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

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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.

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Notes

  1. This must not be understood as a field experiment, incentivised experiment in laboratory or choice experiment. We simply propose different hypothetical scenarios in order to elicit their preferences.

  2. Hence, the ambiguity considered here comes from imprecision and not from conflict or disagreement (Cabantous 2007).

  3. This means that the two models are nested.

  4. We used the Conditional Mixed Process program (CMP) in STATA developed by Roodman (2011) that makes it possible to consistently estimate our two models by taking several forms of endogenous regressors for various qualitative, censored and quantitative variables of interest into account. In particular, we performed Monte-Carlo simulations to check that this procedure was suited to our second model by providing appropriate parameter recovery.

  5. Note that the variable INCOM12 includes the two first classes of the variable Income < EUR 1000, and Income belongs to [EUR 1000; EUR 2000]. INCOM34 includes classes [EUR 2000; EUR 2500] and [EUR 2500; EUR 3000]. INCOM5 represents the richest forest owners in the sample and is considered as the reference income class.

  6. However, this remains an open issue that needs further empirical investigations.

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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).

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Appendix: Design of the hypothetical survey

Appendix: Design of the hypothetical survey

We implemented a between-subject design with two treatments in terms of probabilistic information about the fire risk. Consequently, 24 participants were assigned to the risk treatment and had accurate information about the fire risk. Sixteen participants were in the ambiguous treatment characterised by uncertainty about the risk probability. Ambiguity is reflected by providing different estimates of the probability of fire occurrence. In each treatment, participants were confronted with eight scenarios. Each scenario is a combination of one type of public assistance (among four possible ones: NA, FA, CFA, IS) and one level of average annual revenue generated by the forest (among two possibilities: low-level of EUR 250/hectare or high-level of EUR 500/hectare). The type of public assistance and the level of revenue are within-subject variables. The order of presentation is always the same for the type of public assistance, while it has been changed for the level of revenue.

The following table (from Brunette et al. 2013) provides an example of a scenario. This is the scenario for the ambiguous treatment for the FA type of public assistance and low income. Participants had to complete two tasks: WTP for insurance task and choice task. First, they had to specify their maximum annual premium in order to be fully insured. Second, they had to choose one of the five contracts offered, defined by different indemnities and premiums.

General context

You are participating in an experiment on decision-making processes. For each question, assume that you own a forest of 12 hectares of maritime pine in the Aquitaine region. This provides you with annual revenue, the amount of which depends on the situation described. Your forest faces a risk of fire

Information about the probability

Throughout the experiment, assume that there is some uncertainty about the annual probability of fire damage. Four experts have given you four different estimates of the probability of a fire destroying your forest this year: 0.05, 0.15, 0.25 and 0.35%

Information about your income

Your forest provides an annual income of EUR 250 per hectare, i.e., EUR 3000 per year

Information about the government programme

If a fire occurs, your forest will be completely destroyed, and the government is committed to giving you a fixed compensation of EUR 1500 in order to offset part of your financial losses. You can also choose to take out an insurance policy. Thus you can combine the public assistance with an insurance indemnity

WTP for insurance task

What is the maximal annual premium (in EUR/hectare) that you are willing to pay to be fully covered against potential losses due to fire?

Choice task (introduced for consistency check)

We propose several contracts. You can only choose one. Each of these contracts has different costs and decreases the risk of loss in a different way. If you decide to take out an insurance policy, the insurance price will be directly deduced from your income

Which insurance contract do you purchase?

Contract A: Indemnity = EUR 500/ha, premium net of tax = EUR 1/ha

Contract B: Indemnity = EUR 375/ha, premium net of tax = EUR 0.75/ha

Contract C: Indemnity = EUR 250/ha, premium net of tax = EUR 0.5/ha

Contract D: Indemnity = EUR 125/ha, premium net of tax = EUR 0.25/ha

Contract E: Indemnity = EUR 0/ha, premium net of tax = EUR 0/ha

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Brunette, M., Couture, S., Foncel, J. et al. The decision to insure against forest fire risk: an econometric analysis combining hypothetical real data. Geneva Pap Risk Insur Issues Pract 45, 111–133 (2020). https://doi.org/10.1057/s41288-019-00146-6

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