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Testing a Rational Choice Model of Airline Hijackings

  • Laura Dugan
  • Gary LaFree
  • Alex R. Piquero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3495)

Abstract

Using data that combines information from the Federal Aviation Administration, the RAND Corporation, and a newly developed database on global terrorist activity, we are able to examine trends in 1,101 attempted aerial hijackings that occurred around the world from 1931 to 2003. We have especially complete information for 828 hijackings that occurred before 1986. Using a rational choice theoretical framework, we employ econometric time-series methods to estimate the impact of several major counter hijacking interventions on the likelihood of differently motivated hijacking events and to model the predictors of successful hijackings. Some of the interventions examined use certainty-based strategies of target hardening to reduce the perceived likelihood of success while others focus on raising the perceived costs of hijacking by increasing the severity of punishment. We also assess which specific intervention strategies were most effective for deterring hijackers whose major purpose was terrorism related. We found support for the conclusion that new hijacking attempts were less likely to be undertaken when the certainty of apprehension was increased through metal detectors and law enforcement at passenger checkpoints. We also found that fewer hijackers attempted to divert airliners to Cuba once that country made it a crime to hijack flights. Our results support the contagion view that hijacking rates significantly increase after a series of hijackings closely-clustered in time. Finally, we found that policy interventions only significantly decrease the likelihood of non-terrorist-related hijackings.

Keywords

Rational Choice Rational Choice Theory Metal Detector Contagion View Federal Aviation Administration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Laura Dugan
    • 1
  • Gary LaFree
    • 1
  • Alex R. Piquero
    • 2
  1. 1.Department of Criminology and Criminal JusticeUniversity of MarylandCollege Park
  2. 2.Department of Criminology, Law and SocietyUniversity of FloridaGainesville

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