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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

The effects of personal experience on choice-based preferences for wildfire protection programs

Thomas P. Holmes A D , Armando González-Cabán B , John Loomis C and José Sánchez B
+ Author Affiliations
- Author Affiliations

A Southern Research Station, USDA Forest Service, PO Box 12254, Research Triangle Park, NC 27709, USA.

B Pacific Southwest Research Station, USDA Forest Service, 4955 Canyon Crest Drive, Riverside, CA 92507, USA.

C Department of Agricultural and Resource Economics, Colorado State University, B310 Clark Building, Fort Collins, CO 80526, USA.

D Corresponding author. Email: tholmes@fs.fed.us

International Journal of Wildland Fire 22(2) 234-245 https://doi.org/10.1071/WF11182
Submitted: 24 December 2011  Accepted: 30 June 2012   Published: 14 September 2012

Abstract

In this paper, we investigate homeowner preferences and willingness to pay for wildfire protection programs using a choice experiment with three attributes: risk, loss and cost. Preference heterogeneity among survey respondents was examined using three econometric models and risk preferences were evaluated by comparing willingness to pay for wildfire protection programs against expected monetary losses. The results showed that while nearly all respondents had risk seeking preferences, a small segment of respondents were risk neutral or risk averse. Only respondents who had personal experience with the effects of wildfire consistently made trade-offs among risk, loss and cost and these respondents were willing to pay more for wildfire protection programs than were respondents without prior experience of the effects of wildfire. The degree to which people with prior experience with the effects of wildfire can effectively articulate an economic rationale for investing in wildfire protection to other members of their own or other communities facing the threat of wildfires may influence the overall success of wildfire protection programs.

Additional keywords: expected utility, heuristics, natural disasters, prospect theory, risk aversion, risk seeking.


References

Adamowicz W, Dupont D, Krupnick A, Zhang J (2011) Valuation of cancer and microbial disease risk reductions in municipal drinking water: an analysis of risk context using multiple valuation methods. Journal of Environmental Economics and Management 61, 213–226.
Valuation of cancer and microbial disease risk reductions in municipal drinking water: an analysis of risk context using multiple valuation methods.Crossref | GoogleScholarGoogle Scholar | [Published online early 31 October 2010]

Araña JE, León CJ (2009) Understanding the use of non-compensatory decision rules in discrete choice experiments: the role of emotions. Ecological Economics 68, 2316–2326.
Understanding the use of non-compensatory decision rules in discrete choice experiments: the role of emotions.Crossref | GoogleScholarGoogle Scholar |

Araña JE, León CJ, Hanemann WM (2008) Emotions and decision rules in discrete choice experiments for valuing health care programmes for the elderly. Journal of Health Economics 27, 753–769.
Emotions and decision rules in discrete choice experiments for valuing health care programmes for the elderly.Crossref | GoogleScholarGoogle Scholar |

Arrow K, Solow R, Portney PR, Leamer EE, Radner R, Schuman H (1993) Report of the NOAA panel on contingent valuation. Federal Register 58, 4602–4614.

Bennett J, Adamowicz W (2001) Some fundamentals of environmental choice modeling. In ‘The choice Modeling Approach to Environmental Valuation’. (Eds J. Bennett, R Blamey) pp. 37–69 (Edward Elgar Publishing, Inc.: Northampton MA)

Boxall PC, Adamowicz WL (2002) Understanding heterogeneous preferences in random utility models: a latent class approach. Environmental and Resource Economics 23, 421–446.
Understanding heterogeneous preferences in random utility models: a latent class approach.Crossref | GoogleScholarGoogle Scholar |

Boyle K (2003) Contingent valuation in practice. In ‘A Primer on Non-Market Valuation’. (Eds P Champ, K Boyle, T Brown) pp. 111–170. (Kluwer Academic Publishers: Dordrecht, the Netherlands)

Browne MJ, Hoyt RE (2000) The demand for flood insurance: empirical evidence. Journal of Risk and Uncertainty 20, 291–306.
The demand for flood insurance: empirical evidence.Crossref | GoogleScholarGoogle Scholar |

Camerer CF, Kunreuther H (1989) Decision processes for low probability events: policy implications. Journal of Policy Analysis and Management 8, 565–592.
Decision processes for low probability events: policy implications.Crossref | GoogleScholarGoogle Scholar |

Cameron TA, Englin J (1997) Respondent experience and contingent valuation of environmental goods. Journal of Environmental Economics and Management 33, 296–313.
Respondent experience and contingent valuation of environmental goods.Crossref | GoogleScholarGoogle Scholar |

Cohen M, Etner J, Jeleva M (2008) Dynamic decision making when risk perception depends on past experience. Theory and Decision 64, 173–192.
Dynamic decision making when risk perception depends on past experience.Crossref | GoogleScholarGoogle Scholar |

Cummings RG, Brookshire DS, Schulze WD (1986) ‘Valuing Environmental Goods: an Assessment of the Contingent Valuation Method.’ (Rowman & Allanheld: Totowa, NJ)

Deaton A, Muellbauer J (1980) ‘Economics and Consumer Behavior.’ (Cambridge University Press: New York)

Ganderton PT, Brookshire DS, McKee M, Stewart S, Thurston H (2000) Buying insurance for disaster-type risks: experimental evidence. Journal of Risk and Uncertainty 20, 271–289.
Buying insurance for disaster-type risks: experimental evidence.Crossref | GoogleScholarGoogle Scholar |

Gigerenzer G (2001) The adaptive toolbox. In ‘Bounded Rationality: the Adaptive Toolbox’. (Eds G Gigerenzer, R Selten) pp. 37–50. (Oxford University Press: New York)

Harless DW, Camerer CF (1994) The predictive utility of generalized expected utility theories. Econometrica 62, 1251–1289.
The predictive utility of generalized expected utility theories.Crossref | GoogleScholarGoogle Scholar |

Hirshleifer J (1983) From weakest-link to best-shot: the voluntary provision of public goods. Public Choice 41, 371–386.
From weakest-link to best-shot: the voluntary provision of public goods.Crossref | GoogleScholarGoogle Scholar |

Holmes TP, Adamowicz W (2003) Attribute-based methods. In ‘A Primer on Non-Market Valuation.’ (Eds P Champ, K Boyle, T Brown) pp. 171–220 (Kluwer Academic Publishers: Dordrecht, the Netherlands)

Holmes TP, Kramer RA (1995) An independent sample test for yea-saying and starting point bias in dichotomous-choice contingent valuation. Journal of Environmental Economics and Management 29, 121–132.
An independent sample test for yea-saying and starting point bias in dichotomous-choice contingent valuation.Crossref | GoogleScholarGoogle Scholar |

Holmes TP, Abt KL, Huggett RJ Jr, Prestemon JP (2007) Efficient and equitable design of wildfire mitigation programs. In ‘People, Fire, and Forests: a Synthesis of Wildfire Social Science’. (Eds TC Daniel, MS Carroll, C Moseley, C Raish) pp. 143–156. (Oregon State University Press: Corvallis, OR)

Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47, 263–292.
Prospect theory: an analysis of decision under risk.Crossref | GoogleScholarGoogle Scholar |

Krupnick A, Alberini A, Cropper M, Simon N, O’Brien B, Goeree R, Heintzelman M (2002) Age, health and the willingness to pay for mortality risk reductions: a contingent valuation survey of Ontario residents. Journal of Risk and Uncertainty 24, 161–186.
Age, health and the willingness to pay for mortality risk reductions: a contingent valuation survey of Ontario residents.Crossref | GoogleScholarGoogle Scholar |

Kunreuther H, Slovic P (1978) Economics, psychology, and protective behavior. The American Economic Review 68, 64–69.

Loomis J, Hung LT, González-Cabán A (2009) Willingness to pay function for two fuel treatments to reduce wildfire acreage burned: a scope test and comparison of White and Hispanic households. Forest Policy and Economics 11, 155–160.
Willingness to pay function for two fuel treatments to reduce wildfire acreage burned: a scope test and comparison of White and Hispanic households.Crossref | GoogleScholarGoogle Scholar |

Louviere JJ, Hensher DA, Swait JD (2000) ‘Stated Choice Methods: Analysis and Applications.’ (Cambridge University Press: Cambridge, UK)

Luce MF, Bettman JR, Payne JW (1997) Choice processing in emotionally difficult decisions. Journal of Experimental Psychology. Learning, Memory, and Cognition 23, 384–405.
Choice processing in emotionally difficult decisions.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK2s3jsVOgtA%3D%3D&md5=2df82334b74fc8b1ca3ae7a140cc8f59CAS |

McClelland GH, Schulze WD, Coursey DL (1993) Insurance for low-probability hazards: a bimodal response to unlikely events. Journal of Risk and Uncertainty 7, 95–116.
Insurance for low-probability hazards: a bimodal response to unlikely events.Crossref | GoogleScholarGoogle Scholar |

National Fire Plan 2001. A collaborative approach for reducing wildland fire risks to communities and the environment: 10-year comprehensive strategy. National Fire Plan. Available at http://www.forestsandrangelands.gov/resources/plan/documents/11-23-en.pdf [Verified 21 August 2012]

Payne JW, Bettman JR (2001) Preferential choice and adaptive strategy use. In ‘Bounded Rationality: the Adaptive Toolbox’. (Eds G Gigerenzer, R Selten) pp. 123–145. (Oxford University Press: New York)

Runkle JR (1985) Disturbance regimes in temperate forests. In ‘The Ecology of Natural Disturbance and Patch Dynamics’. (Eds STA Pickett, PS White) pp. 17–33 (Academic Press, Inc.: San Diego, CA).

Scarpa R, Thiene M (2005) Destination choice models for rock climbing in the northeastern Alps: a latent-class approach based on intensity of preferences. Land Economics 81, 426–444.

Schoemaker PJH (1982) The expected utility model: it’s variants, purposes, evidence and limitations. Journal of Economic Literature 20, 529–563.

Starmer C (2000) Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk. Journal of Economic Literature 38, 332–382.
Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk.Crossref | GoogleScholarGoogle Scholar |

Talberth J, Berrens R, McKee M, Jones M (2006) Averting and insurance decisions in wildland-urban interface: implications of survey and experimental data for wildfire risk reduction policy. Contemporary Economic Policy 24, 203–223.
Averting and insurance decisions in wildland-urban interface: implications of survey and experimental data for wildfire risk reduction policy.Crossref | GoogleScholarGoogle Scholar |

Train KE (2002) ‘Discrete Choice Methods with Simulation.’ (Cambridge University Press: New York).

Tutsch M, Haider W, Beardmore B, Lertzman K, Cooper AB, Walker RC (2010) Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern Gulf Islands, British Columbia, Canada. Canadian Journal of Forest Research 40, 2104–2114.
Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern Gulf Islands, British Columbia, Canada.Crossref | GoogleScholarGoogle Scholar |

Tversky A, Kahneman D (1973) Availability: a heuristic for judging frequency and probability. Cognitive Psychology 5, 207–232.
Availability: a heuristic for judging frequency and probability.Crossref | GoogleScholarGoogle Scholar |

Varian HR (1984) ‘Microeconomic Analysis’, 2nd edn. (WW Norton & Company: New York)

Winter G, Fried J (2001) Estimating contingent values for protection from wildland fire using a two-stage decision framework. Forest Science 47, 349–360.