Wood 2014 From Loss to Looting

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From Loss to Looting? Battleeld Costs andRebel Incentives for Violence

Reed M. Wood

International Organization / Volume 68 / Issue 04 / September 2014, pp 979 - 999DOI: 10.1017/S0020818314000204, Published online: 13 August 2014

Link to this article: http://journals.cambridge.org/abstract_S0020818314000204

How to cite this article:Reed M. Wood (2014). From Loss to Looting? Battleeld Costs and RebelIncentives for Violence. International Organization, 68, pp 979-999 doi:10.1017/S0020818314000204

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From Loss to Looting? Battlefield Costs andRebel Incentives for ViolenceReed M. Wood

Abstract Research into the causes of civilian abuse during civil conflict hasincreased significantly in recent years, yet the mechanisms responsible for changes inactors’ tactics remain poorly understood. I investigate how the outcomes of discrete con-flict interactions influence subsequent patterns of rebel violence against civilians. Twocompeting logics suggest opposite influences of material loss on violence. A stylizedmodel of rebel-civilian bargaining illustrates how acute resource demands resultingfrom recent severe conflict losses may incentivize insurgent violence and predation. Ialso identify several factors that might condition this relationship. I evaluate hypothesesbased on these expectations by first analyzing the behaviors of the Lord’s ResistanceArmy using subnational conflict data and then analyzing a cross-sectional sample ofpost–Cold War African insurgencies. Results from both the micro- and macrolevel ana-lyses suggest that rising battlefield costs incentivize attacks on civilians in the periodimmediately following the accrual of losses. However, group-level factors such as effec-tive control over territory and the sources of rebel financing condition this relationship.The findings suggest potential benefits from examining the interaction of strategic con-ditions and more static organizational characteristics in explaining temporal and geo-graphic variation in rebel violence.

Shortly after Christmas 2008, the Lord’s Resistance Army (LRA) attacked multiplevillages along the border of the Democratic Republic of Congo (DRC) and Sudan,killing and abducting more than 1,000 people.1 LRA brutality is certainly not uncom-mon, but both the scale and timing of these attacks were largely unanticipated. AsFigure 1 demonstrates, LRA violence varies significantly over time, and the“Christmas Massacres” followed a relative lull in violence during previous years.What explains such surges in violence? More broadly, why do rebels engage inhigh levels of victimization at some moments yet act with relative restraint atothers? The answers to these questions are both normatively and substantively im-portant for scholars and policy-makers interested in ameliorating the human costsof conflict.

I thank Chris Butler, David Cunningham, Mike Findley, Scott Gates, Mala Htun, Kelly Kadera, KendraKoivu, Brian Lai, Mark Ramirez, Sara Mitchell, Andrew Schrank, Bill Stanley, and Cameron Thies fortheir useful feedback on earlier drafts of this manuscript. I would also like to thank the anonymousreviewers and the editor for their detailed suggestions. I am also grateful to the National ScienceFoundation for helping to support this research (SES-0921702).1. Human Rights Watch 2010.

International Organization 68, Fall 2014, pp. 979–999© The IO Foundation, 2014 doi:10.1017/S0020818314000204

Although research on civilian abuse during civil war has increased significantly inrecent years, the mechanisms responsible for changes in actors’ tactics from one timepoint to the next remain poorly understood. Several influential studies link rebel vio-lence (or other behaviors) to groups’ organizational configurations, resource bases, orthe interplay of these factors.2 These arguments highlight the factors that predisposegroups to violence but provide few insights into why victimization occurs at specificmoments and in specific places because group behavior is viewed as a byproduct oflargely fixed group characteristics. By contrast, instrumentalist approaches view civil-ian victimization as one of multiple strategies through which rebel groups achieve theirgoals. Studies adopting this perspective note that civil wars occur within a capriciouslandscape and have identified a number of diverse mechanisms to explain both tem-poral and geographic variations in civilian targeting. Such studies theorize that insur-gents target civilians to attain war resources, impose costs on adversaries and theirsupporters, exert control over territory, and shape local civilians’ behavior.Moreover, researchers link group strategies to factors such as control, popularsupport, power distribution, threat perception, and resource availability.3

The emerging instrumentalist literature increasingly presumes a relationshipbetween conflict interactions—either single events or broader patterns of events—

FIGURE 1. LRA attacks on civilians per month

2. See Hovil and Werker 2005; Humphreys and Weinstein 2006; Weinstein 2007; and Staniland 2012.3. See Balcells 2011; Hultman 2007; Kalyvas 2006; Metelits 2009; and Wood 2010.

980 International Organization

and motives for civilian targeting. To the extent that the outcomes of conflict eventsshape factors such as control, resources, and support, they should also influence rebelviolence strategies. Despite this intuition, few analyses have explicitly investigated theimpact of battlefield wins and losses on patterns of rebel violence.4 Consequently,their impact on subsequent rebel behavior remains unclear. Establishing clear linkagesbetween conflict processes and victimization would contribute to the field’s under-standing of the motives for rebel violence. Moreover, evidence of a systematicrelationship might allow policy-makers, military strategists, and aid workers tobetter anticipate the timing and location of victimization and respond to such tragediesmore effectively.I examine the potential relationship between the outcomes of discrete conflict inter-

actions (for example, the severity of wins and losses) and intentional rebel violenceagainst civilians. Two competing logics suggest opposite influences of loss on vio-lence. I critique these logics and then use a stylized model of rebel-civilian bargainingto illustrate how acute resource demands may incentivize insurgent violence andpredation in the period immediately following significant conflict losses. I evaluatehypotheses based on these expectations by analyzing a single case (the LRA) disag-gregated to the subnational level as well as a cross-sectional sample of post–ColdWarAfrican insurgencies.

Battlefield Outcomes and Civilian Targeting

Two competing logics suggest potentially opposing influences of rebel conflict losseson civilian targeting. On the one hand, heavy conflict costs may suppress rebel vio-lence; on the other, losses may create short-term incentives for civilian victimization.With respect to the former, significant losses may severely weaken insurgents’ mili-tary capacity and constrain the geographic area over which the group can effectivelycarry out military operations. Dwindling capabilities might therefore limit rebels’ability to carry out large-scale attacks against civilian population centers, particularlyif rebels are driven into peripheral areas of the state. This logic appears popular withpolicy-makers, and it serves as the basis for military strategies such as the “surge” inIraq, in which the United States deployed 20,000 additional troops to Iraq in 2007.5

Similarly, rebels may attempt to improve their treatment of civilians to attract newrecruits and to replenish depleted resources following losses. In this case, risingresource demands could theoretically enhance civilians’ bargaining position relativeto rebels, thereby allowing them to leverage better behaviors. If either of these situ-ations obtains, rebel violence might be expected to decline in the wake of major losses.

4. Hultman 2007 and Lyall 2009 represent notable exceptions.5. John McCain and Joe Lieberman “The Surge Worked,”Wall Street Journal (Internet ed.), 10 January

2008.

Battlefield Costs and Rebel Incentives for Violence 981

Although plausible, this perspective overlooks several key facts regarding thenature of irregular conflict. First, as the history of the LRA and events such as therecent wave of extremist bombings in postwar Iraq demonstrate, insurgents requireonly limited military capabilities to inflict significant costs on civilian populations.6

Previous studies also suggest that whereas rebels do vary their war strategies inresponse to changing capabilities and resource access, they tend to employ conven-tional tactics more (rather than less) frequently as capabilities increase.7 Severe ca-pability loss and resource depletion might therefore reduce the risk of rebel attackson well-defended (hard) targets but would not necessarily deter attacks againstunarmed civilians, particularly in areas where government control is weak. Rather,this logic suggests that more vulnerable locations will become increasingly attractivetargets of predation by desperate rebels.It is likewise unclear how losses might lead rebels to seek rapprochement with civil-

ians. Following major losses, rebels’ time horizons often shrink, leading them to prior-itize short-term strategic objectives such as resource acquisition over longer-term goalslike institution building or the provision of services to constituent populations. Facingacute resource demands, rebels often turn to local civilians who typically represent themost proximate source of food, shelter, recruits, and so on. Even where a nominallypositive history of civilian-rebel interactions exist, rebel demands may exceed civilians’willingness to voluntarily comply following losses. This situation leaves rebels tochoose between coercively acquiring resources and foregoing them.Civilians certainly possess some level of agency during civil conflicts, and existing

research suggests that they retain some ability to bargain with rebels over the extent oftheir cooperation.8 However, civilian-insurgent bargaining is largely asymmetricalgiven that rebels retain the outside option of taking resources by force. The likelihoodof violence is therefore largely related to the outcome of rebel-civilian bargaining.Factors that facilitate successful bargaining should reduce the likelihood of victim-ization whereas those that impede it should increase the likelihood of violence.The outcomes of recent conflict events—particularly significant losses—likely

represent an influential factor in this relationship. First, the costs accrued throughconflict interactions determine the severity of the demands rebels impose on civilians.Second, observed gains and losses shape civilians’ expectation regarding the balanceof risks and rewards for supporting rebels. Importantly, civilians’ support oftendeclines following losses and major setbacks because it is often linked to civilians’subjective assessments of the group’s likelihood of success and the promise offuture benefits.9 Furthermore, weakening rebel control over territory and populationsreduces civilian compliance, increases defection rates, and raises the likelihood of

6. See “Car Bombs Kill Scores Across Iraq,” Aljazeera (Internet ed.), 11 August 2013; and “Baghdad Hitby New Wave of Deadly Bomb Attacks,” BBC (Internet ed.), 28 August 2013.7. See Byman 2008; Butler and Gates 2009; Lockyer 2010; and Taber 2002 [1965], 28–29; 145–47.8. See Baines and Paddon 2012; Barter 2012, 554–57; Lyall 2009, 337; Mampilly 2011, 66–67; and

Wood 2003.9. See Wickham-Crowley 1987, 486; Wood 2003, 238–39; and Taber 2002, 54.


civilian targeting.10 Coercive violence and predation are therefore more likely when(and where) rebel demands outpace civilian compliance and civilians fail to accede torebel demands. Without other immediately available resources, rebels become morelikely to resort to predation and coercion in the wake of significant material losses.

H1: Civilian victimization increases following material losses.

Rebel-civilian relations are not strictly determined by resource demands. Factors suchas organizational capacity, the ability to credibly provide benefits to supporters, andthe presence of alternative resource streams likewise influence rebel-civilians inter-actions. These factors also influence which sets of strategies appear most acceptableto a group in the wake of losses and thus structure how the group chooses to respond.Consequently, these largely static characteristics condition the expected relationshipbetween battlefield fortunes and civilian targeting.All else equal, insurgent demands are more easily met when the group enjoys the

support of the local population. But all else is rarely equal during insurgency, andsympathy for rebels does not necessarily insulate civilians from violence. Civilianshave strong incentives to hide their preferences during war, and even sympatheticpopulations are often reluctant to collaborate in the face of uncertainties about waroutcomes and actor credibility.11 Rebels may therefore resort to violence toenforce compliance and extract resources, even among populations that supportinsurgent goals. However, as Kalyvas demonstrates, territorial control increases civil-ian compliance and constrains violence.12 It may also mitigate the influence ofmaterial losses on victimization. When rebels control large swaths of territory theyare able to transfer resources from areas of consolidated control to areas wherecontrol is weakened by recent losses, thereby ameliorating the resource burdensimposed by losses and reducing demands on local civilians. Moreover, even in theface of battlefield losses, control allows rebels to credibly signal their ability toprovide security and other benefits to loyal civilians. Given the virtues territorialcontrol extends to rebels, groups that effectively control territory should be compara-tively better able to successfully bargain with civilians for support, even in the wakeof material losses.

H2: Groups that exercise substantial territorial control are less likely to target civil-ians following material losses.

Previous research suggests that groups who rely on external support or who financethe rebellion using conflict resources are more likely to target civilians.13 These

10. Kalyvas 2006.11. See Kalyvas 2006, 101–3; and Wood 2003.12. Kalyvas 2006.13. See Beardsley and McQuinn 2009; Hovil and Werker 2005; and Weinstein 2007.


resource streams allow rebels to forego more traditional, community-based organiz-ational strategies and contractual bargaining for local support. This contributes tomutual indifference, increased ideological distance, and limited positive interactionsbetween rebels and civilians. Where these conditions exist, civilians are less likely tosupport (or understand) insurgent goals, and insurgents are more likely to engage inviolence and looting.14 Furthermore, the lack of strong community connections andabsence of historical patterns of positive interactions with civilians are likely toexacerbate rebels’ resource dilemma following losses. Although the sale of commod-ities or infusion of additional resources from foreign patrons may eventually permitrebels to replenish resources without relying on civilians, major losses imposeimmediate resource constraints. Insurgents may therefore still face pressures toextract resources from a local population that they have likely neglected or abusedin the past. This situation complicates their ability to successfully bargain overresources and encourages predation.

H3a: Groups with foreign sponsorship are more likely to target civilians followingmaterial losses.

H3b: Groups financed through the sale of conflict resources are more likely to targetcivilians following material losses.

Finally, organizational structures may also influence rebels’ response to losses. Clearcommand-and-control structures as well as strong internal discipline reduce the likeli-hood that troops abuse civilians.15 Because the strength of organizational structuresinfluences the group’s ability to engage cooperatively with local communities,16 weakstructures may impede the group’s ability to successfully bargain when they facesevere resource constraints. Furthermore, following losses, individual members maypursue self-serving, predatory goals—even to the detriment of group goals—simplybecause the leadership in unable to police their behaviors. Consequently, weak organiz-ational structures exacerbate the influence of material loss on civilian victimization.

H4: Groups with weaker organizational structures are more likely to target civiliansfollowing material losses.

Data and Analyses

Analyses conducted at the subnational level are useful for evaluating microlevel con-flict processes. However, it is important to acknowledge that macrolevel processesoften drive microlevel events. Consequently, there is much to learn from “scaling-

14. Mkandawire 2002.15. See Johnston 2008; Humphreys and Weinstein 2006; and Weinstein 2007.16. Humphreys and Weinstein 2006, 444.


up” microlevel theories and testing them at the meso- and macrolevels.17 I thereforeexamine the proposed relationship at two distinct levels of analysis. I first conduct amicrolevel analysis of LRA violence disaggregated to the district-week, whichaccounts for conflict dynamics obscured at higher levels of aggregation. A singlecase analysis cannot, however, account for factors that vary across groups.Therefore I also conduct across-sectional time-series analysis of post–Cold WarAfrican insurgencies that allows me to both verify the validity of the central hypoth-esis across a range of diverse cases and to investigate the conditional hypotheses.

Microlevel Analysis

The microlevel analysis focuses on the LRA for two reasons. First, the group’s brutal-ity leads many observers to conclude that there is little logic to its actions, suggestingsomething of a hard case for rationalist arguments. Second, temporal and geospatialvariation in the group’s violence suggests that static factors (for example, resourceendowments, organizational structures, leader psychology) cannot fully account forthe group’s tactics. Focusing on a case where these factors are largely constantover time allows me to evaluate the hypothesis that conflict interactions influence pat-terns of violence.Data on LRA conflict events come from the Armed Conflict Location Events

Dataset (ACLED).18 I aggregate these data to the subnational (for example, district)unit-week to closely examine the sequencing of events. Rather than employing arbi-trarily sized grid-squares (or hexagons) as the unit, I instead rely on actual adminis-trative divisions because they often reflect important geographical, ethnic, or politicalboundaries. Gridded units sacrifice the potential underlying importance of these div-isions in exchange for consistency in unit size. Such small units (often fifty squarekilometers) also dramatically increase the number of nonevent observations withina sample, especially in time-series analyses. Because of substantial differences inthe size of administrative units across states, I aggregate to different levels of admin-istration for each state: the district in Uganda and the Sudan, the territory (zone) in theDemocratic Republic of Congo, and the sub-prefecture in Central AfricanRepublic.19 This aggregation creates more equivalently sized units. I also controlfor the size of the units. Subnational administrative unit data are taken from theGADM database on Global Administrative Areas.20

ACLED includes event-level data on battles, attacks on civilians, changes in ter-ritorial control, and other conflict-related events. These data are coded from avariety of secondary sources, including local and regional press accounts, the

17. Kalyvas 2012.18. Raleigh et al. 2010.19. This represents the first administrative level under the central government in Uganda, the second levelin the CAR, and the third in both the Sudan and DRC.20. Available at <www.gadm.org>, accessed 13 December 2012. Global Administrative Areas 2012.


http://www.gadm.org

Integrated Regional Information Network (IRIN), Relief Web, Factiva, varioushumanitarian agencies, and information available from the Uppsala ConflictData Program (UCDP) archives.21 I rely on ACLED’s data because theyprovide relevant substantive information on conflict events and their outcomesthat I use to create key variables employed in the analysis. The sample for theanalysis includes all subnational units in which the LRA were active between1997 and 2010. A unit enters the sample once an LRA-related conflict eventoccurs within it and remains active thereafter. I therefore exclude from the analysisareas in which no conflict events occurred (for example, 90 percent of the DRC).Aggregating the data to the unit-week produces a sample with approximately13,500 observations, including nearly 600 unique attacks on civilians committedby the LRA as well as more than 900 other conflict events spread over fifty sub-national units in four countries.The dependent variable in the analysis is a weekly count of the number of attacks

on civilians committed by the LRA in a subnational unit. Relying on counts of attacksrather than estimates of deaths offers both benefits and drawbacks. This operation-alization treats attacks in which dozens of civilians are killed as equivalent toevents in which a single civilian is killed. However, deaths are only one form of vio-lence inflicted upon civilians—abductions, torture, beatings, sexual violence, and thedestruction of property occur frequently during civil wars. These forms of violenceare captured by ACLED but excluded from most other data sets. Furthermore, dataon the occurrence of an attack are often more reliable than estimated death counts.Indeed, media reports often agree where and when an event occurred but sharplydiverge on details such as the number of persons killed or injured.I create the key independent variable using information available in ACLED.

Specifically, I construct a weekly count of conflict interactions that produced substan-tial material losses for rebels. First, ACLED indicates whether a given battle resultedin a territorial gain, loss, or no change for an actor. Second, ACLED makes availableshort summaries of the relevant events captured in the data set. These coding notesinclude information on the context and outcome of various conflict interactions. Iuse this supplementary information plus the existing ACLED coding of changes interritorial control to create the loss measure.22 I code an event as a material loss ifthe interaction produced any of the following outcomes: the loss, capture, defection,or deaths of twenty or more rebel troops; the liberation of twenty or more abductees orchild soldiers;23 the destruction of rebel camps; or the capture or destruction of rebelsupply stores. I aggregate these events to the district week and lag the measure by one

21. Raleigh et al. 2010, 656.22. The ACLED project manager suggests that these summaries are suitable for creating the variablesdescribed. E-mail correspondence, Clionadh Raleigh (10 January 2012).23. I include liberation of child soldiers and abductees given the frequency with which the LRA (and otherrebellions) utilize them for both combat and noncombat duties. While typically less combat capable thanvoluntary adult recruits, children nonetheless serve a strategic purpose, and their loss represents a resourcecost for the group. See Beber and Blattman 2013.


period. Summary statistics for relevant variables are located in Table 1. Figure 2shows the locations of major LRA losses (points) mapped onto the frequency ofanticivilian violence (shading) for the effected region.24

I control for the number of attacks on civilians committed by rival armed factions(for example, the Sudanese People’s Liberation Army (SPLA) and Uganda forces) aswell as the number of battles between the LRA and other armed factions that did notproduce significant losses. I include a one-week lag of LRA attacks on civilians and

TABLE 1. Descriptive statistics

Variables Mean Standard deviation Minimum, maximum

LRA VICTIMIZATION 0.064 0.388 0, 9NON-LRA VICTIMIZATION 0.008 0.104 0, 4MATERIAL LOSSES 0.008 0.092 0, 2BATTLES∼ MATERIAL LOSSES 0.070 0.402 0, 9

FIGURE 2. LRA losses and attacks on civilians

24. Maps were created and spatial data were organized using Quantum GIS (v1.8.0).


spatial lags (contiguous districtt−1) of each of the conflict events variables to controlfor temporal and spatial dependence. I also control for district-level infant mortalityrate, logged population size,25 logged district land area (km2), and whether themajority of the district’s population is ethnic Acholi (which the LRA claimed to rep-resent).26 Finally, I include a dummy variable for the year 2002 to account for a sig-nificant shift in the conflict landscape. Although the LRA received significant supportfrom Sudan during the 1990s, improved relations between Sudan and Uganda in theearly 2000s led Sudan to terminate this support. Moreover, in March 2002 Ugandantroops gained permission to pursue the LRA into Sudan during operation “OperationIron Fist.” These events represented a period of sustained setback for the LRA thatmay correlate with increased violence.

Results for the microlevel analysis are presented in Table 2. Most units do notexperience attacks on civilians at most time points, creating an overabundance of

TABLE 2. Microlevel analysis results

Model 1 (full sample) Model 2 (reduced sample)

Variables Count Inflation Count Inflation

MATERIAL LOSSES‡ 0.429*

(0.147)–1.778*(0.880)

0.356*(0.145)

–1.737*(0.831)

BATTLES∼MATERIAL LOSSES‡ 0.110

(0.088)–0.760†

(0.431)0.105(0.072)

–0.514(0.351)

NON-LRA VICTIMIZATION‡ 0.532†

(0.316)1.554(0.899)

0.459(0.382)

1.021(0.934)

LRA VICTIMIZATION‡ 0.126*

(0.038)–2.569*(0.815)

0.117*(0.041)

–1.572*(0.403)

ETHNIC ACHOLI 0.022(0.324)

–1.401*(0.349)

–0.364(0.425)

–1.349*(0.582)

INFANT MORTALITY –0.023(0.021)

–0.053*(0.022)

–0.027(0.018)

–0.036*(0.018)

POPULATIONln 0.567*(0.145)

0.804*(0.244)

0.618*(0.143)

0.582*(0.225)

AREAln 0.537(0.391)

–0.146(0.370)

0.811*(0.343)

0.609(0.441)

2002 0.331(0.215)

0.074(0.372)

0.379†

(0.232)0.284(0.364)

TIMEln 0.356*(0.171)

0.401(0.406)

0.112(0.247)

–0.179(0.493)

Constant –12.819*(3.185)

–2.601(4.860)

–13.903*(2.760)

–6.039(5.024)

N (nonzeroes) 13,539 (552) 6,340 (552)Units 50 50Wald X2 1,682.71 972.28Log-pseudolikelihood –2,260.563 –2,116.554

Notes: Coefficients and standard errors (clustered by spatial unit). * = p ≤ .05; † = p ≤ .10 (two-tailed test). ‡ = Lagged oneperiod. Spatial lags suppressed.

25. See CIESIN 2005a and 2005b.26. Geo-Referencing of Ethnic Groups data set (GREG); see Weidmann, Rød, and Cederman 2010.


zeroes in the sample.27 I therefore employ a zero-inflated negative binomial model.This model estimates the relationship between covariates and the dependent variablein two stages. First, the inflation step employs logistic regression to predict whether agiven observation belongs to a population of true zeroes. Thus, the model takes intoaccount that some units are simply not exposed to rebel attacks on civilians at sometime points. The second step models the events count according to a negative bi-nomial distribution, which includes a parameter accounting for overdispersion inthe count. Because the LRA was simply not active in some districts during muchof the sample period, I also test a second model using a reduced sample limited todistricts in which the LRA was active during the year. In both models I cluster thestandard errors on the subnational unit to account for within unit correlation.The results from both models provide support for H1. In the count equations of

both models, LRA losses positively correlate to more attacks on civilians in the dis-trict during the subsequent week, and the coefficients are statistically significant. Thenegative and significant coefficients on the material loss measure in the inflationequations of both models suggest that losses reduce the likelihood that the districtexperiences no attacks in the subsequent week. Put otherwise, losses increase the like-lihood that the district is at risk for an attack. There is only weak support for the argu-ment that violence spiked in 2002 when Sudan cut off support to the LRA andUgandan troops pursued them into Sudan. It is possible that accounting specificallyfor the conflict events that this macrolevel event produced simply washes out theinfluence of the cruder year dummy. This suggests a connection between macrolevelevents such as shifting alliances and microlevel conflict events that may not be obser-vable at higher levels of aggregation.Figure 3 displays the substantive impact of the relationship found in Model 2.28

The panels reflect the probabilities of observing zero, one, and two attacks on civil-ians over the number of LRA losses in the previous week. As the first panel illus-trates, for districts in which the LRA was active during the year, the baseprobability of zero attacks on civilians is approximately 94 percent. However, thisprobability declines when the group experiences material losses, falling to 80percent after a single loss and to roughly 65 percent after two losses in the district.The second panel illustrates the probability of observing a single attack on civiliansin a district in a week. When the group experienced no losses in the previous week,the risk of a single attack on civilians is about 5 percent. This risk increases toapproximately 14 percent for weeks following a substantial rebel loss and to morethan 20 percent when the group experienced two major losses in the previousweek. A similar pattern emerges for the risk of two attacks. The likelihood of twoattacks in a week is under 2 percent when the group has not recently experienced sig-nificant losses yet increases to 5 percent and 9 percent when the group experiencedone and two setbacks respectively.

27. The ratio of zeroes to nonzeroes in the full sample is approximately 20:1.28. Predictions generated using SPost software; see Long and Freese 2006.


These results help shed light on notable episodes of LRA violence. For example, theChristmas Massacres occurred in the wake of the Garamba Offensive, which destroyedup to 70 percent of the LRA’s camps and severely depleted the group’s resources.29 Asnoted, there is also some evidence that violence spiked in 2002 following OperationIron Fist. Indeed, Figure 1 shows that LRA attacks spiked in mid-2002. The timingof these attacks follows shortly after the Ugandan military’s pursuit of the LRA intosouthern Sudan in early April of that year.30 Following this incursion the LRA attackedrefugee centers and villages along the border of Sudan and Uganda, killing hundreds ofcivilians.31 Similarly, the LRAs first major wave of mutilations (as well as a generaluptick in violence) followed closely on the government’s 1992 counterinsurgencyoffensive, which included organizing and arming local militias.32

Macrolevel Data and Analysis

To determine the generalizability of these findings, I conduct a separate analysis ofpost–Cold War African conflicts using the group-month as the level of analysis. I

FIGURE 3. Probability of LRA attacks

29. See “Christmas Massacres ‘Killed 400’,” BBC News (Internet ed.), 30 December 2008; and MartinPlaut, “Behind the LRA’s Terror Tactics,” BBC News (Internet ed.), 17 February 2009.30. Keith Somerville, “Uganda’s Rebels Keep the Faith,” BBC News (Internet ed.), 3 July 2002.31. See Baines and Paddon 2012, 235; BBCNews, 17 February 2009; and Refugee Law Project 2004, 30–31.32. Branch 2005, 16–17.


construct the sample by merging the Non-State Actor Dataset (NSA)33 and the UCDPGeocoded Events Dataset (GED).34 I aggregate events data from the GED to thegroup-month and merge these with the NSA to produce a data set with nearly4,000 observations, reflecting more than eighty individual groups in twenty-eightcountries. I choose the group-month as the unit of analysis because the argumentassumes that violence occurs shortly following losses. Analysis at higher levels oftemporal aggregation would therefore obscure this relationship.The dependent variable is the estimated monthly count of noncombatants killed

through one-sided violence by a specific rebel faction as reported in the GEDdata set.35 The UCDP defines one-sided violence as the intentional and direct useof violence against noncombatants. This definition necessarily excludes deaths bysiege or infrastructure damage as well as deaths from battlefield error, negligence,or crossfire.36 For instance, the execution of unarmed tribal leaders during therebel occupation of a village would be included in the data set whereas civiliandeaths resulting from exchanges of gunfire between rebel and government forcesin a densely populated urban area would not. This conceptualization makes thedata appropriate for testing hypotheses on the causes of strategic (as opposed to inci-dental) violence.I operationalize the severity of material losses as the estimated proportion of a

group’s forces lost in battle in the previous month. I scale losses to group sizebecause the resource burden losses impose depends on the size of the group. Forinstance, whereas the deaths of twenty-five troops may reflect a trivial cost to arobust force of 25,000 troops (0.1 percent), it would literally decimate a rebellionof 250 guerillas. Data on troop deaths are taken from the GED data set whileannual troop estimates are taken from the UCDP Conflict Encyclopedia.37 I alsoaccount for the severity of government troop losses, operationalized in the samemanner.Accounting for the contingent relationships hypothesized in this study requires

group-level indicators related to territorial control, group organizational structure,and funding sources. Cross-sectional data on such characteristics are rare;however, a handful of recent contributions provide reasonable proxy measures.Data on territorial control, group structure, and foreign support are taken from theNSA data set. Territorial control indicates whether or not a rebel group exerted effec-tive control over territory within the conflict state during the year. Central controlreflects whether a central command structure existed and exercised at least a moderatedegree of control over constituent units or troops in the field. Foreign supportaccounts for whether the group received material support from an external statesponsor. I use data from Rustad and Binningsbo to account for whether the

33. Cunningham, Gleditsch, and Salehyan 2009.34. Sundberg and Melander 2013.35. GED and ACLED code from similar source data. See Eck 2012, for a comparison of the data sets.36. Eck and Hultman 2007.37. Troop data are from the UCDP database, available at: <www.ucdp.uu.se/database>.


http://www.ucdp.uu.se/database

organization financed the rebellion through the sale of conflict resources (minerals,gems, or drugs).38 I interact each with the loss variable to test the potential contingentrelationships I discussed.I also employ several control variables. Because civilian deaths are likely a func-

tion of fighting severity, I include the logged value of rebel and government deathsaccruing in the current month.39 I also include the log-transformed value of govern-ment one-sided violence during the previous month of one-sided violence to accountfor the possibility of reciprocal victimization. I account for temporal dependence byincluding the lagged value of rebel one-sided violence. I also control for whether theconflict involved secessionist aims,40 the conflict state’s population size (logged),41

and conflict duration in logged months since onset.I employ negative binomial regression in the macrolevel analysis because the

dependent variable is a monthly count of rebel one-sided violence, and it presentssignificant overdispersion. In each model, standard errors are clustered on thegroup. Table 3 presents the results of these regression analyses. Model 1 reportsa base specification with sparse controls. Model 2 includes additional control vari-ables. In both models, the rebel troop loss variable is positive and statistically sig-nificant, suggesting that as losses mount civilian victimization increases. Theseresults offer some support for H1 and are consistent with a wealth of anecdotalevidence. Kalyvas, for instance, recounts that in Colombia guerillas increasinglyresorted to indiscriminate violence after they were driven from the town ofTame in the early 2000s by the military and allied paramilitary forces.42

Wickham-Crowley similarly highlights numerous cases in which Peruvian andVenezuelan guerillas attacked uncooperative civilians in the immediate aftermathof battlefield defeats in the 1960s and 1970s.43 Moreover, these results are consist-ent with those from the microlevel analysis of the LRA case, suggesting the gen-eralizability of those findings.Although not specifically theorized in this article, it might also be expected that

rising regime costs, which are likely correlated with rebel victories, should be nega-tively correlated with rebel victimization. The variable for government losses is nega-tive in all specifications except for the sparse first model and generally achievesstatistical significance. This suggests a possible supportive corollary to H1: notonly does violence spike after losses, but it may also decline in the wake of rebelvictories.Models 3 through 6 serve as preliminary tests of potential refinements to the

general model. Although the general argument posits a relationship between rebellosses and civilian victimization, it is highly likely that this relationship is conditioned

38. Rustad and Binningsbø 2012.39. Violence variables are aggregated from the GED.40. Harbom, Melander, and Wallensteen 2008.41. Singer, Bremer, and Stuckey 1972.42. Kalyvas 2006, 214–15.43. Wickham-Crowley 1990.


TABLE 3. Macrolevel analysis results

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

REBEL LOSSES(%)‡ 10.374*

(4.838)8.078*(3.065)

9.308*(3.116)

3.427(8.251)

2.558(4.297)

10.916*(5.221)

GOVERNMENT LOSS(%)‡ 17.549

(45.937)–41.220*(26.283)

–41.304*(19.419)

–40.849†

(22.056)–41.238†

(23.670)–41.943*(19.918)

GOVERNMENT OSVln‡ 0.160

(0.176)0.413*(0.194)

0.415†

(0.194)0.409*(0.197)

0.407†

(0.206)0.411*(0.103)

REBEL OSV‡ 0.015†

(0.009)0.010(0.008)

0.010(0.008)

0.011(0.009)

0.011(0.009)

0.010(0.008)

POPULATIONln‡ 0.155

(0.271)0.147(0.293)

0.154(0.298)

0.167(0.296)

0.164(0.271)

BATTLE DEATHSln 0.451*(0.096)

0.452*(0.097)

0.454*(0.097)

0.454*(0.096)

0.454*(0.096)

SECESSIONIST CONFLICT –3.369*(0.638)

–3.368*(0.631)

–3.420*(0.654)

–3.443*(0.637)

–3.374*(0.639)

CENTRAL CONTROL –0.386(0.515)

–0.387(0.514)

–0.421(0.540)

–0.383(0.522)

–0.321(0.531)

TERRITORIAL CONTROL –0.987†

(0.610)–0.945(0.608)

–0.965(0.614)

–1.021†

(0.612)–1.020†

(0.622)FOREIGN SUPPORT 0.900*

(0.361)0.891*(0.360)

0.829*(0.381)

0.881*(0.349)

0.921*(0.396)

RESOURCE FINANCED 1.117*(0.552)

1.123*(0.550)

1.120*(0.557)

1.040†

(0.569)1.114*(0.550)

DURATION(ln) 0.333*(0.151)

0.332*(0.151)

0.339*(0.158)

0.341*(0.154)

0.331*(0.149)

TERRITORIAL CONTROL × REBEL LOSS –9.845*(3.690)

FOREIGN SUPPORT × REBEL LOSS 6.168(9.163)

RESOURCE FINANCED × REBEL LOSS 13.257†

(7.119)CENTRAL CONTROL × REBEL LOSS –5.245

(6.654)Wald X2 72.28 369.75 359.52 373.82 360.05 365.45Log-pseudolikelihood –4,535.50 –4,375.43 –4,374.54 –4,375.08 –4,373.64 –4,375.07N 3,688Groups 82

Notes: Coefficients and standard errors (clustered by dyad). * = p≤ .05; † = p≤ .10 (two-tailed test). ‡ = Lagged one period.

by the group-level characteristics that organizational and structural theories suggestpredispose groups to greater overall levels of violence. Model 3 includes an inter-action term between the loss variable and a binary indicator for territorial controlin order to evaluate H2. Models 4 and 5 examine the conditioning effects of conflictresources and foreign sponsorship to evaluate H3a and H3b. Model 6 evaluates H4 byincluding an interaction term composed of the loss variable and an indicator forstrong central organization.

H2 to H4 propose that loss is conditioned by specific factors, and the inclusion ofthe interaction term in the empirical model asserts this statistically. It is therefore notuseful (and potentially incorrect) to infer statistical or substantive effects from eitherthe constituent term coefficient or from those on the interaction terms.44As such, Iestimate the marginal effects for the hypothesized interactions and illustrate themin Figure 4.45 The graphs included in the figure correspond directly to Models 3 to6 in Table 3. Each panel illustrates the difference between expected one-sided

FIGURE 4. Marginal effects for interactions

44. It is not possible to interpret the coefficient of X (for example, rebel loss) as the average effect of X onY. This coefficient captures the effect of X on Y only when Z is 0. The insignificant sign on the coefficientfor the loss variable in Models 4 and 5 is therefore of little consequence. Similarly, the significant coeffi-cient estimates for the variable in Models 3 and 6 provide limited information and must be interpreted in thelight of conditioning effect of the other terms. See Brambor, Clark, and Golder 2006; and Braumoeller2004.45. Predictions generated in Stata12.


violence by groups that possess the specified characteristics over the range of recentrebel losses compared with those that do not.The first panel illustrates the effect of territorial control. Based on the confidence

intervals shown here, the difference between the two group types does not becomestatistically significant until a loss rate of approximately 3 percent per month andthen remains significant until roughly 10 percent per month—the window in whichthe upper bound of the confidence intervals is below the zero line. At a monthlyloss rate of 3 percent groups that maintain effective territorial control are predictedto kill approximately seven fewer civilians per month than those that lack suchcontrol. At a loss rate of 10 percent per month the difference increases to approxi-mately eleven fewer civilians. This result provides some support for H2, whichsuggested a conditioning influence of territorial control on the relationship betweenrebel troop losses and civilian victimization.The second panel illustrates the influence of foreign sponsorship on the relation-

ship between victimization and monthly troop losses. Although neither the rebeltroop loss variable nor the interaction term are statistically significant according tothe results presented in Table 3, the marginal effects reported here suggest that awindow of losses exists in which the two group types are in fact statistically differentfrom one another.46 Specifically, at a loss rate up to 11 percent foreign sponsorshipexerts a statistically significantly conditioning influence on rebel violence. In sub-stantive terms, when no rebel troops were killed in the previous month, rebels withforeign support are expected to kill approximately five more civilians than thosegroups not supported by a foreign government. This difference increases toroughly twelve deaths at a loss rate of 11 percent. This result provides support forthe H3a. Foreign sponsorship therefore exerts a significant exacerbating influenceon the relationship between rebel losses and civilian targeting.Despite the marginally significant coefficient for the interaction term, the third

panel reveals only weak support for the conditioning influence of conflict financing.The lower bound of the confidence interval hugs the zero line between 0 percent and3 percent before dropping noticeably below it. This result provides little support forH3b. The last panel demonstrates the influence of strong central organization ongroup violence. According to the graph, at no point along the range of loss valuespresented does the variable exert a statistically significant effect. Consequently,there is no support for the logic that stronger organizational capacity dampensrebel violence following losses. The results presented in this study thereforeprovide modest support for two of the corollary hypotheses while failing toprovide convincing evidence for the other two.Turning quickly to the controls, the results suggest that victimization increases as

conflict intensity increases. They also suggest that—at least in Africa—secessionist

46. Lack of significance in the interaction term does not necessarily reflect the absence of a significanteffect over the range of the interaction. It is therefore necessary to evaluate the effects of the variablesover a range of substantively relevant values. Brambor, Clark, and Golder 2006, 73–74.


conflicts produce lower levels of intentional civilian deaths compared with revolu-tionary conflicts. The coefficient for the lagged government victimization variableis positive and significant in most specifications, providing some support for the argu-ment that conflict violence is often reciprocal. As in the microlevel analysis, civiliantargeting tends to increase as conflict duration increases. A few of the characteristicshypothesized to condition the effect of rebel losses exert an independent influence onviolence according to the results from Model 2. Territorial control exerts a marginalreductive effect on rebel violence whereas both foreign support and resource finan-cing exert a positive and significant influence. There is no evidence that organiz-ational structures independently influence violence.

Conclusion

The results from both the micro- and macrolevel analyses provide preliminary evidencethat rebels are increasingly likely to target civilians following significant materiallosses. The argument proposed that this empirical relationship likely results fromrebels’ inability to satisfy acute resource demands created by substantial materiallosses. Where rebels are unable to contract with civilians for the provision of resources,they engage in looting or coercive violence to acquire necessary goods and services.Although looting and coercive violence are common methods of resource extrac-

tion, they are neither the group’s sole strategic options nor the inevitable outcome,even in the wake of major setbacks. Rebels might choose alternative strategiessuch as offering additional resources to civilians in exchange for support, shifttheir ideological position closer to those of civilians, or attempt to otherwiseimprove their relations with civilians. However, the constraints imposed by lossesoften complicate or preclude less coercive methods of resource extraction. As pre-vious research suggests, perceptions of group strength, which are correlated withthe likelihood of victory, and the credible provision of resources are the factorsmost likely to attract civilian support. Consequently, ideological appeals, promisesof future benefits, and improved behaviors are likely to carry little weight amongcivilians where rebels have recently suffered major losses.The logic presented in this study also suggests that factors that facilitate bargaining

with civilians should diminish the odds of rebel violence. Indeed, as the macrolevelanalysis suggests, additional factors may mitigate this relationship. Where groupscontrol territory or where they are already heavily reliant on civilian support, theinfluence of material loss is likely muted, thereby constraining victimization. By con-trast, groups funded by alternative resource streams—and that are therefore less likelyto possess strong social connections to civilians—are generally more likely to targetcivilians following losses.Despite the consistency in these results, they present only correlational evidence

for the relationship between loss and violence and cannot fully evaluate the extentto which bargaining failure contributes to rebel violence. Assessing whether postde-feat violence results from failed civilian-insurgent bargaining, an attempt to signal


resolve or to impose costs on the regime,47 battlefield frustration, or some othermechanism would be quite challenging. However, a fuller understanding of theunderlying mechanisms driving this relationship would benefit both theorists of pol-itical violence and policy-makers and practitioners interested in ameliorating thehuman costs of civil war. For instance, better tests of the proposed mechanismscould help illuminate potential strategies for reducing such violence. Advances indata collection and the movement toward microlevel analysis of conflict haveallowed researchers to get closer to the action and to improve our understanding ofconflict dynamics over space as well as time. That said, future research in the formof fine-grained case analyses, survey-research conducted in areas recently afflictedby conflict violence, and agent-based modeling approaches that directly modelcomplex interactions in a dynamic environment could help further tease out andtest the underlying mechanisms that drive violence.Despite these limitations, the discussion and results highlight the importance of

accounting for conflict interactions and other dynamic elements of the conflictenvironment in explanations of civilian targeting. As the microlevel analysis demon-strated, static factors and initial conditions may predispose actors to violence orrestraint, but changes in the conflict landscape heavily influence temporal andspatial variations in violence. Future research in this area is likely to prosper byfurther examining the interaction of organizational or structural factors and conflictdynamics. Although the measures of insurgent characteristics used in this articleare among the most current and most nuanced available, they are nonethelesscrude proxies for difficult-to-measure features. More refined measures would allowresearchers to better evaluate these mechanisms and assess how organizational andstrategic factors interact to produce conflict violence.Finally, these findings have potentially important policy implications. The

results suggest that successful counterinsurgency operations or other efforts tothwart brutal nonstate actors may produce unexpected consequences. As theevents surrounding both Operation Iron Fist in 2002 and the Garamba Offensivein 2008–2009 demonstrated, attacks that deal heavy blows to insurgents but ulti-mately fail to defeat them may exacerbate civilian victimization. This is consistentwith previous research that suggests that biased interventions or even peacekeep-ing missions may create incentives for rebels to attack civilians.48 Such findingsare disheartening given that they suggest that there is indeed a high cost to defeat-ing rebellion, even where the government attempts to limit the costs of its owncounterinsurgency operations on the population. These results highlight the needfor state governments, international organizations, and nongovernmental organiz-ations (NGOs) involved in peacekeeping missions and other foreign interventionsto devise strategies to better protect civilians from the potential backlash theymight inadvertently create.

47. See, for example, Hultman 2007; and Wood and Kathman 2013.48. See Hoffman 2004; Hultman 2010; and Wood, Kathman, and Gent 2012.


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Wood 2014 From Loss to Looting