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7/29/2019 Terrorist Organizational Success, Violence, and State Sponsorship
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TERRORIST ORGANIZATIONAL SUCCESS, VIOLENCE, ANDSTATE SPONSORSHIP
By: Evan Kalikow
Dissertation submitted in part-fulfillment of the Masters Course in SecurityStudies, UCL, September 2013.
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Abstract
This study aims to examine the relationship between the state sponsorship of a
terrorist organization and the organization’s level of success, as well as the
relationship between state sponsorship and an organization’s level of violence. It
is hypothesized that, because of the resources provided by sponsorship,
organizations with sponsorship from a state will be more successful than their
non-sponsored counterparts. The study also hypothesizes that, because states
do not want to be associated with violence, state sponsored groups will commit
fewer fatalities. These hypotheses are tested by running, respectively, logistic
regressions and ordinary least square (OLS) regressions, with group success
and fatalities committed as the respective dependent variables. Ultimately, the
regressions provide no evidence of a statistically significant relationship between
state sponsorship and success or state sponsorship and violence. However, the
findings show that there is a positive relationship between terrorist group size
and success, terrorist group size and level of violence, terrorist groups with
religious ideologies and level of violence, and a negative relationship between
groups with religious ideologies and success, as well as level of freedom of a
group’s host country and violence. The study closes with an examination of its
limitations, the implications of its results, and the opportunities for future
research.
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Acknowledgements
First, I would like to thank my dissertation advisor, Dr. Alex Braithwaite.
His advice and help throughout the academic year was indispensible, and this
dissertation would be a much weaker piece without his input.
I would like to thank my parents, Ellen and Richard Kalikow, for their
endless support and love.
I would also like to thank my dear friend Amanda Kreider, whose Stata
expertise is unparalleled.
Finally, my fellow students of the 2012/13 MSc Security Studies program
deserve special acknowledgement. You have all become close friends over the
past year, and you have all challenged me intellectually and kept my spirits high.
See you at graduation next year.
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Table of Contents
INTRODUCTION 2
LITERATURE REVIEW 3
THEORETICAL FRAMEWORK 8
RESEARCH DESIGN 11
RESULTS 15
LIMITATIONS OF STUDY 23
IMPLICATIONS OF STUDY 26
CONCLUSION 28
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Introduction
The tactic of terrorism has been extensively studied and analyzed by
policymakers and scholars, and that is unlikely to change in the near future.
Terrorism has seen numerous forms in both tactics and ideology—David
Rapoport identified four distinct waves of terrorism (anarchism in the 1800s, anti-
colonialism in the 1920s, the new left in the 1960s, and the religious wave of the
late 1970s)—and its properties continue to evolve today (Rapoport, 2002). One
recent trend in contemporary terrorism studies is the state sponsorship of
terrorist organizations. In the past decade alone, state sponsored organizations
such as Hamas and Hezbollah have risen in prominence to become official ruling
parties in the respective governments of their host states. To ignore this trend
would be a fool’s errand at best and irresponsible at worst.
But there remains an important question: how are state sponsored groups
different from groups that do not have state sponsorship, if at all? If state
sponsored organizations are indeed different than groups without sponsorship, it
would behoove policymakers to recognize what makes them different and how to
better manage the threats posed by such groups. Similarly, if groups with state
sponsorship status do not behave noticeably differently than groups without state
sponsorship, then understanding that lack of distinction has significant
implications on research and counterterrorism policy.
This dissertation will primarily explore two research questions. (1) Are
state sponsored terrorist groups more or less successful than groups that are not
state sponsored? and (2) Are state sponsored terrorist groups more or less
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violent than groups that are not state sponsored? In the next section, I will
examine the existing terrorism literature and determine what has been
established and what holes exist in the research field. Following that, I will define
key concepts, dependent variables, and independent variables and determine a
logical link between them in order to test hypotheses related to the research
questions. In the research design section, I will delve into more detail about the
design of my quantitative analysis, including the data sources, the measurement
and operationalization of variables, and the specific statistical regression tests
that will be run using the data. That section will be followed by a demonstration
and explanation of the results produced by the aforementioned quantitative
analysis. Preceding that will be a section discussing the limitations of this study,
followed finally by a section discussing the policy and scholarly implications of
this study and opportunities for future research.
Literature Review
The expanded public profile of terrorism since the 1990s has led to a
significant amount of scholarly attention regarding the tactic. In 2006, for
instance, James Piazza sought to determine the root causes of terrorism. At the
time, the popular consensus was that terrorism was caused by poverty,
inequality, and economic factors (Piazza, 2006: 159). However, Piazza’s study
directly refuted this conventional wisdom; he found no empirical support for that
hypothesis. Rather, Piazza posited, “social and cultural stratification is linked to
sociopolitical institutions like party system [and] may be a better explanatory tool
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for terrorism.” (Piazza, 2006: 171). The policy implications were stunning at the
time—instead of investing resources to reduce poverty, Piazza argued that
efforts to reduce the onset of terrorism would be better focused on improving
political party systems in host countries and allowing for more opportunities for
groups to participate in the political process.
In 2009, Piazza explored another aspect of modern terrorism accepted by
the zeitgeist: that groups that adhere to a religious ideology (and Islam in
particular) commit more violent acts of terror than other groups. Ultimately,
Piazza found that violence differed more among terrorist groups based on the
group’s goals than on ideology (e.g. a group with more abstract goals like al
Qaeda is more prone to violence than a group with strategic goals, even if both
groups have the same religious ideology) (Piazza, 2009: 82). This study (as well
as Piazza’s 2006 article) further disrupted some of the conventional wisdom
associated with terrorism studies and contributed to a more meticulous analysis
of terrorist organizations, allowing scholars to study the topic with greater nuance
than before.
There has also been much leeway in the literature regarding the success
and survival of terrorist groups. An article authored by S. Brock Blomberg,
Khusrav Gaibulloev, and Todd Sandler found that groups are more likely to
survive when they are larger, diversify their types of attacks, remain more
domestic in nature than transnational, commit fewer civilian fatalities, and when
they are based in the Middle East or North Africa (Blomberg et al, 2011: 461).
These findings confirmed the importance of independent variables such as group
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size and violence, which seem to have some form of significance regarding the
extent to which terrorist organizations survive and prosper.
In addition to the aforementioned study, Gaibulloev contributed to the
terrorism survival literature with the help of Todd Sandler. Their conclusions were
quite similar to those found by Blomberg et al; however, Gaibulloev and Sandler
also found that “Religious fundamentalists have better survival prospects than
left-wing, right-wing, and nationalist/separatist terrorist groups” (Gaibulloev and
Sandler, 2013: 791). This article confirmed the significance of group ideology as
an independent variable and seems to complicate the conclusions found by
Piazza regarding religious ideology.
More recently, researchers have attempted to understand state sponsored
terrorism. Navin Bapat set out to determine why a state might seek to sponsor a
terrorist organization. Through creating a game theoretic model, Bapat found
that, because the risk of sponsoring a terrorist organization is so high, only states
that are moderately weak (e.g. Syria and Pakistan) will take the risk, and they are
the states most likely to gain from the potential benefits of sponsorship (Bapat,
2012: 26-27). In addition, a study by Daniel Byman and Sarah Kreps determined
that, because of the often-tumultuous relationship between states and the groups
that they sponsor, efforts that exploit this relationship (in particular the
information gap between states and groups, as well as their distrust in one
another) could provide fruitful counterterrorism results (Byman and Kreps, 2010:
13-14). Understanding the characteristics of groups that are more likely to be
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state sponsors of terrorism is critical to formulating sound policy to combat the
risks of state sponsored terrorism.
Most of the literature surrounding state sponsored terrorism has focused
on the ways state sponsored groups can be stopped in relation to groups without
state sponsorship. Drury, Mohn, and Moore sought to explore nonmilitary
methods of handing the state sponsors themselves, in particular economic
sanctions. This study determined that economic sanctions are insufficient in
stopping state sponsored terrorism, but that they can be useful when used in
tandem with other tactics (Drury et al, 2011: 18).
The research surrounding the ways in which democracies can respond to
state sponsored terrorism (and the ways in which they are constrained) is
perhaps the most robust of any of the research surrounding the subject. Dating
back to 1989, Richard Erickson determined several factors constricting a
democracy’s response to this form of terrorism. In his report, Erickson touched
upon several factors—international law, a state’s right to self-defense, and
collective action of multiple states—that may be used as justifications for
attacking a state sponsored terrorist organization or may be seen as constricting
factors (Erickson, 1989). This is especially noteworthy, as it was written and
published 12 years before the attacks of September 11 and the War on Terror.
In 2002, John Alan Cohan wrote one of the more comprehensive articles
relating to the legitimate responses of democracies to state sponsored terrorism.
Cohan argued, firstly, that democracies must be timely, proportionate, and
targeting only combatants in their responses to terrorism in order to maintain
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legitimacy (Cohan, 2002: 103-107). His more significant finding, however, was
the way in which state sponsorship of terrorism can affect that state’s population,
using Afghanistan as a primary example. His claim that “State-sponsored
terrorism not only impacts the targeted population, but tends to unhinge the very
structure of the local society” (Cohan, 2002: 109) underlines the importance of
understanding these organizations and the impact state sponsorship may have
on them and their surrounding societies.
Some of the conventional wisdom regarding state sponsored terrorism has
been challenged in recent years. Richard Snyder and Angélica Durán Martínez,
for instance, sought to determine whether state sponsored protection inhibits or
promotes violence. Although their study focused on the illicit drug trade, the
principles are similar enough to warrant inclusion in the state sponsorship of
terrorism literature. Snyder and Martínez found that the establishment of state
sponsored protection can have a pacifying relationship with violence, and that the
abolition of these protections can make non-state actors more violent (Snyder
and Martínez, 2009: 87). These findings further complicate the debate among
state sponsored terrorism scholars and necessitate examinations of the issue.
It would be remiss to not mention one of the more prominent datasets
used in the study of terrorism that is not used in this particular piece: the Global
Terrorism Database (GTD). This database contains information about terrorist
groups and attacks from 1970 to 2011, including the number of types of attacks
perpetrated by various groups (START, 2012). Because other databases contain
information more relevant to this particular study, the GTD was not used;
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however, it has been an invaluable tool in the literature for terrorism research and
has been used in many of the aforementioned articles.
Although there has clearly been much research and thought put forward
for how to best handle the issue of state sponsored terrorism, each of these
studies fails to ask some basic yet essential questions: namely, how is a group
affected by the presence of state sponsorship? These articles start from the
assumption that state sponsored terrorism presents new challenges and requires
new counterterrorism tactics to meet these challenges. While this may be true,
little (if any) research has been done to determine to what extent state
sponsorship affects the terrorist group being sponsored. Moreover, the majority
of research has looked at state sponsored terrorism from the perspective of the
sponsoring state rather than the group that receives sponsorship. This study
hopes to bridge that gap by examining the effects of state sponsorship on group
success and fatalities committed by the group.
Theoretical Framework
Before proceeding any further, it is of paramount importance to define the
concepts of terrorism, state sponsorship, and success. Defining a terrorist
organization, in particular, can be difficult and contentious. For instance, A.P.
Schmidt and Al Jongman identified over 100 working definitions of terrorism in
the political science literature 25 years ago, and the number and scope of the
definitions have certainly expanded since (Schmidt and Jongman, 1988). In
2006, Bruce Hoffman defined terrorism as “the deliberate creation and
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exploitation of fear through violence or threat of violence in the pursuit of political
change” (Hoffman, 2006: 40), a definition that works quite nicely for this study.
Any non-state organization that adheres to those principles can be considered a
terrorist organization.
State sponsorship is also a relatively murky concept to define. Is a state
considered a sponsor of a terrorist organization if it speaks in support of the
group in an official capacity? Or does sponsorship only apply if the state has
supported the group in some capacity? And what defines “support”? Different
scholars have varying answers to these questions, but for the purposes of this
study, the most important definition comes from Victor Asal and R. Karl
Rethemeyer, whose BAAD1 Lethality dataset serves as one of the primary data
sources for this study. Asal and Rethemeyer coded state sponsorship as defined
by financial transfers from a government to an organization, a definition that will
suffice for the scope of this essay (Asal and Rethemeyer, 2008).
In terms of success, it is best to examine the RAND Corporation’s End-of-
Terror Data Set. This dataset identifies five potential ways in which a terrorist
organization ends: policing, splintering, politics, victory, and military force (Jones
and Libicki, 2008: 142). Because the dataset only extends to 2005, a sixth
outcome—that the group’s activities are still ongoing—can be added to these
five. Intuitively, we can classify a group as having failed if it ended in splintering,
policing, or in the use of military force. Similarly, a group can be said to have
succeeded if its target goals have been met (victory) or if its issues have become
part of the political process (politics). This study will measure group success with
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those guidelines in two ways: one in which an ongoing conflict is deemed a
failure, and one in which ongoing conflicts are removed from the data sample.
With these core concepts defined, it is now appropriate to establish hypotheses.
Hypothesis 1: Holding all else constant, state sponsored organizations
are more successful than groups without state sponsorship. The logic behind this
hypothesis is not difficult to understand. Any organization, terrorist or otherwise,
will have a greater chance to succeed if it is provided with more resources. The
sponsorship of a state through financial transfers would, then, seem to provide
the organization with more opportunities to succeed.
Hypothesis 2: Holding all else constant, state sponsored organizations
are less violent than groups without state sponsorship. There are varying schools
of thought with regard to this hypothesis. On one hand, state sponsorship
provides organizations with more resources, which can be used to purchase and
use more weaponry, increasing the violence of an organization (Wilkinson, 2000:
64). However, a case can also be made that states will either choose to sponsor
organizations that rely less on violence, or that states will urge the groups that
they sponsor to engage in fewer violent activities in order to maintain the state’s
legitimacy (Simon and Benjamin, 2001: 5). The latter line of logic is more
convincing, regardless of whether states become less violent after sponsorship
or are chosen for sponsorship because of their comparatively less violent nature.
In addition to these two main hypotheses, this study will also test several
other variables: the host state’s regime type, the host state’s economy, the size
of the terrorist organization, the nature of the organization’s ideology, and the
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age of the organization. These variables, as with the rest of the study’s research
design, will be explained in greater detail in the next section.
Research Design
This is a quantitative analysis study with data that is country-variant and
time-invariant. As I have alluded to in earlier sections, this study primarily uses
two data sources. The first is Victor Asal and R. Karl Rethemeyer’s Big Allied
And Dangerous (BAAD1) Lethality Dataset, henceforth referred to as BAAD1
(Asal and Rethemeyer, 2008). As it is relevant to this study, BAAD1 contains
data on terrorist groups from 1998 to 2005, including fatalities committed by the
group, state sponsorship, the group’s country of base, the group’s religious
ideologies, and the membership size of the group.
The second data source comes from Seth Jones and Martin Libicki’s End-
of-Terror Data Set, compiled as part of How Terrorist Groups End: Lessons for
Countering al Qa’ida, a paper written for publication by the RAND Corporation
(Jones and Libicki, 2008). This dataset will henceforth be referred to as RAND.
The data in RAND spans from 1968 to 2006, but only the groups that overlap
with the 1998 to 2005 timespan established by BAAD1 will be examined in this
study. RAND includes data relating to the regime type of the organization’s host
country, the economic level of each group’s host country, and the group’s
outcome (policing, politics, splintering, victory, military force, or ongoing as of
2006). After combining the two datasets, there is a sample size of 316
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organizations from 1998 to 2005 that contain a sufficient amount of data to
warrant inclusion in the study.
In order to measure how state sponsorship affects a group’s success
(Hypothesis 1), it is necessary to establish dependent and independent
variables. For testing of the first hypothesis, the dependent variable is a group’s
success (success1 and success2 ). As mentioned earlier, a group is considered
successful if it ends in victory or politics. This study also measures success in
two ways, either with groups that were ongoing as of 2006 considered a failure
(success1) or with ongoing groups omitted altogether (success2 ). As a result,
each of the models with success as the dependent variable was run twice, once
for success1 and once for success2 .
There are seven independent variables for the testing of the first
hypothesis: state sponsorship (statespond ), regime type (regime), economic level
(economy ), organization age (age), number of fatalities committed by the group
from 1998 to 2005 (fatalities), the number of members in the group (ordsize), and
if the group’s ideology had a religious component (ContainRelig ). Statespond is a
binary variable, coded as a 1 if the group was sponsored by a state and as a 0
otherwise. Regime was determined based on Freedom House classifications of
state regime types (Jones and Libicki, 2008); free countries were coded as a 2,
partly free as a 1, and not free as a 0. Economy was based on World Bank
classifications of state economic levels (Jones and Libicki, 2008); high income
states were coded as a 3, upper middle income as a 2, lower middle income as a
1, and low income as a 0. Age was determined by the organization’s age in
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years. Fatalities measured the number of civilians killed from 1998 to 2005.
Ordsize counted the number of members in a group; groups with over 10,000
members were coded as a 3, groups with 1000-10,000 members as a 2, groups
with 100-1000 members as a 1, and groups with 0-100 members as a 0. Finally,
ContainRelig is a binary variable, coded as a 1 if the group had a religious
component of their ideology (possibly in addition to other ideological
components) and as a 0 otherwise (Asal and Rethemeyer, 2008).
Having established the dependent and independent variables, and
because the dependent variable is a binary variable, two logistic regressions
(logit) were run using STATA: once with success1 as the dependent variable,
and once with success2 as the dependent variable. In each case, the results
were clustered around a terrorist organization’s country of base (ccode) to
account for heteroskedasticity and multicollinearity.
In order to measure how state sponsorship affects a group’s level of
violence (Hypothesis 2), we must establish the dependent and independent
variables. Fortunately, most of the variables associated with the testing of the
second hypothesis are the same variables that were used for the first hypothesis.
In this case, the dependent variable is fatalities. The independent variables
include regime, economy , success1, success2 , ordsize, age, ContainRelig , and a
measure of whether the organization’s ideology is entirely based on religion
(PureRelig ). Specifically, PureRelig is coded as a 1 if the group’s ideology is
religious in nature and not associated with any other ideological types, and as a 0
otherwise (Asal and Rethemeyer, 2008).
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Because the dependent variable is continuous, this hypothesis was tested
by running an ordinary least square (OLS) regression, clustering around ccode to
account for heteroskedasticity and multicollinearity. For the most accurate
results, this model was run four times: once with success1 and PureRelig as
independent variables, once with success2 and PureRelig as independent
variables, once with success1 and ContainRelig as independent variables, and
once with success2 and ContainRelig as independent variables.
When testing for both Hypothesis 1 and Hypothesis 2, results are
considered statistically significant if the chance of the results happening because
of random change is less than five percent (p<0.05). In the figures in the Results
section, significant results are designated with two stars (**) if p<0.05 and three
stars (***) if p<0.01.
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Results
Figure 1: The effect of state sponsorship on success (success1)
(1)EQUATION VARIABLES success1
success1 statespond -0.787(0.953)
regime 0.248(0.343)
economy 0.100(0.200)
age -0.0156(0.0167)
fatalities -0.00209(0.00229)
ordsize 0.379(0.264)
ContainRelig -1.375**(0.606)
Constant -1.884***(0.549)
Observations 316
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Figure 1 shows the results for a logit regression with success1, the
measure of success counting ongoing conflicts as of 2006 as failures, as the
dependent variable. With regards to Hypothesis 1, that state sponsored groups
are more successful, there is a negative but statistically insignificant correlation;
therefore, we cannot reject the null hypothesis of no relationship. There was also
a negative correlation between success and fatalities, which was statistically
insignificant as well. In addition, regime, economy , age, and ordsize all had
positive, statistically insignificant relationships with the dependent variable. The
sole statistically significant correlation was between the dependent variable and
ContainRelig , which was a negative relationship. Essentially, this means that,
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holding all other independent variables constant, groups that do not have a
religious component in their ideology are more successful than groups with
partially or entirely religious ideologies.
Figure 2: The effect of state sponsorship on success (success2 )
(1)EQUATION VARIABLES success2
success2 statespond -0.362(1.126)
regime 0.347(0.333)
economy 0.0261
(0.266)age -0.00611
(0.0149)fatalities -0.00394
(0.00398)ordsize 0.994**
(0.469)ContainRelig -0.886
(0.581)Constant -1.220*
(0.674)
Observations 127
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Figure 2 shows the results for a logit regression with success2 , the
measure of success omitting ongoing conflicts as of 2006 from the model, as the
dependent variable. Because of these omitted cases, there are only 127
observations in this model, compared to the 316 in Figure 1’s model. Regarding
Hypothesis 1, as examined in Figure 1, there is a negative but statistically
insignificant relationship between state sponsorship and success. Because of
this, we fail to reject the null hypothesis of no relationship between state
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sponsorship and group success. Interestingly, ContainRelig , which had a
negative and statistically significant relationship with the dependent variable in
Figure 1, has a negative and statistically insignificant relationship with success in
Figure 2. Age and fatalities also had statistically insignificant negative
relationships with success, while regime and economy demonstrated statistically
insignificant positive relationships. The sole statistically significant relationship in
this model was ordsize. This means that, holding all other independent variables
constant, terrorist organizations tend to be more successful if their membership
numbers are larger.
Figure 3: The effect of state sponsorship on violence (with PureRelig )
(1) (2)VARIABLES fatalities fatalities
statespond -34.47 -5.605(48.39) (13.53)
regime -18.48** -4.930(8.240) (7.010)
economy 4.915(4.693)success1 -16.53
(13.05)ordsize 67.72** 22.74**
(28.33) (9.336)PureRelig 75.52 22.75
(76.73) (28.61)age -0.278 0.272
(0.621) (0.294)success2 -7.369
(7.547)Constant 9.977 9.262(18.35) (15.21)
Observations 316 127R-squared 0.080 0.145
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
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Figure 3 shows the results for an OLS regression with fatalities, the
measure of how many fatalities were attributed to a group between 1998 and
2005, as the dependent variable. Figure 3 also uses PureRelig as an
independent variable instead of ContainRelig , and it demonstrates the model
being run twice: once with success1 and 316 observations (as shown in the first
column), and once with success2 and 127 observations (as shown in the second
column).
In the first model (with success1), when testing for Hypothesis 2, there is
a negative but statistically insignificant relationship between state sponsorship
and violence; therefore, we fail to reject the null hypothesis of no relationship
between the variables. There was also a negative but statistically insignificant
relationship between success1 and the dependent variable, as well as between
age and the dependent variable. The independent variables economy and
PureRelig each demonstrated a positive but statistically insignificant relationship
with fatalities. This model yielded two statistically significant relationships with the
dependent variable: a positive relationship with ordsize, and a negative
relationship with regime. This means that, holding all other independent variables
constant, groups tend to be more violent when there are more members of the
group. Additionally, holding all other independent variables constant, terrorist
organizations tend to commit more fatalities if their host country is less free.
The second model in Figure 3 differs from the first in several ways. It uses
success2 instead of success1, therefore reducing the observations to 127. The
economy independent variable is dropped from the model due to issues with
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multicollinearity. The R-squared value of the model is 0.145 compared to the first
model’s 0.08, meaning that the model explains 14.5% of the change around the
dependent variable over the first model’s 8%; thus, the second model is a better
“fit” than the first. Regarding Hypothesis 2, there is a negative but statistically
insignificant relationship between statespond and the dependent variable,
meaning that we cannot reject the null hypothesis of no relationship. The
independent variables regime and success2 each have a negative but
statistically insignificant relationship with fatalities, while PureRelig and age each
yield positive but statistically insignificant relationships with the dependent
variables. The sole statistically significant independent variable was ordsize,
which, as it did in the first model, demonstrated a positive relationship with
fatalities (the other significant result in the first model, regime, was not significant
in this model). This means that, holding all other independent variables constant,
terrorist groups with greater membership sizes tend to be more violent.
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Figure 4: The effect of state sponsorship on violence (with ContainRelig )
(1) (2)VARIABLES fatalities fatalities
statespond -37.87 -2.535(48.27) (12.25)
regime -12.44 -1.345(9.353) (5.753)
economy 6.926*(4.107)
success1 -15.37(12.73)
ordsize 68.17** 22.34**(27.16) (8.687)
ContainRelig 61.95** 30.23*
(25.70) (15.38)age -0.196 0.297
(0.508) (0.272)success2 -6.630
(6.073)Constant -11.39 -0.277
(18.10) (8.722)
Observations 316 127R-squared 0.079 0.176
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Figure 4 shows the results for an OLS regression with fatalities, the
measure of how many fatalities were attributed to a group between 1998 and
2005, as the dependent variable. Figure 4 also uses ContainRelig as an
independent variable instead of PureRelig , and it demonstrates the model being
run twice: once with success1 and 316 observations (as shown in the first
column), and once with success2 and 127 observations (as shown in the second
column).
In the first model (with success1), when testing for Hypothesis 2, there is
a negative but statistically insignificant relationship between state sponsorship
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and the dependent variable; therefore, we fail to reject the null hypothesis of no
relationship. The variables regime, success1, and age also demonstrate negative
but statistically insignificant relationships with the dependent variable. The
variable economy yields a positive relationship with fatalities, and it is significant
to p<0.1. Because this study only considers results statistically significant if they
are significant to p<0.05, this makes the economy findings statistically
insignificant. The significant findings for this model were for the variables ordsize
and ContainRelig , which each had a positive relationship with fatalities. In
layman’s terms, that means that, holding all other variables constant, terrorist
groups with more members tend to commit more fatalities. Additionally, holding
all else constant, groups that have a religious component in their ideology also
tend to commit more fatalities.
As was the case in Figure 3, the second model in Figure 4 differs from the
first in a few significant ways. It utilizes success2 instead of success1 as an
independent variable, which means that the sample size is smaller. The economy
variable is dropped due to multicollinearity issues, and the model overall is a
better fit for measuring the dependent variable, yielding an R-squared value of
0.176 (17.6% of the variance of the dependent variable) against the first model’s
R-squared value of 0.079 (7.9% of the variance). With regards to Hypothesis 2,
there is a negative but statistically insignificant relationship between state
sponsorship and fatalities; therefore, we cannot reject the null hypothesis of no
relationship. The variables regime and success2 also have negative but
statistically insignificant relationships with the dependent variable. There is a
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positive but statistically insignificant relationship between age and the dependent
variable, as well as between ContainRelig and the dependent variable—the latter
is significant to p<0.1, but is not considered significant in this study. The one
statistically significant relationship is a positive relationship between ordsize and
fatalities. That means that, holding all other independent variables constant,
groups with larger memberships tend to commit more fatalities.
In summary, none of the six models yielded results that could confirm
Hypothesis 1 or Hypothesis 2. We therefore cannot establish any meaningful
relationship between state sponsorship of a terrorist organization and the
organization’s success (using either success1 or success2 ), nor can we establish
a significant relationship between state sponsorship of a group and the group’s
level of violence (measured by fatalities). Despite these non-findings, there were
some fascinating results from the tests. There was moderate statistical support
for larger groups (measured by ordsize) being more successful, as well as
significant support for larger groups being more violent. There was moderate
support for groups with a partial or complete religious ideology (measured by
ContainRelig ) being less successful, as well as moderate support for those same
groups to be more violent (meanwhile, there was no statistically significant
support of any relationship between PureRelig and either of the dependent
variables). Finally, there was moderate support for groups based in less free
states (measured by regime) to be more prone to committing fatalities.
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While the findings of this study were not as they were anticipated when
forming the study’s hypotheses, they nevertheless pose important questions,
implications, and pathways for future research and policy.
Limitations of Study
Before considering the implications of the results found in this study, it is
important to understand some of this study’s limitations in order to put the results
into context. The first limitation is the sample size of the study. There were a total
of 316 cases with sufficient data for statistical analysis; when measuring success
by dropping all of the cases in which the group’s activities were still ongoing, that
number drops to 127. The former is a sufficient sample size, but the latter, while
not being untestable through statistical analysis, remains more problematic. The
smaller sample size of the results associated with tests run using success2
makes those results potentially less indicative of reality.
Another limitation of this study regards its sample selection. As described
earlier, the universe of cases was taken primarily from the BAAD1 dataset (and
enhanced with data from RAND), which measured terrorist organizations from
1998 to 2005. This seven-year span encompassed a significant number of
groups and cases, but the findings would have been even more impressive were
the window of the findings larger. Unfortunately, as BAAD1 is the only widely
available dataset with data regarding state sponsorship, collecting the data for a
wider time period would have been both daunting and beyond the scope of this
study.
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The range of the sample selection is not the only issue with regards to the
sample, however. One significant limiting factor is the fact that the data from
BAAD1 and RAND vary by country but not by time. For instance, BAAD1 and
RAND contain data from terrorist groups ranging from states all over the world,
which is a positive aspect of the sample selection. But what is more concerning is
that the data are not time-varying; that is, all of the data points are static. Time-
varying data would include data from each terrorist organization for each year of
activity. If the data were time-varying, one would be able to examine terrorist
organizations before and after obtaining state sponsorship, and a clearer picture
of how that designation would affect their success or level of violence would be
able to be examined. That extent of data, sadly, does not exist at this point in
time. There is hope for the future, though, as Asal and Rethemeyer are compiling
an update to BAAD1—fittingly titled BAAD2—which seeks to improve upon
BAAD1, most notably by including data that varies in a time series (Asal and
Rethemeyer, 2008). With the completion of this dataset, further progress can be
made with regards to understanding state sponsored terrorist organizations.
As discussed earlier, the omission of ongoing terrorist campaigns from the
success2 models presented challenges regarding the models’ sample sizes. But
those missing data points also limited the comprehensiveness of the study by
showing how incomplete the data are. Deciding whether to code ongoing
conflicts as failures or to omit them from the model altogether was by far the
most difficult coding decision I had to make; what is presented in this dissertation
is a fine compromise, but the results would be more robust with a more complete
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coding of those variables. The data ranged from 1998 to 2005. It is now 2013,
and many of those 189 cases that were omitted in success2 can now be coded
more accurately. Future research would be wise to take this into account.
Evidence presented in the Results section of this study suggests that the
success2 models may prove to be better fits than the success1 models. By
completing the success variable coding, future studies could very well prove this
to be correct.
The way in which state sponsorship is measured exposes additional
limitations. As discussed in the Theoretical Framework section, a state is
considered to be sponsoring a group if the state engaged in financial transactions
with the group. While this is an accurate way of measuring state sponsorship,
there are two problems. The first problem is that this measure of state
sponsorship does not account for any degree of sponsorship beyond a binary
value—a state providing a group with £100 is considered just as much a sponsor
as a different state providing a different group with £10,000. The second problem
is that data regarding financial transfers from a state to a terrorist organization
can be difficult to find. The state sponsorship data found in BAAD1 is accurate;
yet, because of the difficulty in knowing the true extent of state-terrorist financial
transfers, there is a chance that the state sponsorship findings are incomplete.
Thus, there is a possibility of the extent of state sponsorship being
underrepresented in this study.
One final limitation of this study regards the model fit for OLS regressions.
Each of the four OLS regressions run in this dissertation yielded R-squared
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values ranging from 0.079 to 0.176. These values range from poor to
satisfactory, and they demonstrate that there are other independent variables
that may account for change in levels of violence more than the ones analyzed in
this study. Other factors that may need to be considered include (but are not
limited to) territorial control, if an organization is domestically or transnationally
focused, political (not religious) ideology, a host country’s access to educational
opportunities, and the organizational structure of the terrorist group.
Despite its limitations, however, this study can still provide numerous
lessons for the academic and policymaking fields. The study’s implications will be
presented in the following section.
Implications of Study
From a policy standpoint, this study sought to answer a fundamental
question: Should state sponsored groups be treated differently than groups
without state sponsorship? Conventional wisdom and common intuition would
suggest that, yes, state sponsored organizations provide a higher degree of risk
and danger to international security and should therefore be handled differently in
order to limit their success and influence. Of course, state sponsorship directly
affects international diplomacy and relations between democracies and states
that sponsor terrorist organizations. However, the results of this study seem to
suggest that, when trying to cope with the group itself rather than the supporting
state, some of the focus on state sponsorship may be misguided.
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If a group’s status as state sponsored does not have a significant impact
on its success or its level of violence, as is suggested by the results of this study,
then perhaps it is more productive for policymakers to attempt to address the
factors that do influence a group’s success and fatality rate. Take, for instance,
larger groups and groups with a religious component in their ideology. Each of
these types of groups appears to be more violent than their counterparts, state
sponsored or otherwise. Prudent policymakers must take note of this and be
mindful to prevent groups from getting too large or allowing religious groups to
gain too much power or influence.
The findings related to groups with religious components to their
ideologies (ContainRelig ) present two interesting implications. The first is that,
while the findings for groups associated with ContainRelig were significant, none
of the findings for groups with ideologies that were exclusively religious
(PureRelig ) were significant. That would seem to suggest that, while religious
ideologies may be significant in determining a group’s success or level of
violence, it is most significant when it is not the sole ideological framework for a
group and is compounded with a separate, nonreligious ideological aspect.
The second interesting implication is that, while groups with a religious
component in their ideology proved to be more violent, they were also found to
be less successful. This provides a crucial opportunity for counterterrorism
policymakers. Violence is generally seen as a means to an end—a group
commits acts of violence in order to meet their strategic goals. If terrorist
organizations are responsible for more fatalities without successfully reaching
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their goals (as is suggested by the ContainRelig data), then they are not being
effective. Policymakers, therefore, have an opportunity to turn the terrorist
group’s population against itself and erode popular support by showing that the
organization is succeeding in killing innocents but failing in effecting meaningful
change.
Academics can find use in this study as well. The dissertation’s limitations,
as described in the previous section, show on one hand the ways in which this
study is flawed. Yet the study’s limitations also provide a roadmap for future
studies and shows opportunities for significant advancements in the state
sponsored terrorism field. By challenging the conventional wisdom associated
with state sponsored terrorist organizations and by demonstrating results that at
least preliminarily support the questioning of state sponsorship as a significant
factor toward a group’s success and propensity to violence, perhaps this study
can inspire future researchers to explore the subject with a critical eye.
Conclusion
When this study started, I expected to find quantitative support for the
ideas that state sponsored terrorist organizations were more successful than
their counterparts while also being less violent. The regression models that were
run to test these hypotheses proved that there was no statistically significant
quantitative relationship between either state sponsorship and success or state
sponsorship and violence. The models did, however, yield some secondary
results in the form of a positive relationship between group membership size and
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success, a positive relationship between group membership and fatalities, a
positive relationship between religious ideology and fatalities, and a negative
relationship between religious ideologies and success (not to mention a negative
relationship between regime type (level of freedom) and fatalities). While these
results were not the expected results, they still prove to be quite useful.
Future quantitative studies will be able to build off of these preliminary
results and produce better models, making sure to learn from the aforementioned
limitations of this study and create models and datasets that are more
representative and statistically sound. Moreover, this study provides an excellent
opportunity for qualitative analyses. A scholar hoping to show the effect (or lack
thereof) of state sponsorship on group success and violence would be wise to
examine several terrorist groups, state sponsored and not sponsored, operating
under similar circumstances (similar group sizes, host states, ages, etc.) and see
the extent to which state sponsorship impacted their eventual outcomes. Such a
study would provide important insights that would be more difficult to capture in a
quantitative analysis.
State sponsorship of terrorist organizations remains an integral aspect of
terrorism studies for both academics and policymakers. By providing financial
resources in significant quantities, states have the potential to legitimize terrorist
groups and increase their potential threats. While the concept of state
sponsorship may not be fading from the policy or academic realms anytime soon,
it would be wise to question conventional wisdom and explore further avenues of
research regarding the impact of state sponsorship not on the sponsor, but on
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the group receiving the sponsorship. A failure to fully examine this relationship
could result in dire consequences; however, the opportunities to even more fully
understand how terrorist organizations work and how they may be stopped or
weakened are boundless.
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