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Inter-Rebel Alliances in the Shadow of Foreign Sponsors



From the Patriotic Front struggle against the minority rule in Rhodesia to the seven-party mujaheddin alliance in Afghanistan, inter-rebel alliances make the armed opposition more resilient and successful in the face of government repression. Why then do some rebel groups cooperate with each other while others do not? Drawing on the principal-agent theory, I argue that the presence of foreign sponsors is likely to encourage alliance formation in civil wars especially when two rebel outfits share a state sponsor. Shared sponsors may demand cooperation between their agents and credibly threaten to punish them for non-compliance. They may also insist on the establishment of umbrella institutions to improve their monitoring and sanctioning capacity, and to increase the legitimacy of their agents. I test this argument using the UCDP Actor dataset with new data on alliances between rebel groups. I find strong evidence that shared sponsors increase the probability of inter-rebel alliance.
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International Interactions
Empirical and Theoretical Research in International Relations
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Inter-Rebel Alliances in the Shadow of Foreign
Milos Popovic
To cite this article: Milos Popovic (2017): Inter-Rebel Alliances in the Shadow of Foreign
Sponsors, International Interactions, DOI: 10.1080/03050629.2017.1414812
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Inter-Rebel Alliances in the Shadow of Foreign Sponsors
Milos Popovic
Columbia University
From the Patriotic Front struggle against the minority rule in
Rhodesia to the seven-party mujaheddin alliance in
Afghanistan, inter-rebel alliances make the armed opposition
more resilient and successful in the face of government repres-
sion. Why then do some rebel groups cooperate with each
other while others do not? Drawing on the principal-agent
theory, I argue that the presence of foreign sponsors is likely
to encourage alliance formation in civil wars especially when
two rebel outfits share a state sponsor. Shared sponsors may
demand cooperation between their agents and credibly threa-
ten to punish them for non-compliance. They may also insist
on the establishment of umbrella institutions to improve their
monitoring and sanctioning capacity, and to increase the legiti-
macy of their agents. I test this argument using the UCDP
Actor dataset with new data on alliances between rebel
groups. I find strong evidence that shared sponsors increase
the probability of inter-rebel alliance.
Alliance; civil conflict;
insurgency; machine
learning; principal-agent
Numerous Western appeals to Syrian rebels to rally against the government
and ISIS rest on an idea that coordination, shared resources, and joint efforts
should increase the odds of rebel victory. Indeed, the ZANU-ZAPU (Patriotic
Front) alliance toppled down the white minority government in Rhodesia,
the TPLFs web of alliances with other Ethiopian groups brought down the
Mengistu regime, and the seven-party mujahideen alliance ushered in the
Soviet withdrawal from Afghanistan. Confronted with a unified opponent,
governments must invest more in military operations than if the rebels were
divided. Yet, among 1,000 rebel groups fighting in todays Syria some form
alliances on their own (for example, former Jabhat al-Nusra and Free Syrian
Army in 2014/2015 against Assad and Hezbollah) or in the shadow of foreign
sponsors (for example, US-sponsored Syrian Democratic Forces or Iran-
CONTACT Milos Popovic Saltzman Institute of War and Peace Studies,
Columbia University, 420 West 118th Street, Suite 1336, New York, NY 10027, USA.
*I thank Milada Vachudova, Erin Jenne, Juraj Medzihorsky, Vujo Ilic, Editor Michael Colaresi as well as three
anonymous reviewers for their comments. My gratitude goes to Juraj Medzihorsky for running the analysis on
powerful workstation and Levente Littvay for replicating the material on the CEU server. All the remaining errors
are my own.
Color versions of one or more of the figures in the article can be found online at
Supplemental data for this article can be accessed on the publishers website.
© 2017 Taylor & Francis
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backed Shiite groups) while others like Al-Qaeda or Jaysh al-Islam foster few
or no alliances.
If inter-rebel alliances empower rebel groups and increase their odds of
victory, why do some make them while others do not? Akcinaroglu (2012)
finds that less than a half of all civil conflicts since World War II featured
cooperation among government opponents. What factors encourage alliances
between rebel groups? Why does foreign support in some conflicts foster
rebel cooperation, while in other conflicts leads to belligerent rebels?
Analyzing these questions is crucial for explaining how external govern-
ments can affect civil war dynamics, an issue that has only recently incited
more scholarly interest in conflict studies. Previous research on civil war
dynamics mostly focuses on how interactions among multiple armed actors
affect conflict duration (Cunningham 2011), violence and civilian casualties
(Asal and Rethemeyer 2008; Bakke et al. 2012; Horowitz and Potter 2013;
Metelits 2009), and various war outcomes (Akcinaroglu 2012; Cunningham
2011; Cunningham et al. 2009; Nilsson 2008; Phillips 2014). An emerging
stream of conflict literature shifts the focus to interactions themselves, show-
ing that rebel groups may cooperate or fight each other (Bapat and Bond
2012; Bond 2010; Christia 2012; Fjelde and Nilsson 2012; Furtado 2007;
Nygård and Weintraub 2014). This research has focused extensively on
internal factors, including rebel objectives (Furtado 2007), balance of power
and winning coalitions (Christia 2012), shared identity (Bond 2010), and
rebel capabilities (Bapat and Bond 2012). Although we know more about the
impact of balance of power on rebel interactions, little empirical research
exists on how different configurations of foreign sponsors influence rebel
propensity for cooperation and conflict. This is a serious omission given that
previous studies show that foreign sponsors can decisively affect the organi-
zation, effectiveness and survival of rebel groups (Fjelde and Nilsson 2012;
Salehyan 2010; Salehyan et al. 2014; Sinno 2008).
By introducing foreign sponsorship into the study of inter-rebel dynamics,
I expand current theoretical insights with the focus on how different config-
urations of sponsors can influence cooperation among rebel groups. This
approach contributes to an emerging work on state sponsorship of rebel
groups (Salehyan 2009; Salehyan et al. 2011,2014; Popovic 2015a,2015b;
Szekely 2016) as well as to the rich scholarship on interstate rivalry (e.g.
Akcinaroglu and Radziszewski 2005; Colaresi 2005; Maoz and San-Akca
2012) by linking incentives of external states to the actual use of rebel groups
to shape and shove civil war dynamics. In doing so, I depart from more
prominent studies on inter-rebel alliances, including Bapat and Bond (2012)
who argue that rebel capabilities take precedence over international factors in
explaining alliance-making. In contrast, I break new ground in this emerging
literature by arguing that sponsors make the first move by providing support
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for the alliance between two rebel groups, irrespective of their individual
I use the principal-agent framework to show that when one or more
sponsors act as common principalsof both partners (agents)inany
monitoring, and sanctioning mechanisms. Shared principals enjoy a mon-
itoring and sanctioning capability greater than that of single principals
because they can credibly threaten to deny resources to both agents for
underperformance or transgression. If one agent underperforms relative to
the other, it gets punished by the principal. An agent may disapprove of
this system, but it may lack alternatives particularly when the other
partner is rewarded for good performance. Shared sponsors are likely to
demand cooperation between their agents, and devote considerable
resources to this goal because the alliance may allow them to buttress
their control over the rebellion, and more efficiently navigate a dyad rather
than multiple groups. Unless sponsors are poised to stir chaos, an alliance,
therefore, decreases transaction costs associated with the monitoring, con-
trol and sanctioning of separate groups.
I borrow the measure for rebel alliance from Bapat and Bond to show that
the type of foreign sponsors matters more than other predictors
, but I arrive
at different results due to differences in the time-frame used for analysis
(starting from 1975 instead of 1946, and ending in 2009 rather than 2001),
the proxies for foreign support and the statistical model.
resolution, especially in relation to the Syrian war where multiple rebel
groups are fighting the government. Foreign sponsors can play a decisive
role in bringing rebel groups together or deepening divisions between them.
Given the previous findings that rebel alliances may prolong civil wars or
lead to rebel victory, this article suggests that dealing with rebel alliances in
multiparty civil wars such as the Syrian conflict requires third parties to
devote attention to external governments who often foster inter-rebel
cooperation. Third parties and mediators sometimes assume that rebel
groups operate either independently from each other or from foreign
governments. This article indicates that understanding civil conflicts
requires understanding the international dimension of intrastate wars.
Understanding the connection between state sponsors and militants pro-
vides important directions for conflict resolution; the failure to account for
these ties may lead policy makers to unintentionally prolong the civilian
This article is divided into four parts. First, I use the principal-agent
framework to shape my theory of rebel alliance and offer hypotheses on
I am thankful to Kanisha Bond for sharing the dataset.
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the relationship between state sponsors and inter-rebel cooperation. Second,
I discuss my data and methods. In particular, I focus on a rebel dyad as a unit
of analysis rather than on a group or conflict as a whole. After this, I present
my statistical results, including those from 10-fold cross-validation, indicat-
ing that the model with shared sponsors has the strongest explanatory and
forecasting power. I conclude by exploring the implications of this study for
future research and policy.
Alliance-making in civil conflict
Civil wars are increasingly characterized by a web of inter-dependencies
between multiple armed actors. Multiparty conflicts last longer
(Cunningham 2011), increase the number of civilian casualties (Bakke et al.
2012; Metelits 2009) and produce various war outcomes (Cunningham et al.
2009; Nilsson 2008). While armed confrontation between the host govern-
ment and rebels is often the most visible aspect of civil wars, rebel groups
also form violent or cooperative relationships with each other. Rebel groups
clash over access to recruits, resources and territory (Fjelde and Nilsson 2012;
Nygård and Weintraub 2014), but they sometimes cooperate. Rebel alliances
improve the chances of opposition survival and victory (Akcinaroglu 2012;
Phillips 2014), terrorist, increase the lethality of rebel attacks (Asal and
Rethemeyer 2008; Horowitz and Potter 2013), and hamper conflict resolution
(Cunningham 2011).
Despite such important effects, the phenomenon of inter-rebel alliances
remains an understudied topic. There is a handful of studies exploring the
existence of inter-rebel alliance. For example, Furtado (2007)developsa
typology of rebel groups based on their goals and available resources to
argue that alliance formation depends on the ability of groups to credibly
commit to cooperation, and on the magnitude of counterinsurgency.
Similarly, Bond (2010) argues that shared identity facilitates cooperation
between armed groups, while power considerations drive the outlook of
alliances. In another study, Christia (2012) draws on realist theory in IR to
suggest that rebel groups are attempting to simultaneously be on the
winning side of a war while also gaining the greatest possible benefits in
doing so (the minimum winning coalition). This means that a driving
factor in alliance formation is the relative strength of the different alliance
partners vis-a-vis one another and vis-a-vis other alliances. Due to fre-
quent shifts in relative strength, Christia concludes that alliances may be
preserved, if there is an external party capable of enforcing cooperation.
Following this conclusion, Bapat and Bond (2012) argue that alliance
I understand rebel alliance as a formal or informal arrangement for security cooperation(Walt 1990:12) between
two rebel groups.
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formation is more likely in conflicts where the militants are weaker than
the government. To overcome distrust and form an alliance, weaker
groups need a foreign sponsor that can use material supplies to enforce
cooperation and deter defection (Bapat and Bond 2012:11). Bapat and
Bond find that foreign support has an impact on alliance only in interac-
tion with relative rebel strength.
While empirical results on rebel alliance shed light on how the balance of
power is associated with inter-rebel cooperation, present studies are limited
in examining the influence of external actors. External actors serve as a
supporting factor to relative capabilities in understanding alliance-formation.
The assumption is that balance of power considerations dictate the search for
foreign support even though foreign sponsors may approach rebel groups
Looking at foreign sponsors offers anewwaytoaddressrebelalliances.
This approach acknowledges that sponsors play a decisive role in rebel
behavior, organization and survival (Salehyan et al. 2014; Sinno 2008). As
Salehyan et al. (2014) show, foreign support from multiple governments
encourages rebel groups to be more violent toward civilians because no
single state can effectively restrain the organization. Foreign support is
critical for both weak and strong rebels because it increases their military
power and cohesiveness (Staniland 2014). The more sources of support,
the more likely rebels are to survive government repression (Sinno
2008:290). While weaker rebels should be more easily induced to coopera-
tion, both weak and strong groups may need an external enforcer because
anarchy stimulates concerns about relative (Grieco 1988:498). Because a
prospective partner may grow stronger from cooperation, rebel groups
may choose to rely on their own capabilities. Therefore, foreign support is
likely to have an effect on alliance making irrespective of relative rebel
Accordingly, Chandler (1983) writes that the disdain for Vietnamsoccu-
pation of Cambodia in the 1970s brought together Maoist Khmer Rouge and
royalist National United Front for an Independent, Neutral, Peaceful, and
Cooperative Cambodia even though the latter was much weaker militarily
than its ally. There is likewise a number of alliances between two capable
groups receiving external support such as, for example, the Tigrayan
Peoples Liberation Front (TPLF) and The Eritrean Peoples Liberation
Front (EPLF), Somali National Movement (SNM) and Somali Patriotic
Movement (SPM) or Peoples Armed Forces (FAP) and Transitional
Government of National Unity in Chad or Hamas and Hezbollah in Israel.
During the civil war in Croatia and Bosnia, respectively, the government in
Belgrade was instrumental in forging ties between the Serbian statelets and
irregular forces, even though, these rebel groups had a parity with the host
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Alliance-making and principal-agent theory in civil wars
Conventional wisdom suggests that anarchy is a distinguishing feature of
international politics (Waltz 2010). Under anarchy, the lack of a central
authority to oversee and enforce prospective deals encourages suspicion
and hampers alliance-making (Snyder 1984). Not only do states incur costs
from negotiating and maintaining the alliance, but they also face the possi-
bility of unilateral defection from the agreement. Partners could minimize
defection using formalized treaties with protective clauses and mechanisms
under international law (Leeds et al. 2009) even though opportunistic abro-
gation seems inevitable when there is a shift in threat perception, power,
values and institutions of alliance members (Leeds and Savun 2007; Walt
1990). Maintaining alliances requires each party to credibly commit to not
cheat the other (Walter 2002). But when actors expect benefits from defec-
tion, prospective partners cannot commit credibly to fulfill agreements. A
credible outside actor with superior strength and abundant resources might
bring them together by offering rewards and punishments or by establishing
institutions (Axelrod and Keohane 1985).
The need for outside arbiters is even more pressing in civil conflicts where
rebel groups cannot rely on international law to safeguard inked deals. One
way to understand the role of external arbiters in rebel alliance-making is
through the principal-agent framework
. At a minimum, the principal-agent
framework includes a principal, who delegates authority to an agent in order
to solve collective decision-making problems, profit from agent expertise or
credibly commit to certain policies. The principal can select, monitor, and
punish its agent by manipulating the provision of resources (McCubbins and
Kiewiet 1991:2734).
In conflict studies, this usually translates into a government providing
money, sanctuary, weapons or other tangible resources to rebel groups in
return for their cooperation over goals, organization and tactics (Byman and
Kreps 2010; Salehyan 2010; Salehyan et al. 2011,2014; Szekely 2016). The
threat to withdraw support allows the sponsor to deter disobedience or
pressure problematic rebel groups into submission (Popovic 2015a;
Salehyan 2010). If this logic holds, then the principal could also induce its
agent to cooperate with other agents. Existing evidence shows that sponsors
have pursued this path, including the efforts to unite the Afghan mujaheddin,
Kashmir insurgents, and, recently, the Syrian opposition in order to increase
their effectiveness against the incumbent government.
However, single principals may have interests that do not necessarily favor
effectiveness. One common interest is to inflict damage on enduring rivals
Principal-agent theory originates from political economy, management, and law, but it has also found its
application in political science (Hawkins et al. 2006; McCubbins and Kiewiet 1991; Nielson and Tierney 2003;
Pollack 1997).
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without necessarily committing to forging inter-rebel ties (Maoz and San-
Akca 2012; Salehyan et al. 2011). Rivals can, for example, exploit ethnic and
ideological connections with rebels for domestic purposes (Byman and Kreps
2010; Saideman 2002), or provide support for weakly organized rebels to stir
instability (Salehyan et al. 2011). Ultimately, sponsors may funnel resources
to their agents to fight other groups whose interests are seen as hostile to the
sponsors political goals (Fjelde and Nilsson 2012). Hafiz Assads Syria, for
instance, supported Palestinian organizations such as the Popular
Democratic Front for the Liberation of Palestine (PDFLP), al-Saiqa, and
the PFLP-GC against Fatah to check Arafats influence (Byman and Kreps
2010:11). Pakistan supervised the rise and fall of the Kashmir insurgency,
pitting Hizbul Mujahideen against the Jammu and Kashmir Liberation Front
(JKLF), and later Lashkar-e-Taiba against Hizbul fearing that a dominant
Kashmiri organization could take on a life of its own and make a compro-
mise with India (Haqqani 2010:290). Therefore, single principals, especially
those involved in enduring rivalry, may not necessarily be inclined to
unconditionally support alliance formation but rather fuel instability that
can ultimately lead to international war and recurring conflict (Colaresi 2014;
Salehyan 2009).
While single principals may lack interest in forging ties among rebels, the
presence of two agents serving different principals generates collective action
problems at the top of the delegation chain. Frequently multiple sponsors
have widely different agendas (for instance, Sudan and France in the Chadian
civil war), which may have a detrimental effect on the ability of groups to
. Sponsors must agree that the cooperation between their agents is
desirable, find a mutually acceptable framework, and work together toward
the alliance formation. Assuming that sponsors agree on the goals and means
of prospective alliance, they are faced with the division of labor problem
who should incur greater costs of monitoring, supplying and sanctioning the
agents. This lack of unity is particularly exacerbated with the increase in the
number of principals who can veto coordination efforts. As a consequence,
disunited principals can send contradictory signals to their respective agents,
which in turn hampers agentsalliance proclivity.
If the lack of common supervision fuels uncertainty between the prospective
partners, and sows the division between the principals, then alliance formation
might benefit from two agents sharing a principal. Shared principals may come
into being in two ways. In the ideal case, shared sponsors offer assistance
simultaneously to both groups under the condition that they cooperate against
a third-party (e.g. U.S. support for the Syrian Democratic Forces composed of
Kurdish and Syriac groups). This implies that a sponsor appears for the first
time in a conflict without much history of interference in the target country.
I thank a reviewer for pointing out this issue and example.
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Yet, this seems implausible because civil conflicts frequently attract neighbors
who either seek to exploit the turmoil or prevent war diffusion across their
own borders (Kathman 2010;Salehyan2009). Another, more plausible, possi-
bility is that a sponsor aims to transform its role from a single or different to a
shared principal. In this case, the sponsor may seek to increase the effective-
ness of its proxy forces and legitimize the opposition struggle in the eyes of the
international community. To do so, the sponsor must first rein in its protégé
prior to soliciting other rebel groups. One approach is to grant a sanctuary to
potential partners, as Sudan did with the Chadian rebels in 2000s. This leaves
rebels without much choice, but to comply with the sponsorsdemands.
Another possibility is that the sponsor systematically appoints loyal cadres to
the proteges leadership. For example, Pakistan solicited cooperation between
Lashkar-e-Taiba and Hizbul Mujahideen in the 1990s by appointing Pakistani
militants and foreign mercenaries as commanders of [.. .] the Hizbul
Mujahideen(Tribune 1998).
Once in control of the partners, shared principals enjoy leverage greater
than that of single principals, because they can credibly threaten to deny
resources to both agents for underperformance or transgression
the sponsor supports one rebel group, the insurgents have considerable
leverage in the relationship, and can ignore the demands of the principal.
But when the support simultaneously flows to another rebel group, the
leverage of the first protégé is weakened. Foreign sponsors benefit from
finding an additional protégé so as to increase their leverage over their
agents. If one rebel group is disobedient relative to the other, the sponsor
may divert resources to the more loyal rebel group, thereby reducing the
relative strength of the problematic agent. For example, if one rebel group
fights more often against its partner than the government, it is likely to
lose the sponsorsfavor.YousafandAdkin(1992:150) demonstrate this
mechanism in the case of the Afghan mujahedeen who received arms and
supplies from Pakistan only, if they attacked Soviet troops; passive alliance
members were temporarily denied support if they failed to spend ammu-
nition in combat. If sponsors offer support sequentially, then it may
behoove them to reduce the moral hazard of offering support to only
one rebel group through finding an additional dissident organization to
support. Once the patron is sponsoring two rebel groups, the principle has
the leverage to push both rebel groups to enter into an alliance. An agent
may dislike this scheme, but it often lacks viable alternatives, especially in
the case where the other partner reaps the rewards for good performance.
Thus, this relative performance evaluation should mitigate the fear of
exploitation that would otherwise exist in schemes with single and differ-
ent principals.
I thank a reviewer for highlighting leverage as a distinguishing feature of shared principals.
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There are also pitfalls for alliance formation even in the shadow of shared
sponsors. Shared sponsors may unknowingly incite competition between
their agents. Cooperation with foreign sponsors in Chad, Eritrea, or
Lebanon generated significant divisions within rebel groups. In particular,
Libyan willingness to support FAN and FAP against the government led to
serious disagreements and ultimately spelled the end of the Second
Liberation Front in Chad.
Even worse, once external support helps rebels
ascend to power, ties to shared sponsors may compromise externally-backed
groups in the post-conflict environment in which the public may frame them
as traitorsor agents of foreign powers (Colaresi 2014). Less benevolent
shared principals may pit their agents against one another in order to
increase their control (e.g. Pakistan pitting Pakistani groups against indigen-
ous Kashmiri groups). This should boost the agents fear of exploitation from
both the principal and partner and weaken the alliance in the long run.
Further complicating things, multiple shared principals can issue conflicting
orders to the alliance partners, damaging the alliance in two ways. For
example, Free Syria Armys (FSA) key sponsors pursue different policies
regarding Moscow-backed Astana talks on Syria: while Turkey participates
in the talks, the United States has opted out. The agents may play the
principals against one another to increase their autonomy. While the agents
decrease the fear of exploitation from the principals, they simultaneously
become more vulnerable to each other as the external arbiter is unable to
sanction their behavior. This leads to a scenario in which despite shared
sponsors, rebels end up fighting each other. For instance, skirmishes broke
out between CIA-sponsored Fursan al Haq, and Pentagon-backed Syrian
Democratic near Aleppo last year
. Simultaneously, benevolent agents may
fall prey to the conflict between their principals. Because the agents may be
confused whose orders to follow, their alliance might run the risk of rupture.
The potential costs of foreign support may lead rebels to seek cooperation
without external guidance. Indeed, civil wars feature inter-rebel alliances
absent sponsors (e.g. Guatemala, El Salvador, Myanmar). One alternative is
that similar ethnic background may bring groups together. However, co-
ethnic groups might see each other as competitors for popular support and
territorial control, and engage in outbidding (Bloom 2004). Eliminating a
rival co-ethnic group may be more beneficial because it allows the winner to
attract the membership of the defeated. Failing to do so poisons the relation-
ship between co-ethnic rebels and may lead to a vicious circle of rivalry and
violence. In this case, shared sponsors could serve as a barrier to internecine
fighting while single or different sponsors may fuel outbidding. A more
viable alternative is the ideological compatibility. Communist insurgencies
I thank a reviewer for bringing up this example.
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in Latin America during the Cold War and, more recently, jihadi coalitions
in Syria and Iraq suggest that shared ideology may act as catalyst for alliance.
In some cases, rebel groups embrace certain ideologies to appeal to specific
sponsors (San-Akca 2016). For example, leftist ideology served groups to
make themselves look favorable to USSR during the Cold War period. In this
respect, it may be that rebel ideology depends on the sponsors ideology, and
that this, in turn, depends on access to foreign support. It is unclear whether
shared sponsors also prefer ideologically congruent agents beyond the Cold
War (e.g. Irans support for alliance between Shia Hezbollah and Sunni
Hamas). Thus, it may be that ideology can have both independent and
intervening effects on alliance-making, depending on the absence or presence
of foreign support
In sum, these examples suggest that while shared sponsors are not without
imperfections, they promise to have a more direct effect on inter-rebel
alliance than shared ethnicity or ideology.
Foreign sponsors and inter-rebel alliance
If sponsors can delegate authority to individual rebel groups (Byman and
Kreps 2010; Salehyan 2010; Salehyan et al. 2014; Szekely 2016), then they
should theoretically be able and willing to foster relationships, belligerent or
cooperative, between two rebel groups. Existing research on rebel alliance
hints at this possibility but either develops no specific mechanisms that
would link sponsors and inter-rebel alliance (Christia 2012) or doubts that
sponsors can mitigate the imbalance of power between the prospective
alliance partners (Bapat and Bond 2012). In contrast, this article links
insights from the literature on foreign sponsorship with the emerging litera-
ture on rebel alliances to argue that alliance-making will depend on the
structure of the relationship between sponsors and rebels. In this article,
there are three possible configurations: 1) sponsors may serve as a principal
of a single rebel group; 2) two groups may act as agents of different sponsors;
3) two groups may be agents of a shared sponsor.
The first form is likely to offer narrow prospects for alliance formation due
to the imbalance of power between two prospective partners. The externally-
backed rebel group may be uninterested in the cooperation with a rebel
group lacking external backing because the presence of foreign support
may boost the capabilities and self-confidence of the former. Consequently,
the externally-backed group may view its bargaining position as more favor-
able and decide to dictate preconditions for cooperation. Ultimately, the
externally-backed group may choose belligerence over cooperation in an
attempt to eliminate the competition. For example, this logic corresponds
The model in Table 2 in the Appendix features shared ethnicity, ideology as alternative control variables.
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with the observation of the British liaison officer Captain Hudson in the
Chetnik headquarters in occupied-Yugoslavia during World War II.
Analyzing the failure of the Partisans and Chetniks to form a viable alliance
against the German forces in 1941, Hudson notes that:
The British promise of support had the effect of worsening Chetnik-Partisan
relations. When I first arrived at Ravna Gora and Uzice, at the end of October,
1941, before Chetnik-Partisan hostilities, Mihajlovic already knew by telegram that
he would get British support. He felt rightly that no one outside the country knew
about the Partisans or that he alone was not responsible for the revolt (Maclean
A similar pattern is visible in the Sri Lankan civil war where the Tamil
Tigers used Indian support to wipe out their competitors. Another
possibility is that the sponsor may be disinterested in fostering coopera-
tion. If the sponsor desired genuine cooperation between its agents, it
agent may be unleashed against othergroups.Forexample,Pakistan
encouraged Hizbul Mujahideen to initiate fratricidal attacks against its
former agent, JKLF. Similarly, Fjelde and Nilsson (2012)findthatsuch
sponsors are more associated with inter-rebel violence than cooperation.
This implies that sponsors favoring one rebel group over the other may
hamper their potential for cooperation. This leads to the first
H1: Ceteris paribus, foreign support will have no effect on alliance forma-
tion, if it is directed to only one group.
Another possibility is that both prospective partners receive support,
but from different sponsors. While the imbalance of power becomes less
of an issue, assuming that each sponsor equally contributes to rebels
capabilities, multiple principals face difficulties synchronizing their poli-
cies. Given their diverging preferences, multiple sponsors lack common
standing toward their agents (Salehyan 2010). This, in turn, may lead
principals to issue contradictory directions to their agents. At a maximum,
sponsors may impose their own preferences on each other, preventing
their agents from cooperation. One such example is the failure of two
Congolese rebel groups, Rally for Congolese Democracy (RCD) and
Movement for the Liberation of the Congo (MLC) to preserve their
alliance once their respective sponsors, Rwanda and Uganda, turned
against each other over the spoils of war in the eastern part of the country.
Another example is the inability of Gulf countries to put together an anti-
Assad alliance of their fragmented agents. Sponsors must synchronize
their policies to make their agents cooperate. Due to collective action
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problems, this undertaking is ultimately very costly, and sponsors often
end up issuing contradictory directives to their respective agents. This
produces the second hypothesis:
H2: Ceteris paribus, foreign support will have no effect on alliance formation
when two groups receive it from different sponsors.
The final possibility is that two groups receive support from the same
sponsor or sponsors. Shared sponsors are likely to demand cooperation
between their agents, and may devote considerable resources to this goal
than sponsors of a single group. Reasonably, shared sponsors may pro-
vide support to multiple rebels to instigate chaos or maintain their
influence in the target country without a commitment to their cause.
But the creation of a rebel alliance may signal the sponsorsresolveto
topple down the target government. The presence of a shared principal
with superior monitoring and sanctioning capabilities should minimize
the fear of cheating and exploitation inherent in the anarchic nature of
civil conflicts. Shared sponsors might favor rebel cooperation either,
because it corresponds with their preferences or offers the possibility to
manipulate alliance partners. For example, Iran fostered cooperation
between Hamas and Hezbollah throughout the 1990s and 2000s as a
part of their shared resistanceagainst Israel and the West (Byman
and Kreps 2010). Shared sponsors can also use monitoring and sanctions
to increase the cost of unilateral defection by offering material induce-
ments to make alignment more attractive or by threatening to punish
disloyal regimes(Walt 1997:164). For instance, in their recollection of
Pakistans relationship with the Afghan mujahideen (Yousaf and Adkin
1992:150151) specify how ISI officers manipulated the supply of weap-
ons and ammunition to the seven parties:
For planning purposes we worked on a rough percentage basis for each Party.
These were not permanently fixed; they varied slightly for operational reasons, and
sometimes they were deliberately reduced if a Party was seen not to be pulling its
weight in the field. Such reductions were normally gradual and followed a verbal
warning to the Leader. [. . .] If my officers reported a warehouse was always full,
sometimes for months, it meant that the Party was less than enthusiastic at
prosecuting the war, and as such never qualified for an increased share of arms.
Second, the shared sponsors commitment may also signal its resolve to
consolidate control over insurgency. Shared sponsors may foster the estab-
lishment of umbrella institutions to improve their monitoring and sanction-
ing capacity because it is easier to navigate a collection of groups rather than
multiple outfits. Umbrella organizations also ensure that no single rebel
outfit can negotiate separately with the incumbent government without the
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consensus of the sponsor. These include, for example, Pakistans creation of
the United Jihad Council, an umbrella organization of jihadists based in
Pakistan, Seven-Party alliance in Afghanistan and, the Arab-sponsored
Syrian National Council. Shared sponsors should be most committed to
forging cooperation between their agents when they have strategic interests
in the conflict-ridden country, such as the acquisition of territory, resources
or population, as well as the weakening of their rival (Maoz and San-Akca
2012). For example, the long-desired acquisition of Kashmir was the driving
force behind Pakistans decision to support the alliance between its agents,
Lashkar-e-Taiba and Hizbul Mujahideen, and to later establish the umbrella
institution for Pakistani-based jihadi outfits. The shared sponsors influence
on alliance formation may be buttressed by common ties with its agents such
as ideology, ethnicity or religion. Under such circumstances, shared sponsors
can combine material support with legitimacy to foster cooperation between
their agents.
While alliance formation may serve the shared sponsors interests, rebels
receiving external support are also likely to benefit from cooperation. Rebel
groups may anticipate valuable resources such as weapons, funding or sanctuary.
For instance, Yousaf and Adkin (1992:150155) portray how the ill-equipped
Afghan mujahideen largely toned down their differences to receive external
support. Without such a support the mujahideen would have risked an uncer-
tain future against a stronger foe. Another advantage is that the shared sponsor
may guarantee that alliance partners will not exploit each other even if one of
them becomes much stronger. Shared sponsors can threaten to withdraw
resources or punish an agent for disobedience. This guarantee minimizes fear
and distrust that would otherwise deter cooperation under anarchy. Therefore,
this leads to the third hypothesis:
H3: Ceteris paribus, when two rebel groups share a sponsor, they are more
likely to form alliance.
Data and research design
To examine these hypotheses, I have assembled an original dataset of all
multiparty civil conflicts for the period 19752009 in which rebel groups may
or may not cooperate against the government or other rebel groups. I begin
with the existing UCDP Armed Conflict Dataset (Gleditsch et al. 2002),
which includes civil conflicts with two or more non-state actors that are
fighting against a government. Next, I code those civil conflicts where there
were at least two rebel groups active for any observed year. After selecting
those conflicts, I then arrange the dataset into dyad-years where a dyad
includes two rebel groups. Following Bapat and Bond (2012) a dyad is
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coded only if two rebel groups were active in the same territory and year. For
instance, Hamas and Hezbollah were both militarily active from 1987 until
1996, and MILF and MNLF were active throughout the 1980s, which makes
them potential partners in a respective conflict and time period. In this case, I
analyze the dyadic relationship between Hamas and Hezbollah, and between
MILF and MNLF for the period in which they were active. In most instances,
the government is fighting more than two rebel groups, so I create annual
dyads from every possible combination of the active groups. If two groups
were fighting within a country in two different territorial conflicts (e.g.
Kashmir insurgents and the Naxalites) they were not considered as potential
dyads. In rare instances, (i.e. ELN and FARC in Colombia, MCC and PWG
in India, and SPM and SNM in Somalia) the alliance between two groups
collapsed only to be re-established within one to two years.
The dataset covers the post-1975 period because the UCDP data on
external support used to measure my main independent variable records
information only for this period. To my knowledge, there are no datasets
with similar time-sensitive and robust information on the identity of foreign
sponsors. This limits my ability to fully evaluate my argument and competing
explanations in the pre-1975 period. In total, the dataset includes 165 rebel
dyads nested within 985 dyad-years.
Alliance is a formal or informal cooperation between two rebel groups in
which they share resources or coordinate attacks against the government or
other rebel groups. The data denotes whether there is an alliance or not. I
borrow this variable from Bapat and Bond who measure alliance as
resource-sharing or tactical co-ordination between the groups at some
time during a year(2012:19). Their dataset includes information on alli-
ance-making for the period 19462001 or 1,318 observations with 429
occurrences of an alliance (33%). In the first stage of coding, I excluded
from their dataset cases in which one of the potential partners is an alliance
(e.g. UIFSA in Afghanistan), military faction or nameless group of organiza-
tions labeled various insurgents(these include, for instance, Chadian rebels
in the 1970s, Myanmars Shan insurgents, Lebanons sectarian organizations,
and other non-PLO groups in Israel).
Since my dataset extends to 2009, I then searched for additional evidence on
alliance-making in the UCDP External Support in Armed Conflict Dataset
(Högbladh et al. 2011). Similar to Bapat and Bond (2012), this dataset codes
alliance as the provision of warring (i.e. troops) or non-warring support (e.g.
money, logistics or training) support as well as the coordination of policies,
including information on the identity of partners on an annual basis.
Additionally, I searched for mergers in the UCDP Non-State Actor dataset
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since Bapat and Bond also record them
. Where there was evidence of one group
providing support to or merging with its prospective partner, alliance was coded
1, and 0 otherwise. Following Bapat and Bond, I considered only cases where
two rebel groups cooperated in the same territory and year because transnational
alliances may entail different alliance dynamics and foreign sponsors may not
hold the same influence on rebel groups. Therefore, alliances with militant
movements outside a conflict (e.g. MILF and ASG with Jemayyah Islamiyya)
or with transnational militant movements (e.g. ARS/UIC with Al-Qaeda) were
not considered. In sum, the data include roughly 21% of dyad-years with an
alliance (206 observations) and 79% of dyad-years without an alliance (779
observations), yielding a total of 985 observations.
Shared sponsors
The central argument of this article is that sponsors are likely to boost alliance
formation if they are shared by both members of a dyad. To test this
argument, I draw on UCDP External Support in Armed Conflict Dataset
(Högbladh et al. 2011). The key advantage of this dataset is that it provides
information on the identity of the sponsor and year of support for every rebel
group that fought against the government from 1975 to 2009. This allows me
to identify not only who were recipients of foreign support in a dyad, but also
whether the potential partners shared a sponsor for any given year. UCDP
defines a sponsor as a government of an internationally recognized country
that provides warring or non-warring assistance to a party in an ongoing civil
conflict (Högbladh et al. 2011). This support can take the form of a provision
of weapons, funding, sanctuary, logistics, training, intelligence, regular troops,
and other types of material support. In this article, I consider all these forms
of support together. The resulting variable, Sponsor, measures whether any
member of a dyad receives foreign support in any given year or not.
Next, I determine whether one or both groups have sponsors to test the
hypotheses. This variable is an upgraded version of the previous in that it
displays the source of foreign support in the following way. If no member of
a dyad received support in a given year, the predictor takes the value of 0; if
only one receives external backing the variable takes the value of 1; support
for both partners from different sponsors is coded 2; and if both received
support from the same sponsor it equals 3. It is worth noting that the value of
0 (no support) is regarded as a baseline category in the subsequent analyses.
The frequency plot in Figure 1 shows that no support and shared support
are prevalent in the data, while observations with different and single spon-
sors are rarer. Put simply, this implies that foreign support more often takes
Mergers for the post-2001 period include MCCPWG, UIFSA, and MJPMPCIMPIGO. Mergers from the Bapat and
Bond include PF, SRRC, URNG, and FMLN.
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the form of shared delegation than that of single or competing sponsors.
When these categories are compared with the occurrence and non-occur-
rence of an alliance, visible is a large discrepancy in observations related to
non-support, and a balance regarding shared sponsorship are visible.
Other predictors
Beyond testing the main hypotheses, this article also engages two main
explanations in the literature. The first is advanced by Bapat and Bond
(2012) and includes an interaction between relative rebel strength and for-
eign support. Relative rebel strength (Weak Dyad) denotes whether the
dyad members are weaker than the host government. This variable was coded
following Bapat and Bond, in that I use the weaker of the Non-State Actor
(NSA) dataset figures (Cunningham et al. 2013) for both groups to denote
their ability to resist the governments repression. The variable is coded 1
when both groups are weaker than the government, and 0 if they are a match
to or stronger than the government.
The second explanation comes from Christia (2012) who argues that rebel
groups are more likely to cooperate with groups of similar strength. Ratio
measures the balance of power within any given dyad as a range of values from
0 (extreme imbalance) to 1 (balance). Drawing on the number of troops from
the NSA (Cunningham et al. 2013), I calculate Ratioby dividing the number
of troops of group A by number of troops of group B. If this argument holds,
then Ratioshould be positively associated with the probability of alliance.
None Single Different Shared
Number of observations
0 100 200 300 400 500 600
Ye s
Figure 1. Distribution of type of sponsors by alliance.
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To ensure that my analysis does not simply reflect the impact of other
predictors potentially associated with both alliance formation and with the
main variables of interest, I include a number of dyad-, conflict-, and
country-level controls. In particular, I include the variables suggested by
Bapat and Bond (2012) to control for the impact of environment.
GDP per capitais used to denote the governments ability to control its
territory. Countries with lower GDP per capita should stimulate rebel groups
to cooperate against the government. I draw on Gleditsch (2002) for the
measure of this variable, which is log-transformed for the purpose of this
article. Another proxy for the governments absolute capacity to deal with the
insurgency is military spending (Expenditure). An increase in military
spending should signal the lack of capacity to tackle the insurgency. Thus,
with every unit increase in spending, there should be an increase in the
likelihood of alliance. This variable is borrowed from the COW dataset
(Singer 1988), and log-transformed. Another possibility is that the central
government is recently formed and that multiple rebel groups may seize this
opportunity to join hands in toppling down the incumbent regime. The
duration is taken from Gurr et al. (2010), and logged. Other controls include
non-contiguity denoting countries, like Indonesia or the Philippines whose
capitals are physically separated from the rest of the territory duration,
which controls for temporal dependence in the data, and ethnic and religious
Analysis and discussion
The data is composed of dyad-year observations, where each dyad-year is nested
within a dyad. This implies that observations are not independent of each other
given that there are multiple rebel groups who operate within the same conflict,
and may indirectly interact with each other. Violating the assumption of inde-
pendence of observations can lead to biased estimates of coefficients and their
standard errors (Barcikowski 1981). Accordingly, I use multilevel logit regres-
sion, which is found to mitigate this issue (Gelman and Hill 2006). The multi-
level model allows coefficients to vary across several levels even where
observations are non-independent, correctly modeling correlated error. In this
article, I cluster observations at the government (country) level
The multilevel logit analysis was conducted using a marginal likelihood estimator, and a logit link function as
implemented in the glmmADMB package (Fournier et al. 2012) for the R language (Venables and Smith 2014).
Missing data was corrected with multiple imputation using Rs Amelia package (Honaker et al. 2011), creating 500
imputations. This number is considered very high as the literature recommends only 10 to 50 imputations. Using
a higher number is generally good for safety reasons: to be sure that the results hold even when the model is
faced with more data. The coefficient estimates and standard errors from 500 models were pooled following
Rubins rule(Little and Rubin 2014:8687).
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Figure 2 reports the direction of each of the predictors on alliance forma-
tion, using the coefficient estimates and confidence intervals
. The vertical
dotted line represents no effect; positive coefficients (associated with alliance)
are represented by point estimates to the right of the dotted line, while
negative coefficients (associated with no alliance) are represented by point
estimates to the left of the dotted line. The horizontal solid lines represent the
95% confidence intervals. These intervals display the range of values in which
one can be 95% certain that the true value of the parameter lies. The observed
relationship is regarded as above the conventional critical values when the
interval bars do not include 0. The effect is thus present if 0 lies outside the
intervals, and unclear if 0 is included in the intervals.
Figure 2 includes three models of alliance-making
. I begin my analysis by
presenting Model 1, in which I test H1 and H2 that single and different
sponsors will have no effect on alliance-formation between two given rebel
groups, whereas shared sponsors should increase the probability of coopera-
tion, as H3 suggests. As envisaged, the findings show that the presence of
either single or different sponsors has no effect on alliance-formation given
that their confidence intervals include the line of no effect. This squares with
the wider expectations in the civil war literature that foreign support may not
necessarily have a positive effect on inter-rebel cooperation (Fjelde and
Nilsson 2012). In fact, the external backing may bolster power asymmetry
between potential partners, encouraging competition and fratricide rather
than cooperation. In contrast, confidence intervals for shared principals are
positive and exclude zero, indicating that the effect of shared sponsors has
a practical significance for understanding the onset of alliance-formation.
These results suggest that there is more space for inter-rebel cooperation if
the potential partners are agent to the same foreign sponsor.
Model 2 tests the explanation advanced by Bapat and Bond (2012) that
foreign support (Sponsor) interacted with the strength of the dyad relative
to the government (Weak Dyad) is more likely to lead to alliance formation
in civil wars. I find no support for this argument as the interaction effect
(Sponsor x Weak Dyad) displays an unclear effect on alliance. In contrast,
the interaction term for foreign support (Sponsor) has a positive and
considerable effect on the probability of alliance, indicating that sponsors
of a strong dyad may be more likely to lead to alliance formation. The failure
to replicate findings in Bapat and Bond (2012) is, first, due to a difference in
the time-period covered in my analysis because I omit 236 observations from
the pre-1975 period, and include 97 observations for the post-2001 period.
Following American Statistical Associations (ASA) suggestion to avoid p-values in favor of other approaches
(Wasserstein and Lazar 2016), I choose confidence intervals to present my findings. Conventional regression
output with coefficient estimates, standard errors and p-values is in Table 1 in the Appendix.
I separated the latter from my main predictor because both relative rebel strength and troop ratio are
theoretically endogenous to foreign support.
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Even when I constrain the time period to 19752001 the results do not
. Another reason is that Bapat and Bond use their own measure of
sponsorship, whereas I borrow the measure from the UCDP. But the UCDP
measure covers the post-1975 period. Their measure does not include infor-
mation on identity of sponsors, and that precludes me from using their proxy
to test my argument. Finally, the difference in statistical models might be
driving the outcome. While Bapat and Bond employ probit model, I use
multilevel logistic model, which accounts for the fact that rebel dyads are
nested within conflicts.
Durability (ln)
Ethnic frac.
Religious frac.
Duration (ln)
Expenditure (ln)
GDP p.c. (ln)
Shared sponsor
Different sponsor
Single sponsor
−5.0 −2.5 0.0 2.5 5.0
Coefficient Estimates
Model 1: Alliance in the Shadow
of Sponsor
Durability (ln)
Ethnic frac.
Religious frac.
Duration (ln)
Expenditure (ln)
GDP p.c. (ln)
Sponsor x
Weak Dyad
Weak Dyad
−4 0 4
Coefficient Estimates
Model 2: Sponsor and Weak Dyad
(Bapat and Bond)
Durability (ln)
Ethnic frac.
Religious frac.
Duration (ln)
Expenditure (ln)
GDP p.c. (ln)
−4 0 4
Coefficient Estimates
Model 3: Relative Strength (Christia)
Figure 2. Alliance-making in civil war, 19752009.
Note: Pooled multilevel logistic regression with point mean coefficient estimates and 95%
confidence intervals clustered on conflict level.
See Figure 4 in the Appendix.
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On the other hand, the findings in Model 3 lend support to Christias
argument that a balance of power facilitates cooperation between rebel
groups as the coefficient estimate for balance (Ratio) has a large and
positive effect on alliance. Therefore, the shadow of anarchy constraints
cooperation as rebel groups fear exploitation from stronger partners.
Regarding control variables, the models offer two findings. First, an inter-
rebel alliance is likely to take place when potential partners are facing a
durable incumbent regime. Even though this may run against some expecta-
tions in the civil war literature, the logic follows that of balance-of-threat
theory where actors are likely to resolve their disputes, if their common foe is
perceived as more dangerous. This dynamic is not fully captured in coeffi-
cients for GDP per capita and expenditure, as neither have a clear effect on
alliance formation. Second, I find that an alliance is more likely to take place
in the early years of conflict. Perhaps rebel capabilities are larger even at the
outset of the conflict or the goal of toppling down the government resonates
well across the opposition spectrum. Either way, this finding offers important
implications for Syria where multiple groups have failed to form an alliance
after six years of combat.
Now I turn to Model 1 and Model 3 in detail given that shared sponsors
and Christias troop ratio demonstrate practical significance for alliance-
formation. Using the estimates from Model 1 and Model 3, respectively,
Figure 3 shows the predicted probability of alliance by types of foreign
sponsors (left-hand side boxplot) and troop ratio (right-hand-side line plot
with 95% confidence intervals) while holding other predictors constant. This
includes a prototype case with the following characteristics:
The country is contiguous and polarized along the ethnic and religious
The incumbent regime has been in power for a decade, and spends more
than USD 3 million annually on military with a GDP per capita of
approximately USD 300;
The conflict has been active for two decades.
Figure 3 shows the substantive impact of shared sponsors on alliance-
making as well as a substantive variation in the effects of sponsor types.
The probability of alliance-making is at its lowest when neither of the
prospective allies receives foreign support (less than 0.1). There is a higher
probability of an alliance for those dyads in which at least one group
receives foreign support (around 0.15). However, that is much lower
compared to cases in which both groups receive backing from different
sponsors (0.25). Interestingly, this finding suggests that cooperation is far
more likely among groups with external backing than those that rely
solely on domestic support. Shared sponsors boost the probability of an
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alliance more than all other types of support taken individually. The
probability of an alliance in this case is double that of different sponsors
(0.5 vs. 0.25), and nearly triple that of single sponsors (0.5 vs. 0.18).
Shared sponsors are, therefore, a crucial factor in predicting alliance-
Moving to the right-hand side part of Figure 3, there is a clear upward
trend in the predicted probability of an alliance as the ratio inches closer to
one, that is, the balance of power. As the balance between two groups
increases the probability of alliance grows nearly five times. Although the
effect is impressive the 95% intervals are extremely wide, decreasing the
confidence in the result.
While the results and the substantive effects show the strong effect of shared
sponsors on the presence of inter-rebel alliance, it is equally important to
explore the predictive capacity of the models. I do so using 10-fold cross-
validation, which is a machine learning technique used to address the model
underperformance and overfitting through a random partition and analysis of
No Sponsor Single Sponsor Different Sponsors Shared Sponsor
Pr Alliance
0.00 0.25 0.50 0.75 1.00
Figure 3. Marginal effects on predicted alliance.
Note: Estimates based on Model 1 (left) and Model 3 (right, including 95% confidence intervals in
grey). GDP p.c., ratio, military expenditure, conflict duration, religious and ethnic fractionalization,
and regime durability are held at mean values. The remaining control variables are set to their
model values.
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data (Colaresi and Mahmood 2017)
. I first determine predictive performance
of the models using Receiver Operating Characteristic (ROC) curves.
curve is used in applications in which data are class imbalanced to indicate the
true and false positive rates for a classifier.
In this article, the ROC curve shows the extent to which the model
correctly classifies allianceand non-alliance.TheROCgraphissum-
marized by the Area Under Curve (AUC), which is the probability that
the model correctly ranks positive cases (alliance) versus negative cases
(no alliance), and that one has a greater probability than the other.
Models with greater predictive capacity gravitate toward the upper left
corner of the plot, and have higher AUC scores. This indicates the true
positive rate against the false positive rate for the different possible cut-
points of a diagnostic test. The closer the curve to the diagonal line, the
more the prediction resembles a coin-flip (.5); the closer the curve to the
upper left corner, the more accurate the model a perfect fit would have
the curve touching the top-left corner (1).
AUC scores of 0.70 are regarded as fair, while AUC scores equal to or
higher than 0.80 are considered good. The predictive performance in
Figure 4 varies from 0.85 to 0.87. Model 1 has the highest AUC of 0.87,
while Model 2 and Model 3 have slightly lower AUC scores of 0.85 and 0.85
each. I conclude that my model is slightly more capable in predicting
alliance-making in comparison to Model 2 and Model 3.
These results show the overall performance of my model, but it is also
important to understand how to improve its predictive power. I, therefore,
identify observations that generate lower performance using the model
criticism plot (Colaresi and Mahmood 2017).
The model criticism plot
shows the distance between actual and predicted values by plotting the
latter for each observation on the x-axis. Observations are then colored
according to their observed value (in this case, non-alliance is blue, while
alliance is red). The plot then ranks the predicted values in descending
order on the y-axis. Extremely inconsistent positive values (alliance is
observed, but the model predicts low probability of alliance) are colored
in red and gravitate toward the southwest, whereas highly inconsistent
zero values (non-alliance is observed, but the model predicts high prob-
ability of alliance) appear in blue toward northeast. Observations appear
on the vertical separation plot on the right y-axis, with most discrepant
K-fold cross validation (CV) is a way to analyze how the results of a model apply to an independent sample, i.e.
predictive accuracy of the model. CV randomly partitions the original data into similar folds of subsets, and then
performs analysis on a single subset (training dataset), while validating the analysis on the other (testing
dataset). In my case, I partition the dataset into 10 folds of 98 or 99 observations, carry out CV on 500 multiple
imputations, and pool predicted values for testing data.
I provide additional CV diagnostics such as the precision-recall (PR) plot and confusion-matrix in the Appendix.
I use R package ModelCriticism developed by Zuhaib Mahmood. Package source:
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positive and zero observations being colored in intense red and blue,
respectively. These observations are labeled and connected by lines to
their respective points on the y-axis on the left.
Figure 5 displays the model criticism plot for Model 1.
Similar to ROC
scores, Model 1 demonstrates a solid predictive performance as well as good
separation of negative and positive values given that there are only a few
extremely inconsistent positive values in the southeast. The top 10 discrepant
cases of alliance (red) are: SSDFSNM in 1991 (Somalia), NRAUPM in 1986
(Uganda), JEMSLM in 2007 (Chad), AmalLNM in 19851986 (Lebanon),
SPMSNM in 1989, and EPDMTPLF in 1989 (Ethiopia), FAR-PGT, EGP
ORPA and FARORPA, all in 1979 (Guatemala), and ELNFARC in 1991
(Colombia). Foreign sponsorship is present in none of these instances. The
most common denominators for these observations are weaker capabilities
relative to the government and a rather long time period during which the
dyads entered into alliance (within a decade of their existence). Interestingly,
most of these observations belong to the Cold War period where ideological
Model 1
False positive rate
True positive rate
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
AUC = 0.87
Model 2
False positive rate
True positive rate
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
AUC = 0.85
Model 3
False positive rate
True positive rate
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
AUC = 0.85
Figure 4. ROC plots for pooled cross-validated models of alliance-making.
The model criticism plots for Model 2 and Model 3 are available in the Appendix.
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polarization played a critical role in intra-conflict dynamics as well as in
relation to foreign sponsors. Using Non-State Armed Groups (NAG) dataset
(San-Akca 2016), I check whether the groups in those dyads shared one of
the possible ideologies: left-wing, nationalist, religious, right-wing. I find that
eight out of 10 dyads are ideologically compatible. This suggests that adding
shared ideology could be useful for exploring whether it improves the
performance of the model regarding alliance.
I notice that highly discrepant cases of non-alliance are clustered in the
IsraeliPalestinian conflict where ethnic outbidding and foreign support
AMAL & LNM−1986
AMAL & LNM−1985
FAR & PGT−1979
EPDM & TPLF−1989
ELN & FARC−1991
NRA & UPM−1986
SPM & SNM−1989
FAR & ORPA−1979
SSDF & SNM−1991
EGP & ORPA−1979
PUK & KDP−1975
PIJ & PLO (FATAH)−2002
Observation (ordered by f)
0.00 0.25 0.50 0.75 1.00
Forecast Value
Figure 5. Model criticism plot for model 1.
Shared ideology turns out to be a significantpredictor of alliance when included in a model with shared ideology
and a number of other alternative dyad-level variables (see Table 2 in the Appendix). I have also included a
robustness test for the model with shared ideology/ethnicity and other rebel-level variables by interacting the Cold
War dummy variable with shared ideology as well as including this variable as a predictor. Ideological proximity is
robust to the inclusion of the Cold War variable, which itself demonstrates no effect on alliance-formation. These
results are in Figure 8 in the Appendix.
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among the Palestinian outfits may have prevented the formation of coopera-
tion. This reinforces my previous point that foreign support may not neces-
sarily contribute to inter-rebel alliance unless it comes from shared sponsors.
This article builds on the principal-agent framework to argue that an inter-
rebel alliance in civil conflict is more likely when any two potential partners
share a foreign sponsor. This relationship provides two-way benefits: sponsors
can use alliance to manipulate the dynamics of the conflict while rebels receive
material incentives such as weapons, funding or sanctuary. Moreover, shared
sponsors are likely to invest effort in preventing potential defections through
controlling and monitoring mechanisms. The empirical analysis, using novel
panel data on rebel alliances, suggests considerable support for this argument. I
find that alliance formation is more likely when both potential partners receive
external backing from the same sponsor or sponsors. Moreover, I find that
single and different sponsors have an unclear effect on alliance formation and
that no foreign support appears to offer the worst prospects for cooperation.
Future research on civil war should take the foreign sponsorship of inter-
rebel dynamics more seriously. With a few exceptions, previous quantitative
studies have largely explored the relationship between the incumbent govern-
ment and rebel groups. As this study demonstrates, rebel groups develop ties
with external governments, which can significantly influence their strategies.
Bringing together data on foreign sponsors and rebel groups can shed more
light on the blurry line between external and domestic actors in civil war.
Research on political violence may also benefit from this merger by examining
how links to sponsors affect rebel propensity for violence against civilians.
These insights may better inform both counter-insurgency and conflict
resolution. While governments may deal with separate groups, the alliance
makes rebels a more formidable opponent. Counter-insurgent strategies
should factor in the presence of external ties, and invest more efforts into
resolving issues with foreign sponsors by diplomatic means. Conflict resolu-
tion professionals should also take into account the preferences of foreign
actors before investing considerable resources into mediation efforts. As the
Syrian conflict testifies, there is no shortcut to peace, let alone cease-fire,
when multiple foreign governments interfere in insurgent interactions.
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... There is a small body of work that focuses specifically on the effect of external support on rebel fighting, allying, and splintering. Bapat & Bond (2012) and Popovic (2018) find that a common external sponsor can improve the environment for alliances by providing a space for reiterated talks and mitigate information and commitment problems between rebel groups. Mirroring this, Fjelde & Nilsson (2012) find that rebel groups are not more likely to engage in rebel infighting when they receive external support. ...
... G. Fjelde & Nilsson, 2012;A. H. Kydd & Walter, 2006;Nygård & Weintraub, 2015), but there are mixed findings on how rebel competition and cooperation is related to the international system (Bapat & Bond, 2012;Popovic, 2018;Tamm, 2016). I argue that different forms of external support have heterogeneous effects on conflict dynamics. ...
... Akcinaroglu (2012) and Bapat & Bond (2012) argue that the strength of a rebel group not only derives from their military power, but also from their network with other rebel groups. As an external enforcer, a state can improve the environment for alliances by providing a space for reiterated talks and mitigate information and commitment problems between rebel groups (Axelrod & Hamilton, 1981;Bapat & Bond, 2012;Popovic, 2018). External states that provide nonfungible support have more control over their rebel group due to the increased information and better sanctioning mechanisms. ...
Why do states provide different forms of support to rebels fighting in foreign civil wars? How can external support band disparate rebels together in some conflicts but lead to bloody fratricide in others? My thesis aims to answer these questions. To do so, I make a two-step argument. First, I argue that civil wars are opportunities for states to improve their place in the global balance of power, and they provide different forms of support depending on the risk of retaliation from other states. Second, I argue that different forms of support have heterogeneous effects on rebel dynamics. The influx of money and weapons–which are fungible and exchangeable–induces a competitive conflict environment and leads to greater splintering and rebel infighting as groups compete over important resources. Nonfungible support such as troops shifts the balance of power, alleviates the systemic effects of anarchy, causes bandwagoning among and within rebel groups, and leads to more allying and less splintering. This argument provides the first holistic account of how the international system shapes cooperation and competition in rebellions. I test the empirical grounding of the argument as part of a mixed-method nested research design. First, I conduct two large-N analyses: a temporal network analysis to explain how external states support rebels and a matching analysis of rebel group behaviour on how different forms of support affect the propensity that rebels fight, form alliances, and splinter. Second, I conduct a theory-testing case study of the conflict in Northern Ireland (1968-1998) and a cross-case comparative study of Libya (2011-2019) and Syria (2011-2019). Drawing on archival evidence, secondary and grey literature, and micro-level conflict data, I demonstrate the causal mechanisms underpinning the results of the large-N analyses. I find support for key parts of the argument.
... Rebel organizations with an ethnic constituency advance political claims on behalf of an ethnic group, typically described as subjugated or mistreated. Rebel organizations with an ideological constituency cast political claims on behalf of social groups identified on the basis of non-ascriptive characteristics, which their 1 See also Popovic (2018) for theory and evidence about the effects of shared state sponsorship on rebel alliances. 2 We focus on alliances between organizations fighting against the same government within the same country, thus excluding cross-border rebel alliances. ...
... State co-sponsor is positively signed and significant, indicating that rebel groups receiving external support from the same source are more likely to ally. This result is consistent with that of Popovic (2018) but contrasts with Gade et al.'s (2019) finding that in Syria, rebel groups with shared state sponsors were not more likely to cooperate. ...
Challenging influential perspectives that downplay the role of shared rebel constituencies, we argue that they represent important causes of rebel alliances. Yet, we theorize distinct effects for different types of constituency. While compatible political aspirations push both organizations with a common ideological constituency and those with a common ethnic constituency to ally, for co-ethnic organizations this cooperation-inducing effect is offset by a cooperation-suppressing effect due to their higher risk of inter-rebel war. Leveraging a novel dataset of alliances in multiparty civil wars (1946–2015), we find support for our theoretical expectations. Shared ideological constituencies have a larger and more robust positive effect on the probability of alliances than shared ethnic constituencies. Furthermore, we find that co-ethnic rebel organizations tend to establish informal alliances only, while organizations sharing an ideological constituency are drawn to formal alliances.
... We anticipate rebel alliances to influence the willingness of groups to move to city centers. To operationalize Rebel Alliance Violence, we use Popovic's (2017) data to create a dichotomous variable coded one if a member of the network was actively allied with the group in question. Like our Other Rebel Violence variable, we count the number of battle deaths that occurred between a government and the allied organization(s) (and then take the natural log), expecting more intense competition to be associated with a shift towards city centers. ...
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Rebels that fight near or capture cities gain more concessions from the government than those that remain in the periphery. Yet, not all groups challenge urban centers. Previous scholarship expects rebel strength to explain this strategic decision. However, weak rebel groups challenge cities, too. Our approach focuses on the conflict process more broadly. We argue that as the network of rebels challenging the government increases, opposition groups become more likely to attack cities as either they become emboldened, given the government’s disadvantage in multi-front wars, or they are propelled to strategic and resource centers in competition with the other groups. Statistical analysis of all African conflicts from 1989-2009 strongly supports this logic, while an exploration of most typical cases highlights each of these mechanisms in practice. This project thus links literature on civil war tactics and conflict contagion.
... As a matter of fact, civil wars especially long-term ones, undergo both the formation and disintegration of alliances as the conflict unfolds, at the domestic level -that is within the country and the different groups taking part in the conflict. For instance, strong evidence was found that shared sponsors increase the probability of the creation of inter-rebel alliances (Popovic 2017). ...
This thesis looks at the phenomenon of internationalised civil wars and seeks to theoretically explain, through a large-scale quantitative study, why some intra-state conflicts are more subject to foreign involvement.
... Once the party becomes politically aligned with the rebel cause, it implies that its loyalty to the domestic state is dubious. A growing body of literature confirms that a vast share of rebel groups has had an explicit or widely accepted link with a foreign patron (Byman et al. 2001;Salehyan 2010;Salehyan, Gleditsch & Cunningham 2011;Popovic 2017;Bapat 2012). The KPU had been the prominent advocate of Russian interests in the Ukrainian parliament, agitating for the vision of the USSR as a lost paradise, instigating local grievances and questioning the loyalty of the party to Ukraine as an independent state (Kuzio 2015a). ...
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The article explains why the Communist Party of Ukraine (KPU) became marginalised during the insurgency in Donbas despite its ideological closeness to the rebel cause. The KPU was a popular pro-rebel party during the rebellion, but sharing the rebels’ ideological background doesn't automatically mean the party will profit from the insurgency to expand or retain a share of power in rebel enclaves during the rebel state-building efforts. The KPU officials welcomed the protests against the new government in Kyiv and the onset of the anti-Ukrainian insurgency under the Russian patronage in the Donbas. Still, even despite this open support, the party descended into marginalisation.
... The exogenous covariates are selected based on the literature on inter-rebel conflict. We thus include dummy variables indicating whether two actors share a common ethno-religious identity or receive material support by the same external sponsor as these factors have previously been found to reduce the risk of conflict (Popovic, 2018;Gade et al., 2019). Additionally, we include binary indicators of two actors being both state forces or both rebel groups as conflict may be less likely in the former but more likely in the latter case (Dorff et al., 2020). ...
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As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events.
... While I mainly emphasize military constraint, other sources of constraint are plausible: extending Popovic's argument, a constraining impact of shared sponsors on inter-rebel conflict is likely. 38 Likewise, state sponsors that share a border with insurgents, or are even themselves involved in the conflict, should also constrain rebels. ...
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Theories of interaction among rebel groups in civil wars, like other works in the armed conflict literature, continue to see force as foundational to the trajectory and outcome of conflict. But evidence from inter-rebel conflicts in the Syrian war, which has been one of the major civil wars of our times, shows that military force is not always the preferred tool even in situations where violence is presumably cheap: in conflicts between dominant rebel groups and weaker rivals. Rather than using force, Jabhat al-Nusra, one of the strongest groups in the Syrian conflict, frequently chose to negotiate with rivals. Existing theories of inter-rebel conflict fail to explain such variation in responses. As an explanation of this puzzle of non-force, I argue that the constraint emanating from the conflict with the main enemy determines rebels’ strategies towards rivals. To investigate this argument, the paper draws on the triangulation of original data on inter-rebel conflicts in Syria, encompassing written agreements between Jabhat al-Nusra and other rebel groups, a database of important military operations in the Syrian civil war since 2011, and interviews with civil and military participants in the insurgency. The findings have important implications not only for our understanding of inter-rebel dynamics in the Syrian conflict but also for other complex civil wars concerning the relationship between inter-rebel negotiation, cooperation, success, and war duration.
How does the withdrawal of troops after a military intervention supporting the government affect the number of rebel groups in the long term? This study argues that the withdrawal of foreign support for the government affects the number of rebels by directly provoking a nationalist backlash in the short term and threatening government legitimacy in the long term. Whether or not nationalism is provoked and whether legitimacy is enhanced or eroded depend on whether or not it was a humanitarian intervention. If rebels win, the intervention withdrawals also indirectly affect the number of rebel groups in the long term through the militias’ presence. Using interrupted time-series estimates between 1961 and 2005, this study found that humanitarian intervention withdrawals decrease the number of rebel groups in the long term, whereas nonhumanitarian intervention withdrawals promote the growth of militias and increase the number of rebel groups.
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Which armed organizations form coalitions despite the inherent difficulties of cooperation in civil wars? We introduce the concept of tacit coalitions, which pertains to strategic and informal coalition behavior between civil war actors to address this puzzle. Our theoretical model of coalition behavior takes in theater-wide conflict behavior to allow for predictions that coalitions are more likely to form. It provides novel insights into the way military synergies within potential coalitions affect the trade-off between pooling resources and worrying about the division of gains. The empirical section finds considerable support for our theoretical argument that actors are more likely to engage in tacit coalition behavior (1) if potential coalitions are power balanced, (2) if joint capability of potential coalitions is not too high, and (3) when coalitions can unlock synergies. In addition, it produces evidence for the important role of geography and ethnic ties in generating military synergies.
Contemporary conflict in North and West Africa is characterized by a high degree of social and political complexity. Hundreds of rebel groups and extremist organizations are involved in a shifting series of alliances and rivalries with regional governments and with each other. These changing relationships can be represented as a social network that provides both opportunities and constraints to violent organizations. To better address this complexity, this article models the temporal evolution of both opposition and cooperation networks using detailed information on nearly 40,000 events in North and West Africa from 1997–2020. Using a relational approach called Dynamic Social Network Analysis (DSNA), the article suggests that the increasing number of belligerents, increasing density of conflictual relationships, and polarization on powerful organizations capable of conducting extensive military operations make a peaceful resolution of the North and West African conflicts more elusive than ever.
Rebellion, insurgency, civil war-conflict within a society is customarily treated as a matter of domestic politics and analysts generally focus their attention on local causes. Yet fighting between governments and opposition groups is rarely confined to the domestic arena. "Internal" wars often spill across national boundaries, rebel organizations frequently find sanctuaries in neighboring countries, and insurgencies give rise to disputes between states. In Rebels without Borders, which will appeal to students of international and civil war and those developing policies to contain the regional diffusion of conflict, Idean Salehyan examines transnational rebel organizations in civil conflicts, utilizing cross-national datasets as well as in-depth case studies. He shows how external Contra bases in Honduras and Costa Rica facilitated the Nicaraguan civil war and how the Rwandan civil war spilled over into the Democratic Republic of the Congo, fostering a regional war. He also looks at other cross-border insurgencies, such as those of the Kurdish PKK and Taliban fighters in Pakistan. Salehyan reveals that external sanctuaries feature in the political history of more than half of the world's armed insurgencies since 1945, and are also important in fostering state-to-state conflicts. Rebels who are unable to challenge the state on its own turf look for mobilization opportunities abroad. Neighboring states that are too weak to prevent rebel access, states that wish to foster instability in their rivals, and large refugee diasporas provide important opportunities for insurgent groups to establish external bases. Such sanctuaries complicate intelligence gathering, counterinsurgency operations, and efforts at peacemaking. States that host rebels intrude into negotiations between governments and opposition movements and can block progress toward peace when they pursue their own agendas.
Increasingly, scholars interested in understanding conflict processes have turned to evaluating out-of-sample forecasts to judge and compare the usefulness of their models. Research in this vein has made significant progress in identifying and avoiding the problem of overfitting sample data. Yet there has been less research providing strategies and tools to practically improve the out-of-sample performance of existing models and connect forecasting improvement to the goal of theory development in conflict studies. In this article, we fill this void by building on lessons from machine learning research. We highlight a set of iterative tasks, which David Blei terms ‘Box’s loop’, that can be summarized as build, compute, critique, and think. While the initial steps of Box’s loop will be familiar to researchers, the underutilized process of model criticism allows researchers to iteratively learn more useful representations of the data generation process from the discrepancies between the trained model and held-out data. To benefit from iterative model criticism, we advise researchers not only to split their available data into separate training and test sets, but also sample from their training data to allow for iterative model development, as is common in machine learning applications. Since practical tools for model criticism in particular are underdeveloped, we also provide software for new visualizations that build upon already existing tools. We use models of civil war onset to provide an illustration of how our machine learning-inspired research design can simultaneously improve out-of-sample forecasting performance and identify useful theoretical contributions. We believe these research strategies can complement existing designs to accelerate innovations across conflict processes.