Political Entrepreneurs and the Diffusion & Escalation of Violence:
The Case of Lebanon 1975-19781
Andrea Ruggeri
University of Essex
[email protected]
June 2010
Word Count: 9,526
Civil wars do not start immediately over the whole territory of a country, social
interactions reach conflict levels within specific locations and contexts. Subsequently,
limited occasions of strife can spread to a broader scale and become civil war. This
paper aims to study why and how situated tensions can reach a larger scope and
become an intrastate war. It suggests that, even though structural features are
important, situated tensions and the role of local political entrepreneurs play a crucial
function. These agents link different motives to fight and mobilize actors situated in
different locations using local political opportunities. Therefore, the escalation and
diffusion mechanisms would drive from micro/local tensions to macro/national
cleavages. In order to test these mechanisms a new dyadic geo-referenced dataset of
events during the Lebanese civil war is employed.
1
This is a working paper. Comments are welcome. Thanks for comments and suggestions on previous
versions of this paper to Alex Braithwaite, Edzia Carvalho, David Cunningham, Kristian S. Gleditsch,
Nils Metternich, David Sobek, Vera Troeger, and Nils Weidmann. Errors remain mine.
2
1. Introduction
Civil wars do not start immediately over the whole territory of a country, social
interactions reach conflict levels within a specific location and context. Subsequently,
limited strife can spread to a broader scale and become civil war. This paper aims to
study why and how situated tensions can reach a larger scope and become an
intrastate war. This paper develops on a central argument: conflict dynamics
commence locally; nationwide conflict is an aggregation of situated tensions. It is
argued that even though there are structural motives to fight, crucial triggers are
localised. This paper links mechanisms of mobilization to outcomes of violence:
mobilization is a spatial process triggered by political entrepreneurs, who fuel
tensions between enemies in order to maximize the number of their fighters. As a byproduct, the level of ruthlessness among combatants is increased.
Recent studies of civil war have moved toward a disaggregated analysis of
conflict dynamics in order to gauge within-country variation of many variables
(Buhaug and Lujala 2005; Buhaug and Gates 2002; Buhaug and Rød 2006; Raleigh
and Hegre 2005). They rightfully rely on the assumption – also empirically
confirmed- that usually only limited parts of a country experience civil war or that the
triggering element that leads to mobilization can be very different across a country.
Other recent works have also tried to corroborate the technical and epistemological
concerns with more elaborate theoretical explanations of the role of space in the
patterns of violence (Tilly 2000; Kalyvas 2006, 2003) using disaggregated data on
specific conflicts (Murshed and Gates 2005; Urdal 2008; Bohara, Mitchell, and
Nepal 2006).
3
This paper contributes to the theoretical understanding of spatial dynamics of
conflict by suggesting that two interlinked mechanisms are active during civil wars:
brokerage and avalanche motives. These mechanisms will influence the mobilization
process, the diffusion of the conflict, and its intensity.
This paper employs as a case study, the beginning of the Lebanese civil war in
1975. Lebanon in 1975 had a relatively small population and the GPD per capita of
Lebanese citizens was much higher than most Middle Eastern and African countries.
However, Lebanon experienced a bloody and prolonged civil war despite a low infant
mortality rate (Goldstone et al. 2000; King and Zeng 2001) and a high quality of
education to meet youth bulges (Urdal
2006) and against the most consistent
empirical findings on the reasons such as the role of population size and GDP per
capita for the onset of civil war based on aggregated data (Fearon and Laitin 2003;
Paul Collier and Hoeffler 2004). This paper is divided into three sections. The first
introduces and develops the above-mentioned ‘situated theory of conflict’, the two
mechanisms and the testable hypotheses built upon them. The second section brings
in the case study analysed here, i.e. Lebanon. A brief narrative of the conflict and
different competing explanations of this strife are provided. The research design
highlighting how data have been obtained, their structure and analysis are also
explained. The third section displays descriptive statistics and discuses the results of
the statistical models.
2. Theory
Civil war does not start simultaneously over a whole national territory. Strife
and clashes happen in some locations and then these contextual conflicts spread at the
4
national level and often have transnational implications (Gleditsch, Salehyan, and
Schultz 2008; Salehyan and Gleditsch 2006; Salehyan 2008).2
A study on the dynamics of conflict and violence in Ireland found that
“revolutionary activity and violence were heavily concentrated in some areas and
nearly non-existent in others” (Hart 1997:143). Therefore, this conflict was “not so
much a national conflict as a collection of regional ones”. Thus, it suggested that in
order to understand the origins and outcomes of violent strife, and therefore the study
of interests, organization, mobilization, opportunity and collective action of conflict
(Tilly 1978), it is necessary to analyse the geography of the conflict ( Hart 1997:143)
Therefore, civil wars are local phenomena that can then diffuse at nationwide
scope. In order to study such dynamics the study of civil war needs an improvement
of data to get more disaggregated information. This new avenue of research has been
disaggregating both spatial and temporal information through disaggregated data.
These studies have found that distance from the capital and the characteristics of the
territory - mountains and resources -can play a crucial role in determining the
likelihood of conflict (Buhaug 2006; Buhaug and Gates 2002).
Starting from the assumption that conflict does not start immediately over a
whole territory but as a fire that starts from a room and then spreads to a whole
building, strife commences in certain locations and then diffuses over a larger spatial
scope, this paper intends to study which mechanisms can lead to these dynamics.
Violence is a spatial process; hence mobilization should be seen as a local process
based on local information and influence (Schelling 1978; Weidmann 2009).
In order to elaborate these mechanisms, two conceptual categories are used to
summarise the necessary elements for a civil war: the willingness and opportunity to
2
This paper puts forward the argument that civil wars start from local dynamics. However, it does not
follow that we should assume that civil war is just a domestic phenomenon. On the contrary
translational opportunities can enhance the local tensions (see Ruggeri 2010).
5
fight (Starr 1978; Siverson and Starr 1991; Cioffi-Revilla and Starr 1995). This paper
argues that in order to be faced with large-scale domestic conflict, countries need
groups with the willingness to fight and the opportunity to mobilize. In most of the
cases the willingness to fight is situated in a certain context, for instance local
tensions due to occupation of territory, contested distribution of public goods or local
representation. However, the contested issue does not need to be well-defined and
unique in order to mobilize people for a violent action.
On the contrary, it is argued that political entrepreneurs3 (Lichbach 1995; Tilly
2003) that organise collective violence have an easier mission when there are
overlapping contested issues and the main arguments for hatred are blurred. Thus,
there is a process that links contested issues provoking an ‘avalanche chain’ -contested issues arise from a single tension and political entrepreneurs identify other
contested issues in order to stress the ’otherness’ of groups and embitter the
interactions between these groups. The opportunity to fight is also geographically
situated; political entrepreneurs need to build networks among people and the
windows of opportunity to mobilize them can vary over different locations (Buhaug
and Rød 2006). Political entrepreneurs provide information and logistical support to
latent fighters that are willing to fight but are constrained by lack of opportunities.
Therefore, this study proposes a ‘situated theory of violence’. As investigated
by other studies (see Kalyvas 2006; Gates 2002) social frictions happen within a welldefined spatial context over a specific issue; however this contextualised strife can
spread over to a larger territory.
3
In this paper political entrepreneurs are defined as social actors who coordinate and organize
collective political mobilization. They are often defined in the Civil War literature as rebel leaders.
One of the first formalization of the role of political entrepreneurs has been suggested by Frohlich et al
(1971). Even though some works have analysed the role of political entrepreneurs related to domestic
strife ( see Popkin 1979, Taylor 1988, Lichbach 1995), nowadays most of the focus is on the state
structure and national characteristics ( Fearon and Laitin 2003; Collier and Hoffler 2004) or the dyadic
relationship between government/rebels (Cunningham, Gleditsch , Salehyan 2009).
6
A main pattern triggered and developed by political entrepreneurs is to link
contentious issues and boost the salience of the conflict moving it from a delimited
and localised contest to a broader territory and a general dispute. The pattern is drawn
as a set of interactions between a central agency, rebel political entrepreneurs, and the
local population (Figure 1). The central agency aspires to stay in power; however, its
resources are limited which need to be spatially allocated. The consequent allocation
of these resources can shape the strategy of adverse political entrepreneurs. In fact, the
rebels’ goal is to enrol and mobilize as many people as they can against the central
agency in order to destabilise it and gain political and economic benefits.4 The
number and size of political entrepreneurs, and therefore their capacity of
coordination and organisation, lies between those of the central agency and the whole
population. They act at a meso-level of interaction and can therefore overcome a
problem of collective action.
[Figure 1 about here]
Political entrepreneurs will localise their activity where the level of government
resource distribution is low and there are overlapping issues of strife (even latent).
This is done for two reasons: first, they will encounter low central agency resistance
in places where the allocation of resources is also low and second, they will have a
higher probability of encountering a deeply dissatisfied local population that will be
easier to mobilise. Further, their action will be facilitated in homogenous areas where
4
It is not assumed that the main goal of these political entrepreneurs is to overthrow the central agency;
it can be, but they could also advance political and economic requests. Their action is aimed to increase
their bargaining power and their scope of capacity.
7
local agents are also connected by other features (such as ethnicity, religion and
family ties).
Hence, these agents will link different actors that previously were untied and
shape their strategies and actions to a consistent set of motives to fight. By doing this,
they mobilize actors situated in different locations using local political opportunities
and a network of crystallised identities. Therefore, the escalation and diffusion
mechanisms would drive micro/local tensions to manifest as macro/national strife.
The individual, limited and often negotiable motives to fight are shaped by political
entrepreneurs to a more general willingness to fight that becomes incompatible with
the status quo. Hereafter, I elaborate the two mechanisms and point out which
observable patterns could be derived from them.
3. Brokerage: Tying People
Political entrepreneurs, in order to maximize the number of people enrolled in
their organisation, need to start their “marketing campaign” from certain locations and
then diffuse their ties. They coordinate different actors in different locations; they are
able to provide information on the willingness of other actors to fight to people that
aspire to behave similarly in another location. In fact, political entrepreneurs are vital
to collect actors’ motives and coordinate them; otherwise Gramsci’s remark about
Southern Italian peasants may be applicable in this case: “they are in perpetual
ferment but, as a mass, incapable of providing a centralized expression for their
aspirations and their needs” (Hobsbawm 1971, 10).
A similar dynamic has been defined as a brokerage process in the literature of
social movements by McAdam, Tarrow and Tilly (2001). It means that actors that
would not be involved in the same social dynamic come in contact and have alliances
8
in order to achieve certain goals thanks to the actions of political entrepreneurs. This
mechanism “links two or more previously unconnected social sites by a unit that
mediates their relations with one another and/or with yet other sites. […] It can also
become a relational mechanism for mobilization during periods of contentious
politics, as new groups are thrown together by increased interaction and uncertainty,
thus discovering their common interests (McAdam, S. Tarrow, and Tilly 2001, 26)”.
Hence, brokerage is a mechanism of mobilization and creation of opportunities
for individuals to join a violent collective action.
Why do these political entrepreneurs act like this? Because their aim is to
mobilize the highest number of people to challenge the central agency and, therefore,
they have to coordinate and organize individuals. In fact, dissidence must be
organised, otherwise single voices would be only individual complaints and a weak
signal of dissatisfaction. Hence, when a political entrepreneur– and usually his
organisation- appreciates that a synergic coordination of these singular demonstrations
could lead to a more efficient and profitable mobilization against the central authority,
he will maximize his effort to invest in local dissent to enlarge it and also increase
their possible private benefits of a generalized struggle. As Lichbach (1995-168\160)
highlights, political entrepreneurs increase fighters’ benefit, lower coordination costs,
increase resources, improve the productivity of tactics, increase mutual expectations,
and impose, monitor, and enforce agreements.
[Figure 2 about here]
9
Therefore, I speculate that an outcome of the brokerage mechanism would be a
spatial diffusion of the conflict; over time, political entrepreneurs will network and
mobilize people in different locations.5 As Figure 2 shows, there is a space T, the
whole territory, where a number of actors p is distributed. However, among them
there is a typology of individuals, r, that are willing to fight but they lack
opportunities. Therefore, they are latent rebels facing contextual constraints. The role
of political entrepreneurs is to connect untied people providing information and
supplying logistic support through the creation of spatial networks. This spatial
diffusion could have a sigmoidal shape as many works on diffusion of social
phenomena and techniques have pointed out (Hedstrom 1994; Strang and Soule 1998;
Elkins and Simmons 2005; Strang and Tuma 1993; Myers 2000; Tolnay, Deane, and
Beck 1996). These assumptions would suggest that brokerage leads to a process of
spatial diffusion. Conflict diffusion would have a logistic curve shape over time.
However, as introduced so far, this mechanism would lead to an inertial
diffusion. Once a conflict starts from a spot within a country, this strife would spread
out until it reaches the whole territory. Of course, this is not the case. First, the
homogeneity/heterogeneity diffusion and then catalyst elements of diffusion should
be taken into account. Put differently, certain areas because characterised by certain
features can permit (or not) easier process of conflict diffusion. Second, there could be
a saturation point of diffusion; rebels may be able to network up only a certain area or
they could be willing to spread the conflict only till a certain portion of the territory
(Buhaug 2010).
5
Here I speculate that political entrepreneurs need time to enlarge their network of the mobilized
population and therefore an observable pattern of behaviour would be a diffusion of the conflict over
space and over time. One could argue that political entrepreneurs do not start just from one location but
they act in different places. However, this phenomenon though slightly different from what I suggest,
will also have both spatial and temporal clusters.
10
For the first point, the elaboration of the mechanism would lead to the following
formalization:
Pr( y i = 1) = f (t )
where the probability of diffusion of violence is just a function of time and this
function can be sigmoidal for instance. However, locations can be different over a
territory and these different features can facilitate or prevent the diffusion of a
conflict. Therefore the heterogeneity of the diffusion should be taken in account
relaxing the assumption that diffusion of violence is just a function of time (removing
a quite deterministic aspect of the mechanism). This should also bring into the
mechanism, those elements that can be catalysts for the diffusion (Wood 2003). In
fact, if the main objective of political entrepreneurs is to tie people together, this
process is facilitated if the network is homogenous inside and heterogeneous
compared to other pockets of the territory. As one of the main elements of diffusion is
communication (Deutsch 1966), it is possible to speculate that areas where there are
similar customs/language the diffusion would be more likely. Of course
communication within a similar ethno/linguistic or religious group could be easier
than with groups with different languages and customs. For instance in Rwanda, local
elites drew on kinship and social networks to recruit participants for collective
violence (Fujii 2009). Therefore, the probability that a location experiences a violent
event is a function of a set of local features such as ethnic/religious homogenous
networks, population (because in more populated places it can be easier to start a
network), then the capacity of the government to implement policies:
11
l
Pr( y i ,t = 1) =
e i ,t
l
1 + e i ,t
Where li,t summarises this set of located variables in location i at time t. More
extensively:
l i ,t = β 1 groupidentity i ,t + β 2 populationi ,t + β 3imri ,t + β 4 gdpcapitai ,t ,
Where group identity, or homogenous religious/ethnic network can facilitate the
political entrepreneurs’ strategy. Moreover, locations with larger population will have
higher chances to find latent rebels. Finally, socio/economic features, such as Infant
Mortality Rate (IMR) and GDP per capita, can influence this mechanism as well.
They can be understood as proxies of state capacity (Fearon & Laitin 2003; Goldstone
et al 2010).
However, we have to also take into account the neighbours’ effects and
characteristics because the diffusion process and its strength will also be a function of
the dynamics in the neighbourhood. Therefore, in order to model the spatial diffusion
the features of the neighbouring locations should be taken in account.
n j ,t = λ1conflictspatiallag j ,t −1 + λ 2 spatialreligioncleavage j ,t
The first item is a temporal-spatial lag of conflict based on a Queen contiguity,
namely whether in any of the first order bordering spatial units there was a conflict
event at the previous temporal unit. The second element allows controlling for spatial
heterogeneity of ethnic/religious groups. It is a spatial lag which indicated whether a
12
spatial unit borders other spatial units with different ethnic/religious population.
Moreover, we shall take into account the previous experiences of violence in a
location during the process and we are going to employ a set of variables that take in
account the numbers of days location i has been at peace and its cubic splines (Beck,
Katz, Tucker 1998):
t i = τ 1 peacedays + τ 2 Ispline + τ 3 IIspline + τ 4 IIIspline
Therefore we can rewrite the probability of violent event in a location given the
spatial effect and its past history:
Pr( yi ,t = 1) =
e
l , n ,t i , t
1+ e
l , n ,t i , t
Hence, some testable hypotheses can be elaborated from the brokerage mechanisms:
Homogenous Network
H.1 Areas characterized by homogenous networks (ethnicity /religion) can increase
the risk of conflict diffusion.
Neighbouring Features
H.2 Conflict is a spatial process. Conflict in spatial proximity increases the risk of
conflict diffusion.
4. Avalanche Motives: Linking Contended Issues
13
Most of the research on domestic conflict assumes that civil strife is triggered
by certain structural features within a society. Social actors decide to mobilize against
the central agency that holds power because there are issues such as
underrepresentation or polity organization (Hegre et al. 2002), ethnic disproportion of
the elite (Cederman, Wimmer, Min 2010), or unequal distribution of wealth (Gurr
1970). However, this paper suggests that the causes of violent mobilization are not
only structural but also local; usually conflict starts from local and limited issues and
then national and structural issues commence playing an important role to sustain and
enhance the conflict between parties. Obviously, the national and aggregated
socio/economic characteristics of a country play an important role. But the aim of this
paper is to address the role of local dynamics. One of the many examples is from the
Italian rebels’ movements during the Second World War. The initial interests and
motives of a large proportion of people who became partisans in Italy and fought the
Nazi army and the fascists’ brigades were broad-ranging from private vendettas to
ideological spirit (Pavone 1991). Their preferences were quite heterogeneous and
contextual; many were former Italian soldiers, others long-time opponents of the
fascist regimes, other youngsters that had to flee in order to avoid the fascist call-up
(Ginsborg 1990).
Hence the individual and initial motives were disparate and the partisan leaders
were able to instruct the new members of the resistance with political and ideological
reasons and indoctrinate them on why and how to fight6 (see Kalyvas 2006:381-386).
In fact it is “often the case that local cleavages are distinct from those emphasized in
the rhetoric of the parties in the conflict” (Wood 2008:547). Put differently, “actors
6
Italo Calvino in his “fiction” book “Il sentiero dei nidi di ragno” in a masterly fashion
summarises this process of individual motives to join the partisans squads toward a general
“willingness” to fight basically shaped by officers in charge of “ideology courses”.
14
seeking power at the center use resources and symbols to ally with peripheral actors
fighting local conflicts” ( Kalyvas 2003:476).
Therefore, behaviours and actions that seem to start at the macro level can,
indeed, be triggered by localised micro-dynamics and political entrepreneurs use these
triggers in order to enlarge motivations to fight and to augment their political demand.
For example, if a group has started demonstrations or local violent acts in order to
obtain a certain political goal and concessions, such as control of local economic
production7 or political representation within a local administrative body, then the role
of political entrepreneurs is to link these delimited and localised issues to larger and
more universal conflict issues. Consequently, they shift the matter of the conflict from
a contextual and localised level to a more general scope, which is less likely to be
pacified, and in turn move a delimited political demand to a general necessity to
change the status quo of power. These mechanism aims to enlarge the political scope
and in the main time enlarge the spatial scope of the strife.
Thus, the conflict shifts from a struggle over “divisible goods” to “indivisible
principles” (Hanf 1993), and as this happens, “the intensity of violence is bound to
become more savage and, hence, the prospects for resolving the conflict peacefully
are all the more remote” (Khalaf 2002, viii). Social actors have different reasons and
motives to mobilize, however political entrepreneurs organise these different motives
toward a more general necessity to rebel, they construct a universal willingness to
fight.
This mechanism intends to aggregate individual or local community frustrations
that being limited are possibly negotiable and resolvable, toward broader and broadspectrum motives to revolt and therefore turn into incompatible goals with the
7
Examples are the several fishermen strikes in Lebanon on the eve of the outbreak of 1975 civil war.
Firstly, they were demanding control of local fisheries, and then the clashes were labelled as intrareligious fights.
15
urgency of conflict.
A crucial assumption of this mechanism is that social actors can
adapt their set of preferences (Elster 1983); to be more precise, they can link
individual preferences to the preferences of the collective action (Murphy and
Shleifeer 2004), and therefore they will adopt certain collective strategies and actions
suggested by the political entrepreneurs in order to reach their individual preferences.
It must be stressed that individual actors do not change the ranking of their own
preferences, but what changes is the set of strategies to achieve their own goals. These
strategies are suggested by the political entrepreneurs. Weingast suggests an analogy
with economic theory in order to explain this behaviour: “based on their preferences
and market prices, individuals make choices of what goods and services to purchase.
As the prices change, so do their choices. Even though both prices and choices
change, however the underlying preferences do not” (Weingast 2005:162).
Hardin (1995) has pointed out how individual interests can diverge from group
interests;
however
under
certain
circumstances,
especially
during
violent
mobilization, individual behaviour aligns with group strategies. In turn, intra-group
cooperation, and therefore individual behaviour’s alignment, is more likely since a
community with the same features can punish those who diverge from the group
action and defect (Habyarimana et al. 2009). In the context of civil war it has been
observed that it is not just rebelling that is costly, but also avoiding to mobilize if your
reference group is rebelling (Kalyvas and Kocher, 2007). Civil war is not a pure case
of collective action problem; for some individuals there is higher cost to defect due to
intra-community punishment against who defect. “New information or changing
circumstances have the power to alter the relative attractiveness of various choices
and may therefore change an individual’s preferred actions or strategies, though this
information does not change underlying preferences” ( Weingast 2005:162).
16
Therefore, the mechanism that political entrepreneurs trigger in order to
mobilize rebels has an effect on the outcome of mobilization, namely the level of
violence during the conflict.
The set up of this mechanism could lead to the following outcome: avalanche
motives lead to more intense conflict over time. Therefore, at the start of the event,
there would more low intensity conflict which increases gradually. Hence, the level of
casualties caused by avalanche motives should have an escalation process. Therefore,
this mechanism should create a process that can be summarised by an exponential
curve (Figure 3, Graph a).
However, if we assume that actors do not have fixed strategies, but through a
learning and indoctrination process they can modify their behaviours, it means that
they can move back toward the starting menu of their strategies. This would lead to a
different observable pattern: the intensity of the conflict would increase over time but
at a certain point, actors will update their preferences and the intensity will decrease;
therefore the function would be monotonic. In fact, conflict will increasingly get
ruthless and therefore costly. Actors will reassess their motivations and costs and they
will decide to diminish their level of violence. This pattern is summarised in Figure 3,
Graph b.
[Figure 3 about here]
It follows a testable hypothesis:
H3. The relationship between conflict intensity and time is bell-shaped.
5. Lebanon Civil War
17
In order to test the two aforesaid mechanisms, I analyse the first three years of
the Lebanese civil war from 1975 to 1978. The choice of only the first three years of
the conflict is supported by the literature on this civil war and also on the
methodology of the study of civil wars. In fact, the two mechanisms aim to explain
the first mobilization and diffusion of the conflict and not the duration of the conflict,
which is a different phenomenon that should be analysed with different methods (see
Hegre 2004). Moreover, the Lebanese civil war gets an internationalized profile in
1978, first with the invasion of Israel and, then, with the deployment of UN
peacekeepers (UNFIL mission). In fact, some scholars drastically suggest that after
1978, the Lebanese conflict cannot be defined as a civil war but an internationalized
conflict;8 however most of the studies on Lebanese civil war agree that the 1978 is the
end of the first phase of the conflict (Rasler 1983; Makdisi and Sadaka 2004, Harris
2007, Winslow 1996, Gilmour 1983, Khalidi 1983).
The outbreak of the Lebanese civil war is usually dated to 13th April 1975 when
the Phalangists and a group of Palestinians exchanged fire. During this clash, two
members of the Kataeb party (Chrisitan Maronites) died. The next day, armed
members of the Kataeb and Liberal Nationalist Party attacked a bus which was
carrying Palestinian refugees (some of them armed) and 27 people were killed. This
was the tipping point that triggered the conflict between Christian and Palestinian
militias. However, the belligerent parts and alliances changed over time and were
quite unstable. For instance, the organisation Amal which was a Palestinian ally
changed fronts during the conflict.
8
Another case, which undermines the main
See Guerses (2007). Also a well-known dataset created by Doyle and Samabnis (2000) gives 1978 as
the termination date of the Lebanese conflict, though they coded a new outbreak in 1982.
18
religious explanation of the conflict, is the Southern Lebanese Army, in which the
Christians and the Muslims fought together.
The Lebanese case is interesting since the features of this case (at least at the
aggregate level) do not confirm the main results of the quantitative literature on civil
wars (Fearon & Laitin 2003; Collier & Hoeffler 2003). A replication of the Fearon
and Laitin (2003) model predicts 1.13% probability of a civil war in Lebanon in 1974.
On same lines, Collier and Hoeffler model predicts a 2.6 % of risk of civil war in
1970, where the average risk of civil war in their sample for 1970 is 6% and for
Fearon and Laitin for 1971 is 2.1%.
The more consistent results of the quantitative literature highlight that the
countries that are more at risk of civil war are those with a large population and a low
GDP per capita.
These two variables have been used as proxies for competing
theories (see Gleditsch & Ruggeri 2010); however they are the two variables which
have been found consistently significant when estimating empirical models (Sambanis
and Hegre 2006). Yet, in 1975 Lebanon had a small population, around 3.5 million,
and a high GPD per capita compared to the Middle Eastern and African countries in
those years, whose GDP per capita in 1975 was 15,087 US dollars9. The Lebanese
economy was quite liberalized and the private sector had room for development
(Makdisi and Sadaka 2004). The economic growth of Lebanon between 1950 and
1974 was on average 7% and then there was a low inflation rate (2-3%) until 1971.
Another indicator which suggests a low structural likelihood of conflict was the low
infant mortality rate (IMR), often it has been used as a proxy to operationalize the
level of state capacity (see Goldstone et al 2010, King and Zeng 2001). Moreover, it
has been suggested that youth bulges and low school enrolment can lead to an easier
9
Real GDP per capita from Penn World Tables 6.1 , constant prices at 2005 US $. In 1975 in countries
close to Lebanon the GDP per capita was as follows: Jordan 3,862 $; Israel 14,940 $; Syria 1,940 $.
19
enrolment in rebels groups (Urdal 2006); however 74% of Lebanese youngsters were
enrolled in primary school. Indeed, several structural indicators were against a
prediction of conflict, as shown also by the low probability of the abovementioned
models.
An element which could explain the outbreak of Lebanese conflict is the
presence of ethnic\religious cleavages; however the debate on ethnic fractionalization
and the risk of civil war is quite still open. If research using aggregated data ( e.g.
Fearon and Laitin 2003 ) suggest that ethnic fractionalization does not affect the
likelihood of civil war, new disaggregated data on ethnic groups do find that political
exclusion based on ethnic cleavages exacerbates social interactions and can lead to
violent mobilization (Cederman and Girardan 2007). However, a sophisticated data
such as the GREG project (see Weidmann, and Cederman, 2010) which uses GIS
technology to locate ethnic groups since it is based on linguistic cleavages codes
Lebanon as a homogenous country since all groups10 speak Arabic.
A further explanation could be the role of external actors into the Lebanese civil
war (Rasler 1987). The Syrian and Israeli armies directly intervened in Lebanese
territory, and Palestinian organizations had their headquarters in Beirut and
surrounding refugee camps causing political instability to the country (Hudson 1982).
Moreover, Arab countries, the Soviet Union and Western countries provided both
financial and logistical support to the fighting parts. However, the international
dimension of this domestic conflict appears more as a facilitating and prolonging
element of the conflict rather than a sufficient element to explain its outbreak; this is
also because these interventions were substantially after the outbreak of the conflict.
10
Except a very small Armenian minority.
20
The political system in Lebanon was based on a division of power between
religious communities, the President of the republic used to be a Christian Maronite,
the Prime Minister Sunni and the Speaker of the Chamber Shi’a. This institutional
balance was based on a demographic census dated 1932 (Maktabi 1999), however the
growth of different demographic groups put at risk the political supremacy of the
Maronites (see Table 1). This has been one of the explanations advanced by many
analyses of the Lebanese conflict (McDowell 1996, Winslow 1996).
[Table 1 about here]
However, to these different structural explanations some stylized facts also
suggest that the dynamics were rooted at the micro level and that local political
entrepreneurs were exploiting these tensions in order to channel the grievances for
their own political goals. A leader of the Maributu party, which was Sunni but selfdefined as Left Nassirist, Ziad Hafed in his confused interview ( MERIP 1985)
highlights how many overlapping divisions such religious, economic, or ideological,
were playing an important role and that political leaders were using those in different
contexts with different motivations. For instance, Suleiman (1972) stresses that
Lebanese peasants’ “needs and the demands are simple and specific rather than
ideological and utopian. Their ties are to the family-kinship group, the village and the
sect rather a political movement, the country or the nation” (1972:13-14). Similarly
another social group which has been a vector of political mobilization, the working
class, has been more concerned on “bread and butter issues, not ideological ones”
(Suleiman 1972:14). In this context, the role of the political entrepreneur was crucial
in order to shift the tensions and conflict from mere material and local issues to more
general ones. Musa Al Sadr in 1974 established the political movement, Amal which
later became an armed movement. He organized the movement within a homogenous
21
network, the Shi’as, but based on social /economic issues (Norton 2007). However, he
used a religious rhetoric which overwhelmed the economic motivations for rebellion.
Another example of local political entrepreneurs was the Franjiyeh family, which
directly controlled the Liberation Army of Zaghorta in northern Lebanon. Even
though this armed organization clearly safeguarded the interests of this family, the
leaders of the family labelled their organization as Maronite.
Familiar networks and local political entrepreneurs (Za’im system) always ruled
areas of the country and decided both economic and political developments (Gilsenan
1996). In fact, most of the political alliances were not definable on categories such as
right/left, or Muslim/Christian. The Lebanese political alliances have been always
very flexible and more based on Za’im. The evolution of the concept of Za’im moved
from mere traditional feudal leadership to political entrepreneurship (Suleiman 1972).
Antfin Sa'dah, the founder of the Syrian Social Nationalist Party (SSNP), for instance,
was not only a za'im but the za’im. Also, Pierre Jumayyil, Rashid Karami, 'Adnan Hakim,
Kamal Junblat, Camille Sham'uina and Raymond Edde qualified themselves with the title
of za'im as are the more traditional Majid Arslan, Sabri Hamadah, Kamil al-As'ad or
Sulayman Franjiyah (Suleiman 1972:15).
Of course these are only some stylized facts on the role of local political
entrepreneurs in Lebanon; the next section introduces a research design in order to test
the hypotheses drawn from the two suggested mechanisms.
6. Research Design
Since case selection can be a serious issue (Geddes 1990) first it must be
explained why and how this paper analyses Lebanon. It has been pointed out that case
studies can be crucial in order to produce theoretical conjectures (Sambanis 2004;
22
Collier and Sambanis 2005) and this is the main goal of this paper. Further, this paper
and its collection of data move more to a mixed methodology, where qualitative
research meets the quantitative method (Lieberman 2005). Finally, because Lebanon
is usually recognized as the example of internationalized civil war with the
intervention of external actors (Rasler 1983), this makes Lebanon probably the most
‘least probable case’ to confirm the main theory of the paper (Landman 2003), hence
getting a very high level of falsifiability and a quite scientific approach for the study
of social interactions (Popper 1959).
Data
In order to test empirically the aforementioned hypotheses, this analysis
employed a new dataset on Lebanon. These are violent event data with geo-reference
going from January 1975 to March 1978; all violent acts have been coded using as a
resource the chronology prepared by the Middle Eastern Journal (MEJ) that collects
newspapers such as the New York Times, The Washington Post, and The Jerusalem
Post. The end of this spell also has a theoretical reason: in fact since 23rd March 1978,
the UNFIL peacekeeping mission was deployed in Lebanon after the Israeli invasion
of the Southern Lebanon.
In order to disaggregate the patterns of the Lebanese civil war every event has a
spatial location through latitude and longitude. Once located the events on the
Lebanon map I have over-imposed a fishnet with grids of 10 kilometres in order to
use these grids as a geographic unit of analysis.
This dataset also attempts to disaggregate the temporal aspect of social
interactions. The temporal units are days and an event is highlighted when something
happened to change the routine of everyday life as the literature of data events has
23
usually done (Olzak 1989; Schrodt 1994). These are conflict events and they are
based on a tripartite scale: low, medium, high. A low event means that the conflict
event is symbolic such as demonstrations, strikes and boycotts. Then an event reaches
the medium category if there are fights and fire exchanges with a maximum of ten
casualties. The high-level conflict category is coded when usually there are bombings,
shelling and artillery exchanges and more than ten casualties.
Map 1 shows the
geographical distribution of the conflict events in Lebanon.
Variables
The unit of analysis for both dependent variables is a 10x10 km grid-day. In the
models, the dependent variable for the first mechanism is a dummy variable that count
as one if there was a conflict in the cell on that day. For the avalanche motive model,
the dependent variable is the number of casualties in a certain cell on a certain day.
As mentioned above, more populated places can facilitate the development of a
network. This variable comes from the Gridded Population of the World (CIESIN) for
the year 1990. The infant mortality rate comes from CIESIN as well and the GDP per
capita from the geographically based Economic data (G-Econ) by Nordhaus et al.
Further, I have digitalised the religious group in Lebanon (Map B in Appendix) and
then overimposed the fishnet and every grid got the value of the group that had the
majority. I have created three variables: I assigned to every religious group – therefore
every single grid- the percentage of population that a group had according to the
census of 1932 that was used as the distributional base for the institutional asset of the
country. Then, I did the same operation for the unofficial census of the 1975 (CIA and
Gordon 1983). Further, a variable useful to capture an important cleavage has been
the difference between the two censuses. The rationale is that this difference can
measure the political under- (or over-) representation of certain groups given the
24
demographic dynamics of the forty years between the two observations. In fact, even
though the demographic asset of the country changed, the institutional redistribution
remained the same. A positive value of this variable means that a group was
underrepresented (e.g.
Shi’a). A negative value was assigned to overrepresented
groups (e.g. Maronite). This variable is important in order to control for the argument
suggested by previous work on the role of demographic growth in different groups
and political tensions.
Further, I control for temporal dynamics. A count variable (days at peace)
captures how many days a location has experienced peace. From this count variable, I
estimate three splines to control for temporal dependency (Beck, Katz and Tucker
1998). In order to test the first two hypotheses, I have created two different spatial
lags: a conflict spatial lag is based on Queen contiguity and it assigns a value of one if
there is a conflict event in the proximity. This variable is lagged by one temporal unit
in order to capture the diffusion effect. The other spatial variable signals whether a
grid is surrounded by grids with the same religious groups or at least one
neighbouring grid has a different religion. Finally, since many conflict events (just
below a third) happened in Beirut, I am controlling for the “special” grid where Beirut
is located.
7. Descriptive Statistics
Brokerage Mechanism and Spatial Diffusion
The first graph represents the diffusion of conflict events over different
locations and time. It is quite clear that the geography of the conflict in the first
period (basically the first year) is quite steady and limited; afterwards more locations
experience conflict events over time. It should be noticed that data are right-censored
25
and therefore the spatial diffusion has not reached a saturated point yet, therefore the
curve seems more exponential than logistic. This trend could suggest that political
entrepreneurs at the beginning of the civil war struggled to network and mobilize
people in different locations; however after a period of brokerage the spatial diffusion
has started.
[Figure 4 about here]
A second graph describes the variance of conflict intensity over time. In the first
stage most of the conflict events are symbolic (black line) such as demonstrations,
strikes or boycotts but after a few months more intense violent actions happened. The
dotted line represents conflicts such as bombings, shelling with a large number of
casualties (at least ten) and the dashed line represents medium conflict events such as
fighting and fire exchanges (with less than ten casualties). Therefore, after a certain
temporal span the interactions seem to shift from a majority of symbolic acts of
violence to more intense violence. In fact, the avalanche motives mechanism would
shift the interactions between belligerents from “divisible goods” to “indivisible
principles”, moving to a more intense and ruthless conflict.
[Figure 5 about here]
A third graph shows the cumulative numbers of casualties over time. In the
Lebanese civil war the first year deaths-tool seems to be quite steady, however in the
area delimited by two dashed red lines the trend becomes quite steep. Over this period
there is peak of number of casualties, afterward again some casualties over time but
26
the line is almost horizontal. Therefore, there is an escalation effect. In this graph I
highlight what an aggregated study of civil war would do: it would study a set of
explanatory variables at time t where the graph shows that the conflict has not reached
its high intensive stage, on the other hand a duration study would miss the central
dynamics focusing more on the variables preceding the end of the conflict. However,
it should be stressed that this graph is right censored, in March 1978 Israel invaded
Lebanon and UN peacekeepers were deployed.
[Figure 6 about here]
8. Results and Discussion
For the first mechanism and give the nature of the dependent variable I employ
a rare logit estimator (King & Zeng 2001), which is apt for limited dependent
variables with rare occurrence. Table 2 reports the models testing the first mechanism
of diffusion (brokerage). All models suggest that population has a positive effect on
the likelihood that a location can experience a violent event. Then, if a grid has a high
infant mortality rate there is a lower probability that that location can experience a
violent conflict. This result is counterintuitive and opposite to most of the results in
the aggregate literature on civil war. This odd result goes hand in hand with GDP per
capita result; its sign is opposite to conventional understanding of the relationship.
Richer areas have a higher probability to experience a conflict. A possible speculation
27
is that richer areas with better public good services (low IMR) are targets of
discontented rebels, i.e. conflict as revenge.
Furthermore, areas where the religious group are overrepresented have a higher
risk of conflict. Indeed, Beirut has a higher risk of conflict. Model 3 and 4 include the
spatial variables. My first two hypotheses can be confirmed: conflict is a spatial
process; areas where in the contiguity there is conflict have a dramatic risk of conflict
diffusion. In addition, conflict is more likely to diffuse in areas with homogenous
religious groups.
[Table 2 about here]
The peace count variable tells us that the more a location has been at peace, the
lower is the likelihood this area will experience a conflict.
In table 3, I report the models to test the hypotheses on the second mechanism.
As estimator is appropriate a count model, however a Poisson estimator is not the
proper one since I cannot assume that the counts are not over-dispersed; further a
simple negative binomial regression cannot account for the large number of nonevents. Therefore, I opted for a zero-inflated negative binomial regression. This
choice has been proved appropriate both by the level of dispersion of the dependent
variable (level of the parameter α different from zero) and the Vuong test has
confirmed that a zero-inflated negative binomial regression performed better that a
negative binomial.
28
I introduce the variables on duration of the conflict and its squared term in the
model. The results would suggest that the relationship between time and intensity of
the conflict is a bell shaped one. The other variables give us further information of
violence intensity. In populated areas and where there is a high infant mortality, there
is a lower number of casualties. I found that areas with homogenous religions have a
higher number of casualties.
[Table 3 about here]
Robustness
In table 4, I report the fourth model of table 2 using different temporal units. The goal
is to check, first, whether the inflation of the sample size bias the results, second, to
analyse whether the spatial diffusion has a certain temporal window. The first model
has as temporal unity day-observations, the second weekly-observations and the third
one monthly-observations. In fact, the spatial lag in the first model is lagged by one
day, in the second by one week and in the third by one month. All results are stable
except for the temporal spatial lag when the temporal unit is a month.
In order to explore when the temporal window of spatial diffusion loses its
effect, I present two graphs. They report the coefficients of the spatial lags with 95%
confidence intervals at different temporal lags. The first one shows a 30 day window,
the diffusion effect is statistically significant until a 10-day lag in lines with the first
two models of table 4. However, since the effect was moving up again, I have plotted
the effect of the spatial lags with a larger temporal window (60 day). It does confirm
that there is a spatial diffusion until the 10 day lag and then it becomes consistently
insignificant.
29
[Table 4 about here]
[Figure 7 about here]
In order to test the robustness of the second mechanisms I have run an orderedlogistic regression employing the three different thresholds of violence ( low, medium
and high). The results confirm the bell-shaped temporal trend of the level of violence.
Moreover, in order to account for the large number of zeros have run ordered-logistic
models resampling the zeros. Both models employing 10% and 30% of the zeros with
random assignment and 1000 iteration for both sampling confirm the results.
9. Conclusion
This paper aims to study the diffusion of violence within the country and the
dynamics of violence intensity. Two mechanisms explain the mobilization process
and the escalation of violence. Political entrepreneurs exploit local tensions in order to
organise latent rebels. This mechanism leads to a spatial diffusion of the conflict
which is facilitated by homogenous networks. In order to mobilize people political
entrepreneurs aim fuel inter-group tensions creating as a by-product a higher level of
violence. However, over time this escalation of violence decreases since the fighters
reassesses the cost of fighting ruthlessly given their preferences. I test these
mechanisms using a new event dataset on the Lebanese civil war; the results confirm
the testable hypotheses.
This
analysis of Lebanon on violence intensity and
casualties seem to be similar to the dynamics highlighted by other researches using
disaggregated data such as Nepal (Bohara, Mitchell, and Nepal 2006; Murshed and
Gates 2005), India (Urdal 2008), Bosnia (Weidmann 2009) and Guatemala (Gulden
2002).
30
This working paper contributes to an ongoing stream of research aiming to link
theoretical explanation and methods innovations, these findings show an interesting
light. However, further research employing other methods such as agent-based
modelling and in-depth cases studies can help to test and elaborate these mechanisms.
31
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Figure 1
Figure 2
Mechanisms of Temporal and Spatial Diffusion
Brokerage
Connecting untied people
Providing information
Supplying logistic support
p
p
r
p
p
r
p
p
Creating spatial network
p
Figure 3
p
r
p
r
p
p
38
Table 1
Map 1
39
40
Figure 4
41
Figure 5
42
Figure 6
43
Table 2
Table 2
Diffusion: Rare Logit Models
Model 1
(Intercept)
pop
ADJIMR
GDPPC_1990
Difference
Census
Beirut
Model 2
-5.958***
-0.364
0.000***
0.000
-3.222***
-0.377
0.000***
0.000
-0.040***
-0.008
0.000
0.000
-0.034***
-0.008
0.000
0.000
-0.093***
-0.025
5.613***
-0.248
-0.094***
-0.026
3.199***
-0.263
Peace Days
-0.034***
-0.003
X_spline1
-0.000***
0.000
0.000***
0.000
-0.000**
0.000
X_spline2
X_spline3
Different Group Contig.
Model 3 Model 4
2.762***
-2.787***
-0.386
-0.387
0.000**
0.000**
0.000
0.000
0.043***
-0.042***
-0.009
-0.009
0.000*
0.000*
0.000
0.000
0.173***
-0.171***
-0.032
-0.032
4.207***
4.173***
-0.354
-0.355
-0.033***
0.033***
-0.003
-0.003
0.000***
-0.000***
0.000
0.000
0.000***
0.000***
0.000
0.000
-0.000**
-0.000**
0.000
0.000
1.177***
-1.183***
-0.246
-0.246
Temporal Spatila
Lag
Nagelkerke R-sq.
0.348
Deviance
2993.642
AIC
3005.642
N
148095
*** p<0.01, ** p<0.05, *
p<0.1
0.432
2610.839
2630.839
148095
0.437
2586.829
2608.829
148095
0.766*
-0.299
0.439
2577.412
2601.412
147960
44
Table3
Avalanche Motives: Violence Intensity
Inflated
Causalites
Zeros
deathlag
Population
RelPolRep
Time at
Conflict
Time Sqrd
0.0978*
(0.0528)
-0.000275***
(6.33e-05)
0,083333
(0.0962)
-0.00107***
(8.86e-05)
-0.519***
(0.0650)
-0.000589***
(5.37e-05)
-0.545***
(0.0559)
-0.00163
(0.00221)
2.43e-06
(2.20e-06)
-0.0287
(0.0348)
-1.138**
(0.535)
-4.453***
(0.808)
15.32***
-1.446
148120
-8.70e-05
(0.00293)
9.80e-07
(2.68e-06)
0.00383
(0.0205)
-0.340
-1.713
10.07***
-1.207
0.0105**
(0.00424)
-1.20e-05***
(3.87e-06)
-0.452***
(0.111)
0,10208333
(0.925)
-4.935***
-1.146
23.64***
-4.824
148120
2.256***
(0.631)
148120
Spatial Lag
GeoRelCle
lnalpha
4.568***
(0.272)
Observations
148120
Standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
Inflated Zeros
0,27013889
(0.0339)
-0.000542***
(8.92e-05)
-0.655***
(0.217)
0.0112**
(0.00473)
-1.19e-05***
(3.78e-06)
IMR
Constant
Causalites
45
Table 4
Conflict Diffusion and Temporal Units
Population
Infant Mortality
GDP pc
Religion Pol Rep
Beirut
Temp Sp lag
Different Religion NBs
T Units at Peace
1st Cudeb spline
2nd cubed spline
3rd cubed spline
Constant
Observations
Pseudo R2
Prob>chi2
Log likelihood
ProbitDay
ProbitWeek
ProbitMonth
1.17e-05***
-4,00E-06
-0.0142***
-0,00293
4.07e-05**
-1,80E-05
-0.0525***
-0,0103
1.738***
-0,119
0.359***
-0,135
-0.392***
-0,083
-0.0101***
-0,000877
-1.30e-07***
-1,67E-08
1.03e-07***
-1,81E-08
-4.05e-08***
-1,21E-08
-1.746***
-0,134
1.15e-05***
-4,00E-06
-0.0156***
-0,00358
5.64e-05***
-2,13E-05
-0.0488***
-0,0128
2.180***
-0,177
0.510***
-0,128
-0.459***
-0,109
-0.0632***
-0,00828
-3.73e-05***
-7,23E-06
2.82e-05***
-7,38E-06
-1.06e-05**
-4,76E-06
-1.196***
-0,187
1.74e-05**
-8,13E-06
-0.0220***
-0,00514
5.62e-05*
-3,20E-05
-0.0457***
-0,0172
2.570***
-0,33
0,204
-0,153
-0.494***
-0,149
-0.251***
-0,0421
-0.00284***
-0,000737
0.00219***
-0,000763
-0,000747
-0,000464
-0.534**
-0,256
147960
0,429
0,000
-1309,6104
21060
0,444
0,000
4860
0,382
0,000
-583,77087
-303,13995
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
46
Figure 7
47
Appendix
Map A, Grids and Conflict Events 1975-1978
48
MAP B, Religious Groups