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Inter-Rebel Alliances in the Shadow of Foreign Sponsors

December 2017
International Interactions 44(3):1-28

DOI:10.1080/03050629.2017.1414812

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Leiden 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 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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Inter-Rebel Alliances in the Shadow of Foreign

Sponsors

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

ABSTRACT

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.

KEYWORDS

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 TPLF’s 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 today’s 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 milos.agathon@gmail.com 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 www.tandfonline.com/gini.

Supplemental data for this article can be accessed on the publisher’s website.

INTERNATIONAL INTERACTIONS

https://doi.org/10.1080/03050629.2017.1414812

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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

strength.

I use the principal-agent framework to show that when one or more

sponsors act as common “principals”of both partners (“agents”)inany

givendyad,thealliancewillbebuttressedthroughtheuseofselection,

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.

Thefindingsinthisarticleoffernovelandpromisingavenuesforconflict

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

suffering.

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

beforehand.

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

strength.

Accordingly, Chandler (1983) writes that the disdain for Vietnam’soccu-

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

People’s Liberation Front (TPLF) and The Eritrean People’s Liberation

Front (EPLF), Somali National Movement (SNM) and Somali Patriotic

Movement (SPM) or People’s 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

government.

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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 1991:27–34).

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

sponsor’s political goals (Fjelde and Nilsson 2012). Hafiz Assad’s 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 Arafat’s 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

cooperate

. 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 agents’alliance 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 sponsor’sdemands.

Another possibility is that the sponsor systematically appoints loyal cadres to

the protege’s 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

.When

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 sponsor’sfavor.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 “traitors”or 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 agent’s 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 Army’s (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.

http://www.latimes.com/world/middleeast/la-fg-cia-pentagon-isis-20160327-story.html.

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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 sponsor’s 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. Iran’s 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

1957:126).

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

wouldhaveprovidedsupporttobothgroups.Instead,theempowered

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

hypothesis:

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 sponsor’sresolveto

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 “resistance”against 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

Pakistan’s relationship with the Afghan mujahideen (Yousaf and Adkin

1992:150–151) 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 sponsor’s 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, Pakistan’s 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 Pakistan’s 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 sponsor’s 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 sponsor’s 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 (1992:150–155) 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 1975–2009 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

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 1946–2001 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, Myanmar’s Shan insurgents, Lebanon’s 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 MCC–PWG, UIFSA, and MJP–MPCI–MPIGO. 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 government’s 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 “Ratio”by dividing the number

of troops of group A by number of troops of group B. If this argument holds,

then “Ratio”should be positively associated with the probability of alliance.

None Single Different Shared

Sponsor

Number of observations

0 100 200 300 400 500 600

607

514

Alliance?

Ye s

Figure 1. Distribution of type of sponsors by alliance.

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Controls

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 capita”is used to denote the government’s 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 government’s 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

fractionalization.

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 R’s 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

“Rubin’s rule”(Little and Rubin 2014:86–87).

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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 Association’s (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 1975–2001 the results do not

change

. 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.

Non−Contiguity

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.

Non−Contiguity

Duration (ln)

Expenditure (ln)

GDP p.c. (ln)

Sponsor x

Weak Dyad

Sponsor

Weak Dyad

−4 0 4

Coefficient Estimates

Model 2: Sponsor and Weak Dyad

(Bapat and Bond)

Durability (ln)

Ethnic frac.

Religious frac.

Non−Contiguity

Duration (ln)

Expenditure (ln)

GDP p.c. (ln)

Ratio

−4 0 4

Coefficient Estimates

Model 3: Relative Strength (Christia)

Figure 2. Alliance-making in civil war, 1975–2009.

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 Christia’s

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 Christia’s 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

lines;

●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-

formationinfuturecivilconflicts.

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.

Forecasting

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

0.1

0.2

0.3

0.4

0.5

No Sponsor Single Sponsor Different Sponsors Shared Sponsor

Sponsor

Pr Alliance

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Ratio

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.

INTERNATIONAL INTERACTIONS 21

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data (Colaresi and Mahmood 2017)

. I first determine predictive performance

of the models using Receiver Operating Characteristic (ROC) curves.

ROC

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 “alliance”and “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: https://github.com/zsmah

mood89/ModelCriticism.

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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: SSDF–SNM in 1991 (Somalia), NRA–UPM in 1986

(Uganda), JEM–SLM in 2007 (Chad), Amal–LNM in 1985–1986 (Lebanon),

SPM–SNM in 1989, and EPDM–TPLF in 1989 (Ethiopia), FAR-PGT, EGP–

ORPA and FAR–ORPA, all in 1979 (Guatemala), and ELN–FARC 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

AUC = 0.87

Model 2

False positive rate

True positive rate

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

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

Israeli–Palestinian 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

HEZBOLLAH & AMAL−1998

HAMAS & PFLP−GC−1987

FATAH AL−INTIFADA & HEZBOLLAH−1987

PUK & KDP−1975

FATAH AL−INTIFADA & HEZBOLLAH−1985

FATAH AL−INTIFADA & HEZBOLLAH−1986

PIJ & PLO (FATAH)−2002

PFLP−GC & HEZBOLLAH−1985

PFLP−GC & FATAH AL−INTIFADA−1985

PFLP−GC & PLO (FATAH)−1989

250

500

750

1000

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 “significant”predictor 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.

24 M. POPOVIC

Downloaded by [Rutgers University] at 03:27 28 December 2017

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.

Conclusion

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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A Relational Theory of Jihadist Affiliate Behaviour: The Case of Al Qaeda and the Islamic State

Article

Mar 2024
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In this article, I focus on why AQ and IS affiliates develop different regional interactions with one another, ranging from high conflict to high cooperation.

Does Withdrawal of Troops After Military Intervention Reduce Rebel Groups?

Article

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Wakako Maekawa

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.

Capture the Fort: Explaining the Timing of Rebel Assaults on Cities During Wartime

Article

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The uninvited guest: understanding Islamic State’s alliances and rivalries in the Afghanistan-Pakistan region

Article

Feb 2023
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Civil Wars: Domestic fights, International competitions

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Which factors contribute to the cohesion of alliances among rebel groups during civil wars? Through the case of the Syrian civil war, which experienced a high level of fragmentation and cohesion among rebel groups, we emphasise how the establishment and implementation of power-sharing mechanisms in horizontal wartime institutions ensure lasting cohesion in alliances. Moreover, we posit how the mere establishment of these mechanisms is insufficient, and they must be effectively implemented. The implementation of power-sharing mechanisms in horizontal wartime institutions increases the costs of defection, and it reduces the incentives for unilateral defection from an alliance by coalition members.

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Borgerkrigen i Syria har gitt opphav til svært mange opprørsgrupper av ulik idelogi. Hovedfienden til opprørerne er Assad-regimet, men samtidig har det vært både strid og samarbeid mellom de ulike opprørsgruppene. Denne artikkelen tar for seg i hvilken grad, og hvorfor, mer ekstreme islamistiske grupperinger har vært i stand til å samarbeide og inngå allianser.

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Why do some rebel organizations form a united front when others confine themselves to a loose partnership? Existing research on rebel movements reveals that insurgents should quickly leave cooperative agreements if doing so will provide particular advantages in a post-conflict setting. Still, rebel groups may build diverse alliances, from joint attacks to shared command structures. If rebels are indeed purely pragmatic, why would they tie themselves to partnerships that hinder their ability to defect easily? In this article, I argue that three relational factors shape how armed groups cooperate: rebel-civilian relations (civilian support), inter-rebel relations (partners), and rebel-sponsor relations (external patrons). Groups connected through these social networks will be more likely to join a united opposition with their shared channels of trust. Developing a new dyadic dataset, I find that sharing allies or civilian support is positively associated with forming united fronts. Yet, state sponsorship does not produce similar results.

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Husain Haqqani

States in Disguise: Causes of State Support for Rebel Groups

Book

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Belgin San-Akca

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Abdulkader H. Sinno

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Statistical Analysis with Missing Data.

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Rebels without Borders: Transnational Insurgencies in World Politics

Book

Jan 2009

Idean Salehyan

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.

Do the robot: Lessons from machine learning to improve conflict forecasting

Article

Mar 2017
J PEACE RES

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.

The ASA's Statement on p-Values: Context, Process, and PurposeRonald L. Wasserstein American Statistical Association, 732 North Washington Street, Alexandria, VA 22314-1943Correspondenceron@amstat.org & Nicole A. Lazar American Statistical Association, 732 North Washington Street, Alexandria, VA 22314-1943

Article

Jan 2016

Introduction: Causes of State Support for Rebel Groups

Chapter

Oct 2016

Belgin San-Akca

Inter-Rebel Alliances in the Shadow of Foreign Sponsors

Abstract

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