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DOI: 10.1126/science.1222240, 858 (2012);336 Science

et al.Joan EstebanEthnicity and Conflict: Theory and Facts

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REVIEW

Joan Esteban,1 Laura Mayoral,1 Debraj Ray1,2*

Over the second half of the 20th century, conflicts within national boundaries became increasinglydominant. One-third of all countries experienced civil conflict. Many (if not most) such conflicts involvedviolence along ethnic lines. On the basis of recent theoretical and empirical research, we provide evidencethat preexisting ethnic divisions do influence social conflict. Our analysis also points to particular channelsof influence. Specifically, we show that two different measures of ethnic division—polarization andfractionalization—jointly influence conflict, the former more so when the winners enjoy a “public”prize (such as political power or religious hegemony), the latter more so when the prize is “private”(such as looted resources, government subsidies, or infrastructures). The available data appear tostrongly support existing theories of intergroup conflict. Our argument also provides indirect evidencethat ethnic conflicts are likely to be instrumental, rather than driven by primordial hatreds.

There are two remarkable facts about socialconflict that deserve notice. First, within-country conflicts account for an enormous

share of deaths and hardship in the world today.Figure 1 depicts global trends in inter- and in-trastate conflict. Since the Second World War,there have been 22 interstate conflicts with morethan 25 battle-related deaths per year, and 9 ofthem have killed at least 1000 over the entirehistory of conflict (1). The total number of at-tendant battle deaths in these conflicts is es-timated to be around 3 to 8 million (2). The sameperiod witnessed 240 civil conflicts with morethan 25 battle-related deaths per year, and almosthalf of them killed more than 1000 (1). Estimatesof the total number of battle deaths are in therange of 5 to 10 million (2). Added to the directcount of battle deaths are the 25 million non-combatant civilian (3) and indirect deaths due todisease and malnutrition, which have been esti-mated to be at least four times as high as violentdeaths (4), as well as the forced displacements ofmore than 40 million individuals by 2010 (5). In2010 there were 30 ongoing civil conflicts (6).

Second, internal conflicts often appear to beethnic in nature. More than half of the civil con-flicts recorded since the end of the SecondWorldWar have been classified as ethnic or religious(3, 7). One criterion for a conflict to be classifiedas ethnic is that it involves a rebellion against thestate on behalf of some ethnic group (8). Suchconflicts involved 14% of the 709 ethnic groupscategorized worldwide (9). Brubaker and. Laitin,examining the history of internal conflicts in thesecond half of the 20th century, are led to re-mark on “the eclipse of the left-right ideologicalaxis” and the “marked ethnicization of violent

challenger-incumbent contests” (10). Horowitz,author of a monumental treatise on the subjectof ethnic conflict, observes that “[t]he Marxianconcept of class as an inherited and determinativeaffiliation finds no support in [the] data. Marx’sconception applies with far less distortion to eth-nic groups.… In much of Asia and Africa, it isonly modest hyperbole to assert that the Marxianprophecy has had an ethnic fulfillment” (11).

The frightening ubiquity ofwithin-country con-flicts, as well as their widespread ethnic nature,provokes several questions. Do “ethnic divisions”predict conflict within countries? How do we con-ceptualize those divisions? If it is indeed true thatethnic cleavages and conflicts are related, howdo we interpret such a result? Do “primordial,”ancestral ethnic hatreds trump “more rational”forms of antagonism, such as the instrumentaluse of ethnicity to achieve political power oreconomic gain? To discuss and possibly answersome of these questions is the goal of this review.

Class and Ethnicity as Drivers of ConflictThe study of human conflict is (and has been) acentral topic in political science and sociology.Economics—with relatively few and largely re-cent exceptions—has paid little attention to theissue. [For three recent overviews, see (12–14).]Perhaps textbook economics, with its traditionalrespect for property rights, often presumes thatthe economic agents it analyzes share that respectand do not violently challenge allocations per-ceived to be unfair. Yet one of the notable excep-tions in economics—Marx—directly or indirectlydominates the analytical landscape on conflict inthe rest of the social sciences. Class struggle, ormore generally, economic inequality, has beenviewed as the main driver of social conflict inindustrial or semi-industrial society (15). In Sen’swords, “the relationship between inequality andrebellion is indeed a close one” (16).

Yet, intuitive as it might seem, this relationshipdoesn’t receive emphatic empirical endorsement.In a detailed survey paper on the many attemptsto link income inequality and social conflictempirically, Lichbach mentions 43 papers on thesubject, some “best forgotten” (17). The evidenceis thoroughly mixed, concludes Lichbach, as hecites a variety of studies to support each possiblerelationship between the two, and others thatshow no relationship at all. Midlarsky remarkson the “fairly typical finding of a weak, barelysignificant relationship between inequality andpolitical violence … rarely is there a robust rela-tionship between the two variables” (18).

The emphasis on economic inequality as acausal correlate of conflict seems natural, andthere is little doubt that carefully implementedtheory will teach us how to better read the data(see below). Yet it is worth speculating on whythere is no clear-cut correlation. Certainly, eco-nomic demarcation across classes is a two-edgedsword: While it breeds resentment, the very

1Institut d'Anàlisi Económica, CSIC andBarcelonaGSE, ES08193,Spain. 2New York University, New York, NY 10012, USA.

*To whom correspondence should be addressed. E-mail:[email protected]

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Fig. 1. Armed conflicts by type. Sources: Databased on UCDP/PRIO armed conflict database. Conflictsinclude cases with at least 25 battle deaths in a single year.

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poverty of the have-nots separates them fromthe means for a successful insurrection. In addi-tion, redistribution across classes is invariably anindirect and complex process.

The use of noneconomic “markers” such asethnicity or religion addresses both these issues.Individuals on either side of the ethnic divide willbe economically similar, so that the gains fromsuch conflict are immediate: The losing group canbe excluded from the sector in which it directlycompetes with the winners [e.g., (11, 19, 20)]. Inaddition, each group will have both poor andrich members, with the former supplying conflictlabor and the latter supplying con-flict finances (21). This suggests aninteresting interaction between in-equality and ethnicity, by which eth-nic groups with a higher degree ofwithin-group inequality will be moreeffective in conflict (22). Moreover, ithas been suggested that “horizontal”inequality (i.e., inequality across eth-nic groups) is an important correlateof conflict (23–26).

There are two broad views on theethnicity-conflict nexus [e.g., (10, 27)].The “primordialist” view (28, 29)takes the position that ethnic differ-ences are ancestral, deep, and irrec-oncilable and therefore invariablysalient. In contrast, the “instrumental”approach pioneered by (19) and dis-cussed in (10) sees ethnicity as astrategic basis for coalitions that seeka larger share of economic or politicalpower. Under this view, ethnicity is adevice for restricting the spoils to a smaller set ofindividuals. Certainly, the two views interact. Ex-clusion is easier if ethnic groups are geographicallyconcentrated (30, 31). Strategic ethnic conflict couldbe exacerbated by hatreds and resentments—perhaps ancestral, perhaps owing to a recent clashof interests—that are attached to the markersthemselves. Finally, under both these views, in eth-nically divided societies democratic agreementsare hard to reach and once reached, fragile (32);the government will supply fewer goods and ser-vices and redistribute less (33, 34); and societywill face recurrent violent conflict (11).

Either approach raises the fundamental ques-tion of whether there is an empirical, potentiallypredictive connection between ethnic divisions andconflict. To address that question, we must firstdefine what an “ethnic division” is. Various mea-sures of ethnic division or dominance (35–37)have been proposed. The best-known off-the-shelfmeasure of ethnic division is the fractionalizationindex, first introduced in the 1964 edition of theSoviet Atlas Narodov Mira, to measure ethno-linguistic fragmentation. It equals the probabil-ity that two individuals drawn at random fromthe society will belong to two different groups (seeBox 1 for a precise definition). Ethnic fractional-

ization has indeed been usefully connected to percapita gross domestic product (GDP) (38), eco-nomic growth (39), or governance (40). But(7, 35, 41, 42) do not succeed in finding a con-nection between ethnic or religious fractionaliza-tion and conflict, though it has been suggestedthat fractionalization appears to work better forsmaller-scale conflicts, such as ethnic riots (43).By contrast, variables such as lowGDP per capita,natural resources, environmental conditions favor-ing insurgency, or weak government are often sta-tistically significant correlates of conflict (12, 44).Fearon and Laitin conclude that the observed

“pattern is thus inconsistent with… the commonexpectation that ethnic diversity is a major anddirect cause of civil violence” (7).

But the notion of “ethnic division” is com-plex and not so easily reduced to a measure ofdiversity. The discussion that follows will intro-duce a different measure—polarization—that bet-ter captures intergroup antagonism. As we shallsee, polarization will be closely connected tothe incidence of conflict; moreover, with a mea-sure of polarization in place and controlled for,fractionalization, too, will matter for conflict.

Fractionalization and PolarizationAs already discussed, the index of fractionalizationis commonly used to describe the ethnic structureof a society (see Box 1). This index essentiallyreflects the degree of ethnic diversity. Whengroups are of equal size, the index increases withthe number of groups. It reaches amaximumwheneveryone belongs to a different group.

When one is interested in social conflict, thismeasure does not seem appropriate on at least twocounts. First, as social diversity increases beyond apoint, intuition suggests that the likelihood of con-flict would decrease rather than increase. After all,group size matters. The fact that “many are in this

together” provides a sense of group identity in timesof conflict.Moreover, groups need aminimum sizeto be credible aggressors or opponents. Second,not all groups are symmetrically positioned withrespect to other groups, though the measure im-plicitly assumes they are. A Pushtun saying is il-lustrative: “Me against my brothers, me and mybrothers against my cousins, me and my cousinsagainst the world.” The fractionalization mea-sure can be interpreted as saying that every pairof groups is “equally different.”Often, they are not.

Consider now the notion of polarization asintroduced in (45–47). Polarization is designed to

measure social “antagonism,” which isassumed to be fueled by two factors:the “alienation” felt between membersof different groups and the sense of“identification”with one’s own group.This index is defined as the aggregationof all interpersonal antagonisms. Its keyingredients are intergroup distances(how alien groups are from each other)and group size (an indicator of the levelof the group identification). Using anaxiomatic approach (45, 48), we obtainthe specific form used in this article; seeBox 1 for the precise formula.

In any society with three or moreethnic groups, the polarization measurebehaves very differently from fraction-alization. Unlike fractionalization, po-larization declines with the continuedsplintering of groups and is globallymaximized for a bimodal distributionof population. This is shown in Fig. 2,where groups are always of equal size

and intergroup distances are equal to 1. Rather thanbeing two different (but broadly related) ways ofmeasuring the same thing, the two measures em-phasize different aspects of a fundamentally multi-dimensional phenomenon. As we shall see, thedifferences have both conceptual and empirical bite.For instance, Montalvo and Reynal-Querol (49),using a simplified version of the index of polariza-tion, show that ethnic polarization is a significantcorrelate of civil conflict, whereas fractionalizationis not. Their contribution provides the first pieceof serious econometric support for the proposi-tion that “ethnic divisions” might affect conflict.

Despite their divergent performance in em-pirical work, the two measures are linked. In-deed, they are even identical if (i) group identitydoes not play a role and (ii) individuals feel equallyalienated frommembers of all other groups.Whichindex is best to use is therefore determined by thenature of the problem at hand: on whether thesense of identity, of intergroup differentiation, orboth are relevant. Group identification matterswhen we face problems of public import, in whichthe payoffs to the entire community jointly matter.Intergroup differentiation is relevant wheneverthe specific cultural characteristics of the othergroups affect the policies that they choose, and

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Fig. 2. Polarization, fractionalization, and the number of groups. In this illus-tration, all groups are of equal size, and intergroup distances are set equal to 1.

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therefore create implications for any one group.In contrast, if social groups compete for narroweconomic gains that accrue to the winners andare excludable from the losers, no opponent’svictory means more or less than any other. In thetheory that we outline below, these are preciselythe factors that receive greatest emphasis.

Marrying Theory and FactsA systematic econometric exploration of the linksbetween ethnic divisions and conflict will generallytake the form of a multivariate regression. The“dependent variable” we seek to explain is somemeasure of conflict. On the other side of the re-gression is ourmain “independent variable,”whichis a particular measure of “ethnic divisions,” aswell as a host of “control variables” that are in-cluded to capture other influences on conflict thatwe seek to filter out. This much is evident. Theproblem is (and this is true of empirical researchmore generally) that little discipline is often imposedon the specification of that regression. Much of thatresearch involves the kitchen-sink approach of in-cluding all variables—usually linearly—that couldpossibly play a role in ethnic conflict. Such an ap-proach is problematic on at least three counts.First, the number of plausible variables is un-bounded, not just in principle but apparently also inpractice: 85 different variables have been used inthe literature (50). Trying them out in various hope-ful combinations smacks uncomfortably of data-mining. Second, even if we could narrow downthe set of contenders, there are many ways tospecify the empirical equation that links thosevariables to conflict. Finally, the absence of atheory hinders the interpretation of the results.

From a statistical perspective, fractionaliza-tion and polarization are just two, seeminglyequally reasonable, ways of measuring ethnic di-visions. Yet they yield very different results inconnecting ethnicity to conflict. Do we acceptthis inconsistency as yet another illustration of“measurement error”? Or is there somethingdeeply conceptual buried here?

The results we are going to present are ob-tained from an explicit game-theoretic model ofconflict. We then bring the predicted equilibriumof this model to data. This allows us both to testthe theory and to suitably interpret the results.Perhaps the most important contribution of thetheory is that it permits both polarization andfractionalization as joint drivers of conflict andexplains precisely when one measure acquiresmore explanatory salience than the other.

We begin by presenting the recent analysisthat links polarization and fractionalization toequilibrium conflict (48). We then describe someof the empirical findings obtained in (51) whenconfronting the predictions of the model with data.

Polarization, Fractionalization, and Conflict: TheoryA situation of open civil conflict arises when anexisting social, political, or economic arrangement

is challenged by an ethnic group. Whether theethnic marker is focal for instrumental or primor-dial reasons is an issue that we’ve remarked onearlier, but at this stage it is irrelevant for our pur-pose. [For more on ethnic salience, see (52–54).]In such a situation, the groups involved will un-dertake costly actions (demonstrations, provoca-tions, bombs, guerrilla or open warfare) to increasetheir probability of success.We view the aggregateof all such actions as the extent of conflict.

More precisely, suppose that there arem groupsengaged in conflict. Think of two types of stakes orprizes in case of victory. One kind of prize is“public,” the individual payoff from which isundiluted by one’s own group size. For instance,the winning group might impose its preferrednorms or culture: a religious state, the abolition ofcertain rights or privileges, the repression of alanguage, the banning of political parties, and soon. Or it might enjoy political power or thesatisfaction of seeing one’s own group vindicatedor previous defeats avenged. Let uij be the payoffexperienced by an individual member of group iin the case in which group jwins and imposes itspreferred policy; we presume that uii > uij, whichis true almost by definition. This induces a no-tion of “distance” across groups i and j: dij≡ uii− uij,which can be interpreted as the loss to i of livingunder the policy implemented by j. Note that amember of group i might prefer j rather than kto be in power, and that will happen preciselywhen dij < dik.

The money-equivalent value of the publicpayoffs—call it p—tells us how much moneyindividuals are ready to give up to bring the im-plemented policy “one unit” closer to one’s ownideal policy. Its value depends in part on the ex-tent to which the group in power can imposepolicies or values on the rest of society. Thus, amember of group i assigns a money value ofuijp to the ideal policy of group j.

The other type of prize is “private.” Ex-amples include the material benefits obtainedfrom administrative or political positions, specifictax breaks, directed subsidies, bias in the allo-cation of public expenditure and infrastructures,access to rents from natural resources, or justplain loot. Private payoffs have two essentialproperties. First, group size dilutes individualbenefits: The larger the group, the smaller is thereturn from a private prize for any one groupmember. Second, the identity of the winner isirrelevant to the loser since, in contrast to the“public” case, the loser is not going to extractany payoff from that fact. (If there are differen-tial degrees of resentment over the identity ofthe winner, simply include this component underthe public prize.) Let m be the per capita moneyvalue of the private prize at stake.

Individuals in each group expend costly re-sources (time, effort, risk) to influence the prob-ability of success. Conflict is defined to be thesum of all these resources over all individuals and

all groups. The winners share the private prizeand get to implement their favorite policies (thepublic prize). The losers have to live with the po-licies chosen by the winners. A conflict equilib-rium describes the resulting outcome. (“Conflictequilibrium” perhaps abuses semantics to an un-acceptable degree, our excuse being that we ob-serve the game-theoretic tradition of describingthe noncooperative solution to a game as a Nash“equilibrium.”) It is a vector of individual actionssuch that each agent’s behavior maximizes ex-pected payoffs in the conflict, given the choicesmade by all other individuals. Note that by theword “payoff” we don’t mean only some narrowmonetary amount, but also noneconomic returns,such as political power or religious hegemony.

But what does the maximization of payoffsentail? Individuals are individuals, but they alsohave a group identity. To some extent an indi-vidual will act selfishly, and to some extent he orshe will act in the interest of the ethnic group. Theweight placed on the group versus the individualwill depend on several factors (some idiosyncrat-ic to the individual), but a large component willdepend on the degree of group-based cohesion inthe society; we return to this below. Formally, wepresume that an individual places a weight of aon the total payoff of his or her group, in additionto their own payoff.

Let usmeasure the intensity of conflict—call itC—by the money value of the average, per capitalevel of resources expended in conflict. In (48) weargue that in equilibrium, the eventual across-groupvariation in the per capita resources expendedhas a minor effect on the aggregate level of con-flict. Thus, in practice the population-normalizedintensity of conflict C can be approximated wellby ignoring this variation, and this simplificationyields the approximate formula

C

pþ m≃ a½lP þ ð1 − lÞF� ð1Þ

for large populations, where l ≡ p/(p + m) is therelative publicness of the prize, F is the frac-tionalization index, and P is a particular memberof the family of polarization measures describedearlier, constructed using intergroup distancesdij derived from “public” payoff losses. (Box 1describes these measures more formally and alsoprovides a more general version of Eq. 1.) Thus,the theory tells us precisely which notions of eth-nic division need to be considered. Moreover, therelationship has a particular form, which informsthe empirical analysis.

This result highlights the essential role oftheory for meaningful empirical work. The exog-enous data of the model—individual preferences,group size, the nature and the size of the prize,and the level of group cohesion—all interact ina special way to determine equilibrium conflictintensity. The theory shows, first, that it sufficesto aggregate all the information on preferences and

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group sizes into just two indices—F and P—capturing different aspects of the ethnic com-position of a country. Second, the weights onthe two distributional measures depend on thecomposition of the prize and on the level ofgroup commitment. In particular, the publicnessof the prize (reflected in a high value of l) re-inforces the effect of polarization, whereas highprivateness of the prize (low l) reinforces the ef-fect of fractionalization. Not surprisingly, highgroup cohesion a enhances the effect of bothmeasures on conflict.

The publicness of the prize is naturally con-nected to both identification and alienation—andtherefore to polarization. With public payoffs,group size counts twice: once, because the pay-offs accrue to a larger number, and again, becausea larger number of individuals internalize that ac-crual and therefore contribute more to the con-flict. Intergroup distances matter, too: The precisepolicies interpreted by the eventual winner con-tinue to be a cause of concern for the loser. Boththese features—the “double emphasis” on groupsize and the use of distances—are captured by thepolarization measure P; see Box 1 for more de-tails. By contrast, when groups fight for a privatepayoff—say money—one winner is as bad as an-other as long as my group doesn’t win, and mea-sures based on differences in intergroup alienationbecome useless. Moreover, with private payoffs,group identification counts for less than it doeswith public payoffs, as group size erodes the percapita gain from the prize. The resulting indexthat is connected to this scenario is one of frac-tionalization (see Box 1).

In short, the theory tells us to obtain data onP and F and combine them in a particular way.It tells us that when available, we should attemptto obtain society-level data for group cohesion aand relative publicness l and enter them in theway prescribed by Eq. 1. With this in mind, wenow bring the theory to the data.

Taking the Theory to DataWe study 138 countries over 1960 to 2008, withthe time period divided into 5-year intervals. Thatyields a total of 1125 observations (in mostcases). Some of the variables in the theory are notdirectly observable, and so we will use proxies.For a complete set of results, see (51) and theaccompanying Web Appendix.

We measure conflict intensity in two ways.The first is the death toll. Using data from thejointly maintained database under the UppsalaConflict Data Program and the Peace ResearchInstitute of Oslo (UCDP/PRIO) (1), we constructa discrete measure of conflict—PRIO-C—forevery 5-year period and every country as fol-lows: PRIO-C is equal to 0 if the country is atpeace in those 5 years; to 1 if it has experiencedlow-intensity conflict (more than 25 battle-relateddeaths but less than 1000) in any of these years;or to 2 if the country has been in high-level

conflict (more than 1000 casualties) in any of the5 years. Despite the overall popularity of UCDP/PRIO, this is an admittedly coarse measure ofdeaths, based on only three categories (peace, lowconflict, and high conflict) defined according toad hoc thresholds, and it reports conflicts onlywhen one of the involved parties is the state. Toovercome these two problems, we use a secondmeasure of intensity: the Index of Social Conflict(ISC) computed by the Cross-National Time-SeriesData Archive (55). It provides a continuous mea-sure of several manifestations of social unrest,with no threshold dividing “peace” from “war.”The index ISC is formed by taking a weightedaverage over eight different manifestations of in-ternal conflict, such as politically motivated as-sassinations, riots, guerrilla warfare, etc.

Our core independent variables are the in-dices F and P. To compute these indices, we needthe population size of different ethnic groups forevery country and a proxy for intergroup dis-tances. For demographic information on groups,we use the data set provided by (9), which iden-tifies over 800 “ethnic and ethno-religious” groupsin 160 countries. For intergroup distances, wefollow (9, 56, 57) and use the linguistic distancebetween two groups as a proxy for group “cul-tural” distances in the space of public policy.

Linguistic distance is defined on a universallanguage tree that captures the genealogy of alllanguages (58). All Indo-European languages,for instance, will belong to a common subtree.Subsequent splits create further “sub-subtrees,”down to the current language map. For instance,Spanish and Basque diverge at the first branch,since they come from structurally unrelated lan-guage families. By contrast, the Spanish andCatalan branches share their first seven nodes:Indo-European, Italic, Romance, Italo-Western,Western, Gallo-Iberian, and Ibero-Romance lan-guages. We measure the distance between twolanguages as a function of the number of steps wemust retrace to find a common node. The resultsare robust to alternative ways of mapping linguis-tic differences into distances.

Linguistic divisions arise because of pop-ulation splits. Languages with very different ori-gins reveal a history of separation of populationsgoing back several thousand years. For instance,the separation between Indo-European languagesand all others occurred around 9000 years ago(59). In contrast, finer divisions, such as thosebetween Spanish and Catalan, tend to be the re-sult of more recent splits, implying a longer his-tory of common evolution. Consistent with thisview, there is evidence showing a link betweenthe major language families and the main humangenetic clusters (60, 61).

The implicit theory behind our formulationis that linguistic distance is associated with cul-tural distance, stemming from the chronologicalrelation of language trees to group splittings and,therefore, to independent cultural (and even ge-

netic) evolution. That argument, while obviouslynot self-evident, reflects a common trade-off.The disadvantage is obvious: Linguistic dis-tances are at best an imperfect proxy for theunobserved “true distances.” But somethingcloser to the unobserved truth—say, answers tosurvey questions about the degree of intergroupantagonism, or perhaps a history of conflict—have the profound drawback of being them-selves affected by the very outcomes they seekto explain, or being commonly driven (along withthe outcome of interest) by some other omittedvariable. That is, such variables are endogenousto the problem at hand. The great advantage oflinguistic distances is that a similar charge can-not be easily leveled against them. Whether thetrade-off is made well here is something that amixture of good intuition and final results mustjudge.

In our specifications, we also control for othervariables that have been shown to be relevantin explaining civil conflict (12): population size(POP), because conflict is population-normalizedin the theory; gross domestic product per capita(GDPPC), which raises the opportunity cost of sup-plying conflict resources; natural resources (NR),measured by the presence of oil or diamonds,which affects the total prize; the percentage ofmountainous terrain (MOUNT), which facilitatesguerrilla warfare; noncontiguity (NCONT), re-ferring to countries with territory separated fromthe land area containing the capital city either byanother territory or by 100 km of water; measuresof the extent of democracy (DEMOC); the de-gree of power (PUB) afforded to those who runthe country, which is a proxy for the size of thepublic prize (more on this below); time dummiesto capture possible global trends; and regionaldummies to capture patterns affecting entire worldregions. Finally, because current conflict is deeplyaffected by past conflict, we use lagged conflictas an additional control in all our specifications.

Our exercise implements Eq. 1 in three ways.First, we run a cross-sectional regression of conflicton the two measures of ethnic division. Second,we independently compute a degree of relativepublicness of payoffs for each country and in-clude this in the regression. Third, we add separateproxies of group cohesion for all the countries.Each of these steps takes us progressively closerto the full power of Eq. 1, but with the potentialdrawback that we need proxies for an increasingnumber of variables.

To form a relative publicness index by coun-try, we proxy p and m for every country. Beginwith a proxy for the private payoff m. It seemsnatural to associate m with rents that are easilyappropriable. Because appropriability is closelyconnected to the presence of resources, we ap-proximate the degree of “privateness” in the prizeby asking if the country is rich in natural re-sources. Typically, oil and diamonds are the twocommodities most frequently associated with the

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“resource curse” (62, 63). Data on the quantity ofdiamonds produced is available (64), but infor-mation on quality (and associated price) is scarce,making it very difficult to estimate the monetaryvalue of diamond production. Diamond pricesper carat can vary by a factor of 8 or more,from industrial diamonds ($25 a carat in 2001)to high-quality gemstones ($215 per carat in2001) (63). Hence, we focus exclusively on oilin this exercise. We use the value of oil reservesper capita, OILRSVPC, as a proxy for m.

Next, we create an index of “publicness,”PUB, by measuring the degree of power affordedto those who run the country, “more democratic”being regarded as correlated with “less power”and consequently a lower valuation of the publicpayoff to conflict. We use four different proxiesto construct the index: (i) the lack of executiveconstraints, (ii) the level of autocracy, (iii) thedegree to which political rights are flouted, and(iv) the extent of suppression of civil liberties.Weuse time-invariant dummies of these variablesbased on averages over the sample, because short-run changes are likely to be correlated with theincidence of conflict.

Our proxy for the relative publicness of theprize is given by

L ≡ ðgPUB� GDPPCÞ=ðgPUB� GDPPCþ OILRSVPCÞ ð2Þ

where we multiply the PUB indicator by per ca-pita GDP to convert the “poor governance” var-iables intomonetary equivalents. The “conversionfactor” g makes the privateness and publicnessvariables comparable and allows us to combinethem to arrive at the ratio L. In the empirical ex-ercise we present here, we set g equal to 1. But theresults are robust to the precise choice of this pa-rameter; see the Web Appendix to (51).

Finally, we proxy the level of group cohesiona by exploiting the answers to a set of questionsin the 2005 wave of the World Values Survey(65). We use the latest wave available because itcovers the largest number of countries. One couldargue that the answers might be conditioned by theexistence of previous or contemporary conflict.Hence, the questions we have selected do not askabout commitment to specific groups but addressissues like adherence to social norms, identificationwith the local community, the importance of helpingothers, and so on. We compute the country averageof individual scores on this set of questions anddenote this by A; see (51) for a list of the questions.

What the Data SayAs already mentioned, we proceed in threesteps. First, we examine the strength of the cross-country relationship between conflict intensityand the two indices of ethnic division, with allcontrols in place, including time and regionaldummies. The estimated coefficients will addressthe importance of the two independent variables

as determinants of conflict intensity. In the secondstage,we step closer to the fullmodel and interact thedistributional indices with country-specific measuresof the relative publicness l of payoffs, just as inEq. 1. Finally, we test the full model by adding tothe previous specification the extent of groupcohesion a independently computed for eachcountry. In both the second and third stages, wealso retain the two distributional indices withoutinteraction to verify whether the significancecomes purely from the ethnic structure of thedifferent countries or because this structure interactswith l and a in the way predicted by the theory.

In stage 1, then, we regress conflict linearlyon the two distributional indices and all other

controls. Columns 1 and 2 in Table 1 record theresults for each specification of the conflictintensity variable—PRIO-C and ISC. Ethnicityturns out to be a significant correlate of conflict,in sharp contrast to the findings of the previousstudies mentioned above. Throughout, P is high-ly significant and positively related to conflict.F also has a positive and significant coefficient.

Apart from statistical significance, the ef-fect of these variables is quantitatively important.Taking column 1 as reference, if we move fromthe median polarized country (Germany) to thecountry in the 90th percentile of polarization(Niger), while changing no other institutionalor economic variable in the process and evaluating

Box 1. A model of conflict and distribution.

The two measures of ethnic divisions discussed in this article are both based on the sameunderlying parameters: the number of groups m and total population N, the population Ni ofeach group, and the intergroup distances dij. Polarization and fractionalization are given by

P = ∑i=1

m∑j=1

mni2 njdij and F = ∑

i=1

m∑j≠1

mninj

where ni = Ni/N is the population share of group i. The distinction between P and F is superficial atfirst sight but is of great conceptual importance. The squaring of population shares in P means thatgroup size matters over and above the mere counting of individual heads implicit in F. In addition,fractionalization F discards intergroup distances and replaces them with 0 or 1 variable.

The theory developed in (48) and summarized below links these measures to conflict incidence.There are m groups engaged in conflict. The winner enjoys two sorts of prizes: One is “private” andthe other is “public.” Let m be the per capita value of the private prize at stake. Let uij be the utility toan individual member of group i from the policy implemented by group j. For any i the utility fromthe ideal policy is strictly higher than any other policy; that is, uii> uij. Then, the “distance” between iand j is dij ≡ uii − uij, so that the loss to i from j’s ideal policy is dij. Let p be the amount of money anindividual is willing to give up in order to bring the implemented policy one unit toward her idealpolicy. Then, we can say that themonetary value to amember of group i of policy j is puij and the lossrelative to the ideal policy is pdij. Individuals in each group expend resources r to influence theprobability of success of their own group.Write the income equivalent cost to such expenditure as c(r)and assume that c is increasing, smooth, and strictly convex, with c’(0) = 0. Add individual con-tributions in group i to obtain group contribution Ri. Assume that the probability of success for groupi is given by pi = Ri /RN, where RN ≡ ∑ iRi. Measure conflict intensity in population-normalized formby r = RN /N.

The direct payoff to a person in group i who expends resources r is given bypmii + pim /ni − ∑j=1

m pjpdij − c(r). Individuals also care about the payoff to the other groupmembers. When deciding on how much r to contribute, individuals seek to maximize the sum oftheir direct payoff and the total of the other group members, weighted by a group commitmentfactor a. Note that the optimal contribution ri by a member of group i depends on thecontributions made by all other individuals. We focus on the Nash equilibrium of this strategicgame: the vector of actions with the property that all are the best response to each other. We provethat such an equilibrium always exists and that it is unique.

Note now that c’(r) is the implicit “price” in sacrificed income that an individual is willing to payfor an extra unit of effort contributed to conflict. We then define the per capita normalized intensityof conflict C as the value of the resources expended, C = c’(r)r. Hence, [C/(p + m)] is the ratio of theresources wasted in conflict relative to the stakes, all expressed in monetary terms. Proposition 2 in(48) shows that the equilibrium intensity of conflict C is approximately determined as follows:

Cp+m ≃ a[lP + (1 − l)F] + l(1 − a) GN + (1 − l)(1 − a)(m − 1)

N

where l ≡ p/(p + m) is the relative publicness of the prize, and where G is a third measure ofethnic distribution, the Greenberg-Gini index: G = ∑m

i=1∑mj=1ninjdij. Its influence wanes with

population size, and we’ve ignored it in this essay, though (48, 51) contain a detailed discussion ofall three measures.

For large populations, the expression above reduces to the one in the main text.

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those variables at their means, the predicted prob-ability of experiencing conflict (i.e, the probabilityof observing strictly positive values of PRIO-C)rises from ~16 to 27%, which implies an increaseof 69%. Performing the same exercise for F(countries at the median and at the 90th percentileof F are Morocco and Cameroon, respectively)takes us from 0.19 to 0.25% (an increase of 32%).These are remarkably strong effects, not leastbecause in the thought experiment we change onlythe level of polarization or fractionalization,keeping all other variables the same.

Figure 3 depicts two world maps. The dotsin each map show the maximum yearly conflictintensity experienced by each country; smallerdots meet the 25-death PRIO criterion, whereaslarger dots satisfy the 1000-death criterion. Al-though these maps cannot replicate the deeperfindings of the statistical analysis, they clearly

show the positive relationship between conflictand ethnic divisions.

In stage 2, we consider the cross-countryvariation in relative publicness; recall our proxyindex L from (2). In columns 3 and 4 in Table 1,the main independent variables are P*L andF*(1 − L), just as specified by the theory; seeEq. 1. This allows us to test whether the inter-acted indices of ethnic fractionalization and po-larization are significant. We also include thenoninteracted indices to examine whether theirsignificance truly comes from the interactionterm. Indeed, polarization interacted with L ispositive and highly significant, and the same istrue of fractionalization interacted with 1 − L.These results confirm the relevance of both po-larization and fractionalization in predicting con-flict once the variables are interacted with relativepublicness in the way suggested by the theory.

It is of interest that the level terms P and Fare now no longer significant. Indeed, assumingthat our proxy for relative publicness accuratelycaptures all these issues at stake, this is preciselywhat the model would predict. For instance, po-larization should have no further effect over andbeyond the “l-channel”: Its influence should dipto zero when there are no public goods at stake.That our estimate L happens to generate exactlythis outcome is of interest. But the publiccomponent of that estimate is built solely on thebasis of governance variables. If this eliminatesall extraneous effects of polarization (as it indeedappears to do), it could suggest that primordialfactors such as pure ethnic differences per se havelittle to do with ethnic conflict.

Finally, in our third stage, we allow groupcohesion to vary across countries. Unfortunately,we are able to proxy A for just 53 countries, andthis restricts the number of our observations to447. Columns 5 and 6 of Table 1 examine thisvariant. In this specification, the independent var-iables are exactly in line with those describedby the model, though we’ve had to sacrifice data.We use precisely the combinations asked for bythe theory: polarization is weighted both by Land by A, and fractionalization by (1 − L) and byA again. We continue to use the direct terms Pand F, as well as the controls. The results con-tinue to be striking. The composite terms forpolarization are significant, whereas the levelsare not. The composite term for fractionalizationis highly significant when we focus on smaller-scale social unrest, as measured by ISC, but it ismarginally nonsignificant in column 5. The levelterms of F continue to be insignificant. Thisbehavior of fractionalization mirrors previousresults that showed the nonrobust association ofF and different manifestations of conflict (7, 35).

What Have We Learned?Existing ethnographic literaturemakes it clear thatmost within-country social conflicts have a strongethnic or religious component. But the ubiquity ofethnic conflict is a different proposition from theassertion of an empirical link between existingethnic divisions and conflict intensity.We’ve arguedin this article that such a link can indeed be un-earthed, provided that we’re willing to write downa theory that tells us what the appropriate notionofan “ethnic division” is. The theorywediscuss pointsto one particular measure—polarization—whenthe conflict is over public payoffs such as politicalpower. It also points to a different measure—fractionalization—when the conflict is over privatepayoffs such as access to resource rents. Indeed,the theory also tells us how to combine the mea-sures when there are elements of both publicnessand privateness in the prize. With these consider-ations in mind, the empirical links between eth-nicity and conflict are significant and strong.

The theory and empirical strategy togetherallow us to draw additional interesting inferences.

Table 1. Ethnicity and conflict. All specifications use region and time dummies, not shown explicitly. Pvalues are reported in parentheses. Robust standard errors adjusted for clustering have been used to computez statistics. Columns 1, 3, and 5 are estimated by maximum likelihood in an ordered logit specification, andcolumns 2, 4, and 6 by OLS. GDPPC: log of gross domestic product per capita; POP: log of population; NR: adummy for oil and/or diamonds in columns 1 and 2 and oil reserves per capita (OILRSVPC) in columns 3 to 6;MOUNT: percentage ofmountainous territory; NCONT: noncontiguous territory (see text); POLITICS is DEMOCin columns 1 and 2, and the index PUB times GDPPC (the numerator of l) for the remaining columns; LAG,lagged conflict in previous 5-year interval; CONST, constant term.

Variable1

PRIO-C2ISC

3PRIO-C

4ISC

5PRIO-C

6ISC

P ***5.16(0.001)

***19.50(0.002)

–1.48(0.606)

–16.33(0.227)

–1.47(0.701)

–23.80(0.212)

F *0.93(0.070)

*3.56(0.061)

0.76(0.196)

0.31(0.878)

0.87(0.403)

–0.16(0.710)

PL ***11.174(0.003)

***61.89(0.001)

F(1 − L) *1.19(0.097)

***10.40(0.000)

PL A *12.65(0.087)

***90.32(0.010)

F(1 − L)A 2.54(0.164)

**13.15(0.018)

GDPPC **–0.34(0.047)

***–2.26(0.004)

*–0.36(0.080)

***– 3.02(0.001)

–0.25(0.375)

***–3.68(0.007)

POP ***0.24(0.000)

***1.14(0.000)

***0.21(0.001)

***1.30(0.000)

*0.09(0.166)

**1.29(0.013)

NR –0.27(0.178)

–0.53(0.497)

–0.00(0.570)

0.00(0.432)

**0.00(0.011)

*0.00(0.090)

MOUNT 0.00(0.537)

0.02(0.186)

0.00(0.362)

*0.03(0.061)

*0.01(0.060)

**0.05(0.020)

NCONT ***1.06(0.001)

***4.55(0.001)

**0.77(0.026)

***4.28(0.001)

***1.37(0.004)

***5.89(0.000)

POLITICS 0.18(0.498)

0.29(0.789)

–0.00(0.328)

**–0.00(0.026)

0.00(0.886)

–0.00(0.374)

LAG ***1.99(0.000)

***0.46(0.000)

***1.94(0.000)

***0.44(0.000)

***1.84(0.000)

***0.40(0.000)

CONST – 0.90(0.915)

– 9.19(0.398)

– 15.40(0.328)

(Pseudo)-R2 0.35 0.43 0.36 0.44 0.40 0.43Observations 1125 1111 1104 1090 447 443Countries 138 138 138 138 53 53

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First, we find conclusive evidence that civilconflict is associated with (and possibly drivenby) public payoffs, such as political power, andnot just by the quest for private payoffs ormonetary gain. Otherwise only fractionali-zation would matter, and not polarization. Sec-ond, the disappearance of the level effects ofP and F once interactions with relative public-ness are introduced (as specified by the theory)strongly suggests that ethnicity matters, not in-trinsically as the primordialists would claim, butrather instrumentally, when ethnic markers are

used as a means of restricting political power oreconomic benefits to a subset of the population.

Onemight object that the results are driven bythe peculiarities of some regions that exhibit bothhighly polarized ethnicities and frequent and in-tense conflicts. Africa is a natural candidate thatcomes to mind. However, if we use regional con-trols or repeat the exercise by removing one con-tinent at a time from the data set, we obtainexactly the same results (51).

It is too much to assert that every conflict inour data set is ethnic in nature and that our ethnic

variables describe them fully. Consider, for in-stance, China or Haiti or undivided Korea, whichhave experienced conflict and yet have lowpolarization and fractionalization. All conflict issurely not ethnic, but what is remarkable is thatso many of them are, and that the ethnic charac-teristics of countries are so strongly connectedwith the likelihood of conflict. Yet we must endby calling for a deeper exploration of the linksbetween economics, ethnicity, and conflict.

This paper takes a step toward the establish-ment of a strong empirical relationship between

A

B

Fig. 3. Ethnicity and conflict. Dots represent the maximum yearlyconflict intensity that each country has experienced over the period;smaller dots meet the 25-death PRIO criterion, whereas larger dots

satisfy the 1000-death criterion. Darker colors signify higher degrees ofpolarization (A) or fractionalization (B). Countries for which no data areavailable are depicted in gray.

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conflict and certain indicators of ethnic groupdistribution, one that is firmly grounded in the-ory. In no case did we use income-based groupsor income-based measures, and in this senseour study is perfectly orthogonal to those thatattempt to find a relationship between economicinequality and conflict, such as those surveyed in(17). Might that elusive empirical project benefitfrom theoretical discipline as well, just as theethnicity exercise here appears to? It well might,and such an endeavor should be part of the re-search agenda. But with ethnicity and economicsjointly in the picture, it is no longer a questionof one or the other as far as empirical analysis isconcerned. The interaction between these twothemes now takes center stage. As we have al-ready argued, there is a real possibility that theeconomics of conflict finds expression acrossgroups that are demarcated on other grounds: re-ligion, caste, geography, or language. Suchmark-ers can profitably be exploited for economic andpolitical ends, even when the markers them-selves have nothing to do with economics. Astudy of this requires an extension of the theoryto include the economic characteristics of eth-nic groups and how such characteristics influ-ence the supply of resources to conflict. It alsorequires the gathering of group data at a finerlevel that we do not currently possess. In short,a more nuanced study of the relative importanceof economic versus primordial antagonisms mustawait future research.

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Acknowledgments: We gratefully acknowledge financialsupport from Ministerio de Economía y Competitividadproject ECO2011-25293 and from Recercaixa. J.E. and L.M.acknowledge financial support from the AXA Research Fund.The research of D.R. was funded by NSF grant SES-0962124.We thank two referees for valuable comments and are gratefulto R. Jayaraman for suggestions that improved the manuscript.

10.1126/science.1222240

REVIEW

Moshe Kress

Armed conflicts have been prevalent throughout history, in some cases having very great consequences.To win, one needs to understand the characteristics of an armed conflict and be prepared with resourcesand capabilities for responding to its specific challenges. An important tool for understanding thesecharacteristics and challenges is a model—an abstraction of the field of conflict. Models have evolvedthrough the years, addressing different conflict scenarios with varying techniques.

Armed conflicts start because peopledisagree. They disagree on controllingterritory, economic interests (such as nat-

ural resources), religion, culture, and ideology. Dis-

agreements may lead to tense disputes, whichcan result in armed conflicts of various scales.History is cluttered with armed conflicts involv-ing tribes, states, insurgencies, guerrilla groups,

and terrorist organizations. Attaining victory is as-sociated with questions regarding the use of re-sources, strategy, and tactics. Answering thesequestions is not easy; it requires a clear definitionof objectives, a detailed review of capabilities andconstraints, and a careful analysis of possible sce-narios and courses of action. For this, military anddefense analysts use models, which are abstrac-tions of armed conflicts, their environments, andtheir possible realizations. These models, hence-forth called armed conflict (AC) models, are usedfor analyzing threat situations, military operations,and force structures.

Operations Research, Naval Postgraduate School, Monterey,CA 93943, USA. E-mail: [email protected]

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