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.



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


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




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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



29

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



30

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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Terrorist Organizational Success, Violence, and State Sponsorship

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