FROM NON-VIOLENT TO VIOLENT CONFLICTS: EXAMINING CONFLICT MILITARISATION*

Henrikas Bartusevičius Department of Political Science and Government, Aarhus University Word count: 9 353

Corresponding author: henrikas@ps.au.dk

* Paper prepared for presentation at the Danish Political Science Association’s annual meeting, Vejle, Denmark,

October 25-26 2012.

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Abstract

A number of present day states have experienced one or other form of domestic political crises

that had potential of escalating into an armed conflict. Yet, there have been more states that

have managed to solve these crises through the use of non-violent means than those that have

not. What explains the violent turn in non-violent conflicts? More specifically, what are the

factors that contribute to conflict militarization? Despite their academic relevance and potential

implications for policymakers, these questions have attracted relatively little attention in

conflict research. The present study aims to address this gap. It presents a new approach to

conflict analysis that focuses on variables linked to conflict militarization. In contrast to

traditionally posed questions of ‘what are the causes of armed conflicts’, this study asks: ‘what

are the factors that lead from potential armed conflicts to the actual armed conflicts?’ In

contrast to commonly used comparisons of states ‘at peace’ with those ‘at war’, this study

compares states ‘likely to be at war’ with those that are at actual war. The study offers three

contributions to conflict research. First, it provides a conceptual delimitation of non-violent and

violent phases of conflict that facilitates isolation of the factors that contribute to conflict

militarization. Second, it empirically demonstrates that likelihood of conflict militarization

significantly depends on (1) conflict history, (2) rebels’ ability to recruit (3) and states’ military

capacity. Third, the study reveals that onset of non-violent and violent conflicts are linked to

different factors. More specifically, the analysis shows that low GDP per capita well explains the

onset of non-violent conflicts, but fails to account for why non-violent conflicts become violent.

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Introduction

In the early 1990s, three Baltic countries – Estonia, Latvia and Lithuania, and three

Transcaucasian countries – Armenia, Azerbaijan and Georgia gained formal independence from

the Soviet Union. The initial years of independence introduced a number of challenges to the

newly-established states. Political stability was hard to come by.1 Economic problems reached

an unprecedented scale.2 Russian forces – despite the official recognition – refused to leave the

territory of all six states. Tensions with ethnic minorities (Russians in Estonia and Latvia,

Russians and Polish in Lithuania, Azeris in Armenia, Armenians in Azerbaijan and Abkhazians

and Ossetians in Georgia) developed into open political confrontation.3

All six countries thus shared characteristics (though to varying degree) that conflict researchers

consider to be significant risk factors for the outbreak of intrastate armed conflicts or civil wars

(further violent conflicts)4: imperial past (Wimmer, Cederman & Min: 2009); newly established

states (Fearon & Laitin, 2003), democratic transition (Hegre et al., 2001); economic

underdevelopment (Fearon & Laitin, 2003; Collier & Hoeffler, 2004); and external military

presence [reference]. Moreover, the six countries were in actual conflicts with their ethnic

minorities. Thus, not only opportunities for violence were present, but reasons as well.

This notwithstanding, Estonia, Latvia and Lithuania, came out of the post-independence period

without major political violence. The story was different in Transcaucasia. Armenian minority in

Azerbaijan’s region of Nagorno-Karabakh embraced separatism and, with support of Armenia,

fought Azerbaijan’s forces in what is now known as Nagorno-Karabakh War. Similarly,

1 During the first five years of independence Estonia experienced four cabinet collapses, Latvia three and Lithuania

five. An average cabinet duration for the period of 1991-2003 was below one year in Latvia (339.9 days) and just over one year in Estonia (476.9) and Lithuania (446.9) (Muller-Rommel, Fettelschoss & Harfst, 2004: 876). Azerbaijan and Georgia experienced several bloody coups (coup attempts) and, as described below, large-scale civil wars. Armenia did not experience major political violence within its borders, but took part in the Nagorno-Karabakh War (see below). 2 Between 1990 and 1994, GDP per capita dropped by 25.6% in Estonia, 43.3% in Latvia, 43.7% in Lithuania, 46.6%

in Armenia, 54.4% in Azerbaijan and 70.8% in Georgia (Maddison, 2008). 3 For Baltic countries see, for example, Vetik (1993) and Fearon & Laitin (2006); for Transcoucasian countries, see

references cited in Footnote 5. 4 In the text below I use the term ‘violent conflict’ interchangeably with the term ‘armed conflict’, and ‘non-violent’

interchangeably with ‘non-armed conflict’. These terms are used exclusively to refer to intrastate conflicts. Definitions are provided in Section Three.

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Abkhazian and Ossetian separatists fought Georgia’s forces in what is now known as the War in

Abkhazia (1992-1993) and the 1991-1992 South Ossetia War. Armenia, while free from armed

conflicts in its own territory, engaged in the warfare with Azerbaijan over Nagorno-Karabakh.

The three conflicts resulted in thousands of dead and hundreds of thousands displaced.5

Why did the conflicts with ethnic minorities turn so violent in Transcaucasia, but not in Baltics?6

Or, in more general terms, why do non-violent conflicts turn violent (or militarize) in some cases

and why do they not in the others? We know from previous research a number of variables that

increase the risk of armed conflict onset (e.g., Dixon, 2009; Hegre & Sambanis, 2006). Yet, we

know little about which of these variables account for why conflicts start in the first place (‘root

causes’) and which for why conflicts become violent (i.e., militarize). Reasons for conflict are as

important as factors that make armed conflicts violent – both are necessary conditions for the

outbreak of armed conflict. Yet, it is the latter that decides whether conflicts are managed

peacefully or turn into an armed confrontation, which produces the dire consequences that the

researchers and decision-makers care most about.

The present study provides the first cross-national tests of the variables potentially linked to

conflict militarization. It introduces a new approach to conflict analysis that focuses on the

transfer from non-violent to violent conflicts. In contrast to traditionally posed questions of

‘what are the causes of armed conflicts’, this study asks: ‘what are the factors that lead from

potential armed conflicts to the actual armed conflicts?’ In contrast to commonly used

comparisons of states ‘at peace’ with those ‘at war’, this study compares states ‘likely to be at

war’ with those that are at ‘actual war’.

The study employs newly introduced Conflict Information System (CONIS) data set that codes

all conflict events – violent and non-violent – in the world between 1945 and 2008. The study

finds that conflict militarization significantly depends on (1) conflict history, (2) rebels’ ability to

5 The Nagorno-Karabakh War left an estimated 25 000 dead and over one million displaced (Human Rights Watch,

1994: ix); the War in Abkhazia resulted in some 8000-9000 dead and 200 000 displaced (Human Rights Watch, 1995: 5-6); the 1991-1992 South Ossetia War left 1000 dead and 40 000 – 100 000 displaced (International Crisis Group, 2004). 6 Note that some studies (e.g., Conflict Barometer, 2010), indeed, refer to ethnic conflicts in Baltic countries

(excluding Lithuania) as ‘non-violent conflicts’.

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recruit, and (3) states’ military capacity. In addition, the study reveals that onset of non-violent

conflicts and conflict militarization are linked to different factors. More specifically, the analysis

shows that GDP per capita – one of the most commonly employed predictor of violent conflict

(e.g., Hegre & Sambanis, 2006) – well explains the onset of non-violent conflicts, but fails to

account for why non-violent conflicts become violent.

The study proceeds as follows. Section One provides a brief overview of the previous literature

on the causes of armed conflicts and introduces an analytical framework that distinguishes

between ‘underlying’ and ‘facilitating causes’ of armed conflicts; Section Two provides a

discussion on the link between ‘underlying’ and ‘facilitating’ causes on the one hand and non-

violent and violent conflicts on the other; Section Three discusses plausible mechanisms

through which non-violent conflicts may become violent; Section Four proceeds to empirical

analysis; Section Five discusses the implication of the results of the empirical analysis; and

finally, the Conclusion offers suggestions for further research.

1. Underlying and facilitating causes of armed conflicts

What causes intrastate armed conflicts or civil wars? Conflict researchers have dealt extensively

with this question. It has been shown that certain political (e.g., Hegre et al, 2001), economic

(e.g., Collier & Hoeffler, 2004), ethnic (e.g., Ellingsen, 2000), demographic (e.g., Homer-Dixon,

1999) and geographic (Buhaug, 2006) variables cross-nationally correlate with the incidence

and onset of intrastate armed conflicts (for an overview of specific variables see Dixon, 2009).

These variables could be grouped into two broad categories. The first category encompasses

variables related to the underlying (or ‘root’) causes of conflicts – factors that generate

incompatibilities between certain actors. Traditionally, these factors have been associated with

the so-called ‘grievance approach’ (Gurr, 1970; 2000) and include such variables as social

inequality (e.g., Østby, 2008) or political repression (e.g., Auvinen, 1997). The second group

encompasses variables linked to facilitating causes (or so called ‘catalysers’) – factors that make

armed conflict practically plausible. Commonly, these factors have been associated with the so-

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called ‘opportunity’ approach (Tilly, 1978) and include such variables as ‘lootable resources’ or

rough terrain (e.g., Fearon & Laitin, 2003).7

The two categories of variables play rather different roles in the outbreak of armed conflicts.

Underlying causes of conflicts generate incompatibilities (see the next section); yet, they are

rarely linked to the actual outbreak of violence. Consider, for instance, restricted access to

education for ethnic minorities. This may serve as a reason for conflict, but it cannot provide

any means to initiate an armed struggle, nor to sustain it. Similarly, facilitating variables

account for the actual outbreak of armed struggle, but seldom for the generation of the original

incompatibilities. Consider, for example, mountainous terrain – a factor that makes armed

conflict more plausible – it provides a physical cover to the rebels (who are, in most cases,

militarily too weak to openly confront the state armies) (e.g., Fearon & Laitin, 2003). Yet,

mountainous terrain can hardly account for why conflicts start in the first place.

It is evident thus that these two categories of causes are linked to different conflict processes.

More specifically, underlying causes are linked to motivation while facilitating causes are linked

to opportunities. An outbreak of armed conflict always requires both; yet, they are often

independent of each other and affect conflict processes through different ways.8 As the present

study demonstrates, the distinction between motivation and opportunities could be plausibly

applied to the distinction between non-violent and violent conflicts.

2. Non-violent and violent conflicts

Hereby I define conflict, in broad terms, as a contested incompatibility, where incompatibility

means ‘incompatible difference of objective – i.e., in its most general form, a desire on the part

of both contestants to obtain what is available only to one, or only in part’ (Dahrendorf, 1959:

135). Subsequently, in line with previous work (e.g., Gleditsch, et al., 2002; Small & Singer,

1982), I define intrastate conflict as a contested incompatibility over government and/or

7 There is also a third category that is beyond the scope of this study – ‘triggers’ – idiosyncratic events that mark

the actual outbreak of violence. Triggers include assassination of political leaders (e.g., Rwanda in 1994), influx of refugees (e.g., Democratic Republic of Congo in 1996) and election fraud (e.g., Kenya in 2008). 8 Motivation and opportunities could be related though – high opportunities may provide motivation, and high

motivation may outweigh opportunities (see below).

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territory between two or more politically organized actors – the one of which is a government

of a state – that takes place primarily within the borders of one state. Finally, I define intrastate

armed conflict as a contested incompatibility over government and/or territory between two or

more politically organized actors – the one of which is a government of a state – that takes

place primarily within the borders of one state and involves systematic use of armed force.9 10

It should be clear, then, that non-violent and violent conflicts are, essentially, just two phases of

the same process – contested incompatibility, distinguished by the presence or absence of a

systematic use of armed force. I assume, therefore, that the incompatibility over which conflict

evolves is, in substance, the same in the violent and non-violent phases. For example, a non-

violent conflict over autonomy of a particular region has, essentially, the same motivation as

the subsequent violent conflict over the autonomy of the same region. The intensity of the

factors that provide motivation, for instance, intensity of ethnic discrimination, may change

over time, and give an impetus for violence. Yet, the basic issue at stake remains the same, and

rarely changes as the non-violent conflict phase moves to the violent one.

Therefore, I treat motivation as a constant factor in the process of conflict militarization. While

it may explain the incidence of non-violent conflicts, motivation cannot, in most cases, account

for why non-violent conflicts become violent. Accordingly, I propose that analysis of the onset

of non-violent conflicts and analysis of why non-violent conflicts become violent should focus

on motivation for conflict and opportunities for an armed conflict respectively.

3. What accounts for the violent turn in non-violent conflicts?

9 Systematic implies that armed force is used in an organized fashion and commonly over an extended period of

time. Thus, ‘relatively spontaneous, unorganized political violence with substantial popular participation, including violent political strikes, riots, political clashes, and localized rebellions’ (Gurr, 2011: 11) fall outside the category of violent conflict in the present study. 10

Note, therefore, that contrary to some of the previous work (e.g., Gleditsch, et al., 2002; Small & Singer, 1982) human casualties are considered here as an attribute that commonly accompanies intrastate armed conflicts, but not as a defining feature of a phenomenon of an intrastate armed conflict (I address this issue in detail in Section Four).

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The ‘why men rebel model’ (Gurr, 1970) serves as the basic theoretical framework in the

following discussion on why non-violent conflicts become violent. In the Introduction to the

Fortieth Anniversary Edition of Why Men Rebel (2011) Gurr has pointed out that:

The essential argument of the why men rebel model is that to understand protest and rebellion in

general, and in specific instances, we should analyse three general factors. First is popular

discontent (relative deprivation), along with its sources. Second are people’s justifications or

beliefs about the justifiability and utility of political action. Third is the balance between

discontented people’s capacity to act – that is, the ways in which they are organized and the

government’s capacity to repress or channel their anger (ix).

Paraphrasing Gurr, the outbreak of an armed conflict depends on (1) the would-be rebels’

motivation for an armed conflict, (2) the extent to which would-be rebels justify the use of

political violence and (3) the chances of success (as perceived by the would-be rebels) in an

armed struggle against a state.11

As mentioned above, the question of motivation is largely extraneous for the analysis of

conflict militarization. The present study, thus, focuses on the questions of what justifies

political violence and what determines the chances of rebels’ success in an armed struggle

against a state.

Justifying political violence

First of all, we have to acknowledge the fact that would-be rebels’ decision to take up arms

depends, among other things, on normative justifications for using political violence, more

specifically, the degree to which society (including would-be rebels) justifies the use of

violence against a government of a state:

‘[Men] are likely to hold norms about the extent to which and the conditions under which

violence generally, and political violence specifically, is proper… The greater men’s normative

justifications for violence, the more likely they are to be willing to participate in political violence

(Gurr, 2011: 157)

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Where success is defined as an engagement in a military action against a state over a prolonged period of time, that eventually results in a complete or partial fulfilment of rebels’ political objectives and/or capture of a state.

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Further, we have to recognize the fact that societal norms and the degree to which people

justify political violence also influences governments’ decision to use or not to use violence

against the would-be rebels. A state that employs violence to deal with potential contenders

puts its legitimacy at risk if a society, in which the violence is projected, is highly averse

towards the use of (political) violence. In contrast, a state will have fewer inhibitions to use

violence against contenders if a society, in which the violence is projected, supports the use

of (political) violence. Thus, I propose the following hypothesis:

H1: the more the would-be rebels justify the use of violence against a state (and vice versa),

the higher the chance of conflict militarization, ceteris paribus.

Calculated (or perceived) chances of success

Even if would-be rebels justify political violence against a state, armed conflict will not take

place if the would-be rebels perceive their strength inferior to the strength of government.

Thus, we also have to acknowledge, that decision to take up arms and participate in a violent

collective action involving high risks must be based, at least partly, on the calculation of the

chance of success and potential costs. As Hendrix puts it:

Rebellion is an inherently militarized act that entails the risk of capture, injury, imprisonment, and

death, and we assume potential rebels factor the size, strength, and skill of state forces into their

decision to rebel. Ceteris paribus, as smaller or less organized army should pose less threat than a

larger or more organized one’ (2010: 274).

The question, then, is: what are the potential factors that increase the chance of rebels’ success

in an armed conflict against a state? I argue here that, among others, these are the rebels’

ability to recruit and the states’ military capacity.

While military history provides numerous examples of small groups successfully fighting state

armies over prolonged periods, successful rebellion often requires significant manpower. The

actual size of rebel organization, as well as the projected size of potential pool of recruits,

should be an obvious indicator of success (in a potential armed conflict against a state) for the

leadership of (would-be) rebellion. Thus, other things being equal, the higher the ability to

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recruit rebels, as perceived by rebellion leadership, the higher the chance that rebellion will

turn violent. Thus, in a more formalized way:

H2: The higher the rebels’ ability to recruit, the higher the chance of conflict militarization,

ceteris paribus.

State’s military capacity, often indicated by its national army size, military technology,

organisation and skills, is another obvious indicator of the rebels' chances. Ceteris paribus,

chances of rebel success in a conflict against a highly capacious (in military terms) state will be

lower than in a conflict against a militarily weak state. Further, militarily strong states,

exercising extensive control over their territories and populations, have higher capacity to

monitor and prevent would-be rebels from establishing a rebel organization, as well as to track

down and capture illegal flows of arms that can potentially be used by the rebels. Thus, I

propose the following hypothesis:

H3: the more capacious the state (in military terms), the lower the chance of conflict

militarisation, ceteris paribus.

4. Empirical analysis

The hypotheses were tested in a standard logistic regression analysis covering all conflict dyad-

years in the world recorded in the period of 1961-2008. The primary unit of analysis was a

conflict dyad-year, which was coded with ‘1’ if the conflict reached the level of ‘violent-conflict’

and ‘0’ if the conflict remained ‘non-violent’ (operational definitions are provided below). Given

that this study was primarily focused on the onset of conflict (not incidence), on-going years of

violent conflict were dropped. Further, in line with other studies (e.g., Buhaug, 2010:118), I

applied the two-year intermittency rule – if the intensity of the conflict dropped below the level

of ‘violent-conflict’ in two successive years and reached the level of a 'violent conflict' in the

third, I coded the third dyad-year as a new onset of a violent conflict.

The Conflict Information System (CONIS) database

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To construct the proxy of the outcome variable, I employed Conflict Information System

(CONIS) database. At present, CONIS provides a beta version of the data set that includes all

conflict events (intrastate and interstate) recorded between 1945 and 2008.12 Conflict in CONIS

is defined as follows:

the clashing of interests (positional differences) over national values of some duration and

magnitude between at least two parties (organized groups, states, groups of states, organizations)

that are determined to pursue their interests and achieve their goals (Conflict Barometter, 2010:

88).

In contrast to similar projects (e.g., Gleditsh et al., 2002; Small & Singer, 1982), CONIS employs

qualitative criteria for coding conflicts, which has several advantages. First of all, qualitative

coding avoids the need to rely on arbitrary quantitative thresholds of battle-related deaths

(BRD) to include conflict events into the conflict list. If we code an armed conflict based on the

number of casualties the conflicts results in, what threshold should we choose – 25 BRD

(Gleditsch et al., 2002), 500 BRD (Sambanis, 2004) or 1000 BRD (Fearon & Laitin, 2003)? The

way we understand the phenomenon of an armed conflict is largely independent on how

effective the conflicting parties are at killing each other’s troops. What matters is whether a

systematic use of an armed force over a prolonged period of time with an aim to advance

political objectives was involved or not. Disregarding this crucial aspect of a concept and relying

on BRD results in conflict lists that include events at odds with an intuitive understanding of an

armed conflict or civil war, for example, the September 11 attacks or the Grand Mosque Seizure

in Saudi Arabia (1979).

Second, qualitative coding avoids relying on the number of BRD to code the start and end of a

conflict. How many individuals should conflicting parties kill over a calendar year to code the

conflict year as an ‘armed conflict onset year’? The onset of conflict coincides with the time

when conflicting parties decide to use an organized armed force against each other to address

their incompatibilities, and not with the time when the use of that armed force results in 25,

100 or 1000 BRD. Similarly, the end of conflict coincides with the time when conflicting parties

12

The beta version of the data is available upon request from Heidelberg Institute for International Conflict Research (www.hiik.de).

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come to an agreement according to which they consent to refrain from the use of armed force

against each other, and not with the time when armed hostilities result in fewer than 25, 100 or

1000 BRD.

Finally and perhaps most importantly, qualitative coding criteria allows coding of non-violent

phases of conflicts. Table I shows the five phases of conflict coded in the CONIS data set.

[Table I here]

Such a classification allows researchers to analyse conflict escalation, de-escalation and, most

importantly for the present study, conflict militarization.

Conflict coding on the basis of qualitative criteria has disadvantages as well. The coding rules

for including conflict events into the CONIS data set are somewhat evasive. This leaves more

space for interpretation of what constitutes a conflict and which intensity category the conflict

fits in – an issue that is largely avoided in the data sets that employ rigid, quantitative criteria

such as the threshold of 25 battle-related deaths over the calendar year. Therefore, coding

based on qualitative criteria risks producing a sample of conflict events that are more

heterogeneous. However, as I demonstrate in the following sub-section, intrastate violent

conflicts coded on the basis of the CONISs qualitative criteria yields a list of conflicts that – to a

large extent – overlaps with the list of conflicts in UCDP/PRIO Armed Conflict Dataset (Gleditsch

et al., 2002; Themner & Wallensteen, 2012) (further UCDP/PRIO) – one of the most commonly

employed conflict data sets that employs quantitative coding criteria.

Outcome variable

CONIS data set collects information on both interstate and intrastate conflicts. Given that this

study was exclusively focused on intrastate conflicts, I dropped conflict events coded in the

original CONIS data set as conflicts between two (or more) states. In addition, I excluded ‘non-

state conflicts’ – conflict events between non-state actors that are typically considered distinct

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from intrastate conflicts (e.g., Sundberg, Eck & Kreutz, 2012). In line with previous studies, I also

excluded ‘anti-colonial’ (or ‘independence’) conflicts.13

Subsequently, following the definitions introduced in the previous section and the criterion of

the systematic use of force, I aggregated latent conflicts, manifest conflicts and crises listed in

the original CONIS data set (see Table I) into the category of ‘non-violent conflict’ and severe

crises and wars into the category of ‘violent conflict’. Finally, I operationalized conflict

militarization as a change in the conflict category from ‘non-violent conflict’ to ‘violent conflict’.

The figures below provide descriptive statistics on the incidence of intrastate violent and non-

violent conflicts listed in CONIS data set.

[Figure 1 here]

[Figure 2 here]

As Figure 1 demonstrates, the number of violent conflicts peaked in the early 1990s – a pattern

that is also reflected in the UCDP/PRIO data set (Figure 2). Indeed, as shown in Figure 2, the

pattern of conflict incidence and onset in the CONIS data set closely approximates the pattern

of conflict incidence and (to a lower degree) onsets in the UCDP/PRIO data set.14 Further, the

figure reveals that CONIS data set contains fewer violent conflict years (i.e., incidence), as well

as new onsets, than UCDP/PRIO data set – an indication that the coding criteria employed in

CONIS results in somewhat more conservative conflict list. Yet, as demonstrated in Table II, the

effect of some of the commonly employed conflict predictors – population size (Maddison,

2008), GDP per capita (Maddison, 2008), ethnic fractionalization, ethnic polarization (Alesina et

al., 2003) xpolity scores, xpolity scores squared (Vreeland, 2008), oil and gas production and oil

and gas production squared (Ross, 2011) is almost identic on the UCDP/PRIO intrastate armed

conflicts and CONIS intrastate armed conflicts.

[Table II here]

13

I also removed conflicts coded as ‘partition’ in CONIS (e.g., India in 1947), considering them as part of ‘independence’ conflicts. 14

The difference in onsets is partly determined by the fact that UCDP/PRIO codes conflict onsets on the basis of

accumulated BRD (see above).

14

Measuring justification for violence

The extent to which people (or would-be rebels) justify the use of violence against a state

can hardly be measured directly – a task that is particularly complex in the context of a

cross-national comparison. The present study, therefore, relied on proxies that potentially

correlate with the extent the would-be rebels’ justify the use of political violence. What are

the likely indicators that would-be rebels will be willing to justify the use of violence against

a state? I argue here, that we can account for this – at least partly – by the simultaneous

presence or history of political violence (hereafter conflict history). Ceteris paribus, would-be

rebels will more likely justify the use of violence against a state, if, at the same time, they

will observe others using violence against a state - ‘we acquire our norms about violence

partly from how we are taught to deal with aggressive impulses, partly from our cultural

heritages of civil peace and conflict’ (Gurr, 2011: 193). Similarly, other things being equal,

would-be rebels will be more inclined to use violence if they will observe that such violence

has been used in the past by others.15 In addition, it is likely that governments that have

used violence against contenders in the past will have fewer normative inhibitions to use

violence against contenders in the future. In other words, would-be rebels (as well as

governments) will find the use of force – as a means to address political incompatibilities –

more legitimate in countries where such means have been practised on numerous occasions

than in those where such means have never been employed. Thus, I coded conflict dyads

with ‘1’ in a given year if a state involved in the dyad has experienced (since 1946 or

independence) or experiences a conflict with another actor16 and ‘0’ if a state involved in the

dyad is not experiencing or has not experienced any conflict in the past.

Measuring rebels’ ability to recruit and states’ military capacity

15

Indeed, in the original hypotheses on violence justification Gurr has proposed that history and frequency of political violence should vary with justification for political violence: ‘Hypothesis JV.2: The intensity and scope of normative justifications for political violence vary strongly with the historical magnitude of political violence in a collectivity’ (Gurr, 2011: 170) and ‘Corollary JV.2.1: The more frequent the occurrence of a particular form of political violence in a collectivity, the greater the expectation that it will recur’ (Ibid.) 16

Conflicts between same actors may relapse because of other reasons, such as persisting hatreds or remaining rebel organization (e.g., Collier, Hoeffler & Rohner, 2009).

15

What are the factors that potentially favour the recruitment of the would-be rebels? It has

been argued that these are, among others, youth bulges (Urdal, 2006) and low GDP per capita

(Collier & Hoeffler, 2004). According to Collier (2000), existence of large cohorts of young

people in population lowers the costs of recruitment. This is because opportunity costs for

youth are relatively low. Youth unemployment rates are generally higher than unemployment

rates for other age groups. The income of young people is also generally lower than the income

of the other age groups. Thus, young individuals have typically lower opportunity costs to join

rebellion – they have less to lose that to gain. Following Urdal (2006), I measured youth bulges

with a % of 15-24-year-olds in the total adult population (15 years and above). The data was

taken from World Population Prospects (further WWP) (United Nations, Department of

Economic and Social Affairs, Population Division, 2011). The data in WWP is quinquennial. To

adjust it to the dyad-year regression, I imputed annual observations with linear ipolate function

in Stata.17

Rebel leaderships’ ability to recruit also depends on the income of would-be rebels. Potential

rebels with a low income will be more willing to join a rebellion than those with a high income

because of the same opportunity costs – the former will have less to loose than the latter. Thus,

rebel leadership in states with low GDP per capita should have higher chances of successful

rebel recruitment. The income of would-be rebels was proxied with a natural log of GDP per

capita (t-1) (hereafter GDP per capita). Data on GDP per capita was taken from Maddison

(2008).

What are the potential indicators of states’ military capacity? In line with other studies (e.g.,

Mason & Fett, 1996; Balch-Lindsay, Enterline & Joyce, 2008; Walter, 2006) I proxied states’

military capacity by the size of military personnel. Other things being equal, states that are able

to maintain large national armies should be militarily stronger than countries keeping smaller

national armies. The data on military personnel was taken from the Material Capabilities

dataset (v4.0) (Singer, Bremer & Stuckey, 1972; and Singer, 1987).

17

Note that autocorrelation in the time-series on age cohorts is very high; therefore, linear imputation should have not resulted in significant measurement error.

16

In addition to the indicator of an absolute state capacity, I introduced a proxy that captures the

relative state capacity vis-à-vis the rebels – vertical income inequality. Vertical inequality

indicates a distance between the ‘advantaged’ and the ‘disadvantaged’, who typically represent

different sides in intrastate conflicts (i.e., the government and the rebels):

Conflict protagonists in a society are often divided into two groups: the challenging groups, i.e.,

the have-nots or the disadvantaged, who seek economic equality by attacking the status quo

distribution of resources; and the established groups, i.e., the haves or the advantaged, who

perpetuate economic inequality by defending the status quo distribution of resources (Lichbach,

1989: 432).

Thus, while vertical income inequality cannot directly capture the relative capacity of the rebels

(vis-a-vis the government), it can account, at least partly, for the relative share of resources

available to the government and the rebels, and thus, their abilities to recruit, equip and

maintain military forces. The more affluent the rebel organization is vis-à-vis the state, the

higher its ability to recruit, equip and retain would-be rebels. Similarly, the more affluent the

state is vis-à-vis the rebels, the higher its ability to recruit, equip and retain army soldiers.

Income inequality was measured with the Gini Index of Net Income Inequality (hereafter

income Gini). The values of the index range between 0 and 1, 0 indicates a perfect equality (i.e.,

perfectly equal distribution) and 1 – a perfect inequality (i.e., perfect concentration). The data

on income Gini was taken from the Standardized World Income Inequality Database (Solt, 2009)

(hereafter SWIID). SWIID contains a substantial number of missing observations. To complete

the data, I employed multiple imputation software Amelia II (Honaker, King & Blackwell,

2011).18 19

Control variables

18

The missing observations were filled in with the average value of ten imputed data sets. The imputation model included the Gini Index of Educational Inequality (Benaabdelaali, Hanchane & Kamal, 2011), GDP per capita, annual GDP per capita growth (Maddison, 2008), xpolity scores and xpolity scores squared (Vreeland, 2008) and years in peace since the last conflict (1946 or independence), as well as cross-sectional and time-series terms. Imputation diagnostics are available upon request. 19

While multiple imputation cannot substitute complete data sets, it can (and, under general conditions, does) outperform listwise deletion. See, for example, King et al. (2001).

17

In addition to the main predictors, I introduced two control variables – population size and

peace years. The size of military personnel is obviously dependent on the country size – large

and populous countries need larger national armies. Large and populous countries are in turn,

more prone to intrastate armed conflict than the small and less populous ones (e.g., Buhaug,

2006). To account for this, I introduced a measure of population size (Maddison, 2008).

As a standard procedure I also controlled for the time dependence by introducing a continuous

variable of peace years (e.g., Urdal, 2006: 617). To account for the fact that every additional

year in peace decreases the risk of conflict exponentially, I adopted the following decay

function: .20 The variables are summarized in Table III.

[Table III here]

Results

Table IV reports regression estimates of the effects of the proxies on the militarization of non-

violet conflicts. The table shows that countries that experience violent conflicts (or have

experienced violent conflicts in the past), indeed, have significantly higher likelihood of conflict

militarization (Model 1.1). The effect remains highly significant even when additional covariates

are added to the block (Models 1.2-1.5). The same is true for the measure of youth bulges – the

effect is positive and significant in all five blocks at p < .001 level. As expected, GDP per capita

negatively affects conflict militarization; yet, the coefficient is insignificant (Model 1.3 – 1.5),

just as the coefficient of the income Gini (Model 1.5). In contrast, the effect of military

personnel is statistically significant, and, in line with expectations, negative (1.4-1.5). Finally, the

table reveals that population size has no effect on the outbreak of violent conflicts. In contrast,

as shown in Models 1.2-1.5, peace years have significant and negative effect on the

militarization of conflicts, which suggests that, indeed, countries recovering from a violent

conflict have higher likelihood of violent conflict onset.

[Table IV here]

20

Peace years stand for the number of years since the last conflict (independence or 1946). X represents the rate of decay that decreases the effect of peace years with every additional year in peace. Following Hegre et al. (2001), I set x to 4, which halves the effect of the peace years with every additional three years in peace.

18

What are the marginal effects of the previous conflict, youth bulges and military personnel on

the militarization of non-violent conflicts? Other things being equal, a country that experiences

(or has experienced) a violent conflict, has almost two times higher likelihood of another non-

violent conflict militarization than a country that has never experienced violent conflicts in the

past (2.5% compared to 4.3%).21 A country where 15-24-year-olds constitute 37% (95th

percentile) of the total adult population has almost four times higher probability of conflict

militarization than where 15-24-year olds constitute 17% (fifth percentile) of the total adult

population (5.8% compared to 1.5%). A country of 200 million people (similar to a present-day

Brazil) with every additional 20 000 increase in its military personnel decreases the chance of

non-violent conflict militarization by approximately 0.3%. Finally, a country of 200 million

people that maintains 193 000 of military personnel, that has never experienced a violent

conflict in the past and where 15-24-year-olds constitute just 17% of the total adult population

has almost six times lower probability of conflict militarization than a country of the same size

that keeps 63 000 of military personnel, that experiences (or has experienced) a violent conflict

and where 15-24-year-olds constitute 37% of the total adult population (1.2% compared to

6.7%). The marginal effects are summarized in Figure 3.

[Figure 3 here]

Additional tests

A number of studies have demonstrated that certain variables affect different types of armed

conflicts in a non-uniform way (e.g., Buhaug, 2006; Sambanis, 2001; Wimmer, Cederman & Min,

2009). Hereby, I test whether proxies employed in this study have non-uniform effects on the

militarization of territorial and governmental conflicts (Table V, Models 2.1a and 2.1b), ethnic

and non-ethnic conflicts (2.2a and 2.2b) and ethnic governmental and non-ethnic governmental

conflicts (2.3a and 2.3b).22

[Table V here]

21

The probabilities were estimated using Clarify software (Tomz, Wittenberg & King, 2003; also King, Tomz & Wittenberg, 2000). 22

To derive the different conflict categories, I matched CONIS conflict list to the conflict list in Author (2012).

19

As shown in the table, the effect of previous conflict, youth bulges and military personnel on

territorial and governmental conflicts follows a similar pattern. A slight difference is indicated

by the measure of military personnel – while it negatively affects both conflicts, the p-value for

the coefficient in Model 2.1b is below level of significance (note the reduced sample size). The

same is true in the model of non-ethnic conflict (2.2b) (note that the effect remains negative

and the p-value is just marginally below level of p < .10). Model 2.2b also reveals that previous

conflict has somewhat weaker effect on the outbreak of non-ethnic conflicts. Further, Models

2.3a and 2.3b demonstrate that previous conflict has also weaker effect on non-ethnic

governmental conflicts compared to ethnic governmental ones. Finally, the models reveal that

the effect of military personnel remains negative on the former but becomes positive on the

latter.

Interestingly, youth bulges exert positive and significant effect in all six blocks. The same is true

for the measure of peace years. In contrast, the effect of GDP per capita remains consistently

insignificant in all six models. Notably, income Gini exerts strong negative effect on ethnic

governmental conflicts – a finding I address in the subsequent section. In sum, Table V reveals

that the effect of the proxies on disaggregated conflicts – despite slight deviations in p-values23

– is largely in line with the effect of the proxies on the aggregate conflict.

Finally, to assess whether the variables employed in this study affect non-violent conflicts, I

regressed the same proxies in the model of non-violent conflict onset (Table VI). This model

compared country-years without any conflict (coded '0') with country years with a non-violent

conflict (coded '1') (violent conflicts were set to 'missing').24

[Table VI here]

The table reveals that the effect of proxies on non-violet conflict onset is rather different. Youth

bulges, unlike previously, have consistently insignificant effect on the outbreak of non-violent

23

Some of these deviations can be attributed to the smaller sample size (note, in particular, Models 2.3a and 2.3b).

On the other hand, some of the deviations, for example, the fact that previous conflict has notably stronger effect on

ethnic conflicts, could be worth of further investigation – an issue beyond the scope of the present study. 24

The sample included all annual observations (1946-2008) of states as defined by Gleditsch and Ward (1999). Note that on-going years of active non-violent conflict were dropped (see above).

20

conflicts. The effect of military personnel is positive and significant – an opposite of the

estimates reported in previous tables. Further, GDP per capita, a measure that had no

significant effect on conflict militarization, exerts strong effect on the outbreak of non-violent

conflicts. Note also that peace years, in the full model (3.5), have no effect on the onset of non-

violent conflicts. The only variable that consistently affects non-violent conflict onset and

conflict militarization is previous conflict. The table, thus, reveals a number of unexpected

results – an issue I address in the following section.

5. Discussion

What are the specific implications of these findings? First and foremost, the findings indicate

that experience of previous conflicts, rebels’ ability to recruit (as proxied by youth bulges) and

states’ military capacity (as proxied by military personnel) significantly affect the likelihood of

non-violent conflict militarization. This, in turn, partly confirms the hypotheses introduced in

Section Three – parties to a conflict consider normative justifications for political violence

(Hypothesis 1), as well as their chances of success (Hypothesis 2 and 3), before they decide to

employ violent means to address their incompatibilities. Note, however, that two indicators,

GDP per capita and income Gini (proxies for rebels’ ability to recruit and relative state capacity

vis-à-vis the rebels respectively), fail to predict conflict militarization, suggesting two possible

conclusions: (i) rebels’ ability to recruit is largely invariant to the absolute level of state wealth;

(ii) chances of success, as perceived by rebellion leadership, are largely independent of the

vertical income distribution. The first conclusion confronts the hypothesis proposed by Collier &

Hoeffler (2004), which predicted that rebels’ opportunities to mobilize will be higher in poor

countries (and, thus, poor countries will have higher chance of armed conflict) because of lower

opportunity costs to recruit potential rebels (see above). Yet, the present study has

demonstrated that non-violent conflicts are not any more likely to become violent in poor

countries that in the wealthy ones, implying that the well-established relation between GDP per

capita and armed conflict onset holds because of other reasons (see below). The second

conclusion suggests that vertical distribution of income in the total population is a poor

indicator of the balance of power between potential rebels and states – rebels affluence and, in

21

turn, their military capacity is thus largely independent of the distance between the

‘advantaged’ and the ‘disadvantaged’ people in a given society.

Second, the study reveals that the effect of previous conflicts, youth bulges and military

personnel is largely consistent across different conflict categories. This implies that

militarization of non-violent conflicts – regardless their nature – depends on corresponding

factors. While the estimates are not conclusive for other variables, youth bulges – % of 15-24-

year-olds in the total population – increase the risk of conflict militarization no matter whether

a conflict is territorial or governmental, ethnic or non-ethnic, ethnic governmental or non-

ethnic governmental.

Third, the study finds that income inequality reduces the likelihood of ethnic conflicts, in

particular, ethnic governmental conflicts. The fact that inequality does not affect militarization

of non-ethnic conflicts suggests that the mechanism I proposed to explain the link between

inequality and conflict militarization is implausible. If, as theorized above, inequality reduced

the likelihood of conflict militarization through imbalance of power between the conflicting

parties, the effect of income Gini should have affected both ethnic and non-ethnic conflicts. The

fact that income Gini affects only ethnic conflicts suggest another, cohesion-centred

explanation proposed by Sambanis: ‘High levels of interpersonal inequality in all ethnic groups

may actually reduce the ability to coordinate an ethnic rebellion as they can erode group

solidarity’(2005: 328). In other words, while in class-based conflict high vertical inequality may

increase the cohesion of the ‘disadvantaged’ (Gurr, 2000), in ethnic conflict high inequality

between members of the same ethnic group may reduce cohesion, and, in turn, their ability to

mobilize.

Finally, the study reveals that onset of non-violent conflict and conflict militarization depends

on rather different factors. The analysis has shown that youth bulges consistently affect conflict

militarization, but has no effect on non-violent conflicts. This implies that large cohorts of

young people favour rebellion recruitment (and, in turn, conflict militarization), but has little to

do with the motivation for conflict. In contrast, the analysis has shown that GDP per capita well

explains the onset of non-violent conflicts, but fails to account for why non-violent conflicts

22

become violent. This is an indication that the well-established link between state wealth an

armed conflict has little to do with the opportunities to recruit rebels (Collier & Hoeffler, 2004)

or military opportunities to confront the state (Fearon & Laitin, 2003). The link between state

wealth and armed conflict is thus more likely to be related to the motivational factors such as

grievances over economic mismanagement, corruption, poverty and unemployment. Finally,

the study has found that the effect of military personnel is significantly negative on conflict

militarization and significantly positive on the onset of non-violent conflict. This suggests that

large military personnel may deter would-be rebels from starting an armed conflict. But it also

suggests that large military personnel could be linked to variables that provide motivation for

conflict. Spending required to maintain military personnel is often increased at the expense of

social welfare, health and education expenditures, which may generate discontent among the

population:

Social dislocations wrought from the privileging of the military – beyond the economic downturns

associated with it – are likely to heighten domestic discontent and provide a breeding ground for

insurgency (Henderson & Singer, 2000: 282)

This is yet another indication that the role certain factors play in conflict processes could be

more nuanced that it may appear in a static analysis of an armed conflict onset.

Conclusion

The present study has implemented the first cross-national test of the factors associated with

the violent turn in non-violent conflicts – conflict militarization. In contrast to previous research

that has compared countries ‘at peace’ with countries ‘at war’ this study has compared

countries ‘likely to be at war’ (i.e., non-violent conflicts) with countries that are at actual war

(violent conflicts). In doing so, this study has found that whether a non-violent conflict turns

into a violent one significantly depends on normative justifications for political violence (as

proxied by previous conflicts), rebels’ ability to recruit (as proxied by youth bulges) and states’

military capacity (as proxied by military personnel). Furthermore, the study has demonstrated

that onset of non-violent conflicts and militarization of non-violent conflicts depends on

23

different factors. Based on the theoretical discussion and empirical results, this study provides

three broad suggestions for conflict research.

First and foremost, this study contends that research on the outbreak of armed conflict needs

to be extended to incorporate the non-violent conflict phase. Conflict is not a discrete

phenomenon (Sambanis, 2004: 259) – it is a dynamic process that alternates between violent

and non-violent stages. To explain political conflict, we must focus on both (i) what is it that

makes conflict dyads move from a non-violent stage to the violent one, and (ii) what is it that

makes conflict dyads move from a violent stage to the non-violent one.

Second, this study suggests that conflict research needs to recognize the fact that the reasons

for why conflicts arise and why they turn violent are not necessarily the same. This study has

proposed a two-stage framework for analysing conflict as a dynamic process, the first stage of

which should largely be focused on the underlying causes (factors that motivate conflict) and

onset of non-violent conflicts, and the second on the facilitating causes (factors that make

conflicts plausible) and conflict militarization. The application of such two-stage analytical

framework, as well as appreciation of the fact that factors accounting for the origination and

militarization of conflicts are not the same, could potentially help us to arrive at better specified

empirical models, as well as more explicit (and thus more falsifiable) hypotheses.

Finally, and related to the second, the present study has shown that effect of the same variable

could be non-uniform on onset of non-violent conflict and conflict militarization. Indeed, as

demonstrated in Section Four, the effect of certain factors may even be opposite on the onset

of non-violent conflict and conflict militarization. This implies that the sum effect of such

variables could mistakenly be taken as insignificant in the analyses that focus exclusively on

armed conflict onset. This, in turn, suggests that we should not reject our hypotheses based on

insignificant results derived in static models of armed conflict onset.

24

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Tables

Table 1. Conflict intensity levels

Int. level Name Definition Examples

1 ‘Latent conflict’ ‘A positional difference over definable values of national meaning is considered to be a latent conflict if demands are articulated by one of the parties and perceived by the other as such’.

1. Spain versus Catalan Nationalists (1979-2008) 2. UK versus Scottish Nationalist Party (2007-2009)

2 ‘Manifest conflict’ ‘A manifest conflict includes the use of measures that are located in the stage preliminary to violent force. This includes for example verbal pressure, threatening explicitly with violence, or the imposition of economic sanctions’.

1. Moldova versus Pridnestrovian Moldavian Republic (1993-2008) 2. Iran versus reformists (2000-2008)

3 ‘Crisis’ ‘A crisis is a tense situation in which at least one of the parties uses violent force in sporadic incidents’.

1. Spain versus ETA (1968-2008) 2. Russia versus Dagestan Islamist Rebels (1999-2008)

4 ‘Severe crisis’ ‘A conflict is considered to be a severe crisis if violent force is used repeatedly in an organized way’.

1. UK versus IRA (1968-1998) 2. Colombia versus FARC (2004-2008)

5 ‘War’ ‘A war is a violent conflict in which violent force is used with a certain continuity in an organized and systematic way. The conflict parties exercise extensive measures, depending on the situation. The extent of destruction is massive and of long duration’.

1. Afghan Civil War (1978-1994) 2. Angola versus UNITA (1975-2002 with interruptions)

Source: Conflict Barometer (2010)

30

Table II. The effect of some of the commonly employed predictors on intrastate armed conflict onset

(comparison of UCDP/PRIO and CONIS data sets)

UCDP/PRIO (Incidence)

CONIS (Incidence)

UCDP/PRIO (Onset)

CONIS (Onset)

Population size .103

(.022) .001

.191 (.024) .001

.131 (.036) .001

.219 (.037) .001

GDP per capita (ln) -.678 (.045) .001

-.748 (.054) .001

-.611 (.098) .001

-.549 (.102) .001

Ethnic fractionalization -.123 (.216) .568

.084 (.230) .716

.153 (.414) .711

.440 (.430) .305

Ethnic polarization 1.722 (.220) .001

2.097 (.245) .001

1.279 (.402) .001

1.467 (.420) .001

Xpolity .053

(.010) .001

.021 (.012) .091

.020 (.021) .335

.030 (.025) .222

Xpolity^2 .001

(.003 ) .705

-.005 (.004) .156

-.001 (.006) .962

-.003 (.007) .720

Oil and gas production .275

(.041) .001

.202 (.048) .001

.355 (.084) .001

.167 (.110) .129

Oil and gas production ^2 -.010 (.002) .001

-.006 (.002) .007

-.016 (.006) .004

-.013 (.010) .188

Constant -2.242 (.144) .001

-2.792 (.181) .001

-3.910 (.319) .001

-4.342 (.336) .001

N 6042 6042 6042 6042

Wald χ2

474.05 474.86 108.10 136.08

Coefficients (β) with robust standard errors in parentheses and ρ-values below.

31

Table III. Summary statistics

Name Observations Mean S.D. Min Max

Onset of violent conflict (conflict militarization) N = 6006 .040 .195 0 1

Previous conflict N = 6006 .577 .494 0 1

Youth bulges N = 5945 29.879 6.311 11.059 41.037

GDP per capita ($ thousands) (ln) N = 5687 .824 1.023 -1.575 3.337

Military Personnel (thousands) (ln) N = 5755 4.730 1.611 0 8.466

Income Gini index (average imputed value) N = 5926 .387 .082 0 .744

Peace years (with decay function) N = 5923 .539 .451 0 1

Population size (in 100 millions) N = 5669 1.217 2.667 .002 13.248

32

Table IV. Logistic regression estimates of the onset of violent conflicts

(1.1) (1.2) (1.3) (1.4) (1.5)

Previous conflict .462

(.168) .006

.446 (.165) .007

.436 (.166) .009

.575 (.177) .001

.576 (.177) .001

Youth bulges - .070

(.014) .001

.057 (.018) .001

.066 (.018) .001

.069 (.020) .001

GDP per capita (ln) - - -.126 (.101) .214

-.103 (.106) .332

-.089 (.109) .414

Military personnel (ln) - - - -.114 (.063) .067

-.122 (.062) .050

Income Gini Index - - - - -.632

(1.029) .539

Peace years (decay) -.262 (.165) .112

-.437 (.165) .008

-.491 (.176) .005

-.570 (.173) .001

-.571 (.172) .001

Population size -.007 (.027) .784

.015 (.028) .595

.011 (.029) .705

.057 (.035) .109

.056 (.035) .115

Constant -3.304 (.123 .001

-5.390 (.449 .001

-4.873 (.617 .001

-4.747 (.694 .001

-4.587 (.707 .001

N 5666 5666 5612 5467 5467

Wald χ2

7.73 33.62 33.87 47.27 48.06

Coefficients (β) with robust standard errors in parentheses and ρ-values below.

33

Table V. Logistic regression estimates of the onset of disaggregated violent conflict

Territorial

(2.1a) Governmental

(2.1b) Ethnic (2.2a)

Non-ethnic (2.2b)

Ethnic Governmental

(2.3a)

Non-ethnic Governmental

(2.3b)

Previous conflict .765

(.292) .009

.441 (.224) .049

.854 (.263) .001

.220 (.248) .374

1.070 (.657) .100

.143 (.254) .575

Youth bulges .045

(.027) .088

.114 (.029) .001

.055 (.025) .025

.099 (.034) .003

.137 (.053) .010

.104 (.036) .004

GDP per capita (ln) -.032 (.142) .820

-.129 (.170) .448

-.128 (.144) .376

.008 (.172) .965

-.065 (.444) .884

-.050 (.196) .800

Military personnel (ln) -.177 (.094) .061

-.072 (.088) .410

-.128 (.078) .100

-.147 (.103) .153

.057 (.181) .754

-.117 (.104) .261

Income Gini Index 1.063

(1.806) .556

-1.350 (1.350)

.317

-1.357 (1.346)

.313

1.058 (1.532)

.490

-4.713 (1.972)

.017

.584 (1.612)

.717

Peace years (decay) -.481 (.263) .067

-.830 (.260) .001

-.832 (.239) .001

-.458 (.272) .092

-1.502 (.549) .006

-.688 (.294) .019

Population size .052

(.043) .231

.117 (.065) .072

.038 (.042) .369

.110 (.072) .127

-.012 (.133) .929

.181 (.069) .009

Constant -4.358 (.993) .001

-5.867 (1.049)

.001

-3.740 (.887) .001

-6.300 (1.181)

.001

-5.661 (1.975)

.004

-6.251 (1.277)

.001

N 2880 2587 3432 2035 715 1872

Wald χ2

21.51 34.73 33.01 24.46 31.88 21.10

Coefficients (β) with robust standard errors in parentheses and ρ-values below.

34

Table VI. Logistic regression estimates of the onset of non-violent conflicts.

(3.1) (3.2) (3.3) (3.4) (3.5)

Previous conflict .403

(.181) .026

.419 (.185) .024

.428 (.185) .021

.484 (.194) .013

.479 (.193) .013

Youth bulges - -.004 (.011) .740

-.019 (.015) .189

-.007 (.015) .651

-.010 (.016) .546

GDP per capita (ln) - - -.144 (.089) .105

-.224 (.096) .020

-.227 (.095) .016

Military personnel (ln) - - - .228

(.049) .001

.235 (.052) .001

Income Gini Index - - - - .394

(.870) .651

Peace years (decay) .519

(.303) .087

.531 (.305) .082

.460 (.309) .136

.014 (.348) .967

.019 (.348) .956

Population size .105

(.034) .002

.103 (.035) .003

.094 (.035) .007

-.015 (.046) .750

-.016 (.046) .730

Constant -3.583 (.095) .001

-3.488 (.318) .001

-2.864 (.495) .001

-3.977 (.537) .001

-4.071 (.602) .001

N 5625 5545 5545 5314 5314

Wald χ2

33.50 33.86 35.28 48.24 47.88

Coefficients (β) with robust standard errors in parentheses and ρ-values below.

35

Figures

Figure 1. Incidence of violent and non-violent conflicts around the world in 1946-2008 (CONIS data set)

36

Figure 2. Incidence (left) and new onsets of violent conflicts around the world in 1946-2008

(a comparison of UCDP/PRIO and CONIS data sets)

φc (Cramer’s V) = .66 (incidence) and .30 (onset). Total number of active conflict country-years amounts to

1721 and 1504 in UCDP/PRIO and CONIS respectively. Total number of new onsets amounts to 323 in

UCDP/PRIO and 282 in CONIS

37

Figure 3. Probability of conflict militarization

The figure demonstrates estimated probabilities of conflict militarization as a function of youth bulges and

military personnel (the probabilities were estimated holding other variables in Model 1.5 at their mean

values).