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Individual and community behavioral responses to natural disasters

Abstract

How do people and communities respond to catastrophes? A natural disaster is a type of external, quasi-random and unexpected catastrophic shock that generates psychological, social and economic implications. Using detailed county level administrative data of charitable contributions, crime and natural hazards in the USA in the recent decade, we empirically identify and quantify the causal effect of natural disasters on prosocial and antisocial behavioral reactions. Our main finding is that while monetary contributions decline in the local affected community in the aftermath of natural disasters, the neighboring and more distant communities react by increasing their charitable giving. Additionally, we find that in the affected community, natural disasters effect crime negatively, dispelling popular conceptions regarding looting, and that while federal assistance crowds out charitable contributions, it does not change the residents reaction to natural disasters.

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Notes

  1. 1.

    A traumatic event is defined by its capacity to evoke terror, fear, helplessness or horror in the face of a threat to life or a serious injury (American Psychiatric Association, 1994. Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC).

  2. 2.

    In certain cases, the deterioration of wealth and its effect on the affected community is so great as to yield a decrease in charity within the community affected (De Alessi 1967).

  3. 3.

    We refer to philanthropic donations as monetary donations to qualified organizations in the USA by individuals who itemize deductions.

  4. 4.

    The classic economic theory would suggest that shortage in supply of goods would lead to an increase in prices, given demand remains the same.

  5. 5.

    In this study, our specifications rely on reduced form models. Such models do not allow to pinpoint the exact underlying mechanisms at play, and therefore, several possible mechanisms remain partially or entirely plausible.

  6. 6.

    Loayza et al. (2012) argue this notion. They conclude that disasters do affect economic growth, but not always negatively, with effects that differ across types of disasters, economic sectors and developing and developed countries. A meta-analyses study by Lazzaroni and Bergeijk (2014) indicates that disasters have a negative impact in average in terms of direct costs.

  7. 7.

    Nonprofits’ net assets and revenue found to be positively correlated with disaster event damage levels.

  8. 8.

    https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-zip-code-data-soi.

  9. 9.

    We followed SOI’s recommendations and instructions to aggregate the data to the county level.

  10. 10.

    In 2015, total charitable contributions by individuals and households were estimated at $264.58 billion, 82% of which was itemized (Giving USA 2016). Any interpretation of our findings should be limited to those individuals who itemize deductions.

  11. 11.

    Form 1040, Schedule A.

  12. 12.

    https://www.ncdc.noaa.gov/stormevents/.

  13. 13.

    All monetary variables are indexed by the CPI to 2015 dollars.

  14. 14.

    A contemporaneous model was estimated as well, yielding qualitatively similar results, and is available upon request from the authors.

  15. 15.

    This approach is practical as more than 95% of counties are located at a distance of up to 3000 km from each other.

  16. 16.

    As our data are aggregated at the county level, one should be careful extrapolating our findings on the individual level.

  17. 17.

    This is an approximation, as each county is also the neighbor of its neighbors. The spatial econometrics literature shows that our approach is a reasonable approximation (Elhorst 2014).

  18. 18.

    This does not mean that counties with higher share of college graduates contribute less. It is rather more likely that our fixed effect model is not suitable for testing variables with relatively low within county variation. For a detailed discussion, see section 1 of the online appendix.

  19. 19.

    For example, an ecological fallacy could occur if highly educated individuals increase their donations, but at the same time, counties with a higher share of highly educated people also tend to suffer from more severe economic downturn than those with lower education pulling the average contribution down. If that were the case the decrease in contributions in these counties should not be attributed to the highly educated individuals.

  20. 20.

    Much like the cautionary note following the education variable discussion. This analysis does not suggest that republican counties contribute the same amount. It is rather more likely that our fixed effect model is not suitable for testing variables with relatively low within county variation. For a detailed discussion, see section 1 of the online appendix.

  21. 21.

    Violent crimes include murder, rape, robbery and aggravated assault. Property crime includes burglary, larceny and motor vehicle thefts.

  22. 22.

    Column 2 does not include a separate coefficient for income inequality as the data are only available for a limited number of time periods. Consequently, the variation of this variable within counties is very low (SD = 1.25) compared to the variation between counties (SD = 3.16). We therefore chose to use the level of this variable in 2014 as a measure of each county’s income inequality. Thus, in our analysis, this variable is time-invariant, and is collinear with the county fixed effects.

  23. 23.

    There is no coefficient for the religious diversity index as there is no temporal variation at the county level, and the data were collected only for 2010. The variable is thus time-invariant and collinear with the county fixed effects.

  24. 24.

    The religious groups are: Catholics, Eastern Orthodox Christians, Eastern Religions (Buddhism, Hinduism, etc.), Jews, Mormons, Muslims, Protestants and others.

  25. 25.

    Due to lack of temporal variation in our religiosity variables, we are unable to test the mechanism that natural disasters affect charitable contributions by their effect on religiosity.

  26. 26.

    For example, can the private sector replace government support to charities (Khanna and Todd 1998).

  27. 27.

    Formally, a governor must first request a declaration, and the president may grant or deny it.

  28. 28.

    https://www.sba.gov/about-sba/sba-performance/open-government/digital-sba/open-data/open-data-sources.

  29. 29.

    When models 1 and 2 are estimated with the number of victims of natural disasters instead of number of events, the federal assistance coefficients remain virtually identical. The results are available upon request.

  30. 30.

    One should be careful interpreting this finding as a relationship between natural disasters and charitable giving on the individual level, since the data are aggregated on the county level and is subject to “ecological Fallacy” risk.

  31. 31.

    Unlike the monetary data which were retrieved from administrative datasets, volunteering data are based on surveys and questionnaires and therefore may be subject to a survey bias.

  32. 32.

    http://www.irdrinternational.org/2014/03/28/irdr-peril-classification-and-hazard-glossary/.

  33. 33.

    Hazards and Vulnerability Research Institute (2016): https://cemhs.asu.edu/sheldus.

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Acknowledgements

We thank our colleagues at the Hebrew University’s School of Public Policy and Economics Department for their useful discussions and feedback. We also thank the participants of the Lilly Family School of Philanthropy at Indiana University seminars for useful comments and suggestions. Claude Berrebi is grateful for the warm hospitality of the Princeton School of Public and International Affairs (SPIA) and Princeton’s Empirical Studies of Conflict Project (ESOC) at Princeton University while he was working on this research.

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Berrebi, C., Karlinsky, A. & Yonah, H. Individual and community behavioral responses to natural disasters. Nat Hazards 105, 1541–1569 (2021). https://doi.org/10.1007/s11069-020-04365-2

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Keywords

  • Natural hazards
  • Charitable giving
  • Crime
  • Panel data
  • Natural disasters
  • Philanthropy
  • Prosocial behavior
  • Antisocial behavior
  • United States