, P. (2011). Applying the travel cost method to Minorca beaches: Some policy results. In J. Bennett (Ed.), The international handbook on non-market environmental valuation (pp. 60-73 ...

  11. PDF MEASUREMENT ISSUES IN THE TRAVEL COST METHOD: by

    A review of the travel cost (TC) literature shows that the base measurements of travel time and distance underpinning many studies are often obtained via crude simplifications. This paper presents an application of the TC method conducted using geographical information system (GIS) software. This permits superior measurement of both travel time ...

  12. Travel-cost method for assessing the monetary value of recreational

    Total travel cost: The increase in total travel costs, TC i, negatively affects the log persontrips' frequency (β ̂ = − 0.006), as expected from the travel-cost method. That means, for each 1 TL increase in total travel cost per person, the expected log count of the persontrips decreases by 0.006 while holding all other variables in the ...

  13. PDF Chapter 15. Travel Cost Method of Valuing Environmental Amenities

    willingness-to-pay for related goods (e.g. SSD or hedonics). The travel cost method is another. indirect measure that is useful in certain circumstances, but which has flaws from both an. economist's and an environmentalist's perspective. The central theoretical flaw in the travel cost method, in common with SSD and.

  14. PDF Travel Cost Literature

    The travel-cost method is used to evaluate the demand for hunting trips in Kansas. In contrast to earlier studies, time spent on-site for other recreational activities is explicitly included in the empirical analysis. The demand for hunting trips falls as cost rises.

  15. Economic Valuation of Cultural Heritage: Application of Travel Cost

    The economic assessment of non-marketed resources (i.e., cultural heritage) can be developed with stated or revealed preference methods. Travel cost method (TCM) is based on the demand theory and assumes that the demand for a recreational site is inversely related to the travel costs that a certain visitor must face to enjoy it. Its application requires data about the tourist's origin. This ...

  16. PDF USE OF TRAVEL COST MODELS PLANNING: A CASE STUDY

    This article examines the use of the travel cost method in tourism-related decision malung in the area of nonconsumptive wildlife-associated recre- ation. SpecificalIy, a travel cost model of nonconsumptive wildlife-associated recreauon de- veloped by Zawacki, Marsinko, and Bowker (2000) is used. Thls article examines the model and dis-

  17. Environmental Valuation: The Travel Cost Method

    The design of this work is to estimate the recreational value and to establish functional relationship between travel cost and visitation of Lawachara National Park (LNP) in Bangladesh. This study employed zonal approach of the travel cost method. The work is grounded on a sample of 422 visitors of the LNP. Results showed that the total value ...

  18. Travel Cost Method

    The travel cost method involves collecting data on the costs incurred by each individual in travelling to the recreational site or amenity. This 'price' paid by visitors is unique to each individual, and is calculated by summing the travel costs from each individuals original location to the amenity. By aggregating the observed travel costs ...

  19. The travel cost method and the economic value of leisure time

    Recent estimates of high values for tourist related recreation USA amenity values indicate that allocation of basic water and terrestrial resources to recreation activities should be given precedence over conventional market oriented activities that often degrade or even extirpate the resource. We discuss at length the travel cost method (TCM), a survey based technique that quantifies the non ...

  20. Environmental Economics: Travel Cost Flashcards

    travel cost method is based on 3 observations: 1. cost of recreation site is more than admissions price. 2. people living different distances from recreation site have different costs for using site. 3. if value people place on site doesn't vary with distance then travel costs can be used a proxy for price in deriving a demand curve for the ...

  21. Travel-cost method for assessing the monetary value of recreational

    Given the increasing demands for recreational areas in Istanbul, assessing the non-market benefits of alternative recreational sites and land-use planning decisions should be reexamined. ... From the micro perspective, this paper combines travel cost method with contingent behaviour method to obtain residents' actual behaviour and contingent ...

  22. The travel cost method and the economic value of leisure time

    Recent estimates of high values for tourist related recreation USA amenity values indicate that allocation of basic water and terrestrial resources to recreation activities should be given precedence over conventional market oriented activities that often degrade or even extirpate the resource. We discuss at length the travel cost method (TCM), a survey based technique that quantifies the non-mark

  23. The Hedonic Travel Cost Method

    of user fees, f(Z), travel time costs, C(Z), and travel costs, T(Z), plus the fixed cost of the trips (a) such as the opportunity cost of time associated with the given duration of the trip. The fixed cost is assumed to be invariant to the site chosen.4 The total cost or price per trip is VI(Z) = a, + f(Z) + T(Z) + C(Z). Travel time and ...