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Valuing risks of death from terrorism and natural disasters

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Abstract

This paper uses a random utility model to examine stated preferences for the valuation of public risks of fatalities from terrorist attacks and natural disasters. Traffic-related deaths serve as the common reference point in two series of pairwise risk-risk tradeoff choices. Even after taking into account differences in respondent risk beliefs, the nationally representative sample values the prevention of terrorism deaths almost twice as highly as preventing natural disaster deaths and at about the same level as preventing deaths from traffic accidents, which pose greater personal risk. Education, seatbelt usage, political preferences, and terrorism risk beliefs affect valuations in the expected manner.

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Notes

  1. The earlier, thwarted attempt to bomb the World Trade Center in 1993 may, however, have had some effect on wages.

  2. Terrorist attacks also produce economic costs generally, as found by Enders and Sandler (1996) and Abadie and Gardeazabal (2003).

  3. For analysis of the proper treatment of altruism for policy risk valuations, see Kaplow (1995) and Jones-Lee (1991).

  4. Similarly, there is evidence of a strong local housing market response to hurricanes and near-miss hurricanes, as found in Hallstrom and Smith (2005) and Bin et al. (2008).

  5. Closely related to the issue of public support for protective and compensatory policies is the role of insurance, both from the standpoint of private markets and the government. The issue of who should pay for terrorism risk events is examined by Kunreuther and Michel-Kerjan (2004, 2007).

  6. Kahn (2007) explores how environmental disasters have triggered regulatory policy actions.

  7. An innovative study along these lines is that by Smith et al. (2009), who find that households are willing to pay $100 to $200 to prevent terrorists from making a shoulder mounted missile attack on a commercial airplane.

  8. U.S. Department of Transportation, Office of the Assistant Secretary for Transportation Policy, Memorandum: Treatment of the Economic Value of Statistical Life in Departmental Analyses, 2008.

  9. The prominence of these and other concerns is a central theme of Viscusi and Zeckhauser (2006) and Fong and Luttmer (2007). The demographic characteristics of disaster victims also have an important effect on the public’s attitudes toward disaster policies.

  10. Chilton et al. (2006) provide a detailed exploration of dread risks.

  11. This formulation is also consistent with the approach taken by Chilton et al. (2002) in which risk-risk matching tasks account for whether the respondent is “at average, above average, or below average” risk.

  12. Unless indicated otherwise, these and subsequent statistics will be for what I will term the “consistent sample” of respondents. The responses for the full sample are virtually identical and differ by less than 1 percentage point for any category—e.g., average traffic risk of 47.7% for the full sample versus 47.8% for the consistent sample.

  13. This pattern of risk beliefs could of course be accurate if risks are highly concentrated among a small segment of the driving population, as discussed in Viscusi and Zeckhauser (2006). Or the result could be a form of overoptimism, as in Rethans (1979). Finally, the result could also be due to a framing effect resulting from people’s unwillingness to rate themselves worse than the average.

  14. This distribution of risk beliefs is not unprecedented for risks of natural disasters. See generally Kunreuther et al. (1978) and Camerer and Kunreuther (1989) for documentation of low risk beliefs for natural disasters.

  15. There could, of course, be multiple terrorist attacks in a given year, but the frequency of such attacks in the U.S. is sufficiently low that a single attack scenario is more plausible.

  16. Jones-Lee and Loomes (1995) did not find an effect of the scale of accidents on the valuation of transport safety.

  17. The nature of this conversion is common to similar risk-risk comparison tasks. As Chilton et al. (2002, pp. 213–214) observe: “In order to convert deaths prevented into a risk reduction the respondent must therefore, at least implicitly, take account of (a) the size of the population at risk in that context; (b) whether or not he/she is a member of that population and (c) if so, whether he/she is at average, above average, or below average exposure to the risk.”

  18. Even for demographic groups for which one might expect the greatest differences, such as education, the parallels are quite close. The percentage distribution of the sample is 15.2% for less than high school, 32.1% for high school, 25.5% for some college, and 27.3% for bachelor or higher. The February 2006 CPS counterpart percentages are 15.0, 31.6, 28.0, and 25.4. The college-educated are overrepresented by just under 2%, those with some college are underrepresented by 2.5%, and the other educational categories are within 0.5% of their national averages.

  19. The sample characteristics are quite similar as well. The fraction of female respondents is 0.52 for the full sample and 0.51 for the consistent sample. The average age is 46.9 for the full sample and 47.0 for the consistent sample. The average number of years of schooling is 13.5 for the full sample and 13.7 for the consistent sample.

  20. McFadden (1974) and Train (2003) provide background on this approach.

  21. The conditional logit model also includes fixed effects for each set of tradeoff questions.

  22. These values were estimated using the delta method commands in Stata to calculate the fitted standard errors.

  23. See Huber and Train (2001) for analysis of the close relationship between hierarchical Bayes and mixed logit estimates.

  24. Some important effects do vary with the specification. In the absence of including the terrorism risk belief variables, residence in a metropolitan statistical area boosts the terrorism utility coefficient. This relationship is quite plausible given the pattern of terrorism attacks to date and the strong effect of metropolitan residence on risk beliefs. See Viscusi and Zeckhauser (2003, 2006) and Fischhoff (2003).

  25. See Hakes and Viscusi (2007) for correlation of seatbelt usage with the individual’s value of statistical life.

  26. The omitted regional categorical variable is the Midwest. The other included regional variables for the Northeast and South were not statistically significant as terrorism interactions.

  27. This dummy variable has a value of 1 if the respondent is a member of any of the following environmental groups: Greenpeace, Sierra Club, National Resources Defense Council, Environmental Defense Fund, National Audubon Society, National Wildlife Federation, and Nature Conservancy.

  28. Of course, one cannot rule out the contributory role of bureaucratic mismanagement. Posner (2007, p. 206) critiques what he calls the government’s “mishandling of the crisis created by the flooding of New Orleans as a result of Hurricane Katrina. Preoccupied with the threat of terrorism, the Department of Homeland Security allowed its attention to wander from threats of natural disaster.”

  29. The seminal study of disaster risks documenting this result is Kunreuther et al. (1978).

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Author information

Authors and Affiliations

  1. University Distinguished Professor of Law, Economics, and Management, Vanderbilt University Law School, 131 21st Avenue South, Nashville, TN, 37203, USA

    W. Kip Viscusi

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  1. W. Kip Viscusi
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Correspondence to W. Kip Viscusi.

Additional information

Jason Bell provided superb computer programming support and V. Kerry Smith provided helpful comments.

Appendices

Appendix

1.1 Additional scope tests

In addition to the series of transitivity tests and the positive valuation of reducing all three fatality risk groups, it is also instructive to assess whether these preferences are consistent with broad measures of behavior and attitudes toward risk within an expanded conditional logit model. These behavioral and affective scope tests expand the range of rationality tests beyond the standard scope tests, as described by Smith and Osborne (1996). As discussed by Heberlein et al. (2005), these scope tests will utilize one interaction for each of the three main effects. For traffic deaths, the interaction variable is the percentage of time that the respondent uses a seatbelt (Seatbelt Usage %). Seatbelt users have revealed a higher value of motor vehicle safety through their personal self-protection decision. There is no comparable self protective variable for natural disasters, but there is an indicator variable for whether the respondent has experienced a hurricane (Experienced Hurricane dummy variable—d.v.). One would expect such a disaster experience to raise the valuation for reducing these risks. For terrorism deaths, there is neither a terrorist attack experience measure nor a self-protection variable, but there is a variable for whether the government should provide assistance to future victims of a terrorist attack (Government Assistance to Terrorism Victims d.v.).

The results reported in Table A1 for these interactions are all consistent with rational valuations. Those who use seatbelts more often have revealed a higher value of traffic safety, and this high value is reflected in their greater traffic death utility coefficient. Similarly, victims of hurricanes place a higher value on reducing deaths from natural disasters. Finally, those who favor ex post government financial assistance to terrorism victims also favor prevention of terrorism deaths to a greater degree. These preferences are consistent across these two closely related domains of choice. Because of the potential endogeneity of attitudes toward the ex ante terrorism prevention policy and the ex post terrorism victim compensation policies, this variable is not included in the subsequent regressions but is presented here as a consistency check.

Table A1

Behavioral Scope Test Conditional Logit Estimates of Policy Choicea

 

Coefficient (Standard error)

Traffic deaths

0.0095***

(0.0017)

Disaster deaths

0.0059***

(0.0005)

Terrorism deaths

0.0094***

(0.0010)

Traffic deaths × Seatbelt usage %

3.38 E-5*

(1.86 E-5)

Disaster deaths × Experienced hurricane

0.0022**

(0.0010)

Terrorism deaths × Government assistance to terrorism victims

0.0025**

(0.0012)

  1. aNotes. Figures in parentheses are robust and clustered standard errors. *Significant at the 0.10 level; **significant at the 0.05 level; and ***significant at the 0.01 level, two-tailed test.

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Viscusi, W.K. Valuing risks of death from terrorism and natural disasters. J Risk Uncertain 38, 191–213 (2009). https://doi.org/10.1007/s11166-009-9068-y

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