MODELLING THE EFFECT OF HEALTH RISK PERCEPTION ON PREFERENCES AND CHOICE SET FORMATION OVER TIME: RECREATIONAL HUNTING SITE CHOICE AND CHRONIC WASTING DISEASE THUY TRUONG VIC ADAMOWICZ PETER BOXALL Resource and Environmental Economics University of Alberta
OUTLINE What is Chronic Wasting Disease? Random Utility Models and Choice Set Formation Empirical Approach Data and Some Descriptive Statistics Model Results, Welfare Measures Extensions and Limitations Conclusions
THE PROBLEM: WHAT IS THE ECONOMIC WELFARE IMPACT OF CWD? Chronic Wasting Disease (CWD): prion disease that affects deer, elk and other cervid wildlife species Neurodegenerative disease BSE, Scrapie, CWD, Creutzfeldt Jakob Disease (vcjd) No known link between the consumption of CWD affected meat and human health, but Cautions were provided to hunters CWD might affect a recreational hunter s: choice sets site choice and these effects might change over time
Source: "Chronic Wasting Disease Alliance (www.cwd-info.org)"
ECONOMIC IMPACTS OF CWD IN ALBERTA Hunting Resident Hunters - Recreational Hunting Aboriginal People Traditional Use Cervid Farming Sector Elk Farms Passive Use Values General Public View of CWD and Resource Allocation Risk to Threatened Species Landowners Wildlife Viewing / Recreation Consumers / Food Non-Resident Hunters Source: Alberta SRD http://srd.alberta.ca/biodiversitystewardship/wildlifediseases/ documents/cwd-positivemap-jan-21-2009.pdf
MANAGEMENT OF CWD Government initiated CWD control programs Testing of deer heads Only way to find disease Provide extra tags/licenses Allow more deer to be harvested by hunters Culling of deer experts find deer, remove them, and test for CWD CWD may be perceived as a health risk, or a nuisance, or both, or neither!
CHOICE AND CHOICE SET FORMATION Random Utility Theory U = β + ε jn X jn jn P jn = Pr{ U jn > U j' n, j' C n, j' j} = Pr{ U jn > max( U j' n ), j' C n, j' j} P jn = exp( µ V k C n jn exp( µ V ) kn ) = exp( µβ X ) jn exp( µβ X k C n kn ) A More General Approach (Swait et al) i * n n j C n { U = V ( X, Z ; β ) + ε ( X, Z ; θ )} n ~ n
OBJECTIVES OF THE STUDY Identify whether changes in potential health risks (CWD) result in change in utility of a recreation site change in its availability in the choice set (choice set formation) Analysis of effects over time Examine if there are scale differences over time and data generating mechanism Examine specifications of choice set formation models Examine if approximations to choice set formation provide similar results to a fully endogenous choice set formation model
LITERATURE REVIEW CHOICE SETS Exogenously determined choice sets Define choice set using survey responses: spatial boundaries, distance, familiarity Modeling choice sets (endogenous choice sets) Explicit modeling Haab and Hicks (1997) Manski s approach (Swait 1984, Swait and Ben-Akiva 1987a and Ben-Akiva and Boccara 1995) GenL model (Swait 2001) Hicks and Schnier (2010) Implicit modeling Cascetta and Papola (2001) Martinez et al (2009) Bierlaire et al (2010) Kuriyama et al (2003)
EXPLICIT MODELING OF CHOICE SET Haab and Hicks model: Manski s two stage decision process: Disadvantage: k m ( ) ( ) p = P j j C P j C j k k C C k m ( ) ( ) p = P j C Q C j k k C C Large number of possible choice sets (2 J -1) Intractable for large choice problems Site choice - utility Choice set
INDEPENDENT AVAILABILITY MODEL Swait (1984) Probability of C k being the true choice set Q C ( ) k = j C k A j l C 1 1 1 h C m k A ( ) A l ( ) h 1 A = j + e γ Z 1 ij
IMPLICIT MODELING OF CHOICE SET Implicit Availability and Perception model (Cascetta and Papola 2001) p Availability function: ij = J k = 1 Ae j k µ V Ae 1 = 1 + ij µ V A ij Z e α ik
IMPLICATIONS Long history of concern over choice set misspecification, but little done... Applies to a large class of models / applications Transportation Food Choices (health risks?) Housing Demand Stated Preference Data Sets SP Data with multiple alternatives, etc. Marketing Little theory or understanding of the impact of misspecifed choice sets Behavioral Econ too much choice?
DATA AND ESTIMATION Data: 2 years, stated and revealed preference data Attributes: cwd prevalence, tags, culling and travel cost 11 sites (wildlife management units ) Demographics Estimation Utility function: alternative specific constants, attributes and relevant interactions Scale function: dummy for SP/RP data, time dummy in exponential function Availability function: cwd and its interaction with time dummy, age, urban and hunting years
ESTIMATION CONTINUED Examine probability of site choice, and choice set formation, in random utility framework Evaluate impact of CWD (and other features) over time Differences in utility, choice set formation, and scale Examine welfare measures Contribution from utility function Contribution from choice set formation
MODELS MNL model (utility function only) MNL model with scale function MNL model with scale and availability (CP) RPL (random parameter logit) versions of the models above IAL model (fully endogenous choice set formation) with scale MNL model using a thresholds specification of the availability function
EXAMPLE OF CONTINGENT BEHAVIOUR / SP QUESTION
Do you feel CWD is a threat to human health?
Do you feel CWD is a threat to wildlife?
I no longer consume deer meat because of CWD
CWD has affected my enjoyment of hunting deer
I eat or give away deer meat before I get testing results back from Fish and Wildlife
RESULTS: ESTIMATED MODELS Model Fixed parameter models Random parameter models Loglikelihood Rhosquared Loglikelihood Rhosquared Base MNL -7,583.41 0.275-5,067.19 0.516 With scale -7,500.26 0.283-4,960.53 0.526 With scale and availability -7,375.68 0.295-4,937.84 0.528
RESULTS: FIXED PARAMETER MODEL Utility function Availability function Scale function Travel cost -23.2 (1.08) Constant 4.59 (0.594) Year 2-0.305 (0.068) CWD 0.627 (0.087) CWD -0.768 (0.062) SP -0.468 (0.045) Tags 0.572 (0.105) CWD x year 2-0.116 (0.054) Year2 x SP 0.3 (0.09) Cull -0.961 (0.129) Age -0.077 (0.015) Tc x urban 11.1 (0.776) Urban 10 (2.31) Tags x urban -0.265 (0.136) Hunting years 0.12 (0.012) CWD x urban -0.655 (0.083) Cull x hunt years 0.014 (0.005) CWD x year 2-0.054 (0.027) Loglikelihood Rho-square -7,375.68 0.295
WELFARE CHANGE OF MOVING TO THE WORST SCENARIO CWD SPREAD MNL Year 1 Year 2 Base MNL 15.37 (3.75) MNL with scale 15.00 (5.18) Rural Urban Rural Urban -4.24 (4.38) -18.35 (4.73) -11.23 (3.66) -23.07 (6.28) -40.67 (4.74) -49.93 (15.78) CP MNL Year 1 Year 2 Rural Urban Rural Urban Utility function 248.49 (30.51) -7.26 (3.41) 248.98 (0.63) -28.45 (8.39) Availability factor -81.41 (32.7) -0.35 (1.03) -116.05 (3.4) -0.69 (2.82) Total 68.33 (21.17) -7.69 (2.71) -38.36 (1.98) -29.04 (8.52)
COMPARISON WITH THE IAL MODEL (ENDOGENOUS CHOICE SETS) Scale components similar Utility components similar But CWD has a negative effect for all hunters, in all years. Availability component somewhat different Urban residents do not consider all sites available (although they still have high probabilities relative to rural individuals) CWD effect positive in year 1, negative year 2 interaction. Welfare measures: TBD
RESULTS: IAL MODEL Utility function Availability function Scale function Travel cost -59.608 (4.909) Constant -0.068 (0.267) Year 2-1.377 (0.074) CWD -0.148 (0.053) CWD 1.176 (0.364) SP -0.782 (0.104) Tags 1.929 (0.282) CWD x year 2-0.747 (0.254) Year2 x SP 0.676 (0.113) Cull -2.48 (0.260) Age -0.001 (0.008) Tc x urban 26.472 (2.855) Urban 0.356 (0.186) Tags x urban -1.676 (0.345) Hunting years -0.004 (0.007) CWD x urban -0.203 (0.110) Cull x hunt years 0.0455 (0.012) CWD x year 2-0.164 (0.066) Loglikelihood Rho-square -7402.643 0.292
IAL MODEL Implied Probabilities of Choice Set Size # alternatives Q (probability) 1 alt 0.00 2 alts 0.00 3 alts 0.01 4 alts 0.03 5 alts 0.08 6 alts 0.15 7 alts 0.21 8 alts 0.22 9 alts 0.18 10 alts 0.10 11 alts 0.03
MODEL 3: A THRESHOLDS MODEL Many suggestions surrounding specification of choice formation Hauser 2010 summary thresholds / heuristics We now model availability as a function of CWD threshold (presence / absence) Travel cost threshold (above / below average)
Availability function Constant 28.750 (17.228) CWD Threshold -27.949 (17.227) CWD RP x year 2-1.342 (0.366) Travel Cost Threshold -2.012 (0.264) CWD SP x year 2 18.474 (13.537) Utility function CWD 0.139 (0.027) Tags 0.779 (0.117) Cull -0.882 (0.139) Travel Cost (TC) -21.851 (0.993) TC x urban 9.846 (0.747) Tags x urban -0.662 (0.137) CWD x urban -0.155 (0.023) Cull x hunt years 0.020 (0.005) CWD x year 2-0.174 (0.033) Scale function SP -0.587 (0.042) Year 2-0.220 (0.068) Year 2 x SP 0.187 (0.080) PARAMETERS OF A MNL MODEL EMPLOYING THRESHOLDS IN AVAILABILITY Note: coefficients in italics are NOT significant at 10%. Standard errors are in parentheses.
Welfare Measures from the Thresholds Model (with availability and scale) Year 1 Year 2 Rural Urban Rural Urban Utility Availability Total 46.82 (9.86) -8.38 (4.06) 39.96 (12.92) -1.76 (5.73) -24.51 (4.00) -25.07 (7.67) -27.5 (3.52) -32.85 (7.19) -40.07 (3.64) -60.27 (22.24) -83.45 (13.75) -163.62 (18.87) Note: Welfare change $/trip of moving to the worst case scenario. Standard deviations are in parentheses.
CONCLUSION Using the CP model, CWD is found to have an effect on choice set formation The negative effect of CWD on choice set formation increases over time Time and habit effects? (similar to BSE research) However, the impact of results are sensitive to specification and model type Is the CP approach a tractable choice set formation model?
LIMITATIONS AND EXTENSIONS Limitations: Complex behaviors, observed and unobserved heterogeneity Small sample with possible selection bias Next Steps Checks on robustness of IAL model Endogeneity of CWD? Simulation analysis Extensions / Future Research: A Theory for Choice Sets? Kreps options (1978) or Sarver regret (2008) Choice sets versus cutoffs? Welfare analysis?
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