international learning and the diffusion of civil …/67531/metadc699990/...linebarger, christopher....
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APPROVED: Andrew Enterline, Major Professor Marijke Breuning, Committee Member J. Michael Greig, Committee Member T. David Mason, Committee Member Idean Salehyan, Committee Member Richard Ruderman, Chair of the
Department of Political Science Mark Wardell, Dean of the Toulouse
Graduate School
INTERNATIONAL LEARNING AND THE DIFFUSION OF CIVIL CONFLICT
Christopher Linebarger
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
August 2014
Linebarger, Christopher. International Learning and the Diffusion of Civil
Conflict. Doctor of Philosophy (Political Science), August 2014, 163 pp., 12 tables,
7 figures, bibliography, 175 titles.
Why does civil conflict spread from country to country? Existing research relies
primarily on explanations of rebel mobilization tied to geographic proximity to explain
this phenomenon. However, this approach is unable to explain why civil conflict appears
to spread across great geographic distances, and also neglects the government’s role in
conflict. To explain this phenomenon, this dissertation formulates an informational
theory in which individuals contemplating rebellion against their government, or “proto-
rebels,” observe the success and failure of rebels throughout the international system. In
doing so, proto-rebels and governments learn whether rebellion will be fruitful, which is
then manifested in the timing of rebellion and repression.
The core of the dissertation is composed of three essays. The first exhorts scholars of
the international spread of civil violence to directly measure proto-rebel mobilization. I show
that such mobilization is associated with conflicts across the entire international system,
while the escalation to actual armed conflict is associated with regional conflicts. The
second chapter theorizes that proto-rebels learn from successful rebellions across the
international system. This relationship applies globally, although it is attenuated by
cultural and regime-type similarity. Finally, the third chapter theorizes that
governments are aware of this process and engage in repression in order to thwart it. I
further argue that this repression is, in part, a function of the threat posed by those
regimes founded by rebels.
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Copyright 2014
by
Christopher Linebarger
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ACKNOWLEDGEMENTS
This dissertation proved to be an epic undertaking, with many twists and turns.
Its completion would not have been possible without the advice and support of a host of
individuals. I would first like to thank my dissertation advisor, Dr. Andrew Enterline,
who spent countless hours over the last several years training me in the methods of
social-science and puzzle-driven research, and delivering advice on publication and
teaching. I would also like to thank the members of my committee, who have guided my
intellectual development. Dr. Idean Salehyan and Dr. T. David Mason taught me just
about everything I know about civil war. Dr. Marijke Breuning offered me an early co-
authorship opportunity, resulting in my first publication, while also providing valuable
insight from outside my sub-field. Finally, Dr. J. Michael Greig provided indispensable
advice on theory and methods. Beyond the dissertation, I would also like to thank the
entire political science department at UNT for providing training that was both
intellectually stimulating and professionally rigorous. I am particularly thankful to Dr.
Salehyan and Dr. Cullen Hendrix for the opportunity to work on the SCAD project. I
was fortunate to start at UNT with a particularly strong cohort of grad students, and to
be joined later by others. I am certain that fewer of us could have finished had we not
been so supportive of one another. I would also like to thank the old group from my
Reno and Elko days. Although we have long since scattered across the country to
pursue a variety of careers, our regular conversations have kept the pursuit of the Ph.D.
from becoming overwhelming. Finally, I would like to thank my parents, Janna and
David, and my brother, Kyle. Their support was invaluable while I pursued a Ph.D. in
Texas.
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TABLE OF CONTENTS
Page ACKNOWLEDGEMENTS ............................................................................................ iii LIST OF TABLES ......................................................................................................... vi LIST OF FIGURES ...................................................................................................... vii CHAPTER 1 INTRODUCTION ..................................................................................... 1
1.1 The Puzzle ................................................................................................. 3 1.2 Preview of the Theory ................................................................................ 8 1.3 Pilot Studies and Initial Work .................................................................. 18 1.4 Structure of the Dissertation ..................................................................... 19
CHAPTER 2 CIVIL WAR DIFFUSION AND THE EMERGENCE OF MILITANT GROUPS ........................................................................................................................ 21
2.1 Chapter Abstract ...................................................................................... 21 2.2 Introduction .............................................................................................. 21 2.3 International Diffusion and the Conflict Process ....................................... 23 2.4 Existing Militant Group Data ................................................................... 27 2.5 Data and Research Design ........................................................................ 30 2.6 Analysis ..................................................................................................... 35 2.7 Conclusion ................................................................................................. 42
CHAPTER 3 DANGEROUS LESSONS: REBEL LEARNING AND MOBILIZATION IN THE INTERNATIONAL SYSTEM .......................................................................... 45
3.1 Chapter Abstract ...................................................................................... 45 3.2 Introduction .............................................................................................. 45 3.3 Learning and the Diffusion of Civil Conflict ............................................. 48 3.4 Theory ...................................................................................................... 52 3.5 Data and Research Design ........................................................................ 56 3.6 Analysis ..................................................................................................... 69 3.7 Conclusion ................................................................................................. 77
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CHAPTER 4 PREVENTIVE MEDICINE: REVOLUTIONARY STATES, THE INTERNATIONAL SYSTEM, AND REPRESSION ..................................................... 80
4.1 Chapter Abstract ...................................................................................... 80 4.2 Introduction .............................................................................................. 80 4.3 The Diffusion of Civil Conflict and Repression ......................................... 82 4.4 Theory ...................................................................................................... 86 4.5 Data and Research Design ........................................................................ 92 4.6 Analysis ................................................................................................... 100 4.7 Conclusion ............................................................................................... 109
CHAPTER 5 CONCLUSION ....................................................................................... 111
5.1 Summary of Findings .............................................................................. 111 5.2 Theoretical Implications .......................................................................... 113 5.3 Policy Implications .................................................................................. 116 5.4 Future Research ...................................................................................... 117 5.5 Conclusion ............................................................................................... 125
APPENDIX A REVOLUTIONARY REGIME LIST ................................................... 126 APPENDIX B MILITANT ORGANIZATIONS LIST ................................................. 130 BIBLIOGRAPHY ......................................................................................................... 152
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LIST OF TABLES
Page 2.1 Logit Models of Armed Conflict Diffusion ........................................................... 35
2.2 Negative Binomial Models of Militant Group Emergence .................................... 37
2.3 Coding of Logit Selection and Outcome Stages ................................................... 40
2.4 Selection Model of Militant Group Persistence and Civil War Onset .................. 41
3.1 Descriptive Statistics for Dangerous Lessons Research Design ............................ 68
3.2 Logit Models of Armed Conflict and Militant Group Diffusion ........................... 70
3.3 Logit Models of Learning and Militant Group Emergence ................................... 72
4.1 Descriptive Statistics for Preventive Medicine Research Design ........................ 100
4.2 Ordered Probit Models of State Repression, Revolutionary Regimes, and MID Count ................................................................................................................. 102
4.3 Ordered Probit Models of State Repression, Revolutionary Regimes, and Revolutionary MID Count ................................................................................. 104
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LIST OF FIGURES
Page
2.1 Frequency of Group Emergence ........................................................................... 33
2.2 Expected Count of Group Emergence .................................................................. 39
3.1 Frequency of Militant Group Emergence ............................................................. 59
3.2 Frequency of Revolutionary Regimes Per Year, 1968–2001 ................................. 63
3.3 Militant Group Emergence as a Function of Revolutionary Similarity ................ 76
4.1 Frequency of Revolutionary Regimes Per Year, 1976–2001 ................................. 96
4.2 Substantive Effects of Revolutionary Regimes and Revolutionary MID Count . 107
CHAPTER 1
INTRODUCTION
What explains the spread of civil conflict from country to country, even among those
separated by great distances? The American War of Independence (1775–83) ignited a wave
of liberal revolutions world-wide (Dunn 2000), for example. Current research is primarily
concerned with the physical mechanisms of conflict’s spread, such as refugees flows and cross-
border ethnicity (e.g., Buhaug and Gleditsch 2008; Salehyan 2009). To address this puzzle,
my dissertation formulates a theory of rebellion in which dissidents contemplating rebellion,
whom I term “proto-rebels,” and governments seeking to thwart rebellion, learn about its
utility from information available in the global system.
Drawing on work in political science (e.g., Gilardi 2010; Meseguer and Gilardi 2009;
Simmons and Elkins 2004; Weyland 2009), social movement studies (e.g., Tarrow 2011; Tilly
1978), and sociology (e.g., Rogers 2003; Strang and Soule 1998), I argue that proto-rebels
and governments learn from those international cases in which rebels have achieved military
victory in civil war and then formed their own government. This was arguably the case with
the mobilization of Latin American proto-rebels after Fidel Castro’s victory in 1959 Cuba
(McSherry 2005), and that of extremist Islamist movements tracing their origins to successful
insurgencies in Afghanistan, Iran, and Lebanon during the 1980s (Abrahms and Lula 2012).
Similar mechanisms were also seen at work during the recent Arab Spring, in which the
overthrow of long-standing dictatorships in Tunisia and Egypt prompted a cycle of rebellion
and repression throughout the Middle East (Saideman 2012; Weyland 2012).
This “rebel learning” theory forms the core of several concepts commonly discussed
by policy-makers and media commentators. The domino theory, for example, contends that
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conflict is contagious and that rebel mobilization, the overthrow of regimes, or the inde-
pendence of a new state can produce a cascade of rebellion in nearby states. So described,
domino theory was at the forefront of American foreign policy during the Cold War. Ameri-
can policy-makers, determined to stop the dominos from falling into the hands of the Soviet
Union, constructed an alliance system designed to contain the threat of international com-
munism and undertook military intervention in places like Korea, Vietnam, Nicaragua, and
Grenada (Hironaka 2005; Kalyvas and Balcells 2010; Slater 1987, 1993; Westad 2005). In the
present day, the domino rhetoric is commonly invoked in discussions of Islamist insurgency
and terrorism. Advocates of the theory claim that defeat of insurgency is necessary, lest
proto-rebels in other parts of the world learn from their success (Hironaka 2005).
In building from these insights, I make three conceptual moves that bring innovation
to the literature. The first is the aforementioned theory of learning. The second is a focus
on the consequences of civil conflict. The existing literature on the international spread of
political violence argues that conflict is contagious or, in other words, that conflict begets
conflict (e.g., Buhaug and Gleditsch 2008; Gleditsch 2007). I accept this argument, but
innovate by also arguing that if rebels are victorious in civil war and successfully establish
a new regime, then a powerful example will be provided to proto-rebels. This example is
not limited by geographic distance; indeed, its effects are felt globally. Finally, in the third
conceptual move, I argue that in order for scholars to understand this topic, it is necessary
to move beyond the use of war onset as a dependent variable in quantitative analysis. If
proto-rebels actually are mobilizing in response to international events, and governments are
seeking to deter them, then the proper dependent variable is one that measures the timing
of militant group mobilization. The commonly used variable, armed conflict onset, should
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actually be seen as the conclusion of a long process that is replete with selection effects.
Thus, scholars working in this area have likely underestimated diffusion’s true impact.
Therefore, this dissertation’s topic cannot be timelier. The analyses herein provide
the social-scientific basis necessary for understanding recent events, while also explaining the
puzzling world-wide pattern of rebel mobilization, state repression, and civil war. Theoretical
innovations central to this dissertation will also bring critical new insight to several academic
literatures, and will be of interest to scholars and policy-makers who focus on civil war,
terrorism, repression, and the international effects of revolution.
The remainder of this introductory chapter is structured as follows. First, I provide
a deeper elaboration of the dissertation’s motivating puzzle. Second, I offer a preview of
the theory and define key terms. Third, I make note of exploratory work that provided key
insights for this project. Finally, I explain the dissertation’s structure, which is dominated
by three independent essays.
1.1. The Puzzle
In 1996, the Communist Party of Nepal (Maoist) (CPN-M) launched a rural-based
insurgency against the monarchical government of that country. This campaign took the
form of a “Maoist People’s War,” which is to say that the communists devoted themselves
to guerilla warfare and the capture of territory in the country-side, followed by the revo-
lutionary mobilization of the population and the construction of parallel state institutions.
Captured documents would later show that the CPN-M conscientiously modeled itself on
several contemporary Maoist and revolutionary groups, including the Naxalites of India, the
Khmer Rouge of Cambodia and, most interestingly, the Sendero Luminoso of Peru.
Although Peru and Nepal are separated by many thousands of miles, and differ in
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many key respects, insurgents in both cases learned techniques of mobilization from one
another and both employed the rhetoric and doctrines of Mao Tse-Tung. This example is
not unique — the diffusion of Maoist strategies of mobilization has inspired radical dissidents
for several decades, in places as far removed as Thailand, the Philippines, and Sri Lanka
(Marks 1996, 2003; Marks and Palmer 2005).
This kind of learned mobilization is not limited to Maoism, or even to active insurgen-
cies. The overthrow of regimes, the secession of new states, and the survival and persistence
of rebel-founded regimes have exerted similarly powerful demonstration effects, inspiring
like minded militants to acts of violence throughout the world. A prominent example is the
mobilization of dissidents throughout the military juntas of 1970s Latin America after the
Cuban Revolution, which contributed to the creation of militant groups like the Colombian
National Liberation Movement, the Uruguayan Tupamaros, and the Nicaraguan Sandinistas.
(Brands 2010; Harmer 2011; McSherry 2005; Westad 2005).
Further examples can be found in contemporary conflicts. Modern conflicts in the
Muslim world are driven, in part, by the success enjoyed by Islamist revolutionaries and
insurgents in 1980s Afghanistan, Iran, and Lebanon. The defeat of the Soviet military in
Afghanistan was a crucial inspiration for rebel entrepreneurs seeking to mobilize challenges
to state authorities in a wide array of states, from Algeria to the Philippines. Although
rebels often overestimate the odds of victory, the stunning success of rebels in Afghanistan
inspired in others the belief that military victory against a major military power was possible,
thus contributing to bloody conflict and mass terrorism of the kind seen on 9/11. Finally,
the success of revolutionaries in Tunisia and Egypt during the Arab Spring (2011) was vital
in the mobilization of rebellion in Libya and Syria (Abrahms and Lula 2012; Hamzeh 2004;
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Hegghammer 2010; Jaber 1997; Sedgwick 2007; Weyland 2012).
Learning of this kind is not limited to dissident mobilization. It also affects state
repression. States threatened by the free flow of information about rebellion often react
harshly, initiating mass repression and campaigns of political terror. In one example, each
of the Latin American dictatorships threatened by the Cuban Revolution instituted policies
designed to demobilize leftist dissidents and impede any further mobilization. The most
notorious of these efforts was Argentina’s “Dirty War.” Interestingly, domestic programs
like the Dirty War were aided by an international program, code-named Operation Condor,
in which the affected states shared intelligence with one another, dispatched assassins across
one another’s borders in order to kill rebels, and waged a campaign of targeted killings
against exile dissidents in places as far away as Europe and the United States (Brands 2010;
Harmer 2011; McSherry 2005; Westad 2005; Weyland 2012). Similarly, during the Arab
Spring, the potential for emulation by rebels led threatened states, like Libya and Syria, to
respond with harsh measures (Saideman 2012; Weyland 2012).
Although these anecdotes occur in the post-World War II era, the phenomena they
highlight are timeless. The historical record is rich with examples of proto-rebel learning.
The American War of Independence (1775–83), for example, ignited a wave of liberal revolu-
tions on both sides of the Atlantic Ocean, inspiring dissidents in France to mobilize against
the monarchy, Haitian slaves to revolt against authorities, and Latin Americans to throw off
their Spanish colonial overlords (Dubois 2012; Dunn 2000).
These liberal revolutions were also associated with intense repression by states. Haiti’s
survival as an independent state provided a direct inspiration to the slave population of the
United States, dramatically affecting early American foreign and domestic policy. At the
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behest of the slave-holding states, the early American republic refused to even recognize
Haitian independence until after the conclusion of its own civil war (Dubois 2012). In
Europe, monarchies threatened by the spread of the liberal nationalist ideology instituted a
counter-revolution and waged war in order to preserve their regimes. After the conclusion of
those wars, the European monarchies created their own version of Operation Condor, the so-
called Holy Alliance, designed to defend and affirm monarchy against the forces of liberalism
and nationalism (Cronin 1999).
Together, these anecdotes suggest a unique puzzle that has, thus far, been ignored in
the contemporary study of rebellion and civil strife; namely, that rebel movements, revolu-
tions, and civil wars appear to inspire dissidents contemplating rebellion, or “proto-rebels”
as I term them in this dissertation, to mobilize, even in countries that are otherwise uncon-
nected. Further, future-regarding political authorities, cognizant of the threat posed by these
inspirational effects, initiate repression in order to preserve their own power. The existing
conflict literature offers little insight into these puzzling linkages, instead focusing on those
conditions responsible for the direct spread of conflict and repression among regional and
geographic neighbors (e.g., Buhaug and Gleditsch 2008; Danneman and Ritter 2014).
Consider the following additional anecdotes, which cannot be explained by current
theory:
• The “New Left” militants of the 1960s were directly inspired by civil conflict in the
developing world. Individuals responsible for organizing the German Red Army Fac-
tion, the Greek N17, the Italian Red Brigades, and the French Action Directe closely
monitored the struggles of Third World nationalists, the revolutionary regimes
therein, and the communist insurgents in Vietnam. The Germans specifically mod-
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eled themselves on the leftist Tupamaros of Uruguay, and were familiar with the
works of Latin Americans like Abraham Guillen and Carlos Marighella, radical in-
tellectuals who promoted “urban insurgency.” The Black Panthers and the Weath-
ermen in the United States were similarly drawn to these cases (Abrahms and Lula
2012; Midlarsky, Crenshaw and Yoshida 1980; Varon 2004).
• In response to Islamist mobilization throughout the Middle East during the 1980s,
the Syrian regime of Hafez al-Assad ordered a harsh crackdown in order to restore
order in the city of Hama. Some put the resulting death toll at 20,000 civilians. The
journalist Thomas Friedman would later call this tactic the “Hama Rules.” Under
these “rules,” mobilization by dissidents, particularly those inspired by international
events, are crushed with overwhelming force (Friedman 1995, chap. 4).
• In 1956, the Soviet Union invaded Hungary and crushed the revolution there, not
only to uphold the communist regime and preserve the Warsaw Pact, but also to pre-
vent similarly inspired uprisings from occurring in Eastern Europe (Valenta 1980).
In later developments, post-Soviet Russia gave aid to Belarus to stop the spread
of the Color Revolutions, the Shanghai Cooperation Council promoted authoritari-
anism in Central Asia, and the Gulf Cooperation Council intervened in Bahrain in
order to suppress public dissent (Ambrosio 2008, 2009). In each case, preserving do-
mestic order and forestalling the diffusion of conflict motivated regimes to intervene
internationally.
In order to grapple with the puzzle of variable diffusion, I propose a multifaceted
theory of “rebel learning” that links these anecdotes together. In the next section, I briefly
introduce the logic of this theory. The theory is explored at greater length in subsequent
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chapters and significant empirical validation is found.
1.2. Preview of the Theory
Upon what information do rebels and governments base their decision making? The
dissertation project advances the novel reasoning that that proto-rebels and governments
adopt violent strategies, in part, by observing the consequences and appropriateness of civil
conflict ongoing in the international system, and in doing so gain a greater understanding of
rebellion’s utility – thus driving the diffusion of conflict even at great distances.
In extant scholarship, protest strategies and government responses are thought
to diffuse in a similar manner (e.g., Beissinger 2002; Hill, Rothchild and Cameron
1998; Kuran 1998; Weyland 2009). To date, however, research on the diffusion
of organized political violence, such as rebellion and repression, is mainly con-
cerned with material conditions anchored to regional and geographic proximity (e.g.,
Braithwaite 2010; Buhaug and Gleditsch 2008; Danneman and Ritter 2014; Gleditsch 2007;
Maves and Braithwaite 2013; Salehyan and Gleditsch 2006). While this research has ad-
vanced the study of war diffusion to a significant degree, it can explain neither the linkages
between distant conflicts, nor the unique patterns of preemptive governmental responses.
I therefore build a theory upon the two-sided logic of mobilized dissent and repres-
sion (e.g., Davenport 2007; Della Porta and Tarrow 2012; Lichbach 1987; Moore 1998, 2000;
Rasler 1996; Ritter 2014). This extensive literature shows that mobilized dissent is cotermi-
nous with repression. However, because conflict is always costly (e.g., Fearon 1995) and there
is uncertainty about the utility of any particular strategy or policy (e.g., Rogers 2003), proto-
rebels and government officials must engage in a search for successful strategies to achieve
their respective ends. In doing so, the dissertation project contends that proto-rebels and
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governments rely on international information to learn about the utility of rebellion and
repression. On the rebel side, violent strategies employed by active rebels may capture the
attention of proto-rebel leaders, who then provide their followers with information on the
utility of violence in order to motivate collective action. Conversely, government officials
in states vulnerable to this process may pro-actively deploy repression in order to preempt
violent proto-rebels (Danneman and Ritter 2014; Machain, Morgan and Regan 2011). Gov-
ernments thus react to dissent locally, but are proactive based on information available
globally.
The rebel learning theory thus integrates a vast body of work across the social sciences,
ranging from political science and international relations to sociology and the psychology
of learning. Given this theoretical preview, in the next set of sub-sections I describe the
“building blocks” of my theory, discussing the four conceptual moves I introduce to this
dissertation, as well as those basic social-science precepts underlying its logic.
1.2.1. Diffusion and Learning
The rebel learning theory assumes that individual conflicts are not solitary events that
can be studied independently. Rather, civil conflicts are interdependent and transnational,
diffusing from one country to another. Diffusion is defined as a process in which “the prior
adoption of a trait or practice in a population alters the probability of adoption for remaining
non-adopters” (Strang 1991, 325). So defined, diffusion operates as follows: individuals are
presumed to have imperfect access to information and unable to form a clear estimate of
the consequences of their actions without first taking stock of similar cases from the past
(Rogers 2003; Strang 1991; Strang and Soule 1998).
Although the concept of diffusion is not new, the subject has not always been in favor
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among social scientists. Oliver and Myers (2005, 4) elaborate:
For scholars not used to thinking this way, the transition is difficult, but it is very im-portant if we are to achieve a real understanding of the phenomenon we are studying.The transition perhaps can be compared to that in the study of evolutionary biology,when it is recognized that a species is not a distinct entity which can make choicesabout how to adapt to an environment, but a statistical distribution of traits acrossindividual organisms. Species evolve when the distribution of characteristics withina breeding population changes. Social movements rise when the overall frequency ofprotest events rises in a population, they become violent when the ratio of violentevents to non-violent events rises, and so forth.
As far back as 1967, Manus Midlarsky and Raymond Tanter issued a call for a serious
exploration of the international relations of civil conflict, stating:
Too often it is assumed that national systems are completely autonomous politicalunits, and that phenomena such as political instability may be explained solely withreference to factors internal to the nation-state. Given the universal existence ofnational boundaries, it is not surprising that the assumption of autonomy, defined asself-determination, finds acceptance among social scientists. Yet numerous instancesmay be cited where this notion would be insufficient as an explanatory principle.To cite one contemporary example, the autonomy of the East European nations isapparently limited by the Soviet presence in that region. In addition, the presence ofthe German community in the Sudetenland prior to the Second World War eventuallycompromised Czech political autonomy (Midlarsky and Tanter 1967, 209).
The perspective of completely autonomous political units, called the “closed polity”
approach by Gleditsch (2007), stands in sharp contrast to empirical reality. Although the
closed polity assumption is useful for understanding those domestic conditions that make civil
conflict more likely, and is a necessary assumption for some quantitative research designs, it
raises the issue of Galton’s Problem (Elkins and Simmons 2005; Gilardi 2012; Lee and Strang
2006; Ross and Homer 1976; Simmons, Dobbin and Garrett 2006; Strang 1991). Originally
elaborated in the 19th Century by Sir Francis Galton, a scholar of the British Royal Society
and a cousin of Charles Darwin, Galton’s Problem notes that it is difficult to determine
whether units with similar structures are “dependent among geographic units as a result
of common external influences upon the units, rather than reflecting underlying internal
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structural characteristics” (O’Loughlin et al. 1998). In other words, are events occurring
in similar countries the result of completely external or completely internal factors? This
question has only recently received significant attention in the study of domestic political
violence and civil conflict (Checkel 2013).
There are a variety of mechanisms by which diffusion among mutually dependent
units may occur. Unfortunately, the literature has yet to settle on a comprehensive list
of such mechanisms. Indeed, by one accounting, there are over thirty ways in which a
policy may spread from one location to another (Elkins and Simmons 2005). Within the
International Relations, scholars have generally settled on three possibilities: migration, em-
ulation, and learning (Gilardi 2012; Wood 2013). Migration is the most widely studied
mechanism of conflict diffusion. Here, political violence in one country creates cross-border
flows that impact the internal environment of another country; thus, conflict spreads in
an almost disease-like fashion. Civil conflict has been shown to drive refugee flows into
neighboring states, disrupting them and rendering conflict in the recipient state more likely
(Salehyan and Gleditsch 2006). Civil violence has also been shown to transmit from one state
to another via cross-border ethnic ties (Buhaug and Gleditsch 2008). Additionally, conflict
can create externalities that increase the likelihood of conflict in neighboring states. For ex-
ample, conflict within a single state may disrupt the regional economy, thereby destabilizing
nearby countries (Murdoch and Sandler 2004).
The final two mechanisms, learning and emulation, are my direct objects of concern
and thus require some discussion. Learning, as it is meant in the relevant literature, is
defined as follows:
The process whereby policy makers use the experience of other countries to estimatethe likely consequences of policy change. Before a policy is introduced, its conse-
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quences are by definition uncertain. . . looking at the outcomes in countries that havealready introduced the policy, and maybe comparing them with those of countriesthat have not adopted it, can be a way for policy makers to evaluate what will likelyhappen. This process can be rational, if policy makers elaborate information accord-ing to the laws of statistics, but it can also be bounded, if they rely on cognitiveshortcuts that may introduce errors into the process (Gilardi 2012, 17).
It is the bounded form of learning that I test in this dissertation. Bounded
learning relies upon findings from cognitive psychology, which do not regard people
as natural statisticians, but rather as “cognitive misers” reliant upon “shortcuts” and
superficial and often facile logic to process information (Kahneman and Tversky 1979;
Kahneman, Slovic and Tversky 1982; McDermott 2001; Tversky and Kahneman 1981).
Despite the fact that learning has yet to be applied systematically to the study of civil
conflict, it has seen wide application to foreign policy decision making. Within that field, such
logic is referred to as “analogical reasoning,” or that process in which decision makers reason
by analogy and attempt to draw lessons from history. Here, policy makers search through the
historical record for events they feel closely resemble their own situation. An analogy is then
selected to guide the policy maker, even if that analogy has only a superficial resemblance to
the current situation (Khong 1992; Neustadt and May 1986). Although many analogies are
seriously misleading, they are a necessary component for decision making in international
relations, especially when the decision maker is confronted with a novel situation (Houghton
1996).
Perhaps the best example of analogical reasoning from the foreign policy decision
making literature is the “Munich analogy.” In the Munich analogy, policy makers seek to
avoid appeasing potential rivals, as is said to have occurred at the Munich peace conference
in 1938 when Western policy makers gave in to Nazi demands on Czechoslovakia. Sensing
weakness from the Western powers, the Nazis, or so the popular account goes, pressed on
12
with additional demands that finally resulted in war. The lesson, therefore, is that decision
makers must remain firm now in the face of demands from foreign powers in order avoid a
more serious crisis later (Khong 1992; Neustadt and May 1986).
The Munich analogy has been applied by American policy-makers, and others, in
justifying foreign policy choices in Vietnam, Korea, and elsewhere, even despite the fact
that any resemblance between cases is usually superficial. Beyond Munich, policy makers
have often utilized analogical reasoning, drawing upon the successes of the Marshall Plan
to justify increased foreign aid outlays (Hook 1995). With respect to my dissertation, no
analogy is more apropos than the so-called domino theory.
The domino theory was first proposed by President Eisenhower in a 1954 press confer-
ence, in which he described a process by which the fall of one state to a communist rebellion
would trigger a cascade of revolutions in nearby states, which would then fall into the orbit of
the Soviet Union (Jervis and Snyder 1991; Leeson and Dean 2009; Slater 1987, 1993). The
domino theory has always been a major point of controversy, with critics of the realist school
of thought going so far as to say that it is an over-exaggerated claim; i.e., international re-
bellion and revolution have very little appeal to domestic audiences (Walt 1996). Whether
or not this is true is an empirical question, and one I seek to answer over the course of this
dissertation. Importantly, even if the domino theory is an exaggeration, policy-makers in
threatened states act as though it is true and are hence liable to use repression as a policy
tool in order to defend themselves.
The final mechanism of diffusion is emulation, which, as it is defined in the policy
diffusion literature, is “the process whereby policies diffuse because of their normative and
socially constructed properties instead of their objective characteristics” (Gilardi 2012, 21).
13
In other words, it is less concerned with the consequences of actions as it is with the changing
norms of appropriateness. In one of the best known examples, social actors like states or even
non-governmental organizations take action to redefine that which is considered “appropriate”
in international relations. Thus, organizations like the International Red Cross can take on
an entrepreneurial role and try to teach new norms about humane warfare, or advocates of
transitional justice can press states to adopt criminal prosecutions and truth commissions
following a period of civil war. Eventually, the norm diffuses through the system and a
large number of states adopt the new norm (Finnemore 1993; Finnemore and Sikkink 1998;
Sikkink 2011).
Emulation also applies to my theory in a variety of ways. The most obvious way
is that of tactics; i.e., tactics like urban insurgency or rural guerilla war may be a more
appropriate way for some rebels to wage war against their governments, given the facts of
terrain, the state’s military power, and so on. Indeed, history is replete with examples of
rebel entrepreneurs seeking to “teach” rebellion to others. Some, like Osama bin Laden, have
met with a degree of success and taught new tactics to insurgents around the world, while
others, like Che Guevara, have met with nothing but failure while attempting to do so. A
recent move in the literature has sought to explore the movement of transnational activists
across the international system and the manner in which this impacts domestic conflicts
(e.g., Bakke 2013; Hegghammer 2010, 2013; Kalyvas and Balcells 2010). I do not consider
tactics or methods of rebellion in this dissertation, although it is an obvious next step and
some initial exploration is offered in the concluding chapter.
It thus bears emphasizing that the “learning” I study is not necessarily “genuine”
in the psychological sense of the term. For example, proto-rebels might adopt a particular
14
ideology simply to attract outside support or because it is particularly useful in attracting
followers. A well known case of such behavior occurred in 1970s Angola, where the rebel
group UNITA switched from an overtly Marxist ideology to one that was ostensibly liberal in
order to appeal to American policy makers for aid. To quote Bakke (2013, 35), “learning or
emulation on the part of the domestic insurgents can be based on ideational or instrumental
motives.” Given this discussion, it is useful at this point to briefly define and discuss the
proto-rebels themselves, with an eye to the literature’s view on their motivations.
1.2.2. Revolutionary Regimes
The literature has fairly well established the fact that proto-rebels will learn and
mobilize in response to an ongoing conflict in another state (Kuran 1998; Lake and Rothchild
1998). While ongoing conflict unambiguously demonstrates to proto-rebels that mobilization
and survival against the repressive power of the state is possible, and provides some idea
on the possible costs of conflict, the relevant literature has neglected the benefits of conflict.
Should rebels succeed in overthrowing their regimes, or breaking away from a country and
forming a new one, proto-rebels may take notice and seek to learn from their example.
Revolutionary regimes of this kind act as “beacons” of inspiration for proto-rebels. Thus,
a crucial innovation is a focus on regimes of this kind. As such, this project not only joins
the civil war and militant collective action literatures, it also addresses the literature on
the effect of revolution on international politics (Carter, Bernhard and Palmer 2012; Colgan
2013; Colgan and Weeks n.d.; Enterline 1998; Enterline and Greig 2005; Maoz 1989, 1996;
Walt 1996).
While the quantitative literature does not directly address the linkage between the
logic of dissent, repression, and the existence of revolutionary regimes, history is replete
15
with supporting anecdotes. Ted Robert Gurr, in his widely cited Why Men Rebel, noted
that Ghana’s independence in 1957 raised the expectations of political independence among
Africans throughout the continent, indirectly contributing to political violence in places as
far away as the Belgian Congo or Angola (Gurr 1970, 97).
Weyland (2009) agrees, noting that stunning rebel success, and the establishment
of a revolutionary regime, alert proto-rebels to a new universe of political possibilities, and
inspires in them an almost euphoric desire to topple their own regime and a willingness to take
risks. This phenomenon is not limited to the modern era with its instant communications
and easy travel. As far back as the 1790s, the Marquis de Lafayette threatened to present
Europe with the “contagious example of a dethroned king” (Haas 2005, 7).
1.2.3. Proto-rebels
Proto-rebels are self-motivated dissidents, radicals, rebel entrepreneurs, and other
first-movers who might act violently if given sufficient opportunity or motivation. Building
from Tilly (1978) and resource mobilization theory, proto-rebels exist world-wide, in every
country and society. However, given that these individuals are ubiquitous around the world,
the key causal mechanism in explaining their mobilization and the onset of political violence
is that of a society’s opportunity structure. When this structure of opportunities changes
in such a way as to favor militant action, such as that which occurs during a weakening of
governmental power, then rebel entrepreneurs are more likely to press their advantage by
mobilizing opposition to the government. Violence occurs when the state chooses a repressive
response, or else when proto-rebels perceive a positive utility in its use.
This opportunity driven view of political violence is dominant in the quantitative
civil war literature (e.g., Collier and Hoeffler 2004; Fearon and Laitin 2003). It generally
16
characterizes violent dissidents as opportunistic “joiners” to a rebel movement, motivated
by selective incentives and the possibility of extracting resources, wealth, or other demands
from the government or the population. The role of ideology, grievance, and other forms
of motivation are disregarded. This approach is most famously advanced by World Bank
economist Paul Collier (e.g., Collier 2006), who argues that while rebel organizations might
be motivated by a whole host of considerations, it is the structure of economic opportunities
that determines whether or not it will continue fighting. In other words, “it is the feasibility
of predation that determines the risk of conflict” (Collier 2006, 3).
Opportunity, however, neglects the powerful role to be played by purely informational
forces that may impel participation in a rebel group. Along these lines, recent research has
shown that Marxist and Islamist ideologies have been broadly responsible for motivating
proto-rebels to go so far as to leave their own home countries and participate in broad, inter-
national social networks (Bakke 2013; Hegghammer 2010, 2013). Opportunity also neglects
the role of the first-mover; i.e., the proto-rebel, who is frequently a highly motivated individ-
ual willing to endure significant deprivation in the pursuit of a cause (Kalyvas and Balcells
2010). But, to pose a rhetorical question, how do proto-rebels first achieve collective ac-
tion, particularly in the face of uncertainty? Although the opportunity approach clearly
has great explanatory power, it cannot address the causal mechanisms behind rebellion, re-
pression, or their diffusion. Indeed, a very famous line of argument shows that proto-rebels
are apt to mobilize when they observe an ongoing conflict in another state (Kuran 1998;
Lake and Rothchild 1998). The answer, then, is to be found in the way proto-rebels learn
from revolutionary regimes.
17
1.3. Pilot Studies and Initial Work
Before proceeding into the main body of the dissertation, I briefly describe here two
papers that were written in preparation for the work. These two papers are: “Rebelling
by Example: Proto-Rebels, Learning, and Civil War Outbreak” (Enterline and Linebarger
n.d.), and “The Condor Effect: Revolutionary Communities and Interstate Cooperation”
(Linebarger and Enterline n.d.). These studies were vital in developing my theoretical per-
spective.
The first project, “Rebelling by Example,” is a pilot study for the dissertation. The
paper offers a brief overview of the quantitative civil war literature and the need for a study
of civil conflict’s international and transnational causes and consequences. The study further
notes that civil war diffusion has, to date, been studied primarily with respect to the physical
mechanisms that spread conflict among neighbors. Left unexplored by the literature are the
indirect mechanisms like learning.
“Rebelling by Example” further develops the framework underlying the rebel learning
theory. The revolutionary regime concept is introduced, as is the idea that proto-rebels are
limited in their decision making capabilities. The main dependent variable in the study is war
onset. That study thus studies a phenomenon separate from proto-rebel mobilization, gov-
ernment repression, or even the escalation to war considered in this dissertation. Finally, the
study utilized the Correlates of War (COW) Intra-state War file (v4.1) (Sarkees and Wayman
2010) as the anchor for its empirical analysis. The COW data allows researchers to study a
significant time frame (1816–2007), but at the cost of focusing only upon high intensity con-
flicts (those generating over 1000 annual battle-deaths). “Rebelling by Exmaple” therefore
provides insight into rebel learning from a macro-historical perspective.
18
The second project, “The Condor Effect,” explores some of the foreign policy con-
sequences of the emergence and persistence of revolutionary regimes in the international
system. Specifically, it argued that revolutionary regimes face a hostile international envi-
ronment and so are forced to ally with one another and form communities in order to survive
and then export their ideologies. Regimes threatened by this process form their own counter-
balancing communities and attempt to eliminate those proto-rebels within their borders that
may be inspired by the revolutionary communities. Although quantitative evidence is given,
the paper also uses a brief case study of the Southern Cone of Latin America in the 1970s,
in which the military dictatorships of the region were threatened by the diffusion of radical
Marxism emanating from the communist bloc. Those dictatorships thus engaged in a surrep-
titious campaign of transnational repression in order to eliminate the proto-rebels in their
midst.
Both of these works are intimately connected to this dissertation and provide im-
portant insight into the rebel learning theory. Perhaps as importantly, they establish the
fact that multiple puzzles are answerable by the rebel learning theory, while others await
discovery.
1.4. Structure of the Dissertation
The dissertation is primarily composed of three essays. These essays each study a
phenomenon affected by the rebel learning theory, and they are each designed to stand as
independent papers for publication in peer-reviewed journals. Chapter 2 is designed to be
published as a simple research note. It describes the need to study the pre-conflict process
and the necessity for diffusion scholars to focus on militant groups rather than war onset. It
also shows that the emergence of a militant group as a function of learning by proto-rebels
19
is a necessary pre-condition for war onset.
Chapter 3 elaborates the rebel learning theory and connects the establishment of
a revolutionary regime with the emergence of militant groups. I argue that revolutionary
regimes provide sufficient inspiration to proto-rebels that their effects transcend geographic
distance. I also show that the inspiration effect of revolutionary regimes is dramatic enough
to inspire proto-rebels to action world-wide, regardless of the cultural differences between
them.
Chapter 4 tests the other side of the rebel learning theory. It draws upon insights from
the repression literature, particularly that of the threat posed to regimes by mobilized dissent.
The chapter relies upon a common assumption in the literature; namely, that political elites
across all regime-types seek to maintain the status-quo. When international events, like
the emergence of a revolutionary regime, suggest to proto-rebels that it is possible, through
violent action, to break the power of elites and obtain the benefits of authority for themselves,
then the extant authorities will seek to protect themselves by using repression. This effect
is conditional upon the ability and the will of revolutionary regimes to export revolution
abroad.
Finally, Chapter 5 concludes the dissertation. It identifies puzzles left unresolved by
this dissertation, and proposes papers that may be written for each, essentially establishing
an early career research agenda.
20
CHAPTER 2
CIVIL WAR DIFFUSION AND THE EMERGENCE OF MILITANT GROUPS
2.1. Chapter Abstract
In this essay, I argue that scholars of the international diffusion of civil conflict would
benefit from directly measuring rebel mobilization prior to the onset of armed conflict. To
better understand the way in which international processes facilitate dissidents overcoming
the collective action problem inherent in rebellion, I focus on militant organizations and
model the timing of their emergence. I use several datasets on militant groups and violent
non-state actors, and rely on Buhaug and Gleditsch’s (2008) causal framework to examine
how international conditions predict militant group emergence. While Buhaug and Gleditsch
conclude that civil war diffusion is primarily a function of internal conflict in neighboring
states, once militant group emergence is substituted, I observe that global conditions affect
militant group emergence. A final selection model links militant group emergence with civil
conflict onset, and demonstrates the variable performance of diffusion effects, thereby sug-
gesting that international diffusion is a two-stage process. First, rebels mobilize in response
to more global events, and then escalate their behavior in response to local conditions.
2.2. Introduction
In this essay, I argue that the quantitative literature on the international diffusion
of civil conflict would benefit from analyzing an alternate dependent variable — the tim-
ing of the emergence of militant groups — rather than the onset of armed conflict or civil
war. Theoretical and empirical advances are possible once this shift is made. Whereas the
current literature demonstrates that the onset of armed conflict is partially the product of
spillover from conflicts within the same geographic neighborhood (Buhaug and Gleditsch
21
2008; Salehyan and Gleditsch 2006). I show these same conflicts also produce mobilization,
radicalism, and the formation of new militant groups on a global basis. Thus, the inter-
national diffusion of armed conflicts is a two-step process. Militancy first diffuses globally
from civil conflicts, “priming” certain countries for escalation to armed conflict. Regional
and neighborhood factors then act upon the primed countries, which may then result in
escalation.
I reach these conclusions by using replication data provided by the
Buhaug and Gleditsch (2008) study on civil conflict diffusion, and then substituting
its dependent variable with one that reflects the timing of militant group emergence
as defined in a variety of data, including the Big Allied and Dangerous Data (BAAD)
(Asal and Rethemeyer 2008), and data modified from the Jones and Libicki (2008) study on
terrorist organizations.
The Buhaug and Gleditsch (2008) study is emblematic of the literature on interna-
tional conflict diffusion. It uses the onset of armed conflict as the dependent variable, and
argues that this variable is the product of regional externalities, like the flow of refugees
or cross-border ethnic kin groups, associated with conflicts in neighboring states. These
externalities render it easier for rebel entrepreneurs in neighboring states to overcome the
collective action problem and assemble a rebel army.
There are two reasons why researchers should consider a dependent variable like
militant group emergence in their analyses. First, the armed conflict onset variable does not
capture the theorized conflict process. Indeed, armed conflict onset is an escalatory, action-
reaction sequence involving organized actors that is observed in the common data-sets only
after that conflict yields a certain number of battle-deaths in a given year.1 By such a
1For example, the Armed Conflict Data hosted by PRIO/UCDP records a conflict once 25 battle-deaths are
22
definition, collective action has already been attained. Second, the quantitative analyses
in the literature suggest that armed conflict diffusion is mainly a function of spillover from
neighboring states, with almost no evidence in favor of global or informational mechanisms,
such those provided by emulation and learning, in which would-be rebels initiate collective
action based upon their observations of global conflict (Buhaug and Gleditsch 2008).
There are a variety of international and transnational causal pathways that explain
the transnational diffusion of militancy, including the possibility of learning and emulation
among actors, ideological linkages, diaspora funding, patronage of a group by a strategic rival,
or even the global flow of arms, funds, and foreign fighters (Hegghammer 2010; Horowitz 2010;
Kalyvas and Balcells 2010; Midlarsky, Crenshaw and Yoshida 1980). I make no attempt to
mediate among these causal pathways. Rather, my aim in this essay is to demonstrate that
scholars interested in the diffusion of civil conflict using quantitative measures executed upon
a global sample might consider an alternative approach.
In the next section, I elaborate upon the escalation process, in which the existence
of a militant group or other violent non-state actor is a necessary condition for the onset
of armed conflict. I then execute a quantitative research design using replication data to
provide evidence for my claims. I conclude by demonstrating that the international diffusion
of civil conflict is a two-stage process.
2.3. International Diffusion and the Conflict Process
Recent literature has defined civil armed conflict as a process, rather than a single
event to be correlated with structural variables. The process evolves as follows. First, a dis-
pute occurs between dissidents and the government. Second, rebels and dissidents mobilize
observed in a given year (Themner and Wallensteen 2012).
23
and the state engages in repression. The challenge for the rebels at this stage is to overcome
the collective action problem and then to recruit an army capable of challenging the state,
while the challenge of the government is to deter mobilization or disrupt recruitment. Both
actors may exit this escalatory spiral at any time, with no armed conflict occuring as a result.
However, in a small minority of cases, this action-reaction process of mobilization and repres-
sion escalates beyond an annual battle-death threshold and is recorded as an armed conflict in
the popular datasets (Davenport, Armstrong and Lichbach 2005; Sambanis and Zinn 2005;
Ritter 2014; Young 2013).
Many of these insights originally came to us from Ted Robert Gurr’s Why Men Rebel
(Gurr 1970, 7-14). Gurr saw political violence occurring on a continuum of increasing orga-
nization and severity. The continuum begins with unorganized unrest and violence. Good
examples include protests, riots, strikes, and other forms of social conflict. The continuum
then continues through conspiracy, which includes coups, most terrorism, and insurgency.
Finally, the continuum concludes with war, which is very highly organized and involves mass
participation. Violence in war is intense and intended to overthrow the state or even create
a new one.
Consider the following examples. After the Cuban Revolution, a wave of militancy
swept across Latin America, prompting the formation of groups like the Uruguayan Tupa-
maros, the Argentine Montoneros, and the Nicaraguan Sandinistas (Brands 2010). Dissidents
in each case mobilized without the benefit of direct spillover from the Cuban conflict, and
their emergence predated the onset of armed conflict by many years. The Sandinistas, for ex-
ample, emerged in 1961 (Zimmermann 2000, 72-73), whereas the UCDP Conflict Encyclope-
dia does not record armed conflict in Nicaragua above a threshold of 25 annual battle-deaths
24
until 1977 (UCDP 2012b).
Similarly, the Iranian Revolution exerted a powerful demonstration effect, inspiring
Shi’ite populations throughout the adjacent Persian Gulf region to militant collective ac-
tion. However, its international impact was felt most keenly in Lebanon. The Iranian and
Lebanese Shi’ite clergies shared a deep affinity, both groups having participated in the same
learning circles in the holy cities of Qom and Najaf. Thereafter, Islamist militants began
congregating in Lebanon’s Bekka Valley, forming the nucleus of the Hezbollah organization.
These militants swore loyalty to Ayatollah Khomeini, and were rewarded by Iran with the
deployment of 1500 Revolutionary Guards who armed and trained them (Hamzeh 2004, 17-
25). Hamzeh (2004, 25) summarizes this process, “Hezbullah thus emerged from a marriage
between Lebanese Shi’ite militants and Islamic Iran, and grew to become the most influential
Shi’ite militant movement in the region.”
Finally, European leftist militants of the 1970s were inspired by anti-colonialist libera-
tion wars and the actions of communist insurgents in Vietnam. The German Red Army Fac-
tion specifically modeled itself on the Tupamaros of Uruguay, and were familiar with the work
of Abraham Guillen, a radical intellectual who promoted “urban insurgency” in Latin Amer-
ica. Importantly, no subsequent armed conflict occurred in Germany (Abrahms and Lula
2012; Midlarsky, Crenshaw and Yoshida 1980; Varon 2004).
There are several practical consequences of this discussion for the scholar of civil
conflict. First, it is evident that many disputes occur, but in which armed conflict is never
recorded because dissidents fail to produce an effective organization or escalate their be-
havior. This is especially important for the scholar of diffusion. Researchers in this area
frequently make the argument that international factors like refugee flows, cross-border
25
ethnicity, or the demonstration effects produced by victory in war aid rebel entrepreneurs
in nearby states in overcoming the collective action problem (Buhaug and Gleditsch 2008;
Salehyan and Gleditsch 2006; Maves and Braithwaite 2013). Yet, nearly every study uses
armed conflict onset in the dependent variable. Because conflict onset occurs at the end of a
long process that is replete with selection effects, it can be argued that scholars in this area
have actually underestimated the potential for diffusion. Onset should be seen as a small
subset of the total number of conflicts that arise from diffusion.
The solution I advance in this essay is a shift in measurement, observing the formation
or emergence of militant groups rather than the onset of armed conflict. One possible
alternative to this problem would be to use a measure constructed from event data to capture
dissident activity in the pre-civil war environment. However, because civil wars require a
degree of organization on the part of the rebellion, it behooves the scholar to also identify
the universe of organizations from which armed conflict might emerge.
The second practical consequence concerns the international origin of many civil
conflicts. It is well known that a state is at considerable risk of experiencing armed conflict
when violence erupts in a neighboring state and then generates spillovers in the form of
refugees and arms flows. Yet, it is also the case that the international system itself generates
information on the utility of violence by institutionalizing the structure of its constituent
states, delineating acceptable forms of violence, and even providing the ideological causes over
which actors conflict. Events occurring in distant locales can even provide domestic actors
with information on the utility of violent action. The connection between the Tupamaros and
the Red Army Faction, Cuba and the Sandinstas, and Iran and Hezbollah provide excellent
examples. The present literature is not designed to analyze these kinds of phenomena, and
26
so new analyses are required.
Beyond the academic literature, militant groups have also played a vital role in in-
ternational politics. Groups such as the Black Panthers of the United States, the various
Greek anarchist movements, the German Red Army Faction, and the Japanese Red Army
each exerted an important impact on international politics without an actual armed con-
flict ever occurring. Although a critic might brush this point aside by arguing that these
are examples of terrorist groups and thus studied separately from armed conflict, there is
no good theoretical reason to study the effect of terrorist and other militant actors sep-
arately. Indeed, recent literature suggests that terrorism is a tactic employed by rebels
that lack control over territory or else who are committed to campaign of urban insurgency
(De La Calle and Sanchez-Cuenca 2012). This point is particularly trenchant for the dif-
fusion of civil war. Armed conflict produces externalities that result in the formation of
underground organizations, although subsequent violence may never transform into the kind
of guerilla war or insurgency commonly associated with civil war. Understanding these
processes is possible only by focusing on militant organizations.
2.4. Existing Militant Group Data
Several existing data sets are candidate sources for information on militant group
origination. The Minorities-at-Risk Organizational Behavior (MAROB) project contains in-
formation on ethno-political organizations that move beyond “normal politics” and into
the realm of extremist violence. MAROB codes 118 organizations representing 22 eth-
nic groups in 12 countries of the Middle East and North Africa for the period 1980–2004
(Asal, Pate and Wilkenfeld 2008). The Big Allied and Dangerous (BAAD) codes data on the
characteristics of terrorist organizations (Asal and Rethemeyer 2008). BAAD is derived from
27
original data collected by the Memorial Institute for the Prevention of Terrorism (MIPT)
and, crucially, includes time-varying variables coded on an annualized basis beginning in
1998. Last, the Uppsala Conflict Data Program’s (UCDP) Actor Dataset identifies the par-
ties involved in the universe of civil conflict, non-state violence, and one-sided violence for
the period 1946–2012. These data take as their unit of analysis the unique actor, and they
are compatible with the various UCDP and PRIO data sets (UCDP 2012a).
Despite several strengths, the three aforementioned data sources have limitations that
make them less attractive as data sources for examining the relationship between civil war
diffusion and the emergence of militant groups. For example, the MAROB and BAAD
data are restricted temporally, but more importantly, focus solely on terrorist activity or
ethno-political mobilization. Although there is some significant overlap between the use of
terrorism and the incidence of civil war, by relying on these data one would effectively be
selecting only those groups that are defined in terms of ethnicity, or else those that have
waged terrorist campaigns. Despite the UCDP Actor Data’s significant spatial and temporal
scope, these data are unable to adequately assess the timing of militant group formation due
to the fact that these data record observations only after an annual threshold of violence is
passed.
Given these limitations, I must construct my own sample. To do so, I turn first to
the Terrorism Knowledge Base (TKB). These data are attractive to this study for a number
of reasons. First, they offer global coverage, with a temporal range extending from the late
1960s until 2008. Second, TKB data contain many groups that the UCDP armed conflict
datasets identify as civil war actors. Examples of civil war actors include the Angolan UNITA,
a group that was eventually able to field conventionally organized armies, and the insurgent
28
Democratic Karen Buddhist Army (DKBA) of Burma. The data also include a mix of actors
that are purely domestic, such as the Black Panthers and the KKK in the United States,
as well those engaged in campaigns of international terrorism, such as al-Qaeda. Because
inclusion in the TKB data is not connected to a death threshold, as in the UCDP Actor Data,
it is possible to observe violent actors in that period before the onset of civil conflict. For
example, UNITA is coded with an origination date of 1963, well before the UCDP’s recorded
date for the onset of civil conflict in 1975. Thus, relying on TKB enables the researcher to
model pre-civil war diffusion processes.
The TKB was originally derived from the RAND Corporation’s proprietary Terrorism
Chronology, initiated in 1970. The Chronology recorded information on terrorist incidents,
militant groups, and other information on 3 × 5 cards. RAND held the Chronology data
privately, but by the 2000s the resource had fallen into disuse. In 2001, RAND partnered with
Oklahoma City’s Memorial Institute for the Prevention of Terrorism (MIPT) and Detica,
a British defense contractor.2 Utilizing grants from the US Departments of Justice and
Homeland Security, the TKB was expanded and quickly emerged as a vital, and after it was
posted online in 2004, freely available resource for scholars. The renewed TKB effort ended
in 2008 due to changes in budgetary priorities at the Department of Homeland Security
(Houghton 2008).
Several variants of the TKB data have come into use by researchers since its termi-
nation in 2008. The first is the Terrorist Organization Profiles (TOPs). TOPs is basically
a repository of the terrorist group profiles originally collected by the TKB, and made avail-
able online through the National Consortium for the Study of Terrorism and Responses to
2MIPT resources are available at http://www.mipt.org/default.aspx.
29
Terrorism (START).3 These profiles include the founding date of organizations, their peak
strength, ideologies, listings of known financial sources, case narratives describing founding
philosophies and current goals, and key leaders and related groups.
The second variant is contained in the RAND Corporation monograph How Terrorist
Groups End (Jones and Libicki 2008). Therein, Jones and Libicki create a listing of terrorist
groups contained in the TKB, and then code the start year of groups based on the earliest
evidence that a group existed. Terminal years for groups are also assigned based on the
earliest evidence that the group no longer exists or at least no longer used terrorism. Again,
many prominent civil war actors are included here. A number of contextual variables are also
coded, including group ideology, goals, and peak size. The Jones and Libicki data are relied
upon in subsequent quantitative studies of terrorism (for example, Aksoy and Carter 2012;
Blomberg, Gaibulloev and Sandler 2011; Chenoweth 2010; Cronin 2009; Daxecker and Hess
2013). While the TKB-based data presented by Jones and Libicki (2008) presents several
advantages, in the following section I discuss several modifications that I make to the data
to make it suitable for my inquiry.
2.5. Data and Research Design
In order to provide evidence for these points, I now describe and execute a quan-
titative research design. I anchor this analysis to the replication data provided by
Buhaug and Gleditsch (2008). The primary insight provided by Buhaug and Gledistch study
is that the spread of armed conflict onset is a function of a process of regional contagion
anchored to cross-border ethnicity. Buhaug and Gleditsch utilize the Uppsala Conflict Data
Program’s (UCDP) Armed Conflict Data to define the onset and incidence of conflict, that
3Available online at http://www.start.umd.edu/start/.
30
being a state-year that experiences armed conflict between a governmental actor and an
organized rebel actor resulting in 25 annual battle deaths (Themner and Wallensteen 2012).
This data contains a global sample of 6591 state-years for the period 1950–2001.
My analysis proceeds in several steps. I first replicate Buhaug and Gleditsch, using
Armed Conflict Onset in the dependent variable, showing that conflict diffusion is primarily
a function neighboring civil conflict. Next, I re-estimate the model twice with the same
covariates, but substituting a new dependent variable. In the first analysis, I use the variable
Group Emergence, that records the number of militant groups that emerge in a given state-
year. These data are an expanded version of the Jones and Libicki (2008) list of terrorist
organizations. In the second analysis, I execute a robustness test by replacing this dependent
variable with BAAD Emergence, extracted from the Big Allied and Dangerous (BAAD)
dataset (Asal and Rethemeyer 2008). Finally, I combine these analyses by using a selection
model developed by Sartori (2003), showing that the diffusion of armed conflict is conditioned
upon the prior existence of a militant group.
2.5.1. Dependent Variable: Militant Group Emergence
For the dependent variable, I code the timing of militant group emergence. Several
datasets on militant groups are available to the researcher. Unfortunately, existing datasets,
like the UCDP Actor Dataset (UCDP 2012a), describe only those groups involved in armed
conflict, whereas my needs are for data that describe the theoretical universe of cases from
which conflict arises. I therefore use two datasets on the emergence and behavior of terrorist
groups, the first being a modified version of the terrorist group data provided by the RAND
Corporation study How Terrorist Groups End (Jones and Libicki 2008), which contains data
on 648 organizations and has been widely used in recent research on the life-cycle of terrorist
31
groups (Aksoy and Carter 2012; Daxecker and Hess 2013). One possible criticism of this
data is that it emphasizes international terrorist groups at the expense of homegrown or-
ganizations. Therefore, I also execute a set of robustness tests using the Big Allied and
Dangerous Data (BAAD) (Asal and Rethemeyer 2008), which contains a mix of 395 groups
that select both domestic and international targets.
One possible criticism of both datasets is that they are are examples of terrorist
groups and thus studied separately from armed conflict. However, there is no theoretical
reason to separate militant groups based upon their choice of tactics.4 Both datasets include
a variety of militant groups, many of which, like the German Red Army Faction, were never
involved in armed conflict. Other included groups, like the Uruguayan Tupamaros or the
Angolan UNITA, were central to armed conflicts of varying intensity.
With respect to the Jones and Libicki (2008) data, I also attempt to ameliorate these
issues by adding the founding date of 248 additional groups and pruning out those groups
whose status as an actual organization is questionable.5 Sources of this research include the
Terrorist Organization Profiles (TOPs)6, as well as the Federation of American Scientists’
(FAS) list of Liberation Movements, Terrorist Organizations, Substance Cartels, and Other
Para-State Entities.7 This expansion effort yielded 896 total organizations.
Many of the resulting organizations are quite minor, responsible only for single inci-
dent attacks. For example, there is an active anarchist movement in Greece, which has led
to the occurrence of several dozen minor attacks, such as the immolation of cars, factory
break-ins, and so forth. Often, credit for these attacks is claimed by a previously unknown
4Although such a topic is a rich opportunity for future projects.
5Such as the Oklahoma City Bombers and small Greek anarchist cells.
6Available online at http://www.start.umd.edu/start/.
7Available online at http://www.fas.org/irp/world/para/index.html.
32
group that is never heard from again. There is good reason to believe that many of these
minor groups rely on the same pool of recruits. I therefore vet the aforementioned data of
896 organizations, removing such minor groups that were responsible for one or fewer attacks,
as determined by the case narratives in the TOPs data, as well as those that survived for
less than one month. Although most of these excluded groups are very minor, some are re-
sponsible for extraordinary acts of violence. The Oklahoma City Bombers, for example, are
listed in the Jones and Libicki study as a terrorist organization. These Bombers, however,
constituted a small group of people and had little to no collective action problem to overcome.
The Bombers, like the minor Greek anarchists, are therefore excluded from the data. After
such research, there are 623 groups for analysis. These groups have sufficient capacity to
be defined as an organization, command authority independent from other groups, and are
large enough that overcoming the collective action problem is a continuing challenge for their
leadership cadres. In Figure 3.1, I plot the founding year of organizations in the modified
data.
Figure 2.1. Frequency of Militant Group Emergence Over Time
010
2030
Fre
quen
cy o
f Mili
tant
Gro
up E
mer
genc
e
1940 1960 1980 2000Year
33
Each group in these data is also assigned a country of origination. This was deter-
mined as follows. Each data source lists those states in which militant groups operate. In
most cases, groups emerge and operate in the same country. However, many organizations
operate in multiple countries. Al-Qaeda, for example, is listed in the TOPs data as operating
in 45 separate states. My argument specifies that the only country needed for analysis is the
location of a group’s primary base or point of origination. In many cases, such information
is listed in the Jones and Libicki data, but in others it was determined from the TOPs and
FAS case narratives.
2.5.2. Independent Variables
The set of independent variables are extracted directly from the Buhaug and Gleditsch
study, and the reader is directed there for the details of their specific operationalizations
(Buhaug and Gleditsch 2008, 223-225). Here, I describe two variables from their study that
are central to the assessment of diffusion, and therefore central to my efforts herein. The
first is Neighboring Conflict Dummy, a 0/1 indicator of civil conflict in at least one of a given
country’s contiguous neighbors. The second variable is Neighboring Conflict Incidence. This
variable weights all armed conflicts in the international system by their proximity to the unit
of observation by using an inverse distance weighting scheme. This variable may range from
0, an extreme case in which there is no conflict in the international system in a given year,
to 1, in which all states in the system experience a civil war or in which all of a country’s
neighbors are experiencing civil war (Buhaug and Gleditsch 2008, 223-224).8
8The reader is referred to Buhaug and Gleditsch (2008) for the weighting scheme.
34
2.6. Analysis
In Table 2.1, I replicate the Buhaug and Gleditsch (2008) study of civil war onset
using Armed Conflict Onset in the dependent variable.9 For reasons of space, I do not display
the control variables, although these perform exactly as expected. As per results contained
in the original study, the Neighborhood Conflict Dummy reflects a high degree of statistical
significance (Model 1), while the effect of the complex weighted average of neighboring
conflict, reflected in the performance of Neighborhood Conflict Incidence (Model 2) is not
significantly different from zero. Buhaug and Gleditsch interpret these findings to mean
that the risk of civil conflict outbreak increases as a function of conflict in a neighboring
state, but not in proportion to the share of countries in the system undergoing conflict
(Buhaug and Gleditsch 2008, 225).
Table 2.1. Buhaug and Gleditsch (2008) Logit Models of Armed Conflict Diffu-sion, 1950–2001.
Neighboring Civil War Global Civil WarsVariable (1) (2)Neighboring Civil War 0.38*
(0.15)Neighboring Conflict Incidence 0.13
(0.28)Neighborhood democracy (wa) -0.01 -0.02
(0.02) (0.02)Neighborhood democracy2 (wa) 0.00 0.00
(0.00) (0.00)Neighborhood GDP per capita (wa) -0.04 -0.05
(0.15) (0.15)Democracy 0.00 -0.00
(0.01) (0.01)Democracy2 -0.01** -0.01**
(0.00) (0.00)GDP per capita (ln) -0.27* -0.29*
(0.13) (0.12)Population (ln) 0.28*** 0.30***
Continued on next page.
9See Buhaug and Gleditsch (2008, 226).
35
Table 2.1 —continued from previous page.Neighboring Civil War Global Civil Wars
Variable (1) (2)(0.05) (0.05)
Post Cold War 0.61*** 0.67***(0.15) (0.15)
Peace Years -0.01* -0.01*(0.01) (0.01)
Constant -3.50** -3.28**χ2 152.62 147.69N 6591 6591Notes : Coefficients with robust standard errors in parentheses; sig. levels are two-tailed:
∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1; wa = weighted average; ln = natural logarithm.
The task of replication completed, I turn to an analysis of militant group emergence.
These models differ from the replication in a number of important ways. First, because
Group Emergence and BAAD Emergence are count variables, Models 3–6 utilize negative
binomial models10.
Second, Group Emergence and BAAD Emergence both show an increasing number
of militant groups over time. This may result from an actual increase or, more likely, it is a
function of improving coverage by the media and other source material (Drakos and Gofas
2006). To account for such bias, I insert a time trend and yearly dummy variables in place of
Buhaug and Gleditsch’s Post-Cold War dummy variable into those models in which Group
Emergence or BAAD Emergence is the dependent variable. These models also remove those
state-years occurring before 1968.
Third, rather than report standard coefficients in these models, which are difficult to
interpret, I report the incidence rate ratio (IRR). The IRR is interpreted around the ratio
1:1. Ratios greater than one indicate that the variable in question has a positive effect, while
less than one indicates a negative effect. Thus, an IRR of 2 indicates that a one unit increase
10The negative binomial is superior to alternative count models, such as the poisson, when the variance ofthe dependent variable exceeds its mean. Goodness of fit tests show this to the case with these data.
36
in an independent variable doubles the frequency of events, all else being equal.
Table 2.2. Negative Binomial Models of Militant Group Emergence, 1968–2001.
Group Emergence BAAD EmergenceVariable (3) (4) (5) (6)Neighboring Civil War 2.189*** 2.727***
(0.373) (0.894)Neighboring Conflict Incidence 2.370*** 2.795**
(0.631) (1.311)Neighborhood democracy (wa) 0.962* 0.959* 0.950 0.946
(0.0222) (0.0245) (0.0448) (0.0478)Neighborhood democracy2 (wa) 0.995* 0.995* 0.997 0.998
(0.00285) (0.00283) (0.00401) (0.00399)Neighborhood GDP per capita (wa) 1.957*** 1.946*** 1.301 1.235
(0.411) (0.429) (0.456) (0.498)Democracy 1.066*** 1.063*** 1.091*** 1.087***
(0.0171) (0.0180) (0.0252) (0.0265)Democracy2 0.998 0.997 1.002 1.002
(0.00398) (0.00424) (0.00431) (0.00488)GDP per capita (ln) 0.881 0.852 1.198 1.157
(0.153) (0.153) (0.342) (0.355)Population (ln) 1.646*** 1.686*** 1.770*** 1.796***
(0.110) (0.115) (0.147) (0.152)Time Trend 1.072*** 1.071*** 1.085*** 1.082***
(0.0252) (0.0259) (0.0293) (0.0304)Constant 7.26e-06*** 1.03e-05*** 4.92e-07*** 1.11e-06***
(9.67e-06) (1.36e-05) (9.47e-07) (2.28e-06)N 4,868 4,868 4,868 4,868Notes : Incidence Rate Ratios with robust standard errors in parentheses;
sig. levels are two-tailed (∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1);
wa = weighted average; ln = natural logarithm.
Models 3 and 5 assess the impact of a neighboring civil war on the probability that
a militant group will form in a given state-year. The effect is highly significant, which is
a notable finding. Thus, changing the dependent variable from one that reflects the onset
of a civil war to one that reflects the emergence of a militant organization yields important
and substantive findings. While armed conflict may complicate the internal environment of
nearby states, making armed conflicts more likely, an analysis of the kind of collective action
that precedes an armed conflict yields an equally important finding—that neighboring civil
37
wars contribute directly to dissident mobilization. Whether or not war occurs later in the
causal sequence is yet to be determined. The results are also substantively significant. An
armed conflict in a neighboring state more than doubles the odds that a militant group will
emerge in these models.
An even more important finding is yielded when the analysis turns to Models 4 and
6, incorporating information on all conflicts in the international system, weighted by its
geographic proximity to the unit of observation. Here, the variable Neighborhood Conflict
Incidence is highly significant. I conclude that such information provides important insights
into the emergence of militant groups. The performance of Model 4 most closely matches
the anecdotal cases. Specifically, civil wars in places like Vietnam can provide inspiration to
domestic dissidents like the Red Army Faction, while revolutions and insurgencies in places
like Iran can provide a model for emulation, and also a patron for the supply of arms and
training.
These results are also substantively significant. Although the IRR in Models 4 and 6
indicate that the risk of a militant group emergence doubles as the share of conflictual states
in the international system increases from 0 to 100%, this is a highly unrealistic scenario.
Therefore, I plot the expected count of Group Emergence as Neighborhood Conflict Incidence
increases from its 25th percentile (.002) to its 75th (.332).
In my second analysis, I wish to examine the influence of diffusion processes as they
bear on mobilization of militant groups and the occurrence of armed conflict. To do so, I
execute a binary outcome selection model for the period 1968–2001. One issue with selection
models is that they depend upon an exclusion restriction; that is to say, they require at
least one variable in the selection stage to be excluded from the outcome stage. However,
38
Figure 2.2. Expected Count of Group Emergence, 1968–2001
.03
.04
.05
.06
.07
Pre
dict
ed N
umbe
r O
f Eve
nts
.002 .052 .102 .152 .202 .252 .302Neighborhood Conflict Incidence
most selection models in conflict research employ identical variables in both stages, posing
significant methodological problems and thus requiring scholars to make exclusion choices
not justified by theory. Sartori (2002, 2003) overcomes this issue by providing an estimator
for binary outcome selection models in which both stages specify the same set of covariates.
Estimation is carried out using Sartori’s sartsel logit selection routine for the Stata software
package.11
I construct the dependent variable required by sartsel in the following way. First, I
collapse the modified list of militant groups in the Jones and Libicki (2008) data to obtain the
variable Militant Group Persistence. This variable is coded “1” for any state-year containing
one or more persisting militant groups, and “0” otherwise. Of my sample of 623 militant
groups, 525 (84%) have valid termination dates. In these cases, I assume that such groups
persisted for one year. For the sample of 4868 state-years in the period 1960–2001, 1492
(about 30%) reflect a persisting militant group.
11Available online at the following URL: http://faculty.wcas.northwestern.edu/ aes797/.
39
I then combine Militant Group Persistence and Armed Conflict Onset into a new vari-
able, Conflict Selection, according to directions provided in the sartsel package. Conflict
Selection is coded “0” for state-years with no persisting groups, “1” for state-years containing
a persisting group and no subsequent armed conflict, and “2” for those state-years experi-
encing persisting militant groups as well as the onset of armed conflict. Table 2.3 reports
the distribution of these categories by state-year.
Table 2.3. Coding of Logit Selection & Outcome Stages, 1968–2001.
sartsel Code Observed State-Years %0 No Group Emergence 3376 691 (Stage 1, Selection) Group Emergence & No Armed Conflict 1410 292 (Stage 2, Outcome) Group Emergence & Armed Conflict 82 2Total 4868 100
The results of the selection model are contained in Table 2.4. The model assumes that
the errors in the selection and outcome stages are nearly identical. As Sartori (2002) notes,
this is a good assumption to make when actors are making similar utility-based decisions in
both stages of the model. Despite the rarity of cases in which Militant Group Persistence
coincides with Armed Conflict Onset, Model 7 clearly demonstrates that a neighboring civil
war strongly increases the odds of a state-year containing a militant group selecting into
armed conflict. Model 8 shows the opposite. Neighborhood Conflict Incidence, while strongly
associated with the persistence of militant groups in the selection stage of the model is not
associated with civil war outbreak in the outcome stage. These results also have substantive
meaning. I obtain predicted probabilities for the selection model from code provided in the
sartsel package. The predicted probability of Armed Conflict Onset in Model 7, conditional
on Militant Group Persistence, is about .024.
40
Table 2.4. Logit Selection Model of Militant Group Persistence & Civil War Onset, 1968–2001.
(7) (8)Militant Group Armed Conflict Militant Group Armed Conflict
Variable Persistence Onset Persistence OnsetNeighboring Civil War 0.29*** 0.28**
(0.05) (0.11)Neighboring Conflict Incidence 0.39*** 0.20
(0.09) (0.23)Neighborhood democracy (wa) 0.01 -0.00 0.00 -0.00
(0.01) (0.01) (0.01) (0.01)Neighborhood democracy2 (wa) -0.00*** 0.00 -0.00*** 0.00
(0.00) (0.00) (0.00) (0.00)Neighborhood GDP per capita (wa) 0.41*** 0.08 0.41*** 0.07
(0.05) (0.12) (0.05) (0.12)Democracy 0.02*** -0.01 0.02*** -0.01
(0.00) (0.01) (0.00) (0.01)Democracy2 0.00* -0.00* 0.00* -0.00*
(0.00) (0.00) (0.00) (0.00)GDP per capita (ln) -0.01 -0.06 -0.01 -0.06
(0.04) (0.09) (0.04) (0.09)Population (ln) 0.37*** 0.31*** 0.38*** 0.32***
(0.02) (0.04) (0.02) (0.04)Time Trend -0.03*** -0.01* -0.03*** -0.01*
(0.00) (0.00) (0.00) (0.00)Constant -6.84*** -5.22*** -6.87*** -5.11***
(0.32) (0.78) (0.33) (0.78)N 4,868 4,868 4,868 4,868Notes : Coefficients with standard errors in parentheses;
sig. levels are two-tailed (∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1);
wa = weighted average; ln = natural logarithm.
41
The fact that the selection model demonstrates a statistically significant linkage be-
tween militant groups and neighboring civil wars, yet does not demonstrate a linkage for
militant group emergence and and neighborhood conflict incidence, suggests that a two-
stage diffusion process is at work. In the first stage, civil wars generate externalities such
as the global flow of arms, refugees, weapons, transnational activists, and demonstration ef-
fects. These externalities are capable of generating militant groups and terroristic behavior
even in states unlikely to experience civil war, such as an advanced industrial democracy.
In the second stage, the externalities of armed conflict exert a direct effect on those states
neighboring the conflict, with additional conflict onset conditioned on the earlier diffusion of
militant groups. Global conflicts thus “prime” a state — the global flow of arms, funds, and
information mobilizes radical dissidents. Neighboring conflict then provides the impetus for
escalation.
The results further suggest that even high capacity states that are unlikely to ever
experience civil war are likely to observe an increase in militant activism and terrorism. This
connection between international civil wars and domestic militants is identified in the histor-
ical literature. The inspiration that Vietnam provided to the militant New Left groups of
the 1960s is a good example (Varon 2004), just as some contemporary Islamist groups took
inspiration from relatively far removed conflicts in 1980s Afghanistan, Iran, and Lebanon
(Abrahms and Lula 2012). Yet, this phenomenon is not addressed sufficiently in the quanti-
tative international relations literature.
2.7. Conclusion
The aforementioned analysis suggest several puzzles. First is the two-stage diffusion
process. This area is clearly in need of greater theory-building. For instance, the variables
42
utilized here cannot differentiate among diffusionary mechanisms. Of particular note are
demonstration effects and learning mechanisms. Do dissidents and would-be rebels learn
from active civil conflicts, updating their estimates of utility based on the content of inter-
national information? While important literature suggests this to be the case, it was limited
primarily to the study of ethnic conflict (Kuran 1998). This study suggests that the phenom-
enon is more general, albeit conditional on highly nuanced factors. It is possible that the
pattern of results shown here can be explained as a result of the transmission of international
ideologies, the establishment of revolutionary regimes by former rebels, and the exporting of
revolutionary ideals.
Second, what explains whether or not any given state-year will transition into armed
conflict after a militant group has emerged? In other words, how do underground groups
transition into full rebel organizations? A vibrant research agenda addresses rebel collective
action and how it might be achieved and maintained (Gurr 1970). Yet, how do international
factors influence the growth of militant groups such that armed conflict becomes inevitable?
Similarly, what explains the origins of civil wars that are do not arise out of one of the groups
identified in this paper?
It is further possible that the pattern of evidence here is a function of state repression.
Only one study to date observes the linkage between civil conflict diffusion and repression
(Danneman and Ritter 2014). That study finds that self-interested political elites, fearing
demonstration effects, are more likely to repress challenges to their rule as civil wars grow
more proximate to their country. Yet, it is also possible that newly established revolutionary
regimes and international ideology impel repression in the same manner. The ultimate result
of such a process is ambiguous—one of the central puzzles of repression is that it may decrease
43
dissident behavior, but it may also trigger a violent backlash (Young 2013). Thus, states
repressing in response to civil war diffusion may in fact trigger greater militancy.
The analyses reported herein, and the puzzles they raise, suggest that answers can
be found in the international environment. They further suggest a focus on non-state orga-
nizations arising during the pre-war process. This note thus poses a number of puzzles and
opportunities for unlocking these and other questions.
44
CHAPTER 3
DANGEROUS LESSONS: REBEL LEARNING AND MOBILIZATION IN THE
INTERNATIONAL SYSTEM
3.1. Chapter Abstract
Contemporary research on the spread of civil conflict privileges proximity-based
causes such as refugees and ethnic diaspora. I argue that the diffusion of civil strife can
occur through a learning mechanism and that this phenomena may occur globally. Individ-
uals most likely to rebel, or “proto-rebels,” learn about the utility of rebellion from active
rebels and from governments founded by victorious rebels. Utilizing militant organization
data, I undertake a quantitative exploration of the spatial and temporal relationships between
militant group formation, civil conflict, and revolutionary regimes, using the country-year
as my unit of analysis. I further examine how these relationships are attenuated by cultural
and regime-type similarity. I find, in line with the literature, that civil conflicts generally
inspire mobilization only in directly neighboring states, while regimes established by rebel
victory in civil conflict are associated with mobilization on a global basis. I conclude that
proto-rebels learn to rebel, and that this process can transcend direct experience.
3.2. Introduction
In 1996, the Communist Party of Nepal (Maoist) (CPN(M)) launched a rural based
insurgency against the monarchical government of that country. Captured documents would
later show that the CPN(M) conscientiously emulated the mobilization strategies and war-
fare tactics of several contemporary Maoist groups, including the Naxalites of India, the
Khmer Rouge of Cambodia and, most interestingly, the Sendero Luminoso of Peru. Al-
though Peru and Nepal are separated by many thousands of miles, and differ in many key
45
respects, insurgents in both cases learned techniques of mobilization from one another and
both employed the rhetoric and doctrines originally developed during the Chinese revolution
(Marks 1996; Marks and Palmer 2005).
This kind of learned mobilization is not limited to Maoism or even to active insurgen-
cies. The overthrow of regimes, the secession of new states, and the survival and persistence
of revolutionary regimes has exerted similarly powerful effects, inspiring militants to acts of
violence throughout the world. A prominent example is the mobilization of dissidents across
Latin America after the success of the Cuban Revolution, which contributed to the creation
of militant groups like the Colombian National Liberation Movement, the Uruguayan Tupa-
maros, and the Nicaraguan Sandinistas (McSherry 2005). Many of today’s extremist Islamist
movements can also trace their origins similarly, having drawn their inspiration from the suc-
cess of revolutions in Afghanistan, Iran and Lebanon during the 1980s (Abrahms and Lula
2012; Hamzeh 2004).
Together, these anecdotes suggest a puzzle; namely, that civil conflicts and revolu-
tions appear to inspire mobilization in dissidents contemplating rebellion, or “proto-rebels”
as I term them in this essay, even in countries that are otherwise unconnected. The exist-
ing conflict literature offers little insight into these puzzling linkages, instead focusing on
those physical conditions responsible for the spread of conflict among geographic neighbors
(Buhaug and Gleditsch 2008; Salehyan 2009). While conflict is indeed contagious among
neighboring states, it may also diffuse among non-neighboring states, as in the recent Arab
Spring or in the observed connection between Peru and Nepal.
In order to gain leverage over this puzzle, I argue that proto-rebels learn from inter-
nationally available information on conflict. This learning mechanism is framed according
46
to the logic of collective action. Although proto-rebel entrepreneurs may employ literally
dozens of possible solutions to the collective action problem, the state possesses a decisive
advantage in terms of policing power and military force, rendering militant mobilization and
the pursuit of violent strategies costly, dangerous, and uncertain (Lichbach 1995). Proto-
rebels entrepreneurs may thus look internationally for information on the expected utility of
rebellion, and then provide it to their followers in an effort to aid mobilization.
Although existing literature has engaged in a search for evidence that proto-rebels
learn from global sources of information (Buhaug and Gleditsch 2008; Danneman and Ritter
2014), it has been unable locate evidence of such, instead consistently finding that diffusion is
the result of direct spillover from neighboring states. By contrast, I argue that proto-rebels
may learn from two sources. First, from ongoing civil wars, which demonstrate how and
when the collective action problem might be overcome. Second, from regimes successfully
established by rebels, which demonstrate the benefits of rebellion. If rebels successfully
displace a ruling government or form their own state through secession, a powerful example
is created that may then be learned by proto-rebels globally. In other words, a key missing
element in the puzzle of global learning is the revolutionary regime.
This essay thus integrates theories of learning and demonstration effects (Kuran
1998), rebellion (Lichbach 1995), international civil conflict diffusion (Buhaug and Gleditsch
2008; Danneman and Ritter 2014; Salehyan and Gleditsch 2006; Maves and Braithwaite
2013; Salehyan 2009) and the effect of revolution and regime change upon the interna-
tional system (Colgan 2013; Colgan and Weeks n.d.; Carter, Bernhard and Palmer 2012;
Enterline and Greig 2005; Maoz 1996; Walt 1996). To date, these literatures have considered
their subjects separately.
47
The remainder of this essay is structured as follows. First, I motivate the study with
a look at the international spread of civil conflict and the effect that revolutionary regimes
have upon it. Second, I develop a theory in which proto-rebels learn from and mobilize in
response to international events. Third, in order to evaluate my hypotheses, I describe a
research design using several unique variables during the period 1968–2001. Fourth, I engage
in a quantitative analysis. Finally, I offer a concluding discussion.
3.3. Learning and the Diffusion of Civil Conflict
My primary interest is the interdependence of conflicts, particularly transnational
linkages among them at the systemic level of international relations. Interdependence and
transnationalism are among the defining features of the international environment — yet,
to date, these phenomena have been studied primarily with reference to positive devel-
opments like international human rights norms, international organizations, and political
economy (Finnemore and Sikkink 1998; Simmons, Dobbin and Garrett 2006). By contrast,
the “dark side” of transnationalism has received attention from scholars of international
relations only recently. This “dark side” includes such phenomena as the international
spread of civil conflict and repression (Buhaug and Gleditsch 2008; Danneman and Ritter
2014; Maves and Braithwaite 2013; Gleditsch 2007; Salehyan and Gleditsch 2006), and the
global transmission of tactics (Horowitz 2010), ideologies (Kalyvas and Balcells 2010), the
movement of foreign fighters (Hegghammer 2013), demonstration effects (Beissinger 2002;
Kuran 1998) and even the possibility that rebels emulate others and adopt their rhetoric in
order to draw upon transnational support (Bakke 2013). In this section, I therefore offer a
“big picture” account of the transnational effects of political violence.
At the theoretical core of civil conflict’s international spread is a phenomenon called
48
“diffusion” (Rogers 2003), which is defined as a process in which “the prior adoption of a
trait or practice in a population alters the probability of adoption for remaining non-adopters”
(Strang 1991, 325). The basic social science literature on the topic identifies more than thirty
potential mechanisms of diffusion (Elkins and Simmons 2005). The most relevant of these
for the study of conflict are migration, emulation, and learning (Gilardi 2012; Wood 2013).
Migration is the most studied mechanism of conflict diffusion. Here, political violence
in one country creates cross-border externalities that impact the internal environment of
another country; thus, conflict spreads in an almost disease-like fashion. Civil conflict has
been shown to drive refugee flows into neighboring states, disrupting them and rendering
conflict in the recipient state more likely (Salehyan and Gleditsch 2006). Moreover, civil
violence has been shown to transmit from one state to another via cross-border ethnic ties
(Buhaug and Gleditsch 2008).
While migration is well explored, learning has been neglected. Learning is defined as
a process in which policy-makers use the experience of other countries to estimate the likely
consequences of a policy innovation (Gilardi 2010, 2012; Rogers 2003). Decision makers resort
to this “cognitive reconnaissance” because the outcome of any particular action is uncertain.
Observing others vicariously thus allows actors to evaluate possible courses of action. A rich
line of literature explores the nature of cognitive reconnaissance, labeling such phenomena
“demonstration effects,” although this body of work is limited primarily to ethnic conflicts
and their regional diffusion (Beissinger 2002; Hill, Rothchild and Cameron 1998; Kuran 1998;
Lake and Rothchild 1998). In the simplest version of this mechanism, decision-makers have
prior beliefs but then update them based upon the new data (Elkins and Simmons 2005).
Crucially, actors may not be bound by geographic proximity and may be able to draw
49
information from global sources.
The learning mechanism is also subtly different from emulation. Emulation is a
mechanism in which actors adopt a policy not because they believe it is the best option,
as learning implies, but because actors believe it is best to copy early adopters in order to
signal conformity in a social system. Emulation is therefore a mechanism in which diffusion
and interdependence are socially constructed, rather than arising from a rational calculus.
Emulation among proto-rebels may occur if a particular tactic or ideology proves to be
successful, and later proto-rebels seek to join a community of like-minded dissidents (Bakke
2013; Wood 2013). This is an intriguing possibility, but not one that I consider in this essay.
With the scope of learning now defined, I examine those international sources that
proto-rebels observe. The literature to date has focused on active sources of civil conflict as
a source of learned information (Maves and Braithwaite 2013; Danneman and Ritter 2014).
Yet, one overlooked source of information is that of the revolutionary regime — one that
has been empowered by rebel victory in civil conflict. Indeed, if rebels are successful in their
efforts, violently overthrowing their government or seceding into a new state, a powerful
global example may be provided to other proto-rebels. The impact of revolutionary regimes
like China, Cuba, and Iran upon the international system would seem to bear out these
assertions.
History is replete with examples of the above phenomena. Consider the link between
the American and French Revolutions in the late 18th Century (Dunn 2000). It is com-
monly argued that the American victory over British forces positively reinforced dissidents
in Europe, encouraging them that success against a major monarchical power was possible,
thus triggering a diffusion of liberalism across the continent. Interestingly, these forces then
50
returned across the Atlantic Ocean and inspired former slaves to overthrow the government
of Haiti and seize control of that country in 1803 (Dubois 2012).
Later, the European Revolutions of 1848 proceeded in a similar fashion after the fall of
the French Monarchy. “1848” transpired during a period of large-scale nationalist, socialist,
and liberal mobilization throughout Europe, culminating in the fall of King Louis-Phillipe.
Revolts then spread across Europe with a pace that would shock contemporary observers,
who are often content to believe that modern telecommunications have created a heretofore
unknown process. The cascade of militant action radiating from Paris produced continent
wide civil strife in a matter of weeks (Weyland 2009). In contemporary times, Ted Robert
Gurr, writing in his seminal Why Men Rebel, wrote that the independence of Ghana in 1957
intensified expectations for independence among African leaders. When progress toward this
goal proved too slow, political violence erupted in a host of distant states like the Belgian
Congo and Angola (Gurr 1970, 97).
Together, these anecdotes suggest that rebel victory in civil war can radically shock
the international system, engendering additional mobilization and conflict globally. Suc-
cessful attempts at revolution or secession can create a precedent, encouraging similarly
aggrieved proto-rebels to attempt similar action. Although new research suggests that learn-
ing and demonstration effects are particularly important for proto-rebels in authoritarian
regimes (Maves and Braithwaite 2013), transnational learning and its consequences remain
a relatively unexplored dimension of conflict diffusion, as the literature focuses primarily
upon non-informational variables.
51
3.4. Theory
Upon what information do proto-rebels base their decision making? To answer this
question, I develop a theory grounded in expected utility, in which decision makers judge
courses of action according to their estimated costs and benefits, and a version of the collec-
tive action problem called the Rebel’s Dilemma (Lichbach 1995), in which proto-rebels are
subjected to poor information, free-riding, and the power of the state, each of which mili-
tate against mobilization. When decision-makers labor under such uncertainty, they must
engage in a search for successful strategies (Rogers 2003). In doing so, proto-rebels learn
about the utility of rebellion. Because all human decision-makers are limited in their cog-
nitive processing abilities, I also theorize that proto-rebels will filter information according
to their contextual similarity to its sources—these sources being ongoing civil conflicts and
revolutionary regimes throughout the international system.
Learning aids proto-rebels and thus drives the diffusion of collective action in the
following ways. Rebellion can increase mutual expectations among proto-rebels by signaling
to them that others are similarly ready for action; secondly, dissent increases productivity of
tactics by providing ideas to proto-rebels for leadership, coalitions, and tactics; thirdly, the
combination of the first two solutions improves the probability of winning in that the success
of a rebel group at time t increases other’s estimates of winning at time t + 1; and fourth,
successful dissidents are able to act as principals and patrons for subsequent proto-rebel
groups (Lichbach 1995, 118-120). As Tilly (1978, 155) once noted in a discussion on the
spread of labor strikes: “That is no doubt one of the main reasons ‘waves’ of strikes occur:
the fact that a given sort of group gets somewhere with the tactic spreads the expectation
that employers or governments will be vulnerable to the same tactic in the hands of other
52
similar groups.”
A complete theory of learning contains multiple elements. First, it follows the “logic
of consequences” in that it recognizes that rational actors engage in vicarious observation,
deriving estimates of costs and benefits from the perceived success or failure of events in
the past (Gilardi 2012; Rogers 2003). Second, actors are bounded in their cognitive pro-
cessing abilities and thus reliant on heuristics and analogies to filter learned information
(Kahneman, Slovic and Tversky 1982).
In recognizing these elements, this essay departs from a purely rational theory of
learning. Rational theories of learning contend that actors observe the probabilities of suc-
cess, costs, and benefits of actions undertaken by decision-makers in the past, and then
update their own estimates of utility in a Bayesian fashion according to the laws of statistics
(Elkins and Simmons 2005; Gilardi 2012; Simmons and Elkins 2004). By contrast, under
the bounded rationality advanced by this essay, decision makers do indeed learn from the
experiences of others, but they are limited in their cognitive ability to do so.
The logic summarized above implies that when proto-rebels are considering mobiliza-
tion, and looking to the international system for information radiating from civil wars and
revolutionary regimes, they will analogize from cases they believe are similar to their own. In
other words, proto-rebels operate as though events in places similar to their own are represen-
tative of their situation. Similarity is therefore the foundational element of bounded learning,
describing those conditions under which proto-rebels are likely to process information from
abroad and then mobilize.
Similarity-based bounded learning may occur globally, without respect for the ge-
ographic distance between actors. Indeed, the literature argues that similarity-based
53
global learning may occur within lingual groups, ethnic diasporas, religious creeds, or even
within ideological communities (Hegghammer 2013; Hill and Rothchild 1986; Horowitz 2010;
Kalyvas and Balcells 2010; Kuran 1998; Lake and Rothchild 1998; Weyland 2012). It is also
possible that proto-rebels mobilize when they observe active rebellion occurring in a state
with a similar regime type. This was plainly evident during the Arab Spring, when dissidents
acted out against personalist dictatorships, during the revolutions of 1848, which saw mobi-
lization against monarchies, and during the events of 1989, in which mobilization occurred
against communist-party dictatorships (Kuran 1991; Saideman 2012; Weyland 2012).
From this discussion, I therefor posit two hypotheses:
Hypothesis 3.1. Cultural Similarity. Proto-rebels are more likely to mobilize
when civil conflict occurs in states with similar cultures.
Hypothesis 3.2. Regime Type. Proto-rebels are more likely to mobilize when civil
conflict occurs in states with similar regime types.
While the mobilization of proto-rebels is keyed to their ability to generalize to their
own context, the nature of the information flows themselves constrains that which proto-
rebels are even able to receive. For instance, proto-rebels may be unable to learn from others
due to limitations posed by physical barriers or a remote geographic location. Thus, although
proto-rebels might act upon the representativeness of those analogies from which they draw
information, they are more likely to be interdependent within a geographic neighborhood
(Gleditsch 2002a), per Tobler’s dictum that “everything is related to everything else, but near
things are more related than distant things” (Tobler 1970, 236). Moreover, it is possible that
even if learning is occurring, its impact is overshadowed by the direct, physical factors that
54
operate in the regional environment. For example, rebel success may very well inspire proto-
rebels in a neighboring state, but the mechanism is drowned out destabilizing by refugee flows
among neighboring states. I therefore reason that although civil conflicts and revolutionary
regimes have a global effect, their effects will be felt most notably in geographically proximate
states.
By way of this logic, I hypothesize as follows:
Hypothesis 3.3. Proximity. As the distance of a proto-rebel from the source of
learned information increases, the less likely a proto-rebel is to
rebel.
Finally, learning by proto-rebels may also be a function of the source of the infor-
mation. I reason that the international system provides two types of information: tactical
and inspirational. Tactical information relates to the physical act of rebellion, including
information regarding the possibilities for armed conflict against the state, military tactics
and strategies for doing so, and the costs of conflict.
Inspirational information manifests most clearly in persisting revolutionary regimes
formed by victorious rebels. Weyland (2009) argues that stunning rebel success, and the
establishment of revolutionary regimes, inspires in proto-rebels an almost euphoric desire
to topple their own regime and a willingness to take risks to do so. When such sources of
information are present in the international system, the probability increases substantially
that their effects will permeate the noise of international politics. Even when these beacons
of success lack a revolutionary character, such as those arising from secession, their mere
existence may propel mobilization.
I therefore anticipate that the source of an information signal, be it tactical or inspira-
55
tional, influences the behavior of proto-rebels. However, inspirational sources of information
are less likely to be degraded by geographic proximity. In this way, revolutionary regimes,
which are the source of such information, may inspire proto-rebel mobilization globally. I
therefore posit the following additional hypothesis:
Hypothesis 3.4. Information Source. The more inspirational the source of in-
formation bearing on rebellion, the less likely that proto-rebel
learning will be degraded by geographic distance.
3.5. Data and Research Design
In this section, I describe data and a quantitative research design that assesses
the above hypotheses. I anchor my analysis to the replication data provided by
Buhaug and Gleditsch (2008). The replication data contains a global sample of 6591 state-
years covering the years 1950–2001, although this sample is reduced depending upon the
coverage of my variables. Because my analytical focus is on the timing of proto-rebel mobi-
lization, I am reliant on data describing the timing of the emergence of militant organizations
in my dependent variable. I use two general classes of independent variables: one that cap-
tures similarity of states containing proto-rebels to states undergoing armed conflict, which
is linked conceptually to the tactical mechanism, and one that captures similarity of states
containing proto-rebels to states hosting a revolutionary regime, which is linked conceptu-
ally to the inspirational mechanism. A battery of control variables, discussed below, are
extracted from the replication data. I describe each of these variables, in turn.
3.5.1. Dependent Variable
In order to measure proto-rebel mobilization, I turn to data on the emergence or
formation of militant organizations. Such data capture the moment, as closely as possible
56
given current data, that proto-rebels overcome the collective action problem and assemble an
organization. There are two reasons why researchers should consider a dependent variable
like militant group emergence, rather than the onset of armed conflict. First, the onset of
armed conflict does not capture the theorized mobilization process. Armed conflict onset
is an escalatory, action-reaction sequence involving organized actors that is observed in the
common data-sets only after that conflict yields a certain number of battle-deaths in a given
year.1 By such a definition, learning and mobilization have already occurred. If proto-rebels
are indeed learning from global information, then it is necessary to obtain information from
a stage of the conflict process prior to the onset of armed conflict or war. What is needed,
therefore, is data that does not preference cases in which conflict onset has already occurred.
I collect data on militant group emergence from a variety of sources. These sources
include information originally collected by the Terrorism Knowledge Base (TKB), formerly
available from the Memorial Institute for the Prevention of Terrorism (MIPT).2 These data
were further expanded by Jones and Libicki (2008) to include the start and end year of groups.
Groups in this data are identified according to a definition of terrorism in which organiza-
tions employ violence of a “political nature [involving] the perpetration of acts designed to
encourage political change” (Jones and Libicki 2008, 3). Six hundred and forty-eight orga-
nizations are identified under this definition. The temporal domain of these data runs from
the late-1960s until 2006. I therefore choose 1968 as the starting point for my analysis.
In order to extend the empirical domain of this study, I expand the Jones and Libicki
(2008) data by adding the founding date of 248 additional groups. Sources of this expan-
1For example, the Armed Conflict Data hosted by PRIO/UCDP records a conflict once 25 battle-deaths areobserved in a given year (Themner and Wallensteen 2012).
2Available online at http://www.mipt.org/default.aspx.
57
sion include the Terrorist Organization Profiles (TOPs), a data set based on the TKB and
now available from the National Consortium for the Study of Terrorism and Responses to
Terrorism (START),3 as well as the Federation of American Scientists’ (FAS) list of Libera-
tion Movements, Terrorist Organizations, Substance Cartels, and Other Para-State Entities.4
This expansion effort yielded 896 total organizations. I then prune those groups from the
data whose status as an actual organization is questionable.5 After such research, there are
623 groups remaining. These groups have sufficient capacity to be defined as an organization,
command authority independent from other groups, and are large enough that overcoming
the collective action problem is a continuing challenge for their leadership cadres. These
groups are listed in Table B.1. Figure 3.1 below shows the frequency of militant group
emergence over time.
One possible criticism of these data is that they are primarily composed of terrorist
groups and should therefore be studied separately from armed conflict. However, there is no
theoretical reason in this essay to separate militant groups based upon their choice of tactics.6
Another possible criticism is that the media sources from which the data are collected may be
biased toward groups that have engaged in international terrorism. However, the expanded
dataset includes a mix of groups of all kinds, like the Weathermen, which were strictly
domestic in their targeting, as well as groups that engaged in international terrorism, like
the German Red Army Faction. While it is important to acknowledge the limits of my data,
they capture my theoretical concepts as closely as possible given current data. With this
research complete, I obtain a 0/1 variable indicating the formation of one or more groups
3Available online at http://www.start.umd.edu/start/.
4Available online at http://www.fas.org/irp/world/para/index.html.
5Such as the Oklahoma City Bombers and a number of small Greek anarchist cells.
6Although such a topic is a rich opportunity for future projects.
58
Figure 3.1. Frequency of Militant Group Emergence, 1946—2006
010
2030
Fre
q. o
f Mili
tant
Gro
up F
orm
atio
n
1940 1960 1980 2000Year
in a given state-year. Few states experience more than one group formation in any given
year; thus, collapsing the data in this way is justified. I term this variable Militant Group
Emergence.
3.5.2. Independent Variables
3.5.2.1. Similarity
In order to operationalize the learning concepts described herein, I construct measures
of the similarity of proto-rebels to each informational source. I first define the sources of
information from which proto-rebels learn as follows: (a) tactical information emanating
from armed conflicts in other countries; and (b) inspirational information emanating from
revolutionary regimes.
(1) Armed Conflicts are identified using the Uppsala Conflict Data Program’s (UCDP)
59
Conflict Termination Data (Kreutz 2010). This dataset contains episodes of ongoing
conflicts during the 1946–2010 period. As this theory does not address proto-rebels
inside the government conspiring to replace it, as occurs in a coup d’etat, such cases
are excluded from the data.
(2) States hosting revolutionary regimes are identified for the entire 1946–2011 period,
and coded as originating from civil wars in which rebels are victorious, as defined by
the Conflict Termination Data (Kreutz 2010).7 The start-year for a revolutionary
regime is keyed to the termination year of those conflicts that UCDP identifies as
rebel victories. The termination date for each revolutionary state is keyed to changes
to the form or type of government brought to power by armed conflict, as determined
by regime-data contained in Colgan (2012) and Geddes, Wright and Frantz (n.d.),
or a 30-year cutoff that I impose. I follow this protocol because revolutionary regimes
will undoubtedly decline over time in their appeal to foreign dissidents.
Some alternatives to this coding scheme present themselves. For example, Colgan
(2012), in his study of revolutionary regimes, defines such governments as those including
something similar to a “revolutionary command council” in their governing structures. The
result, for Colgan, is a set of regimes that include those that came to power by methods other
than military victory. My theory, however, is strictly limited to those regimes that came to
power violently. While it is doubtless that other kinds of regimes are quite revolutionary in
their appeal to proto-rebels, my theory is largely silent of such matters. I leave it to future
7In most cases, rebel victory is easily determined from this file. Cases in which one side is victorious arecoded with the Conflict Termination Data variable “outcome” = 4; and the subset of those cases in whichrebels are victorious are coded with the variable “vicside” = 2. In a small number of cases the outcome isset to “other” (“outcome”=6). In such cases, some original research was conducted in order to determineif rebel victory occurred. These include Croatia’s independence from Yugoslavia (conflict ID #190); Mao’svictory over China (#3), and the FNL’s actions in Vietnam (#53).
60
work to explicate those mechanisms.
These coding criteria result in set of regimes in which rebels overthrow the government,
as well as several new states formed from secession. These regimes are listed in Table A.1
in the appendix. There are 61 regimes in the data, with a minimum duration 0 complete
years, a maximum of 51 years, a mean of 11.8 years, and a standard deviation of 10.8 years.
I collapse the data in such a way as to yield a state-year variable, Revolutionary Regimes,
which is a count of the number of regimes persisting in the international system.
Figure 4.1 below reports the trend in the frequency of Revolutionary Regimes for the
period 1968–2001. The number of such regimes is relatively low early in the history of this
time frame, jumping at two key points — the late 1970s, which saw revolutions in Iran and
Afghanistan, and the early 1990s, a period characterized by the end of the Cold War. Each
of these events inspired proto-rebels elsewhere. The Iranian revolution, for example, inspired
aggrieved Shia minorities in nearby Iraq and Saudi Arabia, while also inspiring the creation
of Hezbollah in more distant Lebanon (Jaber 1997).
The list of revolutionary regimes (Table A.1) further shows considerable heterogeneity.
On the one hand, the list contains regimes empowered by truly mass movements responsible
for the successful overthrow of governments. The Chinese Revolution (1949), the Cuban
Revolution (1959), and the Iranian Revolutions (1979) are probably the classic cases. In each
of these examples, mass revolutionary movements adhering to an internationalist ideology
took possession of the state and then actively attempted to inspire proto-rebels abroad and
even provided aid and support to worldwide revolutionary movements (Westad 2005). On
the other hand, however, there are regimes in the list in which warlords with little ideological
agenda beyond the acquisition of power were able to overpower their rivals. The repeated
61
coups and counter-coups in Chad are a good example. In that case, Hissene Habre violently
overturned the government in 1982, ruling until 1990 when he was removed from power by
Idriss Deby, his own military advisor. Revolutionary regimes of this type are unlikely to
appeal greatly to proto-rebels abroad. Without an international ideology to give a meaning
and interpretation to events within a revolutionary regime, it is even possible that proto-
rebels will ignore states of this type, dismissing them purely as warlordism.
Further examination of the list shows that some of the revolutionary regimes are
established due to issues related to international politics. For example, the Guatemalan
junta was empowered in 1954 after an American sponsored coup designed to thwart the
spread of communism in the Western Hemisphere. Such a case demonstrates the domino
logic in action — American policy-makers feared that communism in Guatemala would
spread like a cancer through Central America, eventually threatening key American interests
in Panama and Mexico, and so supported the overthrow of the regime in order to protect
their own interests. Foreign supported regime change of this type therefore represents yet
another type of revolutionary regime. Like those empowered by warlords, foreign supported
revolutionary regimes will provide little inspiration to proto-rebels, and their very existence
may very well be considered illegitimate by proto-rebels.
Indeed, it is even possible that foreign supported revolutionary regimes could inspire
an entirely different kind of proto-rebel activity. The Soviet imposition of a communist
regime on Afghanistan (1979), for example, led to the radicalization of an entire generation of
Islamists. These extremists mobilized in a host of countries and then traveled to Afghanistan
in order to fight against the Soviets (Hegghammer 2010). Far from inspiring proto-rebels,
then, the Soviet imposed regime in Afghanistan actually engendered and aggravated a highly
62
internationalized form of conflict. Despite the international nature of this conflict, it is not
considered rebellion or revolution as it is defined in this project.
Although this heterogeneity of revolutionary regime types present a wealth of oppor-
tunities for future research, my intent here is to establish baseline models of proto-rebel learn-
ing. I do not, therefore, undertake analyses of the different kinds of revolutionary regimes.
Should the analyses herein support the hypotheses, then future research must unpack the
heterogeneity contained in the list of revolutionary regimes.
Figure 3.2. Frequency of Revolutionary Regimes Per Year, 1968–2001.
515
25F
req.
of R
evol
utio
nary
Reg
imes
1970 1980 1990 2000Year
Having identified the two sources of information signals available to proto-rebels,
I now to turn to operationalizing the cultural similarity of proto-rebels to states host-
ing these phenomena in order to test Hypothesis 3.1. Simmons and Elkins (2004) and
Danneman and Ritter (2014), in their studies of diffusion, operationalize cultural similarity
by measuring whether or not two states share a dominant language or religion. I follow this
63
approach by counting the number of states hosting armed conflicts or revolutionary regimes
that share a dominant language or religion with another state. Language and religion data
are extracted from Ellingsen (2000), in which nine different religions and 132 languages are
coded. This yields two variables: Conflict Cultural Similarity and Revolutionary Cultural
Similarity. To account for the contrary cases, in which proto-rebels learn from dissimilar
cases, it is also necessary to include variables that account for dissimilarity. I thus split out
two additional variables, Conflict Cultural Dissimilarity and Revolutionary Cultural Dissim-
ilarity. All similarity variables are paired with their dissimilarity counterparts in all models
in which they appear.
To test Hypothesis 3.2, concerning the similarity of states to others of a similar regime
type, I count the number of states hosting armed conflicts or revolutionary regimes that
share regime type with another state. Regime type is determined according to the six-fold
classification scheme provided by Cheibub, Gandhi and Vreeland (2010) (hereafter called
the CGV data).8 This yields two variables: Conflict Regime Similarity and Revolutionary
Regime Similarity. Like the cultural measures, I split out dissimilarity variants of these two
variables.
3.5.3. Proximity
In order to test the expectation pertaining to proximity (Hypothesis 3.3), I disaggre-
gate all of my variables into two types. I do so by way of the coding of direct contiguity as
defined by the Correlates of War Contiguity Dataset (Stinnett et al. 2002). Each variable
is split into two variants in each state-year: (1) similar states that are contiguous to the
8This data classifies regime type according to (1) parliamentary democracy, (2) semi-presidential democracy,(3) presidential democracy, (4) civilian dictatorship, (5) military dictatorship, and (6) royal dictatorship.
64
unit of observation at time t ;9 and (2) similar states that are non-contiguous to the unit
of observation at time t.10 A complete list of the disaggregated variables is available in
Table 4.1.
Disaggregating by proximity serves a useful purpose beyond hypothesis testing. Be-
cause conflict tends to occur in clusters (Salehyan 2009), it is difficult in the extreme to
separate out learning mechanisms from those direct mechanisms, like refugee flows, occur-
ring among directly contiguous states. Moreover, all diffusion mechanisms overlap to some
extent (Wood 2013). Consider that Sudan in the late 1990s was nearly surrounded by states
whose governments had been empowered by successful rebellion, each of which could have
inspired Sudanese proto-rebels. Yet, these revolutionary states, including Uganda, Ethiopia,
Eritrea, Chad, and the Democratic Republic of Congo were each consumed in their own
civil conflicts. Porous international borders permitted refugee flows into each state’s territo-
ries, each sanctioned and supported rebellion against one another’s governments, and each
attacked and invaded one another in a complex of interlocking wars. Providing evidence
of learning over and above the effect of such a conflict zone is the central challenge of this
essay. In order to provide evidence of learning, it is therefore necessary to show that the
non-contiguous versions of my variables are associated with proto-rebel mobilization.
3.5.4. Control Variables
I extract most of my control variables from the Buhaug and Gleditsch replication
data, which provides a standard benchmark for studies of civil conflict diffusion. The first is
Neighboring Civil War, a 0/1 indicator of civil conflict in at least one of a given country’s con-
9Where the COW variable “contig” variable is set to “5” or less. This includes all cases of direct landcontiguity, as well as those separated by 400 miles of water or less.
10As determined by the Correlates of War Contiguity Dataset, where the “contig” variable is set to “6.”
65
tiguous neighbors. This variable is used primarily in a set of baseline analyses that establish
the fact that international factors are critically important to militant group emergence.
I extract several other variables from the replication data. The first is Civil War, a
0/1 dummy variable indicating the presence of an organized armed challenge against the
government of a state, resulting in at least 25 battle deaths in a given year. This variable
derives from the UCDP/PRIO Armed Conflict Data (Themner and Wallensteen 2012). Be-
cause poverty has been shown to be an important correlate of civil conflict, I insert the
natural logarithm of GDP per capita at t − 1 (GDP per capita (ln)) into the model. This
variable is ultimately derived from the Gleditsch (2002b) Expanded Trade and GDP dataset.
A neighborhood average of this variable is also included, Neighborhood GDP per capita. The
natural logarithm of population is also included, derived ultimately from the Correlates of
War National Material Capability (NMC) data (Singer 1993).
Several additional variables control for those institutional features associated with
militant mobilization. These are Democracy and Neighborhood Democracy. Democracy is a
0/1 indicator, coded “1” if a state is of a democratic type. Neighborhood Democracy, per
Maves and Braithwaite (2013), is that proportion of states within 3000 km of any given
state that are democratic. These variables are constructed using the CGV data. This is
appropriate because the traditional indicator of democracy, the Polity scale, includes features
of political violence within its measure (Vreeland 2008). Using the CGV data thus eliminates
the possibility of endogeneity.
Figure 3.1 demonstrates an increasing number of militant groups emerging over time.
This may be an actual empirical phenomenon or, more likely, it is due to bias in the un-
derlying data sources (Drakos and Gofas 2006). To control for this bias, I add a time trend
66
and year dummies into the model. Finally, I control for temporal dependence by inserting
a count of the number of years between incidences of Militant Group Emergence, as well as
the squared and cubed variants of this term (Carter and Signorino 2010).
67
Table 3.1. Descriptive Statistics for Dangerous Lessons Research Design, 1968–2001.
Dependent Variable Mean Std. Dev. Min. Max.
Militant Group Emergence 0.09 0.37 0.00 5.00Armed Conflict Onset - - 0 1Independent Variables
Conflict Cultural Similarity (contig.) 0.67 0.93 0.00 6.00Conflict Cultural Similarity (non-contig.) 6.66 4.14 0.00 17.00Conflict Cultural Dissimilarity (contig.) 0.47 0.86 0.00 8.00Conflict Cultural Dissimilarity (non-contig.) 17.69 5.43 5.00 38.00Revolutionary Cultural Similarity (contig.) 0.41 0.65 0.00 4.00Revolutionary Cultural Similarity (non-contig.) 3.81 2.28 0.00 14.00Revolutionary Cultural Dissimilarity (contig.) 0.28 0.59 0.00 3.00Revolutionary Cultural Dissimilarity (non-contig.) 12.46 3.39 3.00 23.00Conflict Regime Similarity (contig.) 0.41 0.69 0.69 5.00Conflict Regime Similarity (non-contig.) 5.31 3.50 0.00 16.00Conflict Regime Dissimilarity (contig.) 0.73 0.98 0.00 6.00Conflict Regime Dissimilarity (non-contig.) 19.02 6.08 6.00 37.00Revolutionary Regime Similarity (contig.) 0.29 0.63 0.00 5.00Revolutionary Regime Similarity (non-contig.) 4.18 4.78 0.00 22.00Revolutionary Regime Dissimilarity (contig.) 0.40 0.69 0.00 4.00Revolutionary Regime Dissimilarity (non-contig.) 12.06 5.95 1.00 23.00Control Variables
Civil War - - 0.00 1.00Neighboring Civil War - - 0.00 1.00Democracy - - 0.00 1.00Neighborhood Democracy 0.29 0.24 0.00 1.00Neighborhood GDP 8.14 0.87 6.25 10.33GDP (ln) 8.18 1.07 5.64 10.74Population (ln) 9.03 1.52 5.33 14.06
68
3.6. Analysis
In order to analyze the data, I execute logit regressions with robust standard errors
clustered by country. This is an appropriate model given the binary nature of the dependent
variable and the Time-Series Cross Sectional structure of the data. In order to find support
for my theory, I undertake a multi-step strategy. First, I demonstrate that neighboring civil
wars are significantly related to both Armed Conflict Onset and Militant Group Emergence
(Table 3.2). Second, I undertake an analysis of Militant Group Emergence as a function
of learning from armed conflicts, with learning defined according to cultural and regime
similarity (Table 3.3, Models 3 and 4). Third, I show in Table 3.3, Models 5 and 6, I show
the impact of revolutionary regimes upon Militant Group Emergence, again with learning
defined according to cultural and regime similarity.
In Table 3.2 below, I replicate the Buhaug and Gleditsch (2008) study of civil conflict
diffusion. The primary independent variable of interest is the Neighborhood Civil War. As
Model 1 demonstrates, this variable is significantly related to Armed Conflict Onset, as we
would expect from the literature. Model 2 shows that Militant Group Formation is also a
significantly related to the Neighborhood Civil War.
69
Table 3.2. Logit Models of Armed Conflict Onset and Militant Group Emergence,1968–2001.
Armed Conflict Onset Militant Group EmergenceVariable (1) (2)Neighboring Civil War 0.47* 0.58***
(0.18) (0.16)Civil War 0.66***
(0.18)Neigh. Democracy 0.13 0.10
(0.57) (0.33)Neigh. GDP -0.03 0.29*
(0.17) (0.14)Democracy -0.50+ 0.42**
(0.28) (0.15)GDP(ln) -0.03 0.04
(0.17) (0.13)Population(ln) 0.22** 0.39***
(0.08) (0.06)Time Trend 0.07***
(0.02)Constant -1.62 -8.97***
(1.32) (1.11)χ2 70.06 701.5Log-Likelihood -646.8 -1019N 4,787 4,787Note: Coefficients with robust standard errors in parentheses;
cubic polynomials and year dummies not reported;
sig. levels are two-tailed (∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1).
This alone is a significant contribution to the literature. Existing studies of civil
conflict, including Buhaug and Gleditsch (2008), utilize an indicator of armed conflict onset
in the dependent variable, thereby aggregating the various action-reaction processes that
occur between proto-rebels and governments. In other words, while conflict may indeed
be contagious, neither the theory nor the data in past publications have possessed detail
of a fine enough grade to explore the mechanisms and sequencing of civil conflict and its
international impact on proto-rebel mobilization. This finding represents a phenomenon
separate from conflict onset, although the two are related.
70
Having established that mobilization and the onset of war are both significantly re-
lated to neighboring conflict, and that analytic utility is to be gained, I turn to the next
step in the analysis of my theory, reported in Table 3.3. Because the variants of the conflict
and revolutionary learning variables are significantly related, I cannot insert them into the
same model.11 This makes empirical sense, as a revolutionary government, by definition,
emerges in a country that has experienced civil conflict. I therefore insert the conflict-based
learning variables into Models 3 and 4. The revolutionary regime-based learning variables
are inserted into Models 5 and 6.
Before turning to my analysis, a note on presentation is required. Owing to the
fact that many different learning variables have been constructed, I do not report Conflict
Similarity and Revolutionary Similarity, or their dissimilarity based variants, in separate
rows of Table 3.3. Rather, I label these variables according to that contextual feature whose
similarity is being measured; i.e., Cultural Similarity or Regime Similarity. It must be
understood, however, that these variables are measuring separate concepts in Models 3–4
and Models 5–6.
11The two variable types are related at p < 0.001.
71
Table 3.3. Logit Models of Learning and Militant Group Emergence, 1968–2001.
Armed Conflict Revolutionary RegimesVariable (3) (4) (5) (6)Cultural Similarity (contig.) 0.29 0.56**
(0.62) (0.20)Cultural Similarity (non-contig.) 0.17 0.45**
(0.63) (0.15)Cultural Dissimilarity (contig.) 0.16 0.39**
(0.66) (0.15)Cultural Dissimilarity (non-contig.) 0.14 0.41**
(0.62) (0.14)Regime-Type Similarity (contig.) 0.17 0.38*
(0.64) (0.15)Regime-Type Similarity (non-contig.) 0.13 0.41***
(0.63) (0.12)Regime-Type Dissimilarity (contig.) 0.22 0.48**
(0.65) (0.15)Regime-Type Dissimilarity (non-contig.) 0.13 0.41***
(0.62) (0.11)Civil War 0.87 0.84 0.74*** 0.73***
(0.64) (0.65) (0.17) (0.17)Neighboring Civil War 0.59*** 0.61***
(0.16) (0.17)Neigh. Democracy 0.09 0.12 0.11 0.17
(0.36) (0.36) (0.37) (0.36)Neigh. GDP 0.19 0.26+ 0.29* 0.30*
(0.15) (0.15) (0.14) (0.15)Democracy 0.44** 0.41+ 0.45** 0.40
(0.16) (0.21) (0.16) (0.26)GDP(ln) 0.03 -0.01 0.05 0.03
Continued on next page.
72
Table 3.3 —continued from previous page.Armed Conflict Revolutionary Regimes
Variable (3) (4) (5) (6)(0.14) (0.14) (0.13) (0.13)
Population(ln) 0.42*** 0.37*** 0.40*** 0.39***(0.09) (0.07) (0.06) (0.06)
Time Trend 0.06** 0.07*** 0.07** 0.07**(0.02) (0.02) (0.02) (0.02)
Constant -11.53 -10.98 -15.19*** -14.85***(9.67) (9.86) (1.85) (1.59)
χ2 621.1 613.8 651.0 685.5Log-Likelihood -1029 -1031 -1017 -1018N 4,787 4,787 4,787 4,787Incident Rate Ratios with robust standard errors in parentheses; year dummies not reported;
sig. levels are two-tailed (∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1).
73
In Model 3, the impact of neighboring conflicts, as measured by Cultural Similarity
(contig.) and Cultural Similarity (non-contig.), are not significantly discernable from zero.
Although I have shown that neighboring conflicts are associated with both conflict onset
and with militant mobilization, it can be seen that dissaggregating neighboring conflict by
Cultural Similarity does not add much to the model in terms of explanatory power. This
is consistent with the literature. Conflict tends to cluster as a result of its underlying
determinants, while diffusing as a result of migratory based mechanisms, like refugee flows,
irrespective of the languages spoken among neighbors. The same result holds in Model 4,
which analyzes the impact of Regime Similarity.
It is in Models 5 and 6, however, that the true evidence of learning is apparent.
Model 5 assesses the impact of a state’s cultural similarity to the international system’s
revolutionary regimes. Each of the learning based independent variables are highly significant.
This lends support to many of my hypotheses. Hypothesis 3.1 posits that learning is a
function of cultural similarity. The significance of Cultural Similarity (non-contig.) supports
this contention — proto-rebels learn from international events, even those in non-neighboring
states. Support is also found for Hypothesis 3.4, concerning the impact of inspirational
mechanisms. Recall that I theorized that proto-rebels are more likely to learn from a source
that is highly visible. This is shown by the significance of Cultural Dissimilarity (contig.)
and Cultural Dissimilarity (non-contig.). In other words, cultural similarity does not restrict
those sources from which proto-rebels learn, provided that said source is a revolutionary
regime. Similar findings are evident in Model 6, concerning proto-rebel learning from similar
regime-types.
These findings are entirely new to the literature and demonstrate that significant
74
empirical leverage can be gained by directly measuring proto-rebel mobilization in the de-
pendent variable, rather than the onset of conflict, and by using a measure of revolutionary
success as the independent variable. Moreover, the emergence and survival of a revolutionary
state is such a radical shock to the international system that it inspires proto-rebels globally,
irrespective of cultural or regime similarity.
In order to discern the actual impact of revolutionary regimes upon Militant Group
Emergence, I extract predicted probabilities from Models 5 and 6. I set all continuous
variables to their means and all dummy variables at their modes and then generate a number
of scenarios, each of which is plotted in Figure 4.2. Figure 4.2 (a) presents the effect of
Revolutionary Cultural Similarity (contig.) as it varies from its minimum to its maximum.
As the number of culturally similar revolutionary regimes increases from 1 to 5, there is a
concomitant 400% increase in the probability of Militant Group Emergence. These results
should be compared directly against Revolutionary Cultural Similarity (contig.), as shown
in Figure 4.2 (c). This plot shows a 200% change in the predicted probability. Although the
relative change in the substantive effect is less, the absolute levels are much greater. This
directly supports Hypothesis 3.3, concerning the impact of geographic proximity.
A similar finding is seen in Figure 4.2 (b) and (d), concerning Revolutionary Regime
Similarity. Figure 4.2 (b) shows a particularly dramatic effect — Revolutionary Regime
Similarity (non-contig.) shows nearly the same substantive effect as its contiguous variant.
It may therefore be concluded that while proto-rebels learn from culturally similar states
hosting revolutionary regimes, they are more likely to learn and mobilize form similar regimes.
This result solves the puzzle that opened this essay — proto-rebels react to events in similar
regimes across the world.
75
Figure 3.3. Militant Group Emergence as a Function of Revolutionary Similarity, 1968–2001.
(a) Cultural Similarity (non-contig.)
0.0
5.1
.15
.2P
r(M
ilita
nt G
roup
Em
erge
nce)
1 2 3 4Revolutionary Cultural Similarity (non−contig.)
(b) Regime-Type Similarity (non-contig.).
0.0
5.1
.15
.2P
r(M
ilita
nt G
roup
For
mat
ion)
1 2 3 4 5Revolutionary Cultural Similarity (non−contig.)
(c) Cultural Similarity (contig.).
0.0
5.1
.15
.2P
r(M
ilita
nt G
roup
For
mat
ion)
1 2 3 4 5Revolutionary Regime−Type Similarity (non−contig.)
(d) Regime-Type Similarity (contig.).
0.0
5.1
.15
.2P
r(M
ilita
nt G
roup
For
mat
ion)
0 2 4 6 8Revolutionary Regime−Type Similarity (non−contig.)
76
Finally, the results suggested that revolutionary regimes even overpowered the atten-
uating impact of dissimilarity. Such a finding suggests a strong role for ideology. My opening
anecdote, concerning Peru and Nepal, were connected by proto-rebels adhering to a common
ideological frame — revolutionary Maoism in this case. I do not address this possibility in
this essay, but it is an important area for future research.
3.7. Conclusion
During the Arab Spring in 2011, mass uprisings in Tunisia were credited with pro-
pelling rebellion in neighboring states, such as Libya and Egypt, and influencing levels of
dissent in distant states, such as Syria and Mali. Yet, these early rebellions did not diffuse
to a number of proximate and culturally similar states. These patterns of conflict diffusion
in the Middle East suggest that rebellion is not solely a function of proximity to civil war, or
of the spillover of civil war conditions. Rather, I contend that rebellion is, in part, a function
of observation, interpretation, and learning by individuals considering whether to engage in
rebellion, individuals that I refer to as proto-rebels. At the same time, I seek to move the
literature’s focus away from a sole preoccupation with direct, proximate and material causes
of diffusion, and toward indirect forms.
This study has several implications for the broader analysis of conflict. First, victory
by rebels and the governments that they subsequently lead have a powerful impact on the
decision to rebel, both in magnitude and spatial scope. Second, the diffusion of civil conflict
is more than simply a matter of purely local determinants or neighborhood effects; rather, it
is a phenomenon with global dimensions and can be transmitted via information flows that
are not grounded solely in direct, material experience.
The findings in this essay offer a wealth of options for future research. Although I
77
found that proto-rebels learn on a global basis from revolutionary regimes, irrespective of
culture or regime-type, scholars should begin to explore those informational pathways that
operate at the transnational level. One obvious starting point is that of ideology. Indeed,
some ideologies are known to make universalist claims that transcend commonalities of re-
ligion or language. Some excellent examples of the above phenomenon can be found in the
history of leftist terrorism of the 1960s. At that time, anti-colonial liberation wars in the
Third World provided a great deal of inspiration for leftist militants in Europe, particularly
the German Red Army Faction. In point of pact, the Red Army Faction specifically modeled
itself on the leftist Tupamaros of Uruguay, and its leaders were familiar with the work of
Abraham Guillen, a radical intellectual who promoted “urban insurgency” during the civil
wars of Latin America (Abrahms and Lula 2012; Midlarsky, Crenshaw and Yoshida 1980).
A similar story can be told with respect to the radical New Left movements that emerged in
the United States. Groups like the Weathermen and the Black Panthers were explicitly inter-
nationalist in orientation, learning from the ongoing war in Vietnam and taking inspiration
from the revolutionary regime in Cuba (Varon 2004).
Finally, although this essay has focused on the origins of the conflict process, it has
implications for later stages. Existing literature on the international origins of domestic
political conflict have primarily focused on the onset and duration of civil war. Yet, onset
and duration are primarily the result of bargaining failure and an escalating action-reaction
spiral among rebels and governments. However, in this essay, learned information impacts
collective action even before there are rebel actors that can enter into an escalatory spiral.
Future research should therefore accomplish two items: the state response to proto-rebel
learning should be modeled, and research should attempt to find which militant groups
78
actually survive state repression and enter into an escalating spiral that results in war,
particularly as a function of learning from international sources.
79
CHAPTER 4
PREVENTIVE MEDICINE: REVOLUTIONARY STATES, THE INTERNATIONAL
SYSTEM, AND REPRESSION
4.1. Chapter Abstract
Do governments repress in order to defend themselves against the demonstration ef-
fects produced by the victory of rebels in armed conflicts? I theorize that the victory of rebel
forces in armed conflict, and the subsequent creation of revolutionary regimes, provides an
international model for mobilization to would-be rebels and that this, in turn, leads gov-
ernment authorities to deploy repression in order to defend themselves. This relationship is
conditional upon the international assertiveness of revolutionary regimes and their proximity
to the threatened state. I test these expectations against state-year patterns of repression for
the period 1976–2001. I find that states do indeed repress in order to defend themselves from
revolutionary regimes. Moreover, I find that this effect is relatively insensitive to distance
— particularly assertive revolutionary regimes trigger repression even in distant parts of the
globe.
4.2. Introduction
In 2011, revolution and protest erupted in Tunisia and Egypt and then spread across
the Middle East and North Africa, triggering civil war in Libya and Syria. Yet, the im-
pact of these events was felt in places as far away as China, whose government success-
fully deterred rebellion by arresting human rights activists and imposing internet censorship
(Los Angeles Times 2011). This example highlights an important puzzle for international
relations and the study of civil violence; namely, why does it “pay” for dissidents to rebel
in some times and places, but not in others? I propose that this puzzle can be answered by
80
examining the ability of governments to disrupt dissident mobilization when they are threat-
ened by the international spread of civil violence. Specifically, if rebels successfully displace
a ruling government or form their own state through secession, a powerful example is created
that may then be emulated by other would-be rebels globally. Governments threatened by
this process will attempt to thwart it, using political terror to deter and disrupt dissident
mobilization. Moreover, the severity of government repression will depend upon the level of
threat these rebel-founded regimes exert upon the international environment.
I thus integrate several elements into a single theoretical argument. Specifically, I
argue that those dissidents contemplating rebellion, whom I term “proto-rebels,” and those
government authorities seeking to deter it, learn about the utility of civil and political vi-
olence from information available in the global system. The content of this information
includes the perceived likelihood of successful rebellion, which is demonstrated by the es-
tablishment and survival of rebel-founded governments. States threatened by this process
may then deploy “preventive medicine” in the form of repression in order defend themselves
against revolutionary states and the inspirational effects they have upon domestic proto-
rebels.
Prior literature addresses key elements of this puzzle, but does not capture the en-
tirety of my argument. Indeed, much of the work on the international spread of civil violence
is either limited to ethnic conflict (Kuran 1998), or else focuses on those qualities that make it
more likely that violent rebellion will spread into neighboring states (Buhaug and Gleditsch
2008). Some excellent work also argues that governments are likely to repress in response
to neighboring civil conflict (Danneman and Ritter 2014) or other factors related to inter-
national security (Poe and Tate 1994). This essay therefore builds upon this body of work
81
and innovates by exploring the relationships between conflict termination, regime formation,
and the international origins of repression.
The remainder of this essay is structured as follows. First, I motivate the study with
a look at the international spread of civil conflict and the effect that revolutionary regimes
have upon civil conflict. Second, I develop a theory in which governments threatened by
international revolutionary regimes react with harsh domestic repression. Third, in order
to evaluate my hypotheses, I describe a research design utilizing several unique variables
during the period 1976–2001. Fourth, I engage in a quantitative analysis. Finally, I offer a
concluding discussion.
4.3. The Diffusion of Civil Conflict and Repression
This essay’s topic concerns the interdependence of conflicts, particularly transna-
tional linkages among them at the systemic level of international relations. Interdependence
and transnationalism are among the defining features of the international environment —
yet, to date, these phenomena have been studied primarily with reference to positive de-
velopments like international human rights norms, international organizations, and political
economy (Finnemore and Sikkink 1998; Simmons, Dobbin and Garrett 2006). By contrast,
“dark side” of transnationalism has seen only received attention from scholars of international
relations only recently. This “dark side” includes such phenomena as the international diffu-
sion of political violence and repression (Buhaug and Gleditsch 2008; Danneman and Ritter
2014; Maves and Braithwaite 2013; Gleditsch 2007; Salehyan and Gleditsch 2006), and the
global transmission of tactics (Horowitz 2010), ideologies (Kalyvas and Balcells 2010), the
movement of foreign fighters (Hegghammer 2013), demonstration effects (Beissinger 2002;
Kuran 1998) and even the possibility that rebels emulate others and adopt their rhetoric in
82
order to draw upon transnational support (Bakke 2013).
Empirical research establishes that rebel mobilization and state terror is indeed in-
terdependent at the international level. Conflict is especially likely to have consequences in
directly neighboring states, threatening those states with violence of their own. Conditions
under which such strife is likely spread from state to state include ethnicities that straddle
international borders, refugee flows, transnational rebel sanctuaries, and commonly experi-
enced regional conditions like poverty. Particularly notable examples include the movement
of rebels and refugees between Rwanda and the Democratic Republic of Congo, Libya and
Mali, and Afghanistan and Pakistan. This research, and these examples, are united by a
focus on direct, physical factors that spread conflict within geographical regions. A com-
monly invoked analogy therefore compares conflict to a disease (Buhaug and Gleditsch 2008;
Danneman and Ritter 2014; Salehyan and Gleditsch 2006; Salehyan 2009).
Yet, civil strife also generates indirect effects that may spread conflict into non-
neighboring states under the right conditions. The activation of ethnic grievances in one
state, for example, can alert others in a distant state to their own aggrieved status and
provide a seemingly effective means by which to redress it. These “demonstration effects”
suggest that proto-rebels may therefore learn about the utility of violent rebellion from oth-
ers who have pursued a similar strategy (Beissinger 2002; Kuran 1998; Lake and Rothchild
1998; Maves and Braithwaite 2013).
Indirect effects are notable in their focus on learning as a causal mechanism. Because
the consequences mobilization and warfare are uncertain, proto-rebels and government of-
ficials may turn to the external environment for additional information. By observing the
world, they can evaluate the likely outcomes of any particular decision. In the simplest
83
version of this mechanism, decision-makers have prior beliefs but then update them based
upon the new data (Elkins and Simmons 2005).
Interestingly, the available quantitative literature offers only mixed evidence for learn-
ing as a mechanism for the spread of civil conflict. In an excellent article on the subject,
Danneman and Ritter (2014) posit that government officials should learn about the utility of
violent conflict and repression from countries that are culturally, linguistically, or politically
similar, even if they are separated by great distances. Thus, government officials will fear
that proto-rebels in their states will learn from others in similar places and so commence
with preemptive repression.
Despite the plausibility of this argument, Danneman and Ritter (2014) find no evi-
dence in favor of it. The authors conduct a series of statistical tests on a global sample of
state-years covering the period 1981–2011, employing interaction terms to discern the actual
impact of conflict among similar states. These tests show no statistically or substantively
meaningful evidence that governments repress in response to conflict in socially or politically
similar states. Rather, the available evidence suggests that government officials repress only
in response to direct effects of neighboring conflict, particularly refugee flows.
The Danneman and Ritter (2014) study is significant for other reasons as well. First,
theirs is one of the first studies that examines how human rights violations engender addi-
tional violations internationally. Scholars of international human rights have tended to focus
upon how international actors can improve human rights practices in a positive direction,
thereby reducing the likelihood that a state will resort to repression. Yet, this neglects the
opposite case in which conflict produces rights violations elsewhere (Danneman and Ritter
2014, 20-21). Second, the authors express surprise that government officials do not appear
84
to learn from culturally or politically similar states and speculate that learning among proto-
rebels might be a lengthy process, whereas states must respond quickly before proto-rebel
learning can occur Danneman and Ritter (2014, 20-21). It is therefore important to look
closely into additional mechanisms by which government officials observe and respond to
international threats Danneman and Ritter (2014, 20).
By contrast, I argue that a so-far overlooked mechanism in the international relations
of repression is that of revolutionary success. Most of the pieces reviewed above look only at
the way in which conflict begets additional conflict. Yet, conflict termination also has a vital
role to play. If rebels are successful in their efforts, violently overthrowing their government
or seceding into a new state, the new revolutionary state may provide a powerful example
to other proto-rebels. Government authorities may therefore be threatened on a world-wide
basis and become more likely to use repression as a policy-tool.
The historical record is replete with examples. The fall of the French monarchy in
1848 directly inspired proto-rebels throughout Europe, and within a span of weeks nearly the
entire continent was consumed with radical revolution and conservative counter-revolution
and repression (Weyland 2009). In an earlier case, the sudden independence of the United
States in 1783 directly inspired French proto-rebels on the other side of the Atlantic Ocean,
providing the impetus for a wave of mobilization and counter-revolution in the France of
1789 (Dunn 2000).
So described, these arguments are similar to the domino theory metaphor frequently
invoked during the Cold War. The metaphor, originally coined by President Eisenhower dur-
ing a 1954 press conference, describes a process whereby communist revolutions in one state
could trigger a cascade of militant collective action in surrounding states (Jervis and Snyder
85
1991). Such a concern motivated American assistance to a host of repressive regimes in
order halt the cascade. Although it is certain that the domino theory as it was commonly
represented is an exaggerated view of revolution’s appeal to domestic audiences, govern-
ment authorities act as though it were true (Walt 1996). The importance of this metaphor
therefore lies not in its actual truth, but in its perceived reality. Governments react with
repression in order to contain the diffusion of conflict. In the next section, therefore, I offer
a theoretical argument connecting revolutionary success to repression.
4.4. Theory
Upon what information do proto-rebels and government authorities base their deci-
sion making? It is clear that the decision to rebel or repress is at least in part a function
of international events (Lake and Rothchild 1998). The literature on the international ori-
gins of political violence is notable in its focus on conflict, leaving unaddressed the conse-
quences of a conflict’s end for subsequent episodes of violence and, save for one piece (e.g.,
Danneman and Ritter 2014), it does not examine the repressive role played by government
authorities.
I therefore argue, in line with the literature on domestic repression (e.g., Davenport
2007), that when government authorities perceive a threat to the status-quo they will respond
repressively. I build from this basic foundation by exploring the nature of those perceived
threats with an international origin. Specifically, perceived threats arise when domestic proto-
rebels are made aware of alternative forms of political organization existing in the global
system. In turn, this threat can be disaggregated into three elements (1) the existence
of a revolutionary regime arising out of civil conflict; (2) the international aggressiveness
of revolutionary regimes; and, (3) the proximity of revolutionary regimes to the state in
86
question. I will begin by describing the basic logic of the revolutionary regime before positing
hypotheses.
Before a civil conflict begins, proto-rebels must overcome the collective action
dilemma, one that governments seek to make insurmountable (Lichbach 1995). Because
conflict is always costly and there is uncertainty about the utility of any particular strat-
egy or policy, proto-rebels and government officials must engage in a search for successful
strategies to achieve their respective ends. Such uncertainty is common across all forms
of decision-making (Gilardi 2012), and so social actors of all types are forced to seek out
information on utility from external sources, looking to those examples and analogies that
provide successful strategies. This is the essence of learning.
On the rebel side, violent strategies employed by successful rebels may capture the
attention of proto-rebel leaders, who then provide their followers with information on the
utility of violence in order to motivate collective action. Tilly (1978, 158), writing on the
diffusion of protests, held that “when a particular form of riot or demonstration spreads
rapidly, what diffuses is not the model of behavior itself, but the information—correct or not—
that the costs and benefits associated with the action have suddenly changed.” Learning
provides one basis for the diffusion of conflict, even across great geographic distances, by
providing information to proto-rebels on rebellion’s utility.
Conversely, government authorities in states that perceive a threat may pro-actively
deploy repression in order to deter proto-rebels or disrupt collective action. This occurs
because governments are controlled by self-interested political elites who seek to maintain to
access to power (Danneman and Ritter 2014; Davenport 2007; Pierskalla 2010; Ritter 2014;
Shellman 2006). Governments have a variety of options in their arsenal to achieve this aim:
87
the granting of concessions to dissidents (symbolic or otherwise), bribing the opposition,
and many others (Nordas and Davenport 2013). However, any dissident movement that
threatens the status-quo or poses a direct threat to the survival of the regime and its access
to power is likely to incur a repressive response. The literature finds that repression is, in
part, a function of the perceived level of threat emanating from proto-rebels. Large dissident
movements that directly challenge the state are more likely to be targeted for repression,
as are those that use violent tactics or adhere to extremist or radical ideologies (Davenport
2007; Earl 2011; Earl, Soule and McCarthy 2003).
Although international revolutions and civil wars may have a limited appeal to do-
mestic audiences, the fear of successful revolution is actually a major force in foreign and
domestic policy. For example, regimes threatened by conflict abroad may bolster themselves
by transforming into a police state, undertaking military intervention, or even engaging in
foreign policy balancing against the perceived threat. The spread of political violence is thus
not a one-way process—the very presence of a conflict elicits countermeasures by regimes
that find themselves potentially vulnerable to domestic rebellion (Kathman 2010; Gurr 1988;
Walt 1987).
Although the quantitative literature does not directly address the linkage between
the logic of dissent, repression, and the existence of revolutionary regimes, history is replete
with supporting anecdotes. Ted Robert Gurr, in his widely cited Why Men Rebel, noted
that Ghana’s independence in 1957 raised the expectations of political independence among
Africans throughout the continent, indirectly contributing to political violence in places as
far away as the Belgian Congo or Angola (Gurr 1970, 97).
Weyland (2009) agrees, noting that stunning rebel success, and the establishment
88
of a revolutionary regime, alert proto-rebels to a new universe of political possibilities, and
inspires in them an almost euphoric desire to topple their own regime and a willingness to take
risks. This phenomenon is not limited to the modern era with its instant communications
and easy travel. As far back as the 1790s, the Marquis de Lafayette threatened to present
Europe with the “contagious example of a dethroned king” (Haas 2005, 7).
Although states may initiate repression as response to a single revolutionary state,
it is more likely that repression is a function of an increased presence of revolution within
the international system. Indeed, a single revolutionary state emerging somewhere in the
international environment may be a relatively random event; yet, multiple such states may
reflect a widespread distribution of grievances, with proto-rebels learning and governments
responding according to demonstration effects that tie cases together, as the Ghana ex-
ample highlights, or as a result of a particularly contagious ideology, as in the Cold War
(Kalyvas and Balcells 2010).
The foregoing demonstrates that revolutionary regimes, by their presence, raise the
expectations of proto-rebels. Regimes, feeling threat, respond in kind. I therefore hypothe-
size as follows:
Hypothesis 4.1. Revolutionary Presence. The greater the presence of revolution-
ary states in the international system, the greater the likelihood
that states will implement higher levels of domestic repression.
Although this logic is intuitive, not all revolutionary regimes are equally threaten-
ing to government authorities. While some remain internationally quiescent, others disrupt
the international environment and impel states to repress. There are three reasons for
this. (1) States that have undergone a recent regime change are known to have a higher
89
proclivity to become involved in international conflict and thereby threaten nearby states
(Carter, Bernhard and Palmer 2012; Colgan 2013; Colgan and Weeks n.d.; Enterline 1998;
Gurr 1988; Maoz 1996; Skocpol 1979; Walt 1996). (2) Instability in the international sys-
tem is strongly linked to domestic repression (Poe and Tate 1994), especially if the state
in question is located in an unstable geographic region containing some of the correlates of
conflict, including refugee flows, transnational rebel bases, or foreign support for rebels in
another state (Salehyan 2009). (3) Finally, some revolutionary regimes take an active hand
in exporting their governing structures abroad or, at the very least, advertising the benefits
of rebellion to global proto-rebels (Kalyvas and Balcells 2010).
Communist Cuba is one of the most notable examples in this respect. After displac-
ing the Batista dictatorship, Fidel Castro dispatched individuals like Che Guevara abroad
in order to teach other proto-rebels the ways and means of mobilizationa. Justifying these
policies, Fidel Castro proclaimed in 1959 that “our hemisphere needs a revolution like the
one that has taken place in Cuba! How much America needs an example like this in all
its nations” (Westad 2005, 172). Those regimes threatened by communist proto-rebels re-
acted repressively. The Latin American republics engaged in a series of “dirty wars” against
communist-inspired dissidents (McSherry 2005). Writing on the subject of Cuba, the jour-
nalist Walter Lippman argued that “The greatest threat presented by Castro’s Cuba is as an
example to other Latin American states which are beset by poverty, corruption, feudalism,
and plutocratic exploitation” (Lippmann 1964).
After Che Guevara was killed in Bolivia by American-trained military forces, Walt
Rostow remarked to President Johnson that Guevara’s death “shows the soundness of our
‘preventive medicine’ assistance to countries facing incipient insurgency” (Westad 2005, 178).
90
In other words, states engage in repressive tactics in order to defend from international
threat. It is within that logic that I posit my second hypothesis.
Hypothesis 4.2. Aggressiveness. The greater the aggressiveness of revolutionary
states, the greater the likelihood that states will implement higher
levels of repression.
Although some of the above anecdotes are very dramatic, the nature of international
information constrains the learning mechanism. For instance, proto-rebels may be isolated
by a remote geographic location and so news of their mobilization is unlikely to reach a
wider audience. Government authorities may also do their best to limit the spread of such
information internationally in order to protect themselves — China’s response to the Arab
Spring is a notable example.
Moreover, it is possible that even if learning is occurring, its impact is overshadowed by
the direct, mechanical factors that operate in the regional environment. For example, rebel
success may very well threaten the government of a neighboring state, but the mechanism
is drowned out by refugee flows. This logic leads Buhaug and Gleditsch (2008, 220) to
conclude that diffusion is limited by the geographic distance between the site of a conflict
and the potential site of new conflict. This corresponds to the “first law of geography,” in
which “everything is related to everything else, but near things are more related than distant
things” (Tobler 1970, 236).
Thus, I reason that although the emergence of revolutionary regimes may have a global
effect, those effects will be felt most notably in geographically proximate states. Proto-rebels
near a revolutionary state are more likely to be alerted to the possibilities of revolutionary
action if it occurs close at hand. It therefore follows that government authorities will perceive
91
a greater threat from nearby revolutionary regimes and thus employ repression.
By way of this logic, I hypothesize as follows:
Hypothesis 4.3. Proximity. The greater the proximity of revolutionary states, the
greater the likelihood that that states will implement higher levels
of repression.
4.5. Data and Research Design
4.5.1. Dependent Variables
I anticipate greater domestic repression by governments as a function of international
learning by proto-rebels. A variety of state-year measures are available to measure repression,
the two most prominent being the Political Terror Scale (PTS) (Gibney et al. 2012) and
the Cingranelli-Richards (CIRI) data (Cingranelli and Richards 2010). Both data projects
obtain publicly available country-based human rights reports and then create indices that
measure the level of repression in a society in a given year. The PTS is the most relevant for
my purposes here, as its construction allows me to precisely determine when a state escalates
its repression to include its politically active population; or, in other words, those individuals
most likely to be considered proto-rebels.
At the core of the PTS is the concept of “physical integrity rights,” which are defined
as the freedom of individuals within a society from state-directed extrajudicial killing, dis-
appearance, torture, and political imprisonment. The PTS codes two indices that measure
the extent to which a state respects such rights. The first is based upon country-reports
issued by Amnesty International, while the second is based on reports issued by the US
State Department. These reports are then coded into two separate scales ranging from “1”
to “5,” with “1” representing a state-year in which citizens are secure under the rule of law
92
and “5” representing a state-year in which the government uses political terror indiscrimi-
nately. The PTS scale thus considers both the severity of state terror and the segment of the
population it is applied to. Of particular interest here is that a score of “4” on either of the
PTS indices indicates that physical integrity violations are being inflicted upon those active
in politics, and that this repression has become widespread. The PTS data are therefore
uniquely constructed to test the onset of mass repression of proto-rebels as a function of my
theory. The models I present here are based upon the Amnesty International index, which
I term PTS-Amnesty.1
4.5.2. Independent Variables
Central to my theory is the perception of threat to domestic elites posed by revolution-
ary states. My hypotheses also imply interactive effects between a count of the revolutionary
regimes produced by the military victory of rebels in civil war, the level of threat posed by
these regimes, and proximity. To capture these concepts, I first identify those regimes that
result from rebel victory in a civil war.
4.5.2.1. Revolutionary Regimes
In order to test Hypothesis 4.1, I identify those regimes that emerge from rebel victory
in civil war. States hosting revolutionary regimes are identified for the entire 1946–2011
period, and coded as originating from civil wars in which rebels are victorious, as defined by
1The PTS project explicitly provides no guidance as to the proper handling of the two indices. Each hasits own issues. Both vary in their spatial coverage, and both have been accused of bias. Amnesty reportshave been accused of a favorable bias toward leftist regimes, while the US State Department reports havebeen accused of favorable bias toward right-wing dictatorships and the security interests of the United States.Some scholars average the two scales together, while others model them separately. In a series of robustnesstests, I use the State Department and Amnesty reports separately. Although the State Department variableincludes more observations, those models using this dependent variable, though signed in the same direction,are not significant. This is probably attributable to the variable’s bias. Although PTS-Amnesty has similarissues, I insert variables controlling for the impact of the Cold War into my models. This helps to controlfor the impact of any bias toward leftist regimes. Further details are discussed below.
93
the Uppsala Conflict Data Program’s (UCDP) Conflict Termination Data (Kreutz 2010).2
The start-year for a revolutionary regime is keyed to the termination years for those conflicts
that UCDP identifies as rebel victories.
I take a regime-oriented approach to this data, tying a revolutionary regime’s ter-
mination to the persistence of its government, rather than its political leaders. Such an
approach takes note of the fact that victorious rebel regimes often outlast their founding
revolutionaries, even if the founder’s tenure is violently terminated. The termination date
for each revolutionary state is keyed to changes to the form or type of government brought
to power by armed conflict, as determined by regime-data contained in Colgan (2012) and
Geddes, Wright and Frantz (n.d.), or a 30-year cutoff that I impose. I follow this protocol
because revolutionary regimes will undoubtedly decline over time in their appeal to foreign
dissidents, before institutionalizing into “normal” states.3
Some alternatives to this coding scheme present themselves. For example, Colgan
(2012), in his study of revolutionary regimes, defines such governments as those including
something similar to a “revolutionary command council” in their governing structures. The
result, for Colgan, is a set of regimes that include those that came to power by methods other
than military victory. My theory, however, is strictly limited to those regimes that came to
power violently. While it is doubtless that other kinds of regimes are quite revolutionary in
their appeal to proto-rebels, my theory is largely silent of such matters. I leave it to future
2In most cases, rebel victory is easily determined from this file. Cases in which one side is victorious arecoded with the Conflict Termination Data variable “outcome” = 4; and the subset of those cases in whichrebels are victorious are coded with the variable “vicside” = 2. In a small number of cases the outcome isset to “other” (“outcome”=6). In such cases, some original research was conducted in order to determineif rebel victory occurred. These include Croatia’s independence from Yugoslavia (conflict ID #190); Mao’svictory over China (#3), and the FNL’s actions in Vietnam (#53).
3Auxiliary analyses were carried out using the alternative cutoffs of 10 and 20 years, as well as a set ofmodels using no cut-offs.
94
work to explicate those causal mechanisms.
These coding criteria result in set of regimes in which rebels overthrow the government,
as well as several new states formed from secession. These regimes are listed in Table A.1
in the appendix. There are 61 regimes in the data., with a minimum duration 0 complete
years, a maximum of 51 years, a mean of 11.8 years, and a standard deviation of 10.8 years.
I collapse the data in such a way as to yield a state-year variable Revolutionary Regimes,
which is a count of the number of regimes persisting in the international system.
Figure 4.1 reports the trend in the frequency of persisting revolutionary states for the
period 1976–2001. The number of such regimes is relatively low early in the history of this
time frame, jumping at two key points — the late 1970s, which saw revolutions in Iran and
Afghanistan, and the early 1990s, a period characterized by the end of the Cold War. Each
of these events inspired proto-rebels elsewhere and, in some cases, this process was met with
repression. The Iranian revolution, for example, provided an example of mobilization to
aggrieved Shia minorities in nearby Iraq and Saudi Arabia, while also inspiring the creation
of Hezbollah in more distant Lebanon (Jaber 1997).
4.5.2.2. Aggressiveness
I measure the general international aggressiveness (Hypothesis 4.2) by counting in
each state-year those Militarized Interstate Disputes (MIDs) occurring throughout the inter-
national system. The MIDs are contained in Maoz’s DYADMID variant of the MID data,
which records intentional interstate threats, displays or uses of militarized force.4 I term this
variable MID Count.
MIDs are an appropriate measure of the international threat environment, as they
4These data, the Dyadic MID Dataset (version 2.0), are available online:http://psfaculty.ucdavis.edu/zmaoz/dyadmid.html.
95
Figure 4.1. Frequency of Revolutionary Regimes per Year, 1976–2001.
1015
2025
Fre
quen
cy o
f Rev
olut
iona
ry R
egim
es
1970 1980 1990 2000Year
represent the general level of stability or conflict in the system, a state’s ability to project
power beyond its borders, or the will of a revisionist or revolutionary state to violently alter
the balance of power. Moreover, revolutionary states will stand out as a more prominent
example to proto-rebels, and thus encourage repressive behavior in threatened states, if they
are engaged in more aggressive behavior. This is a good assumption to make, as new regimes
are known to initiate MIDs at a higher rate than other states (Maoz 1996).
It should be acknowledged, however, that MIDs are an imperfect measure of threat.
They are, rather, a proxy for the underlying theoretical process, and there are many different
ways that MIDs could represent that threat without the influence of a revolutionary state.
As noted in my theoretical discussion, states may repress due to the general level conflict or
instability present in the international system (Poe and Tate 1994). Regional rivals are also
known to sponsor rebel movements in one another’s territories, to engage in MIDs, and to
96
utilize repression (Salehyan 2009).
To overcome these competing explanations, I interact MID Count and Revolutionary
Regimes. The interaction of these two variables allows me to discern whether it is MIDs in
general that are threatening, or if it is those occurring in an international environment with
frequent revolutionary activity that represent a special case. Additionally, I create a variant
of MID Count in which I count only those MIDs initiated or joined by revolutionary regimes.
I term this alternate variable Revolutionary MID Count.
4.5.2.3. Proximity
In order to test the expectation pertaining to proximity (Hypothesis 4.3), I disaggre-
gate Revolutionary Regimes and MID Count into two types in order to assess attenuation
of threat as a function of geographic distance between a threatened state and revolutionary
regimes. I do so by way of the coding of direct contiguity as defined by the the Correlates
of War Contiguity Dataset (Stinnett et al. 2002): (a) the threat posed by revolutionary
states that are geographically contiguous at time t ;5 and (b) the threat posed by revolu-
tionary states that are geographically non-contiguous at time t.6 This procedure results
in four variables in each year: (a) Revolutionary Regimes. (contig.), a count of revolu-
tionary regimes contiguous to the unit of observation (lagged one year); (b) Revolutionary
Regimes. (non-contig.), a count of revolutionary regimes not contiguous to the unit of obser-
vation (lagged one year). I repeat this same procedure on MID Count, yielding the following
variables: (a) MID Count (contig.); (b) MID Count. (non-contig.); (c) Revolutionary MID
Count (contig.); (d) Revolutionary MID Count (non-contig.).
5Where the COW variable “contig” variable is set to “5” or less. This includes all cases of direct landcontiguity, as well as those separated by 400 miles of water or less.
6As determined by the Correlates of War Contiguity Dataset, where the “contig” variable is set to “6.”
97
Disaggregating by proximity serves another useful purpose. Because conflict tends
to occur in clusters (Salehyan 2009), it is difficult to separate out learning mechanisms
from those direct mechanisms, like refugee flows, occurring among directly contiguous states.
Consider, for example, that Sudan in the late 1990s was nearly surrounded by states whose
governments had been empowered by successful rebellion, each of which is an example from
which Sudanese proto-rebels might have learned. Yet, these revolutionary states, includ-
ing Uganda, Ethiopia, Eritrea, Chad, and the Democratic Republic of Congo were each
subsumed in their own civil conflicts. Porous international borders permitted refugee flows
into each state’s territories, each sanctioned and supported rebellion against one another’s
governments, and each attacked and invaded one another in a complex of interlocking wars.
Providing evidence of learning over and above the effect of such a conflict zone is the central
challenge of this essay.
In order to provide evidence of learning, it is therefore necessary to show that non-
contiguous, threatening revolutionary states result in an increase in repression. Although
it should be expected the entirely local factors should produce a greater impact, my non-
contiguous variables should also to exert a substantively important effect.
4.5.3. Control Variables
I extract all of my control variables from data the Buhaug and Gleditsch (2008) study
on civil conflict diffusion. The first of these variables is Neighboring Conflict Dummy,
a 0/1 indicator of civil conflict in at least one of a given country’s contiguous neigh-
bors. Including such a variable is necessary, given the importance of neighboring conflict
(Buhaug and Gleditsch 2008; Danneman and Ritter 2014; Salehyan 2009).
Several additional control variables are extracted from the replication data. Civil war
98
is a 0/1 variable that measures the presence of armed conflict on a state’s territory, armed
conflict being defined as an organized challenge to the state resulting in at least 25 battle-
deaths in a given year. GDPpc (ln) is the natural logarithm of GDP per capita, which is
ultimately derived from the Gleditsch (2002b) Expanded GDP dataset. Regime characteris-
tics are also controlled by using the democracy index, (Democracy), as well as its squared
value (Democracy2), ultimately derived from the Polity IV data (Marshall, Jaggers and Gurr
2004). This variable ranges from -10 (most autocratic) to +10 (most democratic). The nat-
ural logarithm of population is also included, derived from the Correlates of War National
Material Capability (NMC) data (Singer 1993).
Spatial variants of the democracy and wealth variables are included (Neighborhood
Democracy and Neighborhood GDP per capita, respectively), controlling for the influence
of the geographic clustering of these variables. Like Neighboring Conflict Incidence, these
variables are regional averages.
I also create a variable, Cold War, a 0/1 indicator that is coded “1” for all years
prior to 1991. These years are notable for the presence of a highly ideological form of
learning in which communist revolution and agitation produced emulation by proto-rebels
on a global basis. The fact that this global ideology was promulgated by a superpower
further highlights the fact these years may have had an entirely different quality to them.
Additionally, the human rights measures I employ in my dependent variable are directly
impacted by the Cold War. The US State Department reports were systematically less
critical of American allies. Similarly, the Amnesty reports were systemically biased in favor
of leftist regimes. Differences among the two even out by the end of the Cold War, and there
is little difference between them beyond that time. Cold War thus helps to control for such
99
issues (Poe, Carey and Vazquez 2001; Wood 2008).
Table 4.1. Descriptive Statistics for Preventive Medicine Research Design, 1976–2001.
Variable Mean Std. Dev. Min Max
Dependent Variable
PTS-A 2.7 1.17 1 5Independent Variables
MID Count (contig.) 0.06 0.4 0 5MID Count (non-contig) 0.06 0.67 0 20Revolutionary MID Count (contig.) 0.005 0.11 0 3Revolutionary MID Count (non-contig.) 0.01 0.16 0 2Revolutionary Regimes (contig.) 0.24 0.55 0 5Revolutionary Regimes (non-contig.) 6.89 6.26 10 23Control Variables
Civil War - - 0 1Neighboring Civil War - - 0 1Neighborhood Democracy -0.49 5.53 -9.88 9.861Neighborhood Democracy2 30.9 28.81 7.35E-07 97.6675Neighborhood GDP 8.18 0.88 6.25 10.33Democracy 0.05 7.52 -10 10Democracy2 56.54 31.52 0 100GDP (ln) 8.23 1.08 5.63 10.73Population (ln) 9.07 1.52 5.32 14.05Cold War 0.54 0.49 0 1
4.6. Analysis
In order to analyze the data, I execute ordered probit regressions with robust standard
errors clustered by country. This model is appropriate given the categorical nature of the
dependent variables and the Time-Series Cross-Sectional nature of the data (Greene 2012).
I undertake a two-step empirical strategy. In the first, reported in Table 4.2, I run
a set of models that interact Revolutionary Regimes and the unmodified variants of MID
Count. In the second, reported in Table 4.3, I model the interaction between Revolutionary
Regimes and Revolutionary MID Count. This allows me to discern if revolutionary regimes
aggravate the unmodified MIDs, or if Revolutionary MID Count has a special quality of
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its own. If this is the case, then those interactions containing MID Count should not be
significant, while those containing Revolutionary MID Count should be significant. This
pattern is born out in the analysis below.
Table 4.2, with the unmodified variants of MID Count, contain three models. In
Model 1, I model the interactive effects of Revolutionary Regimes (contig.) and MID Count
(contig.). In Model 2, I model the interactive effects of Revolutionary Regimes (non-contig.)
and MID Count (non-contig.). In Model 3, I include both sets of interactions. Because
the two interaction terms do not overlap, there is no need to cross-interact their constituent
terms.
101
Table 4.2. Ordered Probit Models of State Repression, Revolutionary Regimes,and MID Count, 1976–2001.
Variable (1) (2) (3)MID Count (contig.) 0.25* 0.25**
(0.10) (0.10)Rev. Regime Count (contig.) 0.01 0.02
(0.07) (0.07)MID Count (contig.) × Rev. Regime Count (contig.) -0.07 -0.07
(0.05) (0.05)MID Count (non-contig.) -0.13 -0.12
(0.13) (0.13)Rev. Regime Count (non-contig.) 0.01 0.01
(0.02) (0.01)MID Count (non-contig.) × Rev. Regime Count (non-contig.) 0.01 0.01
(0.01) (0.01)Civil War 1.30*** 1.33*** 1.31***
(0.14) (0.14) (0.14)Neigh. Conflict dummy 0.21+ 0.22* 0.21+
(0.11) (0.10) (0.11)Neigh. democracy -0.00 -0.01 -0.01
(0.01) (0.01) (0.01)Neigh. democracy2 -0.00* -0.01** -0.01*
(0.00) (0.00) (0.00)Neigh. GDP 0.07 0.11 0.11
(0.07) (0.10) (0.10)Democracy -0.04*** -0.05*** -0.05***
(0.01) (0.01) (0.01)Democracy2 -0.01*** -0.01*** -0.01***
(0.00) (0.00) (0.00)GDP -0.13* -0.12 -0.14
(0.05) (0.08) (0.08)Population (ln) 0.12*** 0.16*** 0.16***
(0.02) (0.04) (0.04)Cold War -0.11+ 0.00 -0.01
(0.06) (0.12) (0.10)N 2,743 2,743 2,743χ2 297.1 310.8 314.0Log Likelihood -3220 -3231 -3219Note: Coefficients with robust standard errors in parentheses;
sig. levels are two-tailed (∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1);
wa = weighted average; ln = natural logarithm.
Interactions containing the non-contiguous variables are not statistically significant
(Table 4.2, Models 2 and 3). However, ordered probit coefficients are difficult to interpret,
and interaction terms require the creation of scenarios and the consideration of alternatives
102
in order to build a case (Braumoeller 2004). Additionally, it is difficult to isolate the role of
revolutionary regimes in this model.
Interestingly, the coefficients on MID Count and Revolutionary Regimes are negative
(Models 1 and 3). This should not be taken as evidence that no learning occurs among
contiguous states but, rather, that learning’s effects are washed out by the local correlates of
conflict and that, furthermore, proximal revolutionary regimes likely have a deterrent effect
upon proto-rebels. Thus, once the decision calculus of proto-rebels shifts in the direction of
deterrence, then the state may not need to violate physical integrity rights and may instead
rely upon less violent means of repression.
While this pattern is interesting, I execute a second run of ordered probit models that
isolates the actual impact of revolutionary regimes. I do this by substituting Revolutionary
MID Count into the model, presented below in Table 4.3. Models 4 and 5 analyze the two
interaction terms separately, while Model 6 analyzes them together. Revolutionary Regimes
(non-contig.) and Revolutionary MID Count (contig.) are highly significant, even in the face
of the significance evidenced by their contiguous counterparts, again providing evidence of
learning.
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Table 4.3. Ordered Probit Models of State Repression, Revolutionary Regimes,and Revolutionary MID Count, 1976–2001.
Variable (4) (5) (6)MID Count (contig.) 0.42 0.44
(0.34) (0.34)Rev. Regime Count (contig.) -0.00 0.01
(0.06) (0.06)MID Count (contig.) × Rev. Regime Count (contig.) -0.15 -0.15
(0.13) (0.13)MID Count (non-contig.) -0.85* -0.83*
(0.41) (0.41)Rev. Regime Count (non-contig.) 0.01 0.01
(0.01) (0.01)MID Count (non-contig.) × Rev. Regime Count (non-contig.) 0.04+ 0.04+
(0.02) (0.02)Civil War 1.32*** 1.34*** 1.34***
(0.14) (0.14) (0.14)Neigh. Conflict dummy 0.21* 0.23* 0.22*
(0.11) (0.10) (0.11)Neigh. democracy -0.01 -0.01 -0.01
(0.01) (0.01) (0.01)Neigh. democracy2 -0.01** -0.01** -0.01**
(0.00) (0.00) (0.00)Neigh. GDP 0.11 0.11 0.10
(0.10) (0.11) (0.11)Democracy -0.04*** -0.04*** -0.04***
(0.01) (0.01) (0.01)Democracy2 -0.01*** -0.01*** -0.01***
(0.00) (0.00) (0.00)GDP -0.13 -0.12 -0.12
(0.08) (0.09) (0.09)Population (ln) 0.16*** 0.16*** 0.16***
(0.04) (0.04) (0.04)Cold War -0.06 0.01 0.01
(0.09) (0.12) (0.10)N 2,743 2,743 2,743χ2 300.7 313.5 327.6Log Likelihood -3229 -3230 -3227Note: Coefficients with robust standard errors in parentheses;
sig. levels are two-tailed (∗ ∗ ∗p < 0.001, ∗ ∗ p < 0.01, ∗p < 0.05, +p < 0.1);
wa = weighted average; ln = natural logarithm.
Next, following from Braumoeller (2004), I create a number of scenarios in order to
discern the substantive impact of these variables. The first scenario I examine is a “baseline
scenario” in which I show that states repress as a response to non-contiguous revolutionary
104
regimes and MIDs, rather than from those in the regional neighborhood. I do this by
manipulating the variables in Model 6, which contains both interactions. Revolutionary
Regimes (contig.) and Revolutionary MID Count (contig.) are set to zero. This allows
me to directly test Hypotheses 4.2. By turning off these variables, I am able to show that
states are repressing as a response to learning from non-contiguous cases, rather than in
response to the behavior of local revolutionary regimes. Cold War is also switched to zero in
order to remove the possibility that the contagiousness of communist revolution affect these
results. In creating this scenario, it is also necessary to rule out the contagion of nearby
conflict. I therefore set the continuous control variables at their means, and Civil War and
Neighborhood Conflict Dummy to zero in order to discern the impact of my independent
variables upon a stable state in a peaceful region.
With this scenario created, I calculate the predicted probability that a given state-
year will reach PTS-Avg=4. This value is of interest, as it represents the threshold at which
a state violates the physical integrity rights of persons with murder, disappearances, and
torture. Yet, rights violations at this level have not extended to the entire population. PTS-
Avg=4 is therefore a useful value to assess state repression of proto-rebels as they become
politically active. The predicted probabilities extracted from this scenario are plotted in
Figure 4.2 (a), showing Revolutionary MID Count (non-contig) as it varies from its minimum
to its maximum, and Revolutionary Regimes (non-contig.) as it varies from its minimum to
its maximum.
The probability that a stable state with peaceful neighbors will engage in mass repres-
sion (PTS-Avg.=4) increases from approximately .06 to approximately .15. This represents
a 150% change. This finding supports Hypothesis 4.1 and Hypothesis 4.2. States engage in
105
repression due to the aggression of rebel-founded states, even as a result of events in other
parts of the globe. Figure 4.2 (a) therefore suggests that a learning mechanism is at work,
with states violating the physical integrity of their citizens as a result of qualities originating
in the international system.
In Figure 4.2 (b), I set the Neighborhood Conflict Dummy to one, thus assessing the
likelihood of domestic repression in a stable state located in an unstable region. This allows
me to rule out contagion and spillover from a neighboring civil conflict. In effect, this scenario
allows me to rule out the possibility that conflict clusters, like those of late 1990s East Africa,
are driving my findings. The probability that a stable state with neighbors experiencing civil
conflict will engage in mass repression (PTS-Avg.=4) increases from nearly .06 to just over
.2, an approximate 200% change. States are therefore initiating political terror as a function
of revolutionary states and their international aggression.
106
Figure 4.2. Substantive Effects of Revolutionary Regimes (non-contig.) and Revolutionary MID Count (non-contig.),1976–2001.
(a) Repression in a State with no Neighboring Civil War as a Re-sponse to Revolutionary Regimes (non-contig.).
0.0
5.1
.15
.2.2
5P
r(P
TS
−A
mne
sty=
4)
0 1 2 3 4Revolutionary MID Count (non−contig.)
Revolutionary Regimes (non−contig.) = 13Revolutionary Regimes (non−contig.) = 27
(b) Repression in a State with a Neighboring Civil War as a Re-sponse to Revolutionary Regimes (non-contig.).
0.0
5.1
.15
.2.2
5P
r(P
TS
−A
mne
sty=
4)
0 1 2 3 4Revolutionary MID Count (non−contig.)
Revolutionary Regimes (non−contig.) = 13Revolutionary Regimes (non−contig.) = 27
107
The evidence presented in Figures 4.2 (a) & (b) is therefore decisive. It shows that
proto-rebels are indeed learning as a response to distant events, with or without civil con-
flicts in the neighborhood, and that states repress as a response. The contagious effects
of neighboring civil wars, although important for civil conflict onset and even repression,
do not account for these findings (e.g., Buhaug and Gleditsch 2008; Danneman and Ritter
2014). States will therefore tend to engage in mass repression as Revolutionary Regimes
(non-contig.) and MID Count (non-contig.) increase.
One interesting set of findings from Figure 4.2 are those in which there are a small
number of Revolutionary Regimes (non-contig.). In both cases there is a general decline in
the probability of mass repression. I take this to mean that MIDs occurring in far away
parts of the globe do not generally impact repression — it requires the aggravating impact
of many revolutionary regimes acting out within the international environment. Without
that aggravation, repression and respect for physical integrity will be primarily a function of
domestic determinants.
Finally, the control variables show a pattern of sign and significance suggested by the
existing literature (Davenport 2007). The neighborhood variables, including Neighborhood
Democracy, Neighborhood Democracy2, and Neighborhood GDP are either not significantly
discernable from zero, or else have coefficients near zero. On the other hand, Democracy is
negative and statistically significant, suggesting that domestic democratic institutions are
the primary guards against state repression. Population (ln) is also positive and significant,
suggesting that larger states are more likely to endure state repression. Overall, then, these
results provide evidence for a thus far understudied but key aspect of international relations
— the effect of revolutionary regimes on the domestic political order of states.
108
4.7. Conclusion
Revolutionary regimes are known for their powers of proselytization and the export-
ing of their ideas across the international system. Regimes threatened by the aggression
of these states react with preemptive repression. Although these findings are intuitive, the
quantitative literature has not explored them sufficiently, nor has it investigated their pos-
sible implications. Indeed, these findings offer substantial support for a “clash of ideas” in
world politics, with rebels and governing authorities locked in a system-wide battle over in-
formation, the stakes of which include the political order of states. There are three possible
areas of research, so far unexplored by political scientists, that arise from this logic.
First, the fact that both proto-rebels and governments are engaged in learning implies
a co-evolutionary process. Thus far, the literature has studied the rebel-side and state-
side of this process separately. the process of proto-rebel mobilization and state repression
implies that as each actor becomes aware of information, both will try to preempt the other,
particularly if that information suggests that successful rebellion is possible. Conflicting
parties may therefore become locked into an escalatory spiral of violence, with the onset of
civil war being the inevitable result. Constructing a two-sided theory of war onset based
upon international learning is therefore a next obvious step.
Second, my findings have important normative implications for global human rights.
Given that regimes react with repression even if they are distantly removed from the original
site of a revolution, scholars should be looking to the consequences of conflict in places that
are not obvious. Indeed, it is possible, even likely, that democratic regimes could succumb to
this process. Simply looking at American history provides support for this assertion. Early
in the Cold War, the country was caught up in the so-called Red Scare in which it was feared
109
that radical internationalist ideologies would inspire a communist Fifth Column within the
government. Whether or not this Fifth Column obtained is arguable; yet, it remains the
case that a number of questionable policies were pursued as a result.
Third, and finally, future research should assess the role of ideology. Although
Danneman and Ritter (2014) suggest that governments repress as a function of conflict in
neighboring states, regardless of identity, it could be that certain ideologies are “transfer-
able,” and capable of transmitting information on mobilization, tactics, and strategies from
one part of the world to another. Similarly, regimes beset by rebellion commonly try to
delegitimize dissidents by claiming that they are mere tools or agents of international con-
spiracy. Russia, China, Libya, Syria, and Iran each made this claim when nearby rebellion
threatened key interests. Although this claim is unexplored in this essay, it offers a powerful
justification for repression and should be explored.
To conclude, the domestic repression arising from foreign revolutions is evident in both
empirical and anecdotal evidence offered herein. These topics have been largely unexplored.
Because states devote such an enormity of resources to preventing proto-rebel mobilization,
engaging in domestic surveillance, to say nothing of state terror, such research is of the
highest importance.
110
CHAPTER 5
CONCLUSION
This chapter concludes the dissertation, and proceeds as follows. First, I briefly sum-
marize my findings. Second, I offer some theoretical implications for the academic literature.
Third, I briefly discuss the dissertation’s policy implications. Fourth, I offer a number of po-
tential projects stemming from the current research. I then make some concluding remarks.
5.1. Summary of Findings
The pattern of findings in this dissertation paint an extraordinary, if nuanced, picture.
Proto-rebels, like all human decision makers, are boundedly rational cognitive misers, and
so have little information with which to judge the utility of violently challenging the state.
Thus, they observe the international environment, seeking out cues to help resolve their
uncertainties. These cues can be classified according to their source, which include active
civil wars and revolutionary regimes. Although much of this information is lost in the noise
of international politics, those sources exhibiting a similarity to the proto-rebel are “louder.”
Such sources are therefore capable of inspiring proto-rebels to action even in distant locales.
Civil wars and revolutionary regimes each generate a variety of separate effects. Chap-
ter 2 showed that civil wars throughout the global system are correlated with the emergence
of militant groups and that, furthermore, conflicts in neighboring states may agitate mil-
itants such that their behavior escalates to war. The international diffusion of political
violence is therefore a two-step process. Proto-rebels may mobilize when they vicariously
observe civil wars throughout the international system, but their overall strength and their
ability to challenge the state is limited unless additional inputs are received from directly
contiguous states. To turn a phrase, proto-rebels “learn and mobilize globally, but escalate
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locally.” Although this finding is useful in highlighting the puzzle of world-wide diffusion,
and indicates that international events have a key role to play, it is not necessarily evidence
of learning. This pattern of findings could arise from the international flow of arms, funds,
and foreign fighters. It is not until Chapter 3 that a theoretical explanation is offered and
evidence of learning is found.
Chapter 3 argues that proto-rebels learn from two sources of international information:
civil wars and revolutionary regimes. The findings uphold the literature’s view that civil
wars are poor sources from which to learn. However, revolutionary regimes produce a global
effect sufficient enough to overwhelm culture and regime-type dissimilarity, inspiring learning
proto-rebels globally. This is a highly significant finding, but it is not the end of the story.
It is highly likely that an unobserved variable helps to explain learning by proto-rebels from
events in dissimilar states: that variable being ideology. Marxist or Islamist proto-rebels are
likely to learn from one another globally, as each ideology type professes a universal appeal
that transcends cultural barriers. I have not addressed this possibility here, but it is an
exciting area for future research.
Chapter 4 argues that governmental authorities are aware that these processes are
underway and act to thwart them. This effect arises because states fear that successful
rebel action decisively demonstrates to proto-rebels the benefits to be had from rebellion
and the creation of subversive “Fifth Columns” within their political systems. Moreover,
revolutionary regimes that are highly activist internationally are more likely to capture the
attention of proto-rebels and thus encourage threatened elites to use repression.
Each of these findings is new to the literature and point to some very important im-
plications. In terms of academic theory, scholars are in need of theories and data-sets that
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adequately explain the purely informational aspects of civil conflict and to better under-
stand the connections between non-neighboring conflicts. From a policy standpoint, each of
the findings within my dissertation implies that policy-makers, particularly those in major
democratic countries like the United States, must give careful consideration to the unin-
tended consequences of their foreign policies. Encouraging regime change or promoting civil
conflict could accidentally encourage proto-rebels elsewhere to learn and mobilize. In the
following sections, I explore each of these implications.
5.2. Theoretical Implications
In this section, I discuss two implications for political science. First, this dissertation
unpacks the international learning process by proto-rebels. In doing so, I have significantly
advanced our knowledge of civil conflict. While learning, revolutionary regimes, proto-rebel
mobilization, and state reaction have each been the subject of significant inquiry, they have
rarely been combined into a single, cohesive framework. The second implication relates to
the clash of ideas in world politics. The rise and fall of revolutionary regimes, the onset
and termination of civil wars, and the appearance and exhaustion of international ideologies
powerfully impels collective action on a world-historical scale. I will therefore discuss each
of these points, in turn.
5.2.1. Diffusion and Learning in Civil Conflict
The international relations of civil war is a rapidly growing area of research. New
theories and data have appeared recently, increasing our understanding of world politics
and its influence on political violence.1 Diffusion is but one area within this field, and it
1See Wood (2013) for a discussion of new theories and data.
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has enjoyed something of a renaissance of attention from scholars, complete with methods
anchored to spatial econometrics.
2 These are extraordinarily welcome developments. Diffusion has not always enjoyed
such respect. Indeed, in sociology, diffusion and similar mechanisms were neglected for many
years. Myers and Oliver (2008, 4) explain:
One reason for this lack is the continuing reaction against Le Bon’s (1895) “contagiontheory” and the connotations of irrationality in pre-1970s behavior theory in whichthe spread of collective behavior was likened to the spread of a disease. But, ofcourse, reasonable choices by people can cause behaviors to diffuse in mathematicalpatterns that are analytically similar to disease contagion patterns, without in anyway implying that the diffusing behavior is a disease. People do “imitate,” but thisdoes not imply that their imitation is mindless or irrational. New communicationtechnologies like the telephone and e-mail spread as a function of their utility andthe number of others who had previously adopted the mode of communication. Sit-ins and other protest tactics spread [during the civil rights era] because they wereproducing successes in breaking down segregation.
Yet, to date, most of the new theories and data in this area are designed to look for
evidence of diffusion, or else to correct for spatial dependence among observations. Until
recently, few have devoted any real attention to unpacking diffusion’s causal mechanisms.
To quote Wood (2013, 243) in a recent summary of the literature:
Despite these significant advances in the analysis of the transnational diffusion ofconflict, the precise causal mechanisms underlying diffusion are not yet adequatelyclear. The findings often identify the conditions under which diffusion is likely (thepresence of transborder ethnic ties, for example), without demonstrating preciselywhat mechanism drives the effect across borders. While new data sets of theoreti-cally relevant variables mark a significant advance over earlier proxies, this literatureremains largely driven by structural configurations and deploys better specified butas-yet inadequate proxies, such as demographic size as a measure of mobilizationalcapacity. The process of conflict diffusion is difficult to observe much less capture in a
2As discussed throughout this dissertation, the literature can be grouped into two general areas: con-flict diffusion as a result of direct, regional mechanisms (e.g., Braithwaite 2010; Braithwaite and Li 2007;Buhaug and Gleditsch 2008; Gleditsch 2002a, 2007; Salehyan 2009; Salehyan and Gleditsch 2006) or demon-stration effects exerted upon ethnic groups in close proximity to the conflict (e.g., Beissinger 2002;Danneman and Ritter 2014; Hill and Rothchild 1986, 1992; Hill, Rothchild and Cameron 1998; Kuran 1998;Maves and Braithwaite 2013; Lake and Rothchild 1996, 1998; Weyland 2009, 2010, 2012).
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cross-national database. The challenge is to identify not only the relevant conditionsbut also specify how particular configurations of actors affect the dynamics of conflict.
It is in this context that I position this dissertation’s theory and empirical work. I
have argued that proto-rebels learn, and I have examined how they learn. My dissertation
is among the first to unpack the learning mechanism of conflict diffusion, among the first to
quantitatively measure the diffusion process connecting civil wars and revolutions to militant
groups. Scholars should therefore recognize that there are deeper interconnections among
conflicts than are commonly recognized.
5.2.2. The Clash of Ideas in World Politics
Over twenty years ago, Siverson and Starr (1991) argued that the conflict scholars
of that time had focused heavily on the correlates of international war, yet had ignored
the spatial and temporal consequences of war’s onset and termination. One of the main
consequences implied in such logic is that of diffusion. International wars are rarely isolated
events, but rather tend to cause, and be caused by, conflicts occurring in other times and
places. Alliance ties, trade contacts, governing structures, and systemic qualities tend to
cause international wars to spread from a single point and to cluster geographically.
My dissertation is positioned similarly. Although a significant number of works have
located causation for civil war onset in the international system, even theorizing that war
in one state places neighboring states at higher risk, few have actually examined the conse-
quences for the way such conflicts end. If rebels are militarily victorious, or even if they are
defeated, proto-rebels and state authorities world-wide take notice and update their utility
calculations accordingly. This engenders a clash of ideas that plays out across the entire
scope of the international system.
The Clash of Ideas in World Politics, the title of a book-length quantitative study by
John M. Owen IV, encapsulates the theme of the dissertation (Owen IV 2010). When proto-
rebel learning and state reaction combine on a global scale, the result is a complex and ever
changing international system. From time to time, events, such as a civil war or revolution,
occur that radically shock the system. It is even possible that when and if highly contagious
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international ideologies emerge, mobilization and repression will generate out-sized effects
and become caught up in a clash of truly global proportions. Such was the logic of the Cold
War and, on a much lesser scale, the current War on Terror.
5.3. Policy Implications
Leaving aside developments within the scholarly literature, and debates over the
“big picture,” my dissertation also has important implications for policy. Perhaps the most
important findings in this dissertation can be distilled as follows: civil wars and revolutions
produce active mobilization globally. This directly informs the discussion of current events,
particularly the Arab Spring.
By way of analogy, consider the following chain of events. In 2011, the fall of Hosni
Mobarak in Egypt and Moammar Ghadaffy in Libya mobilized revolutionaries across the
Middle East and North Africa. The nearby regimes reacted harshly and violently, with
conflict escalating to war in Syria, Mali, and Yemen. Ignoring for the moment issues of
morality and the legitimacy of rebellion, the sudden regime change in Egypt and Libya were
brought about, in no small part, due to the actions of international decision-makers. In
the most dramatic case, NATO intervened in Libya, saving the rebel movement there from
defeat and leading directly to regime-change. Surely, given the theory and findings herein,
it should be expected that international proto-rebels learned from this case and mobilized
internationally. It therefore behooves policy makers to give greater consideration to the
international consequences of their actions. Policy-makers must therefore decide how best
to manage the fact of rebel learning.
Another implication relates to terrorism. My dissertation is strongly rooted in orga-
nized political violence, and so it says little about single individuals taking inspiration from
events overseas. That being said, my findings indicate that there is a strong relationship
between civil wars abroad and the emergence of militant groups at home. This has pro-
found consequences for domestic security which must be balanced against the demands of
democracy.
Finally, my dissertation should inform the “domino debate” that emerges from time
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to time. On the one side of this debate, foreign policy hawks in government and media argue
that the facts of rebel learning necessitate major international involvement. The specifics
of this argument vary with the times. During the Cold War, for example, the fear was that
communist driven regime would create a cascade of militant collective action that would
eventually threaten key security interests of the United States. Moreover, consideration
should be given to the “responsibility to protect” argument. Although understandable in
light of the international community’s failure to respond to the Rwandan Genocide, some
scholars believe such interventions creates a “moral hazard” that encourages proto-rebels
elsewhere that victory is possible (e.g., Kuperman 2008).
Conversely, foreign policy doves and political realists contend that the domino theory
is a myth (Walt 1992). Revolutions and civil wars are mainly domestic in their origins, and
have little consequence for distant states, especially in seemingly secure areas like Europe
or the United States. My results show that this is simply not true, and burying one’s head
in the sand, so to speak, will not protect a country from the externalities of civil conflict.
Rather, the truth of the matter is a highly nuanced affair that neither side in the policy
debate has grasped.
5.4. Future Research
The findings and theoretical implications discussed above suggest a number of puzzles
and directions for future research. Civil conflict diffusion represents an exciting area of
research, with many topics remaining unexplored. Here, I propose several potential research
projects, in order of their immediate feasibility: (1) regime change and the diffusion of
social conflict; (2) incident-based diffusion; (3) the diffusion of tactics; and (4) the evolution
of the international system.
5.4.1. Regime Change and the Diffusion of Social Conflict
My research has quantitatively explored the world-wide diffusion of civil conflict and
its consequences. Yet, this is but one small aspect of diffusion. The rebel learning theory
described herein can be easily expanded to encompass additional phenomena. Consider,
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for example, the phenomena of revolution, social conflict, and other forms of contentious
politics. Collective action and free riding problems abound in these kinds of behavior (e.g.,
Tilly 1978; Lichbach 1995; McAdam, Tarrow and Tilly 2001; Tarrow 2011), yet such acts are
typically characterized by a lesser degree of organization and more mass participation than
the organized militant groups and civil wars discussed throughout this dissertation. Indeed,
extant research indicates that revolution is best likened to an “assurance game,” in which
citizens are unaware of one another’s preferences and, fearing the state’s repressive forces,
strive to keep their opinions secret. However, once protest occurs, citizens are assured that
others have the same preferences and that the risks of political expression are less than they
might have imagined. Thus, a preference cascade radiates through society in which citizens
switch from passive acceptance of the regime and become vocal opponents. The speed of
such cascades and the apparent reversal of citizen attitudes mean that revolutions often
erupt in completely unanticipated times and places (Kuran 1989, 1991).
Preference cascades frequently have an international origin. The recent Arab Spring
provides the obvious example, but there are many others present in the historical record,
such as the Color Revolutions of the mid-2000s and the 1989 fall of communism in Eastern
Europe. Yet, as briefly discussed herein, revolutionary waves are not the product of modern
communications technology. The Arab Spring bears important similarities to the European
Revolutions of 1848, in which the fall of the French Monarchy resulted in a regime crisis in
most of the continent’s monarchies (Weyland 2009, 2012). In that case, the development of
railways, newspapers, and telegraphs provided a means of rapid communication even before
the advent of television and the other accoutrements of the information age. Going back into
the historical record as far as the sixteenth century shows similar developments, wherein the
development of the printing press sped the diffusion of social unrest during the Protestant
Reformation (Zagorin 1982).
There are significant opportunities here. To date, research has focused on the outbreak
of mass social conflict and its international spread. Work on the success of revolutionaries
is more limited, typically focused on the overthrow of regimes and the subsequent onset of
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international war (Maoz 1996). I therefore propose to execute a study connecting regime
change and the persistence of newly empowered governments with the diffusion of social
conflict. Regime change may be defined as per this dissertation — the military victory of
rebels in civil war — or it may be defined more broadly so as to include those cases in which
mass protests compel the fall of a regime, as in Eastern Europe. The survival and persistence
of these regimes should therefore be associated with mass protest behavior internationally.
Newly available social conflict events-data makes this a propitious time to engage in such a
study. The Social Conflict in Africa Data (SCAD) (Salehyan et al. 2012) even allows us to
understand these phenomena in a highly disaggregated way.
5.4.2. The Foreign Policy Consequences of Rebel Learning
Another research agenda arising from this dissertation is already underway, and seeks
to understand the consequences of rebel learning for interstate relations. There are a number
of possible projects. In one paper, for example, a co-author and I argue that because revolu-
tionary regimes face considerable international hostility, they tend to ally with one another
and form international communities. The Communist International is a classic example of
such a community. Non-revolutionary states are threatened by this process and create their
own international communities in order to counter-balance revolutionary states. One of the
best examples of this is that of Operation Condor, the secret effort by the South Ameri-
can juntas to counter-balance the communists and police proto-rebels within their societies
(Linebarger and Enterline n.d.).
Another paper in this area should examine the way in which the international com-
munity reacts to revolutionary regimes in general. Consider that at the beginning of the
Cold War, George Kennan issued his famous “Long Telegram,” heralding the foreign policy
of containment (Kennan 1947). Under containment, the United States was the architect of a
vast network of alliances designed to restrict the appeal of Soviet-inspired proto-rebels to one
area of the world. Although Kennan eventually turned against the military interventionism
associated with containment, the US supplemented its alliance policy with military actions
in Korea, Vietnam, Grenada, and other nations and undertook a major program of economic
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and military assistance to threatened nations.
The proposed paper would therefore analyze military intervention, foreign aid flows,
and alliance ties between the great powers and those states neighboring to a revolutionary
regime. This paper would therefore unify several strands of research, including those explor-
ing the world politics of third party military intervention (Kathman 2011), and civil war
diffusion (Buhaug and Gleditsch 2008).
5.4.3. Incident-based Diffusion
Throughout this entire dissertation, I have defined success with respect to rebel-driven
regime change. For example, successful attacks by rebels and terrorists could engender
emulation by active rebels and proto-rebels elsewhere. One or more studies in this area
could concentrate their focus upon rebel actions within a single country, most likely utilizing
highly detailed geo-referenced events-data, or they could be world-wide in scope, seeking
generalizable propositions at the country-year level of analysis.
By applying the rebel learning theory developed herein, it should be possible to un-
derstand individual incidents of rebellion and terrorism. In one oft-forgotten example, the
bombing of the Murrah Federal Building in Oklahoma City on April 19th, 1995 resulted in ad-
ditional, yet seemingly unrelated actions from the then principle actor in domestic American
terrorism — the Unabomber. Because of the unprecedented destruction in Oklahoma City
pushed the Unabomber from media attention, he sought to recapture it by stepping up his
campaign and demanding the publication of his manifesto (Nacos 2010). This demonstrates
a very unique form of rebel learning.
Although this example deals with individuals and “lone-wolf” style terrorism, rather
than mass political violence and civil war, it highlights several threads running through the
dissertation. Firstly, groups often learn from and react to the actions of others who do
not share their ideology. Secondly, it poses the question: do single incidents engender the
simple copycatting of tactics, or do they provide motivation for like-minded proto-rebels,
who are inspired to undertake actions they may not have otherwise attempted? Although
my dissertation comes down heavily on the latter, both options may be analyzed by using
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the tools provided by rebel learning. Midlarsky, Crenshaw and Yoshida (1980) argued that
both copycatting and inspiration were present in the diffusion of terrorism, with some tactics
much more likely to be copied than others (highly publicized bombings and hijackings were
more likely to be emulated than less-well known raids and assassinations). As Sedgwick
(2007, 106-107) phrased it:
A particular terrorist technique is only of interest to a group that has already madethe decision to adopt a terrorist strategy; a technique cannot on its own cause a resortto terrorism. Similarly, a radical group will normally enter into direct contact withan established terrorist group only once the decision to adopt a terrorist strategy hasalready been made.
Still, the mechanisms by which the spread of terrorism occurs have not seen a great
deal of empirical study, and the area is theoretically underdeveloped. I would therefore
theorize, by way of rebel learning, that successful attacks are likely to trigger international
mobilization and counter-mobilization. Thus, an attack like 9/11 could result in the mobi-
lization and emergence of militant groups in other parts of the world. Counter-mobilization
may then occur as rival groups emerge. Although such a study requires a certain cleverness
of research design, the data is readily available.
5.4.4. Rebel Learning and the Onset of Peace
To date, nearly all of the literature on conflict diffusion has attempted to explain its
onset, with little effort given to the way peace affects diffusion or to the way diffusion is
managed by international communities. Yet, we know from the historical record that dif-
fusion and conflict management seriously affect one another. Along these lines, Beardsley
(2011) argues that the deployment of peacekeepers results from attempts by the interna-
tional community to contain diffusion, while Kathman (2010, 2011) found that third-party
military intervention is a function of that party’s risk of experiencing conflict spillover from
a neighboring state.
These works focus almost entirely on those material factors that drive diffusion among
directly neighboring states. Like the literature on civil war diffusion, there is little emphasis
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on learning and other indirect mechanisms, and there has been no work on the way in
which proto-rebels and governments learn from peace, and then choose to engage in peaceful
conflict resolution behavior, rather than mobilizing for war. I therefore identify two such
mechanisms here: (1) learning from peace and the effect of negotiated settlements; (2) the
deterrent effect of war.
In the first mechanism, learning from peace, I propose to examine how rebels and
governments learn from successful conflict management. It is known that there has been a
general decrease in the amount of armed conflict within the international system since the
conclusion of the Cold War. What explains this puzzle? Various explanations include the
end of superpower support for rebel movements, as well as increasingly successful attempts
at international mediation. Yet, by the logic advanced in this dissertation, active rebels
and governments should also learn from the peaceful management of conflict, resulting in an
overall decrease in conflict.
The proposed “learning from peace” research project would therefore code a new set
of revolutionary regimes, these to be defined as those arising from negotiated settlement at
the end of a civil war. Again, the dependent variable would be proto-rebel mobilization,
operationalized as the emergence of militant organizations. Proto-rebels learn from the
peaceful resolution of war and then choose not to mobilize, perhaps instead devoting their
attentions to participation in the political process.
The second mechanism, the deterrent effect of war, is an extension of Chapter 3 on
the use of repression. Here, social actors take stock of the international environment, paying
particular attention to the costs, and then deciding whether or not to engage in normal
political processes. For example, the devastation wrought by the civil wars in Yugoslavia
could have a cooling effect on ethnic tensions throughout Eastern Europe. Far from creating
demonstration effects that mobilize aggrieved ethnicities, potentially conflicting parties could
decide to enter into peaceful interactions. Continuing the example, Czechs, Slovaks, Hungar-
ians, and other ethnicities may use the Yugoslav example not as a lesson in the possibilities
of armed conflict, but as a lesson in those outcomes they wish to avoid.
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5.4.5. Qualitative Research
This dissertation is devoted to acquiring quantitative evidence in what was termed the
“pattern finding” tradition (Lee and Strang 2006). Yet, such methods are not the only tool
for studying diffusion. Qualitative methods are one important alternative. In this section, I
will therefore briefly discuss the possibilities for qualitative research projects.
Until recently, qualitative methods have been neglected in the study of diffusion,
with one scholar even referring to them as “Sir Galton’s step-children” (Starke 2011). Yet,
as Lee and Strang (2006) and Gilardi (2012) argue, qualitative methods are an important
tool for diffusion scholars. Quantitative methods, although extremely useful in the study of
diffusion and war, suffer in that micro-levels of causation are inferred rather than directly
observed. This is not to say that qualitative methods provide the perfect solution; indeed,
they are unable to provide generalizability and are unable explore individual level cognitive
psychology. In other words, even qualitative methods are unable to look inside the minds of
proto-rebels in order to find the truth of the matter.
Still, qualitative methods have enjoyed renewed credibility of late. Gerring (2007) and
McAdam, Tarrow and Tilly (2008) have each produced works that are designed to aid the
qualitative scholar of political violence and contentious politics. With respect to diffusion,
the primary qualitative approach is that of “process tracing,” in which the transmission of
information and its consequences are followed from one actor to another.
Gilardi (2012) summarizes the above works, providing a step-by-step guide for the
interested scholar. In the first step, the scholar must devote attention to case selection. Al-
though this is true in all qualitative work, the specifics are somewhat different in process
tracing. Case selection should follow a “diverse cases” framework designed to achieve “maxi-
mum variance along relevant dimensions.” This stands in sharp contrast to the comparative
methods first advanced by John Stuart Mill. In Mill’s framework, cases are selected accord-
ing to the logic of most-different or most-similar systems. In the most-different systems
method, cases are selected that have different outcomes, similar controls variables, and dif-
ferent diffusion variables. In the most-similar systems method, cases share outcomes and
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certain key diffusion variables, but are otherwise similar. Unfortunately, most cases do not
fit cleanly into these categories, and so while it is always advisable for the researcher to seek
out cases with varying diffusion mechanisms, case selection must give way to the logic of
process tracing.
In the second step, the researcher engages in the actual act of process tracing, concen-
trating on the diffusion process within cases. Thus, qualitative researchers should aim to find
data that “provides information about context, process, or mechanism, and that contributes
distinctive leverage in causal inference” (Brady and Collier 2004, 277). The unit of analysis
in this framework should be the causal-process itself. This enables the researcher to come
to a good understanding of diffusion. It is even possible for the researcher to discover a
“smoking gun,” in which examples are literally copied and emulated by others.
One important contribution to this area is that of Weyland (2009). In that study,
Weyland engaged in a qualitative study of the Revolutions of 1848, in which the fall of the
French Monarchy set off a cascade of militant collective action across Europe and Latin
America. Within weeks, nearly every monarchy in Europe was experiencing a revolution.
Weyland’s method involved tracking information on mass collective action and its potential
for success as it was carried from Paris by telegraph wire and railway line. Weyland even
found that proto-rebel learning was attenuated by the cognitive filters existing in the minds
of proto-rebels, thus helping to explain variation in diffusion’s outcomes.
There are two possibilities for process tracing the rebel learning theory. In the first,
proto-rebels vicariously observe the consequences of civil war and attempt to learn from its
success or failure. In the second, the actual agents of civil war and revolution travel abroad
and attempt to “teach” rebellion to others. In the first possibility, the I could explore those
cases in which mobilization or repression seems to be linked to a transnational process of
learning. Some examples, alluded throughout this dissertation, include the linkage between
the Nepalese and Peruvian Maoists, Latin American insurgents and New Left terrorists,
mobilization and counter-mobilization in states threatened by revolution, and state responses
of the kind seen in Operation Condor and Southern Africa.
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The second possibility, in which rebel agents teach proto-rebels about rebellion, fol-
lows those norm entrepreneurs that attempt to spread their ideologies internationally. The
most notorious case in recent history, of course, is that of Osama bin Laden and al-Qaeda.
Recent qualitative research has shown that the radical Islamism that motivates al-Qaeda
emerged in the mosques and religious schools of the Hidjaz region of Saudi Arabia, and was
spread along particular channels by Abduallah Azzam, one of al-Qaeda’s early mentors. This
stands in sharp contrast to the received wisdom, in which al-Qaeda emerged from the teach-
ings of Sayyid Qutb (Hegghammer 2010). Conclusions such as these would not be possible
without qualitative research.
To conclude, future research should incorporate knowledge acquired from qualitative
case-work. Such an effort would not only add to the richness of my theory and its empirical
support, but could also discover new puzzles and research questions.
5.5. Conclusion
Over the course of this dissertation, I have argued that would-be rebels, or proto-
rebels, learn from information available in the international system. By employing concepts
from psychology and sociology, I have also shown that proto-rebels are capable of learning
and taking inspiration from extremely distant cases. The sources of the information involved
in such learning including episodes of ongoing civil wars, as well as those regimes brought into
existence by militarily victorious rebels. These elements form the core of the domino theory.
Findings indicate a world-wide two-stage process is at work. First, proto-rebels mobilize;
second, war onset becomes likely if mobilized rebels can draw support from neighboring
countries. Further, regimes established by militarily victorious rebels are associated with
state reaction and repression. Each of these findings is new to the literature, and each
carries important implications for theory and policy.
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APPENDIX A
REVOLUTIONARY REGIME LIST
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This section contains a list of the revolutionary regimes used in this dissertation.
Revolutionary Regimes, 1946–2011 (N= 61)
Country Regime Name Start Years End Year DurationAfghanistan Democratic Republic of
Afghanistan / CommunistAfghanistan
1978 1993 15
Afghanistan Warlord government 1993 1996 3Afghanistan Taliban regime 1996 2001 5Afghanistan Karzai regime 2001 2011 10Algeria Republic of Algeria 1963 1992 29Angola Angola / MPLA 1975 2011 36Argentina Revolucion Libertadora 1955 1958 3Azerbaijan Husseinov regime 1993 2011 18Bolivia The Sexenio 1946 1951 5Bolivia Revolutionary Nationalist
Movement1953 1964 11
Bosnia andHerzegovina
Bosnian regime 1996 2011 15
Burkina Faso Compaore regime 1988 2011 23Burkina Faso Sankara regime 1983 1987 4Cambodia Democratic Kampuchea 1976 1979 3Cambodia Cambodian monarchy 1954 1970 16Cameroon Republic of Cameroon 1960 1983 23Central AfricanRepublic
Bozize regime 2003 2011 8
Chad Transitional Government ofNational Unity (GUNT)
1979 1982 3
Chad Habre regime 1982 1990 8Chad Deby regime 1990 2011 21Chile Chilean Junta 1973 1989 16China PRC 1950 2011 61Comoros Denard regime 1989 1989 0Congo Denis Sassou Nguesso
regime1997 2011 14
Congo Ngouabi regime 1970 1991 21Costa Rica Costa Rican junta 1948 1949 1Croatia Republic of Croatia 1991 2011 20Cuba Communist Cuba 1959 2011 52Cyprus Republic of Cyprus 1960 1973 13Democratic Re-public of Congo(Zaire)
Democratic Republic ofCongo
1997 2011 14
Continued on next page.
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Country Regime Name Start Years End Year DurationEast Timor East Timor 1998 2011 13Eritrea Republic of Eritrea 1993 2011 18Ethiopia Ethiopian Republic /
Zenawi regime1991 2011 20
Georgia Shevardnadze regime 1992 2003 11Ghana National Liberation Council 1966 1969 3Ghana Rawlings regime 1981 2000 19Guatemala Guatemalan junta 1954 1958 4Guinea-Bissau Mane junta 1999 2000 1Guinea-Bissau Guinea-Buisseau / PAIGC 1974 1980 6Haiti Front for the Advancement
and Progress of Haiti1991 1994 3
Haiti Haitian regime 2004 2011 7Indonesia Republic of Indonesia
(Sukarno)1950 1966 16
Iran Islamic Republic of Iran 1980 2011 31Iraq Republic of Iraq/ Qassim
regime1958 1963 5
Iraq Republic of Iraq/ AbdelArif regime
1963 1968 5
Laos Lao People’s Republic 1975 2011 36Liberia Doe regime 1980 1990 10Liberia Taylor regime 1991 2003 12Liberia Sirlief regime 2004 2011 7Madagascar Zafy regime 1993 1996 3Morocco Moroccan regime (Sultan
Mohammed V)1956 2011 55
Mozambique Mozambique / FRELEMO 1975 2011 36Namibia Namibia 1990 2011 21Nicaragua Sandanistas 1979 1990 11Nigeria Military regime 1966 1979 13North Yemen Ahmad bin Yahya / Mo-
hammad al-Badr1948 1966 18
Pakistan Republic of Bangladesh 1971 1975 4Paraguay The Stronato 1954 1989 35Paraguay Republic of Paraguay 1989 2011 22Rumania Romanian republic 1989 2011 22Rwanda Kagame regime 1994 2011 17Somalia Aidid regime 1991 1995 4South Yemen South Yemen 1986 1990 4South Yemen South Yemen People’s Re-
public1968 1990 22
Syria Baathist regime 1966 2011 45Continued on next page.
128
Country Regime Name Start Years End Year DurationTunisia Republic of Tunisia 1957 2011 54Uganda Idi Amin regime 1972 1979 7Uganda Obote regime 1980 1985 5Uganda Mouseveni regime 1986 2011 25Vietnam Socialist Republic of Viet-
nam1976 2011 35
Vietnam Democratic Republic ofVietnam
1955 1975 20
Zimbabwe Mugabe regime 1981 2011 30Sources : Colgan (2012); Geddes, Wright, and Franz (n.d.); Kreutz (2010).
129
APPENDIX B
MILITANT ORGANIZATIONS LIST
130
This section lists the militant groups that emerge between 1946 and 2006.
Country of Emergence Group Name Start Year End YearAfghanistan Hezb-e Azadi-ye Afghanistan 1997 1998Afghanistan Hizb-I Islami Gulbuddin (HIG) 1977 1977Afghanistan Hizb-i Wahdat 1988 1989Afghanistan Hizb-I-Islami 1977 1978Afghanistan Islamic Movement of Uzbekistan
(IMU)1998 1998
Afghanistan Jaish-ul-Muslimin 2004 2004Afghanistan Jund al-Sham 1999 1999Afghanistan Mujahideen Message 2003 2003Afghanistan Saif-ul-Muslimeen 2003 2003Afghanistan Taliban 1994 2006Algeria al-Qaeda Organization in the Is-
lamic Maghreb1996 1996
Algeria Armed Islamic Group 1992 2005Algeria Canary Islands Independence
Movement1977 1978
Algeria Islamic Salvation Front 1989 2000Algeria Unified Unit of Jihad 1993 1994Algeria Union of Peaceful Citizens of Al-
geria1994 1995
Angola Front for the Liberation of theCabinda Enclave
1963 1963
Angola Front for the Liberation of theCabinda Enclave - Renewed
1963 1964
Angola National Front for the Liberationof Angola (FNLA)
1962 1990
Angola Popular Movement for the Liber-ation of Angola (MPLA)
1956 1975
Angola UNITA 1966 2002Argentina Argentine Anti-Communist Al-
liance1974 1976
Argentina Che Guevara Anti-ImperialistCommand
2005 2006
Argentina Che Guevara Brigade 1976 1990Argentina Comit Argentino de Lucha Anti-
Imperialisto1972 1972
Argentina Dario Santillan Command 2004 2006Continued on next page.
131
Country of Emergence Group Name Start Year End YearArgentina Eva Peron Organization 1990 2006Argentina Mariano Moreno National Libera-
tion Commando2005 2006
Argentina Montoneros 1970 1981Argentina OPR-33 1971 1976Argentina People’s Revolutionary Army
(Argentina)1969 1977
Argentina People’s Revolutionary Organiza-tion
1992 1997
Argentina Peronist Armed Forces 1967 1974Australia Yanikian Commandos 1986 1973Austria Bavarian Liberation Army 1995 1996Austria Cell for Internationalism 1995 2006Bangladesh All Tripura Tiger Force (ATTF) 1990 2006Bangladesh Harakat ul-Jihad-i-
Islami/Bangladesh (HUJI-B)1992 1992
Bangladesh Hikmatul Zihad 2004 2004Bangladesh Islamic Shashantantra Andolon
(ISA)2002 2006
Bangladesh Jagrata Muslim JanataBangladesh
1998 2006
Bangladesh Jamatul Mujahedin Bangladesh 2002 2006Bangladesh National Liberation Front of
Tripura (NLFT)1989 2006
Bangladesh Parbatya Chattagram Jana Sang-hati Samity (PCJSS)
1972 1972
Bangladesh Purbo Banglar Communist Party(PBCP)
2002 2006
Bangladesh United Achik National Front 2004 2005Belgium Arabian Peninsula Freemen 1989 2006Belgium Armenian Resistance Group 1995 2006Belgium Communist Combatant Cells 1985 1985Belgium New Armenian Resistance (NAR) 1977 1983Belgium Peace Conquerors 1985 1985Belgium Revolutionary Front for Proletar-
ian Action1985 1985
Bolivia Brother Julian 1987 1987Bolivia National Liberation Army (Bo-
livia)1966 1970
Bolivia National Liberation Army (thesecond)
1987 2003
Bolivia Nestor Paz Zamora Commission 1990 1991Bolivia People’s Command 1986 2006
Continued on next page.
132
Country of Emergence Group Name Start Year End YearBolivia The Inevitables 2003 2003Bolivia The National Anti-Corruption
Front2005 2006
Bolivia Tupac Katari Guerrilla Army(EGTK)
1991 1993
Bolivia Workers’ Revolutionary Party 1988 1988Bolivia Zarate Willka Armed Forces of
Liberation1989 1989
Brazil Alianca Libertadora Nacional(ALN)
1968 1970
Brazil Popular Revolutionary Vanguard 1968 1973Brazil Revolutionary Movement of Octo-
ber 8 (MR-8)1968 1972
Brazil VAR-Palmares 1968 1972Bulgaria Pan-Turkish Organization 1985 1985Burma (Myanmar) All Burma Students’ Democratic
Front (ABSDF)1988 1988
Burma (Myanmar) Democratic Karen BuddhistArmy (DKBA)
1994 1995
Burma (Myanmar) God’s Army 1997 2001Burma (Myanmar) Kachin Independence Organiza-
tion (KIO)1961 1962
Burma (Myanmar) Karenni National ProgressiveParty
1955 1955
Burma (Myanmar) Kayin National Union (KNU) 1959 1959Burma (Myanmar) Myanmar National Democratic
Army1989 1990
Burma (Myanmar) New Mon State Party (NMSP) 1962 1963Burma (Myanmar) Vigorous Burmese Student War-
riors1999 1999
Burundi Conseil national pour la defensede la democratie (CNDD)/Forcespour la defense de la democratie(FDD)
1994 1995
Burundi Parti pour la liberation du peuplehutu (PALIPEHUTU)
1994 1995
Cambodia Cambodian Freedom Fighters(CFF)
1998 2001
Cambodia Khmer Rouge 1951 1998Cameroon Movement for Democracy and De-
velopment (MDD)1991 2003
Canada al-Fuqra 1980 1980Canada Animal Liberation Front (ALF) 1976 1976
Continued on next page.
133
Country of Emergence Group Name Start Year End YearCanada Liberation Front of Quebec 1963 1972Chad Chadian People’s Revolutionary
Movement1982 1988
Chad Movement for Democracy andJustice in Chad (MDJT)
1998 2003
Chile Arnoldo Camu Command 1989 1989Chile Chilean Committee of Support
for the Peruvian Revolution1992 1992
Chile Fatherland and Liberty National-ist Front
1999 2000
Chile Lautaro Youth Movement 1983 1994Chile Manuel Rodriguez Patriotic Front 1983 1989Chile Movement of the Revolutionary
Left1965 2004
Chile Proletarian Action Group 1973 1974Chile United Popular Action Movement 1986 1992China East Turkistan Liberation Orga-
nization2002 2002
China Eastern Turkistan Islamic Move-ment (ETIM)
1990 1990
China Uygur Holy War Organization 2001 2001Colombia April 19 Movement 1970 1990Colombia Guevarista Revolutionary Army
(ERG)1993 1993
Colombia Heroes of Palestine 1991 1991Colombia Jaime Bateman Cayon Group
(JBC)1989 2002
Colombia National Liberation Army(Colombia)
1964 2006
Colombia Pedro Leon Arboleda Movement 1979 1987Colombia People’s Liberation Forces
(Colombia)1998 2000
Colombia Popular Liberation Army (Colom-bia)
1967 1967
Colombia Revolutionary Armed Forces ofColombia (FARC)
1964 1964
Colombia Self-Defense Groups of Cordobaand Uraba (ACCU)
1994 2006
Colombia The Extraditables 1987 1991Colombia United Self-Defense Forces of
Colombia (AUC)1997 2006
Congo, Kinshasa Army for the Liberation ofRwanda (ALIR) / Interahamwe
1994 2002
Continued on next page.
134
Country of Emergence Group Name Start Year End YearCongo, Kinshasa Front contre l’occupation tutsie
(FLOT)1998 1999
Congo, Kinshasa Les mongoles 1999 2000Congo, Kinshasa Mouvement de liberation congo-
lais (MLC)2003 2004
Congo, Kinshasa People’s Revolutionary Party(PRP)
1967 1997
Congo, Kinshasa Popular Self-Defense Forces(FAP)
1993 2006
Congo, Kinshasa Rassemblement congolais pour lademocratie (RCD)
1998 1999
Congo, Kinshasa West Nile Bank Front (WNBF) 1995 2004Costa Rica Revolutionary Commandos of Sol-
idarity1977 1977
Cyprus Cypriot Nationalist Organization(OKE)
2004 2004
Cyprus EOKA 1954 1955Cyprus United Nasserite Organization 1986 1987Djibouti Front for the Liberation of the
French Somali Coast1967 1977
Dominican Republic Maximiliano Gomez Revolution-ary Brigade
1987 1987
Dominican Republic Revolutionary Army of the Peo-ple
1989 1989
Dominican Republic United Anti-Reelection Com-mand
1970 1970
Ecuador Armed Revolutionary Left 2004 2004Ecuador Ecuadorian Rebel Force 2001 2006Ecuador Group of Popular Combatants
(GPC)1994 1994
Ecuador People’s Revolutionary Militias 2003 2003Ecuador White Legion 2001 2003Egypt al-Gama’a al-Islamiyya (GAI) 1977 1977Egypt Battalion of the Martyr Abdullah
Azzam2004 2004
Egypt Egyptian Islamic Jihad (EIJ) 1978 1978Egypt Egypt’s Revolution 1984 1989Egypt International Justice Group 1995 2006Egypt Islamic Glory Brigades in the
Land of the Nile2005 2006
Egypt Islamic Liberation Organization 1967 1985Egypt Takfir wa Hijra 1971 2006Egypt Tawhid Islamic Brigades 2004 2006
Continued on next page.
135
Country of Emergence Group Name Start Year End YearEl Salvador Armed Forces of National Resis-
tance1975 1991
El Salvador Farabundo Marti National Liber-ation Front
1979 1991
El Salvador February 28 Popular Leagues 1978 1991El Salvador People’s Liberation Forces 1970 1991Eritrea Islamic Salvation Movement / Er-
itrean Islamic Jihad Movement1998 1999
Estonia Russian National Unity 1990 1990Ethiopia al-Ittihaad al-Islami (AIAI) 1989 1996Ethiopia Eritrean Islamic Jihad Movement
(EIJM)1980 1980
Ethiopia Eritrean Liberation Front (ELF) 1960 1991Ethiopia Eritrean People’s Liberation
Front1970 1991
Ethiopia Ethiopian People’s RevolutionaryArmy
1976 1988
Ethiopia Ogaden National LiberationFront (ONLF)
1984 1984
Ethiopia Oromo Liberation Front (OLF) 1973 1973Ethiopia Tigray Peoples Liberation Front
(TPLF)1975 1991
France Accolta Nazinuale Corsa 2002 2003France Action Committee of Winegrow-
ers1999 1999
France Action Directe 1979 1987France Affiche Rouge 1981 1986France Armata Corsa 1999 1999France Army of the Corsican People 2004 2006France Autonomous Intervention Collec-
tive Against the Zionist Presencein France
1979 1979
France Breton Liberation Front 1966 1967France Breton Revolutionary Army
(ARB)1971 2000
France Charles Martel Group 1975 1983France Clandestini 1999 1999France Clandestini Corsi 1999 2006France Committee for Liquidation of
Computers (CLODO)1983 2006
France Committee of Coordination 1972 2006Continued on next page.
136
Country of Emergence Group Name Start Year End YearFrance Committee of Solidarity with
Arab and Middle East PoliticalPrisoners (CSPPA)
1986 1986
France Corsican Patriotic Front (FPC) 1999 2000France Corsican Revolutionary Armed
Forces (FARC)1992 1992
France de Fes 1994 1994France Francs Tireurs (Mavericks) 1991 1998France Gazteriak 1994 2000France Gora Euskadi Askatuta 2002 2003France Gracchus Babeuf 1990 1991France International Revolutionary Ac-
tion Group (GARI)1974 1975
France Masada, Action and DefenseMovement
1972 1988
France Meinhof-Puig-Antich Group 1975 2006France Ninth of June Organization 1981 1982France Orly Organization 1981 1983France Palestinian Resistance 1980 2006France Resistenza Corsa 2002 2003France September-France 1981 1981France Spanish Basque Battalion 1975 1982France Third of October Group 1980 1981France Totally Anti-War Group (ATAG) 2001 2001France Youth Action Group 1974 1977Georgia Bagramyan Battalion 1998 1998germany 2nd of June Movement 1975 1981Germany Anti-Imperialist Cell (AIZ) 1994 1996Germany Autonomous Cells 1987 2006germany Baader-Meinhof Group 1968 1977Germany Commando of Croatian Revolu-
tionaries in Europe1981 1982
Germany Guardsmen of Islam 1980 1984germany Red Army Faction 1978 1992Greece 21-Jun 2003 2004Greece Anarchist Faction for Subversion 1998 1999Greece Anarchist Struggle 2000 2001Greece Anarchists’ Attack Group 2000 2001Greece Autonomous Cells of Rebel Ac-
tion1998 1999
Greece Black Star 1999 2002Greece Free Greeks 1967 1974
Continued on next page.
137
Country of Emergence Group Name Start Year End YearGreece Khristos Kasimis Revolutionary
Group for International Solidar-ity
1985 1986
Greece New Revolutionary PopularStruggle (NELA)
2002 2003
Greece November’s Children 1996 2001Greece Popular Resistance (Greece) 2002 2003Greece Popular Revolutionary Action 2003 2005Greece Popular Revolutionary Resis-
tance Group1971 1972
Greece Revolutionary Nuclei 1999 2003Greece Revolutionary Organization 17
November (RO-N17)1975 2002
Greece Revolutionary People’s Struggle 1975 1995Greece Revolutionary Struggle 2003 2003Greece The Committee for Promotion of
Intransigence2003 2004
Guatemala Counterrevolutionary Solidarity(SC)
1983 2006
Guatemala Guatemalan Labor Party 1952 1996Guatemala Guerrilla Army of the Poor 1972 1996Guatemala January 31 Popular Front 1981 1982Guatemala Rebel Armed Forces 1962 1996Haiti Coalition of National Brigades 1973 2006Haiti Hector Riobe Brigade 1982 1984Haiti Tontons Macoutes 1958 2000Honduras Cinchoneros Popular Liberation
Movement1980 1991
Honduras Morazanist Front for the Libera-tion of Honduras (FMLH)
1980 1992
Honduras Morazanist Patriotic Front(FPM)
1988 1995
Honduras Night Avengers 1997 1998Honduras Recontra 380 1993 1997Honduras Revolutionary United Front
Movement1989 1989
India Achik National Volunteer Council(ANVC)
1995 1995
India Adivasi Cobra Force (ACF) 1996 1996India al-Faran 1995 1995India al-Hadid 1994 1994India al-Madina 2002 2006India al-Mansoorain 2003 2006
Continued on next page.
138
Country of Emergence Group Name Start Year End YearIndia Ananda Marga 1955 1979India Azad Hind Sena 1982 2006India Babbar Khalsa International
(BKI)1978 2006
India Birsa Commando Force (BCF) 1996 2004India Bodo Liberation Tigers (BLT) 1996 2003India Borok National Council of
Tripura (BNCT)2000 2006
India Communist Party of India-Maoist 2004 2006India Dima Halam Daoga (DHD) 1996 1996India Dukhtaran-e-Millat 1987 1987India Harkat ul-Ansar 1993 2002India Islamic Defense Force 1997 1998India Jamiat-e-Ahl-e-Hadees 1992 1993India Jihad Committee 1986 2006India Kamtapur Liberation Organiza-
tion1995 1996
India Kanglei Yawol Kanna Lup(KYKL)
1994 2006
India Kangleipak Communist Party 1980 2006India Karbi Longri North Cachar Hills
Resistance Force (KNPR)2004 2006
India Kuki Liberation Army (KLA) 1998 2005India Kuki Revolutionary Army 1999 1999India Lashkar-e-Jabbar (LeJ) 2001 2006India Maoist Communist Center
(MCC)1969 2004
India National Democratic Front ofBodoland (NDFB)
1988 1988
India National Socialist Council ofNagaland-Isak-Muivah (NSCN-IM)
1988 1988
India National Socialist Council ofNagaland-Khaplang (NSCN-K)
1998 1999
India People’s Liberation Army (PLA) 1978 1979India People’s Revolutionary Party of
Kangleipak (PREPAK)1977 1978
India People’s United Liberation Front(PULF)
1995 1995
India People’s War Group (PWG) 1980 2004India Revolutionary People’s Front
(RPF)1979 2006
Continued on next page.
139
Country of Emergence Group Name Start Year End YearIndia Students Islamic Movement of In-
dia (SIMI)1977 1977
India United Kuki Liberation Front(UKLF)
1999 1999
India United Liberation Front of Assam(ULFA)
1979 1980
India United National Liberation Front(UNLF)
1964 1990
India United People’s Democratic Soli-darity (UPDS)
1999 2006
India Zomi Revolutionary Army (ZRA) 1997 1997Indonesia Anti-Communist Command 2000 2000Indonesia Free Aceh Movement (GAM) 1975 2005Indonesia Free Papua Movement (OPM) 1963 2006Indonesia Front for Defenders of Islam 1997 1997Indonesia Jemaah Islamiya (JI) 1993 2006Indonesia Komando Jihad (Indonesian) 1975 1981Indonesia Laskar Jihad 2000 2000Indonesia Mujahideen KOMPAK 2001 2006Indonesia National Armed Forces for
the Liberation of East Timor(FRETILIN)
1975 1976
Indonesia Nusantara Islamic Jihad Forces 1999 2006Indonesia South Maluku Republic (RMS) 2005 2006Iran al-Ahwaz Arab People’s Demo-
cratic Front2005 2006
Iran Armed Youth of Cherikha-ye Fa-dayee
2005 2006
Iran Fedayeen Khalq (People’s Com-mandos)
1979 1988
Iran Generation of Arab Fury 1989 2006Iran Jund Allah Organization for the
Sunni Mujahideen in Iran2005 2006
Iran Kurdish Democratic Party of Iran 1946 1947Iran Movement of Islamic Action of
Iraq1982 2006
Iran Mujahedin-e-Khalq (MeK) 1971 2006Iran Organisation of Iranian People’s
Fedaian (Majority) OIPFM1963 1964
Iran Peykar 1975 1982Iran Shahin 1992 2006Iraq 1920 Revolution Brigades 2003 2006
Continued on next page.
140
Country of Emergence Group Name Start Year End YearIraq Abu Bakr al-Siddiq Fundamental-
ist Brigades2004 2006
Iraq Abu Nidal Organization (ANO) 1974 2002Iraq al-Ahwal Brigades 2005 2005Iraq al-Bara bin Malek Brigades 2005 2006Iraq al-Faruq Brigades 2003 2003Iraq al-Fursan Brigades 2005 2006Iraq al-Imam Ali Brigades 2006 2007Iraq Ansar al-Sunnah Army 2003 2006Iraq Arab Liberation Front (ALF) 1969 1986Iraq Army of the Followers of Sunni Is-
lam2004 2004
Iraq Brigades of Imam al-Hassan al-Basri
2005 2005
Iraq Divine Wrath Brigades 2004 2004Iraq Fallujah Mujahideen 2003 2004Iraq Holders of the Black Banners 2004 2004Iraq Imam Hussein Brigades 2005 2006Iraq Iraqi Democratic Front 1982 1983Iraq Iraqi Liberation Army 1980 1981Iraq Islamic Action in Iraq 1984 1991Iraq Islamic Action Organization 1961 1962Iraq Islamic Army in Iraq 2003 2006Iraq Islamic Front for Iraqi Resistance
- Salah-al-Din al-Ayyubi Brigades2005 2006
Iraq Islamic Jihad Brigades 2004 2006Iraq Islamic Rage Brigade 2004 2005Iraq Jaish al-Taifa al-Mansoura 2003 2003Iraq Jihad Pegah 2005 2006Iraq Karbala Brigades 2004 2006Iraq Kurdish Democratic Party 1946 1947Iraq Mahdi Army 2003 2006Iraq May 15 Organization for the Lib-
eration of Palestine1979 1985
Iraq Mujahideen Army 2004 2005Iraq Palestine Liberation Front 1977 1996Iraq Partisans of the Sunni 2005 2006Iraq Patriotic Union of Kurdistan
(PUK)1975 1976
Iraq Protectors of Islam Brigade 2005 2005Iraq Saraya al-Shuhuada al-jihadiyah
fi al-Iraq2004 2004
Iraq Saraya Usud al-Tawhid 2004 2005Continued on next page.
141
Country of Emergence Group Name Start Year End YearIraq Soldiers of the Prophet’s Compan-
ions2005 2006
Iraq Supreme Council for Islamic Rev-olution in Iraq (Badr Brigade)
1982 1983
Iraq Swords of Righteousness Brigades 2005 2005Iraq Tawhid and Jihad 1999 2006Iraq Usd Allah 2004 2006Ireland Continuity Irish Republican
Army (CIRA)1986 2006
Ireland Official IRA 1969 1970Israel Abu al-Rish Brigades 1993 1993Israel al-Aqsa Martyrs Brigades 2000 2006Israel al-Fatah 1958 2006Israel al-Fath al-Mubin Troops 2006 2007Israel Black Panthers (West
Bank/Gaza)1988 2005
Israel Committee for the Security of theHighways
1998 2001
Israel Democratic Front for the Libera-tion of Palestine (DFLP)
1969 2006
Israel EYAL (Fighting Jewish Organiza-tion)
1993 1995
Israel Free People of Galillee 2003 2006Israel Hamas 1987 1987Israel Jenin Martyr’s Brigade 2003 2003Israel Kach 1971 1971Israel Kahane Chai 1990 1990Israel Martyr Abu-Ali Mustafa
Brigades2001 2001
Israel Palestinian Islamic Jihad (PIJ) 1978 1978Israel Palestinian Revolution Forces
General Command1985 1987
Israel Popular Front for the Liberationof Palestine (PFLP)
1967 1967
Israel Popular Resistance Committees 2000 2006Israel Revenge of the Hebrew Babies 2002 2003Israel Salah al-Din Battalions 2002 2006Israel Tanzim 1993 1993Italy al-Borkan Liberation Organiza-
tion1984 1985
Italy Anticapitalist Attack Nuclei(NAA)
2001 2006
Continued on next page.
142
Country of Emergence Group Name Start Year End YearItaly Anti-Imperialist Patrols for Prole-
tariat Internationalism1983 1983
Italy Anti-Imperialist Territorial Nu-clei for the Construction of theFighting Communist Party
1995 1995
Italy Armed Revolutionary Nuclei(ARN)
1977 1978
Italy Autonomia Sinistra Ante Parla-mentare
1989 1989
Italy Cooperative of Hand-Made Fire& Related Items
2001 2001
Italy Five C’s 2002 2002Italy Informal Anarchist Federation 2003 2003Italy International Solidarity 1990 1990Italy New Red Brigades/Communist
Combatant Party1984 1984
Italy Nuclei Armati Comunista 1982 1982Italy Ordine Nuovo (New Order) 1969 1970Italy Padanian Armed Separatist Pha-
lanx1998 2006
Italy Proletarian Combatant Groups 2004 2006Italy Proletarian Nuclei for Commu-
nism2003 2003
Italy Red Brigades 1969 1984Italy Revolutionary Front for Commu-
nism1996 2006
Italy Revolutionary Offensive Cells 2003 2003Italy Revolutionary Proletarian Initia-
tive Nuclei2000 2000
Italy Territorial Anti-Imperialist Nu-clei
1995 2006
Japan Aum Shinrikyo / Aleph 1984 2000Japan Chukakuha 1957 1963Japan Japanese Red Army (JRA) 1970 2001Japan Kakurokyo 1969 2006Japan Kenkoku Giyugun Chosen Seibat-
sutai2003 2004
Japan Maruseido (Marxist YouthLeague)
1974 1975
Japan Revolutionary Army 2000 2006Japan Sekihotai 1987 1990Jordan al-Fatah Uprising 1983 1984
Continued on next page.
143
Country of Emergence Group Name Start Year End YearJordan Arab Communist Revolutionary
Party1990 1991
Jordan Black September 1971 1974Jordan Jordanian Free Officers Move-
ment1974 1975
Jordan Jordanian Islamic Resistance 1997 2000Jordan Palestinian Popular Struggle
Front (PSF)1967 1991
Laos Underground Government of theFree Democratic People of Laos
2000 2000
Lebanon Abu Mus’ab al-Zarqawi Battalion 2006 2007Lebanon al-Sadr Brigades 1978 1979Lebanon al-Saiqa 1966 1967Lebanon Amal 1975 1975Lebanon Ansar Allah 1994 1994Lebanon Arab Communist Organization
(ACO)1974 1977
Lebanon Arab Fedayeen Cells 1986 1986Lebanon Armenian Secret Army for the
Liberation of Armenia (ASALA)1975 1997
Lebanon Asbat al-Ansar 1989 2006Lebanon Black Brigade 1985 1986Lebanon Black Hand 1983 2006Lebanon Front for the Liberation of
Lebanon from Foreigners (FLLF)1977 1983
Lebanon Hezbollah 1982 1982Lebanon Islamic Society 1986 1987Lebanon Justice Commandos for the Arme-
nian Genocide1975 1983
Lebanon Lebanese Arab Youth 1977 1977Lebanon Lebanese Armed Revolutionary
Faction1979 1986
Lebanon Lebanese Liberation Front 1987 1989Lebanon Lebanese National Resistance
Front1982 1990
Lebanon Lebanese Socialist RevolutionaryOrganization
1973 1974
Lebanon Liberation Battalion 1987 1988Lebanon Popular Front for the Liberation
of Palestine – General Command(PFLP-GC)
1968 1968
Lebanon Strugglers for the Unity and Free-dom of Greater Syria
2005 2006
Continued on next page.
144
Country of Emergence Group Name Start Year End YearLiberia National Patriotic Front of
Liberia (NPFL)1984 1995
Libya Arab Nationalist Youth for theLiberation of Palestine (ANYLP)
1974 1974
Libya Harakat al-Shuhada’a al-Islamiyah
1996 1996
Libya Libyan Islamic Fighting Group(LIFG)
1995 1995
Macedonia Albanian National Army (ANA) 2002 2002Macedonia Kosovo Liberation Army (KLA) 1992 1999Macedonia Macedonian Revolutionary Orga-
nization2001 2001
Malaysia Kumpulan Mujahidin Malaysia(KMM)
1995 1995
Malaysia Pattani United Liberation Orga-nization (PULO)
1968 1968
Malaysia Sri Nakharo 2001 2006Mexico 23rd of September Communist
League1973 1982
Mexico Armed Communist League 1972 1972Mexico Comando Jaramillista Morelense
23 de Mayo2004 2004
Mexico Justice Army of Defenseless Peo-ple (EJPI)
1997 1998
Mexico People’s Revolutionary ArmedForces (FRAP)
1972 1977
Mexico Popular Revolutionary Army(EPR)
1996 1996
Mexico Revolutionary Armed Forces ofthe People (FARP)
1999 2006
Mexico Revolutionary Worker Clandes-tine Union of the People Party
1970 1970
Mexico United Popular Liberation Armyof America
1960 1961
Mexico Zapatista National LiberationArmy (EZLN)
1983 2005
Morocco Moroccan Islamic CombatantGroup
1990 1990
Morocco Polisario Front 1973 2005Morocco Salafia Jihadia 1996 2006Mozambique Mozambique National Resistance
Movement (RENAMO)1976 1992
Namibia Caprivi Liberation Front 1994 1995Continued on next page.
145
Country of Emergence Group Name Start Year End YearNamibia South-West Africa People’s Orga-
nization (SWAPO)1960 1989
Nepal Akhil Krantikari 1995 1995Nepal Communist Party of Nepal-
Maoist (CPN-M)1996 2006
Nepal Janatantrik Terai Mukti Morcha(JTMM)
2004 2005
Nepal Madheshi Liberation Front(MLF)
2001 2002
Netherlands Free South Moluccan Youth’s 1975 1978Netherlands South Moluccan Suicide Com-
mando1978 1978
Nicaragua Andres Castro United Front(FUAC)
1995 2002
Nicaragua Contras 1979 1980Nicaragua Sandinistas 1960 1979Nigeria Hisba 2000 2006Nigeria Iduwini Youths 1998 1998Nigeria Movement for the Emancipation
of the Niger Delta (MEND)2006 2006
Nigeria Odua Peoples’ Congress 1995 1995Pakistan al-Arifeen 2002 2006Pakistan al-Badr 1971 1971Pakistan al-Badr (the second) 1998 1999Pakistan al-Intiqami al-Pakistani 2002 2002Pakistan al-Islambouli Brigades of al-
Qaeda1995 1996
Pakistan al-Nawaz 1999 2000Pakistan al-Qaeda 1988 1988Pakistan al-Umar Mujahideen 1989 2006Pakistan al-Zulfikar 1977 1981Pakistan Baloch Liberation Army (BLA) 2003 2006Pakistan Black December 1973 2006Pakistan Brigade 313 2003 2003Pakistan Harakat ul-Jihad-i-Islami (HUJI) 1980 1980Pakistan Harakat ul-Mujahidin (HuM) 1985 1985Pakistan Hizbul Mujahideen (HM) 1989 2006Pakistan Islami Inqilabi Mahaz 1997 2006Pakistan Jaish-e-Mohammad (JeM) 2000 2006Pakistan Jamiat ul-Mujahedin (JuM) 1990 1990Pakistan Jammu and Kashmir Islamic
Front1994 1996
Pakistan Lashkar-e-Jhangvi (LeJ) 1996 1996Continued on next page.
146
Country of Emergence Group Name Start Year End YearPakistan Lashkar-e-Taiba (LeT) 1989 2006Pakistan Lashkar-I-Omar 2001 2001Pakistan Mohajir Qami Movement-Haqiqi
(MQM-H)1992 1993
Pakistan Muttahida Qami Movement(MQM)
1978 2001
Pakistan Sipah-e-Sahaba/Pakistan (SSP) 1985 1985Panama December 20 Movement 1990 1992Panama Omar Torrijos Commando for
Latin American Dignity1989 1990
Panama Sovereign Panama Front (FPS) 1992 1999Peru Ethnocacerista 2000 2000Peru Shining Path 1980 2006Peru Tupac Amaru Revolutionary
Movement1982 1982
Philippines Abdurajak Janjalani Brigade(AJB)
1999 1999
Philippines Abu Sayyaf Group (ASG) 1991 1991Philippines Alex Boncayao Brigade (ABB) 1984 1984Philippines Free Vietnam Revolutionary
Group2001 2001
Philippines Indigenous People’s Federal Army(IPFA)
2001 2006
Philippines Moro Islamic Liberation Front(MILF)
1978 2001
Philippines Moro National Liberation Front(MNLF)
1972 1972
Philippines New People’s Army (NPA) 1969 1969Philippines Rajah Solaiman Movement 2002 2002Philippines Rebolusyonaryong Hukbong
Bayan (RHB)1998 1998
Philippines Taong Bayan at Kawal 2006 2006Portugal Popular Forces of April 25 1981 1986Portugal Zionist Action Group 1982 1982Russia Black Widows 2000 2006Russia Dagestan Liberation Army 1999 2004Russia Dagestani Shari’ah Jamaat 2002 2002Russia Ingush Jama’at Shariat 2006 2006Russia Islamic International Peacekeep-
ing Brigade (IIPB)1998 2006
Russia Movsar Baryayev Gang 1998 2002Russia New Revolutionary Alternative 1999 2001
Continued on next page.
147
Country of Emergence Group Name Start Year End YearRussia Riyad us-Saliheyn Martyrs’
Brigade2002 2002
Russia Special Purpose Islamic Regiment(SPIR)
1996 1996
Russia Sword of Islam 1998 2001Rwanda Rwandan Liberation Army 1991 1992Saudi Arabia al-Haramayn Brigades 2003 2006Saudi Arabia al-Qaeda in the Arabian Penin-
sula (AQAP)2004 2006
Saudi Arabia Islamic Movement for Change 1995 1997Senegal Movement of Democratic Forces
in the Casamance (MFDC)1982 1983
Sierra Leone Revolutionary United Front(RUF)
1991 2002
South Africa African National Congress (SouthAfrica)
1961 1990
South Africa Boere Aanvals Troepe (BAT) 1996 1997South Africa Muslims Against Global Oppres-
sion (MAGO)1998 2006
South Africa People Against Gangsterism AndDrugs (PAGAD)
1995 2006
Spain Abu Nayaf al-Afghani 2004 2006Spain Anarchists, The 2000 2000Spain Anti-Terrorist Liberation Group 1983 1987Spain Basque Fatherland and Freedom
(ETA)1959 2006
Spain First of October Antifascist Resis-tance Group (GRAPO)
1975 2006
Spain Iparretarrak (IK) 1973 2000Spain Revolutionary Perspective 2000 2000Spain Spanish National Action 1979 2006Spain Terra Lliure (TL) 1972 1991Sri Lanka Colonel Karuna Faction 2004 2004Sri Lanka Liberation Tigers of Tamil Eelam
(LTTE)1976 1976
Sri Lanka Revolutionary Eelam Organiza-tion (EROS)
1975 1990
Sudan Southern Sudan IndependenceMovement (SSIM)
1991 1996
Sudan Sudan People’s Liberation Army 1983 2005Sudan Uganda Democratic Christian
Army (UDCA)1990 1994
Suriname National Liberation Union 1989 1989Continued on next page.
148
Country of Emergence Group Name Start Year End YearSwaziland Tigers 1989 1998Sweden Global Intifada 2002 2006Sweden Revolutionary Socialists 1999 2000Syria al-Quds Brigades 1978 1978Syria al-Sadr Brigades 1978 2006Syria al-Saiqa 1966 1966Thailand Barisan Revolusi Nasional
Melayu Pattani (BRN)1963 1964
Thailand New Pattani United LiberationOrganization (New PULO)
1995 1996
Thailand Runda Kumpalan Kecil (RKK) 2008 2009Thailand Young Liberators of Pattani 2002 2002Tunisia Tunisian Combatant Group
(TCG)2000 2000
Turkey 28 May Armenian Organization 1977 1977Turkey Apo’s Revenge Hawks 1999 1999Turkey Black Friday 1988 1988Turkey Communist Workers Movement 2001 2003Turkey DHKP/C 1994 2006Turkey HPG 1999 2006Turkey Islamic Great Eastern Raiders
Front1970 2006
Turkey June 16 Organization 1987 1989Turkey Kurdish Islamic Unity Party 1995 1995Turkey Kurdish Patriotic Union 1994 1994Turkey Kurdistan Freedom Hawks 2004 2006Turkey Kurdistan Workers’ Party (PKK) 1974 2006Turkey PKK/KONGRA-GEL 1978 1979Turkey TKEP/L 1990 2001Turkey TKP/ML-TIKKO 1972 2006Turkey Turkish Hezbollah 1982 1982Turkey Turkish Islamic Jihad 1991 1996Turkey Turkish People’s Liberation Army
(TPLA)1971 1980
Turkey Turkish People’s Liberation Front(TPLF) (THKP-C)
1971 1999
Uganda Lord’s Resistance Army (LRA) 1992 1992Uganda National Army for the Liberation
of Uganda (NALU) / ADF1988 1988
Uganda Uganda National Rescue Front 1980 1981Uganda Uganda Salvation Front/Army 1998 1999United Kingdom Catholic Reaction Force (CRF) 1983 1983United Kingdom Dark Harvest 1981 1982
Continued on next page.
149
Country of Emergence Group Name Start Year End YearUnited Kingdom Earth Liberation Front (ELF) 1992 1992United Kingdom Irish National Liberation Army
(INLA)1974 1998
United Kingdom Loyalist Volunteer Force (LVF) 1997 2003United Kingdom Orange Volunteers (OV) 1970 2001United Kingdom Real Irish Republican Army
(RIRA)1998 2006
United Kingdom Red Hand Defenders (RHD) 1998 1998United Kingdom South Londonderry Volunteers
(SLV)1998 2001
United Kingdom Ulster Defence Associa-tion/Ulster Freedom Fighters
1971 2006
United Kingdom Ulster Volunteer Force (UVF) 1966 2006United States Arizona Patriots (AP) 1984 1986United States Armed Commandos of Liberation 1968 1972United States Armed Forces of National Libera-
tion1974 1985
United States Armenian Revolutionary Army 1978 1985United States Army of God 1982 1982United States Black Liberation Army 1971 1985United States Black Panthers 1966 1972United States Covenant Sword and Arm of the
Lord (CSA)1978 1985
United States Croatian Freedom Fighters(CFF)
1976 1982
United States Independent Armed Revolution-ary Movement (MIRA)
1967 1971
United States Jamaat ul-Fuqra 1980 1981United States Jewish Defense League (JDL) 1968 1987United States Macheteros 1976 1999United States Mara Salvatruchas 1980 1981United States May 19 Communist Order 1983 1986United States Mountaineer Militia 1994 1995United States Nation of Yahweh 1979 1995United States New Order 1997 1998United States Omega-7 1974 1983United States Order, The 1982 1984United States Phineas Priests 1990 2006United States Puerto Rican Resistance Move-
ment1981 1981
United States Republic of New Africa 1968 1971United States Republic of Texas (RoT) 1995 1998
Continued on next page.
150
Country of Emergence Group Name Start Year End YearUnited States Revolutionary Cells Animal Lib-
eration Brigade2003 2003
United States United Freedom Front (UFF) 1974 1984United States Weather Underground Organiza-
tion (WUO) / Weathermen1969 1977
United States White Patriot Party (WPP) 1980 1981Uruguay Raul Sendic International
Brigade1974 2006
Uruguay Tupamaros 1963 1985Uzbekistan Islamic Jihad Group (Uzbek-
istan)2004 2004
Venezuela Bolivarian Guerilla Movement(MGB)
2003 2006
Venezuela Bolivarian Liberation Forces(FBL)
1992 1992
Venezuela Bolivarian Liberation Forces (thesecond)
2002 2002
Venezuela EPA (Ejercito del Pueblo en Ar-mas)
2002 2002
Venezuela Red Flag (Venezuela) 1969 1998Venezuela Tupamaro Revolutionary Move-
ment - January 231998 2003
Venezuela United Revolutionary Front 1997 1999Venezuela United Self-Defense Forces of
Venezuela (AUV)2002 2002
Venezuela Venceremos 1988 1991Yemen Aden Abyan Islamic Army
(AAIA)1994 1994
Yemen World Islamic Jihad Group 1998 2006Yemen Yemen Islamic Jihad 1990 2006Yugoslavia Serb Volunteer Guard 1992 1993Zimbabwe Zimbabwe African Nationalist
Union (ZANU)1965 1980
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