Environmental Modeling & Assessment

, Volume 22, Issue 6, pp 563–576 | Cite as

Attitude Towards Risk and Production Decision: an Empirical Analysis on French Private Forest Owners

  • Marielle Brunette
  • Jérôme Foncel
  • Eric Nazindigouba Kéré


This paper deals with the forest owner’s attitude towards risk and the harvesting decision in several ways. First, we propose to characterize and quantify the forest owner’s attitude towards risk. Second, we analyze the determinants of the forest owner’s risk attitude. Finally, we determine the impact of the forest owner’s risk attitude on the harvesting decision. The French forest owner’s risk attitude is tackled by implementing a questionnaire, including a context-free measure borrowed from experimental economics. The determinants of the forest owner’s risk attitude and harvesting decision are estimated through a recursive bivariate ordered probit model. We show that French forest owners are characterized by a relative risk aversion coefficient close to 1 with a DARA assumption. In addition, we find that the forest owner’s risk aversion is influenced positively and significantly by the level of risk exposure, the geographical location of the forest and the fact to be a forester, and negatively by the income. Finally, we obtain that the forest owner’s risk aversion has a positive and significant impact on the harvesting decision.


Forest owner’s risk attitude Risk aversion Harvesting decision Simultaneous equation models Experimental elicitation 



We are grateful to the European project Newforex, the National Institute of Geographic and Forest Information (IGN), and Région Lorraine that funded the survey. The UMR Economie Forestière is supported by a grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Finally, this work has benefited from the support of the Agence Nationale de la Recherche of the French government, through the program “Investissements d’avenir” (ANR-10-LABX-14-01).


  1. 1.
    Abildtrup, J., Delacote, P., Garcia, S., Lambini, C., & Stenger, A. (2012). A forest owner survey instrument and interview guide ready for implementation. Deliverable D3.2 of the NEWFOREX research project.Google Scholar
  2. 2.
    Agreste (2014). Enquête sur la structure de la forêt privée en 2012. Chiffres et données. 222.Google Scholar
  3. 3.
    Alvarez, L., & Koskela, E. (2006). Does risk aversion accelerate optimal forest rotation under uncertainty? Journal of Forest Economics, 12, 171–184.CrossRefGoogle Scholar
  4. 4.
    Andersson, M. (2012). Assessing non-industrial private forest owners’ attitudes to risk: Do owner and property characteristics matter? Journal of Forest Economics, 18, 3–13.CrossRefGoogle Scholar
  5. 5.
    Andersson, M., & Gong, P. (2010). Risk preferences, risk perceptions and timber harvest decisions - an empirical study of nonindustrial private forest owners in northern Sweden. Forest Policy and Economics, 12, 330–339.CrossRefGoogle Scholar
  6. 6.
    Arrow, K. (1970). Essays in the theory of risk-bearing. North-Holland.Google Scholar
  7. 7.
    Azevedo, C., Herriges, J., & Kling, C. (2003). Combining revealed and stated preferences: consistency tests and their interpretation. American Journal of Agricultural Economics, 85(3), 525–537.CrossRefGoogle Scholar
  8. 8.
    Battalio, R., Kagel, J., & Jiranyakul, K. (1990). Testing between alternative models of choice under uncertainty: some initial results. Journal of Risk and Uncertainty, 3(1), 25–50.CrossRefGoogle Scholar
  9. 9.
    Beattie, J., & Loomes, G. (1997). The impact of incentives upon risky choice experiments. Journal of Risk and Uncertainty, 14, 155–168.CrossRefGoogle Scholar
  10. 10.
    Binswanger, H. (1982). Empirical estimation and use of risk preferences: discussion. American Journal of Agricultural Economics, 64, 391–393.CrossRefGoogle Scholar
  11. 11.
    Birot, Y., & Gollier, C. (2001). Risk assessment, management and sharing in forestry, with special emphasis on wind storms. Paper presented at the 14th convocation of Academies of Engineering and Technological Sciences, Espoo, Finland.Google Scholar
  12. 12.
    Bontems, P., & Thomas, A. (2000). Information value and risk premium in agricultural production: the case of split nitrogen application for corn. American Journal of Agricultural Economics, 82, 59–70.CrossRefGoogle Scholar
  13. 13.
    Bougherara, D., Gassmann, X., & Piet, L. (2011). Eliciting risk preferences: a field experiment on a sample of french farmers Paper presented at the EAAE 2011 International Congress, Zurich, Switzerland.Google Scholar
  14. 14.
    Brunette, M., Cabantous, L., Couture, S., & Stenger, A. (2009). Assurance, intervention publique et ambiguïté: une étude expérimentale auprès de propriétaires vés . Économie et Prévision, 190-191(4-5), 123–134.CrossRefGoogle Scholar
  15. 15.
    Brunette, M., Cabantous, L., Couture, S., & Stenger, A. (2013). The impact of governmental assistance on insurance demand under ambiguity : a theoretical model and an experimental test. Theory and Decision, 75, 153–174.CrossRefGoogle Scholar
  16. 16.
    Brunette, M., & Couture, S. (2017). Is forest insurance a relevant vector to induce adaptation efforts to climate change? Forthcoming in Annals of Forest Science.Google Scholar
  17. 17.
    Brunette, M., Couture, S., & Laye, J. (2015). Optimizing forest management under storm risk with Markov decision process model. Journal of Environmental Economics and Policy, 4(2), 141–163.CrossRefGoogle Scholar
  18. 18.
    Brunette, M., Holecy, J., Sedliak, M., Tucek, J., & Hanewinkel, M. (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
  19. 19.
    Cameron, A. C., & Trivedi, P.K. (2010). Microeconometrics Using Stata, Revised Edition. Rev. ed. College Station: Stata Press.Google Scholar
  20. 20.
    Clarke, H., & Reed, W. (1989). The trre-cutting problem in a stochastic environment: the case of age-dependent growth. Journal of Economic Dynamics and Controls, 13, 569–595.CrossRefGoogle Scholar
  21. 21.
    Conway, C., Amacher, G., Sullivan, S., & Wear, D. (2003). Decisions non-industrial forest landowners make: an empirical examination. Journal of Forest Economics, 9(3), 181– 203.CrossRefGoogle Scholar
  22. 22.
    Couture, S., & Reynaud, A. (2008). Multi-stand forest management under a climatic risk: do time and risk preferences matter? Environmental Modelling and Assessment, 13, 181–193.CrossRefGoogle Scholar
  23. 23.
    Cox, J., & Harrison, G. (2008). Risk aversion in experiments, (p. 12). Bingley: Emerald, Research in Experimental Economics.Google Scholar
  24. 24.
    Darses, O., Garcia, S., & Stenger, A. (2012). Drivers of cooperation for private and public goods provision: evidence from a national survey on french private forest owners. Paper presented at the annual congress of the European Association of Environmental and Resource Economists, Prague.Google Scholar
  25. 25.
    Davidson, R., & MacKinnon, J. (1993). Eliciting risk preferences: when is simple better? Estimation and inference in econometrics. Oxford University Press.Google Scholar
  26. 26.
    Dennis, D. F. (1990). A probit analysis of the harvest decision using pooled time-series and cross-sectional data. Journal of Environmental Economics and Management, 18(2), 176–187.CrossRefGoogle Scholar
  27. 27.
    Eckel, C., & Grossman, P. (2008). Forecasting risk attitudes: an experimental study using actual and forecast gamble choices. Journal of Economic Behavior and Organization, 68(1), 1– 7.CrossRefGoogle Scholar
  28. 28.
    Eswaran, M., & Kotwal, A. (1990). Implications of credit constraints for risk behaviour in less developed economies. Oxford Economic Papers, 42, 473–482.CrossRefGoogle Scholar
  29. 29.
    Fuhrer, J., Beniston, M., Fischlin, A., Frei, C., Goyette, S., Jasper, K., & Pfister, C. (2006). Climate risks and their impact on agriculture and forests in Switzerland. Climatic Change, 79, 79–102.CrossRefGoogle Scholar
  30. 30.
    Galarza, F. (2009). Choices under risk in rural Peru. Working Paper MPRA, 17708, 54.Google Scholar
  31. 31.
    Garcia, S., Kéré, N. E., & Stenger, A. (2014). Econometric analysis of social interactions in the production decisions of private forest owners. European Review of Agricultural Economics, 41(2), 177–198.CrossRefGoogle Scholar
  32. 32.
    Gollier, C. (2001). The economics of risk and time. Cambridge: The MIT Press.Google Scholar
  33. 33.
    Gong, P., & Löfgren, K. (2003). Risk-aversion and the short-run supply of timber. Forest Science, 49(5), 647–656.Google Scholar
  34. 34.
    Guiso, L., & Paiella, M. (2006). The role of risk aversion in predicting individual behavior. In Chiappori, P. A., & Gollier, C. (Eds.), Insurance: theoretical analysis and policy implications. Cambridge: MIT Press.Google Scholar
  35. 35.
    Hershey, J., & Schoemaker, P. (1990). Risk taking and problem context in the domain of losses: an expected utility analysis. Journal of Risk and Insurance, 47(1), 111–132.CrossRefGoogle Scholar
  36. 36.
    Holt, C., & Laury, S. (2002). Risk aversion and incentive effects. The American Economic Review, 92(5), 1644–1655.CrossRefGoogle Scholar
  37. 37.
    Hyberg, B., & Holthausen, D. (1989). The behavior of nonindustrial private forest owners. Canadian Journal of Forest Research, 15, 1014–1023.CrossRefGoogle Scholar
  38. 38.
    IGN (2012). La forêt en chiffres et en cartes. Institut national de l’information géographique et forestière.Google Scholar
  39. 39.
    Kangas, J. (1994). Incorporating risk attitude into comparison of reforestation alternatives. Scandinavian Journal of Forest Research, 9, 297–304.CrossRefGoogle Scholar
  40. 40.
    Koskela, E. (1989). Forest taxation and timber supply under price uncertainty: perfect capital markets. Forest Science, 35, 137–159.Google Scholar
  41. 41.
    Lidestav, G., & Ekström, M. (2000). Introducing gender in studies on management behaviour among non-industrial private forest owners. Scandinavian Journal of Forest Research, 15(3), 378–386.CrossRefGoogle Scholar
  42. 42.
    Lien, G., Størdal, S, & Baardsen, S (2007). Technical efficiency in timber production and effects of other income sources. Small-scale Forestry, 6(1), 12.CrossRefGoogle Scholar
  43. 43.
    Lien, G., Størdal, S., Hardaker, J, & Asheim, L (2007). Risk aversion and optimal forest replanting: a stochastic efficiency study. European Journal of Operational Research, 181, 1584–1592.CrossRefGoogle Scholar
  44. 44.
    Lobianco, A., Delacote, P., Caurla, S., & Barkaoui, A. (2015). Accounting for active management and risk attitude in forest sector models: an impact study on french forests. Environmental Modeling and Assessment, doi: 10.1007/s10666-015-9483-1.
  45. 45.
    Lönnstedt, L., & Svensson, J. (2000). Non-industrial private forest owner’s risk preferences. Scandinavian Journal of Forest Research, 15(6), 651–660.CrossRefGoogle Scholar
  46. 46.
    Musshof, O., & Maart-Noelck, S. (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
  47. 47.
    Petucco, C., Abildtrup, J., & Stenger, A. (2015). Influences of nonindustrial private forest landowners’ management priorities on the timber harvest decision—a case study in France. Journal of Forest Economics, 21(3), 152–166.CrossRefGoogle Scholar
  48. 48.
    Provencher, B. (1997). Structural versus reduced-form estimation of optimal stopping problems. American Journal of Agricultural Economics, 79(2), 357–368.CrossRefGoogle Scholar
  49. 49.
    Reynaud, A., & Couture, S. (2012). Stability of risk preference measures: results from a field experiment on french farmers. Theory and Decision, 73, 203–221.CrossRefGoogle Scholar
  50. 50.
    Sajaia, Z. (2008). Maximum likelihood estimation of a bivariate ordered probit model: implementation and monte carlo simulations. The Stata Journal, 4, 282–289.Google Scholar
  51. 51.
    Sauter, P., Musshoff, O., Mohring, B., & Wilhelm, S. (2016). Faustmann vs. real options theory - an experimental investigation of foresters’ harvesting decisions. Journal of Forest Economics, 24, 1–20.CrossRefGoogle Scholar
  52. 52.
    Schelhaas, M., Nabuurs, G., & Schuck, A. (2003). Natural disturbances in the european forests in the 19th and 20th centuries. Global Change Biology, 9, 1620–1633.CrossRefGoogle Scholar
  53. 53.
    Spittlehouse, D., & Stewart, R. (2003). Adaptation to climate change in forest management. BC Journal of Ecosystems Management, 4, 1–11.Google Scholar
  54. 54.
    Størdal, S., Lien, G., & Baardsen, S (2008). Analyzing determinants of forest owners’ decision-making using a sample selection framework. Journal of Forest Economics, 14, 159–176.CrossRefGoogle Scholar
  55. 55.
    Thürig, E., Palosuo, T., Bucher, J., & Kaufmann, E. (2005). The impact of windthrow on carbon sequestration in Switzerland: a model-based assessment. Forest Ecology and Management, 210, 337–350.CrossRefGoogle Scholar
  56. 56.
    Uusivuori, J. (2002). Non-constant risk attitudes and timber harvesting. Forest Science, 48, 459–470.Google Scholar
  57. 57.
    Greene, W.H., & David A.H. (2010). Modeling ordered choices: a primer. Cambridge University Press.Google Scholar
  58. 58.
    Wik, M., Kebede, T., Bergland, O., & Holden, S. (2004). On the measurement of risk aversion from experimental data. Applied Economics, 36(21), 2443–2451.CrossRefGoogle Scholar
  59. 59.
    Williams, D., & Liebhold, A. (1995). Herbivorous insects and global change: potential changes in the spatial distribution of forest defoliator outbreaks. Journal of Biogeography, 22, 665–671.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marielle Brunette
    • 1
  • Jérôme Foncel
    • 2
  • Eric Nazindigouba Kéré
    • 3
  1. 1.UMR INRA - AgroParisTech, Laboratoire d’Economie ForestièreNancy CedexFrance
  2. 2.Université Lille 3 Charles-de-Gaulle, UFR de mathématiques, sciences économiques et socialesVILLENEUVE D’ASCQ CEDEXFrance
  3. 3.African Development BankAbidjanCôte d’Ivoire

Personalised recommendations