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The decision to insure against forest fire risk: an econometric analysis combining hypothetical real data

  • M. BrunetteEmail author
  • S. Couture
  • J. Foncel
  • S. Garcia
Article

Abstract

Storm and fire are the two main natural hazards in Europe. They result in high costs for forest owners. However, behaviour in terms of forest insurance demand is heterogenous across Europe. In this paper we focus on private forest owners’ decisions to insure against fire. We collected data on: i) willingness-to-pay (WTP) for insurance based on hypothetical scenarios incorporating ambiguous risks; ii) real data on insurance decisions and the individual characteristics. We simultaneously estimated real insurance and WTP using a selection equation for zero WTP that we explain by protest responses against insurance under expected utility. We found that real insurance provision is relevant to explaining positive WTP and that unobservable determinants of insurance may explain protest responses. These results confirm the interest in including observed decisions to analyse preferences towards insurance. One additional result is that facing ambiguous risk increases the WTP for insurance.

Keywords

Insurance decision Willingness-to-pay Ambiguity Protest response Corner solution Forest fire 

Notes

Acknowledgements

The UMR BETA is supported by a grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” programme (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).

References

  1. Alary, D., C. Gollier, and N. Treich. 2013. The effect of ambiguity aversion on insurance and self-protection. The Economic Journal 123 (573): 1188–1202.CrossRefGoogle Scholar
  2. Barreal, J., M.L. Loureiro, and J. Picos. 2014. On insurance as a tool for securing forest restoration after wildfires. Forest Policy and Economics 42: 15–23.CrossRefGoogle Scholar
  3. Bedrick, E.J., and C.L. Tsai. 1994. Model selection for multivariate regression in small samples. Biometrics 50 (1): 226–231.CrossRefGoogle Scholar
  4. Bolte, A., C. Ammer, M. Löf, P. Madsen, G.J. Nabuurs, P. Schall, P. Spathelf, and J. Rock. 2009. Adaptive forest management in central Europe: Climate change impacts, strategies and integrative concept. Scandinavian Journal of Forest Research 24 (6): 473–482.CrossRefGoogle Scholar
  5. Browne, M.J., and R.E. Hoyt. 2000. The demand for flood insurance: Empirical evidence. Journal of Risk and Uncertainty 20 (3): 291–306.CrossRefGoogle Scholar
  6. Brunette, M., L. Cabantous, S. Couture, and A. Stenger. 2013. The impact of governmental assistance on insurance demand under ambiguity: A theoretical model and an experimental test. Theory and Decision 75 (2): 153–174.CrossRefGoogle Scholar
  7. Brunette, M., and S. Couture. 2008. Public compensation for windstorm damage reduces incentives for risk management investments. Forest Policy and Economics 10 (7–8): 491–499.CrossRefGoogle Scholar
  8. Brunette, M., S. Couture, and S. Garcia. 2016. Determinants of insurance demand against forest fire risk: An empirical analysis of French private forest owners. Cahiers du LEF, 2016-01, Laboratoire d’Economie Forestiere, AgroParisTech-INRA.Google Scholar
  9. Brunette, M., J. Foncel, and E.N. Kéré. 2017. Attitude towards risk and production decision: An empirical analysis on French private forest owners. Environmental Modeling and Assessment 22 (6): 563–576.CrossRefGoogle Scholar
  10. Brunette, M., J. Holecy, M. Sedliak, J. Tucek, and M. Hanewinkel. 2015. An actuarial model of forest insurance against multiple natural hazards in fir (Abies Alba Mill.) stands in Slovakia. Forest Policy and Economics 55: 46–57.CrossRefGoogle Scholar
  11. Cabantous, L. 2007. Ambiguity aversion in the field of insurance: Insurers’ attitude to imprecise and conflicting probability estimates. Theory and Decision 62 (3): 219–240.CrossRefGoogle Scholar
  12. Cabantous, L., D. Hilton, H. Kunreuther, and E. Michel-Kerjan. 2011. Is imprecise knowledge better than conflicting expertise? Evidence from insurers’ decisions in the United States. Journal of Risk and Uncertainty 42 (3): 211–232.CrossRefGoogle Scholar
  13. Coate, S. 1995. Altruism, the samaritan’s dilemma, and government transfer policy. The American Economic Review 85 (1): 46–57.Google Scholar
  14. Commissariat Général au Développement Durable. 2011. Le risque de feux de forêts en France. Observations et Statistiques. Etudes et Documents n°45, août 2011, pp 44. https://observatoire-risques-nouvelle-aquitaine.fr/wp-content/uploads/sites/2/2018/08/ORRNA-Etudes-documents-le-risques-de-feux-de-forts-en-France-n45-Aot-11.pdf.
  15. Dai, Y., H.H. Chang, and W. Liu. 2015. Do forest producers benefit from the forest disaster insurance program? Empirical evidence in Fujian Province of China. Forest Policy and Economics 50: 127–133.CrossRefGoogle Scholar
  16. Deng, Y., I.A. Munn, K. Coble, and H. Yao. 2015. Willingness to pay for potential standing timber insurance. Journal of Agricultural and Applied Economics 47 (4): 510–538.CrossRefGoogle Scholar
  17. Frey, U.J., and F. Pirscher. 2019. Distinguishing protest responses in contingent valuation: A conceptualization of motivations and attitudes behind them. PLoS ONE 14 (1): e0209872.CrossRefGoogle Scholar
  18. Gan, J., A. Jarrett, and C. Johnson Gaither. 2014. Wildfire risk adaptation: Propensity of forestland owners to purchase wildfire insurance in the southern United States. Canadian Journal of Forest Research 44 (11): 1376–1382.CrossRefGoogle Scholar
  19. Gärdenfors, P., and N.E. Sahlin. 1982. Unreliable probabilities, risk taking and decision making. Synthese 53 (3): 361–386.CrossRefGoogle Scholar
  20. Global Agenda Council on Climate Change. 2014. Climate adaptation: Seizing the challenge. World Economic Forum Geneva Switzerland. https://www.weforum.org/reports/climate-adaptation-seizing-challenge.
  21. Gorobets, A. 2005. The optimal prediction simultaneous equations selection. Economics Bulletin 3 (36): 1–8.Google Scholar
  22. Greene, W. 2008. Discrete choice modeling. In The palgrave handbook of econometrics: vol. 2, Applied Econometrics, Part 4.2., ed. T. Mills and K. Patterson. London: Palgrave.Google Scholar
  23. Jones, A.M. 1989. A double-hurdle model of cigarette consumption. Journal of Applied Econometrics 4 (1): 23–39.CrossRefGoogle Scholar
  24. Kaplow, L. 1991. Incentives and government relief for risk. Journal of Risk and Uncertainty 4 (2): 167–175.CrossRefGoogle Scholar
  25. Kelly, M., and A.E. Kleffner. 2003. Optimal loss mitigation and contract design. Journal of Risk and Insurance 70 (1): 53–72.CrossRefGoogle Scholar
  26. Kim, B.J., and H. Schlesinger. 2005. Adverse selection in an insurance market with government-guaranteed subsistence levels. Journal of Risk and Insurance 72 (1): 61–75.CrossRefGoogle Scholar
  27. Klibanoff, P., M. Marinacci, and S. Mukerji. 2005. A smooth model of decision making under ambiguity. Econometrica 73 (6): 1849–1892.CrossRefGoogle Scholar
  28. Kunreuther, H., J. Meszaros, R.M. Hogarth, and M. Spranca. 1995. Ambiguity and underwriter decision processes. Journal of Economic Behavior & Organization 26 (3): 337–352.CrossRefGoogle Scholar
  29. Lewis, T., and D. Nickerson. 1989. Self-insurance against natural disasters. Journal of Environmental Economics and Management 16 (3): 209–223.CrossRefGoogle Scholar
  30. Musshoff, O., and S.C. Maart-Noelck. 2014. An experimental analysis of the behavior of forestry decision-makers—The example of timing in sales decisions. Forest Policy and Economics 41: 31–39.CrossRefGoogle Scholar
  31. Mills, E. 2007. Synergisms between climate change mitigation and adaptation: An insurance perspective. Mitigation and Adaptation Strategies for Global Change 12 (5): 809–842.CrossRefGoogle Scholar
  32. OECD. 2015. Climate change risks and adaptation: Linking policy and economics. Paris: OECD Publishing.CrossRefGoogle Scholar
  33. Qin, T., X. Gu, Z. Tian, H. Pan, J. Deng, and L. Wan. 2016. An empirical analysis of the factors influencing farmer demand for forest insurance: Based on surveys from Lin’an County in Zhejiang Province of China. Journal of Forest Economics 24: 37–51.CrossRefGoogle Scholar
  34. Raschky, P.A., and H. Weck-Hannemann. 2007. Charity hazard—a real hazard to natural disaster insurance? Environmental Hazards 7 (4): 321–329.CrossRefGoogle Scholar
  35. Roodman, D. 2011. Estimating fully observed recursive mixed-process models with cmp. Stata Journal 11 (2): 159–206.CrossRefGoogle Scholar
  36. San-Miguel-Ayanz, J., T. Durrant, R. Boca, G. Libertà, A. Branco, D. de Rigo, D. Ferrari, P. Maianti, T. Artés Vivancos, E. Schulte, P. Loffler. 2017. Forest Fires in Europe, Middle East and North Africa 2016. EUR 28707 EN, Publications Office, Luxembourg, 2017, ISBN 978-92-79-71292-0.Google Scholar
  37. Sauter, P.A., T.B. Möllmann, F. Anastassiadis, O. Mußhoff, and B. Möhring. 2016. To insure or not to insure? Analysis of foresters’ willingness-to-pay for fire and storm insurance. Forest Policy and Economics 73: 78–89.CrossRefGoogle Scholar
  38. Schelhaas, M.J., G.J. Nabuurs, and A. Schuck. 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology 9 (11): 1620–1633.CrossRefGoogle Scholar
  39. Schlesinger, H. 2000. The Theory of insurance demand. In Handbook of insurance, ed. Georges Dionne, 131–151. Kluwer Academic Publishers.Google Scholar
  40. Seidl, R., W. Rammer, and M.J. Lexer. 2011. Adaptation options to reduce climate change vulnerability of sustainable forest management in the Austrian Alps. Canadian Journal of Forest Research 41 (4): 694–706.CrossRefGoogle Scholar
  41. Spittlehouse, D.L., and R.B. Stewart. 2003. Adaptation to climate change in forest management. BC Journal of Ecosystems and Management 4: 1–11.Google Scholar
  42. Van Aalst, M.K. 2006. The impacts of climate change on the risk of natural disasters. Disasters 30 (1): 5–18.CrossRefGoogle Scholar
  43. Van Asseldonk, M.A.P.M., M.P.M. Meuwissen, and R.B.M. Huirne. 2002. Belief in disaster relief and the demand for a public-private insurance program. Review of Agricultural Economics 24 (1): 196–207.CrossRefGoogle Scholar
  44. Wilde, J. 2000. Identification of multiple equation probit models with endogenous dummy regressor. Economics Letters 69 (3): 309–312.CrossRefGoogle Scholar
  45. Zhang, D., and A. Stenger. 2014. Timber insurance: Perspectives from a legal case and a preliminary review of practices throughout the world. New Zealand Journal of Forestry Science 44: S9.CrossRefGoogle Scholar

Copyright information

© The Geneva Association 2019

Authors and Affiliations

  1. 1.University of Lorraine, University of Strasbourg, AgroParisTech CNRS, INRA, BETANancyFrance
  2. 2.INRA, UR 875 Applied Mathematics and Computer ScienceCastanet-TolosanFrance
  3. 3.University of Lille, LEM-CNRSLilleFrance
  4. 4.University of Lorraine, University of Strasbourg, AgroParisTech, CNRS, INRA, BETANancyFrance

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