The effect of flood experience on household mitigation—Evidence from longitudinal and insurance data

Osberghaus, Daniel

doi:10.1016/j.gloenvcha.2017.02.003

Cited by 73 publications

(39 citation statements)

References 46 publications

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“…The pseudo R 2 values reported in Table 4 and Table 5 are in a typical range for models with binary dependent variables of flood preparedness decisions reported in other studies (e.g. Hudson et al, 2017;Osberghaus, 2017;Peacock et al, 2005;Botzen et al, 2009b). Notes: Standard errors clustered at session level in parentheses ( * p < 0.1, * * p < 0.05, * * * p < 0.01).…”

Section: Behavioral Motivations For Self-insurancementioning

confidence: 88%

Behavioral motivations for self-insurance under different disaster risk insurance schemes

Mol

Botzen

Blasch

2020

Journal of Economic Behavior & Organization

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This paper presents a lab-in-the-field experiment with 2111 Dutch homeowners in floodplain areas to examine the impacts of financial incentives and behavioral motivations for self-insurance under different flood insurance schemes. We experimentally varied the insurance type (mandatory public versus voluntary private) and the availability of a premium discount incentive for investing in flood damage mitigation measures. This setup allowed us to examine the existence of moral hazard, advantageous selection and the behavioral motivations of individual agents who face these different insurance types, without the selection bias that makes a causal inference from survey studies problematic. The main results show that a premium discount can increase investments in self-insurance under both private and public insurance. Moreover, we find no support for moral hazard in our natural disaster insurance market, but we do find a substantial share of cautious people who invest both in private insurance as well as in self-insurance, indicating advantageous selection. The results have implications for the design of insurance schemes to cope with increasing natural disaster risks.

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Section: Behavioral Motivations For Self-insurancementioning

confidence: 88%

Behavioral motivations for self-insurance under different disaster risk insurance schemes

Mol

Botzen

Blasch

2020

Journal of Economic Behavior & Organization

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“…Relevant factors for explaining adaptive behavior are, among others, experienced flood damage in the past (Grothmann & Reusswig, ; Harvatt, Petts, & Chilvers, ; Osberghaus, ; Zaalberg, Midden, Meijnders, & McCalley, ), personal risk perception (Grothmann & Reusswig, ; Terpstra & Lindell, ), fear of being flooded in the future (Harries, ; Terpstra, ), and coping appraisal (including self‐efficacy and response/outcome efficacy) (Bubeck, Botzen, Kreibich, & Aerts, ; Grothmann & Reusswig, ; Terpstra & Lindell, ). Some studies have also identified a positive influence of perceived social norms (Lo, ; Poussin, Botzen, & Aerts, ), local connectedness (Kim & Kang, ), and perceived incentives for adaptive behavior (Poussin et al, ).…”

Section: What Motivates Individual Adaptive Behavior?mentioning

confidence: 99%

The behavioral turn in flood risk management, its assumptions and potential implications

et al. 2020

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Recent policy changes highlight the need for citizens to take adaptive actions to reduce flood-related impacts. Here, we argue that these changes represent a wider behavioral turn in flood risk management (FRM). The behavioral turn is based on three fundamental assumptions: first, that the motivations of citizens to take adaptive actions can be well understood so that these motivations can be targeted in the practice of FRM; second, that private adaptive measures and actions are effective in reducing flood risk; and third, that individuals have the capacities to implement such measures. We assess the extent to which the assumptions can be supported by empirical evidence. We do this by engaging with three intellectual catchments. We turn to research by psychologists and other behavioral scientists which focus on the sociopsychological factors which influence individual motivations (Assumption 1). We engage with economists, engineers, and quantitative risk analysts who explore the extent to which individuals can reduce flood related impacts by quantifying the effectiveness and efficiency of household-level adaptive measures (Assumption 2). We converse with human geographers and sociologists who explore the types of capacities households require to adapt to and cope with threatening events

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“…Most of the empirical data are collected after one specific flood event. Longitudinal studies, which undertook a survey before an event as well as repeatedly after it, are scarce, which hampers the analysis of temporal changes in awareness and precautionary behavior and their relation to flood experience (Weinstein, ; Osberghaus, ). Similarly, not many cross‐sectional data sets covering the same area at different points in time are available to unravel the temporal changes in the relationship between flood magnitude, exposure, private, and institutional risk mitigation and damages (exceptions are, e.g., Kienzler et al, ; Kreibich et al, ).…”

Section: Introductionmentioning

confidence: 99%

The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model

Barendrecht

Viglione

Kreibich

et al. 2019

Water Resources Research

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In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200 years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data.

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The effect of flood experience on household mitigation—Evidence from longitudinal and insurance data

Cited by 73 publications

References 46 publications

Behavioral motivations for self-insurance under different disaster risk insurance schemes

Behavioral motivations for self-insurance under different disaster risk insurance schemes

The behavioral turn in flood risk management, its assumptions and potential implications

The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model

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