Modelling risk adaptation and mitigation behaviour under different climate change scenarios

Brouwer, Roy; Schaafsma, Marije

doi:10.1007/s10584-012-0534-1

Cited by 47 publications

(31 citation statements)

References 40 publications

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“…Higher premiums make flood insurance less attractive, higher flood risks make them more attractive, as expected. This is consistent with results from previous studies (e.g., Kunreuther 1996;Browne and Hoyt 2000;Botzen and van den Bergh 2012;Brouwer and Schaafsma 2013). For each flood risk category (see Table 1), a single dummy variable is included in the model, with the lowest risk -a recurrence interval of at least 200 years -serving as the omitted category.…”

Section: Parameter Estimatessupporting

confidence: 86%

The Demand for Index-Based Flood Insurance in a High-Income Country

Achtnicht

Osberghaus

2016

SSRN Journal

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.

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Section: Parameter Estimatessupporting

confidence: 86%

The Demand for Index-Based Flood Insurance in a High-Income Country

Achtnicht

Osberghaus

2016

SSRN Journal

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“…Survey-based research includes Baumann and Sims (1978), Pynn and Ljung (1999), Blanchard-Boehm et al (2001), Kriesel and Landry (2004), Landry and JahanParvar (2011), Petrolia et al (2013Petrolia et al ( , 2015. More recently and most similar to the current study, researchers in the Netherlands have conducted discrete choice and contingent valuation experiments to collect stated preference data since flood insurance is not currently available in that country (Botzen and van den Bergh 2012a, b;Botzen et al 2013, Brouwer andSchaafsma 2013). Experiment-based studies include Slovic et al (1977), McClelland et al (1993), andGanderton et al (2000).…”

Section: Literaturementioning

confidence: 89%

“…For comparison, similar mixed logit flood insurance models with panel data had values of 0.18, 0.28, and 0.46 (Brouwer and Schaafsma 2013;Botzen et al 2013; Botzen and van den Bergh 2012a). According to Louviere et al (2000), ''between 0.2 and 0.4 are considered to be indicative of extremely good model fits.''…”

Section: Overall Model Goodness-of-fitmentioning

confidence: 99%

Homeowner purchase of insurance for hurricane-induced wind and flood damage

et al. 2017

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Understanding the circumstances under which homeowners will purchase insurance is critical to creating an effective insurance market for hurricane wind and flood loss. This paper contributes to the empirical literature on the subject through an analysis of survey data for homeowners in North Carolina. We develop separate mixed logit models for flood insurance and wind insurance purchasing decisions. The analysis uses stated preference data on the influence of premium and deductible to address some limitations of revealed preference data in which it is difficult to fully decouple effects of premium, deductible, risk, and coverage limit, and mandatory purchase requirements. The results for flood insurance and wind insurance are similar. We find evidence that the following are all significant and associated with higher probability of purchasing insurance-lower premium, lower deductible, more recent previous hurricane experience, location in a floodplain or closer to the coast, higher income, and younger homeowners. However, demand is relatively inelastic with respect to premium and deductible, and the willingness to pay for a $1 reduction in deductible varies throughout the population with some willing to pay more than $1, a behavioral anomaly. The recency of the last hurricane experience is more influential for homeowners who experienced damage than for homeowners who did not. Results suggest that insurance purchase and home retrofits are complements, not substitutes. Finally, the paper presents statistical models that can be used to predict insurance penetration rates for a region under different premium levels.

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“…Empirical evidence on loss aversion is abundant in general (e.g., Horowitz and McConnell 2002), but for preferences regarding public environmental good provision it is limited (for exceptions, see Bateman et al 2009, andSchaafsma 2013). In this paper we add to the literature by empirically testing the combined effects of loss aversion and changing the value of the reference point on WTA and WTP value functions and the WTA-WTP disparity.…”

Section: Introductionmentioning

confidence: 99%

“…The general expectation underlying WTP for flood control or insurance is that people are risk averse when their decision involves potential losses under low probability-high impact conditions, and the corresponding individual choice behaviour is motivated by a desire for security. A study by Brouwer and Schaafsma (2013) is the only study we know of that examines both WTP and WTA in the context of increasing flood probabilities.…”

Section: Introductionmentioning

confidence: 99%

Reference Dependence Effects on WTA and WTP Value Functions and Their Disparity

Koetse

Brouwer

2015

Environ Resource Econ

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In this study we investigate two reference dependence effects in a choice experiment. The first is the effect of the well-known distinction between gains and losses, the second is the effect of changing the reference value on willingness to pay (WTP) and willingness to accept (WTA). The latter has to our knowledge not been studied before. We hypothesize that there are differences between WTA and WTP, and that both value functions and their disparity are affected by the absolute value of the reference point. We test our hypotheses using a choice experiment with trade-offs between changes in flood probabilities and costs. The choice experiments elicit WTP and WTA, using two flood probability reference values, yielding four separate value functions. Our findings show that a substantial WTA-WTP disparity exists, and that this disparity increases when moving away from the reference point. Also, both WTA and WTP value functions are affected by the flood probability reference value, and the WTA-WTP disparity increases when the flood probability reference point increases. Both findings suggest that welfare effects caused by changes in public good provision depend not only on the direction of change (loss aversion), but also the reference value. Moreover, our results show that the latter effect is larger for losses than for gains. We introduce the concept of reference point updating as a possible explanation for these findings.

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Modelling risk adaptation and mitigation behaviour under different climate change scenarios

Cited by 47 publications

References 40 publications

The Demand for Index-Based Flood Insurance in a High-Income Country

The Demand for Index-Based Flood Insurance in a High-Income Country

Homeowner purchase of insurance for hurricane-induced wind and flood damage

Reference Dependence Effects on WTA and WTP Value Functions and Their Disparity

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