who takes the blame? the strategic effects of collateral ... · 168 luke n. condra and jacob n....

Who Takes the Blame? The Strategic Effectsof Collateral Damage

Luke N. Condra University of PittsburghJacob N. Shapiro Princeton University

Can civilians caught in civil wars reward and punish armed actors for their behavior? If so, do armed actors reap strategicbenefits from treating civilians well and pay for treating them poorly? Using precise geo-coded data on violence in Iraq from2004 through 2009, we show that both sides are punished for the collateral damage they inflict. Coalition killings of civilianspredict higher levels of insurgent violence and insurgent killings predict less violence in subsequent periods. This symmetricreaction is tempered by preexisting political preferences; the anti-insurgent reaction is not present in Sunni areas, where theinsurgency was most popular, and the anti-Coalition reaction is not present in mixed areas. Our findings have strong policyimplications, provide support for the argument that information civilians share with government forces and their allies is akey constraint on insurgent violence, and suggest theories of intrastate violence must account for civilian agency.

“When the Americans fire back, they don’t hit thepeople who are attacking them, only the civilians.This is why Iraqis hate the Americans so much. Thisis why we love the mujahedeen.”1 —Osama Ali24-year-old Iraqi

“If it is accepted that the problem of defeating theenemy consists very largely of finding him, it is easyto recognize the paramount importance of goodinformation.”2 —Gen. Sir Frank Kitson (Ret.)Commander-in-Chief UK Land Forces

Why does violence against civilians in civil warsometimes attract civilians to the insurgents’camp and in other cases repel them? Studies

Luke N. Condra is Assistant Professor of International Affairs, Graduate School of Public and International Affairs, University of Pittsburgh,3601 Wesley W. Posvar Hall, Pittsburgh, PA 15260 ([email protected]). Jacob N. Shapiro is Assistant Professor of Politics and InternationalAffairs and Co-Director of the Empirical Studies of Conflict Project, Department of Politics and Woodrow Wilson School of Public andInternational Affairs, Princeton University, Robertson Hall, Princeton, NJ 08544 ([email protected]).

We thank the following people for comments and suggestions: Huda Ahmed, Eli Berman, Chris Blattman, LTC Liam Collins, LTC LeeEwing, Jim Fearon, COL Joe Felter, Kelly Grieco, Paul Huth, Kosuke Imai, Radha Iyengar, Stathis Kalyvas, Adam Meirowitz, Ulrich Muller,Doug Ollivant, Ken Schultz, Gaby Guerrero Serdan, Paul Staniland, John Stark, Elizabeth Wood, MAJ Matt Zais, and participants at the2009 and 2010 annual meetings of the American Political Science Association, the Contentious Politics Workshop at the University ofMaryland, College Park, and the Program on Order, Conflict, and Violence colloquium at Yale University, in addition to three anonymousreviewers and the AJPS editors. We thank Josh Borkowski, Zeynep Bulutgil, and Nils Weidmann for coding the district-level estimates ofsectarian population. Most importantly, we thank our colleagues at Iraq Body Count for their years of hard work documenting the humancosts of the war in Iraq. This material is based upon work supported by the Air Force Office of Scientific Research (AFOSR) under AwardNo. FA9550–09–1–0314, by the National Science Foundation (NSF) under Award No. CNS-0905086, and by the Army Research Office(ARO) under Award No. W911NF-11–1–0036. Any opinions, findings, and conclusions or recommendations expressed in this publicationare those of the authors and do not necessarily reflect the views of the AFOSR, United States Department of Defense, NSF, or ARO. TheSupplemental Evidence and data used for this article are available at http://www.princeton.edu/∼jns/.

1Dexter Filkins, “Raising the Pressure in Iraq,” The New York Times (September 14, 2004).

2Kitson (1971, 95).

of the interaction between civilians and armed actors incivil wars have shown that both outcomes are possible (cf.Stanton 2009; Valentino 2004). Attacks that harm non-combatants may undermine civilian support or solidifyit depending on the nature of the violence, the intention-ality attributed to it, and the precision with which it isapplied (Downes 2007; Kalyvas 2006; Kocher, Pepinksy,and Kalyvas 2011). Existing empirical research on the sub-ject has studied the consequences of large-scale violenceagainst civilians (Valentino 2004), indiscriminate vio-lence against civilians (Lyall 2009), and targeted killingsof specific individuals (Jaeger and Paserman 2009). Un-fortunately, little empirical attention has so far been paidto the consequences of collateral damage, that is, to whathappens when civilians are caught in the cross-fire.

American Journal of Political Science, Vol. 56, No. 1, January 2012, Pp. 167–187

C© 2011, Midwest Political Science Association DOI: 10.1111/j.1540-5907.2011.00542.x

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168 LUKE N. CONDRA AND JACOB N. SHAPIRO

This lack of attention is unfortunate. First, the con-sequences of mistreating civilians are a first-order pol-icy concern, with military units from Western countriesfrequently being called upon to accept high levels ofrisk in order to protect noncombatants. Moreover, thelaws governing international armed conflict codify andstrengthen norms against intentionally harming civiliansbut leave military commanders on all sides substantialtactical latitude (Gray 2000). While liberal democraticstates face substantial pressures to protect civilians inwarfare (Crawford 2003), there is often substantial un-certainty as to what abiding by legal principles such as“discrimination”—the obligation of military forces to se-lect means of attack that minimize the prospect of civiliancasualties—actually entails (Walzer 2000, 138–59). If thedata provide convincing evidence that avoiding civiliancasualties helps an armed actor achieve its military objec-tives, then the case for accepting greater risk in exchangefor killing fewer civilians will be strengthened. Our anal-ysis provides evidence about the impact of civilian ca-sualties on the short-term military objective of reducinginsurgent attacks; we do not address the possibility thatactions which lead to short-term increases in attacks canlead to positive long-term consequences (by leading to anegotiated settlement, for example).

Second, understanding the manner in which civil-ian killings impact subsequent patterns of violence canhelp shed light on theoretical arguments about the na-ture of insurgency and civil war. If collateral damage hasan impact beyond the expected mechanical correlation,i.e., government-caused civilian casualties predict fewerattacks in the short run because they proxy for aggressivecombat operations, that provides strong evidence againsttheories which discount civilian agency in these settings.More specifically, if the impact of civilian killings dependson the political context in which the killings occur—ifkillings by one side have a different impact on subsequentviolence than killings by the other, for example—then the-oretical models should take that into account. This wouldbe especially important for two classes of models. First,for models that seek to explain the consequences of con-flicts, such results would have strong implications as gov-ernment and rebel commanders are constantly makingchoices that affect the probability that they harm civil-ians, choices that would be informed by the dynamicswe study.3 Second, for three-actor models of insurgencythat give a prominent role to civilian decisions (see, e.g.,Berman, Shapiro, and Felter, forthcoming), the potential

3Examples include the size of bombs insurgents use, the timing ofattacks, and rules of engagement at checkpoints.

heterogeneity of civilian responses should be taken intoaccount.

To advance such understandings, we ask a simplequestion: what are the military consequences of collateraldamage? Using weekly time-series data on civilian casu-alties and insurgent violence in each of Iraq’s 104 districtsfrom 2004 to early 2009, we show that both sides pay acost for causing collateral damage. Coalition killings ofcivilians predict higher subsequent levels of insurgent at-tacks directed against Coalition forces, whereas insurgentkillings of civilians predict fewer such attacks in subse-quent periods. These relationships, however, are strongestin mixed ethnic areas and in highly urban districts, sug-gesting the reaction to collateral damage depends on boththe local political environment and the nature of the tac-tical problem facing combatants on both sides.

We explain this variation using a theory of insur-gent violence that takes civilian agency into account. Inline with a long tradition of theoretical work (Berman,Shapiro, and Felter, forthcoming; Kalyvas 2006; Kitson1971), we argue that insurgents’ ability to conduct attacksis limited by the degree to which the civilian populationsupplies valuable information to counterinsurgents.4 Wehypothesize that collateral damage causes local noncom-batants to effectively punish the armed group responsibleby sharing more (less) information about insurgents withgovernment forces and their allies when insurgent (gov-ernment) forces kill civilians. Such actions affect subse-quent levels of attacks because information shared withcounterinsurgents facilitates raids, arrests, and targetedsecurity operations which reduce insurgents’ ability toproduce violence. It thus follows that collateral damageby Coalition forces should lead to increased insurgentattacks against Coalition forces, while collateral damagecaused by insurgents should lead to fewer such attacks.5

Our data not only are consistent with this argument, butalso allow us to cast doubt on several prominent alterna-tive explanations. These findings suggest noncombatantshave substantial agency in armed conflict and that modelsof their interaction with armed actors should take boththis agency and the heterogeneity of civilian reactions intoaccount.

Overall, the civil war in Iraq affords a unique op-portunity to understand the effects of collateral damage.The robust media coverage of the war and remarkabledata collection capabilities of Coalition forces mean weare able to track daily trends in both battlefield violence

4This approach can be contrasted with theories that place insur-gents’ supply of fighters at the center of conflict dynamics (cf. Dubeand Vargas 2009).

5We thank Jim Fearon for this felicitous phrasing of our argument.

WHO TAKES THE BLAME? 169

and civilian casualties for each of the 104 districts of Iraqfrom February 2004 through February 2009. This meansthat the microdata we use avoid two major methodolog-ical pitfalls. First, their resolution allows us to avoid eco-logical inference problems associated with cross-nationalresearch whose hypotheses imply testing at the microlevelbut instead provide quantitative analysis at the nationallevel (e.g., Cederman and Giradin 2007; Lyall and Wilson2009). Second, because the data show patterns of vio-lence in small geographic units across an entire countryin weekly time series over a period of five years, we can cir-cumvent some forms of selection bias that are of concernin qualitative subnational studies.6 Experts conductingcareful fieldwork in conflict situations can only observea small part of the conflict in time and space at any onetime (e.g., Kilcullen 2009) and can rarely follow a prin-cipled sampling strategy in choosing where to work fora host of logistical and security reasons. This study, andothers of its kind, should therefore be a welcome additionto those who care about practical and theoretical issuessurrounding insurgency and the challenges to restoringsocial and political order.

We proceed as follows. First, we briefly motivate thearticle by discussing the literatures on the interaction be-tween armed actors and civilians in civil war and on theeffects that violence against civilians has had on armedactors’ objectives. We then describe our data and iden-tification strategy, discuss the core results, and buttressthem with a series of robustness checks. After presentingthe results, we outline a simple theory regarding the infor-mational aspects of the relationship between civilian ca-sualties and insurgent violence directed against Coalitionforces which can explain our findings and provide evi-dence against some prominent alternative explanations.We conclude by discussing the implications for policy andfuture research.

The Treatment of Civilians in CivilWar and Insurgency

In fighting against each other, insurgents and counterin-surgents (henceforth, armed actors) make many decisionsevery day about how to deal with the noncombatant civil-ian population. At the most general level, encouragingcooperation from civilians and discouraging it with theenemy is a key goal. More specifically, civilians can pro-

6Recent work by Nepal, Bohara, and Gawande (forthcoming) cir-cumvents these problems in the cross-section for all Nepalese vil-lages from 1996 to 2003 but does not have a strong temporal com-ponent.

vide valuable information to armed actors, such as thewhereabouts of the other armed actor or which civil-ians actively aid the other armed actor (Kalyvas 2006).For competent militaries engaged in counterinsurgency,identifying rebel fighters can be the critical tactical chal-lenge (Kitson 1971).

The war in Iraq is no exception; Coalition forces havestruggled throughout the war to identify insurgents andgain the support of the local population (Cockburn 2007).One American soldier neatly summarized the problem:“The hardest part is picking out the bad guys” (Cockburn2007, 138–39). Civilians in Iraq not only provided criticalintelligence to Coalition forces, but also supplied insur-gents with valuable information (Chehab 2006, 7). Be-cause of the importance of information, Coalition forceshave, on occasion, resorted to collective punishment ofIraqi civilians in the hope that the desired informationwill be forthcoming. Farmers belonging to the Khazrajitribe in Dhuluaya village, 50 miles north of Baghdad, sawthis strategy in action first-hand. Cockburn reports that“US troops had told [farmers]. . .that the fruit groves werebeing bulldozed to punish the farmers for not informingon the resistance. A local . . . delegation was told by anofficer that trees and palms were being destroyed as pun-ishment of local people because ‘you know who is in theresistance and you do not tell us’” (2007, 125–26).

Government and insurgent forces in other conflictshave found that violence against civilians can cut bothways: it can motivate civilians out of fear or push theminto the enemy’s camp (Kalyvas 2006, chap. 6; Valentino,Huth, and Balch-Lindsay 2004). Carr (2002) examinescases from the long history of warfare, illustrating thatviolent strategies of civilian punishment or deterrenceare militarily and politically both counterproductive andcostly. Kalyvas (2006) argues that selective violence isstrategically superior to indiscriminate violence, at leastwhen an armed actor exercises control over the popu-lation. After all, unless civilians perceive that violence isbeing used only on the “guilty,” what incentive is there tocomply with the armed actors’ demands? Indeed, a recentstudy of the effect of Israeli Defense Force house demo-litions on subsequent suicide attacks in the Gaza Stripprovides evidence that targeted violence against civilianscan have opposing effects in the same context, dependingon whether civilians perceive the violence as justifiablyinflicted (Benmelech, Berrebi, and Klor 2010).

Yet governments and rebels routinely engage in vi-olence against civilians, perhaps because, in some cases,it works (Birtle 2008). Indiscriminate violence can cowcivilians into submission and cooperation, reducing sub-sequent insurgent violence, either through intimidationor outright large-scale elimination (Downes 2007, 2008).


Lyall’s (2009) finding that random artillery fire directedagainst villages in Chechnya reduced subsequent insur-gent activity in those villages suggests that, under someconditions, indiscriminate violence against civilians canachieve the desired effect.

Despite the ambiguous evidence from academic stud-ies, the consensus among U.S. policy makers is clearly thatmistreatment of civilians provokes insurgent violence.Testifying before Congress in January 2009, Defense Sec-retary Robert Gates highlighted this issue in the contextof operations in Afghanistan: “I believe that the civiliancasualties are doing us enormous harm in Afghanistan,and we have got to do better in terms of avoiding casual-ties . . . because my worry is that the Afghans come to seeus as part of the problem, rather than as part of the solu-tion. And then we are lost.”7 Politicians and commandersbelieve the issue matters enormously because civilians de-cide whom they will support based partly on how they aretreated—or perceive they are treated—by insurgent andincumbent forces.8

In light of this fact, military commanders and theircivilian superiors face quite a dilemma in designing rulesof engagement. How much risk must soldiers absorb intrying to discriminate between noncombatant and insur-gent? Moreover, does it even matter if U.S. forces go theextra mile in order to protect innocent lives? Does thecivilian population in Iraq blame U.S. forces for civiliancasualties, no matter which side caused the damage? Docivilians place some of the blame for casualties on insur-gents, and if so can they punish them short of organizingcompeting militias?9

Data and Descriptive Statistics

Together, then, the academic literature and received wis-dom from the military and policymaking communitiessuggest a broad consensus that collateral damage shouldbe militarily problematic, but there is little systematic ev-

7Congress, Senate, Committee on Armed Services, Hearing to Re-ceive Testimony on the Challenges Facing the Department of Defense,111th Cong., 1st sess., 27 January 2009, 21.

8Michael Mullen, “Building Our Best Weapon,” Washington Post(February 15, 2009), B7.

9Some analysts argue the dramatic reduction in violence in Anbargovernorate which began in mid-2006, the “Anbar Awakening,”occurred when the population’s frustration with excessive civiliancasualties caused by foreign militants led them to turn against theinsurgents. Others disagree, arguing that the local militias whomade up the majority of the insurgents in Anbar switched sides:they turned from fighting the Coalition to working with it. Thereis no solid consensus in the literature, but Long (2008) and Biddle(2008) provide nuanced discussions.

idence for that contention. To develop such evidence, wecombined data from a broad range of sources. This sectiondescribes those data and presents descriptive statistics andfigures to demonstrate just how varied the landscape ofviolence in Iraq has been.

Civilian Casualties

There are no complete or perfect data for civilian casu-alties in Iraq or in any other conflict.10 The casualty dataused in this article come from Iraq Body Count (IBC), anonprofit organization dedicated to tracking civilian ca-sualties using media reports, as well as hospital, morgue,and other figures.11 These data capture 19,961 incidentsin which civilians were killed that can be accurately geo-located to the district level, accounting for 59,245 civiliandeaths.12 The full data run from March 2003 through June2009, but we use a subset to match the data on insurgentattacks.

We divide these killings into four categories: (1) In-surgent killings of civilians that occur in the course ofattacking Coalition or Iraqi government targets; this cat-egory explicitly excludes insurgent killings that are unre-lated to attacks and are better classified as intimidationkillings related to dynamics of the civil war (see below);13

(2) Coalition killings of civilians; (3) Sectarian killings de-fined as those conducted by an organization representingan ethnic group and which did not occur in the contextof attacks on Coalition or Iraqi forces; and (4) Unknownkillings, where a clear perpetrator could not be identified.This last category captures much of the violence associ-ated with ethnic cleansing, reprisal killings, and the like,where claims of responsibility were rarely made and bod-ies were often simply dropped by the side of the road.

10See general discussion of this issue in Spagat et al. (2009).

11See http://www.iraqbodycount.org/. The data we use were pro-duced through a multiyear collaboration with IBC and containseveral improvements on the publicly available IBC data, includingmore consistent geo-coding.

12The full data contain 21,100 incidents, 14 of which cannot begeo-coded to the governorate level and 2,612 of which cannot begeo-coded to the district level. Because media reports sometimesprovide varying information on whether those killed were in factcivilians, or, less often, on how many civilians were killed in a givenevent, each incident in the data has a minimum and maximumvalue of civilian casualties. We use the minimum value of eachcivilian casualties variable to avoid coding combatant deaths ascivilian.

13IBC separates killings that occur during the course of conflictwith Coalition forces from those that occur elsewhere using infor-mation in press reports. Incidents are coded as “sectarian” in nature(category 3 above) when the killing is not incident to an attack onCoalition or Iraq targets and the perpetrator is a clearly identifiedmilitia.


A natural concern is that there is likely to be enor-mous noise associated with attributing casualties acrossthese categories and that such measurement error wouldbe nonrandom with respect to levels of insurgent attacks,posing significant problems for our analysis. To check forsuch a possibility, we investigate whether the percent ofcivilian casualties (both the number of casualties and thenumber of casualty-related incidents) in our “unknown”category is a function of the rate of insurgent attacks(variable described in next subsection). If it is not, thenthe measurement error is likely random with respect toour core dependent variable and therefore less of a prob-lem. Results of this test are reported in the supportingevidence (SE Table 1A). Once we control for the sectariancomposition of the area, or introduce district and timefixed effects, there is no clear relationship between un-known casualty events and attacks against Coalition andIraqi government forces. This suggests that our coding ofcivilian casualties does not suffer systematic measurementerror.

A related concern is that the probability an incidentis excluded from our analysis because it lacks the infor-mation necessary to match it to a district location may becorrelated with violence. If reporters avoid high-violenceareas, for example, then districts with high levels of vio-lence would have more missing data. By contrast, if thedesire for a good story (or other career concerns) pushedreporters to cover the most dangerous places, we mightsee the opposite bias. Because our data include 2,612 inci-dents for which the governorate is known but the districtis not, we are able to test for this possibility by analyzingwhether the proportion of incidents at the governoratelevel that cannot be attributed to a specific district cor-relates with levels of violence. This test is reported in thesupporting evidence (SE Table 1B). There is no signifi-cant relationship between levels of insurgent violence andthe proportion of incidents that cannot be resolved to thedistrict level.

Our analysis focuses mainly on civilian casualties at-tributable to Coalition forces or insurgents (categories 1and 2 above), as these capture collateral damage. Sectar-ian violence and its relationship to insurgent violence areanalytically and theoretically distinct from the conceptof collateral damage and so we deal with those subjectselsewhere.

Attacks

Our measure of attacks against Coalition and Iraqi gov-ernment forces is based on 193,264 “significant activ-ity” (SIGACT) reports by Coalition forces that capture

a wide variety of information about “. . .executed enemyattacks targeted against coalition, Iraqi Security Forces(ISF), civilians, Iraqi infrastructure and government or-ganizations” occurring between February 4, 2004, andFebruary 24, 2009. Unclassified data drawn from theMNF-I SIGACTS III Database were provided to theEmpirical Studies of Conflict (ESOC) project in 2008and 2009.14 These data provide the location, date, time,and type of attack for incidents but do not includeany information pertaining to the Coalition force unitsinvolved, Coalition force casualties, or battle damage in-curred. Moreover, they exclude Coalition-initiated eventswhere no one returned fire, such as indirect fire attacksnot triggered by initiating insurgent attacks or targetedraids that go well. We filter the data to remove attackswe can positively identify as being directed at civiliansor other insurgent groups, leaving us with a sample of168,730 attack incidents.15

Civilian Population Ethnicity

To estimate the ethnic mix of the population we combinedmaps and fine-grained population data from LandScan(2008).16 After collecting every map we could find ofIraq’s ethnic mix, we geo-referenced them and combinedthem with the population data to generate estimates ofthe proportion of each district’s population that fell intoeach of the three main groups (Sunni, Shia, Kurd). Usingthe figures from what we judged to be the most reliablemap, a CIA map from 2003, we coded districts as mixedif no ethnic group had more than 66% of the popula-tion; otherwise the district was coded as belonging to itsdominant ethnic group.17

14ESOC is a joint project based at Princeton University. It collectsmicrodata on a range of conflicts, including Afghanistan, Iraq,Pakistan, the Philippines, and Vietnam.

15We thank LTC Lee Ewing for suggesting the filters we applied.

16The LandScan data provide worldwide population estimates forevery cell of a 30” X 30” latitude/longitude grid (approx. 800m on aside). Population counts are apportioned to each grid cell based onan algorithm which takes into account proximity to roads, slope,land cover, nighttime illumination, and other information. Full de-tails on the data are available at http://www.ornl.gov/sci/landscan/.

17An alternative approach is to code all parties participating inthe December 2005 legislative election, which saw broad Sunniparticipation, according to their sectarian affiliation. Using thatapproach, one can calculate the vote share gained by each group’s(Sunni, Shiite, Kurd) political parties. Unfortunately, the electionresults were never tabulated at the district level for security reasonsand so that approach can only yield governorate-level estimates.Twenty-five of 104 districts are coded differently using these twoapproaches, mostly in districts that were coded as Sunni, Shia,or Kurdish using governorate-level vote shares but were coded as


Population

We use the World Food Program’s population estimatesgenerated in 2003, 2005, and 2007 as part of its food se-curity and vulnerability analysis.18 Using repeated obser-vations of the population helps minimize the probabilitythat our results are sensitive to biases driven by the sub-stantial population movements Iraq suffered during thewar. For districts in Erbil and Dahuk governorates thatwere not surveyed in the 2003 WFP survey, we use NCCIraq estimates.

Descriptive Statistics

For this article we created a district-week dataset, fromFebruary 4, 2004, through February 24, 2009. Descriptivestatistics of key variables for all of Iraq across this timeperiod as well as for Sunni, Shia, and mixed areas are inSE Table 1C.

Figures 1–3 illustrate the variation in insurgent at-tacks and civilian casualties over time for the entire coun-try and within select districts. For the entire country(Figure 1) we see a steady upward trend in attacks (rightaxis) until fall 2007. Total civilian casualties (left axis) fol-low a similar trend, but, looking at the breakdown by thearmed actor responsible, we see that only sectarian killingsmirror the macrotrend. Casualties attributed to Coalitionforces and insurgents remain relatively stable through-out the war, with insurgent-caused casualties generallyhigher.

Most of Iraq’s districts are relatively devoid of in-surgent attacks on a per capita basis (SE Figure 1), butwhere there is violence there is noticeable variation in itsseverity and timing. The trend in violence, for example,does not look the same in two neighborhoods of Bagh-dad, Al Sadr (commonly known as Sadr City), and Karkh(the area across the Euphrates which contains the GreenZone), though both were quite violent at times (Figure2). In terms of civilian casualties, we see similar patterns(Figure 3; SE Figure 2). Sectarian violence in our datais very highly concentrated in mixed districts, suggestingour coding rules accurately distinguish it (SE Figure 3).

mixed using the map-based method. It is not clear a priori whichapproach is more accurate. The vote shares are based on observedrecent behavior and so constitute a direct measure, but suffer fromaggregation issues. The ethnic population shares are based on fine-grained data but ultimately rest on an outside organization’s guessas to the sectarian mix in Iraq.

18See WFP (2004, 2005, 2007) and http://www.wfp.org/.

Empirical Results

This section assesses the relationship between collateraldamage and subsequent insurgent attacks. We first de-scribe our identifying assumption and introduce a simplestatistical model designed to deal with the range of poten-tial complicating relationships that might exist betweeninsurgent attacks and various types of civilian casualties.After some basic robustness checks, we then show howthe relationships we identify vary across sectarian regionsand levels of urbanity and present an alternative matchingestimator as an additional robustness check.

Our identification strategy relies on the randomnessinherent in weapons effects. The path of shrapnel, howa bullet ricochets, and the pattern of overpressure froman IED all have a large stochastic component, as doesthe physical location of noncombatants on a minute-to-minute basis. Once we control for how past levels of vio-lence affect the general care with which civilians conducttheir lives, then whether someone is standing in the wrongspot at the moment a misaimed round enters their house,or whether they happen to be walking by a window atthe moment an IED creates fatal overpressure on theirstreet, is largely random. The implication for estimatingthe effect of civilian casualties is that once one controlsfor the systematic sources of variation in both civiliancasualties and insurgent attacks—Coalition units mightoperate more aggressively in pro-insurgent districts, forexample—then we can give a causal interpretation to co-efficients from a regression of current attacks on pastcivilian casualties.19

We check this identifying assumption in our pre-ferred specification in detail below, but as a first cutconsider one striking pattern: the week-to-week bivari-ate correlation in the number of insurgent attacks perdistrict is .95, but only .04 for Coalition-caused casual-ties and .14 for insurgent-caused ones. To provide vi-sual intuition, we plot (Figure 4) the weekly time seriesof insurgent attacks for Baghdad governorate’s nine dis-tricts with the weekly time series in civilian casualties

19If they were available, this strategy would entail controlling for di-rect measures of each side’s tactical aggression (number of roundsfired and the like). Such measures are not available for the Iraqconflict, but as Coalition and insurgent units moved around muchless than monthly, and as Coalition units clearly developed distinctcultures with respect to their interaction with civilians, differencingand employing a range of time ∗ space fixed effects seems likely todo fairly well at controlling for unit-specific factors. For evidenceon variation in unit culture, see the numerous articles in MilitaryReview and other military professional journals in which comman-ders frequently recount the particular methods that succeeded (orfailed) during their tours. See, for example, Smith and MacFarland(2008).


FIGURE 1 Insurgent Attacks and Civilian Casualties in Iraq byParty Reponsible (Feb. 2004–Feb. 2009)

FIGURE 2 Insurgent Attacks in Al Sadr and Karkh(Feb. 2004–Feb. 2009)

attributable to Coalition and insurgent forces. Thereis no clear relationship between overall insurgent at-tacks and civilian casualties. Even the long-term relation-ship is largely absent in the two single-sect districts ofBaghdad. In Al Sadr (a densely populated Shia area),

the two time series seem to be strongly correlated. InMahmoudiya (a Sunni suburb south of Baghdad) andTarmia (a Sunni suburb north of Baghdad) there arehigh numbers of attacks but few civilian casualties. In AlResafa (a mixed-sect central commercial district) there


FIGURE 3 Civilian Casualties in Al-Muqdadiya and Al-Musayab(Feb. 2004–Feb. 2009)

FIGURE 4 Insurgent Attacks and Civilian Casualties in Baghdad Districts(Feb. 2004–Feb. 2009)


are high numbers of civilian casualties, relatively fewinsurgent attacks, and the two time series seem uncor-related. That is, while the area-specific trends in insur-gent attacks are highly correlated over time, the same isnot true for civilian casualties, which cannot be predictedwell at all on the basis of previous levels. This is evidenceof greater randomness in the occurrence of civilian ca-sualties, which we exploit in trying to identify the causaleffect of those casualties on insurgent attacks.

To motivate the statistical model, suppose that insur-gent attacks in area i at time t are a function of the in-formation available to government forces plus insurgentgroup-specific strategic goals plus time-varying districtcharacteristics. The information that is available to thegovernment is a function of how each side treated civiliansin the recent past and underlying local attitudes. Assumethat civilians respond to past actions so that informationpassed to the government is increasing in past civiliankillings by insurgents and decreasing in past killings bythe government and its allies. This leads to the hypothesisthat attacks in t will be increasing in government killingsof civilians in t-1 and decreasing in insurgent killings ofcivilians in t-1.

We test that prediction by estimating the followingmodel, which uses a combination of differencing andfixed effects to control for factors affecting both civiliancasualties and attacks:

a(i,t) −a(i,t−1) = �(c (i,t−1) − c (i,t−2))+�(�(i,t−1) − �(i,t−2))

+ Gx(i,t) + S(s ,h) + �(i,t)

where ai,t is the number of insurgent attacks per capita intime t against Coalition and Iraqi targets while ci,t−1 and�i,t−1 are the number of civilians killed by the Coalitionand insurgents,20 respectively, in the previous periods.21

There is no reliable time-series survey data for Iraq withsubnational resolution and so we estimate the model infirst differences to control for district-specific underlyingpolitical attitudes which might affect both noncombat-ants’ propensities to share information and the aggres-

20As explained above, insurgent killings are only those that occurredin the course of attacks against military or government targets, sothey do not include intimidation killings.

21We population-weight attacks to avoid spurious correlations aris-ing from the fact that the rate of attacks may be mechanically higherin places with more people (i.e., if 1% of the population are insur-gents, a district with 500,000 people will have 4,000 more insurgentsthan a district with 100,000 people). We do not population-weightkillings as we think it is unlikely that Iraqis take population num-bers into account when processing information on civilian killings,that is, they read the news that 10 people were killed in district Xas carrying the same weight whether district X has 100,000 peopleor 500,000 people. Our core results are strongest in mixed areas,and in these regions the results become substantially stronger whenplacing population-weighted civilian casualties on the RHS.

siveness of insurgents and Coalition forces.22 As we donot know which of the 20-some insurgent groups oper-ating in Iraq were responsible for each attack, we controlfor groups’ political goals by including a sect-by-half-yearfixed effect, Ss,h. These fixed effects account for the meanlevel of insurgent attacks in each sectarian area for eachhalf-year period and are designed to account for time-varying political factors such as the dramatic political re-alignment in Sunni areas from 2006 to 2007, commonlyknown as the Anbar Awakening. Time-varying districtcharacteristics that impact how people live and thus theirsusceptibility to being killed are captured in a vector, xi,t,which includes population density and the unemploy-ment rate. We estimate our model at the district levelas this is the smallest geographic unit for which reliabletime-series population data are available.23

Main Results

Our core results are presented in Table 1. We report thefirst differences specification above as well as the anal-ogous regression in levels to highlight the need to con-trol for underlying local attitudes via the differencing.24

This approach is designed to pick up the average effectacross areas with different levels of insurgent attacks.25 Es-timating the same regression only for district-weeks withabove median levels of insurgent attacks yields somewhatstronger results.

Table 1 shows that Coalition-caused civilian casu-alties in t-1 are positively associated with incidents ofinsurgent violence in period t and insurgent-caused civil-ian casualties are associated with less violence in the

22Given the fact that we see strong district-specific trends in bothtime series (i.e., the time series are serially correlated), differencingis preferred to using fixed effects and estimating the regressions inlevels.

23Unfortunately, there are no highly detailed data that would al-low us to control for Coalition force levels. Concerns that thisintroduces omitted variable bias should be mitigated by the consis-tency of our results in models, including (1) the previous period’strend, (2) a district fixed effect to control for predictable sourcesof variation in the trend, and (3) those that employ nonparametricmatching on the recent violent history of districts.

24Controlling for the average number of civilians killed by each sideover the previous month does not change the results, which shouldease concerns that differencing does not adequately control for themagnitude of previous attacks.

25The results should not suffer omitted variable bias because incen-tives for the mistreatment of civilians vary across Kalyvas’ (2006)zones of control to the extent that the sect∗ half fixed effect anddifferencing account for which zone a district-week is in.


TABLE 1 Predicting Population-Weighted SIGACTs per Week (Linear Regression)

(1) (2) (3) (4) (5)SIGACTs/100,000 SIGACTs/100,000 SIGACTs/100,000 SIGACTs/100,000 SIGACTs/100,000

DV population population population population population

Coalition Killings 0.00249∗ 0.00270∗∗

(lagged difference) (0.0014) (0.0013)Insurgent Killings −0.0165∗∗ −0.0168∗∗

(lagged difference) (0.0081) (0.0081)Sectarian Killings −0.000667(lagged difference) (0.0010)Unknown Killings −0.0133∗∗∗

(lagged difference) (0.0043)Constant 0.00901 0.00897 0.00900 0.00899 0.00898

(0.0070) (0.0070) (0.0070) (0.0070) (0.0070)Observations 26,416 26,416 26,416 26,416 26,416R2 0.002 0.002 0.002 0.002 0.002

Note: All models include sect∗half-year fixed effects. Population density and unemployment rate variables not shown; coefficients arestatistically and substantively insignificant. Robust standard errors clustered by district in parentheses.∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10.

TABLE 2 Predicting First Difference of SIGACTs per Week as a Function of Civilian Casualties(Linear Regression)

DV: SIGACTs/100,000 (1) (2) (3) (4) (5)population (first difference) Entire Country Sunni Mixed Shiite Kurdish

Coalition Killings 0.00270∗∗ 0.0265 0.00275∗∗ −0.0108 −0.0694(lagged first difference) (0.0013) (0.049) (0.0011) (0.0075) (0.070)Insurgent Killings −0.0167∗∗ −0.0323 −0.0176∗∗ −0.00610 −0.0218(lagged first difference) (0.0081) (0.053) (0.0072) (0.0039) (0.055)Constant 0.00897 0.0288 −0.00108 0.000898 0.00308

(0.0070) (0.045) (0.011) (0.0016) (0.0038)N 26,416 4,064 4,826 10,414 7,112R2 0.002 0.002 0.005 0.001 0.000

Note: All models include sect∗half-year fixed effects. Population density and unemployment rate variables not shown; coefficients arestatistically and substantively insignificant. Robust standard errors clustered by district in parentheses.∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10.

subsequent period.26 But is this effect causal? We believethe balance of the evidence suggests it is.

As a first cut, the results do not change if we con-trol for a broad range of potential confounding factors.Controlling for changes in insurgent attacks in previousperiods (�Yt−1) allows for the possibility that selectioninto certain levels of insurgent attacks and civilian casual-ties is due to historical processes captured by preexisting

26An interesting finding in Table 2 is that unknown killings predictlower levels of attacks in subsequent periods. Since most unknownkillings reflect intimidation by anonymous perpetrators (bodiesfound in the street and the like), this suggests selective insurgentviolence is either counterproductive or a substitute for attacks onCoalition and Iraqi forces.

trends—Coalition units that have experienced increas-ing rates of attacks may tend to use more firepower, forexample—and does not change the results (SE Table 2A).Neither do the results change if we add district fixed effectsto control for the possibility that the error term in firstdifferences is predictable across districts (SE Table 2A).The results in Table 1 are also robust to dropping Bagh-dad from the analysis (SE Table 2B), meaning they are notdriven by the peculiarities of the sectarian conflict in thatcity.27

27SE Tables 2E–2K in the online supporting evidence provide ad-ditional robustness checks.


Two further concerns arise with Table 1. One concernis that the results may be driven by natural oscillations inthe time series of attacks and casualties. A second con-cern is that because we rely on a temporal difference toidentify the causal impact of past collateral damage oncurrent levels of insurgent attacks, we may simply be see-ing a correlation because trends in insurgent attacks andcivilian casualties are both driven by some omitted vari-able. Placing the lead of differences in casualties on theRHS instead of the lag, a standard temporal placebo test,can check both possibilities. Leads of casualties have nopredictive power, providing evidence that our results arenot driven by oscillation or parallel trends (SE Table 2C).Put in plain English, future changes in civilian casual-ties are not correlated with current changes in insurgentattacks, but past ones are, on average and in mixed areas.

Finally, the most serious concern with a causal inter-pretation of the results in Table 1 is the possibility thatthe number of civilians being killed by the Coalition orinsurgents reflects their expectations about the level ofattacks in subsequent periods. Suppose, for example, thatCoalition forces correctly predicted high levels of insur-gent violence in the near future. One rational reactioncould be to increase the number of raids and aggressivecheckpoints today, leading to more civilian casualties to-day. This seems unlikely given how robust the results areto including past trends and district fixed effects, but wetest for this in two additional ways.28

First, following a suggestion in Wooldridge (2002),we tested for the possibility that casualties in t-1 are drivenby (correct) anticipation of future attacks by testing H0 :� = 0 in the equation �at = �xt−1b + wt−1� + �ut,where wt−1 is the vector of Coalition- and insurgent-caused civilian casualties in t-1 (the subset of the vari-ables whose endogeneity concerns us) and xt−1 is thematrix of coefficients described above, including differ-ences in civilian casualties. Using a robust Wald test wefail to reject the null for insurgent casualties in all models,which amounts to formally showing that the number ofcivilian casualties in period t-1 is uncorrelated with theresiduals in our core model. For Coalition-caused casu-alties in Table 1, we reject the null at the p = .900 levelin Model (1) and at the p = .953 level in Model (5), sug-gesting we have some reason for concern about selectioneffects with respect to Coalition attacks. When, however,we drop the 693 district weeks that cannot be matchedwith other districts on their history of violence (as de-scribed below in the matching section) and experiencemore than one civilian casualty caused by both actors, the

28We thank one of our anonymous reviewers for emphasizing theneed to formally test for this possibility in a range of ways.

coefficient estimates become slightly larger in the samedirection and the p-values on the test for exogeneity dropbelow the 90% level in both Model (1) and Model (5)(p = .867 and p = .880, respectively). This gives us con-fidence that the selection bias which remains in our corespecification is unlikely to be driving the result. In oursecond test, we directly check for selection effects by re-gressing various types of civilian casualty levels on therate of insurgent attacks in the next period. The lead ofinsurgent attacks does not predict Coalition-, insurgent-,or sectarian-caused civilian casualties once we differenceto account for district-specific characteristics (SE Table2D), providing further evidence that civilian casualtiesare affecting subsequent insurgent attacks on Coalitionforces, not the other way around.29

On balance, Table 1 and the various robustness checksprovide good evidence that Coalition-caused casualtieslead to increased levels of insurgent violence againstCoalition and Iraqi forces, while insurgent-caused ca-sualties have the opposite effect. Given the nature of thedata and aggressive set of robustness checks we employ,the balance of the evidence clearly supports the hypothesisthat Coalition-caused collateral damage leads to increasedattacks and insurgent-caused collateral damage leads toreduced attacks.

Geographic and Functional Heterogeneityin Results

So how do these results vary across areas? We find twokey patterns. First, the effects we observe are being drivenby districts whose population is more urban than themedian district, where 48.5% of the population lives inurban areas according to the WFP surveys (SE Table 2F).The anti-Coalition effect is absent in nonurban districts(in fact, the effect is in the opposite direction) and theanti-insurgent effect is severely muted.

Second, and more critically, the results vary tremen-dously by the sectarian character of an area. Table 2 re-ports our core specification from Table 1 (Model 5) bro-ken down by sectarian mix, showing the effects are beingdriven by mixed areas.30

In mixed areas, a one standard deviation increasein the number of insurgent-caused civilian casualties

29Note that we have the expected spurious correlations in levelswithout a district fixed effect in SE Table 2D, which provides ad-ditional confidence that differencing is accounting for most of theselection bias.

30SE Table 2M reports the same results dropping the 7.6% of in-cidents (n = 397) in which both Coalition forces and insurgentskilled civilians. The results are substantively the same except thatin the full sample, the coefficient on insurgent killings is no longerstatistically significant.


predicts approximately 0.5 fewer attacks in the next weekfor a 12% drop from the average number of attacksper 100,000 in mixed areas.31 Interestingly, the effect issubstantively larger in Sunni areas, especially the anti-Coalition effect, but is less consistent and so is not statis-tically significant.32 Notice that these results are not whatwe would expect to find if it were true that, in the contextof the civil war and sectarian violence, civilians in mixedareas were more likely to look to various militias or in-surgent groups for protection and were thus likely to stayquiet and share no information with Coalition forces forfear of losing their local protection. If that story describedcivilian incentives in mixed areas, we would expect a nullfinding on insurgent-caused casualties in mixed areas.

Finally, we study how the impact of civilian casualtiesvaries across four types of insurgent attacks: (1) direct fireattacks involving weapons that can only be used with adirect line of sight to the target, such as rifles and thelike, thereby exposing insurgents to the risk of detectionwhile setting up and to a high chance of death or captureduring the incident; (2) indirect fire attacks involvingweapons such as rockets which can be fired far from theirtarget, implying less risk of exposure during setup andnone during the incident; (3) IED attacks that involveplanting an explosive which is detonated when Coalitionforces pass by, involving some risk of exposure duringsetup, some risk to the attacker during the incident if theweapon is command detonated, and some risk of failure ifcivilians tip Coalition forces off to the weapon’s presence;and (4) suicide attacks which are typically very resistant toexposure during setup (Berman and Laitin 2008). As SETable 2G shows, the symmetric anti-Coalition and anti-insurgent effects are present for direct fire and IED attacks.The rate of indirect fire attacks, however, is decreasingin Coalition-generated casualties and the rate of suicideattacks is decreasing in insurgent-generated casualties.The overall effects we identify thus seem to be drivenby the two forms of attacks that we believe to be mostsensitive to information sharing by the population.

Alternative Matching Estimator

This section presents an alternative semiparametric ap-proach to estimating the causal impact of civilian casual-

31SE Table 5 reports substantive significance in detail, showing theeffect of a 1SD increase in the number of civilian casualties onthe number of insurgent attacks for the core model estimated ondifferent periods of data.

32The difference in statistical significance is not due to sample size;our coding identifies roughly the same number of Sunni and mixeddistricts.

ties on subsequent insurgent violence. The basic idea forthis alternative, less model-dependent approach is thatwe can estimate the causal effect of collateral damage onsubsequent violence by comparing outcomes across dis-tricts/weeks that are matched on factors influencing thepropensity of both sides to kill civilians, such as averagelevels of violence or whether one side or the other feelsit is winning in an area. Many such factors are unobserv-able, but we might think most of the information aboutthem is captured in the history of violence through timet in district i. If we look at the set of district-weeks thathave experienced similar levels of insurgent attacks in thepast—say t-4 to t—as well as similar trends over that pe-riod, then we might think that Coalition and insurgentforces operating in those district-weeks would face similarincentives regarding the use of force and level of care takento avoid civilian casualties. Put more starkly, insurgentswho believe they are losing an area might be expected tooperate more aggressively than they otherwise would.

This expectation suggests a simple analytical path:33

(1) use a matching algorithm to identify district-weekswith similar histories; (2) within each stratum, use a re-gression model to estimate the relationship between thenumber of civilians killed today and the number of insur-gent attacks in the next period using a cubic time trendat the quarter level to control for broad secular trends;34

and (3) take the average of these results weighting by thesize of the strata. The resulting estimate provides the aver-age treatment effect for district-weeks that experience anyhistory of violence represented in the set of strata used atstep (2).

We match district-weeks using the Coarsened Ex-act Matching (CEM) algorithm implemented in the cempackage for Stata (Iacus, King, and Porro 2008). The pro-cedure is quite simple. First, coarsen the data on eachmatching variable so that it falls into meaningful bins,just as one would when constructing a histogram. For theaverage number of attacks in the last five weeks, for exam-ple, the bins might be zero attacks, one attack, two to fiveattacks, and so on. Second, perform exact matching onthe coarsened data so that all district-weeks with roughlythe same history and intensity of violence are placed ina common stratum. This procedure does not use a para-metric model for selection to treatment and so is veryamenable to matching for continuous treatment vari-ables. It also has a variety of desirable properties relative

33We thank Kosuke Imai for suggesting this approach.

34Many strata are small and so we believe this approach strikes abetter balance between losing small strata and robustness of thecontrol than does half-year or quarter fixed effects.


to more commonly used methods such as propensityscore matching, including reduced model dependence.35

Our core matching solution replicates the intuitionbehind the first-differences specification. To capture in-centives created by the level of insurgent attacks, we matchon average rate of attacks per 100,000 people in periodst to t-4 by dividing that rate into five bins at the 10th,33rd, 66th, and 90th percentiles of the average. To captureincentives driven by the history of violence, we code thedifferences in insurgent attacks week to week according towhether they increase, remain approximately the same, ordecrease.36 This three-level coding over five periods leavesus with 243 possible histories, of which 241 are observedin the data. This approach leaves us with 493 strata withdistrict-weeks in both treatment and control groups andis justified to the extent that we believe matching on pastinsurgent violence effectively controls for characteristicsimpacting the propensity of actors to kill civilians.37

Table 3 summarizes the matching approach in twoways. First, we report the average marginal effect of killingone additional civilian on future insurgent attacks per100,000 in a district-week. Second, to reduce model de-pendence further we report results where we have di-chotomized the independent variable, providing the dif-ference in mean levels of future insurgent attacks per100,000 people between weeks in which Coalition or in-surgent forces kill no civilians and weeks in which theykill one or more civilians.

Three facts stand out from this matching exercise.First, we can confirm our previous findings on Coalition-caused casualties for the entire country and for mixedareas. In the entire country we find a significant posi-tive treatment such that each additional civilian killed byCoalition forces predicts approximately 0.16 additionalattacks in the following week per 100,000 population.This effect is fairly substantial. The median Coalition-caused incident resulted in two civilian deaths. This

35See Iacus, King, and Porro (2008) for a detailed comparison ofCEM to other matching techniques.

36The cut-point for an increase or a decrease is a movement of morethan one insurgent attack per 100,000 residents, approximately the50th percentile of the absolute value changes for the 12,714 district-weeks in which the number of attacks changed from the previousweek. SE Figure 5A shows the results from calling movementsbeyond the 25th percentile of movement (a change of .5 in attacksper 100,000) a –1 or 1. That match achieves similar balance, but overtime civilian casualties appear to have slightly different patterns.

37The challenge in doing this matching is to coarsen the data sothat in matched strata there is zero contemporaneous correlation(or close to it) between insurgent attacks and civilian killings—i.e.,within matched strata civilian killings at t = 0 are uncorrelated withpast insurgent violence—without matching so finely that there aretoo few district-weeks in each history. Full replication code availablefrom the authors. T

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means that for an average district in Iraq—which has277,238 residents—an average Coalition-caused incidentresults in roughly 0.9 extra insurgent attacks on Coalitionforces in the subsequent week. Second, there is also ev-idence that insurgent-caused collateral damage leads tofewer insurgent attacks, though the effects are statisticallyweaker in the matching estimate. An average insurgent-caused incident involves 3.7 civilian deaths, meaning thatit predicts roughly 3.6 fewer insurgent attacks on Coali-tion forces in the next week in the median-sized mixeddistrict of roughly 559,900 people. Third, the positivefinding for Coalition forces is substantially larger in Sunniareas, while the negative one for the insurgents is statisti-cally absent.

Figure 5 provides a graphic intuition for these results.The x-axis in each plot is the number of weeks before orafter period t . The y-axis in the top plot is the aver-age marginal effect of Coalition civilian killings in timet on SIGACTs/100,000 population for the entire sample.The y-axis in the bottom plot is the average marginaleffect of insurgent civilian killings, where those effectsare calculated by averaging coefficients from regressionsrun within matched strata weighting by the size of thosestrata.

If our procedure matched effectively and there is nocausal impact of past insurgent attacks against Coalitionforces on current civilian casualties within matched strata,then these differences will be close to zero through periodt (or at least relatively constant) and will then spike up(or down) for at least one period after week t , reflectingthe effect of killing civilians.38 These plots confirm thatour matching exercise effectively controls for selection onsome unobservable characteristics, at least to the extentthat those unobservables would have led to differentialpretreatment trends in violence. As in our core specifi-cation, greater violence against civilians by the Coalitionpredicts higher levels of insurgent attacks. Once we matchon past histories in this manner, greater violence by insur-gents against civilians (in the course of attacks on Coali-tion and Iraqi government forces) appears to be a weakerpredictor of lower levels of attacks than in the parametricresults. These plots also provide strong intuition for howto think about the duration of the treatment effect. In theCoalition cases, the treatment effect lasts two to five weeksbefore dropping back to statistically insignificant levels.In the insurgent case it lasts one week, is followed by in-creased violence in t+2, and then returns to levels that

38Even if our approach matches district-weeks correctly on the mo-tivations to mistreat civilians, the mechanical correlation betweenattacks and the probability of civilians being killed may create aresidual positive correlation in t = 0.

are higher than, but statistically indistinguishable from,the pretreatment trend.

In interpreting these plots a note of caution is war-ranted. The results from the matching exercise do notexactly match the parametric results. They are, after all,different estimators being applied to different samples(many district-weeks are dropped from the matched sam-ple). What seems clear from Figure 5 is that there is a largepositive change in the rate of insurgent attacks followingCoalition killings. That lasts for at least two weeks. Thereis a smaller negative change in the rate of attacks followinginsurgent killings but it is much less dramatic, and thereis a positive pretreatment correlation between insurgentkillings and the rate of attacks in the matched sample.SE Figure 5A shows the same plot where we employ aslightly looser definition of changes in insurgent attacksto generate the history and use a cubic time trend withinmatched strata to control for residual imbalance (insteadof the year fixed effects and linear time trend in the mainresults). Here the effects through t+4 are more cleanlyin line with the patterns in the parametric results, butthere is a long-run difference in insurgent attacks pre-sumably driven by omitted variables not accounted forby the cubic time trend. This figure highlights the factthat while the exact shape of the semiparametric resultsdepends on how we match, as they should, the substan-tive results are similar over four to five weeks across arange of specifications. There is a clear increase in insur-gent attacks after Coalition-caused casualties for severalweeks and a less dramatic one-week decrease in attacksafter insurgent-caused casualties.

What’s the Mechanism?

This section considers two sets of explanations for ourfindings. The first set consists of theories about the na-ture of counterinsurgent warfare that imply a positivecorrelation between insurgent attacks and past civiliancasualties as a consequence of Coalition unit organiza-tion and tactics. The second set consists of explanationsresiding in the ways in which collateral damage impactsnoncombatant preferences.

Counterinsurgent Organization and Tactics

Two mechanisms implied by prominent theories of coun-terinsurgent tactical behavior imply results similar toours. The first mechanism is based on arguments aboutthe impact of Coalition unit tactical decisions—whether


FIGURE 5 Effect of Civilian Killings on Insurgent Violence inMatched Strata (Matching on Four-Period History ofSIGACTs/100,000, and Average Insurgent Attacks)

soldiers are on foot or mounted in vehicles.39 Lyall andWilson (2009) reason that mounted patrols, as opposedto foot patrols, are less able to foster relationships withthe population and gather valuable intelligence informa-tion about local activity. Furthermore, these units aremore likely to breed enmity among civilians because ofthe inconvenience posed to civilians and the disruptionof their daily lives by mechanized patrols. These factorscombined to lead to higher levels of insurgent attacks inareas patrolled by mounted vehicles.

Lyall and Wilson’s (2009) theoretical logic suggeststwo dynamics by which civilian casualties would increasein areas with more mounted patrols. First, in response tomounted patrols, insurgents could substitute into largerexplosives, meaning that insurgent-caused civilian casu-alties would increase. Second, mounted patrols have ac-cess to heavier weaponry, which are more likely to causecivilian casualties even if aimed accurately. Suppose thatmore mechanized units tend to get attacked more becausethey have less information. The first dynamic would cre-ate a spurious positive correlation between killings by theinsurgents and attacks because the kinds of units thatwere being attacked more would also be the units beingattacked with weapons most likely to lead to insurgent-caused casualties. The second dynamic would create a

39See discussion in supporting evidence for more details on thesealternative hypotheses and how we ruled them out.

similar spurious correlation between killings by the Coali-tion and attacks because the kinds of units that were be-ing attacked more would also be the units equipped withweapons most likely to lead to Coalition-caused casual-ties.

Simple physics dictate that the dynamics above wouldoperate most strongly in areas of higher population den-sity where the consequences of an errant .50 caliber roundor large IED are more likely to kill civilians. Thus, if thereis a spurious positive correlation between killings and in-surgent violence that is driven by mechanization—whichwe cannot directly test because reliable data do not existon units’ areas of operation (AO) in Iraq or on the extentto which tactical behavior correlated with unit equip-ment (i.e., armored units patrolling mounted to a greaterextent)—we should also find that the ratio of civilian ca-sualties to attacks should be higher in more urban andmore densely populated districts. We test this logic byregressing ratios of civilian casualties to insurgent attackson the percent of the district that is urbanized and on thedistrict’s population density.40 These ratios are intendedto capture how precisely the different parties employ

40One would be concerned that these regressions are hopelesslyendogenous if more mechanized units were sent to areas experi-encing higher levels of violence. We think this concern is largelyunfounded. There was no deliberate effort to match more mech-anized BCTs to more violent areas. Private communication, LTC(Ret.) Douglas Ollivant, Ph.D., September 8, 2009. From October2006 to December 2007, Ollivant was Chief of Plans for Multi-


violence with respect to civilians.41 We find no evidencethe ratios are higher in areas of denser population or witha higher percentage of urban populations (SE Table 3).The link between unit characteristics and civilian casual-ties is unlikely to be driving the results.

The second mechanism is that civilian killings byCoalition forces would correlate positively with insur-gent attacks because Coalition units that engage less withthe local community in their AO both kill more civil-ians and suffer more insurgent attacks. One proxy forcommunity engagement by U.S. forces is the initiationof small-scale reconstruction projects by military unitsunder the Commander’s Emergency Response Program(CERP).42 If better information flowing from engagementwith communities allows units to be more discriminate,we should see a drop in the ratio of civilians killed bythe Coalition per attack. If better information makes itharder for insurgents to operate, we should see an in-crease in the ratio of civilians killed by insurgents perattack. Put formally, if a relationship between engage-ment (i.e., interacting directly and repeatedly with civil-ians) and precision (ability to engage insurgents withoutcausing collateral damage) were driving our results, thenthe ratio of Coalition-caused civilian casualties per attackshould be negatively correlated with the number of CERPprojects initiated and the ratio of insurgent-caused civil-ian casualties per attack should be positively correlatedwith the number of projects initiated.

We test for this alternative explanation using twoproxies for engagement, the number of CERP projectsstarted in a given district-quarter and the total value ofthose projects in millions of dollars.43 Regressing casualtyratios on these proxies, we find that neither the num-ber of projects nor levels of spending are associated withoverall casualty ratios, or casualty ratios for any specificactor (SE Table 4). This increases our confidence that therelationships we observe are not driven by the fact thatunits which do not engage with the communities kill morecivilians and suffer more attacks.

national Division-Baghdad and was lead Coalition force plannerfor the development and implementation of the Baghdad SecurityPlan in coordination with Iraqi Security Forces.

41See SE for a complete discussion of these ratios and the sensitivityanalysis we conducted on them.

42This is a noisy proxy given variation in CERP allocation practicesat the division, brigade, and battalion levels. Based on many in-terviews we believe the average correlation between CERP activityand community engagement is positive.

43See Berman, Shapiro, and Felter (forthcoming) for a completediscussion of these data.

Civilian Agency and the InformationalMechanism

There are many ways in which noncombatant reactionsto collateral damage might impact subsequent levels ofviolence. The simplest is that communities might clamorfor revenge when the Coalition causes civilian casualties,leading insurgents to conduct more attacks, and might putpressure on insurgents to rein in attacks after insurgent-caused casualties.44 This explanation seems unlikely togenerate the patterns above for three reasons. First, forthere to be an increase in insurgent attacks because thepopulation clamors for revenge, insurgents would haveto be producing fewer attacks than they were capable ofperpetrating before the civilian casualty incident(s). Thatseems unlikely as a general trend, though it might betrue in some places at some times. Second, this “revenge”mechanism does not have a clear prediction for variationacross more or less urban districts. Third, if insurgents re-sponded to calls to be more discriminate after they causedcasualties, we would expect the reduction in attacks to bestrongest for indirect fire attacks (those involving mor-tars and rockets), which are the least discriminate formof insurgent attack. It is, in fact, positive and statisticallyinsignificant (SE Table 2G).45

Given this and the findings above, we need a mecha-nism that (1) assumes insurgents produce at capacity (orat least at the limit of what the population will tolerate asin the Berman, Shapiro, and Felter [forthcoming] “heartsand minds” model); (2) predicts the impact of civiliancasualties will be strongest in urban areas and mixed sec-tarian regions; and (3) implies a differential response forindirect fire and suicide attacks, which are less sensitiveto information leakage than other forms of attacks.

One such mechanism is the combination of “sensi-ble” civilian reactions to collateral damage with the criti-cal role of information in Iraqi insurgency, what we termthe “informational mechanism.” Versions of this mecha-nism have been implicit in the long tradition of analysisthat emphasizes intelligence collection as the fundamen-tal task for counterinsurgents (e.g., Galula 1964; Kitson1971; Thompson 1966). It seems quite apt in Iraq wherethe ability of Coalition forces to capture and/or kill insur-gents rests not on the Coalition’s ability to project combatpower (they have that in spades), but on the acquisitionof reliable information on the whereabouts of insurgents.

44We thank two of our anonymous reviewers for pointing out theneed to discuss this mechanism.

45The coefficient on the impact of lagged differences in insurgent-caused casualties on differences in indirect fire attacks is positive(.0009) and statistically insignificant (t = .5).


The conflict in Iraq is thus quite different from insurgen-cies where the government’s capacity to produce violenceis in question.46

Three commonsense assumptions about informationin such settings lead directly to the core results above.First, the less information shared with government forcesand their allies, the easier it is for insurgents to operateand the more attacks there will be against Coalition andIraqi forces.47 Second, the more civilians Coalition andIraqi government forces kill, the less information othercivilians share with them voluntarily, and symmetrically,the more civilians that insurgents kill in the course of theirattacks on Coalition and Iraqi government targets, themore information civilians share. Third, Coalition andIraqi forces want fewer insurgent attacks, and insurgentswant to conduct more (given, of course, that they arenot engaged in negotiations or a ceasefire). While eachassumption may seem obvious, the first two merit a bitof unpacking.

With respect to the assumption that information wasthe key constraint on violence in Iraq, there is little qual-itative evidence that the supply of fighters was an im-portant constraint for any of the insurgent organizations.There is, however, copious evidence that insurgents werevery concerned with the population’s willingness to co-operate with them.48 Even if information was not thesole constraint on insurgents’ production of violence inIraq—as it is certainly not in many conflicts—our unitof analysis naturally draws attention to it. There is littlereason to expect the impact of recruits to be highly local-ized in time or space. Insurgent groups move personnelaround for a variety of reasons, and the time lag fromdecision-to-join to attack can vary from days to months.The impact on violence of the population’s willingness toshare information, however, could be quite localized inboth time and space. Because our analysis focuses on howchanges in civilian casualties from one week to the nextimpact subsequent violence within confined geographi-cal areas, it effectively focuses on factors that can respond

46The massive superiority of government forces is, however, com-mon to many counterinsurgency operations through history, sounderstanding what impacts the supply of information has broadrelevance.

47Note that we focus on information shared with governmentforces. The relationship between information shared with insur-gents and the number of attacks is less clear, as we might expectinsurgents lacking precise information on their enemies to engagein more imprecise attacks, shooting rockets into the Green Zone, forexample. Information shared with the Coalition, however, leads toraids and arrests, which we expect to directly affect both the numberand the quality of attacks insurgents can conduct.

48See, for example, the copious collection of Iraqi insurgents’ in-ternal documents cited in Fishman (2009).

almost immediately to changes in the treatment of civil-ians. We believe this approach zeroes in on the impact ofinformational processes as opposed to recruiting ones.

With respect to the second assumption about howcollateral damage impacts information sharing, there is arange of reasons collateral damage could affect civilians’propensity to share information with government forcesin a “sensible” manner. Adjudicating between them is notour goal, but it is useful to lay out just three of them. First,civilian casualties transmit information to the populationabout how a government established and organized byeach armed actor would treat them. As collateral damageincreases, the natural inference is that the actor respon-sible places relatively less value on the lives of (certain)civilians. Collateral damage therefore provides evidenceabout how a government organized and populated byinsurgent- or Coalition-selected officials will value citi-zens’ livelihoods in the future. If information shared byone individual can have a substantial effect on insurgents’ability to produce violence against Coalition forces, thenone logical reaction of forward-looking civilians to mis-treatment is to share more or less information.

Second, civilian casualties transmit information onthe threat that each side poses to the civilian’s physicalsecurity in a specific time and place.49 Civilians seekingto reduce the threat to themselves and their families cantherefore sensibly take actions that support the less dan-gerous armed actor. Certainly intimidation by one sideor the other can dampen this process, but on the mar-gins citizens caring about their personal security shouldbecome more or less willing to share information as oneside or the other reveals how much of a threat they poseto civilians. The balance of opinion in the policy andmilitary communities is that even clearly unintentionalcivilian casualties by government forces lead to reducedcooperation from civilians (see, e.g., Their and Ranjbar2008).

Third, civilian casualties can create motivations forrevenge on the part of noncombatants. If civilians areaware that sharing information with counterinsurgentsis costly for insurgents, then one way in which they canexact retribution for harm caused by insurgents is to shareinformation with government forces and their allies, andvice versa for harm caused by government forces. Calling

49Violence in Iraq was highly spatially clustered, with clusters ofviolence shifting dramatically over time. Civilian casualties couldthus provide new information on the current threat. The bivariatecorrelations between the count of Coalition-caused civilian casualtyevents in a given district-week and the numbers 5 and 10 weeksbefore in the same district are .25 and .18. The correlation betweenthe number of Coalition-caused casualties in the present week andin past weeks is even lower, approximately .03 at both 5 and 10weeks back.


in tips requires less of a commitment, in terms of time andrisk, than joining a pro- or antigovernment militia and sowe might expect the marginal effect of civilian casualtieson information sharing to be relatively large compared tothe effect on participation—a slightly different revengemechanism whose predictions we discussed above.

A natural concern here is that the marginal impacton subsequent insurgent violence of any one civilian’s de-cision to share information should be trivial, so that sub-stantial civilian coordination would be necessary to mean-ingfully affect insurgent violence. If true, there wouldclearly be a sizable collective action problem to overcomein the context of civil conflict, and the reactions describedabove would not be “sensible.” In Iraq, however, individ-ual decisions could have substantial impact. As a generalrule, Coalition forces would conduct raids based on twoindependent sources of information and would take evensingle-sourced information into account when planningpatrols and the like.50 As even one successful raid coulddramatically reduce insurgent capacity in a given areafor weeks or even months, the decisions of just one ortwo civilians could have massive implications for levels ofinsurgent attacks.51 Over time, Coalition forces becamevery effective at collecting information in ways that mini-mized risks for informants (Anderson 2007). This meantthat sharing information in response to civilian casualtieswas indeed sensible in that it carried relatively little riskand could have large impacts.

Unfortunately, we cannot directly assess the impactof collateral damage on information flow to counterin-surgents because no unclassified data exist on such in-formation transfers. Intelligence from human sources(HUMINT) is among the most highly classified typesof information held by the U.S. military, and no dataon tips provided to Coalition forces in Iraq exist in un-classified form. Instead, as is often the case in politicalscience, what we can do is make a cautious case that theresults are consistent with the observable implications ofthe information mechanism.

That case rests on four findings. First, increases inCoalition-caused civilian casualties at time t predict in-creased insurgent attacks at time t+1 in that district andincreases in insurgent-caused casualties predict reducedinsurgent attacks at t+1.52 This symmetric average re-

50Coalition press releases often cited information from single-sourced tips as driving raids. See, for example, MultinationalDivision-Baghdad (2008).

51See Berman (2009, 29–59) for a thorough discussion of whydefection and information dramatically inhibit insurgents’ capacityto produce violence.

52Mechanisms other than information sharing could generate sim-ilar dynamics, but we provide evidence against the most likely of

sponse is a prediction of the informational mechanismthat is not shared by the revenge mechanism. While othermechanisms could predict such a response, they are un-likely to be driving the results, as discussed in the previ-ous subsection. Second, the effects come from predomi-nantly urban districts. In urban areas there are more peo-ple around who can observe what insurgents are doing,which means that (a) the number of people who couldshare operationally relevant information is greater and(b) it is harder for insurgents to identify informants andthus the sensitivity of information sharing to casualtiesshould be higher. Third, the effects are strongest in mixedsect areas, exactly the places where ingroup policing isharder. In such areas intimidation of civilians to preventthem from responding “sensibly” was likely harder giventhe presence of multiple competing militias.53 Moreover,in mixed areas there were, due to the nature of the war,both people strongly opposed to the Coalition and peoplestrongly opposed to the insurgency. Fourth, the impact ofcivilian casualties on indirect fire attacks is in the oppo-site direction of the main effect. If insurgents substituteinto tactics which are less sensitive to information shar-ing when the population becomes more willing to talk tocounterinsurgents, that is exactly what we should see.

Overall then, the informational mechanism seems tobe the most likely of the explanations we examined be-cause (1) it rests on the plausible argument that if civiliansshare less information with counterinsurgents, then in-surgents can produce more attacks because they are losingfewer men and weapons to raids and the like; and (2) ithas implications for variation in the impact of collateraldamage across districts and types of attacks that are largelyborne out.

Conclusion and Policy Implications

This article answers a simple question: what are the mili-tary consequences of collateral damage in intrastate con-flict? Using weekly time-series data on civilian casualtiesand insurgent violence in each of Iraq’s 104 districts from2004 to early 2009, we show that both sides pay a costfor causing collateral damage. Coalition killings of civil-ians predict higher levels of insurgent violence and insur-gent killings predict less violence in subsequent periods.

those above, and two of our findings follow most clearly from aninformational perspective.

53In ongoing work, Shapiro and Weidmann (2011) find that in-creasing cell phone coverage leads to reduced insurgent attacks, afinding they argue is driven by the role cell phones play in makingit safer to share information with counterinsurgents.


These findings match those of Kocher, Pepinsky, and Ka-lyvas (2011) insofar as aerial bombings in Vietnam causedcivilian casualties and therefore did more to reduce popu-lation cooperation with counterinsurgents than they didto reduce insurgents’ military capacities.

The patterns we find are consistent with a theory ofinsurgent violence that takes civilian agency into account.In line with a long tradition of theoretical and empiri-cal work (e.g., Berman, Shapiro and Felter forthcoming;Kalyvas 2006; Kitson 1971; Lyall and Wilson 2009), weargue that insurgents’ ability to conduct attacks is limitedby the degree to which the civilian population suppliesvaluable information to counterinsurgents. To the extentthat collateral damage causes local noncombatants to ef-fectively punish the armed group responsible by shar-ing less information with that group and more with itsantagonist, collateral damage by Coalition forces shouldincrease attacks by insurgents, whereas collateral damagecaused by insurgents should decrease attacks. Our data areconsistent with this argument and cast doubt on severalalternative explanations for the results.

Critically, we find substantial variation across Iraq inthe response to collateral damage. The effects are strongestin mixed areas and in areas with a largely urban popu-lation. We argue this suggests that the results are in factdriven by the impact of civilian casualties on noncom-batants’ propensities to share valuable information withcounterinsurgents because (a) the population in mixedareas has a more heterogeneous set of political prefer-ences and so there are more people whose behavior canbe swayed by civilian casualties, and (b) in predominantlyurban areas, there are more noncombatants around toobserve insurgents’ activities and it is harder for insur-gents to wield a credible threat of retribution againstinformers.

Alternative mechanisms explaining variation in in-surgent attacks following civilian casualties as a reactionto popular pressure or as a function of Coalition tactics orunit organization receive little support in the data. If therelationship between how Coalition units patrol and theirpropensity for causing collateral damage were driving ourresults, we should have found that the ratio of Coalition-caused casualties to insurgent attacks was greater in ur-ban, high-density areas. If the consequences of engage-ment by counterinsurgents with the community weredriving our results, we should have found that proxiesfor that engagement—CERP spending and projects ini-tiated per capita—reduced the ratio of Coalition-causedcivilian casualties to insurgent attacks and increase theinsurgent casualty ratio. We found no evidence on eitherscore, suggesting these alternative mechanisms are notdriving the results.

There are at least three broad implications of ouranalysis. The first is that exploring civilians’ strategic in-centives is a profitable avenue for better understandingthe dynamics of intrastate conflict. Our results stronglysuggest civilians are making decisions about whom tocooperate with based on constantly changing informa-tion and that these decisions affect traditional variablesof interest, such as violence directed against the state andits allies. The bulk of the literature implicitly discountsthe possibility of noncombatants strategically exerting asizable influence, focusing instead on the interaction be-tween insurgents and incumbent forces (Stanton 2009) orbetween elements of their organization (Weinstein 2007).Our evidence suggests this is a potential limitation.

The second implication is that the dynamics of sub-state conflict may depend heavily on the military bal-ance in that conflict. Unlike the modal intrastate con-flict over the last 60 years, the government side in Iraqhas had—with the involvement of the United States—enormous military superiority. The conflict in Iraq is notuncommon in this regard, however. Civil conflicts oftenshowcase such asymmetries; the Mau Mau insurgency inKenya, the Baloch insurgency in Pakistan, and the Na-tionalist insurgency in Northern Ireland are but a fewexamples of insurgents fighting militaries that do not facea challenge in projecting military power into contestedterritory. In these conflicts the binding constraint on in-surgent violence is likely to be an informational one, notthe manpower constraint normally presumed by theoriesdesigned to explain cross-national patterns of conflict.54

When insurgent violence is constrained by manpowerlimitations instead of by the availability of information tocounterinsurgents, there may be a very different relation-ship between collateral damage and subsequent insurgentviolence.

Finally, the third implication stems from the corefinding of the article: both Coalition forces and insurgentspaid for their (mis)handling of civilians, at least in termsof subsequent violence. The argument is often made thateven though terrorists or insurgents may not abide by thelaws of war or seek to minimize collateral damage, abidingby those rules and taking on added risk is a moral obliga-tion for forces representing liberal democracies. It turnsout to be strategically advantageous: such behavior willbe attractive to civilians. It also turns out that insurgents’sanguinary tendencies hurt them, at least in this case,where information is a key constraint on the production

54For the best review of models of intrastate conflict, see Blattmanand Miguel (2010).


of violence.55 In light of our results, it is no surprise thatthe September 2009 iteration of the Afghan Taliban “Bookof Rules” includes the dictate that “The utmost steps mustbe taken to avoid civilian human loss in Martyrdom op-erations.”56 Actions that make it harder for insurgentsto precisely target government forces present insurgentswith a hard trade-off between accepting greater risks totheir forces and triggering adverse civilian reactions andmay therefore deter insurgents concerned with popularperception.

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Supporting Information

Additional supporting evidence may be found in the on-line version of this article and provides additional infor-mation and a series of robustness checks as follows:

1A & 1B: Shows that measurement error in IBC-basedcivilian casualty data is unlikely to be nonrandom withrespect to levels of insurgent violence.

1C: Provides descriptive statistics for the full countryand Sunni, mixed, and Shiite areas.

2A: Shows core results are robust to controlling forpreexisting trends in attacks and district FE to pick uppredictable heterogeneity in trends.

2B: Shows core results robust to dropping Baghdad.2C: Shows placebo test on core results.

2D: Shows results of trying to predict civilian casual-ties with leads of SIGACTs.

2E: Shows core results are not present if differencebetween lagged attacks and average over t to t+3 is placedon LHS.

2F: Shows core results are stronger in areas with morethan the median proportion of their population (48.5%)living in urban areas.

2G: Shows core results for different kinds of insurgentattacks.

2H: Shows core results on insurgent killings are ro-bust to population-weighting districts. Coalition resultsbecome statistically weaker.

2I: Shows core results on insurgent killings are robustto using the log of casualties on the RHS. Coalition resultsbecome statistically weaker.

2J: Shows core results in the full regression (column5) are robust to including the count of incidents by eachparty on the RHS.

2K: Shows core results in the full regression (column5) are robust to allowing a mean shift for district-weeksin which civilians are killed.

2L: Shows core results on insurgent killings are robustto including spatial lag of incidents on the RHS. Coalitionresults become statistically weaker.

2M: Shows core results are robust to allowing meanshift for any week that includes the first day of the month(to which we attribute killings identified through morguereports).

2N: Shows core results on Coalition killings are ro-bust to dropping the 7.6% of incidents involving bothCoalition and insurgent killings. Insurgent results becomestatistically weaker.

2O: Shows difference between Sunni and mixed dis-tricts in Table 4 is robust to dropping the 7.6% of incidentsinvolving both Coalition and insurgent killings.

2P: Shows core results with Coalition and insurgentkillings per 100,000 on RHS.

3: Shows the impact of population density and ur-banity on civilian casualty ratios.

4: Shows the impact of CERP projects and spendingon civilian casualty ratios.

5: Shows effects of a one-SD increase in civilian ca-sualties on rate of insurgent attacks in different periods.

Figure 5A shows an alternative matching solution tothat described in the text.

Please note: Wiley-Blackwell is not responsible forthe content or functionality of any supporting materialssupplied by the authors. Any queries (other than missingmaterial) should be directed to the corresponding authorfor the article.

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