Skip to main content
Log in

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

  • Published:
Environmental and Resource Economics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. CWD is a prion disease that affects elk, deer and moose and is essentially the cervid species form of “mad cow disease” or bovine spongiform encephalopathy (BSE). However, unlike BSE there is no known link between the consumption of CWD affected meat and human health. Nevertheless, 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).

  2. We note that von Haefen (2008) applied a Kuhn–Tucker demand system to model latent consideration sets. This model is attractive because it is tractable for large choice sets and can be estimated using standard econometric techniques. However the von Haefen approach employs a theoretical and empirical framework that is quite different from the RUM approach used in much of the literature. Therefore, we focus on the Haab and Hicks and IAL approach and do not employ the von Haefen model.

  3. We note that the sample sizes employed are small, hence we make no claims about the ability of our study to predict the behavior of all Albertan deer hunters who may be affected by CWD. Rather we employ this data as a convenience sample to examine the usefulness of our empirical approaches.

  4. The CWD threshold is set at 2.5 or greater for the RP data and greater than 2.5 for the SP data. Models with the CWD threshold both set at 2.5 or greater did not converge well—likely the result of limited variation and possibly a differential treatment of RP and SP data by respondents. We also removed the demographic variables (urban, etc.) from the availability function and focused on attribute thresholds as explanators of availability.

Abbreviations

BSE:

Bovine spongiform encephalopathy

CMNL:

Constrained multinomial logit model

CPA:

Cascetta and Papola availability

CWD:

Chronic wasting disease

IAL:

Independent availability logit

MNL:

Multinomial logit

RP:

Revealed preference

RUM:

Random utility model

SP:

Stated preference

WMU:

Wildlife management unit

References

  • Andrews R, Srinivasan TC (1995) Studying consideration effects in empirical choice models using scanner panel data. J Mark Res 32:30–41

    Article  Google Scholar 

  • Ben-Akiva M, Boccara B (1995) Discrete choice models with latent choice sets. Int J Res Mark 12:9–24

    Article  Google Scholar 

  • Bierlaire M (2003) BIOGEME: A free package for the estimation of discrete choice models. In: Proceedings of the 3rd Swiss transportation research conference, Ascona, Switzerland

  • Bierlaire M, Hurtubia R, Flötteröd G (2010) Analysis of implicit choice set generation using a constrained multinomial logit model. Transp Res Rec 2175:92–97

    Article  Google Scholar 

  • 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):249–263

    Article  Google Scholar 

  • Chiang J, Chib S, Narasimhan C (1999) Markov chain Monte Carlo and model of consideration set and parameter heterogeneity. J Econom 89:223–248

    Article  Google Scholar 

  • Clawson M (1959) Method for measuring demand for and the value of outdoor recreation. Reprint No. 10. Resources for the Future, Washington, DC

  • Diana SC, Bisogni CA, Gall KL (1993) Understanding anglers’ practices related to health advisories for sport-caught fish. J Nutr Educ 25(6):320–328

    Article  Google Scholar 

  • Esarey J, Menger (2016) Practical and effective approaches to dealing with clustered data. Working paper. Department of Political Science, Rice University

  • Government of Alberta (2010) Chronic Wasting Disease. http://www.srd.alberta.ca/BioDiversityStewardship/WildlifeDiseases/ChronicWastingDisease/Default.aspx. Cited 8 Feb 2011

  • Haab TC, Hicks RL (1997) Accounting for choice set endogeneity in random utility models of recreation demand. J Environ Econ Manag 34(2):127–147

    Article  Google Scholar 

  • Haab TC, Hicks RL (1999) Choice set considerations in models of recreation demand: history and current state of the art. Mar Resour Econ 14:271–281

    Article  Google Scholar 

  • Hanemann WM (1978) A methodological and empirical study of the recreation benefits from water quality improvement. Ph.D. Dissertation, Harvard University

  • Hauser JR (2010) Consideration-set heuristics. MIT. http://web.mit.edu/hauser/www/Papers/Hauser%20Consideration%20Heuristics%20JBR%202011.pdf. Cited 10 May 2011

  • Hicks RL, Strand IE (2000) The extent of information: its relevance for random utility models. Land Econ 76(3):374–385

    Article  Google Scholar 

  • 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, DC

  • Jakus PM, Downing M, Bevelimer MS, Fly JM (1997) Do sportfish consumption advisories affect reservoir anglers’ choice? Agric Resour Econ Rev 26(2):196–204

    Article  Google Scholar 

  • Jakus PM, Shaw WD (2003) Perceived hazard and product choice: an application to recreational site choice. J Risk Uncertain 26(1):77–92

    Article  Google Scholar 

  • Jones C, Lupi F (1999) The effect of modelling substitute activities on recreational benefit estimates: is more better? Mar Resour Econ 14:357–374

    Article  Google Scholar 

  • 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, Berkeley

  • Kreps D (1979) A preference for flexibility. Econometrica 47:565–576

    Article  Google Scholar 

  • Li L, Adamowicz W, Swait J (2015) The effects of choice set misspecification on welfare measures in random utility models. Resour Energy Econ 42:71–92

    Article  Google Scholar 

  • Manrai AK, Andrews RL (1998) Two-stage discrete choice models for scanner panel data: an assessment of process and assumptions. Eur J Oper Res 111:193–215

    Article  Google Scholar 

  • Manski CF (1977) The structure of random utility models. Theor Decis 8:229–254

    Article  Google Scholar 

  • May H, Burger J (1996) Fishing in a polluted estuary: fishing behaviour, fishing consumption, and potential risk. Risk Anal 16(4):459–471

    Article  Google Scholar 

  • Parsons G, Hauber A (1998) Spatial boundaries and choice set definition in a random utility model of recreation demand. Land Econ 74(1):32–48

    Article  Google Scholar 

  • Peters T, Adamowicz W, Boxall P (1995) The influence of choice set consideration in modelling the benefits of improved water quality. Water Resour Res 613:1781–1787

    Article  Google Scholar 

  • Roberts J, Lattin J (1991) Development and testing of a model of consideration set composition. J Mark Res 28:429–440

    Article  Google Scholar 

  • Sarver T (2008) Anticipating regret: why fewer options may be better. Econometrica 76:263–305

    Article  Google Scholar 

  • Swait J (1984) Probabilistic choice set formation in transportation demand models. Dissertation, MIT

  • Swait J (2001a) Choice set generation within the generalized extreme value family of discrete choice models. Transp Res B 35(7):643–666

    Article  Google Scholar 

  • Swait J (2001b) A non-compensatory choice model incorporating attribute cut-offs. Transp Res B 35(7):903–928

    Article  Google Scholar 

  • 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 1985

  • Swait J, Ben-Akiva M (1987a) Incorporating random constraints in discrete choice models of choice set generation. Transp Res B 21(2):91–102

    Article  Google Scholar 

  • 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):103–115

    Article  Google Scholar 

  • Swait J, Louviere JJ (1993) Role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res 30(3):305–314

    Article  Google Scholar 

  • Timmins C, Murdock J (2007) A revealed preference approach to the measurement of congestion in travel cost models. J Environ Econ Manag 53(2):230–249

    Article  Google Scholar 

  • von Haefen RH (2008) Latent consideration sets and continuous demand systems. Environ Resour Econ 41(3):363–379

    Article  Google Scholar 

  • Zimmer NMP (2009) The Economic impacts of chronic wasting disease on hunting in Alberta. MSc Thesis University of Alberta

  • 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):71–92

    Article  Google Scholar 

  • 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 74:1621–1635

    Article  Google Scholar 

Download references

Acknowledgements

Funding support was provided by the Alberta Prion Research Institute.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thuy Truong.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (xlsx 149 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Truong, T., Adamowicz, W. & Boxall, P.C. Modelling the Effect of Chronic Wasting Disease on Recreational Hunting Site Choice Preferences and Choice Set Formation over Time. Environ Resource Econ 70, 271–295 (2018). https://doi.org/10.1007/s10640-017-0120-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10640-017-0120-0

Keywords

JEL Classification

Navigation