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Valuing Short Term Beach Closure in a RUM Model of Recreation Demand Using Stated Preference Data Stela Stefanova and George R. Parsons Camp Resources XV August 6 – 7, Wilmington, NC Acknowledgements Funded by the National Park Service
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Valuing Short Term Beach Closure in a RUM Model of Recreation Demand Using Stated Preference Data StelaStefanova and George R. Parsons Camp Resources XV August 6 – 7, Wilmington, NC
Acknowledgements • Funded by the National Park Service • Funded by the National Oceanic and Atmospheric Administration’s Coastal Response Research Center at the University of New Hampshire • Presently under consideration for a chapter in: “Preference Data for Environmental Valuation”, eds. John Whitehead, Ju-Chin Huang and Tim Haab
Outline • Motivation • Data • Padre Island National Seashore Park • Linked Model and Welfare • Our Approach to Incorporating Delayed Trips • Coefficient and Welfare estimates • Conclusion
Motivation • Random Utility Models (RUM) are well suited for valuing seasonal closures of sites • However, RUM are not well suited for valuing short term closures when there is substitution across time periods within the same season • Short term closures may have little impact on total visitation to the closed site • People may be delaying trips, in effect substituting across time periods
Data • 884 Texas residents living within 200 miles of the Texas Gulf Coast • 2692 day trips taken to 65 Texas Gulf Coast beaches between May and September, 2001 • Limited choice set to beaches within 300 miles of residence
Padre Island National Seashore Padre Island is located near Corpus Christi, Texas. 66 miles along the Texas Gulf Coast Accessible by car, approximately 30 minutes from Corpus Christi and approximately 2.5 hours from San Antonio. North Beach, Malaquite Beach, South Beach, Little Shell and Big Shell Beaches, Mansfield cut 14% of people visited Padre beaches - 394 trips
A Linked Model of Site Choice and Trip Frequency • Step 1: Discrete choice site selection • Logit • Mixed Logit • Step 2: Trip frequency • Negative binomial • Bockstael, Hanemann, and Kling. 1987. • Herriges, Kling, and Phaneuf. 1999. • Parsons, Jakus, and Tomasi. 2003.
Three Measures of Welfare Loss • Per trip • Per season • Loss to trip ratio
Strategy for Incorporating Delayed Trips Using SP • These welfare measures rely on RP data • Do not capture substitution across time periods in the case of a short term closure • Survey questions offered the following options in case of site closure • visit another site now • stay home now but visit the closed site later to “make up” for the lost trip • stay home without making up the trip later
Strategy for Incorporating Delayed Trips Using SP Two Models Padre Open Model RP data on all trips Padre Closed Model RP data on trips to Padre is replaced with SP data * Trips to other sites assumed the same * The scaling parameter on the SP choices relative to the RP choices vanishes in estimation. Brownstone, Bunch, and Train. 2000.
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP Padre Open (RP data) Choice set: Conventional Approach Our Approach Padre Closed (RP) Padre Closed (RP/SP) Choice set: Choice set: non Padre sites delayed trips to Padre Padre sites
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP Padre Open (RP data) Padre Utility: Conventional Approach Our Approach Padre Closed (RP) Padre Closed (RP/SP) Padre Utility: Padre Utility: 0
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP Padre Open (RP data) Expected Utility: Padre Closed (RP) Padre Closed (RP/SP)
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP Padre Closed - Conventional Approach Padre Closed - Accounting For Delayed Trips
Results Mixed Logit Unconstrained in Padre Closed
Conclusion • Included the alternative of delaying a trip in a conventional RUM • Estimated losses are 72% to 77% lower when delayed trips are incorporated in the model
References • Bockstael, N., W. M. Hanemann, and C. L. Kling. 1987. Estimating the Value of Water Quality Improvements in a Recreational Demand Framework. Water Resources Research 23, no. 5: 951-60. • Parsons, G. R., P. Jakus, and T. Tomasi. 2003. A comparison of welfare estimates from four models for linking seasonal recreational trips to multinomial models of site choice,” Journal of Environmental Economics and Management 38(2): 143-157. • Brownstone, D., D. S. Bunch, and K. Train. 2000. Joint mixed logit models of stated and revealed preferences for alternative fuel vehicles. Transportation Research Record B, 34.
Step 1: Discrete choice site selection • Mixed logit
Step 2: Trip frequency • Negative binomial model