APPROVED: Steven C. Poe, Major Professor T. David Mason, Minor Professor J. Michael Greig, Minor Professor Steven Forde, Director of Graduate Studies James Meernik, Chair of the Department of

Political Science Sandra L. Terrell, Dean of the Robert B.

Toulouse School of Graduate Studies

WEAK STATES, HUMAN RIGHTS VIOLATIONS,

AND THE OUTBREAK OF CIVIL WAR

Nicolas Rost

Thesis Prepared for the Degree of

MASTER OF SCIENCE

UNIVERSITY OF NORTH TEXAS

May 2005

Rost, Nicolas, Weak states, human rights violations, and the outbreak of civil

war. Master of Science (Political Science), May 2005, 77 pp, 4 tables, 3 illustrations,

references, 106 titles.

In recent years, explanations for the occurrence of civil war have mainly

emphasized state weakness as providing an opportunity for greed-based rebellions. Yet,

this explanation leaves many questions open, as it cannot distinguish between weak

states that do and those that do not experience civil war. In this paper, I argue that

abuses of personal integrity rights, committed or sponsored by the government, provide

this missing link. The theory is illustrated and formalized in a game-theoretic model and

then tested empirically, building on earlier work by Fearon and Laitin (2003a) and

Sambanis (2004). The results show that repression is highly significant in both statistical

and substantive terms. According to one model, the probability of civil war onset

increases by a factor of almost 16 in highly repressive countries compared to countries

with no repression. Further robustness tests across alternative civil war lists largely

confirm the importance of human rights abuses in explaining the occurrence of civil

war.

ii

ACKNOWLEDGEMENTS

I would first and foremost like to thank my committee chair Dr. Steven C. Poe

and my committee members Dr. T. David Mason and Dr. J. Michael Greig for their

mentoring with this thesis, but also with other papers during the past two years. Other

professors have offered valuable advice, especially Dr. Emily Clough. I would also like to

thank my fellow students, especially Mehmet Gurses and my office mates Chelsea

Brown and Geoff Dancy. Cece Hannah, Jerilyn Doss, and Lisa Blakeley have always

been supportive of me. I would like to thank those who have supported me financially

during the past two years: My parents, the UNT Department of Political Science, the

Fulbright Commission, International Studies Quarterly, the UNT Graduate Student

Council, and the Robert B. Toulouse School of Graduate Studies. Many thanks to my

friends here and in Germany and my family for their listening, comments, and

encouragements. Finally, my gratitude goes to Savera Kashmiri for her support

throughout the process of writing this thesis.

iii

TABLE OF CONTENTS

Page ACKNOWLEDGEMENTS...........................................................................................ii LIST OF TABLES ...................................................................................................iv LIST OF ILLUSTRATIONS ...................................................................................... v INTRODUCTION ................................................................................................... 1 LITERATURE & THEORY........................................................................................ 6

A. The Repression-Rebellion Link............................................................ 6

B. Democracy and Repression ...............................................................12

C. Weak States and Civil War Onset.......................................................17

i) Economic and Demographic Aspects of State Weakness ..17

ii) Geographic Aspects of State Weakness ..........................19

iii) Potential War Spoils......................................................19

iv) Cultural Factors ............................................................20 A GAME-THEORETICAL MODEL.............................................................................22 EMPIRICAL TESTS ...............................................................................................33

A. The Repression-Rebellion Model ........................................................33

i) The Dependent Variable................................................33

ii) Independent Variables: Repression and Democracy.........34

iii) Other Independent Variables.........................................39

B. Results............................................................................................41

C. Substantial Interpretation and Simulations .........................................46

i) The Substantial Impacts of the Independent Variables ....47

ii) Hypothetical Cases: The Four Subgames ........................50

D. Robustness Tests .............................................................................52 CONCLUSION ......................................................................................................56

APPENDIX...........................................................................................................66

REFERENCES.......................................................................................................70

iv

LIST OF TABLES

Page 1. Logit regression on the probability of civil war onset .....................................61 2. Simulated effects of statistically significant variables .....................................63 3. Logit regression on the probability of civil war onset - robustness tests ..........64 4. Logit regression on the probability of civil war onset - overall repression replaced

by political imprisonment............................................................................65

v

LIST OF ILLUSTRATIONS

Page

1. The onset of civil war in an extensive game form .........................................60 2. Civil war onset and the (lagged) level of democracy......................................62 3. Civil war onset and the (lagged and modified) PTS .......................................62

1

I N T R O D U C T I O N

Why do civil wars break out in some countries but not in others? One of the

difficulties in explaining the occurrence of civil war lies in the collective action problem –

or ‘rebel’s dilemma’ (Lichbach 1995) – that potential rebels face. In recent years, greed-

based explanations of civil war onset have received much academic attention. This class

of explanations assumes that greedy rebels act as soon as the political opportunity

structure allows it, that is, mainly in weak states. The benefits of waging a civil war

represent high selective incentives that make it an individually rational choice to initiate

an insurgency, as long as the state is so weak that it cannot easily defeat an

insurgency. Governments often use a repressive strategy in order to deter violent

rebellions from occurring, providing a motivation for the government and government-

sponsored actors to commit acts of incredible cruelty.

In this study, I argue that government-sponsored abuses of personal integrity

rights increase the probability of a civil war starting, a factor that has been overlooked

in much of the literature that focuses on political opportunity structures. Government

repression often leads to the exact opposite of what it is aimed for: It significantly

increases the probability of civil war onset rather than deterring potential rebels. While

political entrepreneurs – potential rebel elites – react to political opportunities to realize

war spoils, these selective incentives are not available to nonelites that might join the

rebels, at least not to the same extent. Especially at the beginning of a civil war, rebel

groups rarely have the means to compensate all their members. Nonelites, in contrast,

will compare the risks of participating to the costs of not participating. The government,

2

by indiscriminately abusing personal integrity rights, puts a cost on nonelites even if

they do not participate. When government repression and state weakness coexist is civil

war an especially probable outcome.

The theoretical link between state terror and the occurrence of violent rebellion

provides an important part of the explanation of when and why civil wars break out.

First, on an individual level, the repression-rebellion link contributes to an

understanding of the reasons a country’s nonelites might have to join an emerging

rebel movement. The goods a successful rebellion is expected to produce often are

largely public goods. Free-riding is then the optimal choice, as the risks of participating

are avoided, while the public good is available to everyone. Rebel movements can

promise or provide individual incentives to a selected few, but not to all its active and

passive supporters. Once the government1 starts to indiscriminately violate basic human

rights, simple nonelites – whether they oppose the government or not – are faced with

a threat, and free-riding is no longer costless. As the risk faced by simple nonelites

becomes similar to the danger they would face by joining the rebels, the individual

approaches indifference between the two choices (Mason and Krane 1989). In this

study, I conceptualize repression to be one of the most important factors in increasing

the probability of civil war onset. Yet, individuals may have other reasons to join or

support a rebel movement. These include feelings of vengeance or anger, for example,

as well as ideological commitments, social expectations, or low opportunity costs.

Furthermore, rebel organizations exercising control over an area will often try to coerce 1 Throughout this paper, I make the simplifying assumption of the existence of a government and one rebel group fighting each other. Some actual cases, however (like Somalia in the 1990s), are much more complicated.

3

nonelites into cooperation, though they can hardly implement perfect control (Gates

2002).

Second, the repression-rebellion link also provides an explanation of civil war

onset at the country level. Weak states have consistently been found to be more prone

to experience the outbreak of a civil war. Still, governments in weak states face a

strategic choice and weak states are far more common in time and space than are

violent insurgencies. This indicates that even in weak states, armed rebellions may be

avoided. Governments can opt for working towards a peaceful settlement of emerging

conflicts and try to prevent conflicts from arising outside the established political

system. This includes avoiding too much of economic inequality, institutionalizing

democracy, and respecting human rights. Though being weak, governments can try to

accommodate the opposition, and include all crucial contenders in the polity (Tilly

1978). Alternatively, they can use a strategy of repression, hoping to deter potential

rebels. This strategy, however, is dangerous and easily boomerangs, increasing the

probability of civil war instead of deterring it. The characteristics of a weak state –

economic and political discrimination, corruption, the failure of the government to

provide basic services, ‘bad governance’ – may provide both the ideological

legitimization and the practical opportunity for starting a rebellion (e.g., Collier and

Hoeffler 2001; Fearon and Laitin 2003a). But whether enough individuals join and

support the emerging insurgency depends on their alternative options: When

indiscriminate government terror imposes costs on people who lead a ‘normal’ life, their

participating becomes more likely. In other words, state weakness provides the political

4

opportunity for potential rebel leaders to realize spoils from an insurgency; government

repression affects individual preference structures in a way that makes participation a

likely choice for at least parts of society.2

If repression is such a bad choice, then why do governments so often go this

way? Civil wars are distinguished from interstate conflicts by a power asymmetry

between government and rebels (Zartman 1995). Rebel organizations therefore employ

a military strategy of hit-and-run, waging a guerilla war. By doing so, they have to rely

on popular support, and government forces then try to undercut this support by

“draining the sea” (Valentino et al. 2004; Azam and Hoeffler 2002). Where the rebels

hide in the mountains and the government chooses to use a repressive strategy,

civilians become easy and likely targets.

This explains the situation during a civil war, but why do governments repress

before a civil war starts? The more power is concentrated in a political system, the

more contending groups are left outside of the polity (Tilly 1978). In every authoritarian

regime groups are excluded from the political system and even in democracies, some

groups may be barred from power and denied representation. When governments do

not want to include contending groups and do not have other means of accommodating

them (like a high level of economic development), they will resort to repression, even at

the risk of civil war.

In the following section, I discuss the literature on civil wars, state repression,

and democracy, highlighting the theoretical linkages that have been put forward. I

2 For a discussion of the opportunity/willingness framework, see Most and Starr (1989).

5

combine arguments on greed and grievance and collective action problems into a

theoretical framework that includes state weakness and government repression to

explain potential rebel leaders’ and nonelites’ decisions to rebel. I separately discuss the

links between civil war and repression, the political system, and state weakness,

respectively. In section 2, this theory is then formulated in game-theoretic terms. Based

on the hypotheses of the theoretical framework and the comparative statics of the

game, an empirical model is constructed to test the theoretical propositions (section 3).

The results, which mostly support the theory, are interpreted, followed by a discussion

of their possible practical implications and ideas for further research.

6

L I T E R A T U R E A N D T H E O R Y

This section is composed of three sections, discussing the three aspects that

increase the probability of civil war onset: repression, characteristics of the political

system, and state weakness. I review the literature relevant to each section and

generate testable hypotheses to describe the relationship between civil war and

repression, the political system, and state weakness, respectively. Whereas state

weakness provides an opportunity for potential rebel elites to realize considerable war

spoils, nonelites are only willing to join if their costs of not joining are high, as they are

under repressive regimes. In section 2, these three parts are tied together in a game-

theoretical model, which allows distinguishing between the effects of repression in

strong and in weak states.

A. The Repression-Rebellion Link

In the past two decades, quantitative human rights research has proliferated

considerably. Typically, the vast amount of these studies has tried to explain under

what conditions human rights violations occur. Six factors have consistently been found

to impact the level of violations of personal integrity rights (Poe and Tate 1994; Poe et

al. 1999): past repression (Davenport 1995, 1996a; Richards et al. 2001), democracy

(Henderson 1991; Fein 1995; Davenport 1995, 2004; Zanger 2000; Regan and

Henderson 2002; Harff 2003; Davenport and Armstrong 2004), the level of economic

development (Mitchell and McCormick 1988; Carey 2004), population size (Henderson

1993; Carey 2004), international war, and civil war (Krain 1997; Zanger 2000; Harff

7

2003).3 The effects of all these factors, except for past repression, have been

interpreted in a theoretical framework of government strength and the perceived threat

from the political opposition (Poe 2004). In this ‘strength/threat’ framework, the

decision to violate human rights is mainly perceived as a reaction to threats posed to

the government’s legitimacy. A number of empirical studies have confirmed that

repression tends to increase when governments are faced with violent protest (Pion-

Berlin and Lopez 1991; Davenport 1995; Gartner and Regan 1996; Regan and

Henderson 2002; Carey 2004). Repression, however, often has the opposite effect of

provoking violent conflict, rather than deterring it.4

What these studies generally neglect is that protests of any form might be a

reaction to state terror. Numerous studies in the civil war literature have found that

state suppression of political rights and civil liberties, instead of deterring rebellions,

actually helps to provoke uprisings, at least at intermediate levels (Gurr 1970; Tilly

1978; Muller 1985; Muller and Seligson 1987; Muller and Weede 1990; Boswell and

Dixon 1990; Schock 1996). Studies focusing on ethnic rebellion have confirmed this

relationship (Gurr and Moore 1997). Discrimination, another form of repression, has a

comparable effect: The State Failure project found that countries with deliberate ethnic

discrimination are 13 times more likely to experience ethnic civil war as compared to

3 There has been some research on the influence of international treaties (e.g., Keith 1999; Hathaway 2002), economic aid (e.g., Regan 1995), international trade and FDI (e.g., Apodaca 2001), constitutional provisions for human rights (e.g., Davenport 1996b; Keith 2002; Keith and Poe 2004), and ethnic diversity (e.g., Walker and Poe 2002), but findings are rather mixed. 4 Most of these findings, as well as this study, are based on a subset of human rights, the right to personal integrity, which prohibits political imprisonment, torture, ‘disappearances,’ and killings. Some quantitative studies on other subsets of international human rights have been conducted (e.g., Poe et al. 1997; Apodaca 1998), but mostly the discourse has been theoretical or based on case studies.

8

countries with no or little discrimination (Goldstone et al. 2000, 35).5 Some of these

studies have found that ‘semirepressive’ regimes are most civil war prone. The question

is whether government repression provokes or deters violent rebellion. Lichbach (1987)

argues that opposition movements respond to government repression by switching to

alternative strategies. Gupta et al. (1993) find that the effects of government sanctions

vary with the regime type of the country (see also Moore 1998).

Repression, in most of these studies, is either operationalized by using Gastil’s

Freedom House indices of political rights or civil liberties, or by measuring government

sanctions. In this study, I define repression very differently, as human rights abuses

committed or sponsored by the government.6 As I explain below, this latter definition of

government repression has a different theoretical impact on civil war onset, operating

through different causal mechanisms. It further allows a more clear-cut distinction

between democracy and repression (note that the Freedom House indices have

oftentimes been used as indicators of democracy as well). Whereas the citizens in an

authoritarian country may collectively prefer a more open and participatory political

system, it becomes individually rational for nonelites suffering from a repressive regime

to engage in violent rebellion.

The literature on inequality and relative deprivation (e.g., Davies 1962; Gurr

1968; Muller and Seligson 1987; Midlarsky 1988) assumes that economic grievances

lead to violent domestic conflict. The problem with this argument, as pointed out by

5 Fearon and Laitin (2003, 85), on the other hand, find no such effect. 6 My measure of human rights abuses, the modified Political Terror Scale, and Freedom House’s measures of political rights and civil liberties are correlated with a factor of .50 and .56, respectively. From here on, ‘repression’ refers to government-sponsored abuses of personal integrity rights.

9

many scholars, is to explain how the collective action problem is overcome. A higher

average income might be desirable to many, but most prefer to free-ride instead of

shouldering the costs of achieving such a change. While low income reduces the

opportunity costs of rebelling, indiscriminate government repression places costs on

nonelites, even if they do not participate. As a consequence, repression makes it

individually and not only collectively more desirable to join or support the rebels.

On the individual level, Mason and Krane (1989) develop a rational choice model

that lays out how repression creates a conflict spiral and drives civilians into joining the

rebels. They thereby provide an explanation of one important means of overcoming the

collective action problem political movements are faced with (Olson 1965). Other

solutions offered in the literature include selective incentives (Olson 1965; Oliver 1980;

Moore 1995) market, community, contract, and hierarchy (Lichbach 1995, for

community see also Taylor 1988), psychological and reputational incentives (Chong

1991), leadership (Van Belle 1996), and framing processes (Berejikian 1992). Mason

and Krane’s (1989) explanation differs from the ones above in that the incentive to join

the insurgents is not created within the group but externally by the government.

Here, it becomes important to distinguish between potential rebel elites who

initiate an insurgency and nonelites who are willing to join the guerrilla or not (Mason

and Krane 1989, 176). The elites can expect to gain considerable benefits from a

successful rebellion, political or economic. These selective war spoils, however, can

rarely be extended to the majority of participants so that nonelites7 can only realize

7 The rebel elites are nonelites too, of course. Here, I use the word ‘nonelites’ to refer to all habitants of a country except government agents and rebel elites.

10

small material benefits and possibly psychological or reputational ones. Henceforth,

nonelites are willing to join or support the rebels if their costs of not joining are high,

i.e. if the government indiscriminately commits human rights violations. Indiscriminately

does not necessarily mean that the whole population is affected, but merely that the

government does not distinguish between actual rebels and nonelites from, for

example, the same region or the same ethnic, religious, socio-economic, or political

group. Nonelites would prefer to stay out of the conflict (Mason and Krane 1989, 176),

but government repression drives them into rebellion.8

Hypothesis 1: The more repression a government exerts, the higher the probability of a civil war to break out.

Some might question whether repression increases the probability of all kinds of

civil wars. Collier and Hoeffler (2000) distinguish between greed- and grievance-based

civil wars which raises the concern of whether the probability of greed-based civil wars

increases to the same extent as the probability of grievance-based civil wars in weak

states. Empirically, though, rebel movements often shift from one type to the other or

combine aspects of both. The rebel leadership might be driven by greed, whereas the

rank and file members are motivated by grievances. In greed-based civil wars,

insurgents seek to profit from the fighting while it is still ongoing, for example by

trafficking in contraband (Fearon 2004) or looting. This type of insurgency, if victorious,

generates additional amounts of selective incentives, at least for the elites, making it

easier to overcome the rebel’s dilemma. Greed-based rebellions might be seen as

business or investment (Collier et al. 2004). 8 This argument should not be interpreted as a normative excuse or legitimization for violent behavior on an individual level.

11

In grievance-based insurgencies, on the other hand, the civil war is only

interpreted as an investment (Collier et al. 2004). Although the rebels may have to rely

on creating selective incentive structures to attract members, the emphasis is put on

producing a collective good (e.g., an independent country, a new government). Many

rebellions start out as grievance-based insurgencies, but then turn into greed-based

ones. In Peru, for instance, the Sendero Luminoso was an ideological movement in the

beginning, until the rebels discovered the potential profits from growing and smuggling

drugs. Fearon (2004) finds that if the rebels use contraband as a financial source, the

duration of civil wars increases considerably. As a result, the distinction between greed-

and grievance-based civil wars should be more important for studies on war duration

than studies on onset.

Further, I argue that greed should play a role for potential rebel elites, more

than for the nonelites that join them. Elites can usually realize substantial spoils in the

war, and may react to openings in the political opportunity structure by initiating

insurgencies. Nonelites, on the other hand, are willing to join only if the danger of

participating is lower than the costs of not participating. Participants in a rebellion face

the dangers of death or lifelong imprisonment, but under repressive regimes, non-

participants as well face similar risks of being arbitrarily imprisoned, tortured,

‘disappeared,’ or killed. Even if an insurgency is purely greed-based from the beginning,

nonelites will only join if alternative payoffs from not joining are low, as the rebels

cannot provide sizable incentives for too many members. While few civil wars start off

12

as being greed-based, I expect repression to play an important role even in these rare

cases.

A potential rebel elite is of course aware of this phenomenon and will consider

two aspects before making the decision to start a civil war. First, they will evaluate the

probability of success in their struggle, or at least, the probability of surviving long

enough to realize some spoils from the insurgency. Zartman (1995, 9) quotes Kissinger

stating that the “guerilla wins if he does not lose; the conventional army loses if it does

not win.” According to this line of thought, potential rebel leaders do not necessarily

have to expect to win outright before mounting an insurgency, but they do have to

believe that they will not lose in order to realize some spoils. For this calculation, mainly

factors of state strength play a role, as in Fearon and Laitin’s (2003a) argument. But

there is a second, if related, factor: A potential rebel elite will have to reflect on

whether they can gain enough support from the population, both by attracting new

members and receiving enough active and passive support from the population. One

main reason for whether the rebels will succeed is the level of government-sanctioned

terror that nonelites face, i.e. the costs of not joining or supporting the rebels.

B. Democracy and Repression

In this section, I develop theoretical expectations on the effects of democracy on

civil war onset, once repression is controlled. While repression is usually reduced as the

level of democracy increases (Henderson 1993; Poe and Tate 1994; Fein 1995;

Davenport 1995, 2004; Poe et al. 1999; Zanger 2000; Regan and Henderson 2002;

13

Harff 2003; Davenport and Armstrong 2004), democracy and non-repressiveness are

conceptually and empirically distinct phenomena.

Many of the studies that explore whether repression increases the probability of

rebellion use the Freedom House 7-point measure of civil liberties to model repression

(e.g., Muller 1985; Muller and Seligson 1987; Muller and Weede 1990; Boswell and

Dixon 1990; Schock 1996), where ‘1’ represents general respect for civil liberties, and

‘7’ wide-spread suppression of these liberties. Political rights refer to the electoral

process, political pluralism and participation, and the functioning of the government.

Civil liberties refer to the freedom of expression and belief, associational and

organizational rights, rule of law, personal autonomy and individual rights.9 The

problems with this measure are obvious: Civil liberties may be suppressed without

widespread violations of the most basic human rights. Singapore in 1985 and 1986, for

instance, was assigned a ‘5’ on the civil liberties index, but almost no violations of

personal integrity rights occurred.

The relationship between repression and democracy has received much scholarly

attention. While Poe and Tate (1994) found a linear influence (the more democratic a

country is the fewer human rights abuses), Fein’s (1995) ‘More Murder in the Middle’

hypothesis posits that more violations should occur in so-called anocracies. These

countries that are neither fully consolidated democracies nor fully established

autocracies, are expected to experience more repression because governments are too

9 http://www.freedomhouse.org/research/freeworld/2003/methodology.htm, accessed March 2005.

14

weak to effectively deter the opposition and, at the same time, they do not have the

means to politically or economically accommodate opposition demands.

Davenport and Armstrong (2004) explain that the relationship between

democracy and repression is even more complex than Fein’s inverted u-curve. They

develop a threshold-model and find that the level of democracy has no impact on the

level of repression up to a relatively high threshold. Only beyond that level has

democracy an alleviating impact on human rights violations. In other words, there are

about as many authoritarian states and anocracies that repress human rights as there

are that do not. Moreover, fully developed democracy seems to be a sufficient condition

for avoiding very high levels of repression. In their sample, not a single fully developed

democracy experienced the highest levels of repression (there has been one case since,

Israel in 2002 and 2003), and only a couple show scores of intermediate to high levels

of human rights abuses.

The implications of this complex relationship between democracy and repression

for the study of civil war onset are potentially important. Fearon and Laitin (2003a)

have not found democracy to have a significant impact on the likelihood of a civil war to

break out, though a dichotomous measure of anocracy was significant, as well as a

measure of political instability. Sambanis (2004) does not include a democracy

measure, but only the anocracy variable in his models, and finds it of only moderate

influence.

15

Since authoritarian regimes can choose whether or not to repress, it is important

to control for both repression and democracy in a multivariate model.10 While

repression is expected to impact the probability of civil war onset independently of the

level of democracy for the reasons laid out above – the repression-rebellion link – I also

expect democracy to significantly decrease this probability. Democracies do not oppress

political conflicts but provide rules to peacefully solve these conflicts within the political

system. This makes it easier to find compromise solutions and to share political power

across different ethnic or ideological groups in a country. Thereby, fewer groups are left

outside the polity and have an incentive to violently claim inclusion. A higher political

adaptability of democracies may also make it easier to solve conflicts nonviolently

(Lacina 2005). In addition to democratic institutions, democratic norms exclude violent

rebellion as a legitimate option.11

Hypothesis 2a: Once repression is controlled, democracy will exert a significant impact on the probability of a civil war to break out, decreasing this probability.

Hegre et al. (2001) find support for a domestic democratic peace, but they find

that both anocracies and politically unstable countries are more conflict prone. Similarly,

Fearon and Laitin (2003a) and Sambanis (2004) find significant effects for anocracy and

political instability in some of their models. Anocracies are more civil war prone because

neither are democratic institutions fully established, nor has an authoritarian regime

consolidated its power. Anocracies have oftentimes unstable, unconsolidated political

systems. Several groups may compete for control of the government while the rules of

10 Or, in Muller and Seligson’s (1987, 432) and Muller and Weede’s (1990, 647) words, for both structural and behavioral repression. 11 However, I cannot empirically discriminate between these alternative explanations.

16

the game have not been institutionalized. Similarly, political instability delegitimizes the

ones in power and shows that they may be vulnerable.

The same should be especially true in newly independent states. Sambanis

(2004, 837), though, criticizes the use of the ‘new states’ variable in Fearon and Laitin’s

(2003a) study, since it might be correlated with some other variables, especially

instability, anocracy, income, and ethnic fractionalization. The state weakness

argument, however, leads to the expectation that new states are more war prone. In

Fearon and Laitin’s (2003a, 84) Model 1, ‘new state’ was the substantially and

statistically most significant dichotomous variable. Further, new states have been found

to be more likely to experience other forms of political instability, including interstate

disputes (see Brecher et al. 2000; Carment 1993) and genocides and politicides (Krain

1997, 346; Harff 2003, 62).

Finally, military regimes are expected to increase the probability of civil war

onset. Military regimes are defined as any regime with a military person as the chief

executive that comes into power following a successful military coup (Madani 1992).

Military regimes can be expected to represent politically weak governments. They have

installed themselves by staging a coup and rarely enjoy widespread popular support.

Further, the very fact that a coup was staged indicates that government structures

were weak in the first place.

Hypothesis 2b: Civil wars are more likely to break out in anocracies.

Hypothesis 2c: Countries with a recent history of political instability show a higher probability of experiencing a civil war.

Hypothesis 2d: Civil wars are more likely to break out in countries governed by military regimes.

17

Hypothesis 2e: Civil wars are more likely to break out in newly independent countries.

These four hypotheses lead to the next section which discusses state weakness:

All four factors are signs of political state weakness, while in the next section I discuss

economic, demographic, and geographic aspects of state weakness.

C. Weak States And Civil War Onset

State weakness has long been associated with the occurrence of civil war (e.g.,

Skocpol 1979). State weakness produces the openings in the political opportunity

structure that can be seized upon by initiating an insurgency. State strength, in this

theoretical concept, mainly effects the calculations of potential rebel leaders. They see a

greater chance for realizing benefits from an insurgency the weaker the state is,

regardless of whether they perceive rebellion as investment or business. If the state is

strong enough, an insurgency will be crushed soon, leaving no room for any benefits to

materialize. State weakness thus creates an opportunity for rebellion (Collier and

Hoeffler 2001), but it does not necessary create the willingness of nonelites to join or

support the movement.

i) Economic and Demographic Aspects of State Weakness

In Sambanis’ (2004, Table 4) study, the only two variables that show a

statistically significant impact across 12 different lists of civil wars, 1960-1993, are GDP

18

per capita and the log of population size (see also Elbadawi and Sambanis 2002).12 Both

a very low level of economic development and a large population produce relatively

weak states and make it easier for the political opposition to organize and start in a

violent rebellion.13 A low economic standing makes it more complicated for a

government that is not willing to share power to accommodate contending groups.14 In

addition, Mason and Krane (1989) argue that governments that are neither willing to

launch redistributive programs to accommodate opposition demands, nor have the

economic resources to do so, are likely to switch to a repressive strategy. A large

population size, on the other hands, makes it harder for governments to control the

whole country. This also increases the probability of existing contending groups that

feel politically or economically underrepresented. In addition, both these conditions

have consistently been found to be associated with relatively high levels of repression,

in turn increasing the probability of a civil war (Mitchell and McCormick 1988;

Henderson 1993; Poe and Tate 1994; Poe et al. 1999; Carey 2004).

Hypothesis 3a: The level of economic development is negatively correlated with the probability of a civil war breaking out.

Hypothesis 3b: A country’s population size is positively correlated with the probability of a civil war breaking out.

12 With one exception: population size was not significant with Regan’s list of civil conflicts. Both per capita income and population size, as well as all other independent variables in Sambanis’ models, are lagged by one year. Both are also significant over three different model specifications, four different civil war lists, and the 1945-1999 period. 13 Sambanis (2004, 844) argues that the results for population size might be an artifact of the high absolute threshold of deaths used to code civil wars. Indeed, with Regan’s civil conflict data, where a lower threshold of 200 deaths is used, the coefficient for population size is not significant. However, the state strength argument provides some reasons to theoretically expect more civil wars in larger countries, as discussed below. 14 Fearon and Laitin (2003, 76) use the level of economic development also as a proxy for the quality of counterinsurgency measures. They contend that “weak local policing or inept and corrupt counterinsurgency practices […] often include a propensity for brutal and indiscriminate retaliation that helps drive noncombatant locals into rebel forces.”

19

The level of economic development or the average income also impacts the

opportunity costs of participating in an upheaval. The higher the opportunity costs, the

less likely nonelites will be to join or support the insurgents. It becomes thereby harder

for rebel elites to act as political entrepreneurs and to recruit followers and attract

supporters. As potential rebel elites are aware of this difficulty, they are less likely to

initiate a rebellion.

ii) Geographic Aspects of State Weakness

In addition to economic and demographic aspects, geographic aspects of a

country may contribute to state weakness. Specifically, rough terrain makes it easier for

rebels to hide from government troops, to wage a guerilla style war (Collier and

Hoeffler 2001; Fearon and Laitin 2003a), and to traffic contraband.

Hypothesis 3c: Countries with rough terrain will show a higher probability of experiencing a civil war.

iii) Potential War Spoils

Fearon and Laitin (2003a) find that civil wars are more likely to start in states

with a high amount of oil exports. Oil exports are not as much a sign of state weakness

as they represent potential spoils for the rebels should they succeed in overthrowing

the government or creating their own state in an oil rich area. Oil fields can usually not

be exploited by the insurgents during a civil war. Exploitation requires exalted

investment and foreign companies will be turned away by the political and economic

instability of that country. The government that controls oil fields, on the other hand, is

20

in a privileged situation as it can easily exclude large parts of the population from the

economic gains. While oil revenues may encourage rent-seeking and corruption among

government elites, these revenues can also be used to battle the insurgents. If the

rebels succeed, however, large benefits from the oil production materialize. This

increases the incentives for potential rebel leaders to stage a rebellion, but not

necessarily for nonelites, as they cannot expect to receive much of these benefits.

Accordingly, I expect the following hypothesis to hold true.

Hypothesis 3d: Oil exporting states will show a higher probability of experiencing a civil war.

iv) Cultural Factors

While neither Fearon and Laitin (2003a) nor Sambanis (2004) find much

evidence for an increasing civil war probability with rising levels of ethnic and religious

fractionalization, it is important to control for these cultural factors, as many civil wars

are fought over ethnic or religious issues (Kaufmann 1996). Even if ethnicity or religion

is not the primary cause, both government and opposition elites oftentimes

instrumentalize and exploit, or even create, such cleavages (Horowitz 1985; Kaufman

1996). Ethnic and religious cleavages also make it easier for rebel leaders to recruit new

members and retain them as they can concentrate on specific groups in society (Gates

2001). Lake and Rothchild (1996) suggest that ethnic wars arise from information

failures and problems of credible commitment, which should be more likely to occur in

weak states. Fearon and Laitin (1996), however, note that ethnic cooperation occurs far

more often than ethnic conflict; Mueller (2000) even questions the viability of ethnic

21

explanations for civil war. Ethnic and religious fractionalization are included in the

empirical model to test the following two hypotheses:

Hypothesis 3e: Countries with a high level of ethnic fractionalization will show a higher probability of experiencing a civil war.

Hypothesis 3f: Countries with a high level of religious fractionalization will show a higher probability of experiencing a civil war.

To summarize, this theory distinguishes between the opportunity and willingness

to start a civil war. Willingness does not only include the opportunity costs of rebelling,

but also the costs of not joining or supporting the rebels. The theory treats potential

rebel leaders and nonelites separately. Elites perceive state weakness as an opportunity

to start an insurgency, but they depend on a substantial number of nonelites to join or

support the insurgency group. Nonelites are willing to join if the costs of not joining are

high, i.e. if the government indiscriminately commits human rights abuses. In the

following section, a game-theoretic model is constructed to illustrate and formalize the

theory, followed by an empirical test.

22

A G A M E – T H E O R E T I C A L M O D E L

The game described in this section illustrates the theoretical framework outlined

above. From the expected utility functions of potential rebel elites and nonelites,

comparative statics are calculated, which correspond to the hypotheses derived from

the theory. Formalizing a theoretical argument helps to ensure internal logical

consistency. In this case, the game-theoretical model also allows examining different

combinations of the factors increasing the probability of civil war onset. For example,

what impact does repression have, contingent on the level of state weakness?

Comparative statics allow specifying the theoretically expected impact of each factor,

holding other influences constant. These effects can then be tested empirically. From

the formal model below, some additional theoretical expectations are generated. To

give a short summary, civil war is only found to be the equilibrium outcome when state

weakness and state terror coexist. In addition, the size of potential war spoils, and the

opportunity costs of rebelling should also have an effect on civil war onset. Finally, rebel

elites should be more sensitive to changes in any of these aspects compared to

nonelites.

The game tree is depicted in Figure 1. Nature moves first, choosing between a

strong and a weak state. This move is common knowledge to all other players, as the

whole play is one of perfect information. The government moves next, deciding

between a repressive and a non-repressive strategy. Third, the opposition elites (or

extremists) decide whether or not to mount a rebellion and fourth, the nonelites (or

23

moderates) decide whether or not to join the movement. 15 Although in the sequence of

this model, potential leaders first decide whether to mount a rebellion and nonelites

later decide whether to join the movement, reality is much more complex. Opposition

elites, the population at large, and the government interact over an extended period of

time before it comes to a civil war.

In the game tree, state weakness and repression are modeled as dichotomous

choices. In the comparative statics section below, this unrealistic assumption is relaxed

to produce testable hypotheses. Government, in this model, does not necessarily refer

only to the party in power. In a democratic system, even in a weak state, this might

include political opposition parties that are sufficiently tied to the polity and can either

expect to win elections at some time in the future, be part of a government coalition, or

exert political influence regardless of their opposition status. Even in democracies,

however, groups are sometimes excluded from the polity.

For each combination of strong/weak states and repression/no repression, there

are three possible outcomes. These four combinations denote the four subgames of the

game tree. If opposition elites do not start a rebellion, nothing happens. If they do

rebel, on the other hand, the nonelites decide to join the movement or not. If they do

join, a civil war breaks out, as the rebel movement becomes large enough. If they do

15 The model applies equally well if the word ‘elite’ is replaced by the word ‘extremists,’ and ‘population’ or ‘nonelites’ by ‘moderates.’ Elites are similar to extremists in that they are the first to decide whether to mount a rebellion; moderates are similar to nonelites in that they later decide whether to join or not. Further, elites and extremists can expect higher potential benefits from the insurgency, albeit for different reasons. While political-economic payoffs might be more salient to political entrepreneurs, extremists profit from political-ideological benefits.

24

not join, the movement will either break down, or end up as a terrorist group or an

assemblage of criminal gangs.

In order to calculate potential elites’ and nonelites’ payoffs, I construct expected

utility functions for both players. State weakness and repression have an impact on

both potential rebel elites’ and nonelites’ calculations. Potential elites react to

opportunities posed by the weakness of the state. They can benefit from criminal or

terrorist activities, as well as from the spoils of a civil war if the state is sufficiently weak

so that neither is crushed immediately. Potential rebel elites prefer, however, civil war

as they can then share the risk of violently opposing the government with the

participating nonelites. There are many beneficial outcomes for potential rebel leaders:

They may become the new political leaders in a newly formed state after secession or if

the central government is thrown over (insurgency as investment); they may make

profits during the conflict, e.g., from looting, drug trafficking or blood diamonds

(insurgency as business, Collier et al. 2004); or they may realize economic or political

profits from criminal or terrorist activities. These spoils are assumed to exceed the

benefits of leading a ‘normal’ life. For potential rebel leaders, therefore, opportunity is

more important than willingness, as they are always willing to initiate an insurgency.

For nonelites, on the other hand, joining a rebel movement is always risky, as it

involves the danger of capture or death and, in most cases, the abandoning of their

families and jobs. Even a lower level of support – like hiding rebels or providing them

with food or merely not denouncing them – involves risks. While nonelites might turn to

the rebels hoping that they will shelter them from indiscriminate government repression

25

(Mason and Krane 1989), supporters of the rebels will always face the potential danger

of getting caught or killed by government agents, at least as long as the state is not

perfectly weak. They do receive some selective benefits from joining the rebels, but

these will be very low in material terms and not high enough in social (reputational) and

psychological terms to rationally justify participation. Since benefits are low, nonelites

are motivated by willingness more than by opportunity. Specifically, they will compare

the costs of joining to the costs of not joining. These costs mainly stem from

indiscriminate human rights abuses committed by the government against the

population. Either way, if the costs of not joining are sufficiently high to outweigh the

danger of joining, it becomes rational for the individual to join.

The expected utility function for nonelites (C) is as follows:

E(U)C = j [wm – (1 – w) d] + (1 – j) [n – r], (1)

where j is the decision to join a rebel movement and 1 – j is the decision to abstain (j = {0, 1});

w is state weakness (0 ≤ w ≤ 1), 1 – w, therefore, is state strength;

m is the motivation to join the rebels (selective material or reputational benefits, but also irrational feelings of vengeance, ideological reasons, etc.; 0 ≤ m ≤ 1);

d is the danger associated with joining the rebels (d = 1, since it might involve death or lifelong imprisonment);

n are the economic, social, and psychological benefits of leading a ‘normal’ life, i.e. of not joining the rebels (0 ≤ m < n ≤ 1);16 and

r is government repression (0 ≤ r ≤ 1). 17

16 All the variables in this model, but especially m and n should be interpreted as individually perceived payoffs that vary across different people. 17 I decided not to discount repression by state weakness as especially politically and economically weak states seem to experience relatively high levels of government-sponsored human rights abuses.

26

If the individual joins (j = 1), she will realize her material and reputational

benefits m to an extent dependent to the weakness of the state w.18 However, these

incentives are assumed to be small, so that the danger of participating (dependent on

state strength, 1 – w)19 will make the total payoff negative in most cases.

Yet, if she does not join (j = 0), she will receive the benefits of leading a ‘normal’

life (n) but will have to bear the costs of repression (-r). If these are higher than wm –

(1 – w) d, a higher – if still negative – utility can be realized from participating. The

benefits of leading a ‘normal’ life (i.e. the opportunity costs of rebelling) are assumed to

be higher than the benefits from rebelling as a consequence of the collective action

problem or rebel’s dilemma. m refers to selective incentives only, benefits from possible

collective goods are equal for participants and non-participants and are therefore not

included in the game. The decision to join the rebels could also be modeled as a

decision of supporting the rebels to varying degrees, from passive sympathizing to

actual participation in the fighting. j would then be a continuous, rather than a

dichotomous variable.

The expected utility for potential rebel elites (E) is as follows:

E(U)E = i [ws – ((1-w) d - p)] + (1 – i) [n – r], (2)

where i is the decision to initiate an insurgency and 1 – i is the decision not to (i = {0, 1});

w, again, is state weakness (0 ≤ w ≤ 1), 1 – w is state strength;

s are the spoils that could be realized from mounting an insurgency (0 ≤ m < n < s < 1);

18 In other words, state weakness indicates the probability with which these benefits will be realized. 19 Similarly, state strength indicates the probability with which the danger materializes.

27

d is the danger associated with initiating an insurgency (d = 1), as above;

p is the rate of nonelites’ participation in the insurgency (0 ≤ p ≤ 1);

n, as before, refers to the economic, social, and psychological benefits of leading a ‘normal’ life, i.e. of not initiating an insurgency (0 < n ≤ 1); and

r is government repression (0 ≤ r ≤ 1).

If the potential leaders initiate an insurgency (i = 1), spoils s will materialize to

the extent that the state is weak.20 They will have to bear the danger d (as a function

of state strength, 1 – w), but for them, p, the rate of participation, is subtracted from

the danger, because they can share the risk if others join. On the other hand, if they do

not initiate an insurgency (i = 0), elites will have to face the same level of repression r

as the nonelites, and receive similar benefits n.

Participation, in turn, depends on how many nonelites choose to join (j = 1).

E(U)C depicts the expected utility calculation of one individual only, whereas p

expresses the rate of participation, as a result of the aggregation of many individual

choices. Participation, therefore, may be calculated from equation (1) as:

p = wm – (1-w) d + r – n; (3)

where m is now the average perceived motivation to join, over all nonelites, and n

represents the average perceived benefits of leading a ‘normal’ life, over all nonelites.

Thus, p is positive if wm + r > (1-w) d + n. Practically, p cannot be smaller than

0, although it can be mathematically. In other words, nonelites will join a rebel

movement to the extent that costs from repression (plus small selective benefits m)

exceed the danger of fighting (discounted by state strength, plus the benefits of a

20 Once more, state weakness indicates the probability with which spoils are realized.

28

‘normal’ life n). By replacing p, the complete equation for potential elites’ expected

utility then becomes:

E(U)E = i [ws – ((1-w) d – (wm – (1-w) d + r – n))] + (1 – i) [n – r]. (4)

They will then initiate a rebellion, iff:

ws – ((1-w) d – (wm – (1-w) d + r – n)) > n – r;

ws – n > (1 – w) d – p – r; or:

ws + p + r > (1 – w) d + n. (5)

Potential elites will therefore initiate an insurgency to the extent that the state is

weak, spoils are high, participation is expected to be high (as a result of a weak and

repressive state), and repression is high. Even if participation is relatively low (because

repression is low) but the state is sufficiently weak, the expected spoils may outweigh

the danger, and an insurgency will be initiated. In this case, however, few people join

and the insurgency will turn into a terrorist or criminal movement. Since the whole

game is modeled as one of perfect information, potential leaders know about how many

people will join. Still, they might start an insurgency, if they can make profit from doing

so, even if they have to bear all the danger.

If the individual choices to initiate a rebellion by potential leaders are aggregated

from equation (4) to a measure q (q like quarrel), in analogy to the participation rate p,

q = ws – {(1 – w)d – [wm – (1 – w)d + r – n]} + r – n, or:

q = ws – 2d + 2wd + wm + 2r – 2n, (6)

29

where, as before, m is now the average perceived motivation to join, over all nonelites

and potential rebel leaders, and n represents the average perceived benefits of leading

a ‘normal’ life, over all nonelites and potential leaders.

The distribution of payoffs, resulting from the expected utility equations, is

relatively simple. I have left out the payoffs for the government, since I do not

empirically test for the reasons of why human rights abuses are committed by the state

in the first place; the inclusion of government payoffs would also make the game much

more complicated. Basically, however, the government will sometimes choose to pursue

a repressive strategy and sometimes not, both in a strong and weak state. From the

point of view of potential rebel elites and nonelites, then, nature’s and government’s

move can be regarded as external, so that I can concentrate on their payoff structures.

The game tree in Figure 1 depicts the moves by nature and government, elites and

nonelites as dichotomous choices. Nature chooses state weakness w to be either 0 or 1;

similarly the government chooses between no or maximum repression so that r = {0;

1}. The payoffs at the final nodes, henceforth, are calculated by setting w, r, i, and j to

either 0 or 1. The first payoff always denotes the expected utility of the potential rebel

leaders and the second that of the nonelites.

Calculating the subgame equilibria for each of the four subgames gives the

following equilibria: (1) If the state is strong and the government does not repress (the

lower left corner in Figure 1), nonelites clearly do not join, since n > -d. Similarly,

potential elites prefer a ‘normal’ life to both civil war and terrorist/criminal activity,

because n > 1 – d > -d (d = 1). Further, they know that they would not be joined by

30

nonelites so they simply compare n to –d and choose not to initiate an insurgency. (2)

In a strong and repressive state (upper left corner), potential elites again prefer doing

nothing to mounting a rebellion that nobody joins in, since n – r > -d (r = d = 1, n >

0). While they would prefer a civil war to doing nothing (as long as n < 1), they know

that the nonelites would not join, as, again, n – r > -d. The equilibrium, therefore, is

that no movement will be initiated. (3) In a weak, but not repressive state (lower right),

nonelites still prefer not to join, as by assumption n > m. Here, however, the elites

engage in violent activity, even if they are not joined, since the spoils are larger than

the payoffs from doing nothing (s > n, by assumption). They would still prefer a civil

war, since s + p > s, but they do not depend on participation in a perfectly weak state.

Consequently, weak states should see increased criminal and/or terrorist activities

(though I do not test this hypothesis empirically). (4) In a weak and repressive state

(upper right), finally, nonelites will prefer to join an emerging insurgency, as m > n – r.

While potential elites would rebel either way (s > n – r), it is only in this scenario that

they can start a civil war, which they prefer (s + p > s). Only in a weak and repressive

state is the equilibrium a civil war.

On the aggregate level, comparative statics denote the effect of all factors on the

participation rate p and the rate q at which potential rebel elites initiate insurgencies.

The other variables (w, m, d, r, n, s) may assume any value between 0 and 1, subject

to the constraint that s > n > m. Using comparative statics, the following partial

derivatives can be derived from equation (3):

31

δp / δw = m + d > 0

δp / δm = w > 0

δp / δd = w – 1 < 0 if w < 1 and = 0 if w = 1

δp / δr = 1 > 0

δp / δn = -1 < 0

These comparative statics denote the direction of the effect of the variables in

the model on the participation rate p of the nonelites. Similarly, the effects on q, the

rate at which potential rebel leaders try to initiate rebel movements – or criminal or

terrorist groups at least – can be calculated from equation (6):

δq / δw = s + 2d + m > 0

δq / δs = w > 0

δq / δd = 2w – 2 = w – 1 < 0 if w < 1 and = 0 if w = 1

δq / δm = w > 0

δq / δr = 2 > 0

δq / δn = -2 < 0

Interestingly, but consistent with the game-theoretic model, potential leaders

react more sensitively to changes in state strength, repression, and the benefits of

leading a ‘normal’ life. This results from the fact that these factors influence q both

directly and indirectly via p. They are more likely to rebel, sometimes forming criminal

or terrorist groups, even if not joined by the nonelites. More important, the effects of all

factors are in the same direction for elites and nonelites, allowing a test of both aspects

in a single empirical model. State weakness, potential war spoils, and repression

32

increase the likelihood of civil war; state strength and the opportunity costs of rebelling

reduce the likelihood.

These comparative statics easily translate into the testable hypotheses outlined

above. m and d are assumed to be constant. State weakness is operationalized by the

level of economic development, anocracies, political instability, military regimes, new

states, population size, and the percentage of mountainous terrain. Economic

development, on the other hand, also affects n, the opportunity costs of joining the

rebels. The potential spoils s of a successful uprising are (imperfectly) operationalized

by oil exports, which provide successful guerrillas with a stable source of income. When

the insurgents succeed in overthrowing the government, they can realize the rents

associated with oil production. Finally, repression is operationalized as state-sponsored

human rights abuses, including political imprisonment, torture, ‘disappearances,’ and

killings. The operationalization of the variables is discussed in greater detail below. In

the following section, I construct an empirical model to test the theoretically derived

expectations and describe the operationalization of the variables.

33

E M P I R I C A L T E S T S

A. The Repression-Rebellion Model

The dependent variable in this research design is a dichotomous variable

denoting the beginning of a new civil war: civil war onset. I therefore use Logit

maximum likelihood estimation to estimate a multivariate model. All countries for which

data are available are included. Data on abuses of personal integrity rights are available

from 1976 on. Due to the one-year lag of this variable, the time frame of this study is

from 1977 to 1999, the last year which many civil war lists report. Countries with a

population of less than half a million people in 1990 are excluded, as they are not

included in Fearon and Laitin’s (2003) dataset, which serves as a source for many

variables.

i) The Dependent Variable

There are many different lists of civil wars, based on different operational

definitions. Even with the same definition, scholars often do not agree on whether a

specific event represents a civil war or not, since data on battle deaths, ceasefires,

military movements, etc. are often very imprecise. And if they do agree, it is even

harder to concur on the exact start and end dates. Sambanis (2004) describes these

problems in great detail and argues that models of civil war onset, prevalence, and

duration should be tested for robustness across different civil war lists. He claims that

this is especially important with variables like GDP growth, which is itself (negatively)

influenced by an erupting civil war. The same is true for human rights violations, which

34

have been found to increase during civil wars (Poe and Tate 1994; Krain 1997; Poe et

al. 1999; Zanger 2000; Harff 2003). In other words, if a list codes the start of a civil

war ‘too late,’ then the observed human rights abuses in the preceding year might be a

consequence of the war, and not one of its causes. Therefore, I test my model across

six of the twelve civil war lists in Sambanis’ (2004) dataset (see Table A1 in the

appendix). I use his ‘a-versions,’ excluding country-years during an ongoing civil war.

First, I test my model with Sambanis’ (2004) civil war codings and describe the results

in detail. I chose Sambanis’ civil war list as it is one of the most recent and well-

documented efforts to compile data on civil wars in the post-1945 era.21 Then, to test

the robustness of the results, I replace his codings with codings from the most widely

cited studies and datasets on civil war: the COW (Sarkees and Singer 2001) and

Uppsala (Gleditsch et al. 2001) datasets, Fearon and Laitin (2003a), Regan (1996), and

an extended version of Doyle and Sambanis (2000). Regan, in contrast to other studies,

uses a relatively low threshold of 200 battle deaths which allows for further robustness

checks. Similar to the correlation coefficients reported in Sambanis (2004, 834), the

different civil war lists are correlated with factors ranging from .54 to .90, for the years

1977 to 1999.

ii) Independent Variables: Repression and Democracy

In a first set of models, human rights violations are measured on the 5-points

Political Terror Scale (PTS, originally developed by Michael Stohl, see Poe and Tate

21 See the detailed case descriptions available at http://www.yale.edu/unsy/jcr/jcrdatadec04.htm, accessed March 2005.

35

1994; Poe et al. 1999). The updated version of the Political Terror Scale is available

from Gibney (2004). The scale ranges from ‘1,’ where repression is almost but not

necessarily entirely absent, to ‘5’ where state terror is inflicted indiscriminately on the

whole population. Examples of countries coded ‘1’ in 2003 include Burkina Faso,

Canada, and Kazakhstan. Examples of countries coded ‘5’ include Algeria, Colombia,

and Indonesia. Countries are coded according to the following coding scheme:

1 - Countries...under a secure rule of law, people are not imprisoned for their views, and torture is rare or exceptional...political murders are extremely rare.

2 - There is a limited amount of imprisonment for nonviolent political activity. However, few persons are affected, torture and beating are exceptional... political murder is rare.

3 - There is extensive political imprisonment, or a recent history of such imprisonment. Execution or other political murders and brutality may be common. Unlimited detention, with or without trial, for political views is accepted...

4 - The practices of (Level 3) are expanded to larger numbers. Murders, disappearances are a common part of life...In spite of its generality, on this level terror affects primarily those who interest themselves in politics or ideas.

5 - The terrors of (Level 4) have been expanded to the whole population...The leaders of these societies place no limits on the means or thoroughness with which they pursue personal or ideological goals.

The codings are based on country reports compiled by Amnesty International

and the US State Department. The two resulting measures correlate with a factor of

.791 over the 1976-2003 period. Poe et al. (2001) report that State Department reports

have slightly favored US allies and discriminated against leftist foes in the 1970s and

early 1980s, but that these biases have mostly disappeared since. Following Poe and

36

Tate (1994), I use the Amnesty International measure, replacing missing values with

the State Department scores, where possible.22

Genocides and politicides are the most egregious forms of human rights

violations.23 I account for this fact by setting the PTS to its highest value of 5 for every

year during which genocide or politicide occurred in that country.24 By combining the

dichotomous variable for genocides/politicides with the PTS, I avoid potential problems

of collinearity between the two.25 Harff (2003, see also Harff and Gurr 1988) provides a

list of genocides and politicides, based on data collected for the State Failure project

(Goldstone et al. 2000), which have been used in empirical studies on genocides and

politicides (e.g., Krain 1997) and their consequences (Moore and Shellman 2004).

Civil wars tend to increase the level of state repression (Poe and Tate 1994;

Krain 1997; Poe et al. 1999; Zanger 2000; Harff 2003). Considering the time period for

which both civil war lists and human rights data are available, 1977-1999, and

Sambanis’ (2004) civil war data, the mean (modified)26 PTS score during 656 civil war

years is 3.94, compared to a mean of 2.20 for the 2,954 domestically more peaceful

22 Considering Amnesty scores only could introduce a sample bias problem, as Amnesty International, according to their mission, tended to over-report countries with high levels of human rights abuses in earlier years. Today, both sources cover the majority of countries in the world (Poe and Tate 1994; Poe et al. 2001). 23 Genocides are incidents of state-sponsored mass murder in which perpetrators define their victims “primarily in terms of their communal characteristics. In politicides, in contrast, groups are defined primarily in terms of their [perceived] political opposition to the regime and dominant groups” (Harff 2003, 58). 24 In total, ninety country-years were changed from a lower score to ‘5,’ including one (odd) ‘1,’ 23 country-years previously coded ‘3,’ and 64 country-years previously at ‘4.’ 79 country-years with genocide or politicide already coded ‘5’ remained unchanged. 25 Krain (1997, 331, FN1) states that the main difference between genocide/politicide and other state-sanctioned human rights abuses “is one of intentionality. The purpose behind state-sponsored mass murder […] is to eliminate an entire group.” If the main difference is one of intention and not necessarily one of behavior, a combination of the two measures seems justified. 26 From here on, PTS refers to the modified PTS, where all genocides and politicides are coded as ‘5.’

37

country years and an overall average of 2.49. In methodological terms, this could pose

problems of endogeneity.

I employ three strategies in order to counter these problems. First, I delete all

civil war years from the analyses (save the first year which is, of course, included).

Thus, cases in which an ongoing civil war led to an increase in human rights violations

while a second war happened to break out do not affect the results. The level of

analysis is the country, not the civil war. This strategy might also be helpful for the

interpretation of some other variables that could change during an ongoing civil war,

like political instability. If a first ongoing civil war causes political instability and a

second civil war erupts in the same country, a statistical relationship between instability

and civil war onset might be spurious.

Second, as mentioned above, I test my models across six different civil war lists.

Regan’s (1996) list, for example, uses a low threshold of 200 battle deaths, accounting

for what other lists would regard as lower-level violence. In general, the differences

between the start dates (Sambanis 2004, 831) should somewhat alleviate problems of

endogeneity. Even if some civil war lists code the starting date ‘too late’ for some

conflicts, others will code it correctly, guarding against an endogenous effect of civil war

on government repression.

Third, in further alternative tests, I use a different measure for human rights

violations. In an alternative set of models, I include the measure for political

imprisonment only, and exclude the variables for torture, ‘disappearances,’ and extra-

judicial killings. A repressive strategy of political imprisonment should be less closely

38

related to the lower-level violence preceding a civil war. McCormick and Mitchell (1997)

have argued that repression is a multidimensional concept that cannot easily be

captured in a single variable. They propose to disaggregate abuses of personal integrity

rights into at least the two dimensions of political imprisonment and torture/killing.

Cingranelli and Richards (1999), on the other hand, find that state repression can not

only be interpreted as a unidimensional concept; violations, further, occur in a

sequential ordering: Political imprisonment and torture occur more often and at an

earlier stage than ‘disappearances’ and killings. Cingranelli and Richards (2004) have

created a dataset that separately codes for political imprisonment, torture,

‘disappearances,’ and extra-judicial killings. Each of these variables is measured on a 9-

point scale, where, in contrast to PTS, higher scores indicate fewer human rights

abuses. In the robustness tests below, I use their measure of political imprisonment

only.

In order to avoid any conceptual overlap between human rights behavior and

democracy, I use the institutional Polity2 measure. This 21-point measure is created by

subtracting the 11-point autocracy scale from the 11-point democracy scale, resulting in

a scale that ranges from -10 (most autocratic) to +10 (most democratic). The

“institutionalized democracy” scale is composed of four additive dimensions:

competitiveness and openness of executive recruitment, constraint on the chief

executive, and competitiveness of political participation. The “institutionalized

autocracy” scale is composed of the same four dimensions, plus a dimension measuring

the regulation of participation. In the original 11-point democracy and autocracy scales,

39

years of foreign occupation are coded as interruption periods (-66), years of “complete

collapse of the central political authority,” mostly during civil war, are coded as

interregnum periods (-77), and the years during which a new polity is established are

coded as transition periods (-88). The combined Polity2 measure, however, codes only

interruption periods as missing; for interregnum periods, a ‘0’ is coded, and for

transition years, the scores are imputed by simple interpolation (Marshall and Jaggers

2002). Countries with scores from -5 to +5 are dichotomously coded as anocracies. In

order to facilitate the interpretation of the model, the anocracy variable is reversed so

that ‘0’ denotes anocracy and ‘1’ denotes no anocracy.27 As I do not necessarily expect

the effect of democracy to be consistent across anocracies and non-anocracies, I also

include an interaction term between the two variables. I employ Fearon and Laitin’s

(2003a) measure of political instability which is dichotomously coded ‘1’ if there has

been a change of at least three points on the Polity2 scale in any of the three preceding

years, and ‘0’ otherwise.

iii) Other Independent Variables

The level of economic development is approximated by GDP per capita. Although

data is available from the World Bank’s World Development Indicators, there are many

missing values, especially in the cases of interest: weak, repressive countries. Using

World Bank data, even if additional sources like the Penn World Tables are consulted,

many country years during which a civil war began would drop from the analysis. 27 The Polity2 variable is centered on ‘0,’ implying that such a country is an anocracy. The size and interpretation of coefficients and odds-ratios are not affected by the decision to reverse the anocracy variable.

40

Therefore, I use Fearon and Laitin’s data on GDP per capita. They use data from the

Penn World Tables28 and from the World Bank, but estimate missing values using data

on energy consumption from the COW project (Fearon and Laitin 2003b).

The World Development Indicators also provide data on population size. This

variable is logged (skewness = 8.08). Countries are coded as newly independent states

in their first two years after independence, similar to Fearon and Laitin’s (2003a)

measure. To operationalize military regimes, countries are dichotomously coded ‘1’ as

long as a regime led by a military person as the chief executive came to power by

means of a military coup (Madani 1992). The data are adopted from Poe et al. (2005)

who updated it through 2003. Countries whose export revenues from oil sales exceed

one third of their total export revenues are dichotomously coded as oil exporters.

Countries are coded in five-year intervals, based on World Bank data. Rough terrain is

measured as the percentage of mountainous terrain, based on the codings of

geographer A. J. Gerard. Missing values are estimated using the difference between the

highest and lowest point of elevation (see Fearon and Laitin 2003b). Measures for

ethnic and religious fractionalization denote the probability that two randomly chosen

individuals in a country are from two different ethnic or religious groups, respectively.

The measures are based on the 1964 Soviet Atlas Narodov Mira. The measures for oil

exports, mountainous terrain, and ethnic and religious fractionalization are directly

adopted from Fearon and Laitin’s dataset (2003a, 2003b).

28 http://pwt.econ.upenn.edu/, accessed October 2004.

41

These independent variables do not show strong signs of problems of

multicollinearity. Except, of course, for the interaction term between democracy and

anocracy and democracy by itself, no two variables are correlated higher than |.5| and

for only four pairs of variables is the correlation coefficient higher than |.4|: Polity2 and

government repression (-.45), and the log of GDP per capita with Polity2 (.48), with the

presence of a military regime (-.43), and with ethnic fractionalization (-.48). All non-

dichotomous independent variables, except for state repression and democracy,29 are

centered on their means, to facilitate interpretation of the statistical outputs. I subtract

‘1’ from the PTS so that the constant in a logistic regression model now represents the

logarithm of the probability of civil war onset in any given year for a country with

average levels of economic development, population size, mountainous surface, and

ethnic and religious fractionalization. This ‘average country’ has almost no repression

(PTS=1), is an anocracy (since Polity2 = 0), has experienced no political instability in

the past three years, is not led by a military government, and is not an oil exporter (all

dummies are set to ‘0’). The descriptive statistics for all independent variables in their

raw form, i.e. before they are lagged, logged or centered on their mean, are

summarized in Table A2 in the appendix.

B. Results

I first present the results with Sambanis’ (2004) civil war list as the dependent

variable in detail and then, more shortly, the robustness tests across model

29 The Polity2 scale is not centered because the mean (-.031) is very close to zero.

42

specifications with alternative independent and dependent variables. The results from a

logistic regression with Sambanis’ (2004) civil war list are summarized in Table 1.30 The

table gives the coefficients and odds-ratios with their respective standard errors, and

the p-values. These results are compared to the Null-Model without the repression

variable. The coefficients of a logistic regression cannot be easily interpreted in

substantive terms, but the odds-ratios indicate the factor by which the odds of civil war

onset increase with a one-unit increase in the independent variable. Odds, in turn, are

the chances of seeing a civil war divided by the chances of not seeing one. First,

though, I only interpret signs and levels of significance.

In the Null-Model in Table 1 state terror is omitted from the list of variables. As

the results for other independent variables only change marginally, I only discuss the

results from Model 1 in detail. This congruence between the Null-Model and Model 1

indicates that repression adds considerably to the explanation of civil war onset without

altering the effects of other variables, contrary to Hypothesis 2a, not even that of

democracy.

Of the state weakness variables, the level of economic development is

statistically significant. This seems to be one of the most robust findings in the

empirically oriented civil war literature. Surprisingly, population size does not even come

close to statistical significance and the coefficient’s sign is not in the expected direction,

while it was one of the most robust variables in Sambanis (2004) and Fearon and Laitin

30 To test for robustness, I alternatively estimate this and all following models with Probit maximum likelihood estimations. In further tests, I replace the modified PTS that is based on Amnesty International reports by the one that is based on US State Department country reports (replacing missing values with the Amnesty scores, if possible). In both cases, the results are similar to the ones reported here. (The Probit and State Department models are not reported.)

43

(2003a) and significant in at least some of Collier and Hoeffler’s (2001) models. New

states, on the other hand, are much more likely to experience a civil war. In fact, this

variable was dropped from the analysis since it predicted failure perfectly. Sambanis

(2004, 837), despite his criticizing the theoretical expectations related to new states,

reports similar findings.

Next, civil wars are significantly more likely to occur in countries governed by

military regimes. This finding adds to existing theory on the causes of civil wars, as it

was not tested in any of the three studies cited above. Political instability, contrary to

earlier findings (Fearon and Laitin 2003a; Sambanis 2004), was not significant. Contrary

to my expectations, democracy positively impacted the probability of civil war onset.31

However, democracy’s positive impact is only statistically significant within the group of

anocracies, where Polity2’s coefficient is .249. For non-anocracies, the coefficient

reduces to almost zero at .04, and is no longer statistically significant, meaning that

fully consolidated democracies and fully established authoritarian regimes are equally

likely to experience a civil war, ceteris paribus.32 Taken together, both democracies and

autocracies are still less likely to experience civil war than anocracies, as indicated by

the negative and significant coefficient. Taken together, these findings underline the

importance of modeling specific aspects of a country’s political system, rather than just

including the overall level of democracy. Yet, they clearly deserve increased

31 The level of democracy also had a positive coefficient in Fearon and Laitin’s (2003a) Model 1 but was not statistically significant. 32 On the interpretation of interaction terms, see Braumoeller (2004). That the coefficient is not significant for non-anocracies can be seen from an alternative model, where the anocracy variable is not reversed, i.e. anocracies are coded ‘1’ and non-anocracies are coded ‘0’ (this model is not reported). Here the p-value of Polity2 is .211.

44

examination in future studies, especially since all other variables related to a country’s

political system were signed in the expected direction. Wantchekon (2004) describes

how democracy can arise from civil war, but how does a higher level of democracy

(within anocracies) lead to civil war?

Figure 2 (and Table A3 in the appendix) describe the bivariate relationship

between the level of democracy and civil war onset in greater detail, not controlling for

other factors. As can be seen from Figure 2, fully established democracies are very

unlikely to experience a civil war. In absolute terms, a relatively high level of new civil

wars seem to break out in almost fully established authoritarian regimes (at Polity2 = -7

and -6). In relative terms, however, anocracies, and especially more democratic

anocracies (between 0 and +5) experience the highest level of civil war onsets.33 This

finding deserves increased scholarly attention in the future. One explanation might be

that half-hearted democratization processes open accesses to the political system for a

number of groups without effectively sharing political power with these groups. At the

same time, state leaders may feel international and domestic pressures to democratize,

but are not willing to give up power. If they pretend to give in to these pressures and

hold meaningless elections, this might be a signal of state weakness. A full

democratization, on the other hand, firmly includes contending groups in the polity,

making violent rebellion an unnecessary choice. Democracy, however, may have a

different effect on other aspects of civil war. Lacina (2005) finds democracy to be one

of the strongest factors in reducing the number of battle deaths in a civil war, once 33 If Polity2 is replaced by either the Freedom House political rights or civil liberties scale, both have negative signs and respect for political rights significantly decrease the probability of civil war onset, while civil liberties do not seem to have an impact (results not reported here).

45

started. Elbadawi and Sambanis (2002) find that democracy reduces civil war

prevalence. The link between democracy and civil war seems to be more complicated

than the democratic peace in international politics.34 In From Voting to Violence, Snyder

(2000) describes how, under certain circumstances, democratizing and pseudo-

democratic countries may experience nationalist or ethnic conflict.

Oil exporting states face a higher risk of seeing a civil war. As argued above, oil

revenues present an incentive for rebels to initiate an insurgency and this finding

supports Collier and Hoeffler’s (2001) thesis of greed-based rebellions. When the spoils

of a civil war are high, potential rebel elites will react upon opportunities to realize these

spoils. The variables for ethnic and religious fractionalization are not significant, neither

is each of these variables, when the other one is excluded from the model.35 Similarly,

the percentage of mountains of a country’s surface does not seem to be related to the

probability of civil war onset, according to this model. Likelihood-ratio tests reveal that

any of the statistically insignificant variables do indeed not offer additional explanatory

power for civil war onset.

The repression-rebellion link, finally, receives major support from the findings of

this model: Past repression exerts a highly significant impact on the probability of a civil

war to break out. In fact, the coefficient of government repression is the most

significant in statistical terms. The odds-ratio allows a first glimpse at the substantial

impact of this variable: For each 1-point increase on the Political Terror Scale, the odds

34 But even with regard to interstate war, Mansfield and Snyder (2002) report findings that incomplete transitions toward democracy increase the probability of war. 35 The results for these alternative model specifications, with ethnic fractionalization only and religious fractionalization only, are not reported.

46

of civil war onset increase by a factor of 2.039. For a change from the lowest to the

highest level of repression, the odds of civil war onset would increase by a factor of

17.28. Figure 3 (and Table A4 in the appendix) describe the bivariate relationship

between the modified Political Terror Scale and civil war onset.

As can be seen from Figure 3, every increase in repression seems to increase the

probability of civil war onset. However, I also tested for the hypothesized, and often

empirically supported, curvilinear impact of repression on domestic political violence

discussed in previous research (Gurr 1970; Tilly 1978; Muller 1985; Muller and Seligson

1987; Muller and Weede 1990; Boswell and Dixon 1990; Schock 1996).36 These tests do

not show statistically significant results, with or without PTS in the model (results not

reported). Any increase in the level of repression, therefore, seems to increase the

probability of civil war onset, not just up to intermediate levels of repression. Next, I

turn to an interpretation of the substantial effects of the independent variables.

C. Substantial Interpretation and Simulations

The substantial interpretation of coefficients in logistic regression is not as

straightforward as the interpretation of their signs and levels of statistical significance.

For this reason, I have generated a table with the simulated substantial effects of the

significant variables, holding all other variables at their means and setting dichotomous

variables and democracy to ‘0.’ In Table 2, the effect of changes on the statistically

significant variables is simulated. As a point of reference, I calculate the probability of

36 By adding a variable that assumed a value of 0 if PTS = 3, 1 if PTS = 2 or 4, and 2 if PTS = 1 or 5.

47

an ‘average country’ to experience the outbreak of a civil war. For this hypothetical

country, all dichotomous variables and the Polity2 democracy measure are set to ‘0,’

PTS is set to ‘1,’ and the other independent variables are at their respective means. The

expected probability of civil war onset for the ‘average country’ would be .51% in each

year. Clearly, this is a very low probability, even over ten years, the accumulated

probability increases to only 5%. Table 2 denotes the change in the significant

variables, the expected probabilities of civil war onset, and the ratio of these

probabilities over that of the ‘average country.’ In general, one should expect relatively

low probabilities of civil war onset, as it only occurs in about 2% of all country years.

Increases of one unit on any of the independent variables have different effects

depending at which level they occur. For example, a change from 1 to 2 points on PTS

has a different impact than a change from 2 to 3 points; and both effects vary with the

level of other independent variables.

In addition, Table 2 gives the expected probabilities over a time span of five and

ten years, given that the independent variables do not change. For the ‘average

country,’ for example, given that it stays at the means of all independent variables, a

probability of .51% per year accumulates to about 2.5% over five years and to about

5% over a decade. If, on the other hand, such a country experienced a democratization

process, for example, or an economic crisis, the predicted probabilities would change.

48

i) The Substantial Impacts of the Independent Variables

First, an increase of 1 point on the Political Terror Scale would result in an

expected probability of 1.04%, about 2.03 times higher than the probability of the

‘average country.’ An increase by 4 points from the lowest to the highest level of

repression would lead to a probability of 8.21%, almost 16 times higher than for a

country without repression, at a level of ‘1’ on the Political Terror Scale. Over a decade,

an accumulated probability of almost 60% would be expected. In fact, government

repression is the variable that leads to the single largest increase in the probability of

civil war onset when set to its maximum.37 Clearly, repressive governments are much

more likely to be faced with a civil war; repression, it seems, is provocation much more

than deterrence.

Second, the effect of democracy, although not in the expected direction, is only

significant for anocracies. Within the group of anocracies, a change towards more

democracy exerts a sizable impact. The most authoritarian anocracy (at Polity2 = -5)

faces a risk of civil war risk of only 15%, 29% that of the ‘average country.’ For the

most democratic anocracy (Polity2 = +5), on the other hand, the expected probability

of civil war onset is 1.77%, 3.44 times as high as the ‘average country.’ In non-

anocracies, regardless of the level of democracy, the probability reduces to .24%, less

than half that of the average country. While democracy does not seem to play a role for

non-anocracies, the finding that the level of democracy increases the likelihood of civil

war onset within the group of anocracies clearly deserves additional scrutiny.

37 A change from the highest to the lowest level of economic development would increase the expected probability of civil war onset by a factor of almost 15.

49

Third, military regimes greatly increase the probability of civil war onset. The

presence of a military regime increases the likelihood of war onset by a factor 2.25 to

1.16%, holding all other variables, including the level of democracy, constant.

Fourth, the level of economic development had the second largest impact on the

likelihood of civil war onset. An increase from the mean of $2,59838 (constant 1985 US

dollar) to the maximum of $28,901 (United Arab Emirates in 1980) leads to a decrease

in the expected probability to .13%, only 25% that of the ‘average country.’ On the

contrary, a move down to the minimum of $215 (Democratic Republic of the Congo in

1998) leads to an increase to 1.94%, 3.8 times more than the ‘average country.’ Over a

time period of ten years, a country with a maximum level of economic development

faces a probability of war onset of only 1.3%, whereas it is almost 18% for a country at

the minimum. Comparing the two, countries with a minimal level of economic

development were more than 15 times as likely to experience the outbreak of a civil

war as countries with a maximal level of economic development. While the importance

of economic development for civil war onset is one of the most robust findings in the

literature, its practical importance may be relatively low. At least in the short term,

economic development changes rather slowly, whereas repression can be considerably

reduced even over the period of a year or two.

Fifth, oil exporters face a much higher probability of civil war onset, at 1.40%,

2.72 times higher than the ‘average country’ without oil exports. In this model, oil

38 This is the mean of the logged and lagged variable; it is different from the one reported in Table A1 in the appendix.

50

exports represent the dichotomous variable with the strongest impact (not regarding

the new states variable).

ii) Hypothetical Cases: The Four Subgames

How do these effects combine in hypothetical cases? More specifically, what

probabilities of civil war onset can be expected in the ideal types of a strong and a

weak state, with and without repression (i.e. in the four subgames)? The ideal type of a

strong state, in this simulation, refers to a fully consolidated democracy or a fully

established authoritarian (not an anocracy), but not a military regime with a maximum

level of economic development, but no or low-level oil exports. The ideal type of a weak

state refers to an anocracy at Polity2 = 5, governed by a military regime, with oil

exports and a minimum level of economic development. In this hypothetical case, the

opportunity costs of rebelling are low and the potential spoils are high. For both cases,

population, the percentage of mountainous terrain, ethnic and religious fractionalization

are held at their respective means, and both are politically stable, since these variables

were not significant in the Model 1. To represent the four subgames in Figure 1, both

weak and strong states without (PTS = 1) and with high levels of repression (PTS = 5)

are simulated. Strong states with no (or almost no) state-sponsored human rights

abuses, as expected, are the least likely to experience a civil war; the probability

reduces to .06%, only 12% of the probability of the ‘average state.’ Strong states with

high levels of repression are 17.67 times more likely to face a civil war, with a

probability of civil war onset of 1.06% (2.06 times that of the ‘average country’). The

51

effect of state weakness, however, (combined with a low n, a high s, and no

repression) is much more dramatic, increasing the likelihood to 29.96% (more than 58

times that of the ‘average country’ and 500 times that of a strong state with no

repression). Clearly, while repression has the single most substantial effect on civil war

onset, the combination of different aspects of state weakness, large potential spoils,

and low opportunity costs, outweighs the impact of the single repression variable. The

game suggests that state weakness is a necessary condition for a civil war to start,

while state weakness and repression combined are sufficient. Accordingly, repression

still increases the probability of civil war onset, even in weak states. Combining state

weakness and repression, the expected probability of civil war onset skyrockets to 88%,

almost three times higher than without repression, 170 times higher than the ‘average

country,’ and 1,470 times higher than in a strong state with no government

repression.39 From these simulated probabilities, then, one can conclude that state

weakness and repression come quite close to being sufficient conditions.

These simulated probabilities strongly support the theorized combined effect of

state weakness and repression – flanked by low opportunity costs and large potential

spoils – on the probability of civil war onset. Even in a perfectly weak state, the

probability of civil war onset is only about 30%, whereas it is 90% in a weak and

repressive state. How do these simulations compare to actual predicted probabilities of

civil war onset in the sample? The lowest probability is calculated for the United Arab

Emirates in 1983, with .04% equaling that of the ideal type of a strong state with no 39 One might argue that this effect should empirically be tested by using interaction terms. Yet, interaction terms between repression and economic development and, alternatively, repression and democracy, were not significant (results not reported).

52

repression. Indeed, the UAE come close to this ideal type on most independent

variables. The highest probability, on the other hand, is predicted for Uganda in 1981 at

57%. This country comes close to the ideal type of a weak state with widespread

human rights violations (PTS = 5, Polity2 = 3); a civil war broke out that same year.

D. Robustness Tests

Sambanis (2004) makes a strong argument for testing the robustness of civil war

models over different civil war coding schemes. Using the exact same set of

independent variables, I replace the dependent variable (Sambanis’ 2004 list of civil

wars) with five alternatives: the COW list of civil wars (Sarkees and Singer 2001), the

Uppsala Armed Conflict Dataset codings (Gleditsch et al. 2001), Fearon and Laitin’s

(2003a), Regan’s (1996), and Doyle and Sambanis’ (2000, extended) measures. All the

data are adopted from Sambanis’ (2004) dataset. As discussed above, I chose these

alternatives because they represent the most widely used datasets, the most recent and

most widely quoted studies on civil war, and different coding rules to identify instances

of civil war.

As can be seen from Table 3, government repression is the most robust variable

throughout Models 2 to 6.40 With the COW list, the p-value increases to 4.4%, and with

Regan’s list, it increases to 2.5%, while it stays below .001% in all other cases. The

variables for democracy, anocracy, military regimes, and oil exports are much less

robust, despite significant results in Model 1. Democracy, within anocracies, is 40 I also added the modified PTS scores (based on Amnesty International and, alternatively, US State Department country reports) to Fearon and Laitin’s (2003a) Model 1, and found them highly significant, with and without ongoing civil wars included. The results are not reported here.

53

significant in three out of five models, increasing the probability of civil war onset.

Anocracy, however, is never statistically significant. Similarly to democracy, GDP per

capita is only significant in three models, while Sambanis (2004) found it to be one of

the most robust variables in predicting civil war onset. The results for new states are

much more robust; the variable drops out of each model. In contrast to the results from

Model 1, population size and the percentage of mountainous terrain seems to exert

significant impact on the probability of civil war onset in one of the models each.

Finally, the results for political instability, ethnic and religious fractionalization are very

robust, showing no effect on civil war onset in any of the six models, although political

instability comes close to significance in Model 2.

To summarize, repression, relatively democratic anocracies, low economic

development, and recent independence seem to be important factors that strongly

increase the probability of civil war onset, whereas political instability and ethnic and

religious fractionalization do not seem to matter much. The results for military regimes,

oil exports, population size, and the percentage of mountainous terrain, on the other

hand, are more ambiguous, with two of the coefficients changing signs and varying

levels of significance.

As an alternative test of the robustness of the impact of repression on civil war

onset, I replace the general measure of human rights violations with a variable that

only takes into account political imprisonment. Cingranelli and Richards (2004) provide

detailed data on all four dimensions of the Political Terror Scale, of which I use the

measure for political imprisonment. This variable, as the three others, is closely related

54

to both Cingranelli and Richards’ aggregate measure of human rights abuses (.77) and

the modified Political Terror Scale (-.61). In contrast to the Political Terror Scale, higher

measures on all of Cingranelli and Richards’ measures denote fewer abuses.

As can be seen from Table 4, the impact of government repression on civil war

onset is still significant with this alternative measure. In this table, only the results for

Sambanis’ (2004) list of civil wars are summarized; further robustness tests across

alternative civil war lists are deferred to the appendix. In Model 7 (Table 4), none of the

effects of other independent variables change considerably. In Models 10 through 12

(Table A5 in the appendix), in contrast, political imprisonment is not significantly

correlated to civil war onset. This is the only caveat found to the robustness of the main

findings. Possibly, although correlated, political imprisonment alone is not harsh enough

a condition to ignite people to rebel. In terms of the game-theoretic model, political

imprisonment might not increase the costs of not joining a rebellion (r) to a level

sufficiently high as to making participation a rational choice. On the other hand, in three

out of six models, political imprisonment does significantly increase the likelihood of a

new civil war. Overall, these robustness tests still seem to support the repression-

rebellion link.

To summarize, the effect of state repression on civil war onset is found to be

significant across different measures of repression and different dependent variables.

This finding qualifies earlier findings on the importance of opportunity and willingness,

and greed and grievance in an important way. While weak states provide an

opportunity for greedy rebel elites to realize war spoils, nonelites join when the costs of

55

not joining become too high. These individually perceived grievances make them willing

to participate.

56

C O N C L U S I O N

State-sponsored repression is an important factor in increasing the probability of

civil war onset. This finding is robust across alternative civil war lists and different

measures of repression. Further, as can be seen from the simulated effects of state

weakness and repression, it is when these factors operate simultaneously that the risk

of a new civil war reaches high levels. This finding provides support for the argument

that even in weak states governments have a choice between confrontational and

accommodative strategies and can significantly reduce the probability of a civil war to

break out. The game-theoretical model of civil war onset receives similar support from

these findings and simulations.

Rather than restating the results in detail, I conclude by discussing possible

implications and options for further research. First, the findings in this study suggest

that repression leads to civil war, but it is not clear, how exactly this happens. Future

research should examine this process more closely, maybe rather in comparative case

studies than with a large-n research design. The strategic interplay between emerging

rebel movements and the government deserves special attention in this context.

Further, it should be examined whether and when rebel organizations emerge from

formerly nonviolent opposition movements or whether these are separate groups in

most cases. Future research could also investigate the role of human rights abuses

committed by the rebels. If the rebels have to rely on popular support, why do they

sometimes repress the population, as in Uganda, the Democratic Republic of the Congo,

or elsewhere?

57

Second, the link between democracy and civil war onset, but also civil war

duration, intensity, and outcome should be examined more closely. Clearly, the

relationship between democracy and conflict is not as straightforward in the domestic

sphere as it is in international politics. If a democratic domestic peace does exist (Krain

and Myers 1997; Hegre et al. 2001), it embraces more than just the level of institutional

democracy. Also, other factors could be included in multivariate models, for instance,

measures of the international situation (e.g., Boswell and Dixon 1990).

Third, rather than looking at repression alone, future studies could focus on how

governments accommodate opposition demands, and what effects accommodation has

on opposition group strategies. Chong (1991), for example, argues that accommodation

could have the counterintuitive effect of encouraging the opposition, leading to more

extreme demands.

Fourth, repression drives people into rebellion, but it can also lead to forced

migration (Mason and Krane 1989; Azam and Hoeffler 2002). Empirical research on the

causes of refugee movements has consistently found state terror, including politicide

and genocide, to be one of the strongest predictors (Schmeidl 1997; Davenport et al.

2003; Moore and Shellman 2004). Future research should look at the conditions

favoring ‘voice’ over ‘exit,’ or those leading to both responses.

Fifth, the practical relevance of these and related theoretical findings should be

explored. Civil wars are more deadly than interstate wars (Gurr 1970, 3), occur more

frequently (Fearon and Laitin 2003), produce refugee movements (Schmeidl 1997;

Davenport et al. 2003; Moore and Shellman 2004) and a further increase in state

58

repression (Poe and Tate 1994; Poe et al. 1999; Zanger 2000), open the way for

politicide and genocide (Krain 1997; Harff 2003), lead to long-term civilian suffering

(Ghobarah et al. 2003), and provide the environment for organized crime and terrorism

(UN 2004). Specifically, the possibilities of creating an early warning or risk assessment

device for civil war onset should be explored more carefully. Early warning and risk

assessment models have been constructed to forecast a number of events in the social

sciences (see Davies and Gurr 1998 and Carment 2003 for an overview). Looking at the

predicted probabilities of civil war onset (the p-hats), only three civil wars broke out in

countries with a p-hat below the median of .86%, while 47 (a rate of 3.84% over all

country-years) occurred above the median. In the upper 25% of the p-hats (above a

probability of 2.24%), the rate of civil war onsets increases to 5.88%; in the upper 10%

(above a probability of 4.44%) the rate of onsets increases to 10.20%; in the upper 5%

(above a probability of 6.98%) the rate of onsets increases to 13.82%, and in the upper

1% (above a probability of 19.44%) the rate of onsets increases to 25%. This means

that the risk to experience a civil war of the upper 1% is almost 14 times higher than

that of the remaining 99% of the country-years. This indicates a potential for risk

assessment devices, but it remains to be examined whether out-of-sample assessments

are similarly possible.

The results presented in this study modify earlier findings on civil war onset in an

interesting way. They may yield some practical implications and pose questions for

future research. Repression, especially in weak states, paves the way for civil war,

leaving governments with the choice of repressing and risking a war or accommodating

59

opposition demands. This study shows that the choice for repression, despicable on

humanitarian and moral grounds alone, may entail even more disgraceful

consequences.

60

Figure 1. The onset of civil war in an extensive game form.

repression r = 1

-d; n – r s + p; m

n – r; n – r

repression r = 1

no repression r = 0

no repression r =0

n; n

j j

j j

~j ~j

~j ~j

G

i

i i

~i

~i

C

CC

C

~i

~i

E

G

weak state w = 1

strong state w = 0

Nature

E

EE

i

1 – d; -d

n – r; n – r

s; n – r

n; n

s + p; m s; n -d; n1 – d; -d

61

Table 1. Logit regression on the probability of civil war onset. Null-Model Model 1 Sambanis (2004) Sambanis (2004) coef. p-value coef. odds ratio p-value Pesonal integrity abusesa - - 0.713*** 2.039*** 0.000 (0.161) (0.328) Democracy (Polity2)a 0.250*** 0.001 0.249*** 1.283*** 0.001 (0.075) (0.078) (0.100) No anocracya -0.833** 0.020 -0.749** 0.473** 0.037 (0.357) (0.359) (0.170) (Polity2 * No Anocracy)a -0.238*** 0.002 -0.209*** 0.811*** 0.009 (0.077) (0.081) (0.065) Political instability 0.114 0.767 -0.048 0.954 0.902 (0.385) (0.384) (0.366) Military regimea 0.937*** 0.009 0.816** 2.262** 0.026 (0.361) (0.366) (0.828) New states

dropped Dropped

GDP per capitaa, b, c -0.615*** 0.009 -0.548** 0.578** 0.020 (0.236) (0.236) (0.136) Population sizea, b, c 0.173 0.103 -0.013 0.987 0.910 (0.106) (0.118) (0.117) Oil exporting countriesa 1.069** 0.011 1.009** 2.742** 0.017 (0.419) (0.422) (1.158) % mountaineousb 0.010 0.121 0.006 1.006 0.380 (0.006) (0.007) (0.007) Ethnic fractionalizationb 0.160 0.799 0.106 1.111 0.868 (0.627) (0.635) (0.706) Religious fractionalizationb 0.375 0.617 0.820 2.269 0.306 (0.750) (0.801) (1.817) Constant -4.212 0.000 -5.264 0.000 (0.391) (0.482) N 2527 2450 Likelihood-Ratio chi² (11) 66.91 85.2 Pseudo R² 0.1362 0.1745 Note: standard errors are in parentheses * p < .1, ** p < .05, *** p < .01 a lagged by one year b centered on the mean c natural log taken

62

02

46

810

1214

1618

-10 -8 -6 -4 -2 0 2 4 6 8 10

Polity2 (lagged)

# civil war onset% civil war onset

Figure 2. Civil war onset and the (lagged) level of democracy.

02468

1012141618

1 2 3 4 5

modified PTS (lagged)

# civil war onset%civil war onset

Figure 3. Civil war onset and the (lagged and modified) PTS.

63

Table 2. Simulated effects of statistically significant variables.

change in independent variable

probability of civil war onset this year

ratio of probability / probability of 'average country'

probability of civil war onset over 5 years

probability of civil war onset over 10 years

'average-country' 0.51% 1.00 2.55% 5.03%

personal integrity abuses +1 (from 1 to 2) 1.04% 2.03 5.11% 9.97%

+4 (from 1 to 5) 8.21% 15.95 34.85% 57.56%

Democracy/Anocracy Polity2 = +5; anocracy 1.77% 3.44 8.54% 16.35%

Polity2 = -5; anocracy 0.15% 0.29 0.74% 1.47%

no anocracy 0.24% 0.47 1.21% 2.42%

No military regime military regime 1.16% 2.25 5.65% 10.99%

GDP per capita + 1 std. dev. (1985 US

$2,933) 0.29% 0.56 1.42% 2.82%

to max: 1985 US $28,901

(UAE) 0.13% 0.25 0.65% 1.30%

to min: US $215 (DR

Congo) 1.94% 3.77 9.34% 17.81%

No oil exporter oil exporter 1.40% 2.72 6.80% 13.14%

strong state, no repression 0.06% 0.12 0.31% 0.62% weak state, no repression 29.96% 58.19 83.14% 97.16% strong state, high repression 1.06% 2.06 5.18% 10.09% weak state, high repression 88.09% 171.09 100.00% 100.00% lowest predicted chance in sample United Arab Emirates, 1983 0.04% 0.09 0.22% 0.45% highest predicted chance in sample Uganda, 1981 57.21% 111.11 98.56% 99.98% Note: The 'average-country' is a country with all dummies and Polity2 set to '0', PTS at ‘1,’ and all other variables at their mean. A ‘strong state’ has an authoritarian, stable, and non-military government (Polity2 = -10, no anocracy), a high GDP per capita of $28,901 (in 1985 US constant $), is not an oil exporter, and has an average population size and average levels of mountainous terrain, ethnic and religious fractionalization. A ‘weak state’ is a stable anocracy (Polity2 = 5) that is governed by a military regime, has a low GDP per capita of $215, is an oil exporter, shows an average population size and average levels of mountainous terrain, ethnic and religious fractionalization. ‘No repression’ refers to a low level of ‘1’ on the PTS, ‘high repression’ to a level of ‘5.’

64

Table 3. Logit regression on the probability of civil war onset. Model 2 Model 3 Model 4 Model 5 Model 6

coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value

Pesonal integrity abusesa 0.358 0.044 1.291 0.000 0.737 0.000 0.403 0.025 0.602 0.000

(0.177) (0.164) (0.189) (0.180) (0.165)

Democracy (Polity2)a -0.023 0.815 0.160 0.046 0.285 0.002 -0.044 0.733 0.159 0.055

(0.097) (0.081) (0.091) (0.129) (0.083)

No anocracya -0.333 0.436 -0.507 0.162 -0.559 0.187 0.048 0.925 -0.569 0.124

(0.427) (0.363) (0.423) (0.515) (0.371)

(Polity2 * No anocracy)a 0.027 0.789 -0.147 0.075 -0.254 0.007 0.077 0.555 -0.162 0.060

(0.100) (0.082) (0.095) (0.130) (0.086)

Political instability 0.693 0.106 -0.027 0.945 0.092 0.828 0.258 0.552 0.301 0.437

(0.428) (0.386) (0.423) (0.434) (0.387)

Military regimea 0.303 0.439 0.092 0.803 0.532 0.181 0.248 0.517 0.744 0.040

(0.392) (0.368) (0.397) (0.383) (0.363)

New states dropped dropped dropped dropped dropped

GDP per capitaa, b, c -0.526 0.044 -0.234 0.317 -0.742 0.006 -0.829 0.003 -0.337 0.160

(0.261) (0.234) (0.269) (0.277) (0.240)

Population sizea, b, c 0.138 0.263 -0.018 0.877 0.147 0.277 0.231 0.074 0.061 0.595

(0.123) (0.115) (0.135) (0.130) (0.115)

Oil exporting countriesa 0.663 0.157 0.146 0.737 0.462 0.369 1.047 0.018 0.460 0.297

(0.468) (0.433) (0.513) (1.047) (0.441)

% mountaineousb 0.013 0.063 -0.003 0.719 0.010 0.154 0.002 0.777 0.003 0.704

(0.007) (0.007) (0.007) (0.008) (0.007)

Ethnic fractionalizationb 0.050 0.940 0.244 0.692 -0.154 0.820 0.674 0.316 0.644 0.322

(0.664) (0.615) (0.678) (0.672) (0.651)

Religious fractionalizationb 0.545 0.557 0.634 0.459 0.181 0.843 -0.505 0.568 0.648 0.431

(0.927) (0.857) (0.917) (0.885) (0.822)

Constant -5.261 0.000 -6.682 0.000 -5.734 0.000 -5.237 0.000 -5.273 0.000

(0.548) (0.559) (0.574) (0.601) (0.473)

N 2315 2781 2444 1828 2509

Likelihood-Ratio chi² (11) 44.41 124.39 74.84 46.83 60.84

Pseudo R² 0.117 0.237 0.187 0.1193 0.1325 Note: standard errors are in parentheses; coefficients with p < .1 are in bold letters a lagged by one year b centered on the mean c natural log taken

65

Table 4. Logit regression on the probability of civil war onset. Overall repression replaced by political imprisonment. Model 7 Sambanis (2004) coef. odds ratio p-value (Absence of) Political Imprisonmenta -0.606** 0.546** 0.043 (0.300) (0.164) Democracy (Polity2)a 0.346*** 1.413*** 0.000 (0.095) (0.134) No anocracya -0.804* 0.448* 0.057 (0.422) (0.189) (Polity2 * No anocracy)a -0.252*** 0.777*** 0.007 (0.093) (0.072) Political instability -0.392 0.675 0.408 (0.474) (0.320) Military regimea 1.299*** 3.667*** 0.007 (0.479) (1.757) New states

Dropped

GDP per capitaa, b, c -0.897*** 0.408*** 0.002 (0.294) (0.120) Population sizea, b, c 0.186 1.204 0.198 (0.144) (0.174) Oil exporting countriesa 1.183** 3.263** 0.024 (0.523) (1.708) % mountaineousb 0.007 1.007 0.398 (0.008) (0.008) Ethnic fractionalizationb -0.683 0.505 0.364 (0.752) (0.380) Religious fractionalizationb 1.286 3.620 0.177 (0.952) (3.447) Constant -4.086 0.000 (0.579) N 1925 Likelihood-Ratio chi² (11) 61.21 Pseudo R² 0.1834 Note: standard errors are in parentheses * p < .1, ** p < .05, *** p < .01 a lagged by one year b centered on the mean c natural log taken

66

A P P E N D I X

67

Table A2. Descriptive statistics of the independent variables.

Mean Std. Dev. Minimum Maximum Pesonal integrity abuses 2.496 1.220 1 5 Democracy (Polity2) -0.031 7.548 -10 10 No anocracy 0.855 0.352 0 1 Political instability 0.152 0.359 0 1 Military regime 0.247 0.432 0 1 New states 0.022 0.148 0 1 GDP per capita (in 1000) 4.428 4.649 0.196 31.969 Population size (in mio.) 30.329 111.380 0.017 1288.400 Oil exporting countries 0.159 0.366 0 1 % mountaineous 17.478 21.451 0 94.300 Ethnic fractionalization 0.409 0.284 0.001 0.925 Religious fractionalization 0.381 0.218 0 0.783 Note: These are the descriptive statistics for the raw data and not for the logged, lagged, or centered data. Not all the data are included in each analysis, due to missing data on other variables.

Table A1. Descriptive statistics of the civil war lists.

Years

covered

# of country-

yrs

Number of civil wars

% of civil war onsets

Number of civil war-years

% of civil war-years

Sambanis (2004) 1976-1999 3146 62 1.97% 673 21.39% COW 2000 1976-1997 2822 46 1.63% 352 12.47% Gleditsch et al. (2001) 1976-1999 3146 62 1.97% 279 8.87% Fearon & Laitin (2003) 1976-1999 3146 49 1.56% 645 20.50% Regan (1996) 1976-1994 2128 48 2.26% 571 26.83% Doyle & Sambanis (2000, extended)

1976-1999 3146 55 1.75% 604 19.20%

Note: Some observations may drop out in the analyses, due to missing data on other variables.

68

Table A3. Civil war onset and the (lagged) level of democracy.

Polity2

(lagged) No civil war

starts Civil war starts Total # of

country-years %

onset -10 114 1 115 0.88 -9 198 3 201 1.52 -8 157 3 160 1.91 -7 528 11 539 2.08 -6 142 5 147 3.52 -5 73 1 74 1.37 -4 42 1 43 2.38 -3 46 2 48 4.35 -2 63 4 67 6.35 -1 35 1 36 2.86 0 26 4 30 15.38 1 26 0 26 0 2 16 0 16 0 3 12 2 14 16.67 4 35 3 38 8.57 5 54 6 60 11.11 6 113 0 113 0 7 85 3 88 3.53 8 156 3 159 1.92 9 159 1 160 0.63 10 554 1 555 0.18

Note: The calculations are based on country-years.

Table A4. Civil war onset and (lagged) repression. PTS (lagged) No civil war starts Civil war starts Total % onset

1 592 0 592 0.00% 2 851 14 865 1.65% 3 692 16 708 2.31% 4 215 13 228 6.05% 5 50 7 57 14.00%

69

Table A5. Logit regression on the probability of civil war onset. Overall repression replaced by political imprisonment and torture.

Model 8 Model 9 Model 10 Model 11 Model 12 COW 2000 Gleditsch et al. (2001) Fearon & Laitin (2003) Regan (1996) Doyle & Samb. (2000, ext.) Coef. p-value Coef. p-value Coef. p-value Coef. p-value Coef. p-value (Absence of) -0.750 0.047 -1.248 0.000 -0.103 0.752 -0.008 0.981 -0.448 0.139 Political Imprisonment 0.378 0.320 0.326 0.324 0.303 Democracy (Polity2)a 0.028 0.826 0.320 0.000 0.350 0.001 -0.009 0.954 0.283 0.005 0.127 0.091 0.102 0.150 0.100 No anocracya -0.359 0.513 -0.665 0.089 -0.982 0.048 0.022 0.972 -0.590 0.182 0.550 0.391 0.497 0.624 0.442 (Polity2 * No anocracy)a 0.018 0.887 -0.251 0.006 -0.291 0.005 0.045 0.762 -0.256 0.011 0.128 0.090 0.103 0.149 0.100 Political instability -0.008 0.990 -0.535 0.266 -0.283 0.598 0.056 0.918 0.021 0.966 0.587 0.480 0.537 0.549 0.492 Military regimea 0.740 0.158 0.939 0.030 1.272 0.015 0.688 0.202 1.599 0.001 0.523 0.433 0.525 0.539 0.489 New states

dropped dropped dropped dropped dropped

GDP per capitaa, b, c -0.575 0.094 -0.500 0.067 -0.819 0.014 -0.949 0.007 -0.361 0.226 0.343 0.273 0.333 0.353 0.298 Population sizea, b, c 0.231 0.132 0.205 0.097 0.403 0.013 0.591 0.001 0.289 0.033 0.153 0.123 0.163 0.177 0.135 Oil exporting countriesa 0.493 0.435 0.931 0.044 0.579 0.354 1.185 0.029 0.429 0.433 0.630 0.463 0.625 0.541 0.547 % mountaineousb 0.018 0.053 0.005 0.516 0.014 0.121 0.009 0.370 0.007 0.467 0.009 0.008 0.009 0.010 0.009 Ethnic fractionalizationb -0.446 0.590 -0.402 0.566 -0.185 0.827 0.099 0.907 0.220 0.780 0.829 0.701 0.848 0.847 0.788 Religious fractionalizationb 0.981 0.414 0.517 0.568 1.489 0.175 0.020 0.986 1.392 0.150 1.201 0.906 1.099 1.123 0.968 Constant -4.334 0.000 -3.576 0.000 -4.796 0.000 -5.185 0.000 -4.503 0.000 0.685 0.476 0.686 0.818 0.586 N 1737 2195 1917 1309 1983 Likelihood-Ratio chi² (11) 30.52 60.99 55.33 37.56 49.09 Pseudo R² 0.1294 0.1624 0.201 0.1471 0.1537 Note: standard errors are in parentheses; coefficients with p < .1 are in bold letters a lagged by one year b centered on the mean c natural log taken

70

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