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Psychometric and Demographic Predictors of the Perceived Risk of Terrorist Threats and the Willingness to Pay for Terrorism Risk Management Programs

Author

Listed:
  • Jeryl L. Mumpower
  • Liu Shi
  • James W. Stoutenborough
  • Arnold Vedlitz

Abstract

A 2009 national telephone survey of 924 U.S. adults assessed perceptions of terrorism and homeland security issues. Respondents rated severity of effects, level of understanding, number affected, and likelihood of four terrorist threats: poisoned water supply; explosion of a small nuclear device in a major U.S. city; an airplane attack similar to 9/11; and explosion of a bomb in a building, train, subway, or highway. Respondents rated perceived risk and willingness to pay (WTP) for dealing with each threat. Demographic, attitudinal, and party affiliation data were collected. Respondents rated bomb as highest in perceived risk but gave the highest WTP ratings to nuclear device. For both perceived risk and WTP, psychometric variables were far stronger predictors than were demographic ones. OLS regression analyses using both types of variables to predict perceived risk found only two significant demographic predictors for any threat—Democrat (a negative predictor for bomb) and white male (a significant positive predictor for airline attack). In contrast, among psychometric variables, severity, number affected, and likelihood were predictors of all four threats and level of understanding was a predictor for one. For WTP, education was a negative predictor for three threats; no other demographic variables were significant predictors for any threat. Among psychometric variables, perceived risk and number affected were positive predictors of WTP for all four threats; severity and likelihood were predictors for three; level of understanding was a significant predictor for two.

Suggested Citation

  • Jeryl L. Mumpower & Liu Shi & James W. Stoutenborough & Arnold Vedlitz, 2013. "Psychometric and Demographic Predictors of the Perceived Risk of Terrorist Threats and the Willingness to Pay for Terrorism Risk Management Programs," Risk Analysis, John Wiley & Sons, vol. 33(10), pages 1802-1811, October.
  • Handle: RePEc:wly:riskan:v:33:y:2013:i:10:p:1802-1811
    DOI: 10.1111/risa.12033
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    References listed on IDEAS

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    2. Jayajit Chakraborty & Timothy W. Collins & Sara E. Grineski & Alejandra Maldonado, 2017. "Racial Differences in Perceptions of Air Pollution Health Risk: Does Environmental Exposure Matter?," IJERPH, MDPI, vol. 14(2), pages 1-16, January.
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    4. Alejandra Maldonado & Timothy W. Collins & Sara E. Grineski & Jayajit Chakraborty, 2016. "Exposure to Flood Hazards in Miami and Houston: Are Hispanic Immigrants at Greater Risk than Other Social Groups?," IJERPH, MDPI, vol. 13(8), pages 1-20, August.
    5. Bardwell Harrison & Iqbal Mohib, 2021. "The Economic Impact of Terrorism from 2000 to 2018," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 27(2), pages 227-261, May.
    6. Gilboa, Shaked & Seger-Guttmann, Tali & Partouche-Sebban, Judith, 2022. "Increasing customer loyalty and WOM in an age of terror: Cross-cultural development and validation of the customers’ reactions to terror scale (CRTS)," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    7. Michael Siegrist & Joseph Árvai, 2020. "Risk Perception: Reflections on 40 Years of Research," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2191-2206, November.
    8. Reeko Watanabe & Tsunemi Watanabe & Kyohei Wakui, 2021. "Acceptance of Main Power Generation Sources among Japan’s Undergraduate Students: The Roles of Knowledge, Experience, Trust, and Perceived Risk and Benefit," Sustainability, MDPI, vol. 13(22), pages 1-22, November.
    9. W. Kip Viscusi & Richard J. Zeckhauser, 2017. "Recollection Bias and Its Underpinnings: Lessons from Terrorism Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 37(5), pages 969-981, May.
    10. James W. Stoutenborough & Arnold Vedlitz & Xinsheng Liu, 2015. "The Influence of Specific Risk Perceptions on Public Policy Support," The ANNALS of the American Academy of Political and Social Science, , vol. 658(1), pages 102-120, March.
    11. Junghwa Choi & Wesley Wehde & Romit Maulik, 2024. "Politics of problem definition: Comparing public support of climate change mitigation policies using machine learning," Review of Policy Research, Policy Studies Organization, vol. 41(1), pages 104-134, January.
    12. Xinsheng Liu & Kent E. Portney & Jeryl L. Mumpower & Arnold Vedlitz, 2019. "Terrorism Risk Assessment, Recollection Bias, and Public Support for Counterterrorism Policy and Spending," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 553-570, March.

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