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: [email protected]
* Paper prepared for presentation at the Danish Political Science Association’s annual meeting, Vejle, Denmark,
October 25-26 2012.
2
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.
4
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-
6
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).
7
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).
8
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)
11
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.
9
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
10
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
11
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).
12
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
13
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).