weapon of the market-dominant: a market theory of...
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Weapon of the Market-Dominant: A Market Theory of Terrorism in Civil War
Aslihan Saygili, Renanah Miles, and Laura Resnick-Samotin Columbia University
Abstract
How does the distribution of capabilities among rebel groups affect their use of terrorist tactics? We argue that the amount of civil wartime terrorism perpetrated by a rebel group depends on its “market share”—the group’s military capabilities assessed vis-à-vis other rebel groups in the same conflict. Market-dominant rebel groups have both the material capabilities to make proliferate and deadly use of terrorist attacks and the strategic incentives to do so as a means of maintaining their monopolistic position in the conflict. By highlighting the links between relative strength, competition, and tactics, we add nuance to the conventional wisdom that conflicts involving multiple rebel groups are inherently more prone to terrorism. Using data on 342 non-state actors involved in internal armed conflicts between 1970 and 2011, we find that rebel groups who militarily dominate the conflict market use terrorism more extensively. Controlling for rebel strength vis-à-vis the government, terrorism appears to be a “weapon of the market-dominant” among non-state actors involved in civil conflict. Our findings demonstrate the explanatory power of inter-rebel distribution of capabilities for predicting terrorism in civil war and make an important modification to outbidding studies in the terrorism literature.
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1 Introduction
How does rebel strength relative to other rebel groups influence the use of terrorism in
civil war? One of the key points of consensus among terrorism scholars is that terrorist tactics are
primarily employed by “militarily inferior groups who lack other means of coercing the target
government” (Crenshaw 1981, 387). There are two important puzzles, however, that the
conventional wisdom about rebel strength and terrorism fail to explain. First, the vast majority of
armed non-state actors are militarily weaker than the governments they fight, yet we see
significant variation in the extent to which rebel groups rely on indiscriminate attacks against
civilian populations.1 Second, there are many examples of militarily advanced rebel groups who
are notorious for their extreme use of terrorism alongside conventional or guerilla tactics. In East
Africa, for instance, Somalia and Kenya have suffered scores of high-profile terrorist attacks
perpetrated by al-Shabaab, a powerful rebel organization that controls large swaths of territory in
central and southern Somalia and even managed to capture parts of the capital city of Mogadishu
during its peak in 2011. While examples like al-Shabaab do not falsify the common notion of
terrorism as a “weapon of the weak,” they indicate important gaps in our understanding of the
relationship between rebel military capabilities and terrorism.
This study suggests that a closer examination of the ways that the distribution of rebel
capabilities in civil conflict influence rebel tactics will fill in some of these gaps. Although a
growing literature explores the strategic logic of terrorism in civil war (Fazal 2017; Findley and
Young 2012a; Fortna 2015; Stanton 2013; Thomas 2014), the relationship between inter-rebel
distribution of capabilities and terrorism remains poorly theorized. Many empirical studies of
civil wartime terrorism either neglect military capabilities altogether or conceptualize rebel 1 In this study, we use Fortna’s (2015, 522) definition of terrorism as intentionally “indiscriminate violence against public civilian targets to influence a wider audience.”
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strength in strictly dyadic terms relative to the government. This gap in the literature is puzzling
given that “outbidding” competitors is one of the key strategic objectives that terrorism is
considered to serve (Kydd and Walter 2006). Surprisingly, the empirical literature on the
relationship between rebel competition and terrorism also makes minimal reference to relative
capabilities, failing to acknowledge that how rebel groups perceive and respond to potential
competition depends largely on their military strength compared to other actors.
In this article, we posit a more complex relationship between rebel strength, competition,
and terrorism than previously assumed. Our main theoretical proposition is that the amount of
terrorism perpetrated by rebel groups varies their “rebel market share,” defined as the share of
available military resources that they command relative to other groups. These resources
represent the troops, weapons, and materiel that enable rebels to fight. We argue that, as a rebel
group gains a larger share of the resources available in the conflict system and approaches
market domination, both its capacity and incentives for profligate and deadly use of terrorism
increase. Market domination introduces the burden of maintaining a monopolistic position and
preventing potential competitors from arising, which incentivizes the use of terror as a means of
exerting authority. We refer to this dynamic as the “monopoly effect.” Monopoly effects also
create structural conditions that may exacerbate non-strategic use of terrorism; for example, by
reducing a group’s ability to gauge public support for its choice of tactics. At the same time,
additional military capabilities may increase the ability to produce terrorism as well as the
willingness to accept the risks inherent in its use. We call this dynamic the “wealth effect.” The
outcome is larger production of terrorism by militarily superior rebel groups who dominate the
conflict market.
We test our hypothesis using new data on terrorist attacks perpetrated by all rebel groups
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that were operational between 1970 and 2011, which we compile by matching actors from the
Non-State Actor (NSA) data (Cunningham, Gleditsch, and Salehyan 2009; 2013) with those
from the Global Terrorism Database (GTD) (LaFree and Dugan 2007). Our dataset introduces an
original measure of rebel market share that we operationalize by calculating the proportion of
troops that each rebel group controls relative to all rebel troops active in the conflict year. Using
zero-inflated negative binomial estimations, we find strong support for our hypothesis: rebel
groups with a larger market share carry out a significantly higher number of attacks and are
responsible for significantly more deaths from terrorism. The finding is robust to alternative
specifications, demonstrating the explanatory power of inter-rebel power differentials for
predicting terrorism in the context of civil war.
Our study makes several important contributions to the scholarship on terrorism in civil
war. First, we show that the relationship between military strength and terrorist tactics in the
context of civil war is more complex than traditional theories can account for, with terrorism
appearing to be the weapon of the stronger in a given conflict market. Our empirical findings
suggest that, in a given distribution of power among rebel groups fighting the same government,
it is the market dominators and monopolists who make profligate use of terrorist attacks. These
powerful rebel actors not only have the weaponry, manpower, and logistical access to urban
centers that facilitate their execution of deadly attacks, they also have incentives to use terrorism
to deter competition from rival groups or internal factions that emerge from within.
Second, by highlighting the link between relative capabilities and competition, we add
missing nuance to the conventional wisdom that civil wars that feature multiple rebel groups are
naturally prone to increased terrorism. Multiparty conflicts characterized by a roughly even
distribution of capabilities among rebel groups differ significantly from those where one group
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militarily dominates the conflict landscape. While the former may engender competition and
incentives to use terrorism, the latter is a doubly dangerous context where dominant groups
possess both the material capabilities and motives to employ terror extensively.
2 Literature Review
The relationship between rebel capabilities, competition, and choice of coercive tactics
has received widespread attention across literatures on intra-state violence. Studies of terrorism
and civilian targeting offer insights about how militarily disadvantaged parties use attacks
against civilians to compensate for their comparative weakness (Crenshaw 1981; Downes 2008).
Hultman (2007, 206) argues that killing civilians serves as “a militarily cheap and easy strategy
to raise the government’s costs for standing firm” when battlefield losses weaken rebels’
coercive capacity. Others suggest that military losses erode popular support for rebels and
increase defection rates, creating incentives to intimidate civilians through selective targeting
(Kalyvas 2006) or indiscriminate violence (Wood and Kathman 2015).
While some studies treat terrorism and other forms of civilian targeting interchangeably
(e.g., Eck and Hultman 2007; Wood 2010), we differentiate between them, following Stanton’s
(2013, 1009) argument that “different forms of violence are associated with different strategic
objectives.” Terrorist attacks target civilians randomly to coerce a different audience, usually the
government (Fortna 2015, 4). Other civilian targeting, in contrast, often seeks to coerce the
civilians themselves, whether to induce collusion, deter defection, or extract resources.
For its part, the terrorism literature takes the relationship between military weakness and
terrorist violence almost as a truism. Widely characterized as a “weapon of the weak” (Crozier
1960), terrorism is seen as an attractive strategy for non-state actors who seek political
concessions yet lack the physical capacity to inflict material costs large enough to induce a shift
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in government behavior. Polo and Gleditsch (2016), for instance, argue that rebel groups who are
sufficiently equipped to rely on a conventional military strategy have few incentives to employ
terrorism, given its low military impact against the target and potentially counterproductive
effects such as legitimacy costs. For insurgents in a position of strength, terrorism is thus
considered “unnecessary—and even wasteful” (Thornton 1964, 89).
While the “weapon of the weak” argument has found empirical support, it fails to fully
capture the interplay between rebel military capabilities and strategic incentives for terrorism.
Because of the widespread assumption that capabilities only matter insomuch as their inferiority
makes terrorism a necessity, most empirical studies of civil wartime terrorism conceptualize and
measure rebel strength vis-à-vis the government (e.g., Fortna 2015; Polo and Gleditsch 2016;
Stanton 2013). This dyadic measure fails to capture meaningful variation in military capabilities
across rebel groups, as most groups are significantly weaker than the government forces they
oppose. Around 94% of rebel group-year observations in our dataset on civil wars that took place
between 1970 and 2011 are coded as “weaker” or “much weaker” than the government.2 As
such, these studies fail to account for the variation in tactics across the bulk of rebel actors who
are outnumbered by government forces yet sufficiently equipped to fight a civil war.
The literature on inter-rebel competition and terrorist outbidding highlights another
problem with treating government forces as the sole basis of comparison when examining the
link between rebel capabilities and tactics. Not all terrorist attacks are meant to coerce the
government: in multiparty conflicts, rebel leaders may see terrorizing civilians as an effective
instrument to eliminate competition. When rival organizations threaten their access to material
support from the local population, rebel groups may use terrorist attacks to signal their
2 We use the NSA dataset measure of relative strength. Of the 342 rebel groups in our dataset, only six surpass the government forces in strength at any point in the conflict.
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commitment to the cause and attract support away from the competition (Kydd and Walter
2006). Outbidding theories predict an increase in high-profile terrorist attacks on the
government’s civilian constituency when armed groups compete for popular support and recruits
(Bloom 2005; Oots 1989). Terrorist outbidding is hypothesized to help groups secure local
support through attraction rather than intimidation; the assumption is that aggrieved people want
to be represented by “zealots” (Kydd and Walter 2006, 76-78). As Bloom (2005, 78) puts it,
terrorism becomes “the litmus test of militancy” among rival groups vying to represent the
population.
In environments where outbidding may occur, we expect relative strength to be a key
determinant of how each group perceives and responds to threats posed by its rivals. However,
most empirical studies operationalize rebel incentives for outbidding with the number of non-
state actors party to the same conflict, treating competition as an inherent characteristic of
multiparty civil wars (e.g., Findley and Young 2012b; Polo and Gleditsch 2016; Stanton 2013).3
This overlooks the fact that each rebel group is likely to perceive threats posed by potential
competitors differently, depending on where it stands in a given distribution of power. Yet we
cannot accurately assess the strength of a group’s incentives to outbid competitors—or the
conditions under which they translate into increased use of terrorism— without accounting for
the group’s relative conflict standing.
In sum, extant theories of terrorism in civil war fail to fully elucidate how rebels’ military
capabilities influence their use of extreme tactics in civil war. The weapon of the weak thesis
invokes a narrower definition of rebel strength vis-à-vis the government, while studies of inter-
3 An exception is Nemeth (2014, 355), who finds that competition may reduce terrorism, but that the relationship is contingent on the state’s acceptance of violence and the group’s ideology. However, his measure of market share is based on ex post outputs (i.e., number of attacks) rather than the ex ante distribution of capabilities that we measure here.
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rebel competition pay insufficient attention to the ways that rebel strength vis-à-vis other groups
shapes their tactical preferences. Next, we present a novel account of civil wartime terrorism that
seeks to bridge these theoretical gaps.
3 Theory: Rebel Market Share and Terrorism
We argue that rebels’ use of terrorism is affected by the distribution of capabilities among
all rebel groups active in an ongoing civil war. We conceptualize these groups as operating
within a rebel conflict “market.” The market consists of available military resources, including
the troops, materiel, and arms that generate fighting power. Market share thus captures the inter-
rebel distribution of material power: the group with the largest share is the most powerful rebel
actor in a given conflict. This conceptualization draws on economic models of civil war that
characterize conflict as “a kind of ‘industry’ in which different ‘firms’ compete by attempting to
disable opponents” (Hirshleifer 2001, 331). Unlike economic markets, however, conflict markets
are zero-sum and value is redistributed by force (Hirshleifer 2001, 331-333).4
Our main proposition is that in the context of civil war, terrorism is employed more
intensively by rebel organizations that command a larger portion of the military resources
available in the conflict market. Increasing market share affects the use of terrorism in several
ways. As groups amass market share, their incentives for using terror as a means of exerting
authority and deterring potential challengers increase through a “monopoly effect,” which may
also increase nonstrategic use of terrorism. At the same time, their capacity to commit attacks—
and willingness to incur the costs of doing so—increases through a “wealth effect.”
4 Formal contest models of conflict adopt this terminology to examine the effects of combatant strength on war outcomes. In these models, armed groups produce violence, allocate resources to defeat their adversaries and, if they win, appropriate the defeated parties’ resources. For an overview in the context of civil wars, see Blattman and Miguel (2010).
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3.1 Monopoly Effects
Monopoly effects occur when a rebel group controls much or all of a conflict market.
Much like a monopolistic firm in an economic market, rebel groups who attain market
dominance face pressure to maintain their monopolistic position vis-à-vis potential competitors.
This pressure creates strategic incentives to use extreme tactics like terrorism.5 Market-dominant
groups are also more likely to miscalculate in their use of terrorism, much like monopolies
produce inefficiencies in economic markets.6 First, we briefly consider how monopoly effects
may induce non-strategic use of terrorism; next, we discuss how they shape strategic incentives.
In the absence of competition, rebel leaders face a problem of asymmetric information
that limits their ability to assess the level of local support for terrorist tactics. As Nemeth (2014,
337) puts it, “The terrorism literature is replete with accounts of groups miscalculating the
amount of violence a society was willing to endure.” An example is Iraq in 2007, when al-Qaeda
in Iraq’s excessive brutality cost it the support of the local Sunni population. By reducing
groups’ ability to gauge public support—and by reducing their accountability to the public for
support—market-dominant rebel groups become less constrained by societal preferences against
terrorism. Monopolistic rebels can ignore the legitimacy costs of terrorism more easily, whether
because they cannot hear public rebuke or because they feel emboldened not to listen.
Monopoly effects also give rise to strategic incentives to use extreme tactics. When faced
with challenges to their power by rising competitors or internal factions, monopolistic and
5 Importantly, we only examine strategic reasons why groups might choose terrorism—not whether or not terrorism is effective. Some scholars find that terrorism does not “pay” in terms of concessions (Abrahms 2012) or victory (Fortna 2015), but others find that extreme tactics succeed in driving governments to the bargaining table (Thomas, 2014). We simply note that, effective or not, groups continue to use it as a tactic, thus meriting scholarly attention. 6 Vickers (1996) provides a useful discussion of the different ways that monopolies and collusion introduce economic and social inefficiencies in markets. In particular, Vickers explains ways that monopolistic markets can cause “private and social interests” to diverge (13).
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market-dominant groups may perceive terrorism as an effective means to re-exert authority and
raise barriers to market entry by other groups.7 Market-dominant rebel groups enjoy strong
military capabilities and control a large share of all available resources, which facilitates the
execution of terrorist attacks. At the same time, the threat of losing that share in the future raises
concern over preventing incipient challenges. Possession of a dominant market share coupled
with the threat of future monopoly loss thus creates a doubly dangerous situation. Importantly, a
group does not have to be the only non-state actor in a conflict for monopoly effects to occur; the
logic also applies to rebel groups that command a large market share. We expect, however, that
these dynamics will become more pronounced as a group’s market share grows.
One way that market dominators use terrorism strategically to eliminate competition is
through a combination of provocation and outbidding. As theorized in the literature, terrorism is
sometimes used to provoke government backlash, under the expectation that harsh and
indiscriminate counterterrorism responses will radicalize the local population and drive recruits
and supporters into the rebels’ camp (Kydd and Walter, 2006). This strategy also serves
monopolistic and market-dominant rebel groups’ efforts to prevent relatively weaker competitors
from accumulating power. A government backlash so brutal that only the strongest can survive
may be seen as a risky but effective way to eliminate smaller groups who would grab larger
market share if allowed to grow.
The Liberation Tigers of Tamil Eelam (LTTE) in Sri Lanka illustrates how powerful
rebel groups can outbid competitors through terrorist provocation. The LTTE emerged in 1974 as
7 Of course, terrorism is not the only tactic that market-dominant groups use to deter or eliminate competition. Rebel groups often strive to become the “only game in town” by engaging their rivals militarily, using terrorism in conjunction with conventional attacks. Another tactic, widely used by the LTTE, is to physically eliminate the leadership of rival organizations. In contrast to outbidding and provocation, which target competitors through indirect mechanisms, these tactics aim directly to destroy rivals’ organizational and fighting capabilities.
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one of many Tamil separatist groups purporting to fight for Tamil independence. In the 1970s
and 80s, relatively weaker Tamil rebel groups competed with the LTTE for recruits. The LTTE’s
early “archrival,” the People’s Liberation Organization of Tamil Eelam (PLOTE) refrained from
attacking civilians, which a U.S. intelligence assessment concluded had “given it a broader base
among Tamils than LTTE has” (Central Intelligence Agency 1986, 6). The LTTE, however,
believed that terrorism would provoke harsh government reprisals that would target Tamil
civilians, increasing support for the LTTE and wresting it away from their rivals. The strategy
worked: the LTTE’s use of terrorism provoked a massive counter-terrorism response from the
government that helped, along with direct attacks on its rivals, to wipe out the weaker groups. As
such, use of terrorism enabled the Tamil Tigers to consolidate a conflict market monopoly.8
3.2 Wealth Effects
While monopoly effects shape group motives for using terrorism, wealth effects influence
the capacity and risk acceptance needed to execute attacks. Our observation on capacity is
straightforward: for groups already inclined to use extreme tactics like terrorism, any increase in
capabilities will increase their ability to execute attacks. Powerful actors have more weapons,
soldiers, and access to urban centers, all of which facilitate the execution of terrorist attacks.
Scholars find that group size associates positively with terrorism (Asal and Rethemeyer 2008a;
2008b; Boyns and Ballard 2004). Even if smaller groups want to use terrorism, they often lack
the requisite human and material capital, essentially creating a “labor constraint” (Clauset and
Gleditsch 2012, 2) on the production of violence that diminishes as groups grow and gain
experience (Asal and Rethemeyer 2008b). Indeed, the three groups with the highest number of
terrorist attacks in a given year for which we have troop data are Shining Path (Sendero
8 At its peak, the LTTE was one of the best-equipped rebel organizations in the world and the only rebel group to boast its own army, navy, air force, and merchant marine (Mampilly 2011).
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Luminoso; SL hereafter) in Peru (1984), Communist Party of India (CPI-Maoist) (2010), and the
Taliban in Pakistan (2009). Although these groups vary in terms of geography, ideology, and
goals, they share a common feature: disproportionately large shares of the overall capabilities
across rebel groups active in the war.9
Our second point is more nuanced: as groups are able to do more, they become willing to
do more. The economics literature has a familiar term for this dynamic: wealth effect. The
wealth effect posits that as individuals’ assets increase in value, they feel more secure about their
wealth and thus are willing to spend more. In the context of civil war, this means that willingness
to produce terrorism may grow with the capacity to do so. The common wisdom is that as
capabilities increase, terrorism will decrease due to countervailing incentives. Scholars argue that
because the non-material costs of terrorism outweigh its material benefits, rational rebels will
apply all their resources toward conventional military strategies when they can (Polo and
Gleditsch 2016, 818; Wood 2014). While we agree that rebels who can hit government forces
will do so, we question whether use of terrorism is zero-sum or mutually exclusive with other
tactics.
The logic of wealth effects predicts that more capabilities represent extra capacity and
additional expendable assets, encouraging risk-acceptant behavior. Risk acceptance in this
context refers to the costs that terrorism incurs, namely legitimacy and reputation costs.
Wintrobe (2006, 186) similarly argues that an increase in the capacity of rebel organizations
affects their preferences over tactics: “[A]s wealth increases, the leader will switch to a relatively
more risky portfolio, that is, make relatively more use of extremist methods.” In a nutshell,
groups may view control of resources as a license to use all available tactics—not as a reason to
9 At their peaks of violence, CPI-Maoist and the TTP were the only active groups in their conflict while SL commanded 93% of the troops on the battlefield.
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exercise self-restraint.
In civil wars where outcomes hinge on the population’s support, groups may see the
ability to terrorize the people who depend on the government for protection as a way to continue
applying political pressure. As groups grow stronger, the threat to impose costs becomes more
credible and certain strategies of terrorism, such as attrition, become more viable. In attrition,
groups use terrorism to signal the ability and resolve to continue imposing costs in the future
(Kydd and Walter 2006, 59-60). Indeed, evidence suggests that rebels use it to “disrupt and
discredit the processes of government” (Crenshaw 1981, 387) or to ratchet up pressure on the
government to make compromises (Thomas 2014), often in tandem with guerrilla warfare.
As one of the notorious perpetrators of terrorism in civil war, Peru’s SL illustrates the
wealth effects associated with market dominance. SL, along with other left-wing groups,
emerged from the Peruvian Communist Party (PCP) in the 1960s. The group split from the PCP
in 1964 because of a disagreement over tactics; while most of the PCP’s members preferred
peaceful means of change, SL sought a violent people’s war to bring about revolution in Peru
(McCormick 1990). When SL began to use terrorism during the 1980 Peruvian elections, it was
already a comparatively strong insurgent group; in 1987, they were estimated to have between
4000 to 5000 dedicated, full-time cadres operating in the country (Central Intelligence Agency
1987). By 1990, SL had administrative control over an estimated 25 to 40% of the country and
had developed a high degree of military and organizational professionalism (U.S. Congressional
Hearing 1992, 13, 17). Rather than eschew terrorism as their strength grew and the conflict
morphed into a long-running guerrilla insurgency, SL embraced it. Between 1982 and 1999, SL
conducted an average of 121 attacks per year; in 1984 alone, they carried out 260 attacks.
SL’s profligate and effective use of terrorism owed not only to its military strength per se
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but also to its command of a large market share in the absence of strong competitors. Even after
the emergence of the Tupac Amaru Revolutionary Movement (MRTA) in the mid-1980s as a
rival group, SL’s dominant market position as well as its notoriety for widespread terror
persisted. A comparison between the two rebel groups helps illustrate this: “…overall, the
MRTA lacked the lavish finances of Sendero, and paled in most other respects as well. It had
less than 10 percent of Sendero Luminoso’s membership base, and was responsible for less than
10 percent of the number of violent deaths attributed to Sendero Luminoso” (Rochlin 2003, 73).
Our data also support this assessment. During its peak years (1989-1991), MRTA possessed only
6.5% of all rebel troops in the conflict and carried out around 100 attacks in total, while the SL
controlled the rest of the market and executed more than 600 attacks in the same period.
Taken together, monopoly effects predict that market-dominant groups will use terrorist
tactics to consolidate control and prevent emergence of competition. Wealth effects predict that
as rebel group capabilities increase, so does their ability to conduct terrorism and the willingness
to employ potentially risky strategies. Together, these insights yield our main hypothesis that
groups with more rebel market share will use more terrorism.
4 Research Design and Data
To test our hypothesis empirically, we construct a new dataset of rebel group terrorism in
civil wars, matching rebel groups in the NSA dataset with terrorist organizations identified in the
GTD data. The NSA’s inclusion criterion, based on a 25-battle-death threshold of armed conflict,
allows us to create a more comprehensive sample of rebel actors than similar studies that only
include groups involved in conflicts reaching 1000 battle deaths in a given year (e.g., Stanton
2013; Fortna 2015). We expand the NSA spells into panel data for a rebel group-year unit of
analysis, which captures periods of overlapping rebel group activity as well as changes in rebel
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strength over time. We include in the dataset all non-state actors for the years 1970–2011, with
the exception of those involved in a coup d’état, which usually have a short lifespan of a few
days, and groups identified as “irregulars,” which we exclude due to their lack of an
organizational structure. Our final dataset comprises 342 rebel groups and 2,032 rebel group-year
observations.
4.1 Dependent Variable
We use two primary measures of the dependent variable, terror attacks and terror
fatalities.10 The first measure is the annual number of terrorist incidents perpetrated by each rebel
group; the second measure is the number of fatalities from those incidents. We rely on the GTD
coding of terrorist events, selecting only those incidents that match all three of the GTD
terrorism criteria.11 We then filter the GTD dataset to include the following attack types: armed
assault, bombing, hijacking, facility/infrastructure attacks, and hostage taking (barricade and
kidnapping incidents). We exclude incidents that are not clearly perpetrated against a civilian
target (i.e., military, police, and government targets). After applying these filters, we match the
number of incidents and fatalities per year to the groups in our data.12 In most cases, we rely on
exact name matches and only record incidents whose perpetrator in the GTD exactly matches the
rebel group name in NSA.13 Our variables only capture terrorist attacks associated with specific
10 Young (2016, 3) surveys 21 journals to show that the two most common operationalizations of terrorism as a dependent variable are event counts and number of fatalities. He also shows that results are sensitive to alternative specifications, such as number of attacks versus fatalities, which is why we operationalize the dependent variable in two different ways. 11 For inclusion in GTD, incidents must meet at least two of three criteria, namely that attacks have a broader (political, social, etc.) goal, target a larger audience than the immediate victims, and fall “outside the context of legitimate warfare activities” (LaFree and Dugan 2007, 188). 12 Our primary measure follows Fortna’s (2017) coding of terrorist activity by rebel groups. Further details about our coding procedures are in the Appendix. To ensure that our results do not depend on a specific definition of terrorism, we use alternative measures in robustness tests that rely on a more restrictive set of attack and target types. Results are reported in the Appendix. 13 See the appendix for details on name-matching between the datasets in ambiguous cases.
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rebel organizations; we do not attribute unclaimed attacks to any rebel group in the target
country in order to avoid overestimating the amount of terrorism perpetrated by civil war actors.
Nearly 42% of the rebel groups (143 out of 342) in our dataset use terrorism at some point in
their lifetime, while around 62% of the civil wars in our sample have at least one rebel group
using terrorism.
4.2 Independent Variable
Our main independent variable—rebel troop share—estimates the distribution of
capabilities among rebel groups concurrently fighting the government. We create this variable
using the NSA data on the size of rebel armed forces. For each state-rebel group dyad year, we
first identify all other groups that were party to the same conflict in the given year. Next, using
the NSA estimates of rebel forces, we calculate the proportion of troops that each rebel group
controls relative to all the rebel troops active in the conflict year. For example, in 1990,
Revolutionary Armed Forces of Colombia (FARC) fought against the Colombian government
alongside two other groups, National Liberation Army (ELN) and Popular Liberation Army
(EPL). Each of these is represented as a separate rebel group-year in the dataset. Using NSA data
on troop number estimates, we estimate the FARC’s troop share at around 74% for 1990, while
the other two groups have 19% and 7% troop shares respectively.
Table 1 presents the ten rebel groups from our dataset that conducted the highest number
of terrorist attacks between 1970 and 2011. During their peak years of terrorist activity, almost
all of these groups commanded more than 75% of all rebel troops available in the battlefield;
most enjoyed complete monopoly over the conflict landscape.14 The observation that the most
14 The Taliban troop share is not calculated due to the presence of at least one other rebel group in the conflict for whom troop data are not available. Nonetheless, the Taliban is well known as a military powerful rebel actor.
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notorious perpetrators of civil wartime terrorism are market-dominant and monopolistic rebel
groups provides preliminary support for our theory. One exception to the observed trend of large
amounts of terrorism being perpetrated by groups with high market status is the ELN in
Colombia, who used terrorist attacks extensively even though its market share was significantly
constrained with FARC’s rapid growth in the early 1980s.
Table 1. Rebel Groups with Highest Levels of Terrorist Activity and Their Market Share During Peak Years
This empirical pattern holds when we examine the distribution of the dependent
variables—terrorist attacks and fatalities—across market shares. Figure 1 below displays the
distribution of the dependent variables by market share, where groups with troop share below 0.5
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are coded as “low share”; groups with troop share 0.5 or greater, but less than 1, are coded as
market “dominators”; and groups with a troop share of 1 are coded as “monopolists.”
Figure 1. Distribution of Dependent Variables by Market Share
4.3 Control Variables
We include a number of control variables that potentially correlate with both terrorism
and rebel capabilities. We control for the regime type of the government party to the civil war—
democracy—using the Polity2 score (Marshall, Jaggers, and Gurr 2013). The literature suggests
that democracies make attractive targets for terrorists due to their citizenry’s low threshold for
civilian casualties (Hultman 2012; Kydd and Walter 2006; Pape 2003) as well as certain regime
attributes, such as respect for civil liberties, which facilitate the planning and execution of
terrorist operations (Eyerman 1998; Li 2005). Regime type might also be related to rebel
capabilities; in democratic countries where counterinsurgency measures are subject to normative
and institutional constraints, rebels can maneuver more easily to recruit and mobilize supporters.
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At the same time, democracies may also offer alternative channels of political expression to the
aggrieved population, thereby hurting rebels’ ability to recruit sympathizers to their rank
(Eyerman 1998; Ross 1993; Schmid 1992).
Another control variable is log-transformed GDP per capita, which captures the level of
economic development in the target country.15 Given that material resource flows are vital for
rebels’ organizational survival, fighting a high-income target country may increase the perceived
effectiveness of particular types of terrorism, such as hostage-taking and kidnapping for ransom,
which provide the rebels a low-cost source of war funding. Operating as a proxy for state
capacity as well as economic well-being, the GDP variable also is plausibly related to rebel
strength. Higher income levels may hurt the rebel ranks; as Fearon and Laitin (2003, 80) argue,
“recruiting young men into the life of a guerilla is easier when the economic alternatives are
worse.”
We also control for factors operating at the conflict and group levels. At the conflict
level, we use a dummy variable for conflict intensity to differentiate between minor conflict
years (25 – 99 battle deaths) and major conflict years (more than 1000 battle deaths). Terrorism
may increase as violence increases across the board. Moreover, conflict intensity and rebel group
strength are likely correlated. Stronger groups are able to mount larger challenges to the
government and may have more ambitious aims, leading to conflicts being fought more intensely
(Buhaug 2006).
At the group level, we control for rebel strength relative to government. We use the
rebel-to-government troop ratio variable, which we create using the NSA troop estimates and
military personnel data from the Correlates of War Project’s National Material Capabilities
15 GDP per capita data are from Gleditsch (2002) and the Penn World Tables (Feenstra, Inklaar, and Timmer 2015).
20
(NMC) dataset (Singer et al. 1972). We also control for foreign support and group ideology.
Rebel groups who receive financial and military assistance from foreign states are not only better
equipped to execute terrorist operations, but also might undertake “drastic, highly visible
campaigns” to signal to their sponsors their commitment to the rebellion (Hovil and Werker
2005, 7). Data for the foreign support variable come from the NSA dataset. For group ideology,
we include a dummy variable, nationalist-separatist, coded by Polo and Gleditsch (2016)
indicating whether the rebel group pursues a nationalist-separatist agenda. Polo and Gleditsch
argue that ethno-nationalist ideals yield a strong sense of othering within the ethnic community
that rebels claim to represent, thereby increasing the legitimacy of terrorist attacks targeting the
out-group(s) associated with the government. Other studies make the opposite prediction,
suggesting that separatists are less likely to use terrorism as they hope to gain legitimacy and
support for statehood in the international arena (Fazal 2017). Ethno-nationalist ideology may
also influence capabilities, particularly in terms of rebel recruitment. Weinstein (2005), for
instance, argues that ethnic linkages and regional identities are social endowments that help rebel
leaders make credible commitments to deliver the future benefits of joining the rebellion.
Finally, we control for the Cold War time period. Kalyvas and Balcells (2010) argue that
superpower support for both proxy state and rebel actors affected their military capacity, leading
to different warfighting strategies, or “technologies of rebellion.” Different modes of warfighting
might also have influenced rebel decisions over tactics. For example, evidence suggests that civil
war terrorism has increased since the end of the Cold War (Enders and Sandler 1999).
4.4 Model Specification
Given that our dependent variable, the number of rebel group terrorist attacks, is an event
count with overdispersion (i.e., observed variance exceeding the mean) and excessive zeros, we
21
use zero-inflated negative binomial (ZINB) models to test our hypotheses. Similar work suggests
ZINB models as the appropriate method for dealing with the abundance of zeros in the data,
which Drakos and Gofas (2006a, 74) call “an intrinsic statistical property of terrorist counts.”16
ZINB models combine a negative binomial count model with a logit model that predicts
excess zeros, assuming that there are two separate data-generating processes for the zero
outcomes (Greene 1994). Essentially, this approach distinguishes between “at-risk” groups who
have some probability of using terrorism, and “certain-zero” groups who lack the incentives or
means for terrorism due to structural factors and thus never employ the tactic.17 Of course, it is
debatable whether any groups have a truly zero probability of using terrorism since they could
theoretically adopt the tactic at any time. The highly skewed distribution of our attack count
variable as well as the results from Vuong tests (Vuong 1989) suggest that the ZINB model is
appropriate, however.
We use separate sets of covariates in the inflation and count equations, because there are
theoretical reasons to believe that different variables affect the likelihood of any terrorism
occurring versus the amount of terrorism that occurs. Our approach is informed by Li’s (2005,
293) objection to employing the same variables in both parts, which “implies that the data
generation process is one and the same, apparently inconsistent with the rationale for using the
zero-inflated estimator.” In fact, our data show significant overlap between authoritarian settings
and certain-zero outcomes: 82% of the rebel groups in our dataset who have never used terrorism
operate in a non-democratic country.18 Our data also suggest that rebel strength relative to the
16 Similar studies using this method include Piazza (2011); Findley and Young (2012b); and Santifort-Jordan and Sandler (2014). 17 The intuition behind this distinction is that the set of covariates generating a zero count for at-risk groups is different from those that govern the zero-always process (Drakos and Gofas 2006a). 18 Drakos and Gofas (2006b) attribute this overlap to two potential factors: (i) non-democratic regimes’ tendency to underreport terrorist activities and (ii) structural factors that make authoritarian regimes less
22
government is a potential predictor of excessive zeros. For the handful of observations where
rebel forces match or outnumber government forces, terrorist activities is near zero for all groups
except for Sierra Leone’s Revolutionary United Front (RUF) and Somalia’s Al-Shabaab.
Accordingly, we include a measure of democracy and a measure of rebel strength relative to the
government in the inflated portion of the models.
5 Results
Table 2 presents the results. In all of our models, robust standard errors are clustered by
rebel group to allow for intragroup correlation of the error terms. Model 1 tests the “market
share” hypothesis using a count of terrorist attacks by rebel group. The coefficient for our main
explanatory variable, rebel troop share, is positive and statistically significant at the 0.001 level,
providing strong support for our hypothesis. The finding demonstrates that rebel groups who
possess a larger share of all troops available in the conflict execute a higher number of terrorist
attacks. The 1.604 coefficient on troop share corresponds to an incidence rate ratio of 4.97; this
means that the incidence rate of terrorist attacks for a monopolistic group (i.e., troop share=1) is
around 5 times that of a group with near-zero troop share. A group that possesses 20% market
share has an incidence rate of 1.38, whereas a market-dominant group with 80% troop share has
a rate of 3.61.
susceptible to terrorism. They also underline that, while the ZINB model addresses the issue of excessive zeros in a “methodologically progressive” way, it cannot disentangle between these two zero-generating factors.
23
Table 2. Zero-Inflated Negative Binomial Models of the Effect of Rebel Market Share on
Terrorism
In Model 2, we use the terror fatalities count as the dependent variable. Similar to Model
1, the coefficient for rebel troop share is significant at the 0.001 level. Our analysis of incidence
rate ratios shows that the rate of fatalities from terrorist attacks perpetrated by a monopolistic
group is 2.68 times that associated with a militarily powerless group with near zero market share.
24
In line with our theory, both findings from Models 1 and 2 suggest increasingly more extensive
use of terrorism by rebel groups who establish military dominance in the conflict landscape.
Figure 2 displays the predicted number of attacks and fatalities at different values of rebel
market share.19 For rebels who are substantially weaker than their competitors and command
only a minuscule share of troops in the conflict market, the predicted amount of terrorist activity
is around 2-3 attacks and 7-8 fatalities. In Figure 2a, as we move along the x-axis towards the
50% threshold (the range above which hosts market dominators and monopolists), the predicted
number of terrorist attacks by rebel group more than doubles. Similarly, as displayed in Figure
2b, the predicted number of fatalities for rebel groups with 50% market share is significantly
larger than the predictions associated with groups that have a low market share. Within the %90-
100 range for rebel troop share which corresponds to groups with the highest market power, the
predicted attack count more than quadruples (>10 attacks) and the fatalities count more than
doubles (>20 fatalities) the amount of terrorist activity associated with relatively weaker rebel
groups with troop shares in the %0-10 range.
Figure 2: Predicted Number of Attacks and Fatalities by Rebel Market Share
19 Predicted attacks and fatalities are calculated with all controls held at their median values.
25
The models also illustrate that that the negative coefficient for rebel-to-government troop
ratio fails to achieve statistical significance (p-values: 0.07 in Model 1; 0.22 in Model 2). This
finding suggests that the “weapon of the weak” thesis holds limited explanatory power when
applied to terrorism in the context of civil war. In line with our expectations, rebel strength
relative to potential competitors appears to be a much stronger predictor of civil wartime
terrorism than rebel strength relative to government. The other control variables behave largely
as predicted. Rebel groups involved in conflicts with more intensive fighting, as well as those
operating in democratic countries, engage in higher levels of terrorist activity, although the
democracy variable is statistically significant only in Model 1. On the other hand, rebel groups
with nationalist-separatist ideology carry out significantly fewer attacks. The coefficients for
GDP per capita, foreign support and post-Cold War variables fail to achieve statistical
significance in either model.
In the inflated models, the polity measure has a negative and statistically significant
coefficient, which suggests that rebel groups operating in countries with higher levels of
democracy are much less likely to be in the certain-zero category. This is in line with our
observation that regime type predicts excessive zeros, with the majority of non-terrorist rebels
operating under authoritarian regimes. While the positive coefficient of the rebel strength
variable is consistent with the literature’s prediction that exceptionally powerful rebels are more
likely to be in the certain-zero group, the variable does not achieve statistical significance. These
two findings confirm our intuition that, while several factors influence the amount of terrorist
activity carried out by rebel groups, rebels’ strategic decision to employ or eschew the tactic
altogether is largely determined by structural factors such as regime type.
Our findings about the relationship between rebel market share and terrorism are robust
26
to a variety of alternative measurement and model specifications, including more restrictive
measures of terror attacks and fatalities and estimations with fixed-effects negative binomial
regression. Due to space constraints, the results are presented in the Appendix.
5.1 Effects of Market Share in Multi-Party Conflicts
Roughly two-thirds of the observations in our data set represent monopolized conflict
markets, where the rebel group is fighting the government in the absence of any competition.
While we expect monopolistic groups to produce high amounts of terrorism in civil wars, our
theory offers similar predictions about stronger rebel groups operating in multi-actor conflicts,
who possess the capabilities for proliferate terrorist activity while also having incentives to use
terrorism as a means of safeguarding their market-dominant position vis-à-vis competitors. In
order to confirm that our findings also hold for conflicts that resemble competitive markets, we
examine the variation in terrorist activity across rebel groups involved in multi-party civil wars.
Table 3 presents models estimated using a subsample from our dataset that only includes
observations where a rebel group has at least one competitor in the given year. Model 3 looks at
the effect of rebel market share on terror attacks; Model 4 re-estimates the same model using
terror fatalities as the dependent variable. Similar to our earlier estimations with the full sample,
the troop share variable has a statistically significant and positive effect on the number of
terrorist attacks by rebel group. The incidence rate of attacks for market-dominant rebels with
80% troop share is 1.86 times that of groups commanding 20% market share; the ratio from the
same comparison is 2.03 when we look at the incidence rates of terror fatalities. These findings
confirm our expectation: in competitive conflict markets, the rebel organizations that are stronger
than their potential rivals tend to produce larger amounts of terrorism.
27
Table 3. ZINB Models of Rebel Terrorist Activity in Competitive Conflict Markets
Our closer examination of multi-party civil wars provides the additional insight that,
when studying terrorist activity at the group level, we need to consider how competitive
dynamics may play out differently for rebel organizations depending on their power position
relative to potential competitors. As such, explanatory variables that only focus on the number of
28
rebel groups in the conflict, commonly employed in empirical analyses of terrorist outbidding,
fail to capture the difference between the strong and weak actors in terms of their military
capabilities and perception of threats posed by rival groups, thereby making only loose proxies
for inter-group competition.
6 Conclusion
In this article, we advance a market theory of civil wartime terrorism that highlights the
relationship between rebels’ military strength relative to other armed groups and their use of
terrorist tactics. Our main proposition is that rebel groups who control a larger portion of
resources in the conflict landscape will employ terrorist attacks more extensively, with increased
market share driving terrorism through a mix of capabilities and strategic incentives. As rebel
groups secure a larger market share in the conflict, they become more capable of carrying out
widespread terrorist attacks and more acceptant of risks associated with the tactic, such as
legitimacy costs. In addition, groups who obtain dominant market shares become incentivized to
use terrorism to maintain their position vis-à-vis potential competitors. We test our argument
using a new measure of rebel troop share on all rebel groups in civil wars between 1970 and
2011. Our hypothesis finds strong support—groups possessing larger shares of military
capabilities perpetrate significantly more terrorism, both in terms of attacks and fatalities.
Our findings diverge from much of the current literature on the determinants of civil war
terrorism. First, we add nuance to the widespread belief that multi-party conflicts are inherently
more likely to produce terrorism. What matters is the distribution of power within the rebel
conflict system, not the number of actors per se. A multi-party conflict where one rebel group
controls a dominant share of the fighting capabilities is more likely to experience terrorism than
a conflict with the same number of groups but a more equal distribution of power.
29
This insight has important implications for the literature that models conflict as an
economic process. Most of this scholarship assumes that conflict inverts traditional economic
logic, with competition, not monopoly, leading to worse outcomes for civilians (e.g., Metelits
2010; Wood and Kathman 2015). We find instead that conflict markets operate much like
economic ones insomuch as the presence of dominant actors drives inefficiencies (in our
interpretation, violence against civilians in the form of terrorism). This suggests the need for a
more nuanced understanding of the effects of competition in the context of civil war.
We also contribute to the literature by proposing a new measure of rebel market share,
which we operationalize by measuring each group’s troop number as a proportion of the total
rebel fighting force within the same conflict. The market share variable provides important
information about a rebel group’s military standing in a conflict, which cannot be captured in
standard comparisons of strength between rebel groups and the target government. Our approach,
somewhat paradoxically, also makes rebel-to-government measures more informative. For
example, imagine two rebel groups, A and B, with similar troop numbers in two separate
conflicts, each coded as “weaker” than the government. Group A is the smallest of several rebel
actors involved in the conflict and, according to our market theoretical logic, will commit
comparatively little terrorism. Group B, on the other hand, is the sole non-state actor and thus
commands all of the available resources in the conflict market. We demonstrate empirically that
Group B poses a greater threat for extensive and lethal use of terrorism, controlling for other
factors. This insight also has implications for counter-terrorism policies. In particular, it suggests
that relative strength between groups is a better indicator for assessing the threat of terrorism
than relative strength vis-à-vis the government.
30
Finally, future research should focus on developing even more accurate measures of rebel
group competition, taking into account not only military capabilities, but also other aspects of
intergroup interaction such as geographical overlap and clashes over territory. An important area
for inquiry is how shifts in capabilities drive changes over time. Evidence from the civilian
targeting literature suggests that declines in relative capabilities, induced by new market entrants,
may lead to increased brutality (Wood and Kathman 2015); whether this logic holds in the
context of terrorism is an important empirical question to investigate.
31
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Appendix for “Weapon of the Market-Dominant: A Market Theory of
Terrorism in Civil War”
I Constructing the Dataset For this project, we constructed a dataset that matches rebel groups in the Non-State
Actor (NSA) dataset (Cunningham, Gleditsch, and Salehyan 2009; 2013) with terrorist
organizations identified in the Global Terrorism Database (GTD) (LaFree and Dugan 2007).
Using the NSA dataset, which expands on the Uppsala-PRIO Armed Conflict Dataset (ACD)
(Melander, Pettersson, and Themner, 2016), allowed us to gather more information on rebel
group characteristics such as military capabilities and organizational structure.20
Matching NSA to GTD
To build our dataset, we expanded NSA’s state-rebel group dyad spells into panel data
rather than using the ACD state-rebel group dyad years. We did so to avoid losing data on rebel
group behavior for years during which a group was active but missing from the dataset due to its
battle death numbers falling under the civil war threshold. The ACD excludes any year in which
the level of conflict falls below 25 battle deaths for each state-rebel group dyad, regardless of
whether the rebel organization involved in that conflict remains operational in the given year,
whereas the NSA dyad spells end only “if there is a period exceeding two calendar years in
which the level of conflict falls below 25 battle deaths in a year” (Cunningham, Gleditsch and
Saleyhan 2013, 519).
The ACD’s restrictive inclusion criterion may introduce selection bias when modeling
the relationship between rebel capabilities and use of terrorism, particularly if intermittent drops
in level of violence are correlated with both variables. For ongoing armed conflicts, excluding
years with low-level or no violence from analyses could mean losing information on how rebel
tactics respond to exogenous shifts in conflict intensity (e.g., the government announces a
ceasefire) or to sudden changes in the distribution of military capabilities among conflict parties.
20 Our approach is similar to Polo and Gleditsch’s (2016) ACD2GTD dataset, which matches GTD perpetrators and incidents with rebel groups in the ACD.
37
We attempted to overcome these problems by matching the GTD data with NSA dyad spells,
which cover intermittent periods of low-level violence as well as high-intensity conflict years.
Matching Group Names
Within the GTD dataset, many attacks are either unattributed to a specific group—i.e.,
the group name variable (gname) is “unknown” —or attributed to a group identified only by a
vague descriptor (e.g., “Kachin insurgents”). In a limited number of situations, where a vague
group name clearly matched only one group in the conflict, we added those attacks to the group’s
total. In other situations, the GTD group name was not a perfect match, but clearly referred to a
specific group. These situations include cases where one group has two different naming
conventions in the literature or cases where one data source used the group’s full name while the
other used the acronym. The following chart shows which vague identifiers or slightly
mismatched names in GTD (gname) were matched with groups in NSA (side b).
Country GTD gname NSA side b
Myanmar Kachin Insurgents Kachin Independence Army
KIO
Myanmar Shan State Army-North SSPP
Nicaragua Contras FDN
FDN/Contras
Niger Air Azawak Liberation Front FLAA
Rhodesia ZANU ZAPU Zimbabwe Patriotic Front
ZANU-PF (only matched for the period after 1976)
Russia Chechen Rebels Republic of Chechnya
Rwanda Hutus Opposition alliance
Somalia Islamic Courts Union (ICU) ARS/UIC
Soviet Union Armenian Extremists Armenian Guerrillas Armenian Nationalist Armenian Militants
Government of Armenia and ANM
38
South Sudan South Sudan Liberation Army (SSLA)
SSLM/A
Sudan Sudan People’s Liberation Army (SPLA)
SPLM
Thailand Muslim Separatists Islamic Extremists
Patani Insurgents
Uganda NRM UPM/NRA
Yugoslavia Kosovo Liberation Army (KLA)
UCK
A number of extremely vague descriptors were unable to be matched back to specific
groups in NSA. We delete the following gnames from both the restricted and expanded versions:
● Other
● Unaffiliated Individual(s)
● Miscreants
● Narco-Terrorists
● Youths
● Villagers
● Unemployed Persons
● Terrorists
● Taxi Drivers
● Students
● Strikers
● Strike Enforcers
● Squatters
● Rioters
● Protesters
● Political Group
● Political Activists
● Opposition Group
39
● Mob
● Individual
● Gunmen
● Armed People
● Anti-Government Group
● Anti-Government Demonstrators
GTD and the Missing 1993 Data
As reported in Lafree and Dugan (2007, 186), GTD is missing data from the year 1993.
During the process of physically transferring PGIS—the data source off which GTD was built—
to the University of Maryland, the authors discovered that the data for 1993 had been lost in an
earlier move by the PGIS team. GTD2 attempts to re-collect this missing data, but the collection
methods vary from the rest of the original data set. Because there is no way to rectify this (and
because we can reasonably treat the data as missing at random), we follow the standard
procedure in the terrorism literature of simply using the GTD data as-is.
Handling the Weakest Groups
Some rebel groups in states experiencing a civil war may be too weak to appear in our
dataset, given the NSA battle death inclusion threshold. In this case, the group would have been
eliminated from our dataset, even if they were recorded in GTD as having committed terrorist
attacks which met our inclusion criteria. This exclusion of the smallest and weakest groups is
standard in the literature on terrorism within the context of civil war (Fortna 2015, 531). We do
not believe that excluding these groups would significantly change our results.
II Data and Coding Procedures Independent Variable: Rebel Troop Share
Data for our rebel troop share variable come from the NSA data, which provides
estimates of rebel organizations’ armed forces. For each state-rebel group dyad year, we first
identify all other rebel groups that were party to the same conflict in a given year, using the ACD
conflict identifier. We then use the NSA estimates of rebel forces to calculate the proportion of
troops that each rebel group controls relative to all the rebel troops active in the conflict year.
40
Specifically, we use the NSA rebestimate variable, which provides “the best estimate of the size
of rebel armed forces” (Cunningham, Gleditsch, and Salehyan 2012, 4). Because we expand the
NSA data from state-rebel dyad spells into panel data, we impute the troop information, which is
provided at the spell level, to the corresponding panel years.
Dependent Variable: Terror Attacks and Fatalities
The data for our dependent variables—Terror Attacks and Terror Fatalities—are from
the GTD, which contains global information on domestic and transnational terrorist events from
1970 to present. In order for the GTD to include an incident, it must be: (1) intentional, (2)
violent, or have the threat of violence, and (3) perpetrated by a non-state actor. Additionally, an
incident must satisfy two of three criteria (LaFree and Dugan 2007, 188):
(1) The act must be aimed at attaining a political, economic, religious, or social goal. In terms of economic goals, the exclusive pursuit of profit does not satisfy this criterion. (2) There must be evidence of an intention to coerce, intimidate, or convey some other message to a larger audience (or audiences) than the immediate victims. And (3) the action must be outside the context of legitimate warfare activities; that is, the act must be outside the parameters permitted by international humanitarian law (particularly the admonition against deliberately targeting civilians or noncombatants.
Our sample only includes incidents that meet all three criteria in an effort to exclude as many
other forms of violence as possible.
The GTD allows researchers to apply additional filters, including attack and target. For
our primary specifications of the dependent variable, we filter the dataset to include only the
following attack types: hijacking, hostage taking (kidnapping and barricade incidents), armed
assault, bombing/explosion, and facility/infrastructure attacks. This excludes three attack types:
assassination, unarmed assault, and unknown, following Fortna (2017, 12). In particular,
assassinations do not meet our criteria for deliberately indiscriminate attacks, given that they are
the targeted killing of a single politically-relevant individual.
We likewise filter targets to include only the following target types: airports and aircraft,
business, educational institution, food or water supply, private citizens and property, religious
figures/institutions, telecommunication, tourists, transportation, utilities, and unknown. We
exclude target types that are unlikely to be truly indiscriminate, such as journalists and NGOs.
Although these groups are often reprehensibly targeted for violence in conflict zones, we argue
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that these attacks tend to be selective and thus not terrorism as defined here. We also exclude
governmental and military targets. Rebel groups almost always are weaker than the government,
which means that by default they use asymmetric means. To mitigate this potential bias, which
would over-represent the use of terrorism, we only count attacks if they are directed against
civilian targets.
III Descriptive Statistics Table 4. Summary Statistics
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Figure 3. Distribution of Single and Multi-Party Conflict Years
Figure 4. Distribution of Rebel Group Troop Share
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Figure 5. Distribution of Rebel Group Market Share
Table 5. Control Variables
Variable Operationalization Sources Rebel-to-government Troop Ratio
Ratio of rebel to government troops
NSA dataset and the Correlates of War National Material Capabilities (NMC) dataset (Singer et al. 1972)
Democracy
Polity2 variable, which ranges from -10 to 10
Polity IV dataset (Marshall, Jaggers, and Gurr 2013)
Logged GDP per capita
Logarithmized Gross Domestic Product per capita in constant 2005 US$
Gleditsch (2002) and the Penn World Tables (Feenstra, Inklaar, and Timmer, 2015)
Conflict Intensity
Binary indicator that takes a value of 2 in major conflict years (> battle deaths) and 1 in minor conflict years (25-99 battle deaths)
Uppsala-PRIO Armed Conflict Dataset (ACD) (Melander, Pettersson, and Themner, 2016)
Foreign Support
Dummy variable that takes a value of 1 if a group receives NSA dataset
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financial or military assistance from foreign states
Nationalist-Separatist
Dummy variable that takes a value of 1 if a rebel group has a nationalist-separatist agenda
Polo and Gleditsch (2016)
Post Cold War
Dummy variable that takes a value of 1 for years > 1989
Number of Competitors
Count of the number of other rebel groups active within the same conflict-year (used in multi-party models)
IV Robustness Checks
We run additional statistical analyses to demonstrate that our findings are robust to
different operationalizations of the dependent variable and alternative model specifications.
Models 5 through 15 present the results from the robustness checks. First, to ensure that our
results do not rely on the particular coding of terrorism, we re-estimate the models employing
different operationalizations of terrorist activity. The first one is an alternative count variable for
terror attacks, which is coded using a more restrictive definition of terrorism. Our restricted
count limits attack types to the two most frequent modes of attack: armed assault and
bombing/explosion. We keep the target types, but exclude any unknown targets from this coding.
The other variable, employed in Model 6, is the restrictive attack count from Polo and Gleditsch
(2016).21 The results, presented in Table 6 and displayed in Figure 6, are largely consistent with
21 Polo and Gleditsch (2016, 821): “Since definitions of terrorism are disputed, we use two operationalizations with different event inclusion criteria. The most inclusive is the GTD definition covering all events that satisfy at least two of the criteria in the GTD codebook…. A more restrictive version requires that events satisfy all three criteria, and excludes all attacks against military targets, even if classified by the GTD as falling outside guerrilla warfare.”
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the original findings and support our rebel market share hypothesis.
Next, we examine whether our findings hold with alternative model specifications. First,
we estimate negative binomial regression models, using both the extensive and restrictive
measures of terror attacks and fatalities. Next, we specify fixed-effect negative binomial (FENB)
models, which account for the time-invariant, unobserved group-specific effects. We estimate
these using rebel group dummies (i.e., unconditional FENB) instead of the conditional FENB
method proposed by Hausman et al. (1984), which has been shown to fall short of removing
individual fixed effects in count panel data (see Allison and Waterman 2002, Greene 2005;
Guimarães 2008). The results from negative binomial (Models 7-10) and unconditional FENB
models (Models 11-14) mirror our original findings; in all models, the coefficients for rebel
troop share are statistically significant. One notable difference from original findings is that, in
some of the models where the DV is terror attacks, the rebel-to-government troop ratio variable
has a negative and statistically significant effect on rebel group terrorism. This suggests some
evidence in favor of the traditional weapon of the weak hypothesis, although the finding is not
too robust and the market share variable still appears as the stronger predictor of terrorist attacks.
Finally, in Model 15, we include a quadratic term in the ZINB model of terror attacks to
check whether is a non-linear, U-shaped relationship between rebel market share and terrorism.
The results, displayed in Figure 7, do not indicate the presence of a curvilinear effect.
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Table 6. Re-estimating ZINB Models with Alternative Measures of Rebel Group Terrorism
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Figure 6: Predicted Number of Attacks and Fatalities by Rebel Market Share (Using Alternative Measures of Terrorist Activity by Rebel Group)
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Table 7. Negative Binomial Regressions of the Effect of Rebel Market Share on Terrorist Activity
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Table 8. Unconditional Fixed-Effects Negative Binomial Regression of the Effect of Rebel Market Share on Terrorist Activity
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Table 9. ZINB Model Examining the Presence of a Non-monotonic Relationship Between Rebel Market Share and Terrorism
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Figure 7: Predicted Number of Attacks by Rebel Market Share (ZINB Model with Quadratic Term)
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http://privatewww.essex.ac.uk/~ksg/eacd.html. ———. 2013. “Non-State Actors in Civil Wars: A New Dataset.” Accessible online at:
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