EMPIRICALLY ASSESSING THE CAUSES OF
CIVIL WAR
Scott Gates
International Peace Research Institute, Oslo (PRIO)
and
Michigan State University
gates@pilot.msu.edu
March 2002
Paper prepared for delivery at the annual meeting of the International Studies
Association, New Orleans, March 24-27, 2002. An earlier version of this paper was
presented at the Economic Research Seminar, ISS, the Hague, Netherlands, March 7,
2002. This paper is a product of the UNU/WIDER project 'Why Some Countries
Avoid Conflict While Others Fail'. In addition, I thank the Research Council of
Norway and the Development Research Group at the World Bank for their support. I
also thank Arjun Bedi, Nils Petter Gleditsch, Håvard Hegre, John Mueller, Michael
McGinnis, Mansoob Murshed, and Håvard Strand for their useful comments.
Abstract
Research on civil war and armed civil conflict has exploded in the last few years.
Quantitative analyses of civil wars, in particular, have flourished. This essay provides
an overview of the recent econometric research of civil war and the data that are used
to study intrastate conflict. This essay reviews works that have examined the causes of
civil war onset and duration. In addition to the problem of simply defining a civil war,
researchers in this area must deal with five fundamental problems affecting data on
armed civil conflict: non-independence, unmeasured heterogeneity (a general problem
related to omitted-variable bias), endogeneity, and the rareness of the outcome
variable. This essay also identifies controversies in the field and suggests ways to
improve this research, through the development of better datasets and the use of better
econometric techniques.
1
1
Introduction
The end of the Cold War led some to optimistically predict an end to war. A decade
later we find this optimism was premature. War persists. After the fall of the Berlin
Wall, there have been well over 100 armed-conflicts, 33 of which were still active in
2000 (Wallensteen & Sollenberg, 2001). Most armed conflicts today are conducted
within the boundaries of existing states, though there is often external participation
and spillover into neighboring states. Civil war is the predominant form of war in the
contemporary age.1 Yet, civil war is both under-theorized and lacking systematic
empirical study in comparison to interstate war. Fortunately, recent work has started
to address these problems. This essay provides an overview of the recent econometric
research of civil war and the data that are used to study intrastate conflict. My
overview tends to focus more on recent research, but this only reflects the fact that
most econometric research on civil war is quite recent. I intentionally have left out a
considerable number of works, most of which are purely theoretical or case studies.2
The objective of this survey is to inform someone who knows some econometrics
how to go about doing quantitative research of civil conflict.
Civil war is an important problem. Indeed, civil war constitutes the most
common form of war. Of the 220 armed-conflicts involving at least 25 battle
casualties, fought between 1945 and 2000, 157 were intrastate compared to 42 that
were interstate (Gleditsch et al., 2001).3 Over this period, the percentage of intrastate
conflict as compared to all other types of conflict has grown, peaking in 1993 and
1994. The largest number of intrastate conflicts was in 1992. Since the end of the
Cold War, well over 90% of all armed conflicts have been intrastate. See Figure 1.
[Figure 1 about here]
1
For some conflict researchers, war and armed conflict constitute distinct phenomena distinguished by
casualty thresholds. Later in the essay I will explore this issue in depth, but for the majority of this
essay I will use the terms as synonyms, since this is how most people use the terms.
2
See Sambanis (2002) for a review of recent research on civil wars.
3
In addition to the 157 intrastate wars, there were 18 intrastate wars that became internationalized
through external intervention. In addition to the 42 interstate wars, there were 21 extrasystemic wars.
Extrasystemic wars are wars fought between a state actor and a non-state actor that is not an intrastate
conflict.
2
Civil war also causes horrible suffering. The consequences of civil war since
World War II have been staggering. Casualty figures number in the millions. Most of
these deaths have been civilian non-combatants. Several tens of millions more have
been displaced by civil war. Refugees often flee across immediate borders seeking a
more peaceful setting only to find that their presence in large numbers is destabilizing,
which in turn can lead to more conflict and more displacement. Such humanitarian
crises often pressure international organizations or Western states to become
involved. The large influx of refugees from Haiti resulted in the US intervention in
1992 while NATO intervention in the wars in the former Yugoslavia was designed to
stem the flow of refugees and limit the scope of the war (de Soysa & Gleditsch, 1999:
9).
Tremendous economic costs are also associated with civil war. Armed combat
destroys capital. Buildings and bridges are literally blown up. Roads are mined or
made impassable. Agricultural land and crops are destroyed. Civil war drives out
investment capital and few investors venture into warzones. Warfare also chases away
labor, especially those with the best skills and training. In sum the economic
consequences of civil war are dire. Given that most civil wars occur in relatively poor
countries, civil war has particularly important consequences for economic
development.
This essay is organized as follows. First, I review the data used to study
intrastate conflict, discussing how variables are conceptualized and operationalized.
Next, I provide an overview of the econometric research of civil war. The corpus of
empirical study of intrastate conflict helps answer the following questions about civil
war – What do we know? What might we know, but really are not certain? And what
is being debated. I then examine a variety of econometric issues affecting the
quantitative analysis of civil war. Then I explore areas where further work is needed
and conclude with a data wish list.
2
2.1
Measurement Issues
Defining a civil war
Defining a civil war is not as straightforward as one would imagine. Different views
as to what distinguishes civil war from other forms of organized violence have
resulted in the creation of competing datasets. Nearly all researchers of agree that civil
war is different from other forms of violent activity such as crime, genocide, or
3
interstate war. A widely accepted definition of civil war is an armed conflict between
two domestic parties over a contested incompatibility resulting in a number of
casualties exceeding a certain threshold. The issue then becomes, what threshold?
The Uppsala University Conflict Data project distinguishes between large
conflicts or wars (with annual battle-deaths exceeding 1,000) and minor conflicts
(with a minimum of 25 battle-deaths) (Wallensteen & Sollenberg, 2000: 648–649).
The Correlates of War (COW) project sets the threshold for a civil war at 1,000 battle
deaths per year. Most statistical analyses of civil conflict have used the COW dataset
or some minor variation of it.
For over three decades, the most used civil war dataset has been the one
developed by the Correlates of War (COW) project. Their dataset includes 214 civil
wars between 1816 and 1997. The COW criteria for intra-state wars as specified by
Small & Singer in Resort to Arms, includes armed conflicts involving two intrastate
combatants that resulted in at least 1,000 deaths in a single year, including civilian as
well as military deaths (1982: 213).4 In the 1992 COW dataset update, the threshold
was lowered to 1,000 battle deaths for the entire civil war (Singer & Small, 1994: 2;
Sarkees, 2000: 129; 2001: 13). The 1,000 battle-deaths criterion has become the
accepted threshold for distinguishing a war from other forms of armed violent
conflict, but the framework for counting has been a subject of big debate. Indeed, the
COW community is still debating whose deaths to count when tallying battle deaths.5
It is clear from this debate that there is movement to push the criteria for civil war
back to the strict threshold of 1,000 battle deaths per year. Two issues are at stake:
Should civilian deaths be counted along with battle deaths or should only battle deaths
be counted? And, should the 1,000 death criteria apply to the entire war period or to a
single year? Researchers outside the COW community have developed datasets that
are based on the COW civil war dataset, but vary in some key way. Three noteworthy
4
The COW criteria for war varies across types of war, whereby interstate wars are defined as:
sustained armed combat between two or more state members of the international system; threshold of a
total of 1,000 battle related fatalities. Extra-systemic wars – sustained combat between a state member
of the international system and a political entity (not a system member) outside its territorial
boundaries; threshold of 1,000 battle-related fatalities per year. COW intrastate war – sustained combat
between two armed forces within the territorial boundaries of a state; threshold of 1,000 battle
casualties per year.
5
See the COW2 Forum: http://WWW.COW2.LA.PSU.EDU.
4
examples include Doyle & Sambanis (2000), Collier & Hoeffler (2001), and Fearon &
Laitin (2001).
The State Failure Project (Gurr, Harff & Marshall 2000, 2001; Gurr et al.,
2001) has also developed a conflict dataset connected to its study of political systems
and political change (Polity). They classify conflict by type, but ascribe similar
thresholds for inclusion. Each belligerent party must mobilize 1,000 or more people
and an average of 100 or more fatalities per year must occur during the episode.
Outside of the State Failure Task Force, few have used this data to study civil war.
More research should be done comparing analysis across datasets.6
A new dataset that promises to be a major competitor with the COW project is
the Armed Conflict 1946-2000 dataset, or what is better known as the Uppsala dataset
(Gleditsch et al., 2001).7 This dataset extends the work of the Uppsala Conflict Data
Project, updated annually since 1990, back to the end of the Second World War,
thereby encompassing the period, 1946-2000.8 The threshold for inclusion in this
dataset is 25 battle-related deaths per annum and it includes interstate and intrastate
conflict. They also distinguish between wars and armed conflict by defining a war as
a conflict that results in 1,000 battle deaths in a year. Of the 220 intrastate conflicts
between 1946 and 2000, 95 have been civil wars according to the Uppsala project
(Gleditsch et al., 2001).
The lower threshold of 25 has several advantages. The strict 1,000 battle
deaths criterion excludes several well-known armed conflicts, most notably the
Northern Ireland conflict. The lower threshold does not exclude such enduring
conflicts that never cross the 1,000 deaths criterion. Another advantage the 25 battledeath criterion has over the 1,000 deaths threshold is that it helps avoid against a
selection bias against small countries. Indeed, ceteris paribus, small countries are less
likely to incur 1,000 casualties than countries with large populations. To make this
point clearer, consider two countries, one with a population of less than 100,000 and
one with 100 million. The two countries may exhibit the same propensity for
rebellion, but due to the differences in population, the smaller country is less likely to
6
See Gleditsch et al. (2001) for a comparison of the conflict data from a number of datasets.
7
This dataset is a joint product of the Conflict Data Project in the Department of Peace and Conflict
Research at Uppsala University and of the Conditions of War and Peace Program at the International
Peace Research Institute, Oslo (PRIO).
8
See Wallensteen & Sollenberg (2001) for example.
5
cross the 1,000 battle death threshold. To estimate the severity of civil war without a
selection bias, it may be better to measure it in terms of the proportion of a country’s
population killed in civil war.
Yet another advantage of using a 25 death threshold is that it allows a
researcher to differentiate large and small armed conflicts, using the 1,000 battle
deaths threshold to distinguish between the two. The 25 battle-death criterion is also
sufficiently significant to ensure against not reporting an armed intrastate conflict.
Lower thresholds, say 5 battle deaths, are more likely to be not reported in certain
countries, especially if the western press is preoccupied with other news somewhere
else in the world. In general, casualty figures are extremely unreliable. Reports from
the battlefield are often wildly inflated or under-inflated (depending on how the
belligerents want their role to be perceived). Given this lack of reliability researchers
have tended to want to play it safe and strictly rely on a set threshold. For example, if
the Uppsala project faced a conflict candidate that is reported to have between 10 and
100 casualties, they will not list it as a minor armed conflict because it does not
strictly meet the 25 battle death criterion. Ideally, the actual range of casualties would
be presented in these datasets, allowing the researcher make decision as to how to use
the data.
In addition to the issue regarding a battle death threshold is the issue of whose
death do you count? The criterion of battle deaths is consistent with interstate conflict
datasets. But with interstate wars one is dealing with the deaths of soldiers of
government armies. The problem with intrastate armed conflict is that one of the sides
is not representing the government. Identifying who is or is not a member of a rebel
army is often difficult. Moreover, civilians often tend to be the targets of violence in
civil wars (Azam, 2001; Kalyvas, 2001). Accounting for battle deaths alone
underestimates the scale of violence in a civil war. The COW data include civilian
casualties (though as mentioned above, this issue is being debated) and the Uppsala
data do not.
Several other issues are also relevant for distinguishing armed civil conflict
from other forms of intrastate violence. In particular this means excluding cases of
armed repression of non-violent groups or unorganized groups even if it involves
considerable numbers of civilian casualties as in genocide (Rummel, 1994)..
According to the Uppsala conflict data project:
6
An armed conflict is a contested incompatibility that concerns government
and/or territory where the use of armed force between the two parties, of which
at least one is the government of a state, results in at least battle-related deaths
(Gleditsch et al., 2001: Appendix 1: 22).
This definition thereby excludes armed conflict between two non-governmental
armies, as well as genocides such as the massacre in Rwanda in 1994. Neither the
COW data nor the Uppsala data include armed conflicts between two rival rebel
groups. With regard to genocides, as long as the 1,000 death threshold is crossed, the
COW project includes these conflicts. The Uppsala project does not.
Another controversy regarding data on armed conflict regards distinguishing
between different types of war. The COW project distinguishes between three kinds
of war, interstate, intrastate, and extrasystemic (which are defined as wars between
nation-states and political units not identified as being part of the international system,
e.g. colonies, dependent territories). Fearon & Laitin (2001) choose to integrate
aspects of both the COW civil war and extrasystemic datasets. They argue that
decolonialization wars are civil wars and not substantially different than secessionist
civil wars. Their data on civil war therefore include what the COW project defines as
civil wars as well as extrasystemic wars. The Uppsala dataset, of course, puts all three
types in one dataset, but allows the researcher to distinguish between the types of
conflict. (See Figure 1).
The problem of distinguishing different types of war becomes especially
thorny when the sides fighting disagree as to whether the war is intrastate or
interstate. The conflicts between Serbia and Croatia/Bosnia or between the US and the
Confederate States of America serve as examples. While neither the US nor Serbia
recognized these seceding states as legitimate, there were international actors that did.
Such cases lead one to question whether the definition of civil war is determined by
successful secession? The internationalization of a civil war due to external
intervention also can create problems. Indeed the prospects of a third party (an
external nation-state) intervening in a civil war may alter the decision of a potential
rebel leader to actually initiate conflict.
When is the war over? War termination is another particularly difficult
problem. The Uppsala dataset defines the conflict on the basis of a annual 25 battle
death criteria, but supplements this with a precise date of war onset and termination.
7
War termination is thus defined in terms of the resolution of the incompatibility that
served as the basis for the conflict. Such precise dating is particularly important for
duration analysis of civil conflict and for the use of proportional hazard models to
assess the probabilities of war onset or termination/settlement. The COW project also
provides a date associated with the end of a war. The particular date of conflict onset
and termination can be quite subjective. The signing of a peace treaty or the defeat of
one of the armies can serve as a date, but not all war ends have such clear markings.
Where is the war located? Most conflict datasets only indicate what nationstate or nation-states are involved in conflict. The COW data are organized around the
concept of a country being at war or not. As such, coterminous wars are not
distinguished. One of the chief innovations of the Uppsala conflict dataset is that the
conflict itself serves as the unit of analysis so that two conflicts being fought in one
country are identified as such. A further innovation associated with the PRIO
partnership with the Uppsala conflict data project has been to provide data regarding
the geographic location of each case of armed conflict (Buhaug & Gates, 2002). Such
data may be valuable for understanding the diffusion of conflict and war.
3
An Overview of Quantitative Civil War Research
Civil wars originate, persist, and terminate with human decisions. To have a war,
someone must make the decision to take up arms and engage in violence.
Substantively empirical research of civil war has examined the role of
macroeconomic factors (unemployment, poverty, income inequality, etc.), democratic
and other political institutions, geographical and environmental factors (the nature of
state boundaries, terrain, location of ‘lootable’ natural resources, and other physical
features that affect the way war is fought), factors of identity (ethnic or cultural), and
the relative capabilities of the state to exercise authority. The February 2002 special
issue of the Journal of Conflict Resolution (Collier & Sambanis, 2002) features a
number of articles that examine these relationships.
While the case study method is the dominant form of research on civil war, the
focus here is exclusively on large-N quantitative analyses, which attempt to determine
the factors relevant to the onset, duration, and resolution of civil conflict in a
probabilistic sense. A review of the case study literature is beyond the scope of this
article. Moreover, case studies research suffers from two fundamental problems, non-
8
generalisability and selection bias.9 Case studies of civil war are better able to
describe a single war or the events regarding a particular war, but not civil war in
general. Nonetheless, case studies when used in conjunction with large-N quantitative
analysis can be useful for developing theory, addressing measurement issues,
analysing outlier, and maybe most importantly examining the ‘dogs that did not bark’
– cases where statistical analysis predicts war, but there is no war. Unfortunately very
little work of this nature has been conducted.10
One of the most important findings regarding the quantitative analysis of civil
war is that the factors associated with the onset, duration, and resolution of armed
conflict are distinct from one another. Of course there is significant overlap between
the factors and that relate to the onset of civil war and those that affect the dynamics
and resolution of conflict.
Almost all of the quantitative studies of civil war have used the 1,000 death
threshold. With the recent availability of the Uppsala dataset, more studies will most
likely begin to use these data on intrastate armed conflict. We may find that wars with
1000 battle deaths are different from those with fewer casualties.
3.1
The Onset and Prevalence of Civil Conflict
As there is no theoretical consensus regarding the causes of civil war, there is no
consensus on a comprehensive set of variables to be used for statistical analysis.
Nevertheless, a number of empirical findings appear to be robust across diverse
operationalizations and methodologies. Despite a plethora of theories, a wide
assortment of measurements and a variety of methodologies a consensus of sorts has
emerged around several variables as associated with the onset of prevalence of civil
war. The following variables are generally agreed to be associated with a higher risk
of civil war: (1) poverty, lack of economic opportunities, and level of economic
development, (2) time since previous civil war and conflict history, (3) ethnic
dominance, and (4) political instability. In the following sections I will first examine
these four factors then I will discuss the factors for which there is good reason to
believe they are related to civil conflict but for some reason or another there is some
9
Please see King, Keohane, and Verba (1994) for an overview of these problems and an excellent
overview of qualitative research.
10
See Gates (2003) for a more thorough discussion of how case studies can be used in conjunction with
large-N quantitative analysis.
9
controversy surrounding them. I will also examine factors that have engendered
debates. 11
3.1.1 Lack of economic opportunities and economic development
The ability to recruit soldiers for rebel armies is critical to forming a rebel army
(Collier, 2000;Gates, 2002). Since reliable data on troop strengths in rebel movement
are unavailable, proxy variables of opportunity costs of joining a rebellion must be
used. Economic opportunities and the state of the labor market matter in this regard. A
variety of indicators have been used. Collier & Hoeffler (2001) use the rate of
economic growth per capita and the secondary school enrolment rate for males. Both
variables are lagged by one five-year period to avoid endogeneity problems. Esty et
al. (1995, 1998) and Gurr & Harff (1997) use infant mortality as part of the State
Failure Task Force’s series of studies.
Most studies simply rely on a general indicator of economic development.
GDP is the most commonly used. Hibbs (1973: 21-3) found a curvilinear relationship
between development and internal conflict, supporting his proposition that rich
economies will exhibit less violence, but that economic development will cause
dislocation and lead to increased violence in very poor economies. Collier & Hoeffler
(2001) and Fearon & Laitin (2001) also find evidence for a moderate curvilinear
pattern. Hegre et al. (2001) use energy consumption per capita as a proxy for
economic development to maximize the temporal domain of their study. They also
find support for the curvilinear pattern.
3.1.2 Conflict history
Time heals all wounds. The longer the period since the last civil war, the lower the
risk of civil war onset, eventually approaching the level of risk associated with
countries that have never experienced civil war (Collier & Hoeffler 2001). Using a
Cox regression analysis Hegre et al. (2001) operationalize the time since last the last
civil war, such that: Proximity of civil war = exp(- time in days since the last civil war
11
The criteria for claiming consensus are based on the degree and nature of debate and the
sophistication of the econometric analysis in question. I do not treat critiques based on single case
studies as serious challenges.
10
ended / ), where  is some chosen divisor by which a half-life can be estimated.12
Hegre, et al. (2001) found that 1 year after a civil war, the hazard of civil war was 1.8
times higher than the baseline for the 1964-1992 period with a corresponding 16 year
half-life value. For a longer period of analysis, 1816-1992 they found similar patterns.
Time since the last civil war proved to be one of the most substantively important
factors explaining civil war in Hegre et a.l (2001).
3.1.3 Ethnic dominance
In light of recent civil wars fought in the Balkans and ethnic conflict in parts of Africa
and Asia, heterogeneous ethnic composition has become associated with intrastate
conflict. Researchers have operationalized ethnic composition along two different
dimensions. The first dimension is fragmentation: the more groups, or the higher the
probability that two individuals drawn on random are from different groups, the
higher the level of fragmentation. There is a consensus that this is negatively related
to conflict risk if related at all (Collier & Hoeffler, 2001; Sambanis, 2001). The other
dimension is polarization or dominance. Reynal-Querol/Esteban’s indices of
polarization are highest when there are two groups of exactly the same size. This is
empirically indistinguishable from ethnic dominance, operationalized by Collier &
Hoeffler if the largest ethnic group has 45-80% of the population. There is a
consensus that this variable is positively related to conflict (Bates, 1999;Collier &
Hoeffler, 2001;Ellingsen, 2000;Hegre et al., 2001;Eldawabi & Sambanis, 2002;
Reynal-Querol, 2002).
These studies tend to rely on ethnolinguistic fractionalization indices applied
to data from the Atlas Naradov Mira (USSR, 1964). Collier & Hoeffler use an index
of polarization developed by Esteban & Ray (1994, 1999), such that:
n
P  K
i 1
n

j 1
1
i
 j d,
where i represents the share of people that belong to group i in the total population,
such that i=1,…n. Such a measure of polarization depends on the parameters K and .
K is used for normalization and  is bounded, such that 0<<*, where * 1.6. The
degree of antagonism between the two groups is defined by d. Since the degree of
12
A half-life here exhibits the properties that you would expect it to – a half-life of 16 years
corresponds to a reduction of the initial effects to ¼ after 32 years and 1/8 after 48 years.
11
antagonism is not measurable across all cases, it was simply coded as a dummy
variable, such that d=1, if ij and d=0, if i=j. There has been some debate about
indices and operationalizations, but for the most part there is a consensus. Ethnic
dominance and not ethnic fragmentation is positively associated with armed civil
conflict.
Examining the goals and motivations of rebel groups may provide a better
understanding of how a number of these variables interrelate to affect the risk of civil
conflict. The problem with this approach is that identity-based goals, may not reflect
the goals of rebel leaders, but may in fact be simply mechanisms for recruitment
(Gates, 2002). Given that the salience of ethnic identity is alterable, ethnic identity is
likely to play a dynamic role in causing war (Kaufman, 1996, 1998).
3.1.4 Political instability and political change
Politically unstable regimes are more likely to experience civil war. A number of
studies have examined the relationship between political authority and the onset of
civil war (see below for a discussion), but few have empirically evaluated the role of
political instability and political change. Hegre et al. (2001) study the role of political
institutions and civil war (defined by the 1,000 battle death threshold) over two
temporal periods, 1816-1992 and 1946-92. Gates et al. (2001) applies a similar
analysis to the post-Cold War period using an earlier version of the Uppsala data
(Wallensteen & Sollenberg, 1998). These studies demonstrate a strong statistically
significant relationship between political change and intrastate conflict. Hegre et al.
(2001) also examines the effects of small and large democratization as well as small
and large autocratization. Democratization and autocratization are both dangerous –
especially when towards the institutional inconsistent middle away from strongly
consistent autocratic or democratic institutional arrangements.
Recently independent countries are also more at risk, presumably due to a lack
of institutional development. Another aspect of the unsettled nature of newly
constituted states is that their borders may be in dispute. In the case of post-colonial
independence, colonial borders may have cut across ethnic or religious groupings,
thereby providing the young state a host of internal problems that could escalate to
armed violence. Hegre et al. (2001) find that time since independence (essentially
12
meaning the time since the state was created) is one of the strongest predictors of civil
war. Fearon & Laitin (2001) report similar findings.
Four factors then are identified by a series of empirical studies to be associated
with a higher risk of civil conflict. The quantitative conflict community for the most
part agrees that poverty and lack of economic opportunities, conflict history, ethnic
dominance, and political instability are important factors for understanding
international conflict. We have good reason to believe that the following set of factors
are associated with a higher risk of civil war, but for some reason or another there is
some doubt: dependence on natural resources, ethnic diasporas, total population and
geographical dispersion of the population, rough terrain, political institutional
structure, and state strength.
3.1.5 Dependence on natural resources
The campaign against blood diamonds in recent years has produced an image of
rebels motivated not by justice or grievance but by greed. As Global Witness was
beginning its campaign against conflict diamonds, scholars of civil war were writing
about loot-seeking rebels (Collier & Hoeffler, 1998). Focusing not just on diamonds,
they argue that an abundance of natural resources tempt rebels to capture such ‘loot’
so as to finance the purchase of weapons, food, and labor. Primary commodity exports
constitute lootable resources that invite predation. Civil war occurs when a rebel
group and the government compete for control of the territory where the primary
commodity is located, such as a diamond mining area. The essence of the argument is
that rebellions more closely resemble organized crime than freedom fighters
struggling against injustice or more succinctly, ‘rebellion is large-scale predation of
productive economic activity’ (Collier, 2000). Indeed, in their analysis, Collier &
Hoeffler (2001) find that the variable generally associated with the highest risk factor
is primary commodity export dependence.
Dependence on natural resources not only serves as a temptation for rebels,
but leads to fundamental economic distortions (Dutch disease). Their proxy for
natural resource dependence, the ratio of primary commodity exports over GDP,
serves better to estimate this problem of economic distortion than as a measure of
loot-seeking. Collier & Hoeffler (2001) find a strongly significant parabolic
relationship between natural resource dependence and civil war. Fearon & Laitin
disagree. Theoretical work tends to support Collier & Hoeffler, but there are
13
unresolved issues regarding this variable. The primary problem with this variable is
that there are so many missing cases and the limited temporal domain.
3.1.6 Ethnic diasporas
Collier & Hoeffler (2001) are the first to demonstrate in a systematic manner that
ethnic diasporas, such as the Tamil community in Canada or the Irish community in
the United States, are associated with the onset of civil war. This variable is
operationalized by the proportion of nationals of the war-affected country living in the
US as compared to the numbers still residing in the home country. This finding is
rather significant, but it constitutes a tenuous proxy of the financial flows from a
population abroad to a rebel group. Collier & Hoeffler (2001) provide a simple
migration model to test for endogeneity in their appendix, but the direction of
causality between the onset of conflict and the motivations for emigration need to be
further explored.
3.1.7 Total population and the geographic distribution of the population
Collier & Hoeffler (2001) have found that various population characteristics of a
country are positively associated with the onset of civil war. Most intriguing of these
measures regards the geographical distribution of the population. To operationalise
this variable, they calculated a Gini coefficient of population dispersion. Analogous to
an income Gini coeffiecient, if a country’s population is concentrated in a relatively
small area of the country, the Gini coefficient of population dispersion will be high.
Concentration. High population dispersion Gini coefficients are positively associated
with civil war.
Total population, population density, and proportion of population living in
urban areas are all positively associated with civil war (Collier & Hoeffler, 2001).
These results, particularly with regarding total population, are most likely due to the
bias of using the 1,000 battle death threshold to define civil war. Small countries are
simply less likely to cross this threshold since the number of fatalities is so large.
3.1.8 Rough terrain
Physical geography also affects civil conflict. Mountains and forest cover help rebel
groups engaged in insurgency movements. Guerrilla tactics are aided by rough terrain.
Fearon & Laitin (2001) find strong statistical support indicating that such factors
14
increase the chances of the onset of war. Collier & Hoeffler (2001) find no support for
these variables as a cause of war onset. They do, however, find that these factors do
work to lengthen the duration of conflict. Fearon (2001) does as well.
3.1.9 Governance and political institutions
A big debate is now raging over the role political institutions play in mitigating the
risk of civil war. Collier & Hoeffler (2001) find that there is no relationship between a
country’s political institutions and the probability of civil war. Fearon & Laitin (2001)
also find no support for this proposition.13 These non-significant results are typically
associated with shorter temporal periods of analysis.
Consider the two controversies regarding democracies and natural resources
and the onset of civil war. In these two cases, Fearon & Laitin’s work serves as the
odd-man out. There is good reason for this. In their analysis they conflate civil wars
and wars of colonial independence. So a democratic France is coded as having a civil
war in Algeria. I would never argue that democracies would be less associated with
wars of colonial independence, but I would argue that democracies are less prone to
civil war. Civil wars are distinct phenomena from colonial wars. I am convinced that
almost all the odd findings in Fearon & Laitin trace back to the conflation of these
two concepts.
Collier & Hoeffler also find no support for a democratic peace thesis. The
problem here has to do with the way they have coded democracy. They structure their
analysis in 5 year bunches, taking the first year value for each independent variable.
This is fine for relatively non-volatile factors. But this also means that changes in
governance that occur before the outbreak of civil war are not estimated. 14 A plethora
of studies, alternatively, find solid evidence indicating that political institutions do
affect the chances of civil war. Taking a scale of democracy, which ranges from
strong autocracy to strong democracy, a large number of studies have found a
13
They do, however, find a significant and positive relationship between human rights abuses and the
onset of civil war.
14
By re-analyzing Collier & Hoeffler’s data, but using the lowest democracy score in
a five year period produces statistically significant results supporting the democratic
peace thesis. See Sørli (2002).
15
parabolic relationship between democracy and the risk of civil war (Muller & Weede,
1990; Ellingsen & Gleditsch, 1997; Benson & Kugler, 1998; Ellingsen, 2000; Yalcin,
2001; Hegre et al., 2001; Elbadawi & Sambanis, 2002). The basic argument is that the
concentration of authority in a strong autocracy serves to suppress any potential
challengers. A strong autocracy (either a dictatorship or a kingdom) possesses a set of
institutions that mutually reinforce the central authority of the autocrat. This
concentration of authority serves to prevent the rise or development of alternative
sources of power or authority, by which rebellion can be fomented.
In contrast, democratic authority is diffuse, legitimate, and open; thereby, for a
potential challenger, the expected utility of entering the political system is much
higher than organizing armed rebellion. Democratic systems also possess a set of
mutually reinforcing institutions that serve to address grievances and thereby reduce
the likelihood of civil war. The problem occurs in systems for which political
authority is still fairly concentrated, thereby making the capture or control of the
political system a nice prize. Furthermore, because of a lack of openness these inbetween regimes often exhibit aspects of repressive governance, which in turn feeds
grievances, which can lead to rebellion.
Most econometric works examining the role of political systems on the onset
of civil war have used a version of the Polity dataset (Jaggers & Gurr, 1995)
(Marshall 2000, 2001). While some economists have used the Freedom House scale
of democracy (Karatnycky, 1999), this is a big mistake for cross-temporal analysis.
The data is produced as an evaluation of the state of democracy in the world in a
single year. The scale changes over time and it is not designed as a series. Indeed,
some cases (e.g. Mexico) rise and fall along the scale in association with global
changes in the number of countries that are democratic in years in which the country
in question exhibited no institutional change. This is particularly problematic in the
middle parts of the Freedom House scale. It should never be used in a panel.
An alternative measure, Polyarchy, has been developed by Vanhanen (2000).
This index is composed of a measure of participation and political competition. Gates
et al. (2001b) have developed a composite measure of democracy that combines
aspects of the Polity measure (executive recruitment and executive constraints) and a
transformed measure of participation taken from the Polyarchy measure. They
distinguish between institutionally consistent autocracies and democracies and
institutionally inconsistent regimes.
16
Rather than simply relying on a readily available index of democracy, scholars
of civil conflict should be further examining the characteristics of these indices. A
starting point would be to unpack these indices and examine the relationship between
specific institutional structures and the onset of civil conflict.
From the work of Przeworkski (2000) and Gates et al. (2001a) there is
considerable evidence demonstrating that both institutional inconsistency and political
instability are associated with one another. Autocracies and Kingdoms with
concentrated authority and well developed institutions for executive recruitment are
relatively stable. Likewise, democracies with extensive voter enfranchisement, elected
executives, and institutionally constrained executives are also relatively stable.
Institutionally inconsistent regimes are relatively unstable. Hegre et al. (2001) tested
for both political instability and for level of democracy (institutional consistency),
accounting for level and change and found both significant. The work by Gates, et al
(2001) demonstrates that the nature of the relationship between institutional
inconsistency, political instability, and civil conflict is very likely to be endogenous.
This issue needs to be further explored.
We also need more work relating specific types of political institutions to civil
war. We do not know enough about how specific democratic institutions serve to
mitigate against armed rebellion. When we do find armed rebellion in democracies
(e.g. Northern Ireland, the Basque Country), are there differences between
majoritarian and plurality systems with regard to the propensity for civil conflict?
Likewise, how are presidential and parliamentary systems related to the risk of civil
war? Utilizing Colomer’s (2000) data on institutional change, Reynal-Querol (2002)
is able to answer these questions by comparing across different governance
arrangements, parliamentary-majoritarian, presidential and semipresidential, and
parliamentary-proportional representation, arranged on a scale of political
inclusiveness.15 She finds that the more inclusive a system of governance, the less
likely the chances of civil war.
Autocratic institutions, in general, also need to be better understood. More
specifically, we need to work on differentiating different types of autocracies. We also
15
Colomer (2000) includes 123 attempts at democratization and major democratic
institutional changes in 84 countries, 1894-99.
17
need to better understand the dynamics of political institutional change and how these
dynamics relate to political instability and the risk of civil conflict.
3.1.10 State strength
Civil war is really about state failure, the inability of the state to maintain a monopoly
on violence. Many theories of the causes of civil war include aspects of state failure.
Addison & Murshed (2001) and Azam (2001) attribute the failure of the state to abide
by the conditions of the social contract. Indeed many of the factors discussed above
either critically interact with state strength or serve as proxies for state strength.
Collier & Hoeffler (2001) argue that the parabola associated with natural resource
extraction and conflict is due to high state capacity in the countries with extremely
high primary commodity dependence levels, e.g. the oil states of the Middle East.16
The ability to tax determines whether governments can govern. No other
governmental activity is so critical to the health of a political system. The ability to
tax ensures adequate fiscal resources to implement policies. State strength can thus be
defined as the ability of the state to politically extract wealth (Organski & Kugler,
1980; (Organski et al., 1984). But state capacity cannot be measured simply by
looking at government revenues. The problems relating to the resource curse tell us
this. What is relevant is a comparison of the revenues collected by a state as compared
to what they potentially could have collected. Such a ratio of actual governmental
resources over the predicted revenues accounts for the differences in economic
endowments across countries.17 Such an indicator serves as a more direct measure of
state capacity.
Benson & Kugler (1998) are the only researchers who have applied this
concept to civil conflict. Interestingly, they also apply this measure to the opposition
group as well as the government. In this way, Benson & Kugler (1998) have
operationalized the contest success functions,
18
which characterise so many of the
theoretical analyses of civil violence, measured on a scale ranging from 1 to 25. They
find that the political capacity is significantly associated with civil violence.
16
17
Also see Ross (2001) with regard to oil dependence and state strength.
The predicted revenues of a country are estimated by means of a cross-national econometric analysis
of economic endowments.
See Organski & Kugler (1980) and Organski et al. (1984). Also see
Arbetman & Kugler (1997) and Benson & Kugler (1998).
18
For example, see Tullock (1980), Hirshleifer (1987), Grossman (1991) and Skaperdas (1996).
18
Substantively, the political capacity of the government is strongly negatively
associated with civil war. Strong governments are much less likely to go to war. The
political capacity of the opposition is positively related to violence. Benson & Kugler
also interact the political capacity of the government and the level of democracy
(Polity scale); this negative relationship is strongly significant. Democracy alone is
not statistically significant, but the squared term is. They did not interact state
capacity and democracy-squared. These are intriguing results. Unfortunately, the
analysis is limited to 26 countries. Severe data limitations with respect to the measure
of state and opposition capacity constrain the scope of the analysis. Regardless, this
analysis demonstrates the relevance of institutionally weak environments with regard
to armed civil conflict.
3.1.11 Importance of interaction and endogenous effects
Too few of analyses of civil wars have examined interaction effects between
variables. Benson & Kugler (1998), who examine the interaction between state
strength and political system, researchers have included interactive terms in their
analysis so as to account for the relationships between explanatory variables.
Elbadawi & Sambanis (2002) examine the interaction between orthogonal
ethnolinguistic and religious diversities.
Endogenous relationships also should be explicitly modeled. For example,
ethnic fractionalism and dominance have been shown to be associated with economic
and political institutions (Easterly & Levine, 1997; Alesina, Baqir & Easterly, 1999;
Collier & Binswanger, 1999; (Rodrik, 2000). Elbadawi & Sambanis (2002) apply the
Rivers & Vuong (1988) technique for estimating a two-stage probit model on panel
data, and thereby are able to test for the endogenized relationships. They find
evidence of endogeneity with regard to a lagged index of political rights.
Other relationships that should be examined regard the relationship between
state strength and institutional consistency, refining the work of Benson & Kugler
(1998). The interaction between political stability and economic factors (Barro, 1990;
Przeworski et al., 2000) should be further investigated as this relationship relates to
the onset of civil war. The endogenous relationship between civil war and growth
should also be examined further, extending the work of Blomberg & Hess (2002) and
Sandler & Murdoch (2002).
19
3.2
Duration of Civil War
Few researchers have examined the duration of civil war. Almost all econometric
analyses have been devoted to model war onset. But what is most interesting is that
the factors that seem most important for explaining the onset of civil war are different
than the factors that affect the sustenance of war (Collier, Hoeffler & Söderbom,
2001). They find that contrary to their findings regarding the onset of war (Collier &
Hoeffler 2001), geographical features (mountains and forests), post 1980 conflicts,
and ethnic diversity are statistically associated with the duration of conflict. Similar to
their analysis of war onset, Collier, Hoeffler & Söderbom (2001) find that primary
commodity exports and economic opportunities are statistically significant.
Most duration analyses (and even in this regard we are only talking about a
handful of studies) have examined the effect of external intervention on the duration
of civil war. Regan (2000, 2002) and Elbadawi & Sambanis (2000) produce a set of
similar conclusions regarding the effects of intervention on civil war duration.
Elbadawi & Sambanis (2000) test an endogenized model regarding war duration and
the decision to intervene. They conclude that the net effect of external intervention is
to increase war duration; this relationship far outweighs the substantive effect of other
variables. Regan (2000, 2002) also examines the factors that determine successful
intervention. Success varies with respect to the intensity of the conflict. In general,
interventions involving a mixed economic and military component are more
successful. Interventions in ideological conflicts tend to be less successful. As of yet,
we lack the data to examine how the threat of potential intervention could influence
the strategic decision-making of a rebel leader thereby affecting the probability of war
onset.
4
4.1
Econometric Issues
Problems with binary time-series cross-sectional data
The quantitative analysis of civil wars must address a number of problems relating to
the characteristics of cross-national, cross-temporal intrastate conflict data (or conflict
panel data). The fundamental advantage of studying civil conflict through the use
conflict panel data is that the cross-sectional characteristics all for greater flexibility in
modeling differences across countries. Conflict data are based on the concept that
civil war or intrastate armed conflict is an event. Such analyses, therefore, constitute
20
some form of event analysis, regarding civil war onset, duration, and termination.
Conflict data tend to suffer from four fundamental problems, non-independence,
unmeasured heterogeneity (a general problem related to omitted variable bias),
endogeneity, and the rareness of the outcome variable. Different econometric
techniques are better at dealing with these problems than others.19 In this section these
data problems are discussed and the implications they have for econometric analysis.
Conflict data tend to be characterized by complex dependence structures. As
noted above, a country’s conflict history significantly affects the risk of civil conflict.
Obviously civil wars are temporally dependent. They also may be spatially dependent,
though this finding is less clear from the literature. Any econometric analysis of civil
conflict must account for this lack of independence across cases.
Unmeasured heterogeneity is a stickier problem for the econometric analysis
of civil conflict. Indeed, this is a particularly serious problem for all panel data.
Consider a panel dataset of N individuals (for most conflict datasets, an individual is a
country) and T annual observations for each individual (country), expressed with the
basic framework of the regression model, such that:
yit  i   ' xit   it ,
(i  1,..., N ; t  1,..., T ).
(1)
As such, yit is the dependent variable, armed conflict in country i in year t (typically a
binary variable, thereby requiring a ‘nonlinear transformation of the response of the
regression equation’).20 xit constitutes the vector of explanatory variables for country i
and year t, with K regressors not including the constant term. The unobservable effect
for country i is i, which is constant over time t and specific to a country (the crosssectional unit). A common presumption in econometric analysis of civil conflict is
that i =  for all i. This means that the value of i is the same for all countries, or
that the unobserved country specific effects are nonexistent or inconsequential.21
Measurement problems and model specification issues in econometric studies of civil
conflict guarantee that this assumption is violated. Specific countries certainly exhibit
unmodeled characteristics that make them more or less prone to civil war than other
19
See the special symposium in IO regarding the use of fixed effects vs. pooled time series models to
analyze international conflict (Green et al., 2001; Oneal & Russett, 2001; King, 2001).
20
See Agresti (1990: 82-83).
21
See Greene (1997: 615-642).
21
countries. In this regard, the problem of unmeasured heterogeneity is a special case of
omitted variable bias.
In a purely linear setting, unmeasured heterogeneity is not that big of a
problem, as long as the heterogeneity is not correlated with the principal explanatory
variables. But since civil conflict is typically modeled as a binary outcome (with
regard to the onset or settlement of conflict) and the estimation is nonlinear,
coefficients can be biased even if the unmeasured heterogeneity is unrelated to the
key explanatory variable.
Endogeneity is another problem. Most econometric studies of civil war have
examined the onset of armed conflict. They have not endogenized the effect of civil
war on other variables. Countries wracked by years of war will tend to be
characterized by a lack of economic development. Similarly, democratic institutions
are often weakened by civil conflict. This weakening in turn is often associated with
political change and instability. Blomberg & Hess (2002) address this particular issue,
but there are other issues of endogeneity as discussed above that need still to be
addressed.
Another inherent problem characterizing civil war data is the relative rareness
of this event. Consider the nature of conflict panel data, such as the COW civil war
dataset. The temporal span is 1816 to 1992, multiplying this by the hundreds of
countries in the international system each year, produces a tremendous number of
country years (the basic unit of analysis for many analyses). Indeed, in Collier &
Hoeffler (2001) only about seven percent of their observations are coded as having a
civil war in the period 1960-99. This problem is greatly alleviated with the Uppsala
data, with a threshold of 25 battle deaths as opposed to the COW 1,000 threshold.
Approximately 27% of all country years are in armed civil conflict in the 1946-2000
period (Gleditsch, et al., 2001).22 From these numbers we may conclude that the rare
events problem is much more serious for analysis of civil wars defined by the 1,000
death criterion. While civil war may be a somewhat rare event, armed civil conflict is
unfortunately not rare.23
22
Note that this figure and the figures that follow regard war occurrence and not war onset. The
numbers for war onset are lower.
23
The real problem with rare events comes into play when analysing interstate war or militarised
disputes, particularly with a dyadic (pairwise) framework.
22
In binary rare events data, such as civil war, not only is the number of
observations important, so is the rareness of the event. The basic problem is having a
number of countries in a panel that have no civil wars. This means that the countryspecific indicator variables corresponding to the all-zero countries perfectly predict
the zeroes in the outcome variable (no civil war) (King & Zeng, 2001; King, 2001).
Perfect prediction may seem like a good idea, but in this case it wreaks havoc on
many econometric models.
In Collier & Hoeffler’s short series, the rareness of civil war is a problem,
which they address by testing the King & Zeng (2000) rare events logit estimation.
The rare events problem can also be addressed by expanding the number of years
included in the data. By expanding the series, the number of all-zeroes for every
annual observation is approximately 90% of the countries in the COW data (19451992). By lowering the threshold of the dependent variable to 25 battle deaths
(thereby increasing the N), the Uppsala data indicates that about 73% of the cases are
all-zeroes. Expanding the temporal range of the dependent variable does not
completely solve the problem though. Many of the independent variables used in the
econometric analysis of civil conflict are simply not available before 1960. Efforts
need to be made to expand these series.
4.2
Estimation techniques
Pooled cross-sectional models are a common form of econometric analysis of civil
conflict. These models tend to rely on maximum likelihood estimators, probit, logit,
etc. with corrections for temporal dependence.24 The fundamental problem regarding
pooled time series analysis is that it assumes away unmeasured heterogeneity. Indeed
such models rely on a strong assumption of homogeneity, which is untenable.
Fixed effects models have also been applied to the study of civil war. Such
models are designed to account for fixed unobserved differences that stem from
unmeasured heterogeneity, by assuming that differences across units can be captured
in differences in the constant term. Thus in equation (1), each i is treated as an
24
Much of the quantitative analysis of civil war before 1995 tends to simply apply a simple logit or
probit model to the entire panel, thereby assuming independence across time and space. Beck in a
series of papers (Beck & Katz, 1995; Beck & Tucker, 1997; Beck, Katz & Tucker, 1998) discuss
methods for correcting this problem.
23
unknown parameter to be estimated. With the least squares dummy variable (LSDV)
model this is typically done by creating a dummy variable for each country, thereby
accounting for the unmeasured heterogeneity across units. The problem with fixed
effects models for analyzing civil conflict (especially as a binary variable) is that so
many units show no temporal variation on the dependent variables, hence having no
impact on the parameter estimates. The use of fixed effect model to attempt to solve
the problem of unmeasured heterogeneity results in a worse problem due to the
rareness of the event. Beck & Katz condemn the use of fixed effects models with
panels exhibiting the properties of civil conflict data, stating:
It is absurd to exclude over 90 percent of the cases from the analysis (or,
equivalently, to allow them in the analysis but not allow them to affect
the statistical results) and then conclude that some independent variable
like democracy has the opposite effect of what every sensible study has
shown…To include fixed effects in these analyses is never a good idea
(Beck & Katz, 2001: 490).25
Collier & Hoeffler (2001) compare several different estimations, including a
random effects model, a pooled logit with temporal dummies, rare events logit, and a
fixed effects estimation. The results or their analysis are robust across estimations,
except for the fixed effects model. Indeed, Collier & Hoeffler (2001) lost a number of
cases in their fixed effects test due to the lack of variation in the dependent variable
over time. The Uppsala data suffers less from the rare events problem in this regard,
since ‘only’ 83% of the countries are thrown out of the analysis, but even at this level
it seems that the fixed effects model as applied to a binary dependent variable is
problematic.
Random effects models as applied to conflict assume that individual (country)
specific constant terms are randomly distributed across the cross-sectional units. Such
an assumption works best if the panel data is a sample of cross-sectional units drawn
from a larger population. This is not the case for conflict data. In this regard, this may
be a rather heroic assumption for conflict analysis.
Unlike the fixed effects approach, random effects treatments may still suffer
from inconsistency due to omitted variables. The random effects approach treats the
individual (country specific) effects as uncorrelated with other regressors.26 The
25
Beck & Katz (2001) are discussing the analysis of militarized interstate disputes, but the argument is
perfectly applicable to civil conflict.
26
See Chamberlain (1978), Hausman & Taylor (1981), Hsiao (1986), and Balgati (1995).
24
Hausman test can be used to determine whether the individual effects are correlated
with the regressors. With a low Hausman test value, the random effects approach is
consistent and efficient. With a high Hausman test value, we are in trouble given the
problematic nature of fixed effect estimators.
To address the problem of rare events, King & Zeng (2001) have devised a
rare events logit estimator. Collier & Hoeffler (2001) find that this estimator produces
almost the same results as the pooled logit with time dummies and the random effects
test. By decreasing the ‘rareness’ of the event (i.e. lowering the threshold of what
constitutes conflict), the need to correct for rareness is reduced, actually it probably
made completely unnecessary.
To address a problem of endogeneity in their panel study Ebdawali &
Sambanis (2002) extend the Rivers & Vuong (1988) framework for dealing with the
potential endogeneity of continuous right-hand-side variables in probit models. This
means estimating a two-stage probit model, which accounts for the lagged time
structure and the endogeneity.
Hazard models have been applied to understanding the onset of civil war
(Hegre et al., 2001) and the duration of civil war (Collier, Hoeffler & Söderbom,
2001) and Fearon (2001). Hegre et al., (2001) utilize a calendar-time proportional
hazard, or Cox regression model (Cox, 1972) (Raknerud & Hegre, 1997). Since the
Cox regression model is semi-parametric, it is not necessary to make assumptions
about the shape of the baseline hazard function. One only needs to assume that the
two hazard functions are proportional over time.27 Raknerud & Hegre (1997) adapt
the Cox regression model to account for calendar time, in contrast to the typical
application of proportional hazard models, which is to look at time at risk. The Cox
regression method assumes that the hazard of an event (i.e. the onset of civil conflict)
c(t) for country c can be factored into a parametric function of time-dependent risk
factors and a non-parametric function of time itself, the baseline hazard:
27
Parametric hazard models such as those based on a Weibull, gamma, or other similar distributions
assume that these distributions reflect the data generating process. The advantage of such parametric
analysis is that if the assumption regarding the distribution holds, the inferences drawn from such
analysis will be more precise (Collett, 1994: 107). The Cox regression method trades off precision for
flexibility.
25

p

c (t )   (t ) exp   k X kc (t ) ,
 k 1

(2)
where  (t ) is the baseline hazard. X kc t  is an explanatory variable for country c,
which could be time-dependent. k is the regression coefficient. Note that t is
expressed in terms of calendar time – the number of days since the last exchange rate
regime shift. This differs from most survival models, in which t is an expression of
time at risk. This model imposes an exponential-regression distribution (exp(-t/alfa)),
on the time elapsed, with a constant hazard rate. Hegre et al. (2001) do not explicitly
account for unmeasured heterogeneity, observations are assumed to be independent
and the variability associated with each observation is determined by a single
dispersion factor, 2 or .28
Cox regression, proportional hazard models can be adapted to account for
unmeasured heterogeneity (Beck & Katz, 2001). One technique is a split population
model, in which some countries never are in civil war and others are at risk.29
Random effects and Bayesian hierarchical models are other alternatives.
Hazard models are well suited for duration analysis. Fearon (2001) determined
that the differences between the results from using a Cox proportional hazard and the
Weibull duration model were negligible. He does not do this, but one can account for
unmeasured heterogeneity by using a Weibull duration model with gamma
heterogeneity (Beck & Katz, 2001). Collier, Hoeffler & Söderbom (2001) account for
duration dependence with a piecewise exponential estimation of the baseline hazard
and control for unobserved country-specific heterogeneity with a random effect i
(Heckman & Singer, 1984).
Civil conflict panel data is characterized by a set of data problems. Recent
innovations in econometric estimation techniques allow conflict researchers to
address these data problems and account for them in their analyses.
28
29
See McCullagh & Nelder (1989: 432).
This is somewhat analogous to criminal recidivists and those who will never return to prison
(Schmidt & Witte, 1989).
26
5
A data wish-list and conclusion
‘Good data beats better methods every time’ (King, 2001: 505). Conflict researchers
seem to understand this maxim rather well. In June 2001, the Department of Peace
and Conflict Research at the University of Uppsala, the International Peace Research
Institute, Oslo (PRIO), and the World Bank co-sponsored a conference devoted
exclusively to the subject of conflict data, ‘Identifying Wars: Systematic Conflict
Research and Its Utility in Conflict Resolution and Prevention’. The objective of the
conference was to compare definitions and methodology across the various data
collections projects, in order to improve the procedures and make the data more useful
for the study of internal as well as international conflicts. Indeed, the genesis of this
paper took place at this conference.
One of the themes of the conference was what data would you put on your
wish list? In other words, what data that is currently unavailable would you as a
researcher want for an econometric analysis of civil war? To better understand the
actual nature of the rebellion, it would be very valuable to obtain information
regarding the size of rebel armies. Along these lines, information on rebel financial
flows, and military capability estimates would be valuable. Indeed, better military
expenditure data by governments would also be useful. Comparable information
regarding legal and illegal small arms flows would also be valuable.30 Many
researchers could make good use of a data series on the price of various types of
military equipment (e.g. a surface to air missile or a Kalishnikov), particularly if this
price information were broken down regionally.
To better understand the patterns of rebel recruitment, better statistics
regarding the price of labor would be useful. Better information on demographic age
breakdowns back to the 1800s would be very valuable. Better information regarding
the location of rebel base camps is another potentially valuable variable.
As for the dependent variable itself, better data on casualties would be
particularly useful. The Uppsala dataset’s annual estimation of battle deaths is a step
in the right direction. I would like to see a dataset that reports upper and lower bounds
for the number of casualties. This would provide both an idea of the uncertainty
involved in the estimate, and an indication of a more precise figure. It would also be
30
The NISAT (Norwegian Initiative on Small Arms Transfers) project at PRIO is working on doing
this (http://www.nisat.org/).
27
more a more inclusive dataset. If the Uppsala project thinks the lower bound is 10 and
the upper bound is 100, they will not list it as a minor conflict because they have an
idea that it is better to err on that side. However, it would be more useful for data
users to decide for themselves on which side it is best to err, given the problem at
hand. Also an assessment of civilian and combatant deaths would be particularly
valuable. If these casualty figures were reported separately with a similar confidence
bound, the data consumer would be able to make his or her own decisions as to how
to use the data. But I could go on and on with my wishes. In the meantime, the
conflict community is progressing with better and better datasets regarding civil
conflict and relevant explanatory variables.
Recent quantitative research regarding civil war has fundamentally improved
our understanding of the onset and duration of intrastate conflict. Through the use of a
variety of econometric estimation techniques, four fundamental data issues have been
addressed to various degrees of success. Most studies have dealt with the problem of
non-independence rather well, particularly with regard to temporal dependence.
Rareness of the outcome variable (civil war) is not as big of a problem as it is for
dyadic analysis of interstate conflict and using datasets. Moreover, Collier & Hoeffler
(2001) demonstrate that accounting for exceptionality makes no substantial difference
to their results. We can conclude that rareness is not a worrisome problem.
Unmeasured heterogeneity and omitted variable bias are another issue that requires
more work. Some of these problems can be addressed with better estimation
techniques. Efforts to gather better data also help address the issue of omitted variable
bias. Endogeneity is a bigger issue. Clearly this is a direction to be taken in future
research. Some of the big debates in the quantitative literature (i.e. the role of
ethnicity, natural resources, and governance structures) exhibit complex endogenized
relations with regard to each other and with respect to civil war. Theories need to be
developed which help us better understand the nature of these relationships. Finally
and most fundamentally, we in the civil war research community must work on
defining what we are studying. What characteristics define a civil war or a civil
conflict? When does a war start? When does it end? When is organized violence a
civil conflict (or civil war) and when is it some other phenomenon such as interstate
war, genocide, or criminal activity? Clarity with regards to these issues will us better
understand where we have consensus and where we do not with regard to the causes
of civil war and peace.
28
29
6
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