Diversionary Rebel Violence in Territorial Civil War

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Diversionary Rebel Violence in Territorial Civil War

HE L G E HO LT E R M A N N

University of Oslo

Much of the violence carried out by rebels seeking secession or territorial autonomy occurs within the area under dispute.

Still, territory-seeking rebels sometimes attack civilians in other parts of the country. By developing a diversionary theory of violence, this article helps explain why and when they do so. Rebels in territorial disputes aim to keep the government’s forces out of their claimed homeland. Attacking civilians outside of the disputed area may help achieve this aim because it pushes the government to disperse its forces and commit resources to protection. Incentives for such diversionary violence are likely to prove particularly high during military offensives, when the government seeks to concentrate its forces in the contested area. I first assess the theory through a quantitative analysis of territorial conflicts worldwide between 1989 and 2015. Second, I conduct a case study of the Sri Lankan Eelam Wars, combining process-tracing and a quantitative test using new events data.

I find that rebels do tend to escalate violence outside their claimed territory during government offensives and that diversion is an important causal mechanism.

Introduction

Rebel groups that fight for secession or autonomy usually concentrate their armed activity within the territory that they claim. By targeting local security forces and their civilian collaborators, they may strengthen their local control and thereby advance toward their political goals (Kalyvas 2006, 216–18). Yet territory-seeking rebel groups sometimes also use violence in other parts of the country. The Tamil Tigers, for instance, carried out deadly suicide bombings in the capital and raided Sinhalese and Muslim villages in various parts of Sri Lanka (Sri Lanka Ministry of Defence 2011, 96–113). Similarly, the Kurdistan Workers’

Party (PKK) has struck civilians through a number of bombings outside Kurdish-dominated areas of Turkey (Stanton 2013, 1018). Why and when do rebels take such actions?

Scholars give little attention to these questions. Much of the relevant literature focuses on where violence occurs within a country (Balcells 2010; Hirose, Imai, and Lyall 2017), which armed groups use violence (Weinstein 2007;

Stanton 2013), and which conflicts see the most violence (Heger and Salehyan 2007;Balcells and Kalyvas 2014). Less research looks at when violence occurs during civil war.

Within this smaller literature, however, several studies point to the importance of military dynamics for understanding rebel violence against civilians (for example,Kalyvas 2006;

Biddle, Friedman, and Shapiro 2012). One notable finding is that rebel violence tends to increase when combat is intense and rebel losses high. Scholars suggest two local-level explanations: losses may push rebels (1) to predate on civilians (Wood 2014a) or (2) to substitute punitive tactics of terror for military tactics (Hultman 2007,2012). However, previous research fails to analyze whether combat in one place may also cause violence in another. This question matters because rebels, as strategic actors, likely use violence not onlyin responseto combat losses, but alsoto preventthem.

Helge Holtermann is a postdoctoral fellow in the Department of Political Sci- ence at the University of Oslo.

Author’s note:This work was supported by the Norwegian Research Council (230412/F10). I am grateful to Mohamed Fawas and Margaret McWeeney for essential research assistance. I also thank Carl Henrik Knutsen, Indra de Soysa, Tatjana Stankovic, Håvard Strand, the participants of the political science sem- inar at the University of Oslo, the editors, and three anonymous reviewers for helpful suggestions. This work was supported by the Norwegian Research Coun- cil (230412/F10).

Building on this idea, I develop a diversionarytheory of when rebels use violence outside of the disputed territory. I argue that rebels in territorial conflicts will seek to keep the government’s forces out of the areas they claim. Attacking civilians outside this territory (“out of area”) can serve this purpose because it pushes the government to scatter its forces to protect civilian targets. However, diversionary violence also entails costs; it might anger foreign powers and increase the government’s resolve. Thus, rebels will likely reserve this tactic mainly for significant moments in the struggle. One of these is during military offensives within the disputed territory and especially those launched by the government.

I assess my theory using a nested research design (Lieberman 2005). In a first step, I conduct a large-N analysis of territorial conflicts between 1989 and 2015 to test whether military offensives generally bring more out-of-area rebel violence. Based on this analysis, I select a typical case for further study: the Eelam Wars (1990–2009), fought between the Tamil Tigers and Sri Lankan governments. The case analysis begins with a quantitative test using measures of greater precision and ends with a process-tracing analysis drawing upon elite interviews. In sum, the evidence suggests that military offensives launched by the government tend to bring more rebel out-of-area violence and that diversion is an important underlying mechanism.

Explaining Rebel Violence Outside the Disputed Area Participants in civil wars use violence against civilians for many reasons. Recent research suggests that violence can be used, among other things, to deter defection among local civilians (Kalyvas 2006; Cunningham, Bakke, and Seymour 2012, 75), eliminate the opponents’ local supporters (Balcells 2010), strengthen group cohesion (Cohen 2013), or abuse or seize resources from locals (Humphreys and Weinstein 2006;Weinstein 2007;Salehyan, Siroky, and Wood 2014;Wood 2014a;Stewart and Liou 2017). These arguments, however, cannot explain why rebel groups sometimes use violence far from their primary areas of operation.

Some possible answers are found in the terrorism literature. One prominent theory holds that rebels use violence to demonstrate their “power to hurt” and thereby coerce the government into making political concessions

Holtermann, Helge (2019) Diversionary Rebel Violence in Territorial Civil War.International Studies Quarterly, doi: 10.1093/isq/sqz007

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(Pape 2003;Hultman 2007;Stanton 2013; Thomas 2014).

Although the argument has intuitive appeal, it is disputed whether violence against civilians is effective in eliciting concessions.Thomas (2014)finds that groups launching more successful terror attacks received more government concessions, but she does not distinguish attacks on civilians from attacks on armed forces.Abrahms (2012)andFortna (2015), in contrast, make this distinction and find evidence suggesting that reliance on civilian targeting reduced both the chance of concessions and the chance of a negotiated settlement. One reason might be that terror can have an ad- verse effect: governments may perceive it as a sign that the rebels are unreliable extremists who cannot be negotiated with.

Another limitation of most coercion arguments is that they do not specifywhenwe would expect rebels to attack civilians. One exception is thesubstitutionargument, which holds that rebels shift their efforts from military tactics to terror when suffering major combat losses (Hultman 2007, 2012). The underlying logic is that groups too weak for com- bating the opponent’s forces turn to killing his civilian supporters to keep inflicting costs (Bueno de Mesquita 2013).

Empirically, there seems to be no strong empirical relationship between rebel capabilities and violence against civilians, however (Wood 2014b).¹ It is also not clear that one would expect tactical substitution following combat losses;

losses might convince the losing side that military tactics are futile, but they could also motivate them to build military strength to win future battles.

Another argument holds that rebels use terror toprovoke the government into retaliating in a way that alienates the population (Crenshaw 1981;Blankenship 2018, 287;Kydd and Walter 2006). However, asCarter (2016)shows, terror appears to be less likely than military attacks to spur government retaliation, which suggests that rebels use terror mainly to avoid counterstrikes rather than to induce them.

A more plausible theory holds that wartime violence is employed to weaken the opponent’s civilian support base (Valentino, Huth, and Balch-Lindsay 2004; Downes 2006;

Fjelde and Hultman 2014). By collectively targeting the opponent’s suspected supporters, the belligerent may hope to physically remove them or deter them from upholding their support. While this tactic might work, it could also back- fire andincreasethe targeted group’s support for the counterinsurgency (Abrahms 2006, 64). This ambiguity seems to be reflected in the empirical research, which is inconclu- sive about the effect of collectively targeting suspected civilian opponents (Herreros 2006; Downes 2007;Lyall 2009;

Kocher, Pepinsky, and Kalyvas 2011). Furthermore, leaving aside its plausibility, this theory does not seem to predict whenwe should see rebel violence.

A somewhat related argument is that attacking villages supporting the government may have adeterrent effectby sig- naling to other villages the costs of choosing the wrong side (Hirose, Imai, and Lyall 2017, 50). While this motivation is plausible in conflicts over government, such as the Taliban insurgency in Afghanistan, it is less clear whether it applies to out-of-area violence in territorial conflicts. In territorial conflicts, rebels mainly seek support from peoplewithinthe disputed area, and these people may not look at events outside their area to learn about the costs of collaborating with the government.

Despite their limitations, these theories do point to some plausible motivations for rebel violence outside the disputed area. One motivation is to keep the government’s forces out

1Notably,Polo and Gleditsch (2016)find that rebel weakness is associated with more terror attacks, but not with a prioritization of soft over hard targets.

of the claimed homeland. Violence might help achieve this goal by imposing enough costs on the government that it decides to withdraw its forces (Pape 2003). But it may also further this aim in another way: throughdiversion.

The Logic of Diversion

Diversionary tactics are employed for various reasons. In diversionary theories of war, the motive ispolitical: leaders use force against other states to improve their domestic political prospects (Levy 1989;Miller 1995;Smith 1996). More specif- ically, by launching foreign military adventures, the leader hopes to distract the public’s attention from domestic prob- lems or spark a patriotic rally-around-the-flag effect (Tarar 2006;Tir 2010). Notably, such tactics may not apply only to foreign policy.Tir and Jasinski (2008), for instance, argue that politically motivated diversion also helps explain a government’s use of force against its own ethnic minorities.

Actors may also use diversion to furthermilitaryaims, however. Notably, in military-oriented diversion, the aim is to distract theopponentrather than one’s own public. According toClausewitz ([1832] 2009, 364), for instance, diversion is

“such an incursion into the enemy’s country as draws off a portion of his force from the principal point.” While Clause- witz focused on interstate conflict, military-oriented diversion can more generally be conceived as tactics aimed at diverting the opponent’s military efforts from certain contested areas.

Rebel leaders, like their government counterparts, might use diversion to further political as well as military goals.

However, the latter appears more plausible. One reason is that rebel leaders usually depend less upon broad popular support than leaders of democratic governments do. An- other is that there is only mixed empirical support for theories of politically oriented diversion at the international level (Meernik and Waterman 1996;Chiozza and Goemans 2004;

Fravel 2010).

Some existing work also indicates that military diversion can be a motive for rebels during armed conflict.

Theorists of insurgency, for instance, argue that rebels sometimes launch military attacks to divert and scatter government forces (Jones 2016, 39–40;Mao [1937] 2012). Sim- ilarly,Hirose, Imai, and Lyall (2017, 50), in their study of the Taliban, suggest that insurgents attack progovernment areas partly to increase the government’s protection costs. How- ever, while these works suggest that diversion is relevant for understanding rebel behavior, they do not elaborate upon its logic or discuss its empirical implications. I now turn to these tasks.

Diversionary Rebel Violence

The basic logic of diversionary rebel violence is the following: by attacking civilians outside the disputed area, rebels hope to tie down the state’s forces and render them militarily useless. Such attacks can have a diversionary effect mainly because governments face pressures to protect civilians and particularly theselectorate—those who have a say in choosing political leaders (Bueno de Mesquita, et al. 2005).

When people in the selectorate are attacked, a failure to improve their security could harm the government’s political prospects. To avoid such harm, governments are likely to respond to scattered rebel violence by spreading their forces around the country to protect soft targets, rendering fewer forces available to battle the rebels in the disputed area.²

2Research on government responses to terror during civil war is lacking. How- ever, in a study from the United States,Prante and Bohara (2008)find that

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Diversionary violence may yield benefits in territorial as well as in governmental conflicts. However, it should be especially favorable for rebels with territorial goals. Since their supporters tend to reside mainly in the disputed region, territory-seeking rebels can hardly relocate if the government should assume full control over the region. Moreover, unlike groups seeking central power, their core military ob- jective is not to defeat the government army, but to remove it from their claimed homeland (Pape 2003, 344). Conse- quently, forcing the government to keep an armed unit away from their homeland can be as advantageous as beating that unit in combat.

Diversionary violence can be costly, however. Fighters must be trained, armed, and sent on a mission they may not survive. To be effective militarily, resources spent attacking must be smaller than the state resources diverted from the battlefield, meaning that sometimes only diversionary attacks on easy targets have sufficiently low costs (Clausewitz [1832] 2009, 365). While rebels may prefer to attack armed forces, this may be too demanding, especially far away from their stronghold. In such areas, nonmilitary targets may therefore be the only feasible targets for diversionary action.

But diversionary violence has other downsides. Most obvi- ously, it may cause a public opinion backlash that reduces the group’s access to collaborators, funders, and recruits (Condra and Shapiro 2012;Shapiro 2013, 3). The risk of such backlash is limited for many self-determination groups, however. Unlike revolutionary groups, their supporters are often geographically concentrated, meaning they can more easily avoid harming their own supporters (Stanton 2013;

Toft 2002). A greater concern is that violence may turn external actors with human rights agendas against the rebels, making them withdraw their support, impose sanctions, or start supporting the government (Salehyan, Siroky, and Wood 2014, 9). Finally, violence might increase the government’s and its supporters’ determination to fight and not negotiate with the rebels (Abrahms 2012, 383).

To minimize these negative consequences, rebels are likely to use diversionary violence sparingly, reserving it mainly for important moments in the struggle. One such moment is during military offensives. Offensives are essen- tially aggressive military operations involving a large force over an extended period within a limited geographic area.

Typically, the objectives include weakening the enemy force and capturing territory. To reach these objectives, the offensive actor seeks a local superiority of forces. In con- ventional wars, local superiority is important for breaking through defensive lines or seizing strategically important positions (Biddle 2010, 40–42;Clausewitz [1832] 2009, 322). In irregular wars, by contrast, the rebels may not firmly defend any fixed positions. However, they will likely increase guerrilla attacks against invaders. The government, therefore, may not need a large force to advance, but they need it to protect captured positions and pursue insurgents (US Army 2007, chap. 5.52). Indeed, counterinsurgency theorists typically consider a numerical advantage of government forces important, deeming that they need ten or fifteen troops for each insurgent to win (US Army 2007, chap. 1.67).

In essence, during an offensive, the government wants to concentrate its forces within the contested area, while the rebels seek to prevent them from doing so. As a result, the rebels’ potential gain from diversion increases. The rebels could, however, find reason to employ diversionary

regions with higher estimated terror risk received a disproportionate share of defense funds.

attacks even in less critical situations. By occasionally launching such attacks at unpredictable times, they could hope to push the government to maintain a constant security pres- ence throughout the country, thereby draining its military resources. Consequently, although rebels’ incentives for diversionary attacks should increase during military offensives, we should not expect a perfect association between the two.

We may rather expect the following, probabilistic relationship:

H1:Military offensives in the disputed territory are related to in- creased rebel violence against civilians outside the disputed territory.

The local concentration of forces matters in any offensive. Rebels’ incentives to launch diversionary attacks may therefore increase during their own offensives as well as during government offensives. Nonetheless, diversionary incentives will likely be greater for government offensives than for rebel offensives, for two reasons. First, diversionary violence during government offensives has not only an expected short-term effect of diluting an ongoing offensive but also a long-term effect by making the government weigh the risk of diversionary violence when contemplating future military offensives. Second, the rebels’ own offensives are more controllable and less dangerous to themselves, since they allow them to plan and abort the operation if necessary. Con- sequently, the need for diversionary tactics will likely be less urgent. We should therefore expect the following:

H2: Government offensives in the disputed territory are more strongly related to rebel violence outside the disputed territory than are rebel offensives.

Contingencies

Diversionary violence may not be equally likely in all territorial conflicts, however. Striking outside one’s core area at the right time requires planning and communication among widely dispersed units, which may be unfeasible for highly decentralized groups, where armed units are weakly connected to central leadership. The association between government offensives and rebel violence may therefore be weaker when rebel groups lack a clear central command.

Second, the country’s political system could play a moderating role. Both democratic and autocratic regimes will likely worry about civilian fatalities to some extent. How- ever, autocracies have a smaller selectorate, which plausibly makes it more difficult to target the persons that the government cares about (Bueno de Mesquita et al. 2005, 70).

Furthermore, autocracies tend to restrict the media and are thus better able to prevent civilian casualties from becoming widely known (Chenoweth 2010, 17). Consequently, rebels may have less incentive to engage in diversionary violence when facing autocratic regimes.

Finally, diversion could be less likely in symmetric than in asymmetric conflicts. While guerrillas are typically very concerned with avoiding a concentration of government forces (Jones 2016, 39–40;Mao [1937] 2012, chap. 7), rebels whose army can match the government’s might not worry as much. Consequently, rebels in symmetric conflicts may be less likely to respond to an enemy offensive with diversionary violence.

Global Empirical Analysis

I begin by assessing the first hypothesis in a quantitative analysis with global scope. The primary data source is the Uppsala Conflict Data Program’s Georeferenced Event

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Dataset (UCDP-GED) 5.0, which records incidents of lethal violence in armed conflicts. The dataset encompasses all armed conflicts with at least twenty-five yearly battle-related deaths in the 1989–2015 period, apart from those in Syria (Sundberg and Melander 2013;Croicu and Sundberg 2016).³ I look only at conflicts over a territorial issue, as marked by the UCDP’s incompatibility variable (Pettersson and Wallensteen 2015).⁴Furthermore, since my focus is on temporal variation, I include only conflict dyads with at least fifty combat events between 1989 and 2015. Within these conflicts, only dyad-years with twenty-five or more combatant fatalities are included.⁵

I identify the disputed territory for each conflict using information primarily from the UCDP Dyadic Dataset (Harbom, Melander, and Wallensteen 2008), but also from other sources. An event is coded as occurring within the disputed territory if it takes place in a first-order administrative district partly or fully within the region claimed by the rebels.⁶This approach entails a risk that some events occurring outside the disputed area could be erroneously coded, but this does not cause any conceivable bias in favor of the hypothesis.⁷

I aggregate the event data to the dyad-week.⁸I choose the dyad rather than the province level for two main reasons.

First, countries have different numbers of provinces, meaning that countries with few provinces would be given more weight in the analysis. Second, rebel violence against civilians is a rare event, and using the province level would ag- gravate this problem. I choose the week as the temporal unit because rebels have incentives to employ diversion shortly after an offensive begins to hamper or end it as soon as possible.

Measures and Models

The dependent variable,rebel violence outside the disputed area, is measured using UCDP-GED’s rebel one-sided-violence (OSV) events variable, which captures direct and deliberate civilian killings (Eck and Hultman 2007). Consistent with the argument that rebels should restrict their use of diversion, out-of-area violence is relatively rare: among 19,188 dyad-weeks in the dataset, 273 (1.4 percent) saw one or more such events.⁹At the dyadic level, such events occurred in sixteen of fifty-three conflict dyads. A list of all dyads with

3UCDP defines armed conflict as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least [twenty-five]

battle-related deaths in one calendar year” (Pettersson and Wallensteen 2015, 536).

4Conflicts over central government are excluded mainly because precise and temporally disaggregated data on territorial control do not exist for most conflicts.

5One error in UCDP-GED was corrected: UCDP confirmed that all fatalities in the Ethiopia-EPLF conflict should have been coded as unknown. Consequently, this conflict was excluded.

6More disaggregated administrative units are not used because the bound- aries of the disputed territory are often unclear, and precise geocoding of events is often lacking. Three conflict dyads in Bosnia are excluded because they lack coding for administrative units. Two other dyads, the Islamic State versus Afghanistan and the Islamic State versus Nigeria, are excluded because the rebels did not claim a limited territory within these states.

7Erring in the other direction would be more problematic, since local violent responses to offensives could then be captured by the dependent variable.

8The temporal aggregation of UCDP-GED events ranges from the day to the year. For rebel one-sided violence events, 93 percent are at the daily level and 98.6 percent last less than a week. For events stretching across more than one week, I code an event only in the first week.

9Rebel violence within the disputed area is much more common: it occurred in 11.2 percent of dyad-weeks.

the sum of rebel out-of-area-violence events and years in the dataset is found in Table A1 of the online appendix.

The main explanatory variable, military offensive, should be marked by above-normal combat activity over an extended period. I use three steps to measure this concept.

First, I estimate the normal level of activity by calculating the average weekly number of combatant fatalities in the disputed area up to weekt.¹⁰Next, I code weeks as experienc- ing high-intensity fighting if there were ten or more combatant fatalities in the disputed area and if this level of fatalities was above twice the normal level.¹¹Finally, to capture its extended nature, I code the beginning of a military offensive in the first high-intensity week followed by another high- intensity week, and the end in the first low-intensity week followed by another low-intensity week. In other words, military offensives always stretch across two or more consecutive calendar weeks. The measure is lagged by one week because rebels would need time to plan and execute diversionary actions in response to a new offensive.

To illustrate, consider the PKK-Turkey conflict in the spring of 1998. From March to May, five weeks saw more than twice the normal level of combat activity (around twenty-eight weekly combatant fatalities). However, only two are consecutive, resulting in an offensive being coded only in the weeks spanning April 30 to May 13. Notably, this coded offensive overlaps reasonably well with a Turkish military operation in Southeastern Anatolia (“Operation Murat”) that started on April 23 and lasted less than a month (BBC Monitoring 1998).

Diversion is not the only possible link between military offensives and rebel violence. Rebels could also attack civilians topunish the government for an offensive. While the two mechanisms are hard to distinguish empirically, they have somewhat different implications regarding timing: while diversion would be most effectiveduringan offensive, punishment can be equally effective followingan offensive. Ac- cording to a punishment logic, we would therefore expect rebel violence to increase also after an offensive ends. To test this implication, I employ a dummy forrecent military of- fensive, which takes the value 1 in the four weeks following an offensive.

The substitution and predation arguments, on the other hand, suggest that the effect of military offensives is mediated by rebel combat losses. To assess these accounts, I employ two measures: the proportion of the rebels’ armed force lost in combat in the preceding week (rebel losses)¹²and the number of rebel fatalities minus government fatalities for weeks with a negative fatality balance for the rebels (rebel relative losses).¹³

To assess possible contingencies, I use the following measures: forcentral command,I use the “central control” dummy from the Non-State Actor (NSA) Dataset, meant to capture whether the rebels have a clear central command

10I use fatalities rather than events to capture military activity because offensives often involve concentrated attacks causing many fatalities, but not necessarily many events. For combat events that stretch across more than one day, I distribute the fatalities equally among the days. Since the normal level cannot be estimated with much certainty in the beginning of each time series, I set the first six months to missing.

11An absolute threshold of ten fatalities is added to avoid coding an offensive in the case of only one or a few weekly fatalities when the normal level is very low.

12Annual rebel force numbers were compiled fromUCDP’s (2017)Conflict Encyclopedia, and losses were calculated from UCDP-GED. I use linear interpolation to impute missing rebel force values.

13A positive fatality balance for the rebels is coded as 0.

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Table 1.Negative binomial models of rebel violence outside the disputed area

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Military offensive in disputed area, t–1 3.306^*** 3.157^*** 2.841^*** 1.134 1.465 4.558^**

(0.529) (0.529) (0.498) (0.697) (0.904) (2.289)

Recent military offensive 1.203

(0.303)

Rebel losses, t–1 1.018

(0.0320)

Rebel relative losses, t–1 1.002^*

(0.00102)

Central command 1.409

(0.631)

Military offensive * central command 3.261

(2.073)

Democracy 1.197

(0.448)

Military offensive * democracy 2.443

(1.568)

Asymmetry 1.978

(0.822)

Military offensive * asymmetry 0.730

(0.386) Combat events outside disputed area, t–1 1.336^*** 1.350^*** 1.328^*** 1.512^*** 1.329^*** 1.324^*** 1.327^***

(0.0918) (0.0929) (0.0916) (0.0997) (0.0919) (0.0923) (0.0918)

Government OSV, t–1 0.844 0.734 0.849 0.863 0.840 0.865 0.743

(0.126) (0.137) (0.126) (0.129) (0.125) (0.129) (0.139)

Rebel violence outside disputed area, t–1 1.910^*** 1.888^** 1.900^*** 2.589^*** 1.832^** 1.853^** 1.898^***

(0.363) (0.368) (0.361) (0.477) (0.351) (0.354) (0.368)

Random intercepts Yes Yes Yes Yes Yes Yes Yes

Observations 17744 15170 17744 17744 17276 17744 17046

Dyads 53 50 53 53 49 53 47

BIC 2506.6 2355.0 2511.8 2554.3 2518.0 2522.8 2441.0

Notes: (1) Exponentiated coefficients. (2) Standard errors in parentheses. (3) Statistical significance: *p<0.05, **p<0.01, ***p<0.001.

(Cunningham, Gleditsch, and Salehyan 2013).¹⁴To capture asymmetryI combine data on weaponry, taken fromBalcells and Kalyvas (2014), and data on relative force numbers.¹⁵A conflict is coded as asymmetric if the state employed heavy armor and weaponry whereas the rebels did not, and the government controlled at least five times as many troops as the rebels did. Fordemocracy, I useBoix, Miller, and Rosato’s (2013) dichotomous measure, which codes a country as democratic if its political leaders are chosen through free and fair elections, and a majority of adult men have the right to vote.

I also control for possible confounders. To capture local effects of combat on violence, I include the lagged number ofcombat events outside the disputed area. The lagged number of government one-sided violence events in the country is also included to capture possible revenge dynamics. Finally, to handle temporal autocorrelation, I include a dummy capturing whether there was anyrebel one-sided violence outside the disputed areain the previous week.

The online appendix provides summary statistics for all the variables (Table A2), a correlation matrix (Table A3), and the number of imputed observations for relevant variables (Table A4).

14Since this dataset ends in 2011 and the variable has little temporal variation, I carry the last value in the time series forward to December 2015 for groups outlasting the NSA dataset.

15I take annual government force data from theWorld Bank (2017b) and lin- early impute missing values. For the weaponry indicator, which has little temporal variation, I use linear interpolation and carry the first value in the dyad time series backward and the last value forward in time.

Results

I assess the first hypothesis using a negative binomial estimator, which is well suited because the dependent variable displays overdispersion.¹⁶ To handle heterogeneity among groups, I employ random intercepts.¹⁷While doing so does not fully guard against dyad-level heterogeneity, it has the advantage of including dyads that lack variation on the dependent variable.

Hypothesis 1 is consistently supported, as shown by the results inTable 1. In all main models (1–3), military offensives in the disputed territory are associated with more rebel violence outside the disputed area. The coefficient (shown as an incidence-rate ratio) is large and highly significant across the models and suggests that a military offensive on average is associated with between 2.8 and 3.3 times more rebel out- of-area violence.

In line with the diversion theory, the effect of military offensives does not appear to mainly go through rebel combat losses. The military offensive coefficient decreases only marginally when controlling for rebel losses (Model 2) and decreases by only 14 percent when controlling for rebel relative losses (Model 3). Furthermore, there is only tenuous support for a positive effect of rebel losses: of the two measures, only rebel relative losses is significant at the 5 percent level.

16A likelihood-ratio test of the dispersion parameter following a negative binomial regression rejects that it is 0 (p<0.000).

17A likelihood-ratio test comparing a pooled model with a random-intercepts model rejects similarity (p<0.000).

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Figure 1.Average predictive margins for military offensive conditional on other factors Note: Estimates from Model 5 (left panel), Model 6 (center panel), and Model 7 (right panel).

The results do not offer much support for the punishment mechanism. Model 4 shows that, unlike ongoing military offensives, recent military offensives are not significantly related to more rebel violence outside the disputed area (p=0.46). While this result does not rule out that punishment plays a role, it indicates that punishment is not the most important driver.

In the last three models, I add interaction terms to assess whether the role of military offensives is moderated by dyad- level factors. None of the interaction terms are significant at the 5 percent level, although the central command interaction comes close (p=0.06). Notably, however, the small number of dyads having variation on the dependent variable adds uncertainty to these estimates.

To further explore the possible contingencies, I graph the average predictive margins for military offensive by each of the three factors (Figure 1).¹⁸ The graphs indicate that central command and democracy have a moderating effect in the expected direction. For groups lacking a clear central command, military offensive has no discernible effect, whereas for groups with a central command, it yields almost a fourfold increase in the prediction, from 0.012 to 0.044 weekly out-of-area violent events. Put differently, one would expect one such event for every eight-five weeks without an offensive and for every twenty-three weeks during an offensive. The moderating effect of democracy is also substan- tial: military offensive is associated with a three-and-one-half time increase in rebel violence under democracy, but only a 47 percent increase under autocracy. Asymmetry, on the other hand, has no discernible moderating effect, as shown by the almost parallel lines.

The main finding is robust to various changes in measure- ment and modeling.Figure 2graphs the coefficient and 95

18The random component is set to 0 in the predictions. Linear predictions give broadly similar results.

percent confidence intervals of military offensive for several modifications of Model 1.¹⁹The first three rows show results using alternative measures of military offensive: a less restrictive measure that reduces the absolute combatant fatality threshold to 5 (Model 8), a more restrictive measure that increases the threshold to 20 (Model 9), and a third measure that codes the end of an offensive in the first low-intensity week regardless of the following week’s intensity (Model 10). The military offensive estimate decreases slightly only for the less restrictive measure and is significant by a wide margin for all measures.

The next three rows show results from alternative model specifications: Model 11 includes a control for tropical rainy seasons, since such seasons often entail severe weather that might impede both military operations and rebel violence.²⁰ Model 12 controls for the logged number of rebel and government forces, which might also affect both offensives and violence. Model 13 excludes the lagged dependent variable.

None of these modifications cause any notable changes in the results.

Finally, I address the concern that the random-intercepts model is vulnerable to omitted dyad-level confounders.

Reassuringly, the main estimate hardly changes when using a fixed-effects negative binomial model (Model 14), or when using a fixed-effects Poisson model with robust standard errors, which fully removes concerns about dyad heterogeneity (Model 15).

While the relationship is robust, it could potentially be driven by a single case, since relatively few conflicts see any

19The full results are found in the online appendix, Tables A5 and A6.

20Tropical rainy seasons are operationalized as the three months with highest average rainfall in countries that are at least partly tropical. Rainfall estimates (1991–2012) are taken from theWorld Bank (2017a), and data on tropical climate come from the Köppen-Geiger climate type map inPeel, Finlayson, and McMahon (2007, 1642).

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Figure 2.Military offensive coefficients from alternative models

Notes: Each row shows results from separate models. Model labels refer to changes to Model 1. Whiskers represent 95 percent confidence intervals.

Figure 3.Military offensive coefficients when excluding dyads

Note: Estimates from Model 1. Dyads with at least one incident of rebel violence outside the disputed area shown. Whiskers represent 95 percent confidence intervals.

rebel violence outside the disputed area. To address this concern, I assess how removing each dyad that has variation in the dependent variable from the sample affects the military offensive coefficient (Figure 3). Only one conflict, PKK versus Turkey, has a sizable effect: excluding it makes the coefficient decline by nearly one-third. Nonetheless, the coefficient remains fairly large (2.35) and highly significant (p<0.000). It is also worth noting that the PKK case does not amount to only a few deviant observations. The conflict was active in all twenty-seven years of the sample period and saw the highest number of out-of-area civilian killings and the second-highest number of military offensives. Con-

sequently, this case also provides more insight into the relationship of interest than most other conflicts do.

Conflict duration and combat intensity also help explain why some conflicts do not see any variation in the dependent variable. As Table A7 of the online appendix shows, the conflicts with no rebel violence outside the disputed area tend to be considerably shorter and less intense than the conflicts seeing such violence. This is as expected, given that such violence is generally rare and positively associated with military offensives. Furthermore, autocracies are somewhat overrepresented among conflicts not experienc- ing such violence. Poor media coverage of certain conflicts

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also appears to play a role. One indication is that nearly three-quarters of the cases without any out-of-area rebel violence are coded with no rebel violence against civilians at all, which seems implausibly high. Another is that 39 percent of conflicts lacking variation in the dependent variable occurred in countries with an unfree press, as compared with 13 percent for the rest of the sample.²¹ Altogether, these statistics indicate that the true proportion of conflicts seeing rebel out-of-area violence is higher than what the UCDP- GED data suggest.

To summarize, the analysis suggests that military offensives increase rebel violence outside the disputed region, and especially so for centrally organized rebel groups fighting democratic regimes. The results also weaken the plausibility of other causal mechanisms than diversion. In dis- agreement with the substitution and predation mechanisms, most of the effect of military offensives was not due to rebel losses. Furthermore, I find no support for the punishment implication, that is, that rebel violence should increase following arecentmilitary offensive.

The global analysis has some limitations, however. First, the quality of the event data probably varies among the conflicts in the sample, which could weaken the results’ reliabil- ity. Second, since the UCDP-GED dataset lacks information on the initiator of military attacks, it does not allow for testing the second hypothesis, concerning the differential effects of government and rebel offensives. Finally, the analysis does not provide direct evidence bearing on causal mechanisms. To deal with these limitations, I turn to a mixed- methods case study of the conflict between the Liberation Tigers of Tamil Eelam (LTTE) and the Sri Lankan government.

Evidence from the Eelam Wars

For more than three decades, the LTTE fought for an in- dependent state of Tamil Eelam in the north and east of Sri Lanka. The organization formed amid other militant Tamil groups in the 1970s. The first phase of the conflict, Eelam War I, lasted until mid-1987 and saw several Tamil groups waging guerrilla war against the government, while occasionally fighting each other. This phase ended with the deployment of the Indian Peace Keeping Force, which also became engaged in struggle with the Tigers. In early 1990, the Indian forces withdrew, and fighting soon erupted again between the LTTE and the Sri Lankan government. The next phase, Eelam War II, saw a combination of guerrilla and semiconventional warfare, as the Tigers enhanced their military capacity and achieved hegemony within the Tamil militant movement. Eelam War III began in April 1995, following a short ceasefire, and saw a further move toward con- ventional warfare and an expansion of Tiger-controlled territory. In early 2002, the parties agreed upon an externally monitored ceasefire. The war resumed four years later, however, and in the final phase, Eelam War IV, a tenacious government offensive eventually destroyed the LTTE by the end of May 2009.

My analysis looks at the period from June 1990 to May 2009, which encompasses three of the war’s four phases.

While the UCDP-GED data begin in 1989, I exclude the time up to June 1990, since the LTTE’s main opponent in that period was the Indian Peace Keeping Force.

The Eelam Wars case is apt for theory-testing for two main reasons. First, it is atypical case regarding the relationship

21Unfree press is a dummy coded fromFreedom House’s (2017)“freedom of the press status” indicator.

Figure 4.Military offensive coefficients for the LTTE and the full sample

Note: Estimates from Model 1. Whiskers represent 95 percent confidence intervals.

between military offensives and rebel out-of-area violence (Gerring 2007, 89). As Figure 4shows, the military offensive coefficient in a regression with only the LTTE is reasonably close to the estimate for the rest of the sample and well within its confidence interval, indicating that the causal relationship within this case is representative for the population (Lieberman 2005, 444). Second, the case offers a great amount of temporal variation, which makes a statistical analysis viable. Within the global sample, only one conflict saw more violent events outside the disputed area, and only two conflicts saw a larger number of military offensives.

The Eelam Wars also feature both conditions that the evidence suggested play a moderating role: a functioning rebel central command throughout the conflict and a democratic regime for almost the entire study period.²² This aspect of the case increases the chance that the theory applies to it. Notably, however, a majority of territorial conflicts have similar characteristics. Among the conflicts in the dataset, 55 percent featured rebels with a functioning central command and a democratic regime, 39 percent featured one of these conditions, and only 6 percent featured none of them.

Data and Temporal Patterns

The basis for the quantitative analysis is an expanded version of the UCDP-GED (v. 40) dataset. To distinguish between government and rebel offensives, I code the actor initiating each combat event in the dataset as indicated by the original source articles.²³ It is possible to identify the initiator for more than two-thirds of the events, yielding a total of 1,212 rebel attacks and 1,467 government attacks.²⁴ I then code offensives in the same way as in the global analysis, except that I include only fatalities from government attacks when coding government offensives and only fatalities from rebel attacks when coding rebel offensives.²⁵

Figure 5shows combatant fatalities from government attacks and the coded government offensives in the areas claimed as Tamil Eelam. Where known government military operations overlap with the coded offensives, I add their names.²⁶Reassuringly, almost all the coded offensives match

22Boix, Miller, and Rosato (2013)code Sri Lanka as a democracy since 1991.

23The coding rules are found in Appendix C. A research assistant does parts of the coding, but I double-check every entry. Most of the source articles are found in the Factiva database.

24Violence against military captives and unarmed political officials are not included in this measure.

25When sources report different numbers of combatant fatalities, I use the average of the highest and the lowest estimate. I also improve on the fatality data in other ways (see Appendix C). When calculating the normal level of activity, I exclude ceasefire periods.

26The names of operations are collected fromBalasuriya (2009, 181–88),De Silva (2013, 444–46),SATP (2001), and theSri Lanka Ministry of Defence (2011).

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Figure 5.Government offensives in the disputed area of Sri Lanka

with an announced operation.²⁷ We also see that government offensives were relatively frequent; they occurred in 10 percent of the weeks. All phases saw such offensives, but the longest offensive by far occurred during the final “Hu- manitarian Operation” in Eelam War IV.

The Tamil Tigers’ offensives followed a different pattern (Figure 6). Strikingly, in the war’s last phase, there were no Tiger offensives. Moreover, while the Tigers did launch offensives in earlier phases, most were very brief. Tiger offensives were therefore generally rare, occurring less than 2 percent of the time, which has an interesting implication:

if the Eelam Wars case is typical, government offensives are more common than rebel offensives and therefore have a greater bearing on the general combat-based offensive measure employed in the global analysis.²⁸ Consequently, the positive effect of offensives in that analysis is plausibly driven in large part by government offensives.

Finally,Figure 7describes how rebel violence outside the disputed area varied across time. Noticeably, such violence occurred in all phases of the war, but became more frequent during the government’s final offensive in Eelam War IV.

Quantitative Case Analysis

I test the second hypothesis on the new Eelam Wars data using a similar approach as in the global analysis. The dependent variable is rebel violence against civilians outside

27A systematic validation of the measure using qualitative information is not feasible, though, since the available sources do not clearly define a “military operation” and their lists of operations differ.

28The combat-based offensive measure is correlated r=0.32 with rebel offensives and r=0.64 with government offensives in the Eelam Wars data.

the disputed area, and the unit is the week. Since the dependent variable is overdispersed, I use a negative binomial estimator with robust standard errors.

Thegovernment offensivevariable is lagged, as in the global analysis.Rebel offensive, in contrast, is not lagged, since rebels can plan diversionary actions ahead of launching their own offensives. The control variables are similar to those used in the global analysis, apart from the addition of aceasefire dummy, which marks periods in which one or both parties had declared a ceasefire and there were fewer than three monthly combat events. Summary statistics are found in Table B1 and a correlation matrix in Table B2 of the online appendix.

The results shown inTable 2are consistent with the second hypothesis. As expected, government offensives are associated with more rebel violence outside the contested area. The incidence-rate ratio is highly significant and suggests that rebel violence increases more than threefold during government offensives. In contrast, there is no indication that rebel offensives are associated with more rebel out-of-area violence, as the incident-rate ratio is slightly less than 1. However, the limited number of Tamil Tiger offensives makes this estimate very uncertain. A Wald test of the difference between the government offensives and rebel offensives coefficients therefore does not turn out significant (p = 0.235). In other words, while the coefficients are in line with H2, we cannot confidently dismiss the null hypothesis.²⁹

29The main findings hold when including in the dependent variable rebel out-of-area attacks on military targets. The government offensive coefficient decreases somewhat, though, to 1.92. When looking only at rebel out-of-area attacks

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Figure 6.Rebel offensives in the disputed area of Sri Lanka

Figure 7.Rebel one-sided violence outside the disputed area

As in the global analysis, most of the effect of government offensive does not appear to be mediated by rebel losses. Still, the loss variables appear to have a somewhat

on military targets, the coefficient decreases further and loses significance at the 5 percent level (p=0.11) (Appendix Table B4). In other words, the Eelam Wars data only weakly indicates that the main hypothesis applies also to rebel military attacks.

greater effect in this case. The government offensive coefficient decreases by 18 percent when controlling for rebel losses (Model 17) and by 29 percent when controlling for rebel relative losses (Model 18). Also similar to the results in the global analysis, of the two loss measures, only rebel relative losses is significantly associated with more rebel out- of-area violence.

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Table 2.Negative binomial models of LTTE violence outside the disputed area

Model 16 Model 17 Model 18 Model 19

Government offensive in disputed area, t–1 3.207^*** 2.636^* 2.264^*

(1.094) (0.996) (0.910)

Rebel offensive in disputed area 0.948 0.818 0.863

(0.950) (0.821) (0.917)

Recent government offensive 1.222

(0.597)

Rebel loss, t–1 1.061

(0.0404)

Rebel relative loss, t–1 1.003^*

(0.00169)

Combat events outside disputed area, t–1 1.415 1.467 1.355 1.749^*

(0.311) (0.322) (0.299) (0.451)

Government OSV, t–1 0.900 0.909 0.912 0.797

(0.567) (0.574) (0.575) (0.494)

Ceasefire 0.350^* 0.365 0.376 0.293^*

(0.187) (0.196) (0.201) (0.155)

Rebel violence outside disputed area, t–1 0.705 0.731 0.730 0.920

(0.347) (0.363) (0.364) (0.471)

Observations 961 961 961 961

BIC 493.4 499.1 498.2 495.6

Notes: (1) Exponentiated coefficients. (2) Robust standard errors in parentheses. (3) Statistical significance: *p<0.05, **p<0.01, ***p<0.001.

Next, I assess the punishment logic by addingrecent govern- ment offensive,which takes the value 1 in the four weeks following a government offensive (Model 19). As in the global analysis, this test offers scant support for the punishment story: the incidence-rate ratio is quite close to 1 and far from significant.

The positive estimate for government offensive with- stands the same robustness tests used in the global analysis (Appendix Table B3). Modifying the fatality threshold leads to no changes in the offensive measures, since all coded offensives saw more than twenty combatant fatalities. Setting the end of an offensive to the first low-intensity week regardless of the following week’s intensity also has no discernible impact on the results (Model 20), nor does controlling for rainy season (Model 21). Controlling for rebel and government troops leads to a slight decrease in the government offensive coefficient, but it remains highly significant (p=0.004) (Model 22). Excluding the lagged dependent variable does not have a notable impact (Model 23), nor does the use of a Poisson estimator (Model 24). Across all models, government offensive remains positively associated with rebel out-of-area violence, whereas rebel offensive is not.

In sum, the quantitative evidence from the Eelam Wars bolsters the support for the diversionary violence theory.

Although the infrequency of Tiger offensives makes their estimated effect uncertain, the evidence clearly suggests that government offensives are related to more rebel violence outside the disputed territory.

Process-Tracing Analysis

To further probe whether diversion is driving the covaria- tion between government offensives and rebel violence, I turn to tracing the causal processes using qualitative evidence. Process-tracing is challenging in this case, since the LTTE was a secretive organization and most of its leaders have now perished. The ambition is therefore not to give a detailed account of the relevant causal processes, but to

assess some hypotheses that together constitute a relatively strong test of the diversionary violence theory.

I look at the following three implications of the theory:

First, rebel violence outside the disputed areas should tend to be planned and coordinated by the central organization.

This hypothesis amounts to ahoop test: it is a necessary but not sufficient criterion for accepting the diversionary violence account, since it is consistent also with other strategic motives (Collier 2011, 825).

Second, informants with intimate knowledge of the LTTE should mention diversion as a motive for rebel violence out of area. This is a strongstraw-in-the-wind test: it is nei- ther necessary nor sufficient for accepting the theory, since informants may not know the truth or not tell it (Collier 2011, 825). Nonetheless, such statements by former Tiger leaders would definitely strengthen the plausibility of the argument, especially considering that the LTTE officially denied nearly all violence against civilians (Hoffman and McCormick 2004, 261).

Third, the government should tend to respond to rebel out-of-area violence by increasing efforts to protect civilians in these areas. This is also a hoop test: it is a necessary criterion, since the rebels would otherwise not expect a diversionary effect of violence, but it is not sufficient, since it does not prove that diversion was the rebels’ motive.

In addition, I assess an alternative explanation that is difficult to test quantitatively: the increased out-of-area violence during government offensives in the north and east could potentially be due to civilians elsewhere becoming less pro- tected, thus giving the rebels greateropportunity for violence.

If this were true, we should see that security in the rest of the country tended to deteriorate during government offensives.

To assess these implications, I draw on various types of data. The main source is in-depth interviews with elites on both sides of the conflict. Interviewees include two high-ranking former LTTE leaders: Athmalingam Ravindra (alias Rupan), who held various leadership positions in the political wing throughout the war, and Vinayagamoorthy Muralitharan (alias Karuna Amman), who commanded the

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Batticaloa-Ampara region from 1993 until he defected to the government in 2004. As such, he is the most senior LTTE leader known to be alive today. Both interviewees are well- informed about policy-making at the central level of the organization, as they were in close contact with the LTTE leader, Velupillai Prabhakaran. As part of the former leadership, however, they might have had incentives not to disclose LTTE violations of international humanitarian law. Notably, such incentives would make it more difficult to find support for the diversion theory. I also interviewed two high- ranking Sri Lanka Army officers: Lieutenant General Daya Ratnayake, who had a leading role in the final offensive in the east, and Major General Aruna Perera, who headed counterterror efforts in Colombo in the war’s last phase.³⁰ I complement these interviews with information from news articles, reports, and academic studies.

THEORGANIZATION OFVIOLENCE

Throughout its existence, the LTTE was a centralized organization with power concentrated in the hands of its founder, Velupillai Prabhakaran. He headed both the political and military wings of the organization and made all strategic decisions, often without internal debate.³¹ Attest- ing to his dominance, every LTTE cadre had to swear alle- giance not only to the struggle for Tamil Eelam, but also to Prabhakaran (Richards 2014, 18).

While military commanders had the authority to initiate smaller military actions, larger battles as well as most attacks on nonmilitary targets required Prabhakaran’s approval.

According to Karuna, they had to ask Prabhakaran for permission before executing anyone. While this rule might not always have been followed, Prabhakaran’s oversight was probably strong for operations outside the disputed area, since they had greater potential implications (Richards 2014, 21). Decisions to launch terror attacks around the country, Karuna claimed, were indeed made by Prab- hakaran, in cooperation with the powerful chief of the Tiger Organization Security Intelligence Service (TOSIS), Pottu Amman.³²

TOSIS also played a central role in planning and co- ordinating the so-called “external operations” outside the claimed areas. Its operatives established intelligence net- works in Colombo and other towns, which carried out reconnaissance on targets, facilitated the entry of special forces, and ensured that planned attacks were carried out (Richards 2014, 142; Sri Lanka Ministry of Defence 2011, 22). Attached to TOSIS was the Black Tigers unit, which specialized in suicide attacks against civilian as well as military targets. The Black Tigers all underwent a six-month period of specialized training before returning to their regular units, and their identity was revealed to other Tiger cadres only at the end of their deadly missions (Richards 2014, 25).

These organizational tools ensured that the entire process of external operations, from target selection to execution, was directed from the top. Consequently, Prabhakaran was able to use violence strategically; he could plan attacks that would evoke the desired government response at the right time.

30All interviews were in English. The interview with Karuna Amman was recorded, while I took extensive notes for the other three. As such, not all quotes are word-for-word transcriptions.

31Author’s interview with Karuna Amman, Batticaloa, January 2016.

MOTIVES FORVIOLENCE

Like all rebel groups, the Tamil Tigers employed violence for various reasons. Within the areas claimed for Tamil Eelam, they killed many Tamil political opponents and government collaborators. The main purpose of these killings was probably to gain dominance within the Tamil self- determination movement and control over the population (Marks 2007, 494; Staniland 2012, 17; Swamy 2003, 195).

Targeted killings of individuals were less common outside of their claimed territory, where violence often took the form of armed assaults on Sinhalese and Muslim villagers or bombings of public areas.³³

The assaults on Sinhalese and Muslim villagers occurred within the disputed area as well as outside it, in districts such as Polonnaruwa and Anuradhapura (Sri Lanka Ministry of Defence 2011, 106–16). Some of these assaults plausibly followed a logic of forced removal. The LTTE perceived Sin- halese settlements within the claimed Tamil Eelam as government outposts and therefore sought to remove them (Marks 2007, 494;Wickremesekera 2016, 23). This account was corroborated by Rupan, who for years headed the Tiger administration in Trincomalee, a district with many village massacres. Some Sinhalese villages were strategically placed by the government to disconnect Tamil areas and control vi- tal roads, he argued, and they therefore had to be cleared by force.³⁴

But although forced removal motivated village massacres within the claimed territory, it is not a plausible explanation of massacres elsewhere. The villages attacked in Polon- naruwa and Anuradhapura, for instance, were not located in Tamil-dominated areas, and they could hardly impede movement within the LTTE-controlled territory. According to Karuna, attacks in such places followed a different logic:

the Tiger leadership “wanted to spread the army.” Villages in Anuradhapura were not attacked because of their strategic position, but rather because “Anuradhapura [is] very important for the Sinhala people.”³⁵The Sinhalese would certainly not move from those areas even if attacked; rather, they would ask for the army’s protection. This, in turn, would draw the army’s efforts away from the struggle over Tamil Eelam.

During the war, government spokespersons often gave a similar interpretation. In August 1990, for instance, the Min- ister of Defence, Ranjan Wijeratne, claimed that the LTTE was killing innocent Sinhalese and Muslims in the east to keep security forces tied down in that region and retard the state’s military progress on the northern front (Xinhua 1990). Similarly, when the Tigers attacked civilians in the southeast during the final offensive in the northern Wanni, the Defence Ministry stated, “[t]hese are signs of sheer des- peration by the LTTE to divert military attention from the Wanni” (Reddy 2009).

Other forms of violence outside the disputed region are often argued to follow different logics. Among them are revenge and punishment (Selvadurai and Smith 2013, 556).

Swamy (2003, 257), for instance, argues that the Central Bank bombing in Colombo in January 1996 was Prab- hakaran’s way of avenging the loss of Jaffna a few weeks earlier. Punishment certainly appears to be a plausible motive for this attack. However, in line with my reasoning in the quantitative analysis, this punitive action came in the after- math of an offensive rather than during it.

33According to the UCDP-GED data, 32 percent of one-sided Tiger attacks in the claimed territory caused a single fatality, whereas only 17 percent outside the claimed territory did.

34Author’s interview with Rupan, Trincomalee, January 2016.