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Utility in WTP Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps

  • Ricardo Scarpa


    (University of Waikato)

  • Mara Thiene


    (University of Padua Viale dell’Universita`)

  • Kenneth Train


    (University of California at Berkeley)

Destination choice models with individual-specific taste variation have become the presumptive analytical approach in applied nonmarket valuation. Under the usual specification, tastes are represented by coefficients of site attributes that enter utility, and the distribution of these coefficients is estimated. The distribution of willingness-to-pay (WTP) for site attributes is then derived from the estimated distribution of coefficients. Though conceptually appealing this procedure often results in untenable distributions of willingness to pay. An alternative procedure is to estimate the distribution of willingness to pay directly, through a re-parameterization of the model. We compare hierarchical Bayes and maximum simulated likelihood estimates under both approaches, using data on site choice in the Alps. We find that models parameterized in terms of WTP provide more reasonable estimates for the distribution of WTP, and also fit the data better than models parameterized in terms of attribute coefficients. This approach to parameterizing utility is hence deemed promising for applied nonmarket valuation.

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Paper provided by University of Waikato, Department of Economics in its series Working Papers in Economics with number 06/15.

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Length: 23 pages
Date of creation: 15 Dec 2006
Date of revision:
Handle: RePEc:wai:econwp:06/15
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  1. Edward Morey & Kathleen Greer Rossmann, 2003. "Using Stated-Preference Questions to Investigate Variations in Willingness to Pay for Preserving Marble Monuments: Classic Heterogeneity, Random Parameters, and Mixture Models," Journal of Cultural Economics, Springer, vol. 27(3), pages 215-229, November.
  2. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
  3. Cameron, Trudy Ann, 1988. "A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression," Journal of Environmental Economics and Management, Elsevier, vol. 15(3), pages 355-379, September.
  4. David F. Layton & Gardner Brown, 2000. "Heterogeneous Preferences Regarding Global Climate Change," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 616-624, November.
  5. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
  6. Kling, Catherine L. & Bockstael, Nancy & Michael, W., 1999. "Estimating the Value of Water Quality Improvements in a Recreational Demand Framework," Staff General Research Papers 12334, Iowa State University, Department of Economics.
  7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
  8. Andrew A. Goett & Kathleen Hudson & Kenneth E. Train, 2000. "Customers' Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 1-28.
  9. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
  10. Meijer, E. & Rouwendal, J., 2000. "Measuring welfare effects in models with random coefficients," Research Report 00F25, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  11. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
  12. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
  13. Cameron, Trudy Ann & James, Michelle D, 1987. "Efficient Estimation Methods for "Closed-ended' Contingent Valuation Surveys," The Review of Economics and Statistics, MIT Press, vol. 69(2), pages 269-76, May.
  14. Joseph A. Herriges & Daniel J. Phaneuf, 2002. "Inducing Patterns of Correlation and Substitution in Repeated Logit Models of Recreation Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1076-1090.
  15. W. Michael Hanemann, 1989. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Response Data: Reply," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(4), pages 1057-1061.
  16. Bill Provencher & Kenneth A. Baerenklau & Richard C. Bishop, 2002. "A Finite Mixture Logit Model of Recreational Angling with Serially Correlated Random Utility," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1066-1075.
  17. Herriges, Joseph A. & Phaneuf, Daniel J., 2002. "Inducing Patterns Correlation and Substitution in Repeated Logit Model of Recreation Demand," Staff General Research Papers 5035, Iowa State University, Department of Economics.
  18. Riccardo Scarpa & Mara Thiene, 2004. "Destination Choice Models for Rock Climbing in the Northeast Alps: A Latent-Class Approach Based on Intensity of Participation," Working Papers 2004.131, Fondazione Eni Enrico Mattei.
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