Public Disclosure Authorized

Justice-Seeking and Loot-Seeking in Civil War

Paul Collier Public Disclosure Authorized The World Bank

Anke Hoeffler CSAE, Oxford

This Draft: February 17th, 1999 Public Disclosure Authorized Public Disclosure Authorized

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Justice-Seeking and Loot-Seeking in Civil War

1. Introduction

Civil war is both a human tragedy and a major impediment to development. Most of the world’s poorest countries are experiencing or have recently experienced such conflict. We use a comprehensive data set to identify its causes. We group potential causes into two categories: the quest for `justice’ and the quest for `loot’. Clearly, one motivation in rebellion is the alleviation of grievances, real or perceived. Most rebellions are ostensibly in pursuit of a `cause’. However, many rebellions also appear to be linked to the capture of resources: diamonds in Angola and Sierra Leone, drugs in Colombia, and timber in Cambodia. In some cases these two motivations become blurred: for example, in Colombia groups which initially claimed ideological motivation have transmuted into drug baronies.

In Section 2 we develop a simple rational choice model of loot-motivated rebellion in which private costs are equated with private benefits, and propose empirically measurable proxies for its key variables. In Section 3 we turn to the more complex phenomenon of justice-motivated rebellions, distinguishing between the demand for justice, motivated by a variety of grievances, and the supply of justice determined by both private costs and the difficulties of collective action. We again propose empirically measurable proxies for the variables. In Section 4 we test a synthesis of the two models on a comprehensive data set of 152 countries for each of the six five-year periods between 1965 and 1995, giving a total of 1064 potential observations. For 53 of these observations the society was at peace at the start of the period but experienced civil war during it. We use probit regressions to explain these collapses into civil war in terms of characteristics at the start of the period.

2. The Looting Model of Rebellion

We first consider the case in which the objective of rebellion is the capture of loot. We assume that loot is acquired during the process of rebellion rather than being dependent upon prior victory. For example, the rebellions in Angola and Colombia have remained economically profitable for very long periods despite having little prospect of military victory. If, instead, the capture of loot were to be contingent upon rebel victory, then both the expected duration of the conflict and the probability of victory would affect the probability of rebellion. In the present model we abstract from these considerations.

In the looting model, rebellion is distinguished from violent crime only by the scale of the enterprises involved. At the `industry’ level, the presence of lootable resources of a particular value, N, supports looting, L, which in turn induces government resources to be devoted to protection. Protection reduces the returns to looting. The looting sector uses rebel labor, l, and the government uses defense labor, g, both looting and defense being subject to diminishing returns.

L = L(l, g, N); Ll >0, Lg<0, Ln>0, Lll <0; Lgg >0 (1) In equilibrium, the marginal product of rebel labor for given g and N is equated with its opportunity cost. The marginal product of government defense labor, which is some fraction or multiple of the reduction in looting which it achieves, may be assumed to be equated with the marginal benefits of other government expenditure.1

Civil war as conventionally measured occurs if certain threshold rates of mortality are exceeded. A key analytic step is therefore to determine the military laborforce deployed in the conflict. In Figure 1 the space is the laborforce on each side in the conflict. We depict the Nash equilibrium laborforces for two levels of lootable resources, N0 and N1, N1>N0. The loci l(g, N0) and g(l, N0) depict the reaction functions of the rebels and the government for the lower level of lootable resources. An increase in lootable resources raises the productivity of looting labor for a given level of government deterrence and so shifts the rebel reaction function to the right. The increase in looting raises the productivity of deterrence and so similarly shifts the government reaction function upwards. An increase in lootable resources will unambiguously increase the equilibrium quantity of government defense labor. For a given size of rebel laborforce the productivity of looting has increased, so that the value of a given amount of deterrence increases. Further, since lootable resources are also taxable, the government’s tax revenue will increase, so that the opportunity cost of defense expenditure is reduced. An increase in lootable resources will normally, but not necessarily, increase the quantity of rebel labor. The direct effect is always positive through the increase in the productivity of looting at a given size of deterrence. However, a sufficiently large induced increase in deterrence expenditures may reduce the equilibrium size of the rebel laborforce.

The death rate from combat, D, which is the observed variable in civil war, we will assume to be proportional to the product of the manpower on each side:

D = a(g.l). (2)

The definition of civil war used in quantitative measures includes the requirement that total deaths must exceed one thousand within a period of twelve months. In Figure 1 this condition is represented by the rectangular hyperbole, D1000. A second condition is that at least 5% of the deaths must be from each side, so as to distinguish conflict from one- sided massacres. If deaths are assumed to be proportional to the laborforce, then this condition defines a cone, depicted in Figure 1 by the rays D5% and D95% which show the two critical shares of rebel deaths in total deaths. As illustrated, the increase in the endowment of lootable resources shifts the country over the definitional thresholds and hence into civil war. This is not inevitable. Although, the death rate may normally be an increasing function of the endowment of lootable resources, it is quite possible that if

1 At one extreme the government could regard looting simply as a neutral transfer, so that the social costs would arise only from private expenditures upon protection. At the other extreme, the social costs of looting could greatly exceed the value of the property looted because of the undermining of trust in property rights. Since all that concerns us is the sign of the change in the size of government defense forces changes in response to the size of rebel forces and the endowment of lootable resources, it is not necessary to further specify the social costs of looting.

2 they increase sufficiently, the rebel laborforce contracts. As a result, either the product of the two laborforces, and hence the death rate could decline below D1000, or the composition of deaths could fall below D5%.

A third component of the definition of civil war is that the rebel side should be composed of one or a few identifiable organizations. Hence, if looting is conducted only by small scale enterprises it will not be classified as a civil war even if the mortality rate conditions are met. Subject to meeting the mortality rate conditions, whether looting is classified as a civil war thus depends upon the market structure of the sector and this, in turn, depends upon its technology. We will assume that at the level of the individual enterprise looting is characterized by economies of scale. These arise because looting is dependent upon violence, and there are scale economies in the production of violence. A large group of rebels can outgun a smaller group. Given economies of scale, the equilibrium market structure will be monopolistic: the sector will consolidate to a single looter. The size of the monopoly enterprise will be limited by diminishing returns at the industry level, arising from the endowment of lootable resources, which functions as equivalent to a demand constraint. There will thus be a single looting enterprise pitched in violent conflict against government defense forces. Of course, in practice much looting is conducted by small scale organizations. Our assumption of scale economies is meant to demonstrate not that all violent theft must be large scale, but that with reasonable assumptions, the motivation of looting can generate behavior which will be classified as a civil war.

Figure 1: Nash Equilibrium in the Looting Model for Two Levels of Lootable Resources

D5%

g(l, N¹) g l(g, N¹)

l(g, Nº) g(l, Nº)

D95%

D1000

l

3 Empirically, we will proxy loot by the share of natural resource exports in GDP. Natural resource exports are the mainstay of tax revenue in low-income countries, because they are readily taxable. Production is inelastic with respect to taxation, either because the capital stock is fixed so that marginal costs are low, as with coffee and rubber, or because there are huge profits from the control of marketing channels, as with surface diamonds and drugs. In other cases the asset stock can be directly taxed and depleted over a period, as with timber. The characteristics which make natural resources readily taxable by a government, also make them readily lootable by rebels.

In equilibrium, the returns to participation in rebellion will be equated with its opportunity cost, C. The opportunity cost of participation in rebellion thus acts as a constraint upon the rebel leadership, raising the cost of labor to the rebel enterprise. Thus, given diminishing returns at the industry level, the higher is the opportunity cost, the lower will be the rebel laborforce for any given endowment of lootable resources and size of government deterrence. Formally, the conditions for a looting war can thus be summarized as:

WAR iff: a[l(N,g,C).g(N,l)] > 1,000; 0.05 < l(N,g,C)/g(N,l) < 0.95 (3)

Thus, the probability of war will be a decreasing function of its opportunity cost, and normally, an increasing function of the endowment of lootable resources:

WARprob = f(C,N). (4)

The notion that the decision to participate in rebellion will be influenced by its opportunity cost may initially appear unrealistic, and so we illustrate its practical importance by the example of the Russian civil war of 1919-21. This was not only the largest civil war of the twentieth century, it was unusual in that both the Reds and the Whites depended upon improvised armies: in effect, both sides were `rebels’. Recruitment and defection were massive problems for both sides: in total four million men deserted from the two armies. The rate of desertion during the war is therefore a large scale social phenomenon potentially subject to economic regularities. The Russian desertion rate provides strong evidence that opportunity cost is an important consideration. Desertion was ten times higher in summer than in winter: being peasant armies, during the summer months soldiers deserted because they needed to work on the harvest (Figes, 1996).

We investigate two proxies for the opportunity cost of enlistment for the representative recruit. One proxy is mean per capita income, using the Penn World Tables data. The other proxy is the mean number of years of education of the population. Since education raises labor income, and since it tends to be concentrated on young males, it may better proxy the opportunity cost of young male labor. Further, as discussed above, we are already proxying non-labor income through our measure of natural resources.

To summarize, in the looting model of rebellion, the risk of rebellion is normally, but not necessarily, increasing in the natural resource endowment, and decreasing in the

4 opportunity cost, proxied either by per capita income or the per capita human capital endowment.

2. The Justice Model of Rebellion

We now turn to the other motivation for rebellion, the rectification of perceived injustice. The risk of conflict will now reflect both the demand for justice and the cost of supplying it. We consider these in turn.

The demand for justice

The demand for justice can be reformulated as the circumstances which generate grievance. We now consider three types of grievance: hatreds arising from relations between distinct social groups, repression in the process of political decision making, and poor economic outcomes.

Grievance due to Social Composition

The most common image of violent civil conflict in developing countries is of fighting conducted between groups of young males with distinct social identities. The identities might be based upon ethnicity, religion or region, or indeed the three may be coincident. There is indeed evidence, discussed in Horowitz (1998), that different groups often dislike each other. The question is not, therefore, whether innate hatreds between social groups with distinct identities exist, but rather whether they cause civil war.

Two aspects of social composition which capture the above image are measurable: the proportion of the population made up of young males, M, and the degree of social fractionalization, F. There is a well-established relationship between the proportion of young males in a society and its crime rate. The same group provides the main manpower for civil war. Specifically, we test whether the higher is the proportion of a society which is male and aged 15-24 the greater is the risk of conflict.

Although the intensity of group hatreds cannot be comparably measured across societies, we are able to measure the degree of group fractionalization. We measure three distinct types of group fractionalization.

Ethnic Fractionalization

The ethnic composition of societies has been measured by a team of Russian anthropologists: the variable ELF (ethno-linguistic fractionalisation) measures the probability as of 1960 that two randomly drawn people from a national population would be from different ethno-linguistic groups. Hence, 0 represents ethnic homogeneity and 100 represents complete ethnic fractionalisation. The variable differs massively between countries: for example, South Korea scores 0 whereas Uganda scores 93. The variable

5 has already been used in other contexts, for example, in explaining economic growth (Easterly and Levine, 1997).

Religious Fractionalization

Precisely the same proposition can be made concerning religious hatred. To date, there has been no measure of religious fractionalization comparable to that for ethno-linguistic fractionalization. However, due to recent work by Barro (1997) it is now a straightforward matter to construct one. Barro utilized original data from Barrett (1982) to construct the proportions of a society which adhere to each of the major religions.2 From this we have constructed an index of religious fractionalization which precisely corresponds to that on ethno-linguistic fractionalization.3 Again, it differs markedly between societies, with Saudi Arabia scoring 0, whereas Tanzania scores 73. We test the effect of religious fractionalization in the same way as ethno-linguistic fractionalization.

Geographic Fractionalization

We construct an analogous measure of geographic fractionalization. We structure the geographic area of each country into a 10km by 10km grid. Each cell in the grid constitutes a spatial unit analogous to that of an ethno-linguistic or religious group. We then calculate the population in each cell of the grid. Finally, we calculate the probability of any two randomly drawn people from the country being from the same cell. Evidently, spatially small countries with large populations will be less fractionalized on this measure than spatially large countries with small populations.

The effect of geographic fractionalization may differ from that of ethnic and religious fractionalization because conflicts inevitably have a spatial dimension. Rebellions may be easier in if the population is geographically fractionalized because a particular territory can be defended.

Composite Fractionalization

Each type of fractionalization may have distinct effects. For example, religious fractionalization may reduce conflict whereas geographic concentration may increase it. Alternatively, all three may have similar effects because the determining factor is simply fractionalization, rather than the manner of that fractionalization. In the latter case a composite measure of fractionalization is appropriate. In principle, the three different measures of group fractionalization could be either coincident or cross-cutting. Coincident fractionalization would occur if each ethnic group had both a distinct religion and a distinct location. In this case it might be expected that the divisions would be more

2 We would like to thank Robert Barro for supplying his data. The data are described in chapter 2 of Barro (1997). 3 That part of the population which is not assigned to any of the identified religions is assigned to a final category, secular. The probability, p, of two people being drawn randomly from the same group is then the sum of the squares of the shares. The index of religious fractionalisation is then (1-p).100.

6 powerful than if the society had the same ethnic divisions but was homogenous with respect to religion and location. One way of capturing this is to take the arithmetic mean of the three dimensions of fractionalization. If, however, the three fractionalizations are perfectly cross-cutting, then the appropriate composite measure of fractionalization is the product of the individual fractionalizations. Since at present we lack country-specific information as to whether the different fractionalizations are coincident or cross-cutting, we experiment with both measures in the subsequent analysis.

Grievance due to Repression

Repression, or the lack of democratic rights, might increase the risk of rebellion for several reasons. Democracy might produce governments which are more efficient, so that the population as a whole has less basis for grievance. It might produce governments which are fairer, so each social group has less cause for grievance. Finally, it might be perceived as more legitimate, so that the population will accept outcomes from a democratic process which they are not willing to accept from a dictatorial process. might reduce the risk of rebellion because repressive governments are able to resort to methods which would be unacceptable in a democracy.

We measure repression, R, using the Polity III democracy index.

Grievance due to Poor Economic Outcomes

Given the degree of fractionalization and the degree of political repression, group grievance may reflect poor economic outcomes. As noted above, these might be due to poor national economic performance, or to outcomes which are disadvantageous for particular groups. We now proxy each of these.

Poor national economic performance

We measure two aspects of poor national economic performance, growth and inflation. Populations may punish their governments for poor growth performance, G. Since growth performance will be endogenous to whether there is a civil war, we measure it during the preceding five year period. The hypothesis is that slower growth increases the risk of war. Populations may also punish governments for macroeconomic mismanagement. We proxy this by the rate of consumer price inflation during the preceding five year period, CPI. The hypothesis is that higher inflation increases the risk of war.

High Inequality

Groups may have stronger grievances depending upon the degree of inequality in either income or assets. For income distribution, IY, we use the data set compiled by Deininger and Squire (1996). For asset inequality, IA, we use a measure of the distribution of land constructed by the FAO. In low-income societies, land is the major asset of concern to poor people and so is a reasonable proxy for overall asset inequality. Although both of

7 these dimensions of inequality are subject to substantial measurement error, variables based on the two data sets have proved significant in other studies (see, for example, Alesina and Rodrik, 1994).

Grievance due to Interaction Effects

So far we have considered grievance as the potential product of three components: fractionalization, political repression, and poor economic outcomes. However, the effect may also depend upon interactions. For example, inequality may be more dangerous in societies which are highly fractionalized. We test for such effects by introducing interaction terms.

To summarize, the demand for justice, JD, is a function of the following variables, the derivatives all being positive except for G which will be negative:

JD = JD(M,F,R,G,CPI,YI,YA) (5)

The Supply of Justice

We will now assume that the demand for justice is sufficient to warrant rebellion: that is, the level of grievance against the government is such that if all those with grievances supported the rebel movement the government would be defeated at little cost. We focus instead on the supply of justice, JS.

There are two types of cost in achieving justice through rebellion. The first is the opportunity cost of rebel labor already considered in the looting model. The other is the cost of overcoming the collective action problem. Looting rebellions do not face intrinsic collective action problems because the activity is privately profitable. By contrast, justice rebellions face three problems in collective action.

First, justice rebellions produce a public good and so face free-riding. Even though everyone is agreed that rebellion is desirable, it is even more attractive if the costs are borne only by others and the success of rebellion will not be dependent upon the participation of any one individual.

Whereas the free-rider problem occurs because of the non-excludability of the consumption of justice, the second collective action problem arises because of the process of justice production. The technology of justice rebellion is much less favorable than for looting rebellion. Looting rebellions can be profitable without needing to defeat government forces. The rebel force must simply be sufficiently strong to defend itself. By contrast, to attain justice, rebels must defeat the government and so rebellions need to be large.4 Small rebellions risk defeat and punishment. Plausibly, the probability of victory

4 Rebels may be able to achieve their objectives by forcing the government to the negotiating table. However, even in this strategy they face a severe time-consistency problem (see Collier, Hoeffler and Soderbom, 1998), and we do not consider this option further here.

8 will be an increasing, S-shaped function of the rebel laborforce, so that the marginal product of rebel labor initially increases. This range of increasing returns to scale at the `industry’ level, which does not occur in loot-seeking rebellions, creates a coordination problem. Everyone may be willing to join a rebellion, but only if sufficient others do so for their participation to be productive.

Thirdly, unlike looting rebellions, justice rebellions face a time-consistency problem, since the benefits only accrue after the effort of rebellion has been made. In order to defeat the government rebel supporters must create a rival, hierarchical military organization. The time consistency problem arises because, if the benefits of rebellion only accrue after victory, the rebel leader once victorious must be trusted to behave differently from the present government, and in particular to honor promises to supporters.

These problems of collective action can nevertheless sometimes be overcome. We consider three mechanisms by which justice rebellions can occur: through social capital, through bandwagons, and through fads, and identify those circumstances in which the mechanisms are more or less effective.

Communities face many situations in which collective action is needed and have developed means of overcoming the above problems. The continuity of associational life sets each particular decision in the context of a repeated play game and so reduces opportunistic behavior (Platteau, 1994). The networks of associational life in which agents participate thus function as `social capital’. The greater the density of associational life, (the greater the stock of social capital), the better able is the community to undertake collective action. The collective action problem posed by justice-seeking rebellion may therefore be overcome by utilising existing social capital.

Social fractionalisation may limit the scope for social capital to play this role. For example, Keefer and Knack (1997) show that fractionalisation reduces trust. In socially fractionalised societies, the predominant networks are intra-group. It is therefore easier to overcome the collective action problem within groups than between them. This may make justice-rebellions only feasible at the level of the individual social group. However, if the society is sufficiently fractionalised, an intra-group rebellion may be too small to defeat the government and so may not be rational, whereas inter-group rebellions are infeasible because they cannot overcome the collective action problem.

A second route by which the free rider problem may be overcome is through the bandwagon effect, which has been used to model the growth of opposition to repressive regimes (Kuran, 1989). The bandwagon effect arises because public expressions of political support for an opposition group may be costly. As a result, much support for the opposition will be latent. However, the costs of going public with support fall the more people have already expressed support because there is safety in numbers. Hence, support can cumulate: each potential supporter in turn goes public as public support passes the thresholds which trigger decisions.

9 Two factors may reduce the bandwagon effect: the degree of repression and the dispersion of preferences over the population. The more severe is political repression, the higher will be the costs of public expression of support. Recall that we have previously treated the severity of repression as a factor raising the demand for justice. However, it also raises the cost of supplying justice and so its net effect on rebellion is a priori ambiguous. If political preferences are highly dispersed, or clustered, then the bandwagon stops once it reaches a gap in the preference range: the last people from one cluster to go public do not have a sufficient influence on agents in the next cluster to induce further decision changes. Social fractionalization, whether ethnic, religious or geographic, is a likely indication of preference dispersion and so makes the bandwagon effect less likely to occur.

A third route by which the free-rider problem may be overcome is through the process of choice convergence through the informational cascading of fads (Bikhchandani et al., 1998). Such cascades can arise where each agent knows that he has only very limited information, and so accepts that there is information content in the choices of other agents. Analogous to the bandwagon effect, though for a different reason, it is then possible for sequential decisions to create an information cascade, in which the decisions of pioneers are copied by the rest of the population. Political choices have the basic attributes suitable for the generation of fads, in that no individual agent has much incentive to invest in costly information. The decision (typically by young men) whether to join an incipient rebellion is likely to be based on even less complete information than normal political choices and so may be particularly liable to process by which fads are generated. As with bandwagons, fads may need only a few dedicated altruists to start them. Social fractionalization may arrest fads because the choices made by the members of one social group may be regarded as not conveying information pertinent to the choices of members of other groups. Thus, fads may have difficulty spreading between groups.

To summarize, the supply of justice will be negatively related to both the opportunity cost of rebellion, and the difficulties of overcoming the collective action problem:

JS = JS(C, F, R). (6)

The risk of rebellion

The overall risk of justice-seeking rebellion will be an increasing function of both the demand for justice and its supply:

D S WARprob = f(J , J ). (7)

We have proposed three categories of grievance which can be expect to increase demand: adverse social composition, political repression, and poor economic outcomes. On the cost side, opportunity cost can be proxied as in the looting model, by either per capita income, or the per capita endowment of human capital. The difficulties of collective

10 action are increased by social fractionalization, and by political repression, both of which are already measured.

The most striking factor about the justice model is that the effects of social fractionalization and political repression are a priori ambiguous. Although, viewed from the perspective of the demand for justice, or grievance, social fractionalization and repression are likely to increase the risk of conflict, from the perspective of the supply of justice, they should make conflict less likely. However, both are empirically resolvable since both are measurable.

Justice-seeking rebellions and loot-seeking rebellions share one feature, namely that both are deterred by a higher opportunity cost of rebel labor. However, in other respects their determinants differ sufficiently to be empirically distinguishable. Justice-seeking rebellions will be induced by the proxies for grievance, although two of these are ambiguously signed because they also proxy costs unique to justice-rebellions. Loot- seeking rebellions will be induced by natural resources. Because justice-seeking rebellions face such severe difficulties of collective action,, in their pure form they may be very rare. Hence, typically, rebellions may be either pure loot-seeking, or have both motivations. The presence of loot will help to overcome the collective action problem. Thus, a more general model has the risk of war increasing in both lootable resources and the demand for justice, and decreasing in the opportunity cost and the obstacles to collective action:

D WARprob = f(N, J , C, F, R). (8)

This is the model we test in the next section. The complementarity between looting and justice-seeking may be evolutionary. Rebellions may begin as loot-seeking but transmute into justice-seeking if they grow to a scale where there would be good prospects of victory. Equally, rebellions may begin as justice-seeking, but if the prospects of victory wane they may persist due to loot-seeking, as appears to have happened in Colombia.

4. Testing for the Causes of Conflict

The determinants of the probability of war described in (8) constitute testable propositions and we now turn to empirical testing. Potentially, we observe 53 civil war episodes out of 1004 observations among 152 countries over the five-year periods between 1965 and 1995. However, for some wars there is a severe lack of supporting data. For complete data we are currently restricted to 24 war episodes. However, for 16 further wars the data set can be made complete by only minor assumptions or estimations. We therefore present results for both the narrow and the augmented data sets. The sources and precise definitions of the variables used are given in the Appendix.

The dependent variable is the likelihood of civil war during a five-year period, as determined in a probit model. For our definition of civil war and our main data source on its occurrence, we rely upon the standard source of Singer and Small (1994). However, their definition is not scale-neutral, since it imposes an absolute threshold of one

11 thousand deaths per annum as a minimum. Because the size of countries differs so enormously, it is not realistic to rescale the Singer and Small definition so that the threshold would be proportionate to the population. For example, if the threshold for civil war in the Turks and Caicos was set at two deaths during the year, which is obviously ridiculously low, China would not meet the requirement even were civil conflict to kill a quarter of a million people. Hence, the absolute threshold utilized by Singer and Small has much sense to it, even though it makes large countries more prone to civil war. There are other reasons why population size might affect the probability of civil war, but since these cannot be disentangled from the statistical threshold effect, there seems little value in considering them. To allow for the threshold effect, population size will be entered as an explanatory variable.

Our core regressions are shown in Table 1. In the appendix we show that no additional variables are significant when added to these regressions, which outperform all variants.

Table 1: A Probit Regression of the Causes of Civil War, for Five-Year Periods between 1965 and 1995

Narrow Data Set Augmented Data Set

Variable coefficient z-statistic coefficient z-statistic coefficient z-statistic

Education -0.237 -2.85 -0.247 -3.91 -0.258 -3.97 Primary Exports 11.692 2.71 12.401 3.30 11.547 3.07 Primary Exports2 -20.843 -2.16 -25.450 -2.82 -23.578 -2.64 (Religion*Ethnicity)2 -1.66 -1.61 -1.770 -2.17 -1.750 -2.12 Democracy 0.236 1.90 0.158 1.45 0.145 1.32 Democracy2 -0.025 -1.80 -0.017 1.37 -0.015 -1.19 ln(Population) 0.326 3.07 0.347 4.18 0.321 3.82 Constant -7.129 -3.74 -7.318 -4.98 -6.878 -4.63 PreviousWar*Education - - - - 0.152 1.78

N = 459; = 614 =614 log likelihood = -75.44; =-117.36 =-115.89 pseudo r2 = 0.20 =0.21 = 0.22

When the data set is augmented from its core of 24 war episodes to 40, coefficients are little affected and all variables become more significant with the exception of democracy. The core regressions support the proposition that opportunity cost matters. The mean years of education per head is more significant than is mean per capita income, although the two are too highly correlated to be included in the same regression. The effect is powerful: at the mean risk of civil war, a one year increase in education per head reduces the risk of civil war by 20%. There is also support for the looting model since the share of primary commodity exports in GDP is significant. Although the relationship is a

12 quadratic, the risk of conflict peaks at a share of primary exports of 28% of GDP, which is extremely high. Thus, over the range relevant for most countries, the larger is the natural resource endowment the greater is the risk of conflict. Again at the mean risk of conflict, a country with this peak danger resource endowment has a risk of conflict 4.2 times greater than one without resources.

There is also evidence for justice-seeking rebellion. However, those factors which influence the cost of supplying justice through rebellion dominate those which influence the extent of grievance. This is most strikingly the case with respect to social fractionalization. Although neither ethnic nor religious fractionalization are individually significant, their interaction significantly reduces the risk of conflict. The effect is substantial. At the mean risk of conflict, a society with the maximum possible religious and ethnic fractionalization reduces its risk relative to that of a homogenous society by 80%. No society has the maximum possible social fractionalization, but the most fractionalized society in our sample, Uganda, reduces its risk of conflict compared with a homogenous society by 42%. Evidently, the effect of fractionalization on inhibiting collective action is much more important than any effect via an increased level of grievance.5 Similarly, political repression on the whole appears to have more powerful effects through raising the costs of justice-seeking, than through increasing the demand for justice. On the Polity III democracy scale of 0-10, with 0 being maximum repression, societies become more prone to civil war as they democratize, reaching a peak danger when the index is at 4.8, by which time the risk is 68% greater than with total repression. Thereafter, the risk declines again.

On the augmented sample there is some evidence that experiences of slow growth in the previous decade increase the risk of war. This is consistent with both the opportunity cost and grievance hypotheses, but it could also be endogenous, since a future risk of war might discourage investment. There is also some evidence that the higher is the proportion of the population made up of males aged 15-24 the greater is the risk. None of the other influences on the demand for justice are significant: inequality, whether of income or of land, has no effect on the risk of conflict. The interaction of the inequality measures with the social fractionalization measures, which might proxy inequalities between social groups, is also insignificant. High inflation, the rate of growth of the population, and population density, also have no significant effects.

Population is highly significant in the regression. Since both population and the dependent variable, (p/1-p), are measured in natural logs, for low values of p the coefficient is approximately the elasticity of the risk of war with respect to the population. Thus, it is more interesting that the coefficient is significantly much less than unity than that it is greater than zero. As two identical countries are combined, the risk of war much less than doubles. Hence, although the risk increases with population, it is reasonable to regard large countries as being safer than small countries.

5 Alesina et al. (1997) find evidence for an analogous effect of social fractionalization being an impediment to collective action, although in their case the collective action is productive rather than destructive.

13 Finally we analysed the effect of previous conflict. This analysis is only appropriate on the larger, 40-war sample. Within this sample we observe 192 periods which are post-war (although mostly not immediately post-war). Of these, 158 were free of any further war experience. Hence, on average, 18% of our post-war episodes had further war experience. This is a higher incidence than the average risk of first-time war. However, it does not necessarily imply that war itself increases the risk of war. Since those countries which have one war will on average have characteristics which make them more war-prone, it cannot be concluded that a higher incidence indicates any causal effect of war. To test for a causal effect we tested in turn the inclusion of two dummy variables. One took the value of unity if the country had had a previous conflict since 1945. The other took the value of unity if the country had had a conflict within the previous ten years. These are important tests in two respects. First, they test whether we are omitting systematic and persistent causes of conflict. If we are omitting such variables, then in the countries which have had more than one civil war they will tend to take values which make war more likely. In this case, when omitted, the variables would appear collectively as a fixed effect in such countries, so that the dummy variable for a previous conflict would be significant and positive. In fact, the coefficient on the previous war dummy variable is small and insignificant. The coefficient on the dummy variable for war in the previous ten years is small but closer to significance, being significant at 16%. The coefficient implies an increased risk of civil war if there has been a conflict in the previous ten years of around a third at the mean of other variables, which is not a large effect in comparison with the power of the other variables. Because of the near-significance of the variable we explored it further. We created a variable for the length of time since the previous war, (lnpeace). This was not significant (except at 22%) and its coefficient was small. Hence, is there is any gradually decaying additional risk generated by a war experience, it appears to be a weak effect. We then interacted the previous war variable with each of the significant variables to check whether their effects were different in post-war environments. Only one of these interactions was significant, the results being shown in the third panel of results in Table 1. During the first ten years of post-conflict the human capital variable (the mean number of years of education of the population) is significantly less powerful in reducing the risk of conflict than it is normally. This may simply indicate that in post-conflict societies the mean years of education exaggerates the opportunity cost of conflict, since it fails adequately to capture the fall in income. Alternatively, it may suggest that the opportunity cost of conflict is less important as a deterrent to conflict in post-conflict situations. This is supported by the fact that when education is replaced by per capita income, which should better capture the fall in income as a result of conflict, the effect remains significant. The lack of significance of the previous war dummy variables as a fixed effect imply that there are no important and persistent causes of civil war which have been omitted from the analysis. Of course, there are likely to be many important causes of conflict which are systematic but not persistent, and we cannot test for these.

The dummy variables are also of importance for what they tell us of the effects of civil war. A priori, it would seem likely that the risk of subsequent civil war would be endogenous to the experience of civil war. Conflicts might be expected to polarize societies and create further grievances. In fact, this appears not to be the case. Post-

14 conflict societies are not on average more likely to have a further bout of conflict as a result of their experience. Of course, such societies will, on the whole, be those with a relatively high risk of conflict because of their prior characteristics. However, conflict does not appear substantially to compound the problem which they face. Once a conflict has stopped, it is no more likely to start again than is a conflict in a country which has been at peace but has the same underlying characteristics.

In summary, we have tested (8) and found that all but one of the variables are significant with the expected sign. Civil wars are less likely the higher is their opportunity cost, the fewer are lootable resources, and the more substantial are the obstacles to collective action. The variable which is insignificant is the demand for justice. We found no evidence that the level of grievance is an important influence on civil war, nor that post- conflict societies face greater risks or different risk factors as a result of their experience of conflict.

5. Conclusion

The motivation for instigating a rebellion war may commonly be a blend of an altruistic desire to rectify the grievances of a group, and a selfish desire to loot the resources of others, both balanced against the costs of rebellion. The claim that motives are altruistic may serve as a convenient smokescreen for greed, or alternatively, looting may be a necessary means by which altruistic objectives are financed. In this paper we have set out simple models of loot-motivated and justice-motivated rebellion and tested them on a comprehensive data set.

The two motives for rebellion are potentially empirically distinguishable, because looting-rebellions are induced by the endowment of lootable resources, whereas justice- seeking rebellions are induced by grievance. However, the theory suggested that some of the most apparently potent factors inducing grievance were also likely to accentuate the collective action problem endemic to justice-seeking rebellion, and so may reduce rather than increase the probability of such conflicts.

Our empirical test was based on a comprehensive data set of civil war episodes during 1965-95. For 152 countries over six five-year periods, we use probit regressions to explain the probability of civil war in terms of the characteristics prevailing at the start of each period. We have found strong evidence for the looting motivation. The share of primary commodity exports significantly and strongly affects the risk of war: a country with the highest risk endowment is over four times more likely to experience war than a country without primary commodities. We also found evidence supporting the hypothesis that rebels take into account the opportunity cost of rebellion: the endowment of human capital per capita strongly affects the risk of war. There was also evidence for justice- motivated rebellion. However, none of our proxies for the intensity of grievance, whether the extent of inequality, social fractionalization, or poor past economic performance, significantly increased the risk of war. The risk of conflict does not appear to be increased by the severity of objective grievances. On the contrary, justice-seeking rebellions appear to be influenced by the height of the obstacles to overcoming the

15 collective action problem. Factors which increase grievance but reduce the feasibility of collective action, notably social fractionalization and repression, actually significantly reduce the risk of conflict.

Finally, we tested for whether post-conflict countries were more likely to have a further conflict than implied by the characteristics observed in our analysis. We found that a previous war had no significant effect on the overall level of risk. This suggests that we have not omitted any variables which are both significant and persistent. It also suggests that post-conflict societies are not more at risk simply as a result of their experience of conflict. Conflict may nevertheless indirectly increase the risk of further conflict by changing the values of the causal variables. In particular, since civil wars reduce income, post-conflict countries will have a reduced opportunity cost of rebellion.6

6 There may also be an increase in dependence upon natural resources if such activities are atypically resilient to conflict and this would also raise the risk of further conflict. On the effect of war on the level and composition of economic activity see Collier (1999).

16 References

Alesina, A., R. Baqir, and W. Easterly. 1997. ‘Public Goods and Ethnic Divisions,’ The World Bank, mimeo.

Alesina, A. and D. Rodrik. 1994. ‘Distributive Politics and Economic Growth,’ Quarterly Journal of Economics.

Azam, J.-L. 1995. ‘How to Pay for the Peace,’ Public Choice, 83:173-84.

Barro, R.J. 1997. Determinants of Economic Growth: a Cross-Country Empirical Study, MIT Press, Cambridge, Mass.

Barrett, D.B. 1982. (ed.) World Christian Encyclopedia, Oxford University Press, Oxford.

Bikhchandani, S., D. Hirshleifer and I. Welch (1998) `Learning from the Behavior of Others: Conformity, Fads and Informational Cascades’, Journal of Economic Perspectives, Vol. 12, (3), pp.151-170.

Collier, P., 1999, ‘On the Economic Consequences of Civil War’, Oxford Economic Papers, Vol. 51(1), pp. 168-83.

Collier P., and A. Hoeffler. 1998. ‘On the Economic Causes of Civil War,’ Oxford Economic Papers, Vol. 50(4), pp. 563-73.

Deininger, K. and L. Squire. 1996. ‘A new data set measuring income inequality,’ World Bank Economic Review, 10:565-91.

Figes, O. 1996. A People’s Tragedy, Pimlico, London.

Gurr, T.R. 1993. ‘Why Minorities Rebel: A Global Analysis of Communal Mobilisation and Conflict since 1945,’ International Political Science Review 14:161-201.

Horowitz, D.L. 1998. ‘Structure and Strategy in Ethnic Conflict.’ In B. Pleskovic and J.E. Stiglitz, Annual Bank Conference on Development Economics, 1998, The World Bank, Washington DC.

Jain, S. 1975. ‘Size Distribution of Income: A Comparison of Data,’ The World Bank, unpublished manuscript.

Kuran, Timur, 1989, `Sparks and Prairie Fires: a Theory of Unanticipated Political Revolution’, Public Choice, Vol. 61(1), pp. 41-74.

17 Singer, J.D., and M. Small. 1994. Correlates of War Project: International and Civil War Data, 1816-1992. Inter-University Consortium for Political and Social Research, Ann Arbor, Michigan.

18 The Data Set

COUNTRY WAR6096 DUR6096 WAR4560 RGDP60 ELF POP60 SXP65 polr rf70 rf80 gini70 gini90 gin90-70 ALGERIA 1 66 1 1,723 43 10.80 0.19 4.96 2 2 38.73 BENIN 0 0 0 1,100 62 2.05 0.04 4.92 49 50 BURKINA 0 0 0 456 68 4.63 0.04 4.38 57 61 BURUNDI 1 64 0 640 4 2.94 0.08 5.79 48 36 CAMEROON 0 0 1 641 89 5.33 0.14 5.08 73 74 CENTRAL AFRICAN REPUBLIC 0 0 0 704 83 1.53 0.08 5.25 66 62 55.00 CHAD 1 101 0 756 69 3.06 0.09 5.46 70 69 CONGO 0 0 0 1,123 66 0.99 0.12 5.00 61 61 EGYPT 0 0 0 809 4 25.92 0.09 4.21 32 31 39.00 32.00 -7.00 ETHIOPIA 1 209 0 257 69 22.54 0.09 5.42 62 60 GABON 0 0 0 1,789 69 0.49 0.38 4.63 52 52 64.39 GAMBIA 0 0 0 602 73 0.35 0.34 1.71 27 27 GHANA 0 0 0 894 71 6.77 0.14 4.71 70 72 35.36 IVORY COAST 0 0 0 1,120 86 3.78 0.29 4.88 60 63 53.42 36.89 -16.53 KENYA 0 0 1 659 83 8.33 0.13 4.42 68 70 47.90 54.39 6.49 LIBERIA 1 70 0 717 83 1.04 0.45 5.00 55 59 MADAGASCAR 0 0 1 1,191 6 5.31 0.10 3.67 63 64 43.44 MALAWI 0 0 0 380 62 3.53 0.17 5.04 74 73 MALI 0 0 0 535 78 4.35 0.06 5.00 35 33 MAURITANIA 0 0 0 780 33 0.99 0.33 5.50 1 1 42.53 MAURITIUS 0 0 0 2,862 58 0.66 0.28 0.96 66 66 36.69 MOROCCO 1 97 1 815 53 11.63 0.14 3.38 2 1 39.20 MOZAMBIQUE 1 290 0 1,153 65 7.58 0.09 5.10 59 65 NIGER 0 0 0 532 73 3.23 0.04 5.21 24 21 31.60 36.10 4.50 NIGERIA 1 34 0 567 87 40.82 0.12 4.17 69 69 54.07 41.15 -12.92 SENEGAL 0 0 0 1,047 72 3.19 0.15 3.25 19 17 49.55 54.12 4.57 SIERRA LEONE 1 67 0 878 77 2.24 0.09 4.58 56 57 60.79 SOMALIA 1 177 0 1,103 8 3.28 0.13 6.00 1 0 SOUTH AFRICA 0 0 0 2,191 88 17.40 0.08 3.46 62 63 53.00 62.30 9.30 SUDAN 1 259 0 817 73 11.16 0.13 4.79 45 43 38.72 TANZANIA 0 0 0 319 93 10.03 0.19 4.96 73 74 39.00 38.10 -0.90 TOGO 0 0 0 367 71 1.51 0.14 5.54 59 66 TUNISIA 0 0 1 1,101 16 4.22 0.10 4.63 2 1 43.15 40.24 -2.91 UGANDA 1 92 0 598 90 6.56 0.16 4.75 68 65 36.89 ZAIRE 1 62 0 489 90 15.33 0.08 5.54 65 64 ZAMBIA 0 0 0 965 82 3.14 0.50 3.63 66 67 51.00 43.51 -7.49 ZIMBABWE 1 85 0 989 54 3.61 0.27 3.92 56 59 66.27 56.83 -9.44 BARBADOS 0 0 0 2,666 22 0.23 0.14 0.00 20 30 36.90 CANADA 0 0 0 7,258 75 17.91 0.10 0.00 55 58 32.24 27.56 -4.68 COSTA RICA 0 0 1 2,096 7 1.24 0.16 0.00 17 18 44.40 46.07 1.67 DOMINICA 1 5 0 1,195 4 3.23 0.11 1.29 5 7 49.28 50.65 1.37 EL SALVADOR 1 152 0 1,427 17 2.57 0.20 2.21 6 7 40.00 44.77 4.77 GUATEMALA 1 143 1 1,660 64 3.96 0.12 2.83 10 11 29.96 59.06 29.10 HAITI 0 0 0 924 1 3.80 0.07 5.33 27 30 HONDURAS 0 0 0 1,039 16 1.93 0.24 2.67 7 8 61.88 54.00 -7.88 JAMAICA 0 0 0 1,773 5 1.63 0.10 0.79 41 45 41.27 41.79 0.52 MEXICO 0 0 0 2,836 30 36.53 0.04 2.75 7 10 57.80 52.65 -5.15 NICARAGUA 1 108 0 1,606 18 1.50 0.23 3.75 9 10 50.32 PANAMA 0 0 0 1,575 28 1.15 0.10 4.08 25 27 57.00 56.47 -0.53 TRINIDAD 0 0 0 5,627 56 0.84 0.48 0.42 71 71 51.00 U.S.A. 0 0 0 9,895 50 180.67 0.01 0.00 63 67 34.06 37.80 3.74 ARGENTINA 0 0 1 4,462 31 20.62 0.05 2.25 13 15 44.00 47.59 3.59 BOLIVIA 0 0 1 1,148 68 3.43 0.17 2.50 13 14 49.60 42.04 -7.56 BRAZIL 0 0 0 1,784 7 72.59 0.07 2.13 18 22 57.61 59.60 1.99 CHILE 0 0 0 2,885 14 7.61 0.11 3.88 28 31 45.82 56.49 10.67 COLOMBIA 1 154 1 1,684 6 15.94 0.09 1.21 6 7 52.02 51.32 -0.70 ECUADOR 0 0 0 1,461 53 4.41 0.11 2.29 6 7 68.26 43.00 -25.26 GUYANA 0 0 0 1,596 58 0.57 0.47 3.00 72 72 56.16 40.22 -15.94 PARAGUAY 0 0 1 1,177 14 1.77 0.12 3.58 8 7 39.80 PERU 1 139 0 2,019 59 9.93 0.13 2.96 9 9 55.00 43.82 -11.19 URUGUAY 0 0 0 3,968 20 2.54 0.09 2.54 50 51 42.79 42.79 0.00 VENEZUELA 0 0 0 6,338 11 7.50 0.29 0.50 10 10 47.65 53.84 6.19 INDIA 1 106 1 766 89 434.85 0.02 1.33 31 32 30.38 29.69 -0.69 INDONESIA 1 19 1 638 76 94.00 0.11 4.38 66 67 30.70 33.09 2.39 IRAQ 1 154 1 3,427 36 6.85 0.27 5.83 9 8 62.88 ISRAEL 0 0 0 3,477 20 2.11 0.04 0.88 22 21 31.95 33.62 1.67 JAPAN 0 0 0 2,954 1 94.09 0.01 0.42 54 57 35.50 35.00 -0.50 SOUTH KOREA 0 0 0 904 - 25.00 0.02 2.79 68 72 33.30 33.64 0.34 MALAYSIA 0 0 1 1,420 72 8.14 0.36 2.42 68 68 50.00 48.35 -1.65 MYANMAR 1 299 1 316 47 21.75 0.14 5.92 22 24 NEPAL 0 0 0 628 70 9.40 0.01 2.96 22 19 PAKISTAN 1 63 0 638 64 45.85 0.06 3.58 6 6 29.91 31.15 1.24 PHILIPPINES 1 245 1 1,133 74 27.56 0.12 2.92 27 28 49.39 45.00 -4.39 SAUDI ARABIA 0 0 0 3,884 6 4.07 0.61 5.29 1 2 SINGAPORE 0 0 0 1,658 42 1.47 0.67 3.50 65 65 41.00 39.00 -2.00 SRI LANKA 1 164 0 1,259 47 9.89 0.22 1.83 52 52 37.71 30.10 -7.61 SYRIA 0 0 0 1,575 22 4.56 0.10 5.13 20 19 THAILAND 0 0 0 943 66 26.39 0.13 2.79 15 15 42.63 48.80 6.17 AUSTRIA 0 0 0 5,143 13 7.05 0.04 0.00 18 18 29.30 25.84 -3.46 DENMARK 0 0 0 6,760 5 4.58 0.13 0.00 8 9 36.73 33.20 -3.53 FINLAND 0 0 0 5,291 16 4.43 0.07 0.63 10 13 27.00 26.11 -0.89 FRANCE 0 0 0 5,823 26 45.68 0.03 0.00 32 37 44.00 34.91 -9.09 GERMANY, 0 0 0 6,570 3 72.67 0.02 0.00 54 57 28.13 32.20 4.07 GREECE 0 0 0 2,093 10 8.33 0.05 0.92 4 4 35.11 35.19 0.08 ICELAND 0 0 0 4,964 5 0.18 0.25 0.00 5 7 IRELAND 0 0 0 3,311 4 2.83 0.15 0.00 9 9 38.69 34.60 -4.09 ITALY 0 0 0 4,564 4 50.20 0.02 0.17 17 28 41.00 32.47 -8.54 MALTA 0 0 0 1,374 8 0.33 0.04 0.42 4 5 NETHERLANDS 0 0 0 6,077 10 11.49 0.14 0.00 59 62 28.60 29.38 0.78 NORWAY 0 0 0 5,610 4 3.58 0.10 0.00 3 4 36.76 33.31 -3.45 PORTUGAL 0 0 0 1,869 1 8.94 0.06 0.92 6 11 40.58 36.76 -3.82 SPAIN 0 0 0 3,123 44 30.46 0.03 1.04 5 6 34.55 25.91 -8.64 SWEDEN 0 0 0 7,592 8 7.48 0.06 0.04 41 45 30.36 32.52 2.16 SWITZERLAND 0 0 0 9,409 50 5.36 0.02 0.00 52 52 TURKEY 1 66 0 1,622 25 27.51 0.05 1.96 2 2 53.50 44.09 -9.41 U.K. 0 0 0 6,823 32 52.37 0.02 0.00 41 44 25.10 32.30 7.20 AUSTRALIA 0 0 0 7,782 32 10.27 0.10 0.00 52 61 32.02 41.72 9.70 NEW ZEALAND 0 0 0 7,960 37 2.37 0.16 0.00 39 46 30.05 40.21 10.16 PAPUA NEW GUINEA 0 0 0 1,235 42 1.92 0.12 1.05 51 48

2 Appendix

War: Dummy variable which takes a value of one if a civil war broke out and zero if the country did not experience a civil war. If the civil war did not end during the five year period, but continued in the next period we did not consider this continuation period for this country, because in our analyis we want to concentrate on the occurrence of wars and not on their duration. The dummy was created from the civil war and extrasystemic war data provided by Singer and Small (1994) and an update of this data set described in Collier, Hoeffler and S`derbom (1999).

Previous War: dummy variable which takes a value of once the country had a civil war. We consider all civil wars between 1945 and 1995.

Previous War 10: dummy variable which takes a value of one if the country experienced a civil war in during the previous ten years.

Schooling: Average schooling years in the total population aged 15 and over. Source: Barro and Lee (1996).

Share of Primary Exports in GDP: export of primary products (fuels and non-fuels) to GDP ratio. Source: WDI 1998.

Ethno-linguistic fractionalisation: measures the probability that two randomly people from a national population are from different linguistic groups. This index was measured for about 1960. Source: Mauro (1995).

Religious Fractionalization

Gives the probability that two randomly drawn people do not have the same religious affiliation. We would like to thank Robert Barro for the use of his data on religion (original data source is Barrett (1982).

Data was available for 1970 (which we used for 1965 and 1970) and for 1980 (which we used for 1980, 1985 and 1990). For the 1975 value we calculated the average of the 1970 and 1980 value.

Democracy: source Polity III data set (http://www.colorado.edu/IBS/GAD/spacetime/data/Polity.html

General openness of the political institutions, score 0-10, with higher values indicating a greater degree of openness.

Population: total population. Source: WDI 1998.

3 Income Distribution (Income Gini Coefficient)

Source: Deininger and Squire (1996). We only used their high quality data.

Data is measured at the beginning of each sub-period. However, data was not available for all initial years 1965, … 1990. We did not inter- or extrapolate to obtain missing values, but filled in missing observations according to the following rules. When only one data point was available we used this observation for all periods. If however data was available for 1970 and 1980 we used the average of the two values as a data point in 1975. For missing observations which could not be calculated as averages we assumed that the missing observations take the same values as the observation which is most closely in time to the missing observations. For example if data was available for 1970 and 1980, the observation in 1965 takes the value of the 1970 observation and for the years 1985 and 1990 we assumed the 1980 value.

If the data was not available for 1965, 1970, …, 1990 but for slightly earlier or later years we used this data if it was not more than plus or minus two years from the year for which we wanted to use the data.

For a very small number of countries there was only one data point available, which we used for all initial years. For the income GINI we used the 1993 value for Mauretania, Rwanda and South Africa.

If data was available for the year before and after, for example for 1979 and 1981, we calculated the average.

Land Distribution (Land Gini Coefficient)

Source: Klaus Deininger, Worldbank (original source FAO). We used the same data organization rules as for the income distribution data.

Income: PPP adjusted GDP per capita in constant US $ measured at the beginning of the five year subperiods. Source: Penn World Table.

Lagged Growth: average annual growth rate of our income variable.

Lagged Inflation: average annual change in the CPI. Source: WDI 1998.

Aid: aid as a percentage of GDP. Source: Chang and Dollar, Worldbank.

Population density: Source: WDI 1998.

Young Male Population: the percentage of young men aged 14-29 in the total population. Source: Worldbank.

4 Sample:

Our Sample includes 90 countries of which 22 experienced at least one civil war during 1965-95. For two countries, Iraq and Sri Lanka we analyze two civil wars each. The dates indicate when the civil war started.

ALGERIA 90-95 LIBERIA 85-90 SIERRA LEONE 90-95 SUDAN 80-85 UGANDA 65-70 ZIMBABWE 70-75 DOMINICAN 65-70 REP. EL SALVADOR 75-80 GUATEMALA 65-70 GUATEMALA 75-80 NICARAGUA 75-90 COLOMBIA 80-85 PERU 80-85 INDIA 85-90 INDONESIA 70-75 IRAN 75-80 IRAQ 70-75 IRAQ 85-90 MYANMAR 65-70 PAKISTAN 70-75 PHILIPPINES 70-75 SRI LANKA 70-75 SRI LANKA 80-85 TURKEY 90-95 BENIN CAMEROON CENTRAL AFR.R. CONGO EGYPT GAMBIA GHANA KENYA MALAWI MALI

5 MAURITIUS NIGER SENEGAL SOUTH AFRICA TANZANIA TOGO TUNISIA ZAIRE ZAMBIA CANADA COSTA RICA HAITI HONDURAS JAMAICA MEXICO PANAMA TRINIDAD&TOBAGO U.S.A. ARGENTINA BOLIVIA BRAZIL CHILE ECUADOR GUYANA PARAGUAY URUGUAY VENEZUELA ISRAEL JAPAN JORDAN KOREA, REP. KUWAIT MALAYSIA NEPAL PAKISTAN SINGAPORE SYRIA THAILAND AUSTRIA CYPRUS DENMARK FINLAND FRANCE GERMANY, WEST GREECE

6 ICELAND IRELAND ITALY NETHERLANDS NORWAY PORTUGAL SPAIN SWEDEN SWITZERLAND U.K. AUSTRALIA NEW ZEALAND PAPUA N.GUINEA

7