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Journal of Genocide Research

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Habituation to atrocity: low-level violence againstcivilians as a predictor of high-level attacks

Charles H. Anderton & Edward V. Ryan

To cite this article: Charles H. Anderton & Edward V. Ryan (2016): Habituation to atrocity:low-level violence against civilians as a predictor of high-level attacks, Journal of GenocideResearch, DOI: 10.1080/14623528.2016.1216109

To link to this article: http://dx.doi.org/10.1080/14623528.2016.1216109

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Habituation to atrocity: low-level violence against civilians as apredictor of high-level attacksCharles H. Anderton and Edward V. Ryan

ABSTRACT‘Habituation to atrocity’ is characterized as an actor’s increasedwillingness to carry out high-level violence against civilians (VAC)owing to the choice of low-level attacks in an earlier period. Wetheoretically analyse habituation to atrocity using a rational choicemodel in which a government, rebel organization or militia groupallocates resources to fighting, attacking civilians and identityformation to achieve territorial control. Based upon conceptsavailable in the rational addiction literature, the model generatesa demand function for VAC in which substantial additionaldemand arises owing to the ‘bad habit’ generated by previousatrocities. The model guides our empirical inquiry into VAC for asample of forty-nine African countries over the period 1997 to2014. We find that the number of past low-level civilian attacks(even sometimes those involving zero fatalities) significantlyaffects the number of high-level attacks in the present. We alsofind that previous low-level civilian attacks sometimes betterpredict high-level attacks than civil conflict. Our work suggeststhat regional and global datasets on ‘small’ VAC incidents canserve as valuable early warning indicators of more severe atrocities.

Introduction

Since the turn of the twentieth century, the world has endured more than 200 mass atro-cities in which at least 1,000 civilians were purposely killed. In this time, there have alsobeen thousands of acts of ‘low-level’ intentional violence against civilians (VAC) inwhich at least five civilians were killed.1 There exists substantial literature concerning con-ditions that enable VAC, including about three dozen published empirical studies of risksfor mass atrocities and about twenty such studies for low-level VAC. Nevertheless, there islittle empirical work on ‘habituation to atrocity’, which we characterize as an actor’sincreased willingness to carry out high-level civilian attacks owing to earlier choices oflow-level civilian attacks.

This article begins with a brief survey of who intentionally attacks civilians and why, fol-lowed by a summary of empirical literature on risks for low-level VAC. The survey and lit-erature summary inform our development of a rational choice model designed to identifyconditions in which attacking civilians is an ‘optimal’ choice by a government, rebel organ-ization or militia group. The model shows how an actor’s desire to control territory

© 2016 Informa UK Limited, trading as Taylor & Francis Group

CONTACT Charles H. Anderton canderto@holycross.eduSupplementary material for this article is available online at http://dx.doi.org/10.1080/14623528.2016.1216109.

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generates a ‘demand’ for civilian attacks. Additional insights from the rational addictionliterature point to a simple extension of the model into atrocity habituation in which anactor’s foray into civilian killing generates its own impetus (demand) for even more civiliankilling.

The survey of VAC motives, literature summary and theoretical model guide our con-struction of hypotheses about risks for relatively large civilian attacks, i.e. those involving100 or more fatalities and those involving twenty-five to ninety-nine fatalities. We empiri-cally test our hypotheses using a pooled sample of forty-nine African countries from 1997to 2014 based on VAC data from the Armed Conflict Location and Event Dataset (ACLED).To preview our main result, we find that the number of previous low-level VAC attacks sig-nificantly affects the number of high-level attacks. To our surprise, some tests show thatprior low-level civilian attacks better predict high-level attacks than civil conflict. Ourresults are robust over alternative estimators including negative binomial, logit, zero-inflated negative binomial, rare events logit and fixed effects; over alternative measuresof low- and high-level civilian attacks; and over alternative measures of control variables.Given the strength of our results and the emergence of easily accessible data on low-levelVAC, we conclude that low-level civilian attacks (including those with zero fatalities) can bea valuable explanatory variable and early warning indicator of severe atrocities for scho-lars, policymakers and activists working on genocide risk and prevention.

Intentional violence against civilians

By whom?

As distinct from non-political mass murders such as most mall shootings, civilians areintentionally attacked in political contexts by governments and non-state actors includingrebels and militia groups. Figure 1 shows the number of intentional civilian attacks by fatal-ity levels conducted in African nations by governments, rebels and militia groups from

Figure 1. Intentional violence against civilians in Africa by governments, rebels and militia groups,1997–2014.

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1997 to 2014. The data source is ACLED, which defines VAC as ‘deliberate violent acts per-petrated by an organized political group such as a rebel, militia or government forceagainst unarmed non-combatants’.2 The dark columns show the number of attacks bygovernments across the fatality categories. The grey and white columns show the samefor rebel and militia groups, respectively.

The figure shows 28,633 intentional civilian attacks across the three groups in Africanstates over the period. Most attacks (17,120 or 59.8 per cent) involved zero fatalities;they should not be excluded from empirical work on VAC because, even when nobodyis killed, they can involve such harms as kidnapping and/or rape. Moreover, as we willshow, low-level attacks can be precursors to later high-level attacks. The figure alsoshows that militia attacks make up more than half (15,124 or 52.8 per cent) of allattacks across the three groups. In ACLED, such militia groups can be aligned with govern-ments or rebel groups or they can be independent. Note also that attacks involving com-paratively large fatalities are relatively rare; attacks with twenty-five to ninety-nine fatalitiesacross the groups numbered 551 (1.9 per cent of the total), while those with 100 or morefatalities numbered 228 (0.8 per cent of the total).3

Why?

The reasons for VAC can be as numerous as the various motivations of governments,rebels and militias and the particular and changing circumstances in which theyoperate. Nevertheless, scholars generally conclude that such attacks often occur duringwars or other crises involving control of territory and, by extension, the polity.4 Duringcivil wars, for example, governments and rebel and militia groups generally view civiliansas a critical resource. Controlling populations allows a group to control resources, includ-ing financing, safe havens, information and new recruits.5 Contesting groups will use inti-midation and violence to compel civilians to support them or even destroy civilians in aneffort to deny such resources to the enemy. During non-war crises (e.g. severely contestedelections, coups), perceptions of existential threat can lead to drastic choices by stateleaders, including repressive VAC.6 Rebel movements and other non-state groups alsoresort to VAC when facing threats and seeking support.7

Although war is considered a critical risk factor in the VAC literature, it is not necessaryfor VAC to occur. In societies where the government is dictatorial or weak, governmentactors may face few checks and balances to their power. Meanwhile, non-governmentactors may feel little loyalty towards government. Under such conditions, predation of civi-lians can occur through looting, forced relocation, rape and kidnapping.8 These too areobviously civilian attacks, even when fatalities are zero.

Ethnic and religious differences between groups can also foster (or be manipulated topromote) VAC during crises.9 Classification of people into different groups by ethnicity,race, religion, etc. is the first of Stanton’s eight stages of genocide (recently expandedto ten stages).10 During war or other crisis, government, rebel and militia leaders canfind it beneficial to accentuate group characteristics such as ethnicity or religion. Coalesc-ence along group lines can generate ‘group formation economies’ including unity ofpurpose within the group, ability to root out informants, low-cost recruitment of personneland resource support from local and overseas ethnic kin. During crises, people can ‘find iteasy to exaggerate differences between our group and others, enhancing in-group

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cooperation and effectiveness, and—frequently—intensifying antagonism toward othergroups’.11

The lack of democratic checks and balances on power within states can also lowerresistance to VAC among contesting groups.12 Regarding governments, for example,the ‘more repressive and dictatorial a government, the more will fear inhibit opposition[to harming civilians]. Opposition to early steps along a continuum of destruction alsodecreases when free expression is inhibited… ’.13 The lack of checks and balances on con-testing actors within states can also be due to weak external constraints. Some scholarsnote that greater integration by states into the world economy through trade and/or par-ticipation in international organizations provides avenues by which governments (andperhaps other actors who aspire to government power) can be constrained from perpe-trating civilian atrocities.14

The civil war literature covers implications of ‘lootable resources’, such as oil, mineralsand diamonds, on intrastate violence.15 There is substantial debate on how resourcesaffect civil strife, for example whether resources are a key element over which the comba-tants fight (an end), a source of financing for wars driven by other factors (a means) or asource of grievances from perceived distributional injustices. The means/ends/grievancesdistinction does not require an either/or perspective on the roles of lootable resources inintrastate crises since all three can operate. Regarding VAC, lootable resources can providefinancial rewards from participating in atrocities, means by which civilians can be attackedand an opportunity to wreak vengeance against an out-group. Similarly, external sourcesof resources (e.g. aid) can allow organizations to carry out more VAC attacks thanotherwise.16

There are also aspects of behaviour that can habituate actors to VAC. While con-tests over political and territorial control can be brutal and cross into VAC, inter-national laws and norms restrain such atrocities. Once such laws and norms arebroken, it becomes easier for actors to carry out additional and more extreme VAC.One explanation for escalating aggression against civilians is ‘habituation to atrocity’,in which initial low-level VAC incidents lower inhibitions to more numerous andsevere attacks.17 When restraints to VAC are challenged, some people from an in-group will first passively tolerate civilian abuse, later participate in relatively minorVAC incidents and later move to more severe VAC.18 VAC can escalate from the in-group owing to rewards offered by political leaders and via the ‘foot-in-the-doorphenomenon’ in which people who have become habituated to small requests willtend to comply with later larger requests.19 Moreover, vested interests can formaround VAC programmes because they allow people to enrich themselves, achievestatus in a social hierarchy and coerce others to go along with them to reinforce jus-tification of their actions.20 As resistance to VAC within the in-group diminishes, per-petrators can come to justify and even laud their actions by claiming that VAC isessential to their survival, cleanses the territory of a persistent problem group ormakes up for past injustices.

Empirical literature on ‘low-level’ intentional violence against civilians

Supplementary Table S1 summarizes key variables and data sources for twenty pub-lished large-sample empirical studies on risks for low-level VAC.21 Most of these

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studies consider the impact of violent conflict on VAC risk, with many finding a positiveand significant effect.22 Many also find that non-democratic regimes are associated witha significant risk of government VAC,23 but a few find that democracy is correlated witha greater risk of rebel VAC.24 Some also include per capita GDP and/or trade openness.The former proxies a state’s level of economic development and/or income earningopportunities in the regular economy, while the latter captures how a state’s economy(and more broadly its society) is integrated with the rest of the world. Some studiesfind that greater per capita GDP can reduce VAC risk.25 Similarly, a small number ofstudies incorporate trade openness, with a few finding that it significantly reducesVAC.26 Some studies also find that ethnic fractionalization is associated with a greaterVAC risk.27 Finally, some studies find that resources (natural or external support) signifi-cantly increase VAC.28

Several other important themes emerge from the empirical literature on low-levelVAC. First, some studies focus on VAC by governments,29 others on VAC by rebelgroups,30 and some by both governments and rebels.31 None of the studies appear toinclude militia group attacks, but ours will. Second, almost all of the studies measurethe dependent variable by the number or presence of civilians killed.32 Empirical workon civilians killed is vitally important, but there is a scarcity of empirical research onthe number or presence of attacks. Political actors usually choose attacks, not a specificnumber of civilians to kill; the latter is conditional on the former and other circumstancesassociated with the attacks. Hence, our empirical model focuses on the number of civilianattacks. Third, almost all of the empirical studies on VAC control for the presence and/ormagnitude of conflict (and we will too), but additionally, most (sixteen of twenty in TableS1) build their samples around conflict cases (e.g. warring actors, states involved in con-flict, etc.). But wars or even sub-war violence are not necessary for VAC to occur. Hence,in our sample of African states, some are at times involved in violent conflicts but someare not.

Theoretical model: VAC as a rational choice

General background of the model

By rational choice, we mean that actors have objectives (e.g. territorial control) and con-straints (e.g. resource limits) and they attempt to achieve their objectives as best theycan subject to their constraints. It is well known that human choices are more complexthan depicted by ‘narrow’ rational choice models, so economists, social psychologistsand others incorporate additional complexities into rational choice models to capturesuch realities. We do so as well by incorporating perspectives from the economics of iden-tity and rational addiction literatures.33

We assume the model below operates in the context of war or other crises in which agovernment, a potential or actual rebel group or an ethnic/religious community, whichcould form a militia, feels threatened. We assume each group (government, rebel organ-ization and militia group) has solved collective action issues within its organization suchthat each group via its key leaders can be treated as a decision maker. Finally, weassume each group seeks to control territory and consume ‘regular goods’ such as food,clothing and shelter.

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Cobb-Douglas production function for territorial control

Assume a group’s inputs to achieve territorial control (Q) are to fight or contest groups (F )inimical to its security and to perpetrate strategic violence against civilians (V ). Fighting is adirect means to control territory, but it is not the only means. By threatening or attackingcivilians, a group can attempt to keep villages ‘loyal’, thus providing sources of funding,safe havens, support for supply lines and recruits. Moreover, such attacks can potentiallyundermine civilian support for other groups.

We assume an actor’s control can be enhanced by ethnic or religious identity (I ) amongmembers of the group owing to identity-based economies.34 As noted earlier, such econ-omies include unity of purpose within the group, enhanced ability to root out informants,low-cost recruitment of personnel and resource support from ethnic kin.

For a given amount of fighting of other groups, we assume a group’s production func-tion for territorial control (Q) is of the Cobb-Douglas (CD) form:35

Q = FwVyIi 0 , w, y, i , 1, w+ y+ i = 1. (1)

Since the productivity parameters (w, y, i) respectively on fighting (F ), VAC (V ) and iden-tity formation (I ) are positive and less than 1, equation (1) implies positive and diminishingmarginal productivity for each ‘input’. Further, the summation condition (w+ y+ i = 1)implies that ‘production’ of territorial control is governed by constant returns to scale(i.e. raising each input by x per cent will cause the amount of territory controlled to riseby x per cent).

Stone-Geary utility function over territory and consumption goods

Each group achieves utility from territorial control (Q) and a composite consumption good(C ), which represents items such as food, clothing and shelter. For analytical tractability, weassume two types of civilian attacks: (1) strategic (V ), which aid in the control of territoryvia the production function noted above, and (2) gratuitous (V), which can generate utilityfor the group without necessarily having any strategic value. Total civilian attacks will beV + V . Furthermore, following insights into habituation from social psychology and therational addiction literature, we assume that gratuitous VAC in the previous period (V−1)can lower resistance to such atrocities in the present and become a ‘bad habit’.36

A functional form that is particularly useful for modelling the ‘bad habit’ of atrocity isthe Stone-Geary (SG) utility function:

U = bqln(Q− gq)+ bcln(C − gc)+ bv ln(V − V−1)

0 ≤ bj ≤ 1,∑

jbj = 1 (j = q, c, v)

(2)

The β terms reflect the comparative importance of the various ‘goods’ in generatingutility. The gamma terms (gq, gc) show the minimum necessary amount of the respectivegood. Specifically, gq is the minimum amount of territory the group must achieve to beviable and gc is minimum (subsistence) consumption. The final piece of the utility functionis the separate addition to utility from gratuitous civilian attacks, V (recall that strategiccivilian attacks, V, is a means to greater Q via the production function). Following theSG functional form, V−1 can be thought of as a ‘gamma term’ (minimum necessary V)

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for utility to be generated from the ‘bad habit’ or ‘addiction’ of gratuitous civilian attacks.Including a variable minus its previous period value is the simplest way to introducerational addiction into a model.37

Complete statement of the model

An actor facing insecurity has resources (R) available to allocate to fighting (F ), strategicVAC (V ), identity formation (I ), composite good (C ) and gratuitous attacks (V ). The‘prices’ for these inputs are given by Pj, ( j=f,v,i,c), with each price representing theaverage or unit cost per item. We assume for simplicity that the prices of strategic and gra-tuitous civilian attacks are the same (=Pv). Plugging equation (1) into (2), an actor’s con-strained maximization problem is:

maxF,V ,I,C,V

U = bqln(FwVyIi − gq)+ bcln(C − gc)+ bv ln(V − V−1)

subject to R = Pf F + PvV + PiI + PcC + PvV(3)

There is no savings in the model, so all resources are spent as shown by the constraintequation. A key theoretical mechanism posited in (3) is the presence of past VAC in theutility function. This implies that if an organization’s leaders chose atrocities in the past(V−1 . 0), they will have a change in preferences in the present in favour of atrocities,which is the nature of a ‘bad habit’ in the rational addiction literature.

Key theoretical results related to VAC

The model in (3) leads to demand functions for the choice variables, including demandfunctions for strategic and gratuitous VAC (V and V ), as functions of prices, resourcesand productivity and utility function parameters. A rich theoretical analysis of thedemand functions could follow, but our objective is to focus on theoretical predictionsrelated to VAC. We refer the interested reader to a supplementary mathematical appendixin which we derive the most important of these theoretical results. Here we provide anintuitive summary of the key ideas demonstrated there.

The broader context in which the demand for VAC arises in the model is one of inse-curity for groups over territorial control in which fighting and/or other forms of contesta-tion are present. It will, of course, be important to control for this in our empirical inquiry.The supplementary appendix demonstrates that: (1) prior period civilian attacks can gen-erate an additional demand for VAC in the present; (2) greater resources promote VAC; and(3) the ‘law of demand’ holds in which VAC attacks rise when the unit cost of such attacksgoes down.

The theoretical model in (3) represents a critical decision by the leaders of politicalorganizations: should civilians be attacked? The leaders answer ‘yes’ or ‘no’ and, if yes,choose the amount and severity of such attacks. Such choices depend on the ‘benefits’of VAC (e.g. controlling territory, gratuitous utility) relative to the costs (proxied by theunit cost of VAC, Pv). When conditions are relatively peaceful, strategic and gratuitousbenefits of VAC will be low. When internal political constraints on VAC are rigorous (e.g.democratic checks and balances), there is a large economic opportunity cost of violence

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(e.g. the economy is doing well) and external linkages to the rest of the world are robust(e.g. through trade), the cost or price of VAC will be high. If benefits of VAC are sufficientlylow and price sufficiently high, the amount of VAC demanded will be zero. If benefits ofVAC are sufficiently high relative to the costs, however, a positive amount of VAC isdemanded (chosen) in the model. In addition, if an organization is rich in resources, itsdemand for VAC would be greater, everything else the same.

Empirical research design

Habituation hypotheses

Guided by previous literature and our theoretical model, we focus on habituation to atro-city in our empirical analyses. In particular, we hypothesize:

H1: The number of prior period acts of low-level VAC will increase the number of high-levelattacks in the present, everything else the same.

H2: The number of prior period acts of low-level VAC will have a greater impact on the numberof high-level attacks in the present the greater the severity of the prior period acts of low-levelviolence, everything else the same.

To test H1 and H2, we use several estimation methodologies and variable measures. Wedo not estimate separate equations for attacks by governments, rebels and militia groupsbut instead empirically model the aggregate number of such attacks across states andyears. Our objective is not to empirically sort out possible interdependencies of action-reaction (between governments and rebels, for example) or complementarity or substitut-ability (between government and government-aligned militias, for example).38 Instead, weinclude broad measures of political, economic and social conditions that theory and pre-vious empirical work suggest are important for fostering environments in which civiliansare at risk of attack from governments, rebel groups and militias.

Dependent variable

High-level VAC attacksOur dependent variable is the number of high-level civilian attacks in African states peryear by government, rebel and militia groups based on ACLED. VAC events in ACLEDinclude attacks by the three actors as well as fatality estimates per attack, includingattacks with zero fatalities. This allows us to construct counts of VAC attacks per countryper year by various fatality thresholds. Our dependent variable is operationalized by thecount of high-level attacks (100 or more and twenty-five to ninety-nine fatalities) by thethree perpetrator groups.

Key independent variable

Previous low-level VAC attacksOur key independent variable is not the lag of the dependent variable, but the count of theprevious year’s low-level VAC attacks. We operationalize prior ‘low-level’ attacks as thoseconducted by government, rebels and militia groups involving no fatalities (Civilian

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Attacks, Zero Fatalitiest−1), one to four fatalities (Civilian Attacks, 1to4 Fatalitiest−1) and fiveto twenty-four fatalities (Civilian Attacks, 5to24 Fatalitiest−1). By hypothesis H1, we expectthe number of prior low-level attacks to be positively associated with higher-level attacks.By H2, we anticipate that the greater the severity of prior low-level attacks, the larger theimpact on high-level attacks.

Control variables

Conflict MagnitudeFollowing almost all empirical studies of VAC risks, we control for civil conflict. We turn tothe Uppsala Conflict Data Program/Peace Research Institute Oslo (UCDP/PRIO) ArmedConflict Dataset v.4-2015 to construct our conflict measure.39 We create an ordinal scaleof civil conflict magnitude by distinguishing states and years in which there was civilwar (code 3), sub-war civil conflict in which the cumulative intensity of violence reacheda war threshold (code 2), sub-war civil conflict in which cumulative intensity did notreach a war threshold (code 1) and no civil conflict (code 0).

The UCDP/PRIO dataset records civil conflicts in which the government is fighting oneor more rebel groups. In our coding we take this into account by adding the coded valuesfor each civil conflict within a state. For example, in Angola in 1998 there was civil war withthe National Union for the Total Independence of Angola (UNITA) (code 3) and sub-warcivil conflict only with the Front for the Liberation of the Enclave of Cabinda (FLEC) viaits armed wing, the Forças Armadas de Cabinda (FAC) (code 1), for an overall code of4. The conflict magnitude measure is lagged one year. Based on our theoretical modeland previous literature, we expect Conflict Magnitude to be positively associated withhigh-level civilian attacks.

Adjusted Polity2Our measure of a state’s political system is created from the Polity IV project.40 We beginwith the 21-point Polity2 measure, which ranges from −10 for full autocracy to +10 for fulldemocracy. In Polity2 coding, cases of foreign interruption (with standardized authorityscore −66) are treated as missing, cases of interregnum or anarchy (−77) are set to aneutral score of zero and cases of transition (−88) are interpolated when feasible. Wemodify the Polity2 protocol by classifying cases of interregnum or anarchy (−77) asmissing rather than a neutral score of zero.

In an important analysis of Polity2 coding, Vreeland shows that two of the fivecomponents underlying a state’s Polity2 score include instances where a government con-ducts repressive violence and even genocide.41 Among the five component scores ofPolity2, three relate to executive power (XCONST, XRCOMP and XROPEN) and two to politicalparticipation (PARREG and PARCOMP). Vreeland shows that civil war and VAC enter thecoding of PARREG and PARCOMP, implying that Polity2 data may generate a spuriousrelationship between Polity2 and political violence, especially if one is testing a nonlinearrelationship. Vreeland’s solution is to create an alternative Polity2 score from the threeexecutive components only. Following Vreeland, we adjust the Polity2 score by summingthe three executive component scores. The resulting measure, Adjusted Polity2, rangesfrom −6 for perfect autocracy to +7 for perfect democracy, which is lagged one year. Weexpect Adjusted Polity2 to be negatively associated with high-level civilian attacks.

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Log GDP Per CapitaOur gross domestic product per capita measure comes from the World Bank and is theone-year lag of the natural logarithm of real GDP per capita in constant 2005 USdollars.42 We expect Log GDP Per Capita to be negatively associated with high-level civilianattacks.

Trade OpennessFor a state’s trade openness, we use the World Bank’s measure of trade as a per cent ofgross domestic product, which we lag one year.43 We expect Trade Openness to be nega-tively associated with high-level civilian attacks.

Resource ExportsTo construct our measure of resources, we sum (by year and by state) the World Bank’smeasures of fuel exports and ores and metals exports as a per cent of merchandiseexports, which we lag one year.44 We expect Resource Exports to be positively associatedwith high-level civilian attacks.

Ethnic FractionalizationWe include a measure of ethnic fractionalization based on country-specific, time-invariantethnic group shares provided in Alesina et al.45 Being time-invariant, we do not lag ourethnicity measure. We expect Ethnic Fractionalization to be positively associated withhigh-level civilian attacks.

PopulationOur measure for population comes from the World Bank and is the natural logarithm ofpopulation, which we lag one period.46 We make no hypothesis about the relationshipbetween VAC and Population, but it is a frequent control variable in VAC studies.

Table 1 presents descriptive statistics for the variables summarized in this section.

Empirical analyses and results

Negative binomial

Our dependent variable is measured by the count of high-level civilian attacks, so webegin with negative binomial (NB) regression to test our hypotheses. Table 2 shows thecoefficient estimates for the NB model. The dependent variable is measured bythe count of civilian attacks involving 100 or more fatalities. Columns 1 and 2 show theresults of Model 1 in which prior low-level civilian attacks are measured by the count ofsuch attacks involving zero fatalities only (column 2 shows results when the highly insig-nificant GDP per capita and population variables are removed). Results indicate that priorlow-level attacks have a small and insignificant impact on high-level attacks, while conflictmagnitude, resource exports and ethnic fractionalization have positive and significanteffects and more democratic political systems (Adjusted Polity2) reduce high-level attacks.

Columns 3 and 4 in Table 2 represent a change in the measure of our key explanatoryvariable from the number of prior zero-fatality attacks to the number of attacks involvingone to four fatalities. Coefficient estimates for prior civilian attacks are now positive and

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significant and their magnitudes have risen sharply from about 0.004 to 0.02. Coefficientestimates on conflict magnitude, resource exports and ethnic fractionalization remainpositive and significant and that for Adjusted Polity2 remains negative and significant.Note that the coefficient estimate on conflict magnitude has fallen from about 0.52 to 0.33.

Columns 5 and 6 in Table 2 represent a change in the measure of our key explanatoryvariable from the number of attacks involving one to four to those involving five totwenty-four fatalities. Coefficient estimates for prior civilian attacks are positive and signifi-cant and their magnitudes have again risen sharply from about 0.02 to 0.06. Coefficientestimates for resource exports and ethnic fractionalization remain positive and significantand that for Adjusted Polity2 remains negative and significant, but notice that the coeffi-cient estimate for conflict magnitude in column 5 is close to zero and far from significant.Column 6 shows that results remain similar after the highly insignificant variables, conflictmagnitude and population, have been removed.

The results in Table 2 are broadly supportive of our habituation hypotheses. In four ofthe six regressions, the coefficient estimate on prior period low-level civilian attacks is posi-tive and significant, which supports hypothesis H1. Moreover, when our measure of priorlow-level attacks goes from very low fatalities (namely, zero fatalities) to still low but moresevere fatalities (one to four and five to twenty-four), the impact on the number of high-level attacks becomes greater, which supports hypothesis H2. Finally, and to our surprise,we find that when our measure of prior period low-level civilian attacks becomes modestlymore severe (one to four and five to twenty-four fatalities), the effect of conflict magnitudeon high-level attacks diminishes and even vanishes (in a statistical sense).

Logit

We also test our hypotheses using logit in which positive counts of the dependent variableare treated as 1 and zero counts as 0. Columns 1 and 2 of Table 3 show the results of Model1 in which prior low-level civilian attacks are measured by the count of such attacks invol-ving zero fatalities only (column 2 shows results when the highly insignificant population

Table 1. Descriptive statistics.Variable Mean Standard Deviation Minimum Maximum

Civilian Attacks,100+ fatalities

0.26 1.30 0 16

Civilian Attacks,25to99 Fatalities

0.64 3.13 0 45

Civilian Attacks,5to24 Fatalities

3.20 10.15 0 144

Civilian Attacks,1to4 Fatalities

9.16 29.78 0 382

Civilian Attacks,Zero Fatalities

19.72 61.81 0 707

Conflict Magnitude 0.50 1.01 0 4Adjusted Polity2 0.74 3.69 −6 7Log GDP Per Capita 6.60 1.08 4.29 9.58Trade Openness (per cent of GDP) 75.95 46.67 17.86 531.74Resource Exports (per cent of Merchandise Exports) 31.68 31.18 0 99.67Ethnic Fractionalization 0.66 0.23 0.04 0.93Log Population 16.05 1.26 13.05 18.99

Note: N=868 except for Conflict Magnitude (916), Ethnic Fractionalization (912), Adjusted Polity2 (876), Log GDP Per Capita(889), Trade Openness (882), Resource Exports (587) and Log Population (931).

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variable is removed). Results indicate that prior low-level attacks have a small and signifi-cant impact on high-level attacks, while conflict magnitude, resource exports and ethnicfractionalization have positive and significant effects. Unlike Table 2, however, we nowfind that the negative coefficient estimate on Adjusted Polity2 is insignificant.

Moving to columns 3 and 4 and then to 5 and 6 in Table 3 represents changes in themeasure of our key explanatory variable from the number of attacks involving zero fatal-ities to attacks involving one to four and five to twenty-four fatalities, respectively. Whilesome coefficient estimates and significances change across several of the variables, we stillfind support for our habituation hypotheses. Coefficient estimates for prior civilian attacksare positive and significant and their magnitudes rise sharply from about 0.005 to 0.05 to0.11. Meanwhile, the coefficient estimate for conflict magnitude falls from about 0.5 to 0.3and then to about zero.

Table 2. Effects of low-level civilian attacks on high-level civilian attacks.Estimator: Negative Binomial

Dependent Variable: High-level civilian attacks (100+ fatalities per attack)

(1)Model 1Initial

(2)Model 1Refined

(3)Model 2Initial

(4)Model 2Refined

(5)Model 3Initial

(6)Model 3Refined

Constant −4.215(4.388)[0.337]

−4.659***(1.548)[0.003]

−2.316(3.911)[0.554]

−2.276(2.449)[0.353]

−3.085(3.625)[0.395]

−2.250(2.778)[0.418]

Conflict Magnitudet−1 0.518***(0.189)[0.006]

0.524***(0.185)[0.005]

0.330**(0.154)[0.032]

0.331**(0.141)[0.019]

0.050(0.204)[0.806]

Adjusted Polity2t−1 −0.149*(0.079)[0.059]

−0.126*(0.069)[0.069]

−0.198**(0.084)[0.019]

−0.198**(0.079)[0.012]

−0.180**(0.086)[0.036]

−0.176**(0.074)[0.018]

Log GDP PerCapitat−1

−0.292(0.319)[0.360]

−0.396(0.324)[0.222]

−0.396(0.326)[0.223]

−0.449(0.380)[0.238]

−0.446(0.379)[0.239]

TradeOpennesst−1

−0.025(0.016)[0.108]

−0.032**(0.016)[0.045]

−0.021(0.014)[0.138]

−0.021(0.014)[0.136]

−0.018(0.015)[0.218]

−0.019(0.014)[0.176]

ResourceExportst−1

0.036***(0.007)[0.000]

0.033***(0.007)[0.000]

0.038***(0.007)[0.000]

0.038***(0.007)[0.000]

0.035***(0.007)[0.000]

0.035***(0.007)[0.000]

Ethnic Fractionalization 3.172**(1.382)[0.022]

3.258**(1.477)[0.027]

2.246*(1.213)[0.064]

2.247*(1.225)[0.067]

2.768*(1.469)[0.060]

2.764*(1.503)[0.066]

Log Populationt−1 0.060(0.206)[0.772]

0.002(0.200)[0.990]

0.046(0.179)[0.796]

Civilian Attacks,Zero Fatalitiest−1

0.004(0.003)[0.159]

0.005(0.003)[0.118]

Civilian Attacks,1to4 Fatalitiest−1

0.023***(0.009)[0.007]

0.023***(0.008)[0.003]

Civilian Attacks,5to24 Fatalitiest−1

0.063***(0.024)[0.008]

0.065***(0.025)[0.008]

Pseudo R2Log LikelihoodObservations

0.191−144.248530

0.184−145.583532

0.206−141.521530

0.206−141.522530

0.226−137.954530

0.226−138.004530

Notes: Cluster robust standard errors in parentheses; p-values in brackets.*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).

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Zero-inflated negative binomial

In our dataset, there are 868 country-years in which there could have been one or morehigh-level civilian attacks by government, rebels and militias. Among our two countmeasures of high-level attacks, there were 799 country-years in which there were zero100+ fatality attacks and 748 country-years in which there were zero 25 to 99 fatalityattacks. This implies that there could be two distinct processes by which a state mighthave zero counts for high-level attacks. Within one population of states, some mighthave political, economic and conflict magnitude conditions that correlate to zero high-level civilian attacks. But within another population of states, some might never havehigh-level civilian attacks. These latter states are classified as ‘certain zero’ in zero-inflatednegative binomial (ZINB) methodology. Hence, zero counts can arise from either popu-lation of states, but positive counts only come from the former.

Table 3. Effects of low-level civilian attacks on high-level civilian attacks.Estimator: Logit

Dependent Variable: High-level civilian attacks (100+ fatalities per attack)

(1)Model 1Initial

(2)Model 1Refined

(3)Model 2Initial

(4)Model 2Refined

(5)Model 3Initial

(6)Model 3Refined

Constant −6.310(5.873)[0.283]

−2.290(3.348)[0.494]

−2.131(4.977)[0.668]

−0.586(2.655)[0.825]

−4.198(5.810)[0.470]

−0.364(3.545)[0.918]

Conflict Magnitudet−1 0.375**(0.166)[0.024]

0.488***(0.163)[0.003]

0.225(0.159)[0.157]

0.313**(0.124)[0.012]

−0.011(0.223)[0.620]

Adjusted Polity2t−1 −0.095(0.084)[0.254]

−0.070(0.068)[0.300]

−0.177*(0.095)[0.063]

−0.183**(0.083)[0.028]

−0.137(0.103)[0.184]

−0.122(0.076)[0.107]

Log GDP PerCapitat−1

−0.536(0.369)[0.146]

−0.523(0.332)[0.116]

−0.794*(0.413)[0.055]

−0.895**(0.395)[0.024]

−0.930(0.584)[0.111]

−0.995*(0.533)[0.062]

TradeOpennesst−1

−0.024(0.018)[0.197]

−0.025(0.016)[0.123]

−0.010(0.015)[0.488]

−0.008(0.016)[0.615]

ResourceExportst−1

0.031***(0.007)[0.000]

0.033***(0.009)[0.000]

0.030***(0.007)[0.000]

0.031***(0.008)[0.000]

0.024***(0.008)[0.001]

0.027***(0.008)[0.001]

Ethnic Fractionalization 3.215*(1.778)[0.071]

3.605*(1.968)[0.067]

2.035*(1.183)[0.085]

2.174*(1.186)[0.067]

2.547(1.788)[0.139]

3.114(1.908)[0.103]

Log Populationt−1 0.269(0.268)[0.317]

0.106(0.269)[0.693]

0.266(0.320)[0.406]

Civilian Attacks,Zero Fatalitiest−1

0.005**(0.002)[0.026]

0.006***(0.002)[0.010]

Civilian Attacks,1to4 Fatalitiest−1

0.049***(0.011)[0.000]

0.054***(0.010)[0.000]

Civilian Attacks,5to24 Fatalitiest−1

0.110***(0.019)[0.000]

0.117***(0.018)[0.000]

Pseudo R2Log LikelihoodObservations

0.259−91.570530

0.254−92.227530

0.325−83.368530

0.322−83.878531

0.368−78.059530

0.362−78.888531

Notes: Cluster robust standard errors in parentheses; p-values in brackets.*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).

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Note that regression will not distinguish between the two processes by which an exces-sive number of zeros can arise, but ZINB can distinguish the two sources of zeros. Specifi-cally, ZINB combines a binary (logit) model of the predictors of the ‘certain zero’ class and acount (NB) model of the predictors of the count process for those not in the ‘certain zero’class. The correlates of the binary and count portions of the model often differ, i.e. factorsthat affect whether states are in the ‘certain zero’ class can differ from the correlates of thecounts of attacks for non-certain zero states.

Table 4 shows the coefficient estimates for the ZINB model in which the dependentvariable is measured by 100+ fatality attacks. The bottom of the table shows the binary(inflated zero) part of the model. At the bottom of column 1, we find a negative and stat-istically significant effect of prior low-level (zero-fatality) civilian attacks on the likelihood ofbeing in the ‘certain zero’ class. That is, the coefficient estimate of −0.424 implies thathigher counts of prior zero-level attacks make being a ‘certain zero’ less likely. Hence,states that avoid prior attacks (even those with zero fatalities) are more likely to be‘certain zero’. The lower portion of column 1 also shows that conflict magnitude andethnic fractionalization have expected negative signs (more conflict and higher fractiona-lization make ‘certain zero’ less likely) but are insignificant. The top part of the table showsthe count portion of the model in which resource exports and ethnic fractionalization havepositive and significant impacts and Adjusted Polity2 and trade openness have negativeand significant impacts on the count of high-level attacks. Note in the top portion thatprior low-level (zero fatalities only) attacks and conflict magnitude do not have significantimpacts on high-level attacks. Column 2 of Table 4 runs the ZINB model again, but with thehighly insignificant prior low-level attacks, conflict magnitude and GDP per capita vari-ables removed from the top portion and the highly insignificant ethnic fractionalizationremoved from the bottom. Results in the bottom part of the model continue to show anegative and significant coefficient estimate on prior zero-fatality attacks and insignifi-cance for conflict magnitude.

Columns 3 and 4 of Table 4 rerun the regressions of columns 1 and 2, but with a moresevere measure of prior low-level attacks (i.e. those with one to four fatalities). In thebottom portion of the table, coefficient estimates on prior attacks continue to have theexpected negative and significant effects. But note now that the coefficient estimate forethnic fractionalization is highly negative and significant and that for conflict magnitudeis also negative and significant. Hence, ethnic fractionalization and conflict magnitude sig-nificantly and substantially reduce the likelihood of ‘certain zeros’. In the top portion ofTable 4, columns 3 and 4 show that prior attacks and resource exports have positiveand significant impacts on the count of high-level attacks, while Adjusted Polity2 con-tinues to have a negative and significant impact. We also find that ethnic fractionalizationhas a negative and significant effect on the count of high-level attacks. Perhaps most sur-prising in column 3 is the negative and insignificant coefficient estimate on conflict mag-nitude, which we remove in column 4.

Columns 5 and 6 of Table 4 rerun the regressions of columns 1 and 2, but with a moresevere measure of prior low-level attacks (i.e. those with five to twenty-four fatalities). Inthe bottom portion of the table, coefficient estimates on prior attacks continue to havethe expected negative and significant effects and the same for ethnic fractionalization,but note that the coefficient estimate on conflict magnitude shrinks in absolute valueand is no longer significant. In the top portion of the table, columns 5 and 6 show that

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Table 4. Effects of low-level civilian attacks on high-level civilian attacks.Estimator: Zero Inflated Negative Binomial

Dependent Variable: High-level civilian attacks (100+ fatalities per attack)

(1)Model 1Initial

(2)Model 1Refined

(3)Model 2Initial

(4)Model 2Refined

(5)Model 3Initial

(6)Model 3Refined

Count (Civilian Attacks 100+)

Constant 2.144(4.132)[0.604]

1.602(3.948)[0.685]

1.951(4.040)[0.629]

0.160(0.697)[0.819]

5.375(3.901)[0.168]

1.839(2.142)[0.390]

Civilian Attacks,Zero Fatalitiest−1

0.000(0.002)[0.950]

Civilian Attacks,1to4 Fatalitiest−1

0.022***(0.006)[0.001]

0.018***(0.005)[0.001]

Civilian Attacks,5to24 Fatalitiest−1

0.042***(0.013)[0.001]

0.043***(0.012)[0.000]

Conflict Magnitudet−1 0.178(0.185)[0.336]

−0.219(0.217)[0.312]

−0.427(0.282)[0.130]

−0.417*(0.237)[0.078]

Adjusted Polity2t−1 −0.120**(0.058)[0.038]

−0.125**(0.058)[0.032]

−0.181***(0.065)[0.005]

−0.160***(0.059)[0.006]

−0.116*(0.064)[0.069]

–0.163**(0.067)[0.015]

Log GDP PerCapitat−1

−0.114(0.270)[0.673]

−0.252(0.351)[0.472]

−0.423(0.339)[0.213]

−0.604*(0.366)[0.099]

TradeOpennesst−1

−0.028**(0.013)[0.034]

−0.035***(0.012)[0.005]

−0.015(0.012)[0.227]

−0.017(0.011)[0.133]

−0.013(0.014)[0.340]

ResourceExportst−1

0.033***(0.006)[0.000]

0.030***(0.006)[0.000]

0.029***(0.006)[0.000]

0.028***(0.005)[0.000]

0.027***(0.006)[0.000]

0.025***(0.006)[0.000]

Ethnic Fractionalization 2.775**(1.373)[0.043]

2.944***(1.114)[0.008]

−2.449*(1.384)[0.077]

−2.317**(1.057)[0.028]

0.733(1.338)[0.584]

Log Populationt−1 −0.274(0.222)[0.218]

−0.252(0.205)[0.220]

−0.006(0.264)[0.983]

−0.269(0.245)[0.274]

Inflate

Constant 6.876(5.958)[0.248]

3.614***(0.929)[0.000]

12.454***(3.983)[0.002]

12.175***(4.087)[0.003]

9.721***(3.345)[0.004]

10.194***(3.103)[0.001]

Civilian Attacks,Zero Fatalitiest−1

−0.424***(0.130)[0.001]

−0.440***(0.135)[0.001]

Civilian Attacks,1to4 Fatalitiest−1

−0.101**(0.045)[0.027]

−0.104**(0.045)[0.020]

Civilian Attacks,5to24 Fatalitiest−1

−0.919***(0.354)[0.010]

−0.942***(0.332)[0.005]

Conflict Magnitudet−1 −1.772(1.851)[0.338]

−1.311(0.952)[0.169]

−2.515***(0.967)[0.009]

−2.391***(0.924)[0.010]

−1.649(1.231)[0.180]

−1.671(1.259)[0.184]

Ethnic Fractionalization −4.528(7.326)[0.537]

−14.184***(5.295)[0.007]

−14.289***(5.151)[0.006]

−9.189**(3.987)[0.021]

−9.981***(3.641)[0.006]

(Continued )

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prior attacks and resource exports have positive and significant impacts on the count ofhigh-level attacks, while Adjusted Polity2 continues to have a negative and significantimpact. Perhaps most surprising in columns 5 and 6 are the negative coefficient estimateson conflict magnitude (one of which is significant), suggesting that after controlling formodestly severe prior attacks and the ‘certain zero’ process in the lower portion of thetable, conflict magnitude does not elevate the count of high-level civilian attacks.

Each column in Table 4 also includes the Vuong z value, which is used to compare ZINBand NB estimation methods. In each column of Table 4, the z value is statistically signifi-cant, indicating that ZINB is preferred to NB as an estimation method. This Vuongtest result holds for all ZINB models available for this article (including supplementarytables).

We find good support for our habituation hypotheses in Table 4. Specifically, thepaucity of prior low-level civilian attacks explains the tendency of states to be ‘certainzeros’ regarding high-level civilian atrocities. Moreover, the greater the number of priorlow-level attacks, the greater the count of high-level attacks, everything else the same.These results are consistent with hypothesis H1. We also find that such results can bereinforced when we use a more severe measure of prior attacks (i.e. those involvingone to four and five to twenty-four fatalities). Specifically, the absolute values on the coef-ficient estimates on prior civilian attacks are about nine times larger in columns 5 and 6relative to 3 and 4 in the bottom portion and about double in the top portion of Table 4.These results are consistent with hypothesis H2. We also note that the results on the theo-rized positive effect of conflict magnitude on high-level attacks are simply not as compellingas those for prior civilian attacks. Of the ten coefficient estimates on conflict magnitude inthe top and bottom portions of Table 4, only two have the predicted sign and are significant.Meanwhile, of the eleven coefficient estimates on prior attacks, ten have the predicted signand are significant.

We reran the ZINB models in Table 4 using a less severe proxy of high-level VAC for thedependent variable, i.e. the count of attacks involving twenty-five to ninety-nine fatalities.In Table 5, coefficient estimates on prior low-level attacks are not as large in magnitude asthose in Table 4 and fewer are significant (six of eleven vs. ten of eleven). Nevertheless, allcoefficient estimates on prior low-level VAC have the correct sign, and coefficient esti-mates on conflict magnitude are never significant in the top portion of the table and sig-nificant in only three of six cases in the bottom portion.

Table 4. Continued.Estimator: Zero Inflated Negative Binomial

Dependent Variable: High-level civilian attacks (100+ fatalities per attack)

(1)Model 1Initial

(2)Model 1Refined

(3)Model 2Initial

(4)Model 2Refined

(5)Model 3Initial

(6)Model 3Refined

Log pseudolikelihoodObservationsZero ObservationsVuong z value

−131.6455304972.57***[0.005]

−132.4405324993.09***[0.001]

−131.2075304973.05***[0.001]

−132.2555324993.22***[0.001]

−123.0445304972.96***[0.002]

−123.9025314983.69***[0.000]

Notes: Robust standard errors in parentheses; p-values in brackets. For Vuong test, square bracket shows Pr>z forregressions without robust standard errors.

*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).

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Table 5. Effects of low-level civilian attacks on high-level civilian attacks.Estimator: Zero Inflated Negative Binomial

Dependent Variable: High-level civilian attacks (25 to 99 fatalities per attack)

(1)Model 1Initial

(2)Model 1Refined

(3)Model 2Initial

(4)Model 2Refined

(5)Model 3Initial

(6)Model 3Refined

Count (Civilian Attacks 25 to 99)

Constant −1.278(4.459)[0.774]

−0.352(1.138)[0.757]

−1.066(4.038)[0.792]

−1.112(0.948)[0.241]

−0.414(0.495)[0.315]

−0.611(0.860)[0.478]

Civilian Attacks,Zero Fatalitiest−1

−0.002(0.002)[0.435]

Civilian Attacks,1to4 Fatalitiest−1

0.017***(0.006)[0.006]

0.017***(0.006)[0.003]

Civilian Attacks,5to24 Fatalitiest−1

0.029***(0.010)[0.003]

0.026***(0.009)[0.004]

Conflict Magnitudet−1 0.108(0.149)[0.468]

−0.071(0.128)[0.578]

−0.113(0.205)[0.583]

Adjusted Polity2t−1 −0.091(0.056)[0.101]

−0.089*(0.053)[0.096]

−0.175***(0.045)[0.000]

−0.167***(0.049)[0.001]

−0.115**(0.052)[0.026]

−0.107**(0.050)[0.034]

Log GDP PerCapitat−1

0.084(0.229)[0.715]

−0.180(0.219)[0.410]

−0.044(0.247)[0.857]

TradeOpennesst−1

−0.040***(0.011)[0.000]

−0.040***(0.011)[0.000]

−0.026**(0.010)[0.013]

−0.029***(0.010)[0.003]

−0.028***(0.010)[0.007]

−0.027***(0.010)[0.007]

ResourceExportst−1

0.018***(0.004)[0.000]

0.019***(0.004)[0.000]

0.018***(0.004)[0.000]

0.016***(0.005)[0.001]

0.012***(0.004)[0.006]

0.013***(0.004)[0.003]

Ethnic Fractionalization 3.273***(1.167)[0.005]

2.800***(0.940)[0.003]

2.573***(0.964)[0.008]

2.643***(0.855)[0.002]

2.459***(0.951)[0.010]

2.265***(0.761)[0.003]

Log Populationt−1 −0.003(0.217)[0.988]

0.058(0.191)[0.761]

0.008(0.186)[0.965]

Inflate

Constant 0.811(1.820)[0.656]

2.441***(0.427)[0.000]

1.528(1.774)[0.389]

2.001***(0.366)[0.000]

1.827(1.395)[0.190]

2.342***(0.388)[0.000]

Civilian Attacks,Zero Fatalitiest−1

−0.134(0.094)[0.152]

−0.130(0.085)[0.127]

Civilian Attacks,1to4 Fatalitiest−1

−0.086***(0.032)[0.008]

−0.091***(0.036)[0.010]

Civilian Attacks,5to24 Fatalitiest−1

−0.497(0.495)[0.315]

−0.617(0.575)[0.283]

Conflict Magnitudet−1 −1.213(0.749)[0.106]

−1.357***(0.509)[0.008]

−3.778(2.867)[0.188]

−3.660(2.670)[0.170]

−0.805*(0.418)[0.054]

−0.682**(0.299)[0.022]

Ethnic Fractionalization 2.242(2.277)[0.325]

0.672(2.226)[0.763]

0.760(1.812)[0.675]

(Continued )

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Robustness

To test for robustness, we ran numerous additional regressions, which are available in Sup-plementary Tables S2–S10.

Alternative measures of selected independent variables

In Table S2 we replaced the Alesina et al. ethnic fractionalization measure with that fromFearon and reran the regressions of Table 4.47 The signs on the coefficient estimates ofethnic fractionalization are similar across the two measures. More important for ourstudy is that coefficient estimates on prior low-level attacks are similar in magnitudeand significance across Tables 4 and S2.

In Table S3, we added to the regressions of Table 4 a measure of ethnic polarizationfrom Alesina et al.48 Coefficient estimates for ethnic polarization in the top and bottomportions of the table are significant in only one of nine cases. Meanwhile, the coefficientestimates on prior low-level attacks are similar in magnitude and significance across Tables4 and S3.

We also replaced our conflict magnitude measure with a lagged dummy (1/0) variableindicating whether civil war was present during a country-year and reran the regressionsin Table 4. The civil war measure is constructed from the same dataset used to constructour conflict magnitude measure. In Table S4, eight of eleven coefficient estimates on priorlow-level attacks have the predicted sign and are significant. We also find evidence thatthe counts of high-level attacks rise when the severity of prior low-level attacks risesfrom zero only to one to four and then five to twenty-four fatalities. In addition, thecivil war dummy has the correct sign and is significant in eight of eleven coefficient esti-mates. We take this as evidence that both low-level prior attacks and high conflict magni-tude, i.e. civil war, significantly elevate high-level VAC attacks.

Additional independent variables

We also considered how humanitarian aid and peacekeeping affect VAC. We measurehumanitarian aid by the net official development assistance and official aid received bya country per year as a per cent of its GDP based on World Bank data.49 The resulting

Table 5. Continued.Estimator: Zero Inflated Negative Binomial

Dependent Variable: High-level civilian attacks (25 to 99 fatalities per attack)

(1)Model 1Initial

(2)Model 1Refined

(3)Model 2Initial

(4)Model 2Refined

(5)Model 3Initial

(6)Model 3Refined

Log pseudolikelihoodObservationsZero ObservationsVuong z value

−256.0775304683.89***[0.000]

−257.6395324704.52***[0.000]

−251.9895304683.43***[0.000]

−252.6315324703.89***[0.000]

−245.3415304683.55***[0.000]

−245.8095324703.97***[0.000]

Notes: Robust standard errors in parentheses; p-values in brackets. For Vuong test, square bracket shows Pr>z forregressions without robust standard errors.

*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).

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measure, official development assistance (ODA), is lagged one period. Peacekeepingdata are provided by the Stockholm International Peace Research Institute (SIPRI).50

SIPRI data show the yearly number of military troops, civilian police and observers pro-vided to various locations in the world by the United Nations, African Union and othermultilateral organizations. We developed two measures of peacekeeping personnel,total number of personnel (troops, police, observers) and number of troops only, eachlagged one period.

In Table S5 we reran the empirical models of Table 4 with ODA included. Coefficientestimates for ODA did not achieve statistical significance in any regressions, but thosefor prior low-level attacks were similar in magnitude and significance across Tables 4and S5. In Table S6, we reran the empirical models of columns 3–6 of Table 4 for eachpeacekeeping personnel measure (eight new regressions). Peacekeeping personnel hadsignificant negative effects on the count of high-level VAC in four regressions, but inothers the effects were insignificant. More important for our study is that all sixteen coeffi-cient estimates on prior low-level attacks had the correct sign and fourteen weresignificant.

In Table S7 we included a lagged dependent variable (i.e., Civilian Attacks, 100+Fatalitiest−1 and Civilian Attacks, 25to99 Fatalitiest−1) and reran the regressions forcolumns 3–6 of Tables 4 and 5 (eight new regressions). The coefficient estimates on thelagged dependent variable in the top portion of Table S7 were significant in only oneof six cases while those in the bottom portion were significant in only three of sevencases. Meanwhile, all fifteen coefficient estimates on prior low-level attacks had thecorrect sign and ten were significant.

Alternative estimators

We reran the logit models in Table 3 using rare events logit, which corrects for possibleunderestimation of rare event probabilities in finite samples.51 In Table S8, coefficient esti-mates, significances and implications from rare events logit are quite similar to those inTable 3.

Although we have included a relatively large number of control variables in our empiri-cal models, regression analysis can be prone to exclude relevant variables, which is knownas omitted variable bias. In samples in which dependent variable measures change withinstates across time, fixed effects regression can exploit within-state variations to dampenthe effects of omitted variable bias. In our study, however, fixed effects methods comewith a serious drawback. Specifically, fixed effects estimation drops all observations inwhich there is no within-state variation for the dependent variable. Hence, in logitregression, observations for states with no VAC attacks across the sample period (depen-dent variable = 0) or with attacks in each year (dependent variable = 1) are dropped. Simi-larly, in NB regression, observations for states with zero counts of VAC across time aredropped. Owing to the presence of inflated zeros in our dependent variable data, welose more than half of our sample when we apply fixed effects methods to our NB andlogit models.

Despite these drawbacks, in Tables S9 and S10 we reran the initial regressions of Tables2 and 3, respectively, with fixed effects. To increase observations, we also included a lowerthreshold of high-level civilian attacks for the dependent variable (i.e., twenty-five to

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ninety-nine fatalities per attack). Despite the large loss of observations, we still find strongempirical support for our habituation hypotheses. Specifically, all twelve coefficient esti-mates of the effects of low-level on high-level attacks are positive and significant. More-over, there is good evidence of an increase in the magnitude of effect for more severelow-level attacks.

Discussion and conclusions

Our theoretical and empirical analyses provide support for our habituation hypotheses:(H1) prior period acts of low-level VAC increase the number of high-level attacks in thepresent, and (H2) prior period acts of low-level VAC have a greater impact on thenumber of high-level attacks in the present the greater the severity of the priorperiod acts of low-level violence. Of the 127 coefficient estimates for the effects ofprior low-level attacks on current high-level attacks in Tables 2–5 and SupplementaryTables S2–S10, 122 have the predicted sign of which 103 are significant. Further, ofthe seventy-six regressions across these tables that assessed correlates for the countor presence of high-level attacks and in which the measure for prior low-levelattacks increased in severity (e.g. from zero only to one to four to five to twenty-four fatalities), sixty-three show an increase in the magnitude of effect for moresevere low-level attacks.

Our results are generally robust across alternative measures for control variables,additional control variables and various estimation methods. We also find in many ofour regressions that prior and more severe low-level civilian attacks better predict high-level attacks than conflict magnitude, although this result did not hold when the conflictmeasure was the presence of civil war. In numerous ZINB regressions that did not make itinto our supplementary tables, we found that Adjusted Polity2, GDP per capita, tradeopenness and resource exports rarely achieved statistical significance in the inflateportion of the model.

In addressing the risk of high-level VAC attacks, our results can be helpful to scholars,policymakers and activists focusing on atrocity prevention. Our major message is that‘small’ civilian attacks matter, certainly in their own right, but also for the prevention ofmore serious attacks later on. Small attacks (perhaps often below the recognition of theinternational community) can lead to deteriorations in norms against attacking civiliansamong state, rebel and militia actors, which in turn can escalate the number and severityof VAC events.

Future work on atrocity prevention can use regional and global data on ‘small’ VAC inci-dents as early warning indicators of more severe atrocities. For Africa (and several Asianstates) ACLED’s dataset is frequently updated and, as already noted, tracks VAC incidentseven when attacks involve zero fatalities. For global VAC, the Political Instability TaskForce’s Worldwide Atrocities Dataset tracks incidents in which as few as five civilians arekilled and their data is updated regularly.52 VAC incidents involving as few as twenty-five civilians killed are tracked by UCDP’s One-Sided Violence Dataset, which is updatedyearly.53 Finally, UCDP’s Georeferenced Event Dataset provides regularly updated dataon civilian attacks in Africa, the Middle East and Asia in which as few as one civilian iskilled.54

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Notes

1. Charles H. Anderton, ‘Datasets and trends of genocides, mass killings, and other civilian atro-cities’’, in Charles H. Anderton and Jurgen Brauer (eds.), Economic aspects of genocides, othermass atrocities, and their prevention (New York: Oxford University Press, 2016), pp. 52–101.

2. Clionadh Raleigh and Caitriona Dowd, ‘Armed conflict location and event data project (ACLED)codebook 2015’, 2015, p. 13, available at: http://www.acleddata.com/wp-content/uploads/2015/01/ACLED_Codebook_2015.pdf (accessed 4 June 2015).

3. To focus on groups that are likely to support or contest the state, we exclude inter-communalVAC from our sample.

4. Stathis N. Kalyvas, The logic of violence in civil war (New York: Cambridge University Press,2006); Benjamin Valentino, Final solutions: mass killing and genocide in the 20th century(Ithaca, NY: Cornell University Press, 2004).

5. Kalyvas, The logic of violence, ch. 5; Benjamin Valentino, Paul Huth and Dylan Balch-Lindsay,‘“Draining the sea”: mass killing and guerrilla warfare’, International Organization, Vol. 58,No. 2, 2004, pp. 375–407; Jeremy M. Weinstein, Inside rebellion: the politics of insurgent violence(New York: Cambridge University Press, 2007).

6. Christian Davenport, ‘State repression and political order’, Annual Review of Political Science,Vol. 10, 2007, pp. 1–23.

7. Reed M. Wood and Jacob D. Kathman, ‘Competing for the crown: inter-rebel competition andcivilian targeting in civil war’, Political Research Quarterly, Vol. 68, No. 1, 2015, pp. 167–179;Reed M. Wood, ‘Rebel capability and strategic violence against civilians’, Journal of PeaceResearch, Vol. 47, No. 5, 2010, pp. 601–614; Idean Salehyan, David Siroky and ReedM. Wood, ‘External rebel sponsorship and civilian abuse: a principal-agent analysis of wartime atrocities’, International Organization, Vol. 68, No. 3, 2014, pp. 633–661; Weinstein,Inside rebellion.

8. Elisa von Joeden-Forgey, ‘Gender and the genocidal economy’, in Anderton and Brauer, Econ-omic aspects of genocides, pp. 378–395.

9. Hanne Fjelde and Lisa Hultman, ‘Weakening the enemy: a disaggregated study of violenceagainst civilians in Africa’, Journal of Conflict Resolution, Vol. 58, No. 7, 2014, pp. 1230–1257;Geoffrey Robinson, ‘State-sponsored violence and secessionist rebellions in Asia’, in DonaldBloxham and A. Dirk Moses (eds.), The Oxford handbook of genocide studies (New York:Oxford University Press, 2010), pp. 466–488; Leo Kuper, The pity of it all: polarisation ofracial and ethnic relations (London: Duckworth, 1977).

10. Gregory H. Stanton, ‘The 8 stages of genocide’, Genocide Watch, 1998, available at: http://www.genocidewatch.org/genocide/8stagesofgenocide.html (accessed 4 June 2015).

11. James E. Waller, Becoming evil: how ordinary people commit genocide and mass killing(New York: Oxford University Press, 2007), p. 201.

12. Barbara Harff, ‘No lessons learned from the Holocaust? Assessing risks of genocide and politi-cal mass murder since 1955’, American Political Science Review, Vol. 97, No. 1, 2003, pp. 57–73;Rudolph J. Rummel, Statistics of democide: genocide and mass murder since 1900 (Piscataway,NJ: Transactions Publishers, 1998).

13. Ervin Staub, The roots of evil: the origins of genocide and other group violence (New York:Cambridge University Press, 1989), p. 65.

14. Harff, ‘No lessons learned’.15. James Ron (ed.), ‘Paradigm in distress? Primary commodities and civil war’, Journal of Conflict

Resolution, Vol. 49, No. 4, 2005, Special Issue, pp. 441–633; Anke Hoeffler, ‘On the causes of civilwar’, in Michelle R. Garfinkel and Stergios Skaperdas (eds.), The Oxford handbook of the econ-omics of peace and conflict (New York: Oxford University Press, 2012), pp. 179–204.

16. Salehyan et al., ‘External rebel sponsorship’, and Weinstein, Inside rebellion, find that whenrebels have good internal access to natural resources and they receive external supportfrom non-democracies and multiple supporters, they are more likely to conduct VAC.

17. Another plausible explanation for correlation between past and present atrocities is bureau-cratic inertia, which is often empirically modelled using a lagged dependent variable. Below

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we find empirical support for our habituation hypotheses even after including a laggeddependent variable.

18. Staub, The roots of evil, p. 68.19. Waller, Becoming evil, p. 232.20. Kalyvas, The logic of violence, p. 58.21. For surveys of empirical studies of mass atrocity risks, see Anke Hoeffler, ‘Development and the

risk of mass atrocities: an assessment of the empirical literature’, in Anderton and Brauer, Econ-omic aspects of genocides, pp. 230–250; and Charles R. Butcher and Benjamin E. Goldsmith,‘Economic risk factors and predictive modeling of genocides and other mass atrocities’, inAnderton and Brauer, Economic aspects of genocides, pp. 569–590.

22. J. Michael Quinn, ‘Territorial contestation and repressive violence in civil war’, Defence andPeace Economics, Vol. 26, No. 5, 2015, pp. 536–554; Wood and Kathman, ‘Competing forthe crown’; Reed M. Wood, ‘Opportunities to kill or incentives for restraint? Rebel capabili-ties, the origins of support, and civilian victimization in civil war’, Conflict Managementand Peace Science, Vol. 31, No. 5, 2014, pp. 461–480; Salehyan et al., ‘External rebelsponsorship’.

23. Kristine Eck and Lisa Hultman, ‘One-sided violence against civilians in war: insights from newfatality data’, Journal of Peace Research, Vol. 44, No. 2, 2007, pp. 233–246; Reed M. Wood, JacobD. Kathman and Stephen E. Gent, ‘Armed intervention and civilian victimization in intrastateconflict’, Journal of Peace Research, Vol. 49, No. 5, 2012, pp. 647–660.

24. Lisa Hultman, ‘Attacks on civilians in civil war: targeting the Achilles heel of democraticgovernments’’, International Interactions, Vol. 38, No. 2, 2012, pp. 164–181; Wood et al.,‘Armed intervention’.

25. Quinn, ‘Territorial contestation’; Philip Hultquist, ‘Is collective repression an effective counter-insurgency technique? Unpacking the cyclical relationship between repression and civil con-flict’, Conflict Management and Peace Science, 2015, doi:10.1177/0738894215604972; Hultman,‘Attacks on civilians’; Wood, ‘Rebel capability’.

26. Uih Ran Lee, ‘Hysteresis of targeting civilians in armed conflict’, The Economics of Peace andSecurity Journal, Vol. 10, No. 2, 2015, pp. 31–40.

27. Wood, ‘Opportunities to kill’; Hultman, ‘Attacks on civilians’.28. Wood and Kathman, ‘Competing for the crown’; Hultman, ‘Attacks on civilians’; Salehyan et al.,

‘External rebel sponsorship’.29. Hultquist, ‘Is collective repression’; Quinn, ‘Territorial contestation’.30. Martin Ottmann, ‘Rebel constituencies and rebel violence against civilians in civil conflicts’,

Conflict Management and Peace Science, 2015, doi:10.1177/0738894215570428; Wood andKathman, ‘Competing for the crown’; Salehyan et al., ‘External rebel sponsorship’.

31. Sebastian Schuttee, ‘Geographic determinants of indiscriminate violence’, Conflict Manage-ment and Peace Science, 2015, doi:10.1177/0738894215593690; Fjelde and Hultman, ‘Weaken-ing the enemy’.

32. Exceptions in Table S1 are Yuri Zhukov, ‘On the logistics of violence: evidence from Stalin’sgreat terror, Nazi-occupied Belarus, and modern African civil wars’, in Anderton and Brauer,Economic aspects of genocides, pp. 399–424; Wood and Kathman, ‘Competing for thecrown’; and Reed M. Wood, ‘From loss to looting? Battlefield costs and rebel incentives for vio-lence’, International Organization, Vol. 68, No. 4, 2014, pp. 979–999.

33. For defences and criticisms of rational choice theory in the study of VAC, see, respectively,Charles H. Anderton and Jurgen Brauer, ‘Genocide and mass killing risk and prevention: per-spectives from constrained optimization models’, in Anderton and Brauer, Economic aspects ofgenocides, pp. 143–171; and Manus I. Midlarsky, The killing trap: genocide in the twentiethcentury (New York: Cambridge University Press, 2005), pp. 64–74.

34. George A. Akerlof and Rachel E. Kranton, ‘Economics and identity’, The Quarterly Journal ofEconomics, Vol. 115, No. 3, 2000, pp. 715–753.

35. Cobb-Douglas is the most widely taught specific functional form in economics and is availablein virtually all intermediate microeconomics textbooks.

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36. On social psychology and atrocity habituation, see Waller, Becoming evil, pp. 232–233. Aseminal article on rational addiction is Gary S. Becker and Kevin M. Murphy, ‘A theory ofrational addiction’, The Journal of Political Economy, Vol. 96, No. 4, 1988, pp. 675–700.

37. Walter Nicholson and Christopher Snyder, Microeconomic theory: basic principles and exten-sions, 11th edn. (Mason, OH: South-Western, 2012), p. 113.

38. Much more data work is necessary to estimate simultaneous equations for attacks by govern-ments, government-aligned militias, rebels, rebel-aligned militias and independent militiasbecause ACLED does not code militia attacks across the various militia categories. None ofthe studies in Table S1 that estimate separate equations for VAC by government and rebelsuse simultaneous equation methods.

39. Nils Petter Gleditsch, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg and HåvardStrand, ‘Armed conflict, 1946–2001’, Journal of Peace Research, Vol. 39, No. 5, 2002, pp. 615–637; Therése Pettersson and Peter Wallensteen, ‘Armed conflict, 1946–2014’, Journal ofPeace Research, Vol. 52, No. 4, 2015, pp. 536–550.

40. Monty G. Marshall, Ted Robert Gurr and Keith Jaggers, ‘Polity IV project, political regimecharacteristics and transitions, 1800–2014’, 2014, available at: http://www.systemicpeace.org/inscrdata.html (accessed 4 June 2015).

41. James Raymond Vreeland, ‘The effect of political regime on civil war: unpacking anocracy’,Journal of Conflict Resolution, Vol. 52, No. 3, 2008, pp. 401–425.

42. World Bank, World Development Indicators, available at: http://data.worldbank.org/indicator(accessed 4 June 2015).

43. World Bank, World Development Indicators.44. World Bank, World Development Indicators.45. Alberto Alesina, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Wacziarg,

‘Fractionalization’, Journal of Economic Growth, Vol. 8, No. 2, 2003, pp. 155–194. Let pi representthe population share of group i such that

∑pi = 1. Then Ethnic Fractionalization = 1−∑

p2i .Later, we also consider Alesina et al.’s Ethnic Polarization, which is measured as 4

∑p2i (1− pi)

(Jose G. Montalvo and Marta Reynal-Querol, ‘Discrete polarisation with an application to thedeterminants of genocides’, The Economic Journal, Vol. 118, November 2008, pp. 1835–1865). When using these measures, we normalize group shares to sum to 1 and then applythe preceding formulas.

46. World Bank, World Development Indicators.47. Alesina et al., ‘Fractionalization’; James D. Fearon, ‘Ethnic and cultural diversity by country’,

Journal of Economic Growth, Vol. 8, No. 2, 2003, pp. 195–222, available at: https://web.stanford.edu/group/fearon-research/cgi-bin/wordpress/paperspublished/journal-articles-2/(accessed 6 June 2015).

48. Alesina et al., ‘Fractionalization’.49. World Bank, World Development Indicators, available at: http://databank.worldbank.org/data/

(accessed 18 May 2016).50. At the time of this writing, SIPRI’s peacekeeping dataset (https://www.sipri.org/databases/pko)

was unavailable. Data for 1996–2012 were generously provided by SIPRI. Peacekeeping datafor 2013 and 2014 came from the 2014 and 2015 volumes of SIPRI yearbook: armaments, dis-armament and international security (New York: Oxford University Press).

51. Gary King and Langche Zeng, ‘Explaining rare events in international relations’, InternationalOrganization, Vol. 55, No. 3, 2001, pp. 693–715; Michael Tomz, Gary King and LangcheZeng, ‘RELOGIT: rare events logistic regression’, Version 1.1, Harvard University, Cambridge,MA, 1999, available at: http://gking.harvard.edu/relogit (accessed 21 October 2011).

52. Political Instability Task Force Worldwide Atrocities Dataset. Data available at: http://eventdata.parusanalytics.com/data.dir/atrocities.html (accessed 25 February 2016).

53. Eck and Hultman, ‘One-sided violence’. Data available at: http://www.pcr.uu.se/research/ucdp/datasets/ucdp_one-sided_violence_dataset/ (accessed 25 February 2016).

54. Mihai Croicu and Ralph Sundberg, ‘UCDP GED codebook version 4.0’, Department of Peaceand Conflict Research, Uppsala University, 2015, available at: http://ucdp.uu.se/downloads/ged/ucdp-ged-40-codebook.pdf (accessed 24 July 2016).

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Acknowledgements

We are grateful to Robert Baumann, Bryan Engelhardt, Katherine Kiel, Jens Meierhenrich, A. DirkMoses and two anonymous referees for helpful insights on earlier drafts. We also gratefully acknowl-edge support from the Holy Cross College Summer Research Program. We alone are responsible forany errors and omissions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Charles H. Anderton is Professor of Economics and W. Arthur Garrity, Sr. Professor of Human Nature,Ethics and Society at the College of the Holy Cross (Worcester, MA, USA). His research interestsinclude economic aspects of genocides, the bargaining theory of war and rational choice aspectsof violent behaviour. His teaching interests include the following courses: ‘Economics of war andpeace’ and ‘Genocide and mass killing: perspectives from the social sciences’. He is co-editor, withJurgen Brauer, of Economic Aspects of Genocides, Other Mass Atrocities, and Their Prevention(Oxford University Press, 2016).

Edward V. Ryan is an investment analyst at Ballentine Partners, a multi-family office in the Bostonarea where he focuses on portfolio strategy and implementation. He conducted research in conflicteconomics as a summer research assistant and as an honours student at the College of the HolyCross (Worcester, MA). His honours thesis, ‘The Risk Correlates of Violence against Civilians inAfrica’, earned the College’s Freeman M. Saltus award for the best undergraduate paper in econ-omics for the 2014/2015 academic year.

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