Modelling the Effect of Chronic Wasting Disease on Recreational Hunting Site Choice Preferences and Choice Set Formation over Time



Chronic wasting disease (CWD) is a prion disease that affects deer, elk and other cervid wildlife species. Although there is no known link between the consumption of CWD affected meat and human health, hunters are advised to have animals from CWD affected areas tested and are advised against consuming meat from CWD infected animals (Government of Alberta 2010). We model hunter response to the knowledge that deer in a wildlife management unit have been found to have CWD in Alberta, Canada. We examine hunter site choice over two hunting seasons using revealed and stated preference data in models that incorporate preferences, choice set formation, and scale. We compare a fully endogenous choice set model using the independent availability logit model (Swait in Probabilistic choice set formation in transportation demand models. Dissertation, MIT, 1984) with the availability function approach (Cascetta and Papola in Transp Res C 9(4):249–263, 2001) that approximates choice set formation. We find that CWD incidence affects choice set formation and preferences and that ignoring choice set formation would result in biased estimates of impact and welfare measures. This study contributes to the broader recreation demand literature by incorporating choice set formation, scale and temporal impacts into a random utility model of recreation demand.


Recreation demand Random utility models Combined revealed-stated preferences Choice set formation 



Bovine spongiform encephalopathy


Constrained multinomial logit model


Cascetta and Papola availability


Chronic wasting disease


Independent availability logit


Multinomial logit


Revealed preference


Random utility model


Stated preference


Wildlife management unit

JEL Classification

Q260 Q280 



Funding support was provided by the Alberta Prion Research Institute.

Supplementary material

10640_2017_120_MOESM1_ESM.xlsx (150 kb)
Supplementary material 1 (xlsx 149 KB)


  1. Andrews R, Srinivasan TC (1995) Studying consideration effects in empirical choice models using scanner panel data. J Mark Res 32:30–41CrossRefGoogle Scholar
  2. Ben-Akiva M, Boccara B (1995) Discrete choice models with latent choice sets. Int J Res Mark 12:9–24CrossRefGoogle Scholar
  3. Bierlaire M (2003) BIOGEME: A free package for the estimation of discrete choice models. In: Proceedings of the 3rd Swiss transportation research conference, Ascona, SwitzerlandGoogle Scholar
  4. Bierlaire M, Hurtubia R, Flötteröd G (2010) Analysis of implicit choice set generation using a constrained multinomial logit model. Transp Res Rec 2175:92–97CrossRefGoogle Scholar
  5. Cascetta E, Papola A (2001) Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand. Transp Res C 9(4):249–263CrossRefGoogle Scholar
  6. Chiang J, Chib S, Narasimhan C (1999) Markov chain Monte Carlo and model of consideration set and parameter heterogeneity. J Econom 89:223–248CrossRefGoogle Scholar
  7. Clawson M (1959) Method for measuring demand for and the value of outdoor recreation. Reprint No. 10. Resources for the Future, Washington, DCGoogle Scholar
  8. Diana SC, Bisogni CA, Gall KL (1993) Understanding anglers’ practices related to health advisories for sport-caught fish. J Nutr Educ 25(6):320–328CrossRefGoogle Scholar
  9. Esarey J, Menger (2016) Practical and effective approaches to dealing with clustered data. Working paper. Department of Political Science, Rice UniversityGoogle Scholar
  10. Government of Alberta (2010) Chronic Wasting Disease. Cited 8 Feb 2011
  11. Haab TC, Hicks RL (1997) Accounting for choice set endogeneity in random utility models of recreation demand. J Environ Econ Manag 34(2):127–147CrossRefGoogle Scholar
  12. Haab TC, Hicks RL (1999) Choice set considerations in models of recreation demand: history and current state of the art. Mar Resour Econ 14:271–281CrossRefGoogle Scholar
  13. Hanemann WM (1978) A methodological and empirical study of the recreation benefits from water quality improvement. Ph.D. Dissertation, Harvard UniversityGoogle Scholar
  14. Hauser JR (2010) Consideration-set heuristics. MIT. Cited 10 May 2011
  15. Hicks RL, Strand IE (2000) The extent of information: its relevance for random utility models. Land Econ 76(3):374–385CrossRefGoogle Scholar
  16. Hotelling H (1949) Letter to the National Park Service: an economic study of the monetary evaluation of recreation in the National Parks. U.S. Department of the Interior, National Park Service and Recreational Planning Division, Washington, DCGoogle Scholar
  17. Jakus PM, Downing M, Bevelimer MS, Fly JM (1997) Do sportfish consumption advisories affect reservoir anglers’ choice? Agric Resour Econ Rev 26(2):196–204CrossRefGoogle Scholar
  18. Jakus PM, Shaw WD (2003) Perceived hazard and product choice: an application to recreational site choice. J Risk Uncertain 26(1):77–92CrossRefGoogle Scholar
  19. Jones C, Lupi F (1999) The effect of modelling substitute activities on recreational benefit estimates: is more better? Mar Resour Econ 14:357–374CrossRefGoogle Scholar
  20. Kuriyama K, Hanemann WM, Pendleton L (2003) Approximation approaches to probabilistic choice set models for large choice set data. Working paper 967, University of California, BerkeleyGoogle Scholar
  21. Kreps D (1979) A preference for flexibility. Econometrica 47:565–576CrossRefGoogle Scholar
  22. Li L, Adamowicz W, Swait J (2015) The effects of choice set misspecification on welfare measures in random utility models. Resour Energy Econ 42:71–92CrossRefGoogle Scholar
  23. Manrai AK, Andrews RL (1998) Two-stage discrete choice models for scanner panel data: an assessment of process and assumptions. Eur J Oper Res 111:193–215CrossRefGoogle Scholar
  24. Manski CF (1977) The structure of random utility models. Theor Decis 8:229–254CrossRefGoogle Scholar
  25. May H, Burger J (1996) Fishing in a polluted estuary: fishing behaviour, fishing consumption, and potential risk. Risk Anal 16(4):459–471CrossRefGoogle Scholar
  26. Parsons G, Hauber A (1998) Spatial boundaries and choice set definition in a random utility model of recreation demand. Land Econ 74(1):32–48CrossRefGoogle Scholar
  27. Peters T, Adamowicz W, Boxall P (1995) The influence of choice set consideration in modelling the benefits of improved water quality. Water Resour Res 613:1781–1787CrossRefGoogle Scholar
  28. Roberts J, Lattin J (1991) Development and testing of a model of consideration set composition. J Mark Res 28:429–440CrossRefGoogle Scholar
  29. Sarver T (2008) Anticipating regret: why fewer options may be better. Econometrica 76:263–305CrossRefGoogle Scholar
  30. Swait J (1984) Probabilistic choice set formation in transportation demand models. Dissertation, MITGoogle Scholar
  31. Swait J (2001a) Choice set generation within the generalized extreme value family of discrete choice models. Transp Res B 35(7):643–666CrossRefGoogle Scholar
  32. Swait J (2001b) A non-compensatory choice model incorporating attribute cut-offs. Transp Res B 35(7):903–928CrossRefGoogle Scholar
  33. Swait J, Ben-Akiva M (1986) An analysis of the effects of captivity on travel time and cost elasticities. In: Annals of the 1985 international conference on travel behaviour, Noordwijk, Holland, 16–19 April 1985Google Scholar
  34. Swait J, Ben-Akiva M (1987a) Incorporating random constraints in discrete choice models of choice set generation. Transp Res B 21(2):91–102CrossRefGoogle Scholar
  35. Swait J, Ben-Akiva M (1987b) Empirical test of a constrained choice discrete model: mode choice in Sao Paulo Brazil. Transp Res B 21(2):103–115CrossRefGoogle Scholar
  36. Swait J, Louviere JJ (1993) Role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res 30(3):305–314CrossRefGoogle Scholar
  37. Timmins C, Murdock J (2007) A revealed preference approach to the measurement of congestion in travel cost models. J Environ Econ Manag 53(2):230–249CrossRefGoogle Scholar
  38. von Haefen RH (2008) Latent consideration sets and continuous demand systems. Environ Resour Econ 41(3):363–379CrossRefGoogle Scholar
  39. Zimmer NMP (2009) The Economic impacts of chronic wasting disease on hunting in Alberta. MSc Thesis University of AlbertaGoogle Scholar
  40. Zimmer NMP, Boxall PC, Adamowicz WL (2012a) The impacts of chronic wasting disease and its management on recreational hunters. Can J Agric Econ 60(1):71–92CrossRefGoogle Scholar
  41. Zimmer NMP, Boxall PC, Adamowicz WL (2012b) The impacts of chronic wasting disease and its management on hunter perception, opinions, and behaviors in Alberta, Canada. J Toxicol Environ Health Part A 74:1621–1635CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Thuy Truong
    • 1
  • Wiktor Adamowicz
    • 2
  • Peter C. Boxall
    • 2
  1. 1.Department of EconomicsUniversity of Economics Ho Chi Minh CityHo Chi Minh CityVietnam
  2. 2.Department of Resource Economics and Environmental SociologyUniversity of AlbertaEdmontonCanada

Personalised recommendations