Storm Damage and Risk Preferences: Panel Evidence from Germany

Abstract

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.

This is a preview of subscription content, access via your institution.

Notes

  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.

References

  1. Andrabi T, Das J (2010) In aid we trust: hearts and minds and the Pakistan earthquake of 2005. World bank policy research working paper Series

  2. Ashraf N, Karlan D, Yin W (2006) Tying Odysseus to the mast: evidence from a commitment savings product in the Philippines. Q J Econ 121(2):635–672

    Article  Google Scholar 

  3. Barsky RB, Juster FT, Kimball MS, Shapiro MD (1997) Preference parameters and behavioral heterogeneity: an experimental approach in the health and retirement study. Q J Econ 112(2):537–579

  4. Bchir MA, Willinger M (2013) Does the exposure to natural hazards affect risk and time preferences? Some insights from a field experiment in Peru. University of Montpellier, Working paper

  5. Callen M (2011) Catastrophes and time preference: evidence from the Indian Ocean earthquake. University of California, San Diego (Unpublished Manuscript)

    Google Scholar 

  6. Callen M, Isaqzadeh M, Long JD, Sprenger C (2014) Violence and risk preference: experimental evidence from Afghanistan. Am Econ Rev 104(1):123–148

    Article  Google Scholar 

  7. Cameron L, Shah M (2015) Risk-taking behavior in the wake of natural disasters. J Hum Resour 50(2):484–515

    Article  Google Scholar 

  8. Cassar A, Healy A, Von Kessler C (2011) Trust, risk, and time preferences after a natural disaster: experimental evidence from Thailand. Working paper, University of San Francisco

  9. Charness G, Gneezy U, Imas A (2013) Experimental methods: eliciting risk preferences. J Econ Behav Organ 87:43–51

    Article  Google Scholar 

  10. Dionne G, Eeckhoudt L (1985) Self-insurance, self-protection and increased risk aversion. Econ Lett 17(1):39–42

    Article  Google Scholar 

  11. Di Tella R, Galiani S, Schargrodsky E (2007) The formation of beliefs: evidence from the allocation of land titles to squatters. Q J Econ 122(1):209–241

    Article  Google Scholar 

  12. Dohmen T, Falk A, Huffman D, Sunde U, Schupp J, Wagner GG (2011) Individual risk attitudes: measurement, determinants, and behavioral consequences. J Eur Econ Assoc 9(3):522–550

    Article  Google Scholar 

  13. Douglas M, Wildavsky A (1982) Risk and culture: an essay on selection of technological and environmental dangers. California University Press, Berkeley

    Google Scholar 

  14. Donkers B, Melenberg B, Van Soest A (2001) Estimating risk attitudes using lotteries: a large sample approach. J Risk Uncertain 22(2):165–195

    Article  Google Scholar 

  15. Eckel CC, El-Gamal MA, Wilson RK (2009) Risk loving after the storm: a Bayesian-network study of Hurricane Katrina evacuees. J Econ Behav Organ 69(2):110–124

    Article  Google Scholar 

  16. Falk A, Becker A, Dohmen TJ, Enke B, Huffman D (2015) The nature and predictive power of preferences: global evidence. IZA DP, Working paper

  17. Falk A, Becker A, Dohmen TJ, Huffman D, Sunde U (2016) The preference survey module: a validated instrument for measuring risk, time, and social preferences. IZA DP, Working paper

  18. GDV (2015) Naturgefahrenreport 2015. http://www.gdv.de/2015/10/-wohngebaeudeversicherer-zahlten-12-milliarden-euro-fuer-unwetterschaeden/, Gesamtverband der Deutschen Versicherungswirtschaft, German Insurance Association

  19. Gerstorf S, Schupp J (eds) (2016) SOEP wave report 2015. German Socio-Economic Panel study SOEP, DIW, Berlin

    Google Scholar 

  20. Gollier C, Pratt JW (1996) Risk vulnerability and the tempering effect of background risk. Econom J Econom Soc 64(5):1109–1123

    Google Scholar 

  21. Guiso L, Paiella M (2008) Risk aversion, wealth, and background risk. J Eur Econ Assoc 6(6):1109–1150

    Article  Google Scholar 

  22. Guiso L, Sapienza P, Zingales L (2013) Time varying risk aversion (No. w19284). National Bureau of Economic Research

  23. Hanaoka C, Shigeoka H, Watanabe Y (2015) Do risk preferences change? Evidence from panel data before and after the great east Japan earthquake. National Bureau of Economic Research, No w21400

  24. Holt CA, Laury SK (2002) Risk aversion and incentive effects. Am Econ Rev 92(5):1644–1655

  25. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econom J Econom Soc, 263–291

  26. Kandasamy N, Hardy B, Page L, Schaffner M, Graggaber J, Powlson AS, Fletcher PC, Gurnell M, Coates J (2014) Cortisol shifts financial risk preferences. Proc Nat Acad Sci 111(9):3608–3613

    Article  Google Scholar 

  27. Lerner JS, Keltner D (2001) Fear, anger, and risk. J Pers Soc Psychol 81(1):146

    Article  Google Scholar 

  28. Li J-Z, Li S, Liu H (2011) How has the Wenchuan earthquake influenced people’s intertemporal choices? 1. J Appl Soc Psychol 41(11):2739–2752

    Article  Google Scholar 

  29. Liu EM (2013) Time to change what to sow: risk preferences and technology adoption decisions of cotton farmers in China. Rev Econ Stat 95(4):1386–1403

    Article  Google Scholar 

  30. Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psychol Bull 127(2):267

    Article  Google Scholar 

  31. Lönnqvist JE, Verkasalo M, Walkowitz G, Wichardt PC (2015) Measuring individual risk attitudes in the lab: task or ask? An empirical comparison. J Econ Behav Organ 119:254–266

    Article  Google Scholar 

  32. Malmendier U, Nagel S (2011) Depression babies: do macroeconomic experiences affect risk taking? Q J Econ 126:373–416

    Article  Google Scholar 

  33. Mather M, Mazar N, Gorlick MA, Lighthall NR, Burgeno J, Schoeke A, Ariely D (2012) Risk preferences and aging: the “certainty effect” in older adults’ decision making. Psychol Aging 27(4):801

    Article  Google Scholar 

  34. Osberghaus D (2015) The determinants of private flood mitigation measures in Germany—evidence from a nationwide survey. Ecol Econ 110:36–50

    Article  Google Scholar 

  35. Osberghaus D, Philippi A (2015) Klimawandel in Deutsch-land: Risikowahrnehmung und Anpassung in privaten Haushalten 2012 und 2014. Zentrum für europäische Wirtschaftsforschung GmbH, Mannheim, Working paper

  36. Page L, Savage DA, Torgler B (2014) Variation in risk seeking behaviour following large losses: a natural experiment. Eur Econ Rev 71:121–131

    Article  Google Scholar 

  37. Quiggin J (2003) Background risk in generalized expected utility theory. Econ Theor 22(3):607–611

    Article  Google Scholar 

  38. Rosenzweig MR, Stark O (1989) Consumption smoothing, migration, and marriage: evidence from rural India. J Polit Econ 97(4):905–926

    Article  Google Scholar 

  39. Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323

    Article  Google Scholar 

  40. van IJzendoorn MH, Caspers K, Bakermans-Kranenburg MJ, Beach SRH, Philibert R (2010) Methylation matters: interaction between methylation density and serotonin transporter genotype predicts unresolved loss or trauma. Biol Psychiatry 68(5):405–407

    Article  Google Scholar 

  41. Voors MJ, Nillesen EEM, Verwimp P, Bulte EH, Lensink R, Van Soest DP (2012) Violent conflict and behavior: a field experiment in Burundi. Am Econ Rev 102(2):941–964

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Goytom Abraha Kahsay.

Appendix

Appendix

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kahsay, G.A., Osberghaus, D. Storm Damage and Risk Preferences: Panel Evidence from Germany. Environ Resource Econ 71, 301–318 (2018). https://doi.org/10.1007/s10640-017-0152-5

Download citation

Keywords

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

JEL Classification

  • C23
  • D03
  • D81
  • Q54