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Citizen Information, Electoral Incentives, and Provision of Counter-Terrorism: An Experimental Approach

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Abstract

How does incomplete information about counter-terror provisions influence the strategic interaction between a government, terrorist groups, and the citizenry? We investigate this research question using a laboratory experiment and present two key findings. (1) Public counter-terror spending leads citizens to overly frequent “protected” targets such that it makes them easier targets for terrorists. (2) Additionally, we show that citizens over-estimate government counter-terror spending when they are unable to observe it. These findings suggest that asymmetric information and the small probability of a successful terrorist attack may lead to the inefficient provision of counter-terror. We also connect the findings to the larger literature on the principal-agent relationship between citizens and elected officials.

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Notes

  1. From a methodological standpoint, it would be ideal to directly observe and/or manipulate citizens’ and terrorists’ level of incomplete information (from 100 % incomplete to 100 % complete) and observe subsequent counter-terror dynamics. Yet from an ethical standpoint (thankfully), this is not feasible, nor practical. Thus, we accept the artificiality of a laboratory.

  2. For a complete review of the game-theoretical approaches to terrorism, see Sandler and Arce (2003) and Sandler and Siqueira (2009).

  3. Looking more closely at partisanship, Berrebi and Klor (2006) finds that terrorists react strategically by attacking left-wing governments more than right-wing. Both Berrebi and Klor (2008) and Getmansky and Zeitzoff (2014) show that terrorism shifts voters to the right.

  4. With regards to the plausibility of citizens switching transportation methods, Blalock et al. (2009) finds an increase in driving fatalities after 9/11 that can be attributed to travelers substituting road trips in place of air travel. Furthermore, Cox et al. (2011) find a reduction in subway and bus trips after the 2005 London bombings.

  5. Further evidence of this dynamic can be found in Iraq, when American forces pulled back in Iraq behind the heavily fortified ‘Green Zone’, and insurgents stepped up their attacks–even though the area was fortified http://www.nytimes.com/2010/09/30/world/middleeast/30iraq.html.

  6. While empirical work on how protecting a site affects the probability of attack there is difficult to come by, Berman and Laitin (2005) suggest that terrorists change tactics in response to site hardening, with more hardened sites being more likely to attract a suicide attack.

  7. This logic is similar to that of bounded rationality commonly exhibited in the beauty contest game. In the game, a pool of subjects choose an integer [0,100]. An individual who guesses closest to \(\dfrac{2}{3}\) the average of the responses receives a monetary prize.If subjects completely backwardly induct, then they should guess 0 (the Nash Equilibrium). However, observed responses vary between 15 and 40, suggesting that humans have, or believe others have, a limited ability (“level-k”) to reason backward and act fully strategic (Nagel 1995; Stahl and Wilson 1995).

  8. Camerer et al. (2004) estimate that an average person can reason through about one and a half steps of strategic interaction.

  9. We denote Government, Citizen, and Terrorist with capital letters in the experiment. Lowercase government, terrorist, and citizen should be thought of as a more general argument.

  10. In reality, citizens may be less informed about target vulnerability than terrorists—who actively conduct surveillance and planning for attacks http://www.nytimes.com/2012/09/11/opinion/the-bush-white-house-was-deaf-to-9-11-warnings.html. Yet, we show in our experiment that even when the Terrorist and the Citizen have the same incomplete information, the Citizen still does not react as strategically as the Terrorist. This suggests that the dynamic of Citizens behaving less strategic than the Terrorists in our experiment is likely to be even stronger in real life, where terrorists have an informational advantage.

  11. For instance, see the failed plots of the so-called “Shoe Bomber” and the “Underwear Bomber” http://articles.cnn.com/2009-12-25/justice/richard.reid.shoe.bomber_1_terror-attacks-american-airlines-flight-qaeda?_s=PM:CRIME.

  12. In the experiment these were referred to as Players 1, 2, or 3. See Online Appendix for a full list of instructions.

  13. On one hand, the assumption the Government keeps the points not spent on counter-terrorism spending may serve as a model of corruption. However, we find it more realistic that the Government shifts the counter-terrorism funds to another area of policy that helps it stay in power, rather than that the Government is personally appropriating the funds. In this way, the Government faces a trade-off between spending on counter-terrorism and on other policies. For example, see Chapter 3 of the 9/11 Commission Report on the low priority of counter-terrorism spending in the mid-1990s, even after the first World Trade Center Bombing (Kean 2011).

  14. We are agnostic as to what a terrorist “success” represents. It could be the terrorist seeking to inflict maximum casualties against a citizenry. Or it could be the terrorist seeking to strike a high value target. All that is required for our game is that the Terrorist and Citizen have opposing preferences.

  15. The small probability of a successful Terrorist attack when both the Citizen and the Terrorist choose C can be thought of as an event targeted at a particular citizen, such as a kidnapping, whose victims tend to be middle class locals (Forest 2012). Santifort and Sandler (2013) find that, conditional on a successful kidnapping, in only 27.5 hostage incidents do terrorist achieve even some of their goals.

  16. These treatment blocks include Public Harden One, Public Harden Both, and Government Choice in which the Government chose to make hardening public. The results do not change substantially when we exclude the Government Choice treatment.

  17. Throughout the paper we use random effects panel regression. Given that we randomly assigned individuals to groups and treatments, we assume that any baseline differences are orthogonal to the treatment (Greene 2008)—thus random effects is preferable. However, there may be a concern that in some models the use of a lagged dependent variable biases the estimates and random effects. The Online Appendix presents the results from Table 4 and Fig. 5 (Model 3) using fixed effects. The results there are nearly identical to those in Table 4 and Fig. 5 (Model 3), alleviating any concerns about the use of random effects.

  18. A discussion of learning during the experiment and public hardening appears in the Online Appendix.

  19. These are to account for learning in subjects’ behavior both within treatment blocks and across time.

  20. For instance in Private treatment, Visible Harden could only take on zero, whereas in Public Both treatment Visible Harden would take on the value \(I_{G}\). In the Public One treatment, only the investment in option A counts toward the Visible Harden variable

  21. Governments invested on average 3.875 points, so from Model 3 the main effect on the latent \(Bonus = 1.505 \times 3.875+-8.286=-2.454125\)

  22. The Government invested an average of 3.50, 3.875, and 4.60 points in hardening in the Public One, Public Both, and Choice Public treatments, respectively.

  23. Bonuses in the Public Both are on average 1.4 points lower than in the Public One treatment (two-tailed p value=0.07). It should be emphasized that treatment order was randomized across groups.

  24. In fact, when we reformulate the dependent variables \(I_{G}\) and \(B_{Z}\) as binary variables—1 right censored for \(B_{Z}\), 0 otherwise and 1 left censored for \(I_{G}=0\), 0 otherwise, the results are even stronger (see Online Appendix).

  25. See the Online Appendix for a discussion of the expected increase in the Citizen’s payoff per point of hardening, which is calculated to be between .5 and .9.

  26. Powell (2007b) makes a similar point in arguing why strategic defense does not necessarily make targets uniformly safer due to strategic behavior on the part of terrorists.

  27. We argue that a terrorist defeating the government at its strongest may instill fear in the citizenry, making attacks at protected sites even more valuable to the terrorist.

  28. This could also be a high value target, whereby hardening the target signals that it is high value to terrorists (Powell 2007b).

  29. For instance, both Getmansky and Sinmazdemir (2013) (terror attacks) and Johnston (2012) (drone strikes) use the quasi-random success of terror attacks and drone strikes, comparing successes to failures, to estimate their causal effect.

  30. 1 point spent by the government reduced the probability of a successful terrorist attack by 2 percentage points if the terrorist guessed correctly.

  31. I.e., the size of the bonus the Citizen can give the Government does not depend on the success of a Terrorist attack.

  32. The recent uproar over the disclosure by former US intelligence contractor Eric Snowden of secret US surveillance programs run by the National Security Agency (NSA) on ordinary Americans suggests an interesting alternative http://www.theguardian.com/world/the-nsa-files. In our model, the government can shirk by simply not investing in private hardening. However, as the Snowden revelations suggest, they may use secrecy to as a cover for far more invasive surveillance. Modeling this would be an interesting extension.

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Acknowledgments

Funding for this research was provided by NSF award number 0905044.

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Correspondence to Andrew W. Bausch.

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Bausch, A.W., Zeitzoff, T. Citizen Information, Electoral Incentives, and Provision of Counter-Terrorism: An Experimental Approach. Polit Behav 37, 723–748 (2015). https://doi.org/10.1007/s11109-014-9289-x

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