Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-23T10:03:37.061Z Has data issue: false hasContentIssue false

Travel Cost Models of the Demand for Rock Climbing

Published online by Cambridge University Press:  15 September 2016

W. Douglass Shaw*
Affiliation:
Department of Applied Economics and Statistics/204 University of Nevada, Reno
Paul Jakus
Affiliation:
Department of Agricultural Economics and Rural Sociology, University of Tennessee, Knoxville
*
Shaw is corresponding author, but senior authorship is not assigned.

Abstract

In this paper we estimate the demand for rock climbing and calculate welfare measures for changing access to a number of climbs at a climbing area. In addition to the novel recreation application, we extend the travel cost methodology by combining the double hurdle count data model (DH) with a multinomial logit model of site-choice. The combined model allows us simultaneously to explain the decision to participate and to allocate trips among sites. The application is to climbers who visit one of the premiere rock-climbing areas in the northeastern United States and its important substitute sites. We also estimate a conventional welfare measure, which is the maximum WTP to avoid loss of access to the climbing site.

Type
Articles
Copyright
Copyright © 1996 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bockstael, N., Hanemann, W.M., and Strand, I.E. 1984. Measuring the Benefits of Water Quality Improvements Using Recreational Demand Models. Vol. 2 of Benefits Analysis Using Indirect or Imputed Market Methods. EPA contract number: CR-811043-01-0.Google Scholar
Bockstael, N., McConnell, K.E., and Strand, I.E. 1991. “Recreation.” In Measuring the Demand for Environmental Quality, ed. Braden, J.B. and Kolstad, C.D. North Holland: Elsevier.Google Scholar
Cameron, T.A., Shaw, W.D., Ragland, S., Callaway, J., and Keefe, S. 1996. “Using Actual and Contingent Behavior Data with Varying Time Aggregation to Model Recreation Demand.Journal of Agricultural and Resource Economics 21(1): 130–49.Google Scholar
Creel, M., and Loomis, J. 1990. “Theoretical and Empirical Advantages of Truncated Count Data Estimators.American Journal of Agricultural Economics, 72: 434–41.Google Scholar
Economist. 1995. “Climbing Up the Wall.” March 11.Google Scholar
Ekstrand, E. 1994. “Economic Benefits of Resources Used for Rock Climbing at Eldorado Canyon State Park, Colorado.” Ph.D. diss., Department of Agricultural Economics, Colorado State University, Fort Collins, Colorado.Google Scholar
Feather, P., and Hellerstein, D. 1996. “Calibrating Benefit Function Transfer to Assess the Conservation Reserve Program.American Journal of Agricultural Economics. (Available from the authors at ERS/RTD, USDA, Washington, D.C. 20005.) Forthcoming.Google Scholar
Feather, P., Hellerstein, D., and Tomasi, T. 1995. “A Discrete-Count Model of Recreational Demand.Journal of Environmental Economics and Management 29: 214–27.Google Scholar
Greene, W.H. 1994. “Accounting for Excess Zeroes and Sample Selection in Poisson and Negative Binomial Regression Models.” Draft manuscript, Department of Economics, New York University.Google Scholar
Haab, T.C. and McConnell, K.E. 1996. “Count Data Models and the Problem of Zeros in Recreation Demand Analysis.American Journal of Agricultural Economics 78: 89102.CrossRefGoogle Scholar
Hanemann, W.M. 1982. “Applied Welfare Analysis with Qualitative Response Models.” Working paper no. 241, Department of Agricultural Economics, University of California, Berkeley.Google Scholar
Hausman, J., Leonard, G., and 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: 130.Google Scholar
Hellerstein, D. 1992. “The Treatment of Nonparticipants in Travel Cost Analysis and Other Demand Models.Water Resources Research 29: 19992004.Google Scholar
Jakus, P., and Shaw, W.D. 1996. “An Empirical Analysis of Rock Climber's Responses to Hazard Warnings.Risk Analysis 16(4): 581–86.Google Scholar
Johnson, N., and Kotz, S. 1969. Discrete Distributions. New York: John Wiley.Google Scholar
Morey, E.R. 1985. “Characteristics, Consumer Surplus, and New Activities.Journal of Public Economics 26: 221–36.Google Scholar
Morey, E.R. 1994. “What Is Consumer's Surplus per Day of Use, When Is It a Constant Independent of the Number of Days of Use, and What Does It Tell Us about Consumer's Surplus?Journal of Environmental Economics and Management 27: 257–70.Google Scholar
Morey, E.R., Shaw, W.D., and Rowe, R.D. 1991. “A Model of Recreation Participation and Site Choice When Complete Trip Data Are Unavailable.Journal of Environmental Economics and Management 20: 181201.Google Scholar
Parsons, G., and Kealy, M.J. 1995. “A Demand Theory for Number of Trips in a Random Utility Model of Recreation.Journal of Environmental Economics and Management 29: 357–67.Google Scholar
Shaw, D. 1988. “On Site Samples' Regression: Problems of Non-negative Integers, Truncation, and Endogenous Stratification.Journal Econometrics. 39: 211–23.Google Scholar
Shonkwiler, J.S. 1995. “Systems of Travel Cost Demand Equations.” Proceedings, Eighth Interim Report for the W-133 Regional Project, Monterey, Calif. Compiled by Doug Larson, Department of Agricultural Economics, University of California-Davis.Google Scholar
Shonkwiler, J.S., and Shaw, W.D. 1996. “Hurdle Count Data Models for Recreation Demand Analysis.Journal of Agricultural and Resource Economics 21. Forthcoming.Google Scholar
Terza, J., and Wilson, R. 1990. “Analyzing Frequencies of Several Types of Events: A Mixed Multinomial-poisson Approach.Review of Economics and Statistics 72: 108–15.Google Scholar
Yen, J.S., and Adamowicz, V. 1994. “Participation, Trip Frequency and Site Choice: A Multinomial-Poisson Hurdle Model of Recreation Demand.Canadian Journal of Agricultural Economics 42: 6576.Google Scholar