valuing short term beach closure in a rum model of recreation demand using stated preference data...
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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. ParsonsCamp 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.
Beach Characteristics
Beach CharacteristicsNumber of
BeachesMean or % of Beaches
Beach length (miles) 5.35
Gulf access Beach is located on the Gulf 48 74%
State park Beach is part of a state park 4 6%
Remote Beach has a remote location 22 34%
Vehicle free Vehicles not allowed on beach 26 40%
Manual cleaning Beach is routinely manually cleaned 33 51%
Machine cleaning Beach is routinely machined cleaned 36 55%
Rest room Restrooms located at beach 37 57%
Lifeguards Lifeguards at beach 17 26%
Concession Concession located at beach 15 23%
Red tide history Beach has a recent history of red tide 12 18%
Advisory/Closure history
Beach has a recent history of closures and/or advisories
11 17%
Individual CharacteristicsVariable
Mean or % of Sample(Adjusted for Stratification)
Age 41 years
Work Fulltime 62%
Student 5%
Unemployed 5%
Children Under 17 49%
High School 32%
College 24%
Graduate School 10%
Retire 9%
Spanish 9%
Female 60%
Own Boat 24%
Own Pool 24%
Own Coastal Property 7%
Three Measures of Welfare Loss• Per trip
• Per season
• Loss to trip ratio
1 1 2{ } { , }
ln ln /i tc ni i tc nix tc x tcn tci C i C C
w e e
.5 ( )c o cn n n n n nW w T w T T
1 1
/N N
n nn n
ltr W PT
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
SP Data
Option % of SP responses
Another Beach Now 19%
Padre Later 76%
Stay Home 5%
Strategy for Incorporating Delayed Trips Using SP
Two ModelsPadre Open ModelRP data on all trips
Padre Closed Model RP data on trips to Padre is replaced with SP data * Trips to other sites assumedthe 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 ApproachPadre Closed (RP) Padre Closed (RP/SP)Choice set: Choice set: non Padre sites delayed trips to Padre Padre sites
1 2,C C C
1 2,c cC C C *1C C
1C
2C2cC
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP
Padre Open (RP data)Padre Utility:
Conventional Approach Our ApproachPadre Closed (RP) Padre Closed (RP/SP)Padre Utility: Padre Utility:
0
( )oj j tc j j
oU x y tc
( )cj j tc j j
cU x y tc
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP
Padre Open (RP data)Expected Utility:
Padre Closed (RP)
Padre Closed (RP/SP)
1 2
( )( )
& { } { }( ) ln e e
cj tc jj tc j
c
x y tcx y tccRP SP i C j C
E v
1
( )
{ }( ) ln e j tc jx y tcc
RP i CE v
1 2
( )( )
{ } { }( ) ln e e
oj tc jj tc j x y tcx y tco
RP i C j CE v
Strategy for Incorporating Delayed Trips in Welfare Measures Using SP
Padre Closed - Conventional Approach
Padre Closed - Accounting For Delayed Trips
( ) ( ) /c oRP RP RP tcw E v E v
& &( ) ( ) /c oRP SP RP SP RP tcw E v E v
Results LogitPADRE OPEN MODEL
Variable Mean T stat.
Travel cost -0.022 -22.28
Gulf 0.630 4.529
Restroom 0.414 4.438
Lifeguard 0.089 0.89
State park 0.321 1.224
Length 0.232 8.214
Machine clean 0.953 8.52
Vehicle free 0.852 9.087
Remote 0.118 1.142
Manual clean 0.338 3.001
Variable Mean T stat.
Concession -0.330 -3.405
Red tide -1.738 -5.342
Closure -0.526 -2.594
Padre ASC 1.477 7.683
Region 2 1.173 4.224
Region 3 2.150 4.912
Region 4 1.080 2.963
Region 5 1.606 4.843
Region 6 0.503 1.312
PADRE CLOSED MODEL
Padre ASC 1.116 9.289
Results Mixed LogitPADRE OPEN MODEL Fixed parameters Mean T stat.
Travel cost -0.027 -15.28
Restroom 0.419 4.43
Lifeguard 0.111 1.039
State park 0.374 1.398
Length 0.247 8.437
Machine clean 1.085 8.860
Manual clean 0.333 2.893
Concession -0.407 -3.906
Red tide -1.645 -4.905
Closure -0.487 -2.342
Padre ASC 1.911 8.208
Random Parameters Mean T stat. Std. Dev. T stat.
Padre Closed Std. Dev.
Gulf 0.904 2.56 0.836 1.27 0.823
Vehicle free 0.926 8.97 0.006 0.03 0.008
Remote -0.189 -1.01 1.309 3.72 1.299
Region 1 0.000 Fixed 0.000 0.00 0.009
Region 2 1.302 4.19 0.181 0.24 0.136
Region 3 2.218 4.72 0.005 0.02 0.000
Region 4 1.118 2.72 0.072 0.07 0.047
Region 5 1.747 4.58 1.644 5.09 1.704
Region 6 0.240 0.44 1.951 3.61 1.787
PADRE CLOSED MODEL
Padre ASC 1.499 10.93Unconstrained in Padre Closed
Welfare Loss for Closure of All Padre Beaches (2001$)
Logit Mixed LogitConventional approach:Per season 10.13 11.49Per trip 5.48 5.40Loss to trip ratio 70.62 80.10
Accounting for delayed trips:Per season 2.88 2.62Per trip 1.48 1.20Loss to trip ratio 83.66 76.11
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
• Logit
1
( ) ln exp( )nS
n tc ni x ii
E v tc x
1
exp( )( )
exp( )n
tc nk x knt nkt S
tc ni x ii
tc xpr k L
tc x
Step 1: Discrete choice site selection
• Mixed logit
1 1
1( ) ln exp( )
nSRr
n tc ni x ir i
E v tc xR
( ) ( , ) ( | , )nt nkt tc x x x x xpr k L f d
1
1( ) ( )
Rr
ni nk xr
pr k LR
Step 2: Trip frequency
• Negative binomial model
( ( ) / , )n n tc nT f E v z
Pr( ) ( ) / !
where exp{ ( ( ) / ) }
n nnT un nn n
n n tc n
T u e T
E v z
Negative Binomial Results
Variable Coefficient T-Stat.
Intercept -2.96 -3.98
Iv/beta_tc 0.01 0.01
lnage 0.16 -0.10
female -0.09 -0.25
fulltime 0.15 -0.03
childdm 0.22 0.06
property 0.24 -0.03
ownboat 0.31 0.14
own pool -0.17 -0.36
ownsfcst 0.13 -0.02
spanish -0.07 -0.32
gradsch 0.46 0.17
college 0.30 0.11
high sc. -0.23 -0.42
retired -0.09 -0.41
att 0.29 0.22
Dispersion 0.30 0.25
Linked model
Welfare in linked model