The contributors to this volume bring finely honed analyses and nuanced perspectives to the terrorist realities of the twenty-first century—history, analyses, and perspectives that have been too often oversimplified or myopic. They bring a new depth of understanding and myriad new dimensions to the crisis of terrorism.
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And they reach into aspects of counterterrorism that broaden our grasp on such important tools as diplomacy, intelligence and counterintelligence, psycho-political means, international law, criminal law enforcement, military force, foreign aid, and homeland security, showing not only how these tools are currently being employed but how often they are being underutilized as well.
Attacking Terrorism demonstrates that there are no easy answers—and that the road toward victory will be long and arduous, frightening and dangerous—but as Audrey Kurth Cronin states in her introduction, "As the campaign against international terrorism unfolds, a crucial forward-looking process of strategic reassessment is under way in the United States, and this book is intended to be a part of it. James M. A88 Dewey Decimal Classification Rapoport Chapter 3. Diplomacy, Michael A. Sheehan Chapter 5. Paul R.
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Pillar Chapter 6. Law Enforcement, Lindsay Clutterbuck Chapter 7. Table 1. For this exercise, we combine Freedom House measures of civil liberties and political freedoms, each of which varies from 1 to 7, where smaller scores indicate greater liberties or freedom. As is standard, we add these two indices together and have the following three classifications: free 2—5 , partly free 6—10 , and not free 11— For terrorist groups started before , left-wing, nationalist, and right-wing groups were more concentrated in Partly Free and Free countries, whereas religious groups were more centered in not free and partly free countries.
There are two noteworthy changes in those distributions after First, emerging nationalist terrorist groups became more concentrated in not free and partly free countries relative to the earlier period. Second, emerging religious groups became more concentrated in not free countries compared to the earlier period. Table 2 indicates additional details of terrorist groups by listing for the two periods the following: the number of groups that ended by , the number of incidents, the number of casualties, the number of incidents per group, and the number of casualties per group. The four right-hand columns give a picture of the terrorism campaigns waged by each ideology.
For the groups that started before , leftist, followed by nationalist, groups had the largest number of endings. Right-wing and then left-wing groups displayed the greatest demise percentages. During to , all ideologies showed an enhanced resilience for newly formed terrorist groups, which was particularly true for religious and nationalist organizations. At the terrorist campaign level, emerging leftist groups had the greatest influence on the number of incidents during to However, religious and nationalist groups caused more casualties than leftists.
This concurs with leftist intent not to have large casualty tolls Enders and Sandler Despite the relatively small number of pre religious groups, they caused the largest number of casualties, as reflected in their casualties per group. For groups starting during to , religious groups not only engaged in well over five times as many attacks, but these attacks resulted in far more casualties when compared to each of the other three ideologies.
In EDTG, religious terrorist attacks resulted in Moreover, the casualties per religious group were Table 2. Finally, Table 3 depicts further aspects of the terrorist campaigns of the four ideologies during four time intervals. The to and to periods are intended to capture the emergence of terrorist groups toward the end of the RAND data period and thereafter.
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During to , religious groups were most prevalent of the four ideologies in terms of emerging group numbers and attacks. The right-most column of Table 3 indicates that religious groups relied much more on kidnappings than other ideological-based groups in the three post periods. Table 3 displays the breakdown of attacks into domestic and transnational incidents.
For any ideology or period, the share of transnational attacks was a rather small percentage that fell after This highlights why terrorist group data sets must include domestic terrorist attacks. Surprisingly, nationalist and left-wing groups provided the most social services before , but religious groups had the highest percentage of social service—providing groups. Even though emerging religious groups gave the most social services during to , most terrorist groups did not provide such services, which was especially true during to for new groups.
Table 3 also highlights that a very small percentage of terrorist groups held territory during any of the displayed periods. Table 3. With the discrete-time method, one can account for unobserved heterogeneity—using panel regression—and conveniently estimate flexible hazard functions Blomberg, Gaibulloev, and Sandler As a robustness check, we implement the random-effects logit regression, which accounts for unobserved heterogeneity, and a piecewise constant specification of time duration, which is an alternative hazard function.
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The piecewise constant specification uses a set of dummy variables for separate time intervals, assuming constant hazard rate within each time interval. Our dependent variable equals one if a group ends in a given year and equals zero otherwise. Group-specific explanatory variables are the logarithm of a group size at its peak, four dummy variables— left wing, nationalist, right wing, and religious fundamentalist base category —for group ideology, the share of transnational terrorist attacks trans. Other group-specific controls include attack diversity in a given year, the number of bases i.
For example, allies may cooperate on planning and staging attacks, benefit from knowledge spillover, and share resources and training facilities e.
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Survival prospect is found to vary across groups with different ideologies e. Moreover, traveling to and operating on a foreign soil are riskier with large costs tied to cross-border movement of terrorist resources. The impact of the number of casualties is uncertain. Larger casualties may generate a more aggressive government response, which weakens the likelihood of survival. In contrast, more casualties may attract more followers, which enhance the probability of survival. The articles cited in this paragraph provide a further theoretical justification that space does not permit us to elaborate in this data set article.
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The other group-specific variables are explained earlier in this article. See the codebook for more details.
If a terrorist group has more than one base country, then we average the variables across these base countries. High-income base countries may offer skilled recruits for terrorists, but the opportunity costs of engaging in terrorism increases with income.
Attacking terrorism : elements of a grand strategy
Moreover, higher per capita income allows the government to placate material discontent—for example, through welfare spending—and spend more on counterterrorism. Government spending is used as a proxy for counterterrorism, which is difficult to measure owing to a lack of data. We enter the quadratic term of the polity variable Polity squared to test for a possible nonlinear relationship. An index of ethnic fractionalization ethnic frac. Seven regional dummy variables are included to control for geographic location of base countries.
Table 4 presents the main results for to Models 1 to 3 are estimated using the pooled logit estimator. Model 1 includes groups. As we enter more variables, the number of groups drops to as displayed because of missing observations in the fully specified model 3. This variable did not exist for the full sample period in the earlier studies because RAND data did not distinguish between domestic and transnational incidents until after Religious fundamentalist terrorist groups have better survival prospects than left-wing, nationalist, or right-wing organizations.
However, contrary to Gaibulloev and Sandler , casualties per attack and the number of bases are not statistically significant. These results are robust across models. The only exceptions are the left-wing and nationalist ideology variables, which are not statistically significant in the full specification.
Table 4. Logit Regressions of Terrorist Group Failure, to This suggests an inverted U-shaped relationship for which a moderate diversity is more conducive to group failure. We also implement the random-effects logit regression to account for unobserved heterogeneity models 4—6 and obtain qualitatively similar results.
Trade openness, which is positive, now becomes marginally significant. We reestimate Table 4 with a piecewise constant time specification by entering time interval dummies for the s, s, s, s, and to In so doing, the previous results hold. We also estimate models 1 to 3 by holding sample size constant. The left-wing and nationalist ideology variables are never statistically significant indicating that these variables lose statistical significance in Table 4 because of sample size groups reduction rather than inclusion of additional regressors. The other results are not sensitive to different model specifications with constant sample size.
Finally, we examine a subsample of demised groups by excluding groups that remain active at the end of the sample period. That exercise gives three findings that differ from Table 4.
First, nationalist and right-wing ideology variables have negative signs and are generally not significant. Left-wing ideology variable, however, is negative and statistically significant, which indicates that left-wing organizations were more resilient among demised groups. This might be because our sample of demised groups is dominated by major leftist groups that lasted relatively long; most religious fundamentalist groups started relatively recently and are still active.
Second, consistent with Gaibulloev and Sandler , casualties per attack and the number of bases are statistically significant with anticipated positive and negative signs, respectively. The remaining results are consistent with Table 4. Next, we divide our sample between terrorist groups that have been operating during to and those that have been active during to Table A4 in the Online Appendix reports several interesting insights.
Ethnic fractionalization is also significant before, but not after, Additionally, ideology variables are generally not statistically significant before This suggests that religious fundamentalists do not differ from leftists, nationalists, or right-wing groups in terms of survival prospects before However, religious fundamentalist groups are more resilient than groups with other ideologies in the later subperiod.