Storm Damage and Risk Preferences: Panel Evidence from Germany


Individuals’ risk preferences may change after experiencing external socio-economic or natural shocks. Theoretical predictions and empirical studies suggest that risk taking may increase or decrease after experiencing shocks. So far the empirical evidence is sparse, especially when it comes to developed countries. We contribute to this literature by investigating whether experiencing financial and health-related damage caused by storms affects risk preferences of individuals in Germany. Using unique panel data, we find that household heads were more risk-seeking after they experienced storm damage. We do not find evidence of exposure to storm per se (regardless of damage experience), which suggests that household heads have to suffer damage for their risk preferences to be affected. These results are robust across a battery of alternative model specifications and alternative storm damage measures (magnitude of financial damage). We rule out other potential explanations such as health-related and economic shocks. The self-reported storm damage data is broadly confirmed by regional storm damage data provided by the insurance industry. While we cannot identify the channels through which experiencing storm damage affects risk preferences from our data, we suggest and discuss some potential channels. The results may have important policy implications as risk preferences affect, for instance, individuals’ savings and investment behaviour, adoption of self-protection and self-insurance strategies, and technology adoption.

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  1. 1.

    The survey was conducted by the market research institute forsa. For the purpose of representativeness, forsa provides households without internet access with a device connected to their TV. Thereby they can participate in the survey by TV. Forsa identified the household head as the person who is generally taking financial decisions for the household and asks for participation of this person in the survey. According to forsa, this person can generally be assumed to be the respondent in both survey waves, although this cannot be guaranteed in an online survey.

  2. 2.

    See Charness et al. (2013) for a discussion on advantages and disadvantages of various methods of eliciting risk preferences.

  3. 3.

    These results are not presented here, but they are available from the authors upon request.

  4. 4.

    Another line of literature argues that exposure to natural hazards affects risk preferences through changing the environment with which people interact (Douglas and Wildavsky 1982). Other explanations include neurobiological effects (van IJzendoorn et al. 2010) and increasing cortisol levels (Kandasamy et al. 2014). Similarly, Voors et al. (2012) suggest selection effects, changes in beliefs, social structure, and preferences as potential explanations for the observed risk-loving behaviour after exposure to violence in their study.

  5. 5.

    While we do not have prior periods to test the implication of common trend assumption, we estimate our model by re-defining the treatment and control groups through random draws from our data. We do not find a significant treatment effect—which is expected. Similarly, we estimate our model for a sub-sample of our treatment and control groups with a series of random draws. We find a significant treatment effect which is in line with our identification strategy.


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We thank Kibrom A. Abaya, Carlo Gallier, Francois Laisney, and the participants at the EAERE annual conference as well as the young researchers workshop “Environmental and Resource Economics” of the Verein für Socialpolitik for helpful comments and suggestions. Financial support of the German Ministry for Education and Research (BMBF), under grant 01LA1113C, is gratefully acknowledged. The funding source had no involvement in study design, collection, analysis and interpretation of the data, and writing of the article.

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Correspondence to Goytom Abraha Kahsay.



See Tables 7, 8, 9, 10, 11.

Table 7 Wording of the key questions (risk preference and extreme weather experience), translated from German
Table 8 Descriptive statistics
Table 9 Effect of storm damage experience on risk preferences
Table 10 OLS regression of the effect of storm damage experience on risk preferences (using transformed risk variable)
Table 11 OLS regression of the effect of storm damage experience on risk preferences

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Kahsay, G.A., Osberghaus, D. Storm Damage and Risk Preferences: Panel Evidence from Germany. Environ Resource Econ 71, 301–318 (2018).

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  • Extreme weather
  • Risk preferences
  • Risk seeking
  • Storm damage
  • Panel data

JEL Classification

  • C23
  • D03
  • D81
  • Q54