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US foreign aid, interstate rivalry, and incentives for counterterrorism cooperation

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  • Andrew Boutton

    (Department of Political Science, University of Central Florida
    Department of Political Science, University of Central Florida)

Abstract

A common strategy pursued by states targeted by international terrorism is to provide economic and military assistance to the states that host this activity. This is thought to increase their willingness and capacity to crack down on terrorism, but very little work to date has looked at whether this strategy actually leads to desirable outcomes. This article offers an explanation for why a strategy of foreign aid-for-counterterrorism can be successful in some contexts, but counterproductive in situations in which recipients have more pressing strategic priorities. Specifically, I argue that host states receiving US foreign aid that are involved in an ongoing interstate rivalry will use the aid to arm against their rival, rather than to undertake counterterrorism. These states thus have an incentive not to disarm terrorist groups, but rather to play-up the threat from terrorism in order to continue receiving aid concessions. Using data on US foreign aid and terrorist activity in recipient countries, I employ a series of duration and count models to demonstrate that, while US foreign aid can help to decrease terrorist activity in non-rivalrous states, the opposite is true in states with at least one rival.

Suggested Citation

  • Andrew Boutton, 2014. "US foreign aid, interstate rivalry, and incentives for counterterrorism cooperation," Journal of Peace Research, Peace Research Institute Oslo, vol. 51(6), pages 741-754, November.
  • Handle: RePEc:sae:joupea:v:51:y:2014:i:6:p:741-754
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    Cited by:

    1. Garcia-Alonso, Maria D.C. & Levine, Paul & Smith, Ron, 2016. "Military aid, direct intervention and counterterrorism," European Journal of Political Economy, Elsevier, vol. 44(C), pages 112-135.
    2. Lis, Piotr, 2018. "The impact of armed conflict and terrorism on foreign aid: A sector-level analysis," World Development, Elsevier, vol. 110(C), pages 283-294.
    3. Buscema, Massimo & Ferilli, Guido & Sacco, Pier Luigi, 2017. "What kind of ‘world order’? An artificial neural networks approach to intensive data mining," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 46-56.

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