POLICY RESEARCH WORKING PAPER   2355
Greed and Grievance                                                       Of the 27 major armed
conflicts that occurred in
in   Civil W ar                                                           1999, all but two took place
within national boundaries.
Paul Collier                                                              As an impediment to
Anke Hoe ffler                                                            development, internal
rebellion especially hurts the
world's poorest countries.
What motivates civil wars?
Greed or grievance?
The World Bank
Development Research Group                                                         U



POLICY RESEARCH WORKING PAPER 2355
Summary findings
Collier and Hoeffler compare two contrasting                 effect might be present in both greed-motivated and
motivations for rebellion: greed and grievance. Most         grievance rebellions.
rebellions are ostensibly in pursuit of a cause, supported     The authors' results contrast with conventional beliefs
by a narrative of grievance. But since grievance             about the causes of conflict. A stylized version of
assuagement through rebellion is a public good that a        conventional beliefs would be that grievance begets
government will not supply, economists predict such          conflict, which begets grievance, which begets further
rebellions would be rare.                                    conflict. With such a model, the only point at which to
Empirically, many rebellions appear to be linked to the    intervene is to reduce the level of objective grievance.
capture of resources (such as diamonds in Angola and            Collier and Hoeffler's model suggests that what
Sierra Leone, drugs in Colornbia, and timber in              actually happens is that opportunities for predation
Cambodia). Collier and Hoeffler set up a simple rational     (controlling primary commodity exports) cause conflict
choice model of greed-rebellion and contrast its             and the grievances this generates induce diasporas to
predictions with those of a simple grievance model.          finance further conflict. The point of policy intervention
Some countries return to conflict repeatedly. Are they     here is to reduce the absolute and relative attraction of
conflict-prone or is there a feedback effect whereby         primary commodity predation and to reduce the ability
conflict generates grievance, which in turn generates        of diasporas to fund rebel movements.
further conflict? The authors show why such a feedback
This paper - a product of the Development Research Group - is part of a larger effort in the group to study civil war and
criminal violence. For more on this effort, go to www.worldbank.org/research/conflict. Copies of the paper are available
free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please contactAudrey Kitson-Walters, room MC3-
527, telephone 202-473-3712, fax 202-522-1150, email address akitsonwalters@worldbank.org. Policy Research
Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Paul Collier may be contacted
at pcollier@worldbank.org. May 2000. (44 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the autthors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center



Greed and Grievance in Civil War
Paul Collier
The World Bank, CEPR and CSAE
Anke Hoeffler
CSAE, Oxford






1. Introduction'
Civil war is both a tragedy and an impediment to development, affecting most of the
world's poorest countries. It is now far more common than international conflict: of the
27 major armed conflicts listed in the Stockholm International Peace Research Institute
Yearbook for 1999 (SIPRI 1999), all but two were internal.
In Section 2 we compare two contrasting motivations for rebellion: greed and grievance.
Most rebellions are ostensibly in pursuit of a cause, supported by a narrative of grievance.
However, since grievance-assuagement through rebellion is a public good which a
government will not supply, economists would predict that such rebellions would be rare.
Empirically, many rebellions appear to be linked to the capture of resources: diamonds in
Angola and Sierra Leone, drugs in Colombia, and timber in Cambodia. We set up a
simple rational choice model of greed-rebellion and contrast its predictions with those of
a simple grievance model. A second empirical regularity is that some countries are prone
to repeat conflict. This may be either because their underlying characteristics make them
highly conflict-prone, or because of a feedback effect whereby conflict generates
grievance which in turn generates further conflict. We show why such a feedback effect
might be present in greed-motivated rebellions as well as in grievance-rebellions. Finally,
we consider an integrated model in which the motivation for rebellion is both greed and
grievance.
In Section 3 we discuss the construction of a comprehensive data set of 161 countries for
each of the eight, five-year periods between 1960 and 1999, giving a total of 1288
potential observations. For 73 of these observations the society was at peace at the start of
the period but experienced civil war during it. In Section 4 we use logit regressions to
explain these collapses into civil war in terms of characteristics at the start of the period.
We perform non-nested tests on the greed and grievance models. Although the greed
model substantially outperforms the grievance model, we cannot reject the hypothesis
that the grievance model adds to explanatory power. An integrated model, which
incorporates some features of grievance into the greed model, outperforms all other
models. An economic calculus of the costs and opportunities for the control of primary
commodity exports appears to be the main systematic initial impetus to rebellion, with an
additional effect from fear of domination by an ethnic majority. After peace has been
restored, the legacy of conflict-induced grievance enables rebel movements to restart
conflict by drawing on the support of their diasporas. We show that the results are robust
to the inclusion of a wide range of alternative variables, and to tests for random and fixed
effects and to correction for rare events bias. The last section concludes.
'Previous versions of this paper have benefited from presentations at CERDI, NYU, LSE, Oxford,
Princeton, the World Bank and a CEPR conference at Lisbon. We would like to thank participants for
comments, and especially Todd Sandler for helpful written suggestions.
2



2. Theories of Rebellion
Civil war occurs as a result of rebellion. Hence, the phenomenon to be explained is the
emergence of a rebel organization. A rebellion is somewhat analogous to three other
types of organization: protest movements, armies, and organized crime. In popular and
political analysis, the most common analogue to rebellion is protest. The theory of protest
movements, (Kuran, 1989), focuses on the coordination problem. In effect, by joining
each other on the streets, people can create an election. However, the analogy is not very
close. Protest is not a sustained economic activity: the participants are not full-time
employees of the protest organization, and they risk little. Indeed, this is the essence of
Kuran's 'prairie fire' model: protest only takes off if the risks fall fast enough (through
increasing participation) to make it safe. By contrast, participants in a rebellion must be
prepared to fight over a prolonged period against organized force which aims to kill them.
In this, a rebel organization is more analogous to a regular army than to a protest
movement. Like an army it must solve immense problems of hierarchy and cohesion in
order to get people to risk their own death in order to further the military objective of the
group. However, unlike both an army and a protest organization, a rebellion must
generate revenue in order to feed and pay its workers. The payroll of an army is financed
out of taxation which the army itself does not raise. A protest movement does not have a
significant payroll. The rebel organization must generate income despite not being
directly productive, and in this respect rebellion is like crime. Indeed the analogy with
crime is standard in the present economic theory of rebellion For example, Grossman
(1999) states 'in such insurrections the insurgents are indistinguishable from bandits or
pirates' (p.269). However, the very scale of rebellion makes it distinctive from other
crime. Rebel organizations usually have between 500 and 5,000 workers, whereas most
criminals are self-employed or work in small groups. Hence, within crime, the closest
analogy is that with organized crime. Even organized crime is usually a rather small scale
activity, but the largest groups are approximately comparable in size to the smaller rebel
organizations. The recent theory of organized crime (e.g. Konrad and Skaperdas, 1998),
explains its larger scale than other crime by scale economies in extortion, eventually
counterbalanced by policing.
The analogies with the economic theories of organized crime and protest movements
form the basis for the 'greed-rebellion' theory of Section 2.1. In this model rebellion is a
distinctive type of organized crime, although subject to constraints similar to those facing
an incipient protest movement. In Section 2.2 we present a contrasting political model of
rebellion in which we put aside the economic considerations and focus instead upon
various forms of grievance. Section 2.3 compares the two models and considers various
ways in which rebellion might combine both greed and grievance.
2.1. A Model of Greed-Rebellion
Consider a rebellion which, like organized crime, generates its income from extortion.
The rebels menace legitimate economic activities and exact a tribute. Empirically,
rebellion is nevertheless distinctive from other types of organized crime, if only in terms
3



of scale and casualties. We are concerned only with those rebellions which are
sufficiently large and sustained, and come into sufficient conflict with government forces,
to generate at least 1,000 battle-related deaths. This is the conventional empirical
definition of civil war, and this is the phenomenon which we will be seeking to explain.
Hence, rebellion is distinctive from other crime in the scale of organized violence. We
suggest that rebellion is distinctive because the object of extortion is different. The
typical object of criminal extortion is a street of shopkeepers. The criminal organization is
small because the scale economies in menacing shopkeepers are modest, and because the
rate of predation cannot get very high before businesses relocate. Rebellion is at the apex
of organized crime because the object of extortion is not a street of shopkeepers but the
export of primary produce. As a result, both the scale economies of menace and the
sustainable rate of extortion, are atypically high. Primary commodity exports are
sustainable targets for predation because their production is intensive in irreversible and
immobile assets, and because produce must be transported to a port. The owners of
irreversible and immobile assets such as land, trees and mines, receive rents which can be
expropriated without curtailing the activity, whereas similar predation of the incomes
generated by mobile factors would simply produce relocation. As primary commodities
are transported to a port they are exposed to predation at many geographic 'choke points'.
The government itself typically levies taxes at the tightest choke point, namely the port,
but rebels may attack at any point along the transport route.
We begin with a model which, for a country of given population, analyzes how the risk of
such a predatory rebellion is affected by variations in the level of income and its
structure. Corresponding to our subsequent empirical analysis, we will be seeking to
explain only the initiation of conflict, rather than its duration.
We first specify a function for the revenue which a rebellion can generate from predation
upon primary commodity exports, while being opposed by government forces which are
protecting those exports. The gross revenue function has two components, the tribute
which can be exacted conditional upon a successful threat of military force, and a military
contest function. The tribute is assumed to be a function of the value of primary
commodity exports: the tribute increases in this base for predation, but at a diminishing
rate. It is convenient to specify primary commodity exports as the product of income, y,
and the share of income constituted by such exports, n. For the military contest function,
we follow the specification of Konrad and Skaperdas (1998), in which military outcomes
reflect the balance of opposing forces, rebel, r, and government, g. Hence:
Rr =  ny) a[r/(r+g)j.                                                (1)
We next introduce an insight from the economic theory of protest movements (Kuran,
1989). Government forces are not simply trying to impede rebels from predation, they are
trying to kill them. If the rebel force is too small, when it attacks the choke points, the
government forces which it encounters will turn from defense to attack. In Kuran's
model, there is a tipping point of participation above which the protest becomes viable.
4



Here, we assume that there is a threshold size of rebel forces relative to government
forces, below which predation of primary commodity exports is too dangerous to be
viable. We introduce this as a survival constraint: rebel forces must exceed some fraction
of government forces in order to engage in predation without suffering punishment. Thus,
r 2,Bg.                                                                         (2)
The rebel cost function, Cr, is its wage bill:
Cr = rwr.                                                                       (3)
Where wr = rebel wage rate.
The rebel wage is assumed to be linear in the level of income, y:
Wr = (Y.                                                                         (4)
The rebel leader thus chooses the size of the rebel laborforce so as to maximize net
revenue, (1)-(3), subject to (2). Rebellion will occur when net revenue is non-negative,
which we refer to as the financial viability constraint:
Rr-Cr20.                                                                         (5)
We next endogenize the initial government defense effort, g, which precedes the initiation
of rebellion. The government cost function, Cg, mirrors that of the rebels:
Cg = gwg                                                                         (6)
wg = 2Y.                                                                         (7)
We assume that the government sets its precautionary military expenditure as fraction of
its revenue, Rg. Reflecting observed fiscal patterns, government revenue is assumed to be
elastic in income, and in the share of income from primary commodity exports. As
discussed, the same factors make natural resource exports atypically taxable and
lootable.2
Rg = 3y9  +  yny.     O>                                                         (8)
2 Govement military expenditure may also be increasing in the proportion of natural resource exports
because the latter are recognized as being vulnerable to predation. This would simply reinforce the results
below on the non-monotonic effect of natural resource exports on the risk of conflict.
5



Prior to rebellion, the government devotes a given share of its expenditure, v, to military
expenditure. Hence, prior to rebellion, government forces, g, are:
g = V(- S-l + M)/A.                                                        (9)
The minimum size of a viable rebellion is that at which the survival constraint is binding.
If the rebellion is financially viable at this size, the rebel leader may choose to expand it
further. However, since there are diminishing marginal returns to rebel labor, the
rebellion will only be worth expanding beyond the size imposed by the survival
constraint if it is also financially viable at that size. Hence, financial viability at the size
which just satisfies the survival constraint is the condition for the initiation of a rebellion.
Substituting, and rearranging, the financial viability condition is:
Ana/l( +,8) ˇ v(3qy0 a + npyI-a)                                          (10)
Equation (10) is thus the key condition for the initiation of greed-rebellion.
Now consider whether the rebellions so initiated are likely to become sufficiently large to
be recorded as civil wars: that is, whether they generate at least 1,000 combat-related
deaths. For this a key consideration is whether the incipient rebellion generates a phase of
Iarms race' in which both government and rebel forces grow in response to each other. If
there is such a phase, then we will assume that the rebellion is large enough to generate a
civil war.
The sequence of rebellion is as follows. In the pre-history of the rebel movement it builds
its forces until they reach the level which just satisfies the survival condition. At this
stage rebel forces can start operations, and so combat deaths commence. We assume that
the initial response of the government to rebellion is to increase its expenditure upon its
military forces and focus on how rebel forces respond to this increase. If rebel forces
respond by contracting, then it is possible that the rebellion gets snuffed out before
combat-related mortalities reach the critical level of 1,000 at which the conflict is
classified as a civil war. By contrast, if the rebel group initially responds by increasing its
own forces, then there is a phase of mutual escalation, although this phase may end with
the bankruptcy of the rebellion. The Konrad-Skaperdas (KS) military combat function
predicts how the optimal size of the rebel organization changes in response to an increase
in government forces: rebel forces will increase if r>g, and decrease if r<g. However, at
the point at which the rebel organization commences predation its forces are below their
optimal size: the rebel organization builds its forces from zero, and once they satisfy the
survival constraint, operations commence. At this point the government expands its own
forces. Hence, regardless of whether the optimal size of the rebel organization increases
or reduces in response to the expansion of government forces, the actual size must
increase in line with the rise in the minimum size necessary to satisfy the survival
constraint. The rebel organization expands its forces at least in reaction to this increase
6



and perhaps additionally in a continuing adjustment towards the optimum. In response,
the government further expands its own forces.3 Hence, once (10) is satisfied, not only
does the rebellion come into existence, but it enters an arms race phase with government
forces. While this race may end with the bankruptcy of the rebellion, we assume that in
the interim it will have caused sufficient combat deaths to meet the criterion used in our
empirical analysis. Condition (10) is thus both a necessary condition for rebellion and a
sufficient condition for civil war. We now derive from it specific predictions as to how
the risk of rebellion will be affected by the level of income and its structure.
First, consider the level of income. The LHS of (10) is not a function of y. Since 0>1>c;
the derivative of the RHS w.r.t. y is strictly positive:
dRHS/dy = (6oa) v6yoy°a-1 + (1-a) vnopyq` > 0.                             (11)
Hence, the conditions under which rebellion is profitable become more restrictive the
higher is income. Restated, higher income is predicted to reduce the risk of conflict.
Next, consider the structure of income. Differentiating (10) w.r.t. n yields:
d(1O)/dn = [mln a"/(1 +/3)] - vplyI -a"                                    (12)
In the neighborhood of n=O, (10) is negative and (12) is positive, so that as n increases
from zero the risk of conflict increases. However, (12) is not monotonic in n. Setting (12)
to zero and solving for n yields a critical value, n*, below which the conditions for
rebellion become easier as n increases, and above which they become tighter. Hence, n *
denotes that intermediate level of primary commodity dependence at which the risk of
rebellion is at its peak:
= n                                                                     (13)
The above analysis applies to a country of given population. Now consider how the risk
will be affected by variations in population size. If the risk in each of two identical
territories is p, the risk that there will be at least one conflict in the two territories is p +
p(l-p). The elasticity of risk with respect to population, controlling for n and y, will
therefore be (l-p). Globally, over the period 1960-1999 the mean risk of conflict per
period was around 0.06, so that the expected elasticity of conflict with respect to
population is around 0.94.
Thus far, the model has four testable predictions. The risk of conflict should be
decreasing in per capita income, increasing in the share of natural resource exports in
GDP at low levels, decreasing at high levels, and be slightly less than unit elastic in
3 Collier (2000) further analyzes this escalation.
7



population. We now introduce three refinements which introduce further testable
propositions: relative military advantage, rebel costs of recruitment, and start-up finance.
Relative military advantage
The military combat function included in (1) is a convenient simple special case of the
KS function. The more general form of the KS combat function is:
r/(r+g+k),                                                              (14)
where k denotes the relative military advantage of the two forces. For k=0, the special
case we have adopted above, neither force has technological superiority. For k>0
government forces have the advantage, and conversely for k<O. Relative military
advantage affects the necessary conditions for financial viability. Trivially, the greater is
the relative military advantage of the government, (the higher is the value of k), the lower
is the value of the combat function, (14), and hence the lower is gross and net revenue
from rebellion. Rebellion is less likely the greater is the relative military advantage of the
government. We endogenize relative military advantage with respect to geography,
cohesion and motivation.
Rebels need a refuge, and two geographic features, mountains and forests, are commonly
supposed to make counter-insurgency more difficult. We measure these as the proportion
of a country's terrain which is mountainous, m, and the proportion which is forested, A
Additionally, Herbst (2000) has suggested that states such as Zaire are prone to rebellion
because the population is dispersed around the edges of the country, making government
military control more difficult. We measure this by the geographic dispersion of the
population, VI We predict that the risk of conflict is increasing in these three measures.
In war, the size of a fighting force is probably less important than its willingness to fight.
Military history offers many instances in which cohesion and motivation offset numerical
inferiority. The government army has the advantage that it predates the rebellion and so
has had time to build cohesion and motivation. The rebel organization must create these
attributes as it recruits. The need for cohesion places a simple constraint upon the
composition of rebel recruits: the organization cannot afford diversity. If recruitment
spans ethnic and religious divides it will be more difficult to forge the resulting
laborforce into a cohesive fighting force. The strategy of homogenous recruitment will
sometimes come at a cost, since in socially fractionalized societies it will reduce the size
of the recruitment pool and so raise labor costs for any given size of recruitment. Thus,
the more fractionalized the society, the more costly is rebel recruitment (or the less
cohesive is the rebel force). We thus predict that social fractionalization should reduce the
incidence of civil war. We measure social fractionalization, f, by the extent to which a
society is divided by ethnicity and religion.
8



In addition to cohesion, an army needs motivation: rebel forces must be persuaded to
want to kill the enemy. The Leninist theory of rebel organization, which many rebel
groups follow even if they are not Marxist, is that initially the population from which
forces are recruited does not realize that it is oppressed, so that an awareness of grievance
has to be built by the rebellion. Many rebel organizations invest substantial resources in
this process of indoctrination. For example, the highly successful Eritrean People's
Liberation Front routinely withdrew troops from the front line for periods of six months
for ideological training. Thus, subjective grievance is consciously generated by conflict
to enhance efficiency, rather than being either an accidental by-product of conflict, or a
prior cause of conflict. The greed model departs from Leninist theory in assuming that the
ability of a rebel organization to inculcate subjective grievance is unrelated to objective
grounds for grievance. This assumption is partly a convenience to distinguish the model
as sharply as possible from the subsequent grievance model, but also has some a priori
claim to credibility. Many rebel groups target their recruitment on children and drug
addicts, and such selectivity, in conjunction with the phenomenon of relative deprivation,
whereby a majority of the population considers itself to be unfortunate, may enable
indoctrination to be effective in generating grievance regardless of objective
circumstances. This theory of military motivation has two testable predictions. First,
objective causes of grievance will not explain the risk of conflict. We return to this in
Section 2.2 where we measure objective grounds for grievance. Secondly, since conflict
produces military cohesion, rebel military advantage will be greater in societies which
have recently experienced conflict. We measure this by the time, T, which has elapsed
since the end of any previous civil war.
The model implies that in ethnically diverse societies, rebellion will appear to be caused
by ethnic differences even though diversity actually reduces the risk of conflict. Where
rebellion occurs in such societies, the need for homogeneity in recruitment will make it
ethnically specific, and the need for the rebel organization to generate a subjective sense
of grievance may focus the discourse on ethnicity.
The hypothesized effects of geography, cohesion and motivation on relative military
advantage are summarized in (15):
k = k(m, A, Yf,t fT).                                                (l S)
Rebel costs ofrecruitment
In the basic model set out above, rebel and government labor costs are treated as being
symmetrical: for both the wage is simply linear in per capita income. However, this
ignores an important difference between the rebel force and the government army. Our
focus is on the initiation of rebellion, and in this phase the rebel force must grow, and
probably needs to grow rapidly to reach the survival constraint at minimum cost. By
9



contrast, the government army is already in steady state. We would expect this to make
the labor costs of the rebel organization more sensitive to the state of the labor market
than those of the government. The tighter is the labor market, controlling for the level of
per capita income, the more costly would be rebel recruitment, and so the lower would be
the risk of conflict. We measure the state of the labor market through three proxies:
education, population growth, and economic growth. Our education variable is the
proportion of young males enrolled in secondary education, s. This both directly
measures an important alternative activity to rebellion for that part of the population most
likely to be recruited, and proxies income-earning opportunities for the group. The
population growth rate (p), proxies the change in labor supply, and will consequently
proxy the change in pressure on fixed assets, notably land. The growth rate of income
proxies the demand for labor. Income growth decomposes into p and the growth in per
capita income, y, and since the former is already directly included, we measure income
growth net of population growth. The difficulty of rebel recruitment is assumed to be
increasing in s and, y, while being decreasing in p. To reduce problems of endogeneity,
these variables are all measured prior to the period of prediction. In (16) we introduce this
as a modification to the rebel wage function, (4):
Wr = (y.(s, y, p)                                             (16)
Since this change in the wage function affects the financial viability of rebellion, we
predict that the risk of conflict decreases with s and y and increases with y.
Start-upfinance
We now consider how rebel organizations might acquire start-up finance. The survival
condition imposes a minimum size on rebel forces below which they cannot be
operational in resource predation. This implies that there are threshold start-up costs.
Since rebellions may not be able to raise funding from conventional sources, they must
look elsewhere. We consider two potential sources of finance, foreign governments and
diasporas.
Foreign governments may provide pump-priming finance for rebellions which
subsequently become self-sustaining. An unusually clear example was the financing of
Renamo in Mozambique by the government of Southern Rhodesia. The latter collapsed
shortly after Renamo was established, but Renamo sustained itself as a rebel force for
many years before evolving into a political party. To test the proposition empirically, we
need some proxy for the willingness of foreign governments to finance military
opposition to the incumbent government. Among the possible proxies, we regard the end
10



of the Cold War as providing the clearest test of the proposition. Undoubtedly, during the
Cold War the two major powers tried to enlist other governments as allies, and each
power had an incentive to destabilize those governments which allied with the opposing
power. Hence, we predict that with the end of the Cold War, the supply of foreign power
finance for rebellions declined and that this should have reduced the risk of conflict. We
test this by introducing a dummy variable, Dl, for the post-Cold War period. We might
note that this is a controversial prediction. For example, Kaplan, (2000) argues that the
end of the Cold War 'lifted the lid' off previously suppressed conflict.
A further potentially important source of start-up finance for rebellion is a diaspora living
in OECD countries. Such diasporas are usually much richer than the population in their
country of origin. They are better-placed for collective action: emigrants have a cultural
incentive to create diaspora organizations which can then discipline free-riding. They do
not suffer the consequences of the conflicts they finance. As with grievance among the
local population, in the greed-model grievance among the diaspora is assumed to be
manufactured by the rebel organization rather than being an original cause of conflict.
Hence, the diaspora increases the risks of conflict renewal, but not the initial risk of
conflict. We measure the size of diasporas in the USA relative to the population in their
country of origin. We test whether this variable, d, is significant as an initial cause of
conflict, and by interacting it with the time since any previous conflict, d/r; whether it is a
significant risk factor post-conflict. The theory of greed-conflict predicts that only the
latter will be significant.
2.2. A model of grievance-rebellion
In a grievance-rebellion the objective is not predation, but rather to assuage grievance.
This is indeed the standard characterization of rebellion in the popular literature. The
most proximate economic theory is, as noted above, that of protest movements. Within
this framework, a rebellion might be thought of as a protest movement which has failed
to escalate into mass participation. Recall that rebellions seldom have more than 5,000
participants, whereas successful protest movements are generally many times this size. In
Kuran's model, the early phase of a protest movement is generated by a few ardent
adherents who are insensitive to the risks involved. It escalates if this induces a cascade
of participation by those who are initially deterred by fear of government reprisals. A
theory of rebellion as a failed protest movement must thus explain two stages: the initial
impetus for protest, and the failure of escalation.
Consider, first, the circumstances in which some people feel sufficiently strongly to
protest, to the extent of being insensitive to the risks of government reprisal. Evidently,
the impetus for such protest should be rooted in objective grievances, some of which are
potentially measurable. We distinguish three of them: inter-group hatred, political
exclusion, and vengeance.
11



Inter-ethnic or inter-religious hatreds, h, are probably the most common popular
explanation for civil conflict. A possible example of such a conflict might be Bosnia.
There is indeed evidence that such hatreds exist, and since many conflicts are inter-ethnic
or inter-religious, it is widely presumed that the hatreds are the cause of the conflict.
Although such hatreds are usually not directly observed, they can evidently only occur in
societies which are multi-ethnic or multi-religious. Inter-group hatred may be monotonic
in the extent of social fractionalization, or the relationship may be a quadratic: for
example, societies with two groups may have a higher incidence of inter-group hatred
than societies with many groups. Hence,
h = h(, h(0) = 0; h(f>0) > 0                                    (17)
A second presumed cause of grievance is political exclusion, j. The quantitative political
science literature has already explored the relationship between conflict and the political
rights of a society, q, as the latter range from dictatorial repression to full representative
democracy. Econometric studies have found that other than when repression is very
severe, it tends to increase the risk of conflict (Gleditsch and Hegre, 1997). However,
even democracies may generate grievance if one voting block is able to forge a persistent
majority and uses its power to disadvantage a minority. Nornally, such persistent
majorities are illusive because excluded groups are able to make offers which split the
majority. However, one circumstance in which a stable winning coalition can form is
where political allegiance is pre-determined by ethnic identity and one ethnic group
constitutes a majority of the population. Whether such a group uses its power to extract
transfers from the minority depends in part upon the size of the majority. Conditional
upon power, large majorities have less incentive to be exploitative than small majorities.
For example, if there are fixed costs to inter-group transfers, there is a critical size of
minority below which the minority is not worth exploiting. The circumstances in which
one or more ethnic groups are permanently politically exploited by a dominant group thus
depend upon the largest group having a sufficiently large share of the population to
control the political process, but not being so large that exploitation is not worthwhile.
The precise range of population shares over which the largest ethnic group exercises such
dominance cannot be determined a priori, although simple models would bound the
minimum at 50% and the maximum at strictly less than 100%. The grievance model
postulates that a dummy variable for societies characterized by such ethnic dominance,
D2, will be significant and positive, increasing the risk of conflict. A possible example of
grievance-rebellion driven by an excluded ethnic minority might be the conflict in Sri
Lanka, where the Tamil Tigers claim to defend the interests of the 12% of the population
who are Tamil.
A second circumstance of political exclusion is where the poor are marginalized from the
political process. As Hirshleifer (1991) shows, normally the poor will succeed in using
the political contest to ameliorate their economic position. A high degree of economic
inequality, i, is therefore some indication that the poor are atypically politically
marginalized. The 'rage of the poor' at high inequality is indeed probably the single most
popular explanation for conflict after that of inter-ethnic hatred, and may be exemplified
12



by the Castro rebellion in Cuba. A final circumstance of persistent political exclusion is
where a rich minority is heavily taxed by the majority. The rich may contest the
government or, following Buchanan and Faith (1987), they might attempt to secede from
the fiscal jurisdiction of the state. A possible example of the former might be the Contra
rebellion in Nicaragua, and of the latter, the eventually successful secession of the
Eritrean region from the much poorer state of Ethiopia. Since there is more incentive for
the majority to tax the rich the larger is the share of income or wealth accruing to them,
the 'rage of the rich' might also be expected to increase with economic inequality.
Hence, grievance due to political repression is postulated to be a function of the general
level of political rights, the ethnic composition of the society, and the degree of
inequality:
j =j(q, D2, i)                                                             (18)
Finally, much of the case study literature dwells upon history: current rebellion is
motivated by the desire to revenge atrocities committed during a previous conflict.
Corresponding to the greed theory of induced conflict, we assume that the longer the
period since a previous conflict, T, the less strong may be the demand for grievance
assuagement for such atrocities. Thus, the intensity of grievance, G, is assumed to be a
function of these three components:
G =G(h, j, T).                                                             (19)
Now consider the second stage in protest-rebellion, the failure of the initial protest to
escalate into successful political revolution. In the Kuran model the protest movement
succeeds in generating political change if, as people join the protest, the resulting
reduction in the risk of punishment attracts enough new entrants further to reduce the
risk, yielding a cascade. The key parameter is the elasticity of participation with respect
to the risk of punishment. Kuran suggests that this elasticity will be greater the more
homogenous the society. The participation cascade breaks if there are gaps in the
distribution of preferences such as might occur if the society is fractionalized. Hence,
social fractionalization enters twice in a model of rebellion as aborted protest. It is both
an impetus for the initial cadre of protestors, and an impediment to political revolution. If
rebellions are aborted protests they should therefore be significantly more common in
fractionalized societies.
However, unlike a protest, a rebellion which generates a civil war is a sustained, full-time
effort. Adherents must eat, and so, as with a greed-rebellion, the rebel organization is
constrained to be financially viable. Here the grievance-rebellion faces a severe test.
Grievance-assuagement is predominantly a public good and evidently it is one which will
not be provided by the government. Its financing will therefore face acute difficulties of
free-riding. The willingness of non-participants to support the rebel organization
13



financially may be affected by their own level of grievance, so our grievance proxies
might affect the risk of conflict both through generating hard core adherents, and by
increasing the scope for funding. A further possible source of grievance-related funding is
diasporas. There is some evidence that diasporas are more grievance-conscious than the
populations from which they originated. Hence, it is possible that diasporas are willing to
finance the initiation of rebellion even when local populations are not sufficiently
concerned to do so themselves. We have already discussed how this proposition can be
tested. However, the collective action problem is so severe in the temporary provision of
public goods by non-governmental means, that their supply will usually be negligible.
Thus, the principle prediction of an economic theory of grievance rebellion is that such a
rebellion will not occur, regardless of the intensity of the grievance. We test this
prediction using the grievance proxies in (19).
2.3 Greed-Grievance Interactions
We now compare the greed and grievance models of conflict and consider a synthesis.
The greed model postulates that the cause of initial conflict is an economic calculus of
relative military advantage, the government's ability to finance defense expenditure, the
scale of primary commodity exports, and the costs of rebel recruitment. If a conflict
occurs, the rebel organization will generate subjective grievance. Post-conflict, until this
gradually decays, it will increase the risk of subsequent conflict. The extent of this post-
conflict risk depends upon the size of diasporas, since they are able to finance rebellion.
By contrast, the grievance model postulates that the cause of initial conflict is not an
economic calculus but rather is a protest generated by objective grievances: ethnic or
religious hatreds, inequality, oppression, or historical vengeance. Rebellions are protests
which fail to cascade into non-violent revolution. The scope for rebels to find a refuge,
proxied by the three geography variables, can reasonably be added to the grievance model
without upsetting its essentially non-economic spirit.
Greed and grievance can co-habit. Where the conditions for greed-rebellion exist but
those for grievance-rebellion do not, a group initially motivated by grievance may
become dependent upon primary commodity predation for survival, thus transforming
itself into a greed-rebellion. Conversely, greed-rebellions need to manufacture subjective
grievance for military cohesion and may find an objective grievance an effective basis for
generating it. Hence, the presence of primary commodity exports may sustain rebellions
which are motivated by objective grievance, while the presence of objective grievance
may sustain rebellions motivated by predation. Such interdependence may make case
study evidence difficult to interpret.
The two models are evidently not nested, relying largely upon distinct variables, and in
Section 4 we compare their predictive power using standard tests. However, one variable,
ethnic and religious fractionalization, is included in both models with an opposite
predicted sign and so is of particular interest. The grievance model naturally sees ethnic
and religious difference as a potential source of hatreds as compared with a homogenous
14



society, and as an impediment to non-violent revolution. It thus predicts that the risk of
conflict will be lower in homogenous societies. The greed model sees fractionalization as
tightening the constraint on rebellion: the need for organizational cohesion imposes
homogeneity and so if the society is diverse the costs of rebel recruitment are raised.
An integrated model of conflict would allow both greed and grievance to initiate conflict.
Objective grievance could make recruits more willing to join, and so lower the costs of
rebellion. Potentially, diasporas could provide funding for initial rebellions, motivated by
objective grievance, rather than just financing subsequent conflicts motivated by induced
grievance, as proposed by the greed model. The greed and grievance models thus
potentially nest into an integrated model which combines both sets of variables as causes
of initial, as well as subsequent conflicts. Testing these models is the agenda for the rest
of the paper.
3. Data
Our empirical analysis incorporates several new data sets constructed for the study. In
this data section we present a brief description of the war variable and the various proxies
for greed and grievance. The data source for the war variable and all other variables is
Hoeffler and Sambanis (2000) where a more detailed data description can be found.
3.1. War data
We analyze the risk of civil war using a panel data set for 161 countries. For each country
we have potentially one observation for each of the eight sub-periods 1960-64, 1965-69,
1995-99. For 73 of these observations the country was at peace at the beginning of the
period but a civil war started during it. We predict only war-starts, not their continuation.4
Hence, once civil war has commenced, the possibility for a further initiation of conflict
only occurs once peace has been re-established. In fact, most of the countries which had
one conflict went on to have further conflicts. Indeed, 47 of the 73 wars were in countries
which had already had at least one previous civil war since 1945. We define a civil war as
an internal conflict in which at least 1000 battle related deaths (civilian and military)
occurred per year. This definition has become standard in the literature following the
seminal data collection exercise and analysis by Singer and Small (1982, 1994). Table 1
lists the 73 observations for which a war started during a sub-period. Our new war data
set primarily updates that of Singer and Small from 1992 to the end of the decade.
3.2. Rebel Revenue Sources
The three main sources of rebel revenue are primary commodity exports, diasporas and,
during the Cold War, foreign powers.
'In our earlier work (Collier and Hoeffler, 1998), we used a much smaller sample of wars and conflated
war starts with war duration, using a tobit procedure. We now regard this approach as flawed since
duration appears to be determined by rather different factors from starts.
15



Primary commodity exports are measured as a ratio of GDP at the beginning of each sub-
period. This data was obtained from various World Bank sources.
In order to proxy the size of the diaspora we used US immigration data. The US Bureau
of the Census provides detailed figures on the size of the foreign born population.5 Inter-
census figures were interpolated. To capture the relative size of the population we divided
the US immigration figures by the total population of the country of origin.
The end of the Cold War was proxied by a dummy variable which took the value of unity
for the periods 1990-94 and 1995-99.
3.3    Relative Military Advantage
Military advantage reflects geography, rebel cohesion and rebel motivation.
Using geographic data we calculated a Gini coefficient measuring the dispersion of the
population in a country. Analogous to the income Gini coefficient, the Gini coefficient of
population concentration will be high if a large proportion of the country's population is
concentrated in a relatively small area of the country. The more evenly the population is
dispersed across the country the lower is the Gini coefficient of population concentration.
For the calculation of this Gini coefficient we used population data per 400km2 cells.6 We
also experimented with two further measures of population dispersion, population density
and the proportion of the population living in urban areas, neither of which were
significant. The data source for both series is WDI 1998.
Data on forest coverage was obtained from the Food and Agriculture Organization
(FAO)7. For each period this provided an estimate of the percentage of a country's land
area covered in forests and woods. We could find no satisfactory existing data set on
mountainous terrain. The study commissioned a new data set from Dr. Gerrard, a
specialist on the subject. The measure allows for .the ruggedness of the terrain, rather than
simply relying upon altitude and is available from the World Bank.8
We measure social fractionalization by ethno-linguistic and religious fractionalization.
The ethno-linguistic fractionalization data was obtained directly from the Atlas Naradov
Mira (1964) which lists the ethno-linguistic groups for each country. Based on the Altas
Naradov Mira data an ethno-linguistic fractionalization index can be calculated.9 For each
country it measures the probability that two randomly selected people do not speak the
5Source: http://www.census.gov/population
6 We would like to thank Uwe Deichman (World Bank) for extracting the original data from the
Geographic Information System (GIS).
7Source: http://www.fao.org/forestry
'We would like to thank John Gerrard, University of Birmingham, for the compilation of this variable.
9 We would like to thank Tomila Lankina, University of Oxford, for translating the data entries from the
Atlas Naradov Mira. The first study to use this measure in the economics literature is Mauro (1995).
16



same language. An index of 0 means that the entire population speaks the same language
while a higher index indicates a higher degree of linguistic heterogeneity. The maximum
value of the index is 100. Using the same concept we also constructed a religious
fractionalization index which measures the probability that two randomly selected people
do not share the same religious affiliation."0 The ethno-linguistic data was measured at
about 1960 and the religious data was measured in 1970 and 1980. Since these measures
only change very slowly over time we use the linguistic data for all sub-periods and the
1970 data on religion for the observations 1960-70 and the 1980 data for 1980-99. For
1975 we use the average of the 1970 and 1980 data. Social fractionalization can
potentially be equal to, additive or multiplicative in religious and ethnic fractionalization.
For example, if all the religious divisions coincide with some or all of the ethnic
divisions, then the overall fractionalization of the society may be no greater than that
measured by ethnic fractionalization. With such coincident divisions, social diversity
would simply by the maximum of the underlying ethnic and religious diversity measures.
A second possibility is that the religious divisions occur within ethnic groups (or
conversely). In this case, social diversity would be approximated by the sum of the
underlying measures. A third possibility is that cleavages are cross-cutting, so that ethnic
groups are divided by religion and religious groups by ethnicity. In this case social
diversity would be approximated by the product of the underlying measures. More
precisely, since the index for each measure ranges 0-100, cross-cutting cleavages would
be proxied by the product of the two indices plus the maximum of the two underlying
indices. It is necessary to add the maximum of the underlying indices to the product,
since otherwise, a society which is (say) ethnically diverse but homogenous in religion
would appear to be as homogenous as a society that was homogenous in both ethnicity
and religion. In the analysis of Section 4 we use only this third measure of social
fractionalization, demonstrating in Table 8 that it dominates both the other approaches to
aggregating ethnic and religious diversity.
The time since the previous civil conflict is measured in months. If a country experienced
a civil war we measure the peace period as the number of months between the end of the
civil war and the beginning of the sub-period, 1960, 1965, ..., 1995. If a country never
experienced a civil war we measure the peace period since the end of World War II, i.e. in
1960 these countries have 172 months of peace and they accumulate 60 additional peace
months in every sub-period.
3.4   Opportunity Cost
We use four measures of the opportunity cost of recruitment: secondary school enrolment
rates for men, average income, population growth and income growth.
Male schooling is measured by the gross male secondary school enrollment ratio. This
was obtained from the World Bank World Development Indicators (WDI) 1998. Gross
' Most Of the data was kindly made available by Robert Barro (Barro 1997) and for some countries we
used the data from the original source (Barrett 1982).
17



male enrollment ratios are defined as the ratio of total male enrollment, regardless of age,
to the male population of the age group that officially corresponds to the level of
secondary schooling. Thus, the gross enrollment ratio can exceed 100 percent.
Income is measured as the per capita GDP at the beginning of each period. In order to be
able to compare income data over time and across countries we mainly use the real PPP
adjusted figures as provided in the Penn World Tables Mark 5.6 (PWT). For the 1995
data we used the growth rates from the real PPP adjusted GDP per capita data from the
WDI 1998 in order to update the PWT data which only provide data up to 1992.
Population growth is measured as the average annual growth rate of the population in the
previous five years. The main source of the population data is WDI 1998. Income growth
is measured as the average annual growth rate of real per capita GDP in the previous five
years. The income data used to calculate the growth rates is as described above. We
combine population growth and per capita income growth into a single variable, which is
the weighted difference between them. We experimented with weights, choosing that
which gave the variable the highest t-statistic, this being when the population growth rate
was given three times the weight of per capita income growth.
3.5   Sources of Grievances
Here we concentrate on two sources of grievances, political exclusion and inequality,
since the variables which proxy sources of hatred (social fractionalization) and vengeance
(previous conflicts) have already been discussed above.
Data on political exclusion is available in the Polity III data set (Jaggers and Gurr 1995).
We concentrate on the democracy variable which characterizes the general openness of
political institutions. The democracy score ranges from 0 to 10 where 10 denotes a highly
open regime. In addition we tried the autocracy score from the Polity III data set and a
measure of political openness published by Freedom House ("Gastil Index"). II
We constructed a measure of ethnic dominance based on the ethno-linguistic data from
the Atlas Naradov Mira. This dummy takes a value of one if the largest linguistic group
makes up between 45 and 90 percent of the total population and zero otherwise. We
constructed a number of different dummies to proxy ethnic domination. As reported
below, we found that when ethnic dominance was defined by this range the economic and
statistical significance of the variable was at a maximum.
Income inequality is measured as in Deininger and Squire (1996) either as the Gini
coefficient of income distribution or as the ratio of top quintile's share of income to
bottom quintile's share. We proxy land inequality by a Gini coefficient of land
distribution, for a more detailed description please refer to Deininger and Squire (1998).
Source: http://www.freedomhouse.org/ratings
18



In Table 2 we present some descriptive statistics for the main variables of interest,
distinguishing between the peace observations, the war observations and the entire
sample. The war observations are on average characterized by a higher proportion of
previous civil wars, lower opportunity costs of war (lower secondary school enrolment
rates, lower per capita income, lower economic growth and higher population growth)
larger populations, higher ethnic fractionalization, more mountainous terrain, a lower
democracy score and higher income inequality. However, war observations have a similar
average share of primary commodity exports in GDP, similar religious fractionalization, a
similar incidence of ethnic dominance and have a smaller diaspora than peace
observations.
We now turn to the regression analysis to examine the relationship between these
possible causes of conflict and the risk of civil war.
4. Empirical Results
Our empirical analysis attempts to predict the risk that a civil war will start during a five-
year sub-period, through a logit regression in which the explanatory variables are
characteristics at the start of the sub-period.
We start with the greed model (see Table 3). Because per capita income and enrolment in
secondary schooling are highly correlated, they cannot be used in the same regression.
The first four columns include secondary schooling but not per capita income, which
permits a sample of 688 episodes of which 43 are war observations. The final column
replicates the core regression using per capita income instead of secondary schooling,
which permits a sample of 747 episodes of which 47 are war observations.
The first column omits the variables which proxy the effect of previous civil war. The
two opportunity cost proxies are significant with the expected signs. A higher gross
secondary school enrolment rate for males reduces the risk of war. The difference
between income growth per capita and population growth, both measured for the previous
five year period, decreases the risk of war. As predicted in (12), the effect of primary
commodity exports on the risk of war is significant but non-linear. The highest risk of
war is at a share in GDP of about 25 percent. The positive linear effect on risk suggests
that there is a predatory element in rebellion. The negative quadratic effect suggests that
the increased tax revenue eventually augments the capacity of the government to defend
itself sufficiently to offset the attraction of enhanced loot. The elasticity of the risk of
conflict with respect to population is approximately unity, as predicted. A higher degree
of fractionalization makes societies significantly safer, high cross-cutting ethno-linguistic
and religious fractionalization decrease the risk of civil war. Two of the technology
parameters, the dispersion of the population and mountainous terrain, are also significant.
More mountainous countries are more likely to experience a civil war, while a higher
concentration of the population decreases the risk of conflict.
19



We then introduce the effect of previous conflicts. We hypothesize that a previous civil
war may increase the risk of experiencing a civil war. Initially, we test this hypothesis by
including a dummy which equals one if the country experienced a civil war in the past.
Here we include any civil wars which occurred between the end of World War II and
1995. The coefficient on this previous war dummy is highly significant, previous wars
increase the risk of new civil war. However, such a dummy could equally be picking up
unobserved country-specific effects. We then introduce the length of the peace period
measured in months since the end of the last civil war. In column 3 we add this peace
duration variable to the model. The coefficient is negative and significant, i.e. the longer
the peace period the lower the risk of conflict. The coefficient on the previous war
dummy is now insignificant. Thus, the peace period is a more precise measure of the
effect than is the previous war dummy. This indicates that the risk decays after conflict,
as might grievance, rather than being a proxy for unobserved country-specific effects. In
column 4 we therefore drop the insignificant previous war dummy, leaving a model in
which greed causes initial conflict; conflict causes some effect such as grievance; and this
grievance causes further conflict."2 In the last column we present an alternative
specification in which we include the average income per capita as a measure of
opportunity costs instead of schooling. The results are very similar, although the overall
performance of the regression deteriorates.
In Table 4 we investigate whether the effect of post-conflict grievance on the risk of
conflict is reinforced by the diaspora, as suggested by the analytic model. Since the data
set on the size of diasporas in the USA reduces the number of countries on which we
have data, the sample size is consequentially radically reduced from the 747 observations
and 47 wars which is our maximum sample in the previous analysis. In order to preserve
sample size we therefore retreat to a more parsimonious version of the model, dropping
four sample-constraining peripheral explanatory variables: ethnic and religious
fractionalization, geographic concentration of the population, the extent to which the
terrain is mountainous, and the rate of growth in the previous five year period. The
remaining explanatory variables are thus per capita GDP, primary commodity exports,
population, and the number of months since the previous conflict. Even with these data-
restoring deletions, the sample size is reduced to 595 observations (containing 29 war
observations). However, all the included explanatory variables remain significant.
On this sample, we then test for the effect of a diaspora. We measure the size of the
diaspora relative to the resident population in the country of origin. To allow for a fading
post-conflict effect, we interact this measure with the number of months since the
previous conflict, dividing the former by the latter. This variable, dias/peace in Table 4, is
added to the regression, the result being shown in the first column. The variable is
12 We examined this time effect in more detail by including the natural logarithm of the peace variable or its
square, however, a linear decay term provides a better fit. Note that the measure of peace since the end of
the civil war is somewhat imprecise since we only measure it from the end of the war to the initial year of
each sub-period. A duration model of post-war peace would allow a more detailed analysis of this peace
effect, however, the duration model results in Collier, Hoeffler and S6derbom (1998) support the results
presented in this paper.
20



positive and significant, indeed, slightly more significant than the number of months of
peace by itself, although the latter also remains significant. A large diaspora considerably
increases the risk of further conflict. If we compare the post-conflict society with the
largest diaspora against that with the smallest, with other variables at their mean values,
after five years of peace the risk of renewed conflict is around six times greater.
However, while this result is consistent with the analytic model, it is also open to a more
anodyne interpretation. Evidently, diasporas are to an extent endogenous to the intensity
of conflict: when civil war occurs, many people leave and settle in the USA. Hence, the
size of the diaspora might simply be proxying the intensity of conflict. The result may
therefore simply show that more severe conflicts have a higher risk of renewed conflict.
To test for this we decomposed observed diasporas into a component which is exogenous
to the intensity of conflict and a residual component which is therefore endogenous to its
intensity. For this decomposition we estimated a simple migration model, reported in the
Appendix, Table A3. The size of the diaspora in a census year is predicted to be a
function of its size in the previous census, time, per capita income in the country of
origin, and whether there was a war in the intervening period. This model predicts the
size of the diaspora with reasonable accuracy. We then replace the diaspora data used in
the model with estimated diaspora size in all cases where the observed diaspora is for a
year subsequent to a conflict. Thus, all post-conflict observations of diasporas are
estimates which are purged of any effect from the intensity of conflict. The difference
between these estimates and the actual figures are then used as an additional variable,
measuring that part of the diaspora which is potentially endogenous to the intensity of
conflict. Both of these measures are then introduced into the regression in place of the
previous single measure of the diaspora. The results are reported in the second column of
Table 4. The purged measure of the diaspora remains significant, and the size of the
coefficient is only slightly altered. Further, its coefficient is not significantly different
from that on the endogenous diaspora measure. Had the effect of the diaspora been
simply a proxy for the intensity of conflict, neither of these would have been the case.
The purged variable would have been insignificant, and the coefficient on the endogenous
measure would have been larger. This suggests that the substantial effect of the diaspora
on the risk of conflict renewal is indeed due to its financial contribution to war start-up.
In Table 5 we turn to the examination of a rebellion which is motivated only by
grievance. In the first column we examine the relationship between ethnic dominance,
social fractionalization, democracy and the duration of peace. As in the greed model, we
control for geographic military advantage by including the dispersion of the population
and mountainous terrain. Since we are not including any lagged variables we can use 884
observations of which 57 observations experienced an outbreak of civil war. The results
suggest that societies which are dominated by one large ethnic group (45-90 percent of
the population belonging to the same ethnic group) are more likely to experience conflict.
Greater openness of political institutions reduces the risk of conflict. In the second
column we add the gini coefficient of income inequality as an explanatory variable and in
column 3 we add the gini coefficient of land inequality as an alternative measure of
inequality. Neither measure is significant. Note that the sample size is reduced when we
21



include the income inequality measures. However, we are still analyzing a substantial
number of war occurrences (40 in column 2 and 38 in column 3).13All three grievance
models have very low explanatory power, the regressions only have an R2 of 0.14 or
lower.
We now turn to the question of which model, greed or grievance, provides a better
explanation of the risk of civil war. Since the two models are non-nested, i.e. one model
is not a special case of the other, we use the J-test as suggested by Davidson and
MacKinnon (1981). This non-nested test is based on the following artificial nesting
procedure. First we explain the risk of civil war, p, in terms of the two different models,
greed and grievance.
(1) p=f(greed)
(2) p=f(grievance)
Based on these logit regressions we calculate the predicted probabilities and add these
predicted values, p greed and p grievalce to our alternative models.
(1) p=f(greed, p grevance)
(2) p=f(grievance, p greed)
According to the J-test the significance of the coefficients of these added variables
enables us to choose between the two different models. If p grevance is significant in the
greed model we reject the greed model in favor of the grievance model. If p greed iS
significant in the grievance model we reject the grievance model in favor of the greed
model. As can be seen in columns 2 and 3 of Table 6 p grevance is significant in the greed
model and p greed iS significant in the grievance model. Thus, we conclude that we cannot
choose between the two models, grievance adds explanatory power to the greed model
and vice versa.'4 Since we find neither model dominates the other, we next investigate the
combination of the two models as presented in column 3 of Table 6.
In this combined model all variables apart from democracy are significant and the overall
fit is reasonable (pseudo R2 of about 0.3). Since both the grievance and greed models are
nested in the combined model, we can use a likelihood ratio test to determine whether the
combined model is superior. We can reject the validity of the restrictions proposed by
both the greed and the grievance models."5
13 We also tried the ratio of the income shares of the top to the bottom quintiles. This was also insignificant.
" When we tested an alternative specification of the greed model which includes income rather than
schooling, we found that grievance did not seem to add explanatory power to the greed model, i.e. the
coefficient p gf"evae was insignificant.
'5 Using the same sample as for the combined model (n=665) we obtain the following results: Greed model
versus combined model, 2 degrees of freedom, Likelihood Ratio Test statistic 6.34, p-value 0.042;
grievance model versus combined model, 5 degrees of freedom, LRT statistic 41.76, p-value 0.000.
22



Although the combined model is superior to the greed and grievance models, one
variable, democracy, is completely insignificant."6 Dropping it yields the model in column
4. Without the democracy variable the coefficients and standard errors of all of the other
variables remain virtually unchanged. Based on a likelihood ratio test we cannot reject the
hypothesis of a zero coefficient on the democracy variable.'7 While all variables in the
model of column 4 are significant, that for mountainous terrain is only significant at the
ten percent level and in column 5 we investigate a more parsimonious model in which it
is dropped. Since this model is nested within the two previously presented models, we
again use likelihood ratio tests to compare it to those of columns 3 and 4. The tests
narrowly favor this model over a model which includes mountainous terrain.' No further
reduction in the model is accepted, and no additions of variables included in our previous
models are accepted. We refer to the model in column 5 as our baseline model.
Our baseline model allows us to calculate the change in the probability of war-starts for
different values of the explanatory variables. We present these calculations in the
Appendix, Table A5. At the median of all variables a country would have a 1.4 percent
probability of experiencing a civil war, while at the mean of all variables the risk of a
war-start is higher, about 14 percent. Our model predicts that a country with the worst
characteristics (lowest per capita income, lowest GDP growth and highest population
growth, a primary commodity export share of 0.26, the largest population, the lowest
fractionalization, ethnic dominance, a geography Gini coefficient of zero and only one
month of peace) would have a near-certain risk of war while a country with the best
characteristics would be a very safe society. We now calculate how the change in one
variable (while the others are assumed to take their mean values) affects the probability of
experiencing a civil war. Whether a country is characterized by ethnic dominance has a
considerable impact on the risk of experiencing a civil war. Countries without a dominant
ethnic group have a risk of war of about 10 percent, however, the risk is doubled if
countries have a dominant ethnic group. If the male secondary school enrollment rate is
10 percentage points higher than the average, the risk of war is reduced by about four
percentage points (a decline in the risk from 14% to 10%). Higher income growth
reduces the risk of conflict: an additional percentage point on the growth rate reduces the
risk of war by one percentage point (a decline from 14% to 13%). A reduction in
population growth has a larger effect on the risk of war, a one percentage point decrease
implies a 2.5 percentage point reduction in the risk of conflict (from 14% to 11.5%). The
effect of primary commodity exports is very considerable: at the risk maximizing value of
16 We tried different specifications to test for the effect of political repression by including the autocracy
score instead of the democracy score and by using the difference between the two variables as suggested by
Londregan and Poole (1996). We also tried the Freedom House measure of political freedom, but neither of
these alternative political repression measures were found to be significant.
17Using the same sample (n=665) we obtained a log likelihood of-I 11.25 for the restricted model. Thus,
the LRT statistic is 2.01, 1 degree of freedom, p-value 0.16.
I First, using the same sample (n=665) we test the combined model (column 3) versus the baseline model
(column 5). We obtain a LRT statistic of 3.24, 2 degrees of freedom, p-value 0.198. Second, we test the
reduced model (column 4) against the baseline model (column 5). We obtain a LRT statistic of 2.03, 1
degree of freedom, p-value 0.16.
23



the primary commodity export share (0.26), the risk of civil war is about 23 percent,
while a country with no natural resource exports only has a probability of a war-start of
0.5 percent. A society characterized by the maximum social fractionalization has a very
small risk of experiencing a civil war (about three percent), however, the risk is about
eight times higher for a country which is socially homogenous. Directly after a civil war
there is a high probability of a re-start, the risk is about 42 percent. This risk declines over
time, however, sustained peace only slowly decreases the risk of experiencing a new
conflict. Five years after the end of the civil war the risk is still about 37 percent. The
elasticity of the risk of conflict with respect to population is 0.95, close to the analytic
expectation of 0.94 discussed in Section 2. Lastly, we examine the impact of the
dispersion of the population on the risk of conflict. Countries with a highly concentrated
population are very safe from conflict while countries which are characterized by a
homogenously dispersed population have a very high risk of civil war (about 60 percent).
In column 6 of Table 6 we run the baseline model with average per capita income instead
of schooling. Recall that since we have more data on income than on schooling we are
able to use a larger sample which includes four additional war observations. The variables
are all significant at conventional levels, although the overall explanatory power is
slightly lower. Using this maximum sample we analyze the effect of external assistance
as causes of war. Adding a post cold-war dummy (which takes a value of one for the
periods 1990-94 and 1995-99) in column 7 we find that that the risk of civil war was
significantly higher during the cold war period. This is consistent with the prediction that
the super-powers eased the financing constraint on rebellion.'9 Thus, we find some weak
evidence that external assistance increased the risk of civil war during the cold war.
In Table 7 we investigate a number of different estimation issues. We concentrate on the
analysis of random effects, fixed effects, time effects and a correction for rare events.
We re-estimated our models using random effects. For the baseline model, and indeed, all
those models which include a feedback effect from previous conflict, we accept the
hypothesis that we can pool across the observations.20 The estimation of fixed effects
logits was only possible on a very small sub-sample of the observations. The countries for
which the dependent variable does not vary over time (the majority of countries
experienced only peace) cannot be included in the analysis. Although the fixed effects
test is very severe, the non-monotonic effect of primary commodity exports remains
significant. Were the effect of primary commodity exports dependent only upon cross-
section data, it might suggest that the variable was proxying some other characteristic
such as geography. However, the fixed effects regression uses only changes in primary
commodity dependence, and so reduces the scope for alternative interpretations.
' This result only holds for the augmented sample which includes 47 civil wars. Due to data constraints the
baseline model excludes four of these wars all of which started during the cold war period. The wars we
have to exclude are Chad (1980-84), China (1965-69), Jordan (1970-74) and Liberia (1985-89).
20 The null-hypothesis (rho=0) is not rejected: the chi-squared statistic is 0 (p = 0.998). When the feedback
effect from previous conflict is pxcluded, pooling is marginally rejected: the chi-squared statistic is 2.02
(p=0.155).
24



We analyzed whether time effects matter by including time dummies in the model. Based
on a log likelihood ratio test we cannot reject the hypothesis that the time dummies are
zero.21 However, as discussed above, the dummies for 1990-94 and 1995-99 are jointly
significant and support consolidation into a single dummy for the post Cold War period
in the variant of the baseline model.
Finally, in the last column of Table 7 we use a recently developed correction method for
rare events data (King and Zeng, 2000). Our data is characterized by a relatively small
number of events (wars), only about six percent of the observations are characterized by a
civil war outbreak. The results presented in King and Zeng (2000) suggest that standard
logit estimation underestimates the probability of an event occurring when the events are
rare. We used their correction procedure but found the differences between the standard
logit results and the rare events corrected results to be negligible. Using the rare events
logit procedure, all coefficients on the variables have the same signs and are significant at
the same levels. The mean of the predicted probabilities obtained from the standard logit
regression is 0.063 and the predicted probabilities from the rare events logit regression
have a mean of 0.068. The standard deviations and the extreme values of the predicted
probabilities obtained from the standard logit and the rare events logit were also similar.
Thus, contrary to King and Zeng (2000), we do not find that the logit substantially
underestimates the probability of the event occurring.
We examined a number of different model specifications. We found that none of the
following geographic and demographic characteristics were significant: forest coverage,
population density, the proportion of the population living in urban areas and the
proportion of young men aged 15 to 29. In Tables 8 and 9 we present a more detailed
analysis of ethnic fractionalization. In Table 8 we demonstrate that the measure of cross-
cutting cleavages (the product of religious and ethnic fractionalization plus the maximum
of each component separately), dominates the other possible aggregation procedures for
ethnic and religious diversity. When this measure of fractionalization is included with
the ethnic and religious diversity indices either together or individually, it is significant
whereas the underlying indices are not significant. In Table 9 we investigate alternative
definitions of ethnic dominance. In the first column we include the ratio of the largest
ethnic group to the total population in the model and find its coefficient to be
insignificant. We then construct a number of different ethnic dominance dummies, taking
the value one if 45-90, 45-85, 45-95, 30-90, 40-90, or 50-90 percent of the total
population belong to the same ethnic group. As can be seen, the relationship between
ethnic dominance is most significant when the largest ethnic group makes up 45-90
percent of the total population.22
5. Conclusion
21 The LRT statistic is 2.1, 6 restrictions, p=O.91.
22  also experimented with various other definitions of ethnic dominance, such as the dummy taking a
value of one if the largest ethnic group made up 50-75, 50-80, 50-85, 50-90 percent of the population.
Neither of these definitions provided a better fit than our preferred defnition based on 45-90 percent.
25



We started from two simple models of rebellion. Greed-rebellion was motivated by
predation of the rents from primary commodity exports, subject to an economic calculus
of costs and a military survival constraint. Grievance-rebellion was motivated by hatreds
which might be intrinsic to ethnic and religious differences, or reflected objective
resentments such as domination by an ethnic majority, political repression, or economic
inequality. Both of these models could allow for technological constraints upon rebellion
such as differences in geography. We then introduced the possibility of a feedback effect,
whereby conflict increased the risk of subsequent conflict through generating grievance.
In grievance-rebellion this added a simple destabilizing loop: grievance causes conflict
which then causes further grievance. In greed-rebellion the feedback was more complex
because induced grievance only raised the risk of further conflict if it augmented the
potential financial rewards to rebellion. We postulated that such a loop might arise if
grievance enabled rebellions to tap into the resources of diasporas. Finally, we allowed
for interdependence between greed and grievance. Greed-rebellions need to generate
grievance for military cohesion, grievance rebellions might be driven to predation to raise
finance.
Using a comprehensive panel data set of conflict over the period 1960-99 we examine the
risk of civil war using logit regressions. Starting with the distinct greed and grievance
models, we find that most of the proxies for objective grievance are insignificant and that
the best-performing grievance model has very low explanatory power. By contrast, the
simple greed model performs well. The extent of primary commodity exports is the
largest single influence on the risk of conflict. Strikingly, ethnic and religious
fractionalization makes a society safer, as predicted by the greed model, rather than more
dangerous as predicted by the grievance model. We find that a war substantially increases
the risk of subsequent war, although this risk fades over time. Thus, although objective
grievance is not a powerful primary cause of conflict, conflict may generate grievances
which become powerful additional risk factors. However, we can show that by far the
strongest effect of war on the risk of subsequent war works through diasporas. After five
years of post-conflict peace, the risk of renewed conflict is around six times higher in the
societies with the largest diasporas in America than in those without American diasporas.
Presumably this effect works through the financial contributions of diasporas to rebel
organisations.
Finally, we test for an integrated greed-grievance model. We find that only one of the
potential sources of objective grievance significantly adds to the explanatory power of the
greed model, namely ethnic dQminance. Societies in which the largest ethnic group has
between 45% and 90% of the population have around double the risk of conflict of other
societies. Presumably, this is because such societies have both the power and the
incentive to exploit their minorities.
Our results thus contrast with conventional beliefs about the causes of conflict. A stylized
version of these beliefs would be that grievance begets conflict which begets grievance
which begets further conflict. With such an analysis, the only point of intervention is to
26



reduce the level of objective grievance. Our model suggests that what is actually
happening is that opportunities for primary commodity predation cause conflict, and that
the grievances which this generates induce diasporas to finance further conflict. The
policy intervention points here are reducing the absolute and relative attraction of primary
commodity predation, and reducing the ability of diasporas to fund rebel movements.
27



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29



Table 1: Outbreaks of War
country          year    war   prev.      country        year    war  prev.
started war                             started war
Afghanistan      1975-79    1      0      Mozambique    1960-64   1       0
Afghanistan      1990-94    1      1      Mozambique    1975-79   1       1
Algeria          1960-64    1      1      Myanmar/Burma 1965-69   1       1
Algeria          1990-94    1      1      Myanmar/Bunna 1980-84   1       1
Angola           1960-64    1      1      Nicaragua      1975-79   1      0
Angola           1975-79    1      1      Nicaragua      1980-84   1      1
Azerbaijan       1990-94    1      0      Nigeria        1965-69   1      0
Bosnia           1990-94    1      0      Nigeria        1980-84   1      1
Burundi          1970-74    1      0      Pakistan       1970-74   1      0
Burundi          1984-89    1      1      Peru           1980-84   1      0
Burundi          1990-94    1      1      Philippines    1970-74   1      1
Cambodia         1970-74    1      1      Romania        1984-89   1      0
Chad             1980-84    1      0      Russia         1990-94   1      0
China            1965-69   1       1      Russia         1995-99   1      1
Colombia         1980-84   1       1      Rwanda         1960-64   1      1
Dominican Republic 1965-69   1     0      Rwanda         1990-94   1      1
El Salvador      1975-79    1      0      Somalia        1980-84   1      0
Ethiopia         1970-74   1       1      Somalia        1984-89   1      1
Georgia          1990-94   1       0      Sri Lanka      1970-74   1      0
Guatemala        1965-69    1      1      Sri Lanka      1980-84   1      1
Guatemala        1970-74    1      1      Sudan          1960-64   1      0
Guatemala        1975-79    1      1      Sudan          1980-84   1      1
Guinea-Bissau    1960-64    1      0      Tajikistan     1990-94   1      0
India            1980-84    1      1      Turkey         1990-94   1      0
Indonesia        1975-79    1      1      Uganda         1965-69   1      1
Iran             1970-74   1       1      Uganda         1980-84   1      1
Iran             1975-79    1      1      Vietnam        1960-64   1      1
Iran             1980-84    1      1      Yemen          1990-94   1      0
Iraq             1970-74    1      1      Yemen AR       1960-64   1      1
Iraq             1984-89    1      1      Yemen PR       1984-89   1      0
Iraq             1990-94    1      1      Yugoslavia     1990-94   1      0
Jordan           1970-74    1      0      Yugoslavia     1995-99   1      1
Laos             1960-64    1      1      Zaire          1960-64   1      1
Lebanon          1975-79    1      1      Zaire          1990-94   1      1
Liberia          1984-89    1      0      Zaire          1995-99   1      1
Liberia          1990-94    1      1      Zimbabwe       1970-74   1      0
Morocco          1975-79    1      1
30



Table 2: Descriptive Statistics
sample (n=1 174)                      no civil war (n=1 101)              civil war (n=73)
mean    st.dev.  min   max    n       mean  st.dev. min   max    n        mean  st.dev. min    max  n
war starts       0.06                             1174  0                            1101  1                            73
previouswar      0.34                            1174  0.32                          1101  0.64                         73
male secondary    43.3     31      0.3   147    965   44.3  31        0.3   147    902   30.2  26.6    1          102  63
schooling            I_I_I
GDP per capita    4056     4317    222   33946  1014  4200  4388   257   33946  958   1644  1353   222    583   56
(const. US$)                                                                                                      2
GDP per capita    1.62    3.83    -       14.41   864   1.72   3.78   -22.1  14.41   815   -0.05  4.28    -10.66  7.09  49
growth t-1                         22.08               1
population growth  2.15    1.54    -6.38  16.62  991   2.12   1.57   -6.38  16.62   929   2.50   1.01   -0.013  5.81  62
t-1             I__                                                                                                   I
primary          0.17      0.19    0      2.14   1090  0.17  0.19   0        2.14    1023  0.14   0.1    0.01    0.48  67
commodity
exports/GDP
population       25.4     94.5    0.014  1200   1161  24.5  92.9   0.014  1200   1089  40.0  116.4  0.54   715  72
(millions)          I_I_I_I
ethnic           40       28       0      93     1107  39    28       0      93      1039  50    29       4      90   68
fractionalization
religious        36        24      0      79     1147  36    24       0      79      1078  36    25       0       69   69
fractionalization                       I_I_I
geographic       0.57      0.23    0      0.97    1028  0.57   0.23    0     0.97    961   0.60   0.15    0.24    0.92  67
dispersion             I__
mountainous      15.8      20.73   0      94.3   1174  15.12  20.28   0      94.3   1101  26.73  24.2    0        81   73
terrain                                         I_I_I
ethnic dominance   0.47            0      1      1107  0.47                          1039  0.47                    -   68
(45-90%)                                                                                                 I _          I
democracy        3.90     4.21     0      10     987   4.21   4.21    0      10     987   1.67   2.58    0       9    67
diaspora         0.02      0.03    0      0.19    654   0.02   0.03    0     0.19    654   0.004  0.005   0      0.16  37
Income Inequality  41.35    10.27   17.83  63.2   721   40.65  10.27   17.83  63.2   721   41.97  10.30   23.38   62.3  44
(Gini)
31



Table 3: Greed Model
_1                 2                  3                  4                  5
male secondary schooling    -0.036          -0.037             -0.028              -0.027
(0.011)***         (0.011)***        (0.012)**           (0.012)**
In GDP per capita                                                                                     -0.822
(0.288)***
(GDP growth - 3 *population  -0.080         -0.074             -0.084              -0.086             -0.101
growth) t- 1             (0.036)**          (0.036)**          (0.036)**           (0.036)**         (0.035)***
primary commodity        34.088             34.109             32.147              32.058             22.923
exports/GDP              (8.186)***         (8.423)***         (8.497)***         (8.465)***         (6.903)***
(primary commodity      -67.792             -67.172            -62.307             -62.091            039.192
exports/GDP) 2           (18.683)***        (18.679)***        (18.954)***        (18.962)***        (14.710)***
In population            1.026              0.960              0.832               0.832              0.574
(0.191)***         (0.194)***        (0.198)***          (0.197)***         (0.146)***
social fractionalization  -0.0002           -0.0003            -0.0003             -0.0003            -0.0003
(0.0001)**         (0.0001)***       (0.0001)***         (0.0001)***        (0.0001)***
geographic dispersion    -3.517             -3.888             -3.384              -3.289             -1.568
(1.142)***         (1.181)***         (1.200)***         (1.180)***         (1.051)
mountainous terrain      0.019              0.013              0.014               0.015              0.012
(0.009)**          (0.009)            (0.010)            (0.010)            (0.009)
previous war dummy                           1.016             0.252
(0.405)***        (0.527)
Peace duration                                                 -0.004              -0.004             -0.004
(0.002)**          (0.001)***         (0.001)***
n                        691                691                688                 688                747
no of wars               43                 43                 43                  43                 47
pseudo R2                0.25               0.27               0.29                0.29               0.26
log likelihood           -120.98            -117.63            -114.32             -114.43            -129.44
Notes: All regressions include a constant. Standard errors in parentheses.
***, **, * indicate significance at the 1, 5 and 10 percent level, respectively.
32



Table 4: Greed - Diaspora Model
_ _ _ _ _ _ _ _ _ _ _   ~~~1             2
In GDP per capita          -1.032              -1.037
(0.285)***          (0.286)***
primary commodity          23.98                23.78
exports/GDP                (8.542)***           (8.56)***
(primary commodity         -48.163             -47.75
exports/GDP)2              (22.1)* * *          (22.12)* *
In population              0.318                0.319
(1.156)**           (1.155)**
peace duration             -0.002              -0.002
(0.001)***          (0.001)*
diaspora/peace duration    648.77
(367.57)*
predicted diaspora/peace                        687.09
(393.12)*
(diaspora-predicted                             768.9
diaspora)/peace duration                        (562.42)
n                          595                  595
no of wars                 29                   29
pseudo R2                  0.23                 0.23
log likelihood             -89.05              -89.01
Notes: All regressions include a constant. Standard errors in parentheses.
**, **, * indicate significance at the 1, 5 and 10 percent level, respectively.
33



Table 5: Grievance Model
1                  2                   3
social fractionalization  0.00003            0.00000            0.00008
(0.00007)          (0.00009)           (0.00009)
ethnic                   0.263               0.534              0.567
dominance (45-90%)       (0.290)             (0.342)            (0.374)
democracy                -0.117              -0.091             -0.138
(0.046)***         (0.051)**           (0.052)***
geographic dispersion    0.095              -0.276              -0.152
(0.787)            (0.958)             (1.039)
mountainous terrain      0.015               0.011              0.015
(0.006)**          (0.008)             (0.006)**
income inequality                            0.014
(0.016)
land inequality                                                 -0.150
(1.159)
Peace duration           -0.005              -0.004             -0.005
(0.001)***         (0.00i)***          (0.001)***
n                        884                 614                620
no of wars               57                  40                 38
pseudoR2                 0.13                0.11               0.14
log likelihood           -184.17             -132.11            -122.61
Notes: All regressions include a constant. Standard errors in parentheses. ***, **, * indicate significance at
the 1, 5 and 10 percent level, respectively.
34



Table 6: Combined Greed and Grievance Model
1            2             3               4               5                6               7
male secondary schooling    -0.026                   -0.030         -0.031          -0.036
(0.012)**                  (0.012)***       (0.012)***     (0.01 1)***
In GDP per capita                                                                                    -1.007          -1.006
(0.281)***      (0.283)***
(GDP growth -            -0.079                     -0.085          -0.090          -0.085           -0.103          -0.113
3*population growth) t- I    (0.036)**              (0.037)**        (0.037)**      (0.036)**        (0.035)***      (0.035)***
primary commodity        31.902                      33.576          34.220         32.089           22.983           23.025
exports/GDP              (8.333)***                 (8.514)***       (8.610)***     (8.375)***       (6.806)***       (6.795)***
(prirnary commodity     -61.491                     -64.234         -65.676         -62.511          -39.293          -39.026
exports/GDP)2           (18.489)***                 (18.873)***      (19.186)***    (18.964)***      (14.505)***      (14.394)***
In population            0.840                      0.912            0.934          0.946            0.625            0.678
.______ _    (0.203)***           (0.21 1)***      (0.210)***     (0.204)***       (0.148)***      (0.153)***
social fractionalization  -0.0003     0.00003       -0.0003         -0.0003         -0.0003          -0.0004         -0.0003
(0.0001)**    (0.0001)     (0.0001)**       (0.0001)**     (0.0001)***      (0.0001)***      (0.0001)***
democracy                             -0.042        -0.043
(0.058)       (0.058)
ethnic                                0.895         0.909            0.882          0.847            0.623            0.676
dominance (45-90%)                    (0.373)* *    (0.477)* **     (0.374)* * *    (0.370)* *       (0.348)*        (0.351)* *
geographic dispersion    -2.443       0.023         -3.670          -3.600         -3.891            -1.851          -2.020
(1.270)**    (1.016)       (1.208)***       (1.200)***     (1.172)***       (1.006)*        (1.023)*
mountainous terrain      0.003        0.0004        0.016            0.016
(0.011)      (0.009)       (0.010)*        (0.010)*
peace duration           0.001        0.0004        -0.004          -0.004          -0.004           -0.004          -0.004
(0.003)      (0.0015)      (0.001)***       (0.001)***     (0.00i)***       (0.001)***      (0.001)***
post-cold war dummny                                                                                                  -0.980
____________                                                         ~~~~~~~~~~~~~~~~~~~~~(0.499)**
grievance residual       0.897
.____ ____ ____ ____ __ _   (0.481)*
greed residual                        1.066
(0.200)***
n                        665          665           665              688            688              747             747
no of wars               43           43            43               43             43               47              47
pseudo R2                0.30         0.30          0.30             0.31           0.30             0.27            0.28
log likelihood           -112.27      -111.02       -110.97         -111.61         -113.03          -128.71         -126.47
Notes: All regressions include a constant. Standard errors in parentheses. *   **, * indicate significance at the 1, 5 and 10 percent level, respectively.
35



Table 7: Robustness Tests of the Baseline Model
1              2             3                4
Random Effects  Fixed Effects   Pooled Logit plus  Rare Events Logit
Time dummies
male secondary schooling  -0.036     0.0001        -0.034            -0.034
(0.011)***     (0.033)       (0.012)***       (0.011)***
(GDP growth -         -0.085         -0.063         -0.105           -0.080
3 *population growth) t- l  (0.036)**  (0.067)      (0.041)***      (0.036)**
primary commodity     32.089         34.885         32.978           29.399
exports/GDP           (8.375)* **    (14.945)**     (8.648)***      (8.667)***
(primary commodity    -62.511        -63.363        -65.483          -55.982
exports/GDP)2         (18.964)***    (28.824)**     (19.822)***     (22.781)***
In population         0.946          0.373          0.960            0.891
(0.204)***     (1.374)       (0.210)***       (0.155)***
social fractionalization    -0.0003  -0.007        -0.0003          -0.0003
(0.0001)***    (0.005)       (0.0001)***      (0.0001)***
ethnic                0.847                         0.913            0.807
dominance (45-90%)    (0.370)**                    (0.381)**        (0.350)**
geographic dispersion    -3.891      78.555        -3.936            -3.722
(1.172)***     (80.216)      (1.196)***       (1.041)***
Peace duration        -0.004         0.01          -0.004            -0.004
(0.001)***     (0.003)***    (0.001)***       (0.001)***
T70-74                                              -0.960
(0.841)
T75-79                                              0.145
(0.776)
T80-84'                                             -0.002
(0.777)
T85-89                                              0.477
(0.749)
T90-94                                              -0.820
(0.855)
T95-99                                              -0.229
(0.168)
n                     688             135           668              688
no of wars            43             43             43               43
pseudo R2                                           0.33
log likelihood        -113.03        -112.27        -107.44          -113.03
Notes: All regressions include a constant. Standard errors in parentheses. *  **, * indicate significance
at the 1, 5 and 10 percent level, respectively.
36



Table 8: Social Fractionalization
1             2               3               4                5               6               7                8               9                10
Baselinc      Baseline Model   ethnic         Baseline Model   religious       ethnic and      Baseline Model   social frac.=    social frac.=   social frac.=
Model         plus ethnic     fiactionalization   plus religious  fractionalization   religious  plus ethnic and    ethnic plus  ethnic minus    ethnic times
fractionalization   only        fractionalization   only         fractionalization   religious    religious       religious        religious
fractionalization   fractionalization   fractionalization   fractionalization
male secondary  -0.036       0.037           -0.025          -0.037           -0.034          -0.036           -0.037          -0.034           -0.021           -0.036
schooling      (0.011)***    (0.012)***      (0.001)***       (0.012)***      (0.011)***       (0.113)***      (0.116)***      (0.011)***       (0.010)**        (0.011)***
(GDP growth -   -0.085       -0.083          -0.093           -0.084          -0.078           -0.081          -0.084          -0.086           -0.082          -0.085
3*population    (0.036)**    (0.037)**       (0.035)***       (0.037)**       (0.036)**        (0.036)**       (0.037)**        (0.036)**       (0.036)**        (0.036)**
growth) t-l 
primary        32.089        32.700          23.442           32.019          28.368          29.447           32.862          28.809           21.120           32.147
commodity      (8.375)***    (8.425)***      (7.213)***       (8.390)***      (7.872)***       (8.060)***      (8.454)***      (8.003)***       (6.822)***      (8.381)***
exports/GDP
(primary       -62,511       -64.283         -44.962         -62.791          -56.891          -58.464         -64.450         -56.049          -42.019          -62.621
commodity      (18.964)***    (19.044)***    (16.501)***      (19.035)***     (18.260)***     (18.633)***      (19.020)***     (18.491)***      (15.923)***     (18.970)***
exports/GDP)2
In population  0.946         0.925           0.749            0.941           0.828            0.892           0.924           0.904            0.643           0.946
(0.204)***    (0.206)***      (0.177)***      (0.205)***       (0.186)***      (0.201)***      (0.206)***       (0.199)***       (0.164)***      (0.204)***
social         -0.0003       -0.004                           -0.0003                                         -0.0005          -0.014           0.004           -0.0003
fractionalization  (0.0001)***    (0.001)***                 (0.0002)*                          .(0 0003)*                     (0.005)***       (0,007)         (0.000 I)**_*
ethnic                       0.009           -0.0138                                          -0.007           0.011
fractionalization            (0.010)         (0.007)**                                         (0.007)         (0.013)
religious                                                    -0.006           -0.026          -0.023           0.004
fractionalization                                             (0.014)         (0.009)***       (0.009)**       (0.018)
ethnic         0.847         0.754           0.923            0.816           0.773           0.833            0.756           0.940            0.923           0.847
dominance (45-  (0.370)**    (0.381)**       (0.369)***       (0.375)**       (0.372)**       (0.379)**        (0.381)**       (0.369)***       (0.373)***      (0.370)**
90%)
geographic     -3.891        -4.099          -2.530          -4.070           -4.129          -4.129          -4.027           -3.711           -2.581          -3.894
dispersion     (1.172)***    (1.201)***      (1.042)**        (1.242)***      (1,222)***       (1.230)***      (1.245)***      (1.163)***       (1.053)***      (1.172)***
peace duration   -0.004      -0.0004         -0.005          -0.004           -0.004          -0.004          -0.004           -0.004           -0.004          -0.004
(0.001)***    (0.001)***      (0.001)***      (0.001)***       (0.001)***      (0.001)***      (0.001)***       (0.001)***      (0.00l)***       (0.001)***
N              688           688             668              688             688              688             688             688              688             688
no of wars     43            43              43               43              43              43               43              43               43              43
pseudo R2      0.30          0.30            0.27             0.30            0.29            0.29             0.30            0.29             0.26            0.30
log likelihood    -113.03    -112.60         -118.56         -112.93          -114.42         -113.96          -112.57         -114.62          -118.98         -113.00
Notes: All regressions include a constant. Standard errors in parentheses. *    **, * indicate significance at the 1, 5 and 10 percent level, respectively.
37



Table 9: Ethnic Dominance
1                      2            3            4            5            6            7
Largest group as a      45-85%      45-90%       45-95%       30-90%       40-90%        50-90%
proportion of the total
population
male secondary   -0.034                 -0.036       -0.036       -0.032      -0.039        -0.038      -0.035
schooling       (0.011)***              (0.011)***   (0.011)***   (0.011)***   (0.011)***   (0.012)***    (0.011)***
(GDP growth -    -0.079                 -0.084       -0.085      -0.083       -0.085       -0.089        -0.084
3 *population   (0.036)**               (0.036)**    (0.036)**    (0.036)**    (0.036)**    (0.037)**    (0.036)**
growth) t- 1
primary         31.444                  31.351       32.089       30.546       31.918       30.802       31.292
commodity      (8.422)***               (8.289)***   (8.375)* **   (8.296)** *    (8.285)***   (8.218)* **   (8.265)***
exports/GDP
(primary       -62.283                  -61.182      -62.511     -59.257      -63.073      -60.491      -61.363
commodity       (19.037)***             (18.712)***   (18.964)***  (18.837)***  (18.656)***   (18.433)***  (18.776)***
exports/GDp)2
In population   0.842                   0.909        0.946        0.870        0.951        0.915        0.907
(0.196)***              (0.201)***   (0.204)***   (0.196)***    (0.204)***    (0.200)***    (0.198)***
social         -0.0004                  -0.0003     -0.0003       -0.0003     -0.0004       -0.0004     -0.0003
fractionalization   (0.0001)***         (0.0001)***   (0.0001)***   (0.0001)***   (0.0001)***   (0.0001)***   (0.0001)***
ethnic          -1.223                  0.695        0.847        0.371        0.752        0.736       .0.659
dominance       (1.076)                 (0.373)**    (0.370)**    (0.554)     (0.385)**     (0.374)**    (0.373)*
geographic     -3.907                   -4.042       -3.891      -3.891       -4.155       -3.931       -3.686
dispersion     (1.186)***               (1.190)***   (1.172)***    (1.172)***    (1.182)***    (1.170)***    (1.150)***
peace duration    -0.004                -0.004       -0.004       -0.004       -0.004      -0.004       -0.004
(0.001)***              (0.001)***    (0.001)***    (0.001)***    (0.001)***   (0.001)***    (0.001)***
N               688                     688          688          688          688          688          688
No of wars      43                      43           43           43           43           43           43
Pseudo R2       0.28                    0.29         0.30         0.28         0.29         0.29         0.29
Log likelihood    -115.05               -113.98      -113.03      -115.45      -113.69      -113.67      -114.14
Notes: All regressions include a constant. Standard errors in parentheses. ***, **, * indicate significance at the
1, 5 and 10 percent level, respectively.
38



Table 9A: Ethnic Dominance - Descriptive Statistics
N=688                 mean          std. dev.     min          max
Largest group as a    0.70          0.25          0.18          1
proportion of the total
population
45-85%                0.33          0.47          0             1
45-90%                0.44          0.50          0             1
45-95%                0.77          0.42          0             1
30-90%                0.60          0.49          0             1
40-90%                0.55          0.50          0             1
50-90%                0.39          0.49         0              1
39



Country                      year             war started   previous war
Dominican Rep.              1965                  1             0
Nigeria                     1965                  1              0
Burundi                     1970                  1             0
Pakistan                    1970                  1             0
Sri Lanka                   1970                  1             0
Zimbabwe                    1970                  1             0
El Salvador                 1975                  1             0
Nicaragua                   1975                  1             0
Somalia                     1980                  1             0
Peru                        1980                  1             0
Romania                     1985                  1             0
Turkey                      1990                  1             0
Guatemala                   1965                  1              1
Myanmar/Burma               1965                  1              1
Uganda                      1965                  1              1
Iraq                        1970                  1              1
Iran                        1970                  1              1
Guatemala                   1970                  1              1
Philippines                 1970                  1              1
Ethiopia                    1970                  1              1
Morocco                     1975                  1              1
Iran                        1975                  1              1
Guatemala                   1975                  1              1
Mozambique                  1975                  1              1
Angola                      1975                  1              1
Indonesia                   1975                  1              1
Nicaragua                   1980                  1              1
Colombia                    1980                  1              1
Iran                        1980                  1              1
Myanmar/Burma               1980                  1              1
India                       1980                  1              1
Sri Lanka                   1980                  1              1
Sudan                       1980                  1              I
Uganda                      1980                  1              1
Nigeria                     1980                  1              1
Burundi                     1985                  1              1
Iraq                        1985                  1              1
Somalia                     1985                  1              1
Burundi                     1990                  1              1
Algeria                     1990                  1              1
Rwanda                      1990                  1              1
Zaire                       1990                  1              1
Zaire                       1995                  1              1
43            31
40



Table A2: Descriptive Statistics
sample (n=688)                      no civil war (n=645)      civil war (n=43)
mean      st.dev.    min     max    mean   st.dev.  min       max    mean  st.dev.  min    max
War starts                  0.06_
Previous war                0.36                                 0.33                              0.72
Male secondary schooling    44.5       31        1        147    45.6    31       1       147      27.3   21.7    3       87
GDP per capita (const. US$)    4025    4065      222     18993  4182    4137    290       18993   1673   1360    222    5832
GDP per capita Growth t-1   1.63       3.51      -13.1   14.41   1.72    3.43    -13.1   14.41   0.25    4.46    -10.66  7.09
Population growth t-1       2.09       1.19      -6.38    6.61    2.05    1.2    -6.38    6.61     2.73    0.64    0.47    4.09
Primary commodity           0.16       0.14      0       0.94    0.16    0.14    0        0.94    0.16   0.1       0.02    0.48
exports/GDP
Population (millions)       30                _                  30                                40      _
Ethnic fractionalization    41         29        0       93      40      29       0       93       50     30       4      90
Religious fractionalization  36        25        0       79      36      25       0       79       34     25       2      69
Geographic dispersion       0.6        0.21      0       0.97    0.6     0.21    0        0.97    0.57    0.15     0.24    0.91
Mountainous terrain         15.8       20.19     0       82.2   15.06   19.73   0         82.2    26.81   23.77   2.4    74.5
Ethnic dominance (45-90%)   0.28                                 0.27                              0.4
Democracy                   4.26       4.31      0       10      4.43    4.34    0        10       1.92   2.9      0      9
Diaspora                    0.01       0.02      0       0.16    0.01    0.02    0        0.16    0.003   0.02    0       0.16
Income Inequality (Gini)    41.35      10.04     20.97   62.3   41.14    10.0    20.97   62.3    44.33   10.29   23.38   62.3
41



Table A3: A simple Migration model
Dependent Variable: Diaspora,t
Diaspora, ,,                           1.163
(0.045)***
Ln GDP per capita  t-1                -0.002
(0.001)**
War dummy                              0.003
(0.003)
N                                      216
R_2                                    0.76
The regression is based on the following model:
diasit=f(diasj1t-1, lnGDPi, ,, war1,, l, Tt)
Where dias denotes diaspora which is measured as the ratio of emigrants in the USA to
the total population of the country of origin. The variable "war" is a war dummy,
measured at t-1 it takes a value of one if the country experienced a civil war in the
previous period. The method of estimation is OLS. The data is measured at the beginning
of each decade, i.e. 1960, 1970, 1980 and 1990. The regression includes time dummies,
T, which are jointly significant.
Based on this simple migration model we estimated the size of the diaspora at time t.
Dias estj.t=xit*AB
For countries which experienced a previous civil war23 we used these estimated values to
correct for a possible endogeneity problem. We replaced a total of 64 observations. For
countries which did not experience a civil war we use the actual diaspora data. We took
the averages of this corrected diaspora data measured in 1960 and 1970 (1970 and 1980,
1980 and 1990) in order to obtain values for 1965, 1975 and 1985. For 1995 we use the
observations measured in 1990.
In Table A4 we present some descriptive statistics of the original diaspora data, the
corrected diaspora data and the difference between the two variables.
23 Here we only consider wars after 1960.
42



Table A4: Diaspora - Descriptive Statistics
N=704                  mean          st.dev.         min              max
diaspora               0.0161        0.0286          0.00003          0.1948
predicted diaspora     0.0162        0.0283          0.00003          0.1948
diaspora-predicted     -0.0001       0.0043          -0.0200          0.0625
diaspora
43



Table A5: Marginal Probabilities
Variable     Coeff.   Mean ofX  median   mean    worst   best        no ethnic ethnic   plus 10% plus 2%  less 2%
dom.    dom.    education gyl       popgl
education       -0.037   44.485    -1.460   -1.624   -OCI11   -5.366   -1.624   -1.624   -1.989   -1.624   -1.624
gyl-           -0.085    4.696      0.433    0.397    2.804   -2.857    0.397    0.397    0.397    0.226   -0.114
exports        32.089    0.158      3.594    5.054    8.022    0.000    5.054    5.054    5.054    5.054    5.054
exports2      -62.511    0.025    -0.784   -1.551   -3.907    0.000   -1.551   -1.551   -1.551   -1.551   -1.551
Inpop           0.946   17.233     14.909   16.301   19.777   11.256   16.301   16.301   16.301   16.301   16.301
fractionaliz.    -0.0003 1785.878  -0.607   -0.607   -0.004   -2.372   -0.607   -0.607   -0.607   -0.607   -0.607
ethnic dom.     0.847    0.440      0.000    0.373    0.847    0.000    0.000    0.847    0.373    0.373    0.373
geog dispersion  -3.891   0.575    -2.366   -2.238    0.000   -3.778   -2.238   -2.238   -2.238   -2.238   -2.238
peace          -0.004  347.670     -1.507   -1.488   -0.004   -2.534   -1.488   -1.488   -1.488   -1.488   -1.488
constant      -16.443             -16.443  -16.443  -16.443  -16.443  -16.443  -16.443  -16.443  -16.443  -16.443
X*betahat                          -4.231    -1.826   11.081  -22.093   -2.199   -1.351   -2.191    -1.996   -2.337
probability                         0.014    0.139    1.000    0.000    0.100    0.206    0.101    0.120    0.088
Variable     Coeff.   Mean of X  Exports= Exports= min      max      min      5 yrs    10yrs   geog dis- geog dis-
0.26     0        fraction. fraction. peace    peace    peace   perslon=1 persion=O
education       -0.037   44.485    -1.624   -1.624   -1.624   -1.624   -1.624   -1.624   -1.624   -1.624   -1.624
gyl-           -0.085   -4.696      0.397    0.397    0.397    0.397    0.397    0.397    0.397    0,397    0.397
exports        32.089    0.158      8.343             5.054    5.054    5.054    5.054    5.054    5.054    5.054
exports2       -62.511   0.025     4.226             -1.551   -1.551   -1.551   -1.551   -1.551   -1.551    -1.551
Inpo            0.946   17.233     16.301   16.301   16.301 ' 16.301   16.301   16.301   16.301   16.301   16.301
fractionali2    -0.0003 1785.878   -0.607   -0.607   -0.004   -2.372   -0.607   -0.607   -0.607   -0.607   -0.607
ethnic dom.     0.847    0.440      0.373    0.373    0.373    0.373    0.373    0.373    0.373    0.373    0.373
geog dispersion  -3.891    0.575   -2.238   -2.238   -2.238   -2.238   -2.238   -2.238   -2.238    0.000   -3.891
peace          -0.004  347.670     -1.488   -1.488   -1.488   -1.488   -0.004   -0.257   -0.514   -1.488   -1.488
constant       -16.443            -16.443  -16.443  -16.443  -16.443  -16.443  -16.443  -16.443  -16.443  -16.443
Xbetahat                           -1.212   -5.329   -1.223   -3.590   -0.342   -0.595   -0.851     0.412   -3.479
probability                         0.229    0.005    0.227    0.027    OA15    0.356    0.299    0.602    0.030
The probability of a war-start was calculated in the following way:
probability--exp(XA B )I(l+exp(X^ 13))
44






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