While the Obama Administration has ratcheted down the rhetorical commitment to a “global war on terrorism,” the US is still engaged in suppression of terrorism across the globe. The reality of limited resources makes it imperative to focus national assets on those groups with the most potential for destruction and death. In previous work (Asal and Rethemeyer 2008) we used a cross-sectional dataset containing data on 395 terrorist organizations to examine the factors that lead some organizations to kill prolifically. While this work provided important insights, the generality of our results was constrained by our inability to properly examine causation with longitudinal data, the limited organizational coverage of our first effort, and our inability to properly control for simultaneity between network structures and terrorist behaviors. This paper addresses the limits of our first effort with a new dataset and new methods.

Our paper proceeds in two parts. The first focuses on the determinant of organizational lethality using the Big Allied and Dangerous dataset, Version 2 (BAAD2). BAAD2 is a panel dataset that includes more than 900 terrorist organizations across 10 time periods (1998-2007). The dataset is unique in that it includes data on both compositional variables (such size, age, ideology, leadership structure, and sources material support) and network variables (including alliances between organizations and organizational relationships with states). Compiled through extensive hand-coding of tens of thousands of print sources, BAAD2 provides organizations data on more than 97% of terrorist organizations that were recorded to have perpetrated at least one attack in the Global Terrorism Database (GTD). Collectively, these organizations are responsible for more than 20,000 deaths and nearly 5,200 incidents. Because our previous research clearly demonstrates that network structures help to explain organizational lethality and organizational lethality helps to explain network structures, we will use newly a newly developed stochastic social network analysis technique – co-evolution modeling – to account for simultaneity. Additionally, our models will include controls for the nature of the country context in which these organizations are embedded.

The second part of the paper provides an analysis of the policy and strategy implications of our empirical analysis. Our previous work suggests that interrupting the process of organizational growth seems quite important. Here we propose to stand much of the literature on the “liability of newness,” “organizational survival,” and “organizational effectiveness” on its head, seeking levers by which government can thwart organizational expansion and growth and mechanism by which counterintelligence agencies and operatives could identify and undermine terrorist organizational leaders who may have the talent and ability needed to build large organizations. We will also consider the insights from network science regarding the interruption of network formation and evolution and thus any linkages we find between behavior and networks.