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Abstract
Only a few studies on CBRN terrorism have ever been conducted empirically. To build on this scant literature, this study draws on a novel dataset—the CBRN Terrorism Dataset (CTD). Binary logistic regression models (1990–2016) were executed on two outcomes to discern the factors associated with CBRN weapons pursuit. Across the models, the findings were consistent. Religious groups were more likely to pursue CBRN weapons; even when chemical weapons were excluded from the outcome. Ethnic fractionalization was negatively associated with CBRN weapons pursuit, yet wealth, polity, and environmental factors play almost no role in explaining pursuit. The results and ideological classifications suggest that when anti-abortion terrorism is re-classified under the religious category and single-issue terrorism is classified according to ideology, many of the findings in the extant literature lose support. Limitations and recommendations are provided.
ACKNOWLEDGMENTS
The author would like to thank Dr. Stephen Block and Dr. W. Seth Carus for their guidance, feedback, and sharing of materials in the furtherance of this project. Markus Binder at START, University of Maryland was also instrumental in sharing the POICN database. Many thanks also go to Dr. Ronald Heck at the University of Hawaii for all of the feedback, discourse, and assessment of methodological inquiries and issues as well as Thomas Guarrieri for the insight into START data and CBRN terrorism, more generally.
Disclosure Statement
The author reports no conflicts of interest at this time.
Notes
1 The authors imply that religious groups are less likely to take credit for their actions in order to avoid detection, citing Juergensmeyer (2003).
2 A brief analysis of the year–country data reveals that across almost 1,400 observations not a single observation for a group in the US is given and a much higher proportion of the groups are from the developing world or in countries embroiled in conflict or with weak states. Thus, it seems that this database may conflate insurgency and terrorism to some extent, despite providing great data on group dynamics.
3 If one examines their descriptive data or looks into POICN—the newer version of the CB dataset—single-issue actors comprise a small portion of all cases, yet they almost always result in an attack. However, most of these are anti-abortion cases.
4 This was determined via a variable created in 2017 by START researchers called “individual”. This denotes whether the actor was in factor “unaffiliated”. See Codebook: Inclusion Criteria And Variables, October 2019, pp. 6
5 This variable is actually the Ideology3 measure within the CTD.
6 See Hou et al. (2020) for a review of using such a construct.
7 See Hou et al. (2020). Duration is a measure derived from the start and end dates (in year format) provided in EDTG, and measures how long the actor has been active. Such a measure could not be used, however, given the aggregation process. For example, a group that began in 2000 is coded as 17 years old, given that the data expired at the end of 2016. Since this analysis is cross-sectional, using group age to predict an event or attack or pursuit that may have actually occurred earlier is illogical. However, Hou and colleagues do use such a measure; which is difficult to interpret.
8 This is how the EDTG researchers compiled their database.
9 Polity scores emanate from the Polity5 Project (Marshall & Gurr, Citation2020).
10 McCann, Wesley S. (under review). Outbreak: A Comprehensive Assessment of Biological Terrorism.
11 Wealth as measured by income per capita did not explain the use of CBRN weapons by nationalist/separatist or fundamentalist/cult actors (Hou et al., Citation2020; Ivanova & Sandler, Citation2007).
12 These all use a squared transformation of the respective fractionalization measure. This makes interpretation more difficult, since a higher number corresponds with more diversity. Thus, these studies can only say an intermediate level of fractionalization influences some outcome; normally longevity (Blomberg et al., Citation2011; Gaibulloev & Sandler, 2013; Hou et al., Citation2020).
13 See Polity5 Project Codebook.
14 When it was included in these models, the odds ratios ranged from 25–50. This is because almost every group that did pursue BRN weapons had some level of transnational activity.
15 See Supplementary Methodological Appendix.
16 See Supplementary Methodological Appendix.
17 This is how actors within POICN were coded. As such, single-issue individuals and single-issue groups could be disentangled by looking at other variables that classify individuals vs. groups, but doing so would preclude comparisons within the models between religious and single-issue actors completely (e.g. if an “individual vs. group” factor was used instead).