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Elections and Ethnic Conflict: An Actor-Based Network Analysis of Sri Lanka

Abstract

Empirical research on the connection between elections and ethnic conflict often focuses

on how elections can precipitate the onset of conflict. What is omitted in the literature,

however, is that citizens might present similar voting patterns due to their shared

experience in conflict. This research investigates the endogenous nature of the election-

conflict nexus after conflict emerges en masse. Treating administrative units in conflict as

part of a rebellion network, we apply an actor-based network model to the case of Sri

Lanka. We find that rebellion is less likely to occur in Tamil regions if national winners

in presidential elections enjoy high local approval ratings (selection effect). On the other

hand, regions entangled in the rebellion network converge in terms of their support for

the national winners of the presidency (influence effect). Overall, our model-based

simulation analysis shows that the influence effect has a larger impact on the endogenous

relationship than the selection effect.

Keywords: election, ethnic conflict, actor-based model for network dynamics, Sri Lanka

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1. Introduction

Considerable effort has been made in the past decade to examine the relationship

between elections and ethnic conflict (Brancati and Snyder 2011; Cederman et al. 2013;

Cheibub and Hays 2009; Gleditsch et al. 2009; Strand 2007). As a whole, the existing

literature provides empirical evidence showing that elections can give rise to mass

violence in ethnically divided societies and hence greatly improves our understanding of

the issue. Despite its contributions, it is important to note that the present literature

largely limits its scope to the moment at which ethnic violence breaks out en masse. A

major consequence of this limited research horizon is that the connection between

elections and ethnic conflict can only be examined as a one-way effect going from

election to conflict. Thus, it fails to provide empirical evidence to answer many

interesting questions. For instance, can prolonged ethnic violence generate particular

voting patterns, and does the established causal effect from election to conflict persist

after mass ethnic conflict emerges? In other words, a research horizon limited to the onset

of ethnic conflict prevents us from evaluating the election-conflict nexus from an

endogenous viewpoint.

In order to fill the gap, this study focuses on the endogenous relationship between

elections and ethnic conflict after mass conflict appears. We argue that voting outcomes

continue to influence ensuing conflicts because they reveal information that active and

latent insurgents can use to evaluate the costs and benefits of their military maneuvers. In

particular, insurgent ties are more likely to be observed in regions where support for the

central government is low. We also argue that the continuous fighting between

government and rebellion forces impacts local residents’ voting patterns because shared

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experiences in conflict decisively influence voters’ evaluation of the central government

and local insurgents. As a result, regions in conflict are likely to show similar voting

patterns in national elections.

Empirically examining this endogenous relationship between voting and ethnic

conflict is highly challenging. First, the eruption of mass ethnic conflict often interrupts

elections. Thus, we cannot rely on cross-national comparisons to gain new and relevant

knowledge. Instead we must search deep into a single nation and trace the interactions

between voting and conflict dynamics in micro-steps. Second, choosing to observe the

election-conflict nexus from a microscopic viewpoint amplifies the issue of dependence

in both spatial and temporal dimensions. Insurgents’ military presence and retreat in a

subnational unit are unlikely to be independent incidences. Rather, the rebellious actions

are interrelated because of the varied contextual opportunities the insurgents are facing.

In addition, both elections and conflicts have their own dynamics and each of them needs

to be modeled as the endogenous outcome of the other. To deal with the complex

feedback within this system of dependence, we utilize actor-based social network

analysis in this research. Applying this model framework to the case of Sri Lanka, where

a prolonged ethnic civil war coexisted with regular elections, we find evidence in support

of our arguments. On the one hand, the development of a rebellion network is less likely

to be observed in places where local support for the national winner of the presidency is

high (selection effect). On the other hand, pronounced results show that shared

experiences in conflict decide voting patterns in elections. Specifically, we find that

administrative units entangled in the rebellion network converge with respect to their

support for the national winner of the presidency (influence effect). Overall, our model-

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based simulation analysis shows the influence effect to have a larger impact on the

endogenous relationship in Sri Lanka than the selection effect.

The rest of the paper is organized as follows: Section 2 provides an endogenous

account of the election-conflict nexus. Section 3 offers a brief review of the Sri Lankan

civil war. Section 4 introduces our research method and data sources. Section 5 provides

and discusses our empirical results. The last section concludes.

2. Elections and Ethnic Conflict: An Endogenous Viewpoint

The impact of elections on the outbreak of ethnic conflict has been extensively studied

(Brancati and Snyder 2013; Gleditsch et al. 2009; Spencer 2007, for instance). The

literature shows that the likelihood of civil war increases around the time of elections.

The logic behind this finding is that the government’s weakened authority during the

electoral period provides potential insurgents a window of opportunity. For instance,

when ethnic cleavages dominate elections, violent motivations such as grievance or

identity mystery are more easily cultivated (Birnir 2007; Cederman et al. 2013; Denny

and Walter 2014). Furthermore, ethnic groups that lose an election are likely to use

violent means to deny the electoral outcome if ethnic tensions worsen during the race

(Mann 2005; Mansfield and Snyder 1995; Strand 2007). Insurgents thus try to maximize

their chances of success by taking advantage of these opportunities. It is important to note

that recently a limited but growing literature has begun to investigate how in transitional

regimes elections impact the spread of conflict rather than its onset. In these studies,

previous election results in subnational units are used to approximate political instability

or the strength of local social structures, which might facilitate the diffusion of armed

conflict (Cederman et al. 2013; Holtermann 2014; Peterson 2001).

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We argue that once organized ethnic violence has emerged en masse, local election

results will continue to influence insurgent groups’ micro-decisions of aggression and

retreat. This is, because the local electoral results offer valuable information to the

insurgents concerning the feasibility of surviving and growing there. Given their limited

resources, active insurgents should allocate more resources to places where they are more

likely to be successful. Knowing their slim chance of succeeding in pro-government areas,

insurgents are likely to be inactive if the central authority has high local popularity as

evidenced by election results. Similarly, potential insurgents hiding in the local

population will continue to hibernate, which further reduces the supply of labor and other

resources to active insurgents. Thus, if the information revealed in elections is

overwhelmingly in favor of one side or the other, it should influence insurgents’

maneuverings. In particular, we believe that local support for national election winners

provides the most important signal regarding popularity of the central authority. Thus, in

areas where the central government receives a convincing majority, the growth

momentum of the insurgency is likely to be stopped if not reversed. By contrast, areas

with low support for the central government provide insurgent forces an ideal

environment for their survival and expansion. Given these, insurgents are more likely to

be active in regions that show relatively little support for the central authority. Thus, we

have the following hypothesis:

Hypothesis 1: After organized ethnic violence emerges en masse, the chance of

insurgents becoming active is negatively related to local support for the central authority

as evidenced by election results.

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Some previous research on the election-conflict nexus in ethnically fragmented

societies questions the exogenous effect of elections on conflict. It is suspected that high

ethnic tensions during an election are simply a reflection of the inevitable forthcoming

conflict (Birnir 2007; Cheibub and Hays 2009; Collier 2009). Empirically, the literature

has shown that ethnically motivated elections cannot satisfactorily explain ethnic conflict.

For instance, Turks in Bulgaria, Hungarians in Romania, Catalans in Spain and many

other ethnic groups coexisted peacefully with the national majority. Thus, it is imprudent

to claim elections to be an exogenous cause of conflict. In our opinion, once mass ethnic

conflict has broken out, the impact of the conflict on elections becomes stronger. Due to

shared experiences in conflict, local residents in rebellion areas are more likely to

develop similar opinions concerning what policies the central authority should adopt than

those living in insurgent-free areas. For instance, if local residents attribute their suffering

mainly to the insurgent forces, they might prefer a government that adopts harsh

measures in dealing with the insurgents. If they believe on the contrary that the

government is to blame for the current situation, they shall prefer a government that

adopts soft approaches in dealing with the insurgents. In cases where conflict continues

throughout an election campaign, it seems inevitable that candidates for electoral

positions in the central government will clarify their policies regarding how to end the

mass ethnic violence. Because voters who have the most immediate experiences with the

ongoing conflict are likely to develop the same opinion about what policies the central

authority should pursue, the electoral results in those areas are expected to converge.

Thus, we have reached the second hypothesis.

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Hypothesis 2: In comparison with rebellion-free regions, election results in regions

suffering insurgency tend to converge.

Empirically investigating the two hypotheses is highly challenging. Since the

coexistence of elections and ethnic war is often an exception rather than the norm, we

cannot rely on a macro-level cross-national comparison to develop new knowledge with

respect to the endogenous relationship between elections and ethnic conflict. Instead, we

are forced to trace the micro-level interactions between elections and conflict in

individual countries. In this study we apply such a micro-level intra-country design to the

Sri Lankan civil war. The following section provides the background information for this

country case.

3. The Ethnic Civil War in Sri Lanka

The Sri Lankan civil war was a typical ‘son of the soil’ conflict in which ethnic

identity played an important role (Bandarage 2009; Spencer 2007; Stokke 2006).

Although the democracy of Sri Lanka is often viewed as a model for the developing

world, it is undeniable that national policies adopted since independence are

systematically biased toward the Sinhalese — the country’s largest ethnic group. Because

Sinhalese political elites for decades turned a blind eye to the ubiquitous discrimination

against Tamils and other minority groups, support for a moderate solution eroded among

ethnic Tamils (Mampilly 2011; Stokke 2006). As a result, extremist Tamil resistance

groups mushroomed in the island’s Northern Province in the late 1970s. The Liberation

Tigers of Tamil Eelam (LTTE) is the dominant actor among these extremist groups. Its

ambush of a police station in Jaffna in 1983 directly caused the ‘Black July’ — a bloody

riot that resulted in the death of thousands and paved the way for a total civil war. By

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effectively adopting the strategy of guerilla warfare, the LTTE steadily expanded its

military presence across the Northern and Eastern Provinces as the war progressed.

Although the LTTE was uncompromising in its ultimate goal of building a Tamil country

independent from the rest of the island, during the war it basically tolerated the continuity

of national elections in areas under its influence (DeVotta 2004). The coexistence of

ethnic civil war and democratic elections thus makes the case of Sri Lanka especially

useful for the purpose of this research.

The entire Sri Lankan civil war can be divided into four phases according to relatively

stable ceasefire or other forms of conciliatory arrangement (Bandarage 2009; Stokke

2006). They are Eelam War I (1983-1987), Eelam War II (1990-1994), Eelam War III

(1995-2001), and Eelam War IV (2006-2009). The ceasefire mediated by Norwegian-led

Western governments put an end to Eelam War III, which at one time appeared to

terminate the entire war because of the consensus on the post-conflict political process

reached between the Sinhalese central government and the LTTE. Unfortunately,

renewed conflicts came to explode not long after the inauguration of President Mahinda

Rajapaksa, whose government ultimately defeated the LTTE in Eelam War IV. It is

important to note that Eelam War IV is so different from the previous phases that many

regional experts argue it should be studied separately. Unlike the previous phases, during

which neither the Sri Lankan government nor the LTTE were able to gain obvious

superiority in the conflict areas, Eelam War IV was a one-sided smashing campaign

initiated by the Sri Lankan government (Stokke 2006). It is commonly believed that two

major issues jointly contributed to the change in the distribution of capability. First, the

LTTE was listed as a terrorist organization by the United Nations after the 9/11 terrorist

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attacks. This especially hurt the LTTE because it relied heavily on donations from the

Tamil diaspora in Western countries. Second, in the meantime the Sri Lankan

government had acquired considerable military and financial support from China, a rising

global power that has a special interest in exerting its influence in the region (DeVotta

2009; Parasram 2012). Thus, we exclude Eelam War IV from our empirical analysis.

4. Method and Data

We conceptualize the expansion and contraction of conflict among subnational units

as part of a dynamic rebellion network, while the electoral outcome of such units is

treated as the nodal attribute of our interest. Studying the coevolution between the

rebellion network and election outcomes is methodologically challenging due to the

complex system of dependence. First, both the rebellion network and election outcomes

have their own dynamics. Second, the network and voting dynamics should be the

endogenous outcome of the other. Third, changes in the rebellion network and voting

dynamics can also be influenced by other exogenous variables. In order to examine our

hypotheses within this complicated system of feedbacks, we apply the actor-based model

of network dynamics that in recent years has received considerable attention from

political scientists (Manger et al. 2012; Kinne 2013, for instance). Snijders (2005)

provides a formal derivative of this model framework. A less technical introduction can

be found in Pearson et al. (2006), Snijders et al. (2010) and Steglich et al. (2010). Here,

we focus on the application of this framework to our research on the endogenous nexus

between elections and ethnic conflict in Sri Lanka.

Within the actor-based network framework, the coevolution between the rebellion

network and voting behavior can be modeled by two differential effects. On the one hand,

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election outcomes as a nodal attribute are allowed to trigger changes in the rebellion

network. On the other hand, involvement in the rebellion network is allowed to influence

the outcome of elections. In the literature of dynamic social network analysis, the two

modes of determinism are popularly known as the selection effect and the influence

effect, respectively (Veenstra and Steglich 2012). The former focuses on how actors with

certain behaviors (say smoking) are more or less likely to build social networks (say

friendship) with other actors that share these behaviors. The latter focuses on how the

development of various social connections decides the adoption or abolishment of those

behaviors. Figure 1 provides an illustration for the selection and influence effects. Hence,

our hypotheses can be understood as one being about the selection effect — how election

outcomes impact the rebellion network — and the other about the influence effect, or

how changes in the rebellion network impact election outcomes.

In this research we view residents of each divisional secretariat (DS), including active

and potential insurgents, as a collective actor by whom decisions in both rebellion

network formation and voting behavior are drawn. Data on the LTTE’s military presence

are derived from the records of the Sri Lankan Ministry of Defense and Urban

Development (2009). They document the LTTE’s presence in all 79 district secretariats

of the Northern and Eastern Provinces. We apply the following procedure to translate the

information into a dynamic rebellion network. For any adjacent DS units, a unidirectional

tie of influence can be established if the presence of rebellion is observed in sequence.

Independent of geographical contiguity, two insurgency-afflicted DS units are treated as

having a reciprocal tie of influence if the presence of rebellion is not temporally

identified. A tie of influence is removed between a DS and the rest of the rebellion

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network if the DS has been retaken by government forces. Thus, the formation and

removal of ties between the nodal divisional secretariats comprise the dynamics of our

rebellion network. With respect to voting patterns, we focus on the presidential elections

during the civil war because the president of Sri Lanka is exclusively in charge of the

government’s executive agents, including the military, and faces weak institutional

checks (DeVotta 2004; Spencer 2007). We take DS-level support for the national winner

of the presidency as the main indicator reflecting regional loyalty to the central

government. Data on election results came from the Department of Elections of Sri Lanka

(2014). They are available at the level of polling division, the smallest unit of

constituencies. The 79 divisional secretariats under study belong to 25 polling divisions.

When the constituency demarcation is not fully aligned with that of the DS,

approximation was conducted according to the following principles. First, one DS counts

as part of a polling division if its majority lies in that polling division. Second, when a DS

is evenly divided into different polling divisions, the mean from those polling divisions is

used. Third, the raw data are converted to ordinal integers (0-9) by every 10 percent

increase in popular support to meet the requirement of actor-based network analysis that

the behavior must be ordinal. The relatively wide distance (10 percent) used here has an

important function: It helps lower the impact of the first coding principle — which

arbitrarily inputs the same raw support rate for different divisional secretariats of the

same polling division on our empirical analysis. For the reasons provided in the last

section, we only investigate the first three phases of the war (1983-2001). We aggregate

the yearly network data corresponding to presidential elections. As a result, we have a

dynamic network of rebellion including four waves of observations.

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Besides the interplay between the rebellion network and electoral patterns, some other

factors also contribute to the dynamics of the system. First, a considerable volume of

research has emphasized the significance of geo-environmental factors in deciding the

survival and diffusion of rebellion (Cederman et al. 2011; Esman and Herring 2003;

Tollefsen and Buhaug, forthcoming). To control for geographical accessibility, we

include the average distance to town for each DS. The data are collected from Poverty

Mapping Project supported by the CGIAR Consortium for Spatial Information (CGIAR

2006). Figure 2 shows all the divisional secretariats of Sri Lanka, with divisional

secretariats from the Northern and Eastern Provinces colored according to this variable.

Apart from the distance to town, we also added dummies representing whether a pair of

divisional secretariats are adjacent, are on the island country’s coastline or are located on

the land boundary separating the Northern and Eastern Provinces from the rest of the

country. Second, we control for the demographic properties of each DS by including their

size and population. The variables are derived from the GeoHive (2014). Third, we

include two election-related variables — voting turnout and whether DS units within a

dyadic pair are within the same electoral constituency. The data are again from the

Department of Elections of Sri Lanka (2014). Finally, both the rebellion network and

voting patterns under investigation have their own structures of trend. We leave them to

the next section.

5. Empirical Results

In the actor-based model framework, a rate function and an objective function are

constructed for both the network and the behavior dynamics. The rate function models

the frequency with which actors have the opportunity to change a small step either in the

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network or in the behavior. The subjective function models the rule for the changes and

thus is the main interest for most social inquiries. Table 1 provides the estimated

parameters of our objective functions (replication data and codes for this research are

available upon request). Given the complexity of the actor-based model framework, it is

not hard to appreciate that our estimates are based on simulation. The SIENA program

developed by Snijders and colleagues (Ripley et al. 2013) is used for that purpose.

Our specification of the dynamic rebellion network includes three trend structures.

They are outdegree, actors at distance 2, and reciprocity. The definition and description

of these network structures along with our treatment of similarity are included in Table 2.

All of these trend structures providing the basic information of network dynamics are

shown to be significant. First, the outdegree is negative. This means the odds that any tie

will be present versus absent are only 0.0588 ( 2.834e ), if an opportunity for change comes

and we disregard other parameters in the model. Thus, the rebellion network under study

is a sparse one. This is consistent with the existing research on the frequency of armed

conflict contagion (Holtermann 2014). Second, the effect of actors at distance 2 is shown

to be negative. This indicates that the rebellion network in Sri Lanka did not penetrate

very deep. Third, reciprocity is positive. Thus, nodes that receive incoming ties are likely

to send ties back. Furthermore, given that the effect magnitude of reciprocity is larger

than that of the outdegree, two-way connected nodal dyads should be more common than

one-way connected dyads in the rebellion network. For nodal covariates controlling for

the effect of geographical environment, two findings are worth noting. First, adjacent

regions are more likely to develop insurgent ties than geographically separated ones.

Second, the average distance to town of a region provides a major hurdle to the

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development of a rebellion network, which holds from either a recipient (alter)

perspective or a sender (ego) perspective. Such results are consistent with previous

findings on how geographical features determine the accessibility of a region and hence

the decisions made by insurgent groups. For election-related controls, turnout similarity

is strongly significant and positive. This means that in the northern and eastern parts of

Sri Lanka, two places are likely to make a rebellion tie change if they are similar in terms

of their voter turnout.

The effect of elections on conflict (hypothesis 1) is examined in three ways — sender

(ego), receiver (alter), and interactive effects. The results show that when local support

for the national winner of the presidency is high, the region is unlikely to be active in

sending out insurgent ties to other regions. The selection effect, however, cannot be

established if we observe the issue simply from an alter perspective. In other words, local

support for the national winner of the presidency has no immediate effect on whether a

region becomes a more or less popular target for insurgents. Despite this fact, local

support for the national winner of the presidency on the alter side still has an impact

when it works hand-in-hand with that on the ego side. As the interactive term shows,

when support in both the ego region and the alter region increases, the chances of an

insurgent tie developing between them should be low. Because the data in use supports

the selection effect hypothesis in two out of the three perspectives, it is no surprise to see

that an overall Wald test for the joint effect of selection yields a chi-squared value of

29.06 that is significant at the 0.01 level. Given this, it seems reasonable to say that a

selection effect going from election to conflict does exist. Thus, the impact of elections

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on conflict persists even if we conceptualize the Sri Lankan civil war as a dynamic

network and expand our research horizon beyond the moment of conflict onset.

Our specification of the voting dynamics is relatively succinct. The linear and

quadratic effects jointly define a parabola shape of the basic objective function that is

separable from the influence effect and other trend structures. Because both of these

effects are significant and negative, the basic objective function of voting pattern is an

inverted U-shaped distribution. In other words, local support for the national winner of

the presidency is clustered in the center and spread out on both sides. This means there is

a stable local support rate independent from other effects included in our specification of

voting dynamics. Besides the basic parabola, we also control for the effect of voting

turnout and the effect of being separated from the rebellion network. However, neither of

them is significant. Finally, the effect of influence (hypothesis 2) is tested by examining

the impact of rebellion network linkages on the voting similarity amongst DS nodes. As

we expected, winner support similarity is strongly significant and positive. This means

regions involved in the civil war converge in terms of local support for the national

winner of the presidency. Thus, the effect of influence is confirmed by the data.

By now the endogenous relationship between the rebellion network and behavior has

been empirically established. On the one hand, voting patterns as reflected in local

support for the national winner of the presidency negatively affect the development of a

rebellion network. On the other hand, the regions involved in the rebellion network

converge in terms of their support for the national winner of the presidency. Since both

the selection effect and the influence effect contribute to the coevolution between

rebellion and voting dynamics, a natural question to ask is which effect has a larger

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impact. Following Steglich et al. (2010), we apply Kandel’s procedure (1978) to illustrate

the relative contribution of the two effects. Based on estimated parameters, we first

simulate the interplays between the rebellion network and voting solely using the trend

effects that include the outdegree for the network and the linear and quadratic effects for

voting dynamics. Second, we simulate by excluding both the selection effect and

influence effect. Third, we simulate the coevolution by including the selection effect but

not the influence effect. Fourth, we switch on the influence effect but switch off the

selection effect. Finally, we simulate the full model. Each of the above-mentioned

simulation scenarios includes 4,000 trials (1,000 for each wave of network and voting

dynamics). The network autocorrelation of these simulated outcomes thus provides a

useful way to gauge the relative impact of different effects. We use Moran’s I (Moran

1948) and Geary’s C (Geary 1954) to measure network autocorrelation. Both of them are

widely used, often in parallel with the each other. For Moran’s I, a value close to zero

indicates that nodes connected by a tie are not more similar than one would expect under

random matching. When the value is closer to 1, it implies strong network autocorrelation.

Geary’s C is an inverse measure of network autocorrelation — a value close to 1

indicates weak behavior homogeneity, while a value close to zero implies strong behavior

homogeneity. Table 3 provides the simulation of autocorrelation coefficients for different

effects along with those for the observed data. Both Moran’s I and Geary’s C indicate

that the influence effect has a larger impact on the coevolution than the selection effect.

One limitation of the table, however, is that it provides the mean and standard error only

for the autocorrelation coefficients. Hence, we still fall short of appreciating the whole

picture. To this end, we visualize the distributions of the autocorrelation coefficients

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based on our simulation results. Figures 3 and 4, respectively, provide those for the

selection effect and influence effect. As we switch off the selection effect but switch on

the influence effect — moving from Figure 3 to Figure 4 — both Moran’s I and Geary’s

C show changes in support of strong behavior homogeneity. Such a pattern again

confirms that the influence effect has a larger impact on the coevolution between the

rebellion network and voting behavior than the selection effect.

6. Conclusion

By applying actor-based social network analysis to the case of Sri Lanka, this study

empirically examines the coevolution between elections and ethnic conflict dynamics.

The contribution of this research is fourfold. First, our findings are among the first to

provide systematic evidence concerning the endogenous relationship between elections

and ethnic conflict. In terms of the selection effect, we show that local support for the

national winner of the presidency is negatively related to the aggression of insurgents.

With respect to the influence effect, we show that regions involved in the rebellion

network converge regarding their support for the national winner of the presidency.

Furthermore, our model-based simulation results indicate that overall the influence

effect dominates the feedback between elections and conflict in Sri Lanka. Third, this

research reconfirms the significance of geographical factors in deciding insurgency

dynamics after controlling for the endogenous causality between elections and conflict.

According to this study, the development of a rebellion network is more likely to occur

in regions that are a short distance to town whether we observe the issue from the ego

or alter perspective. Besides this, adjacent regions are shown to be more likely to

develop insurgent ties than geographically separated ones. Both these results are

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consistent with previous research concerning how geographical accessibility contributes

to the behavioral patterns of insurgent groups in guerilla warfare. Finally, this research

illustrates the usefulness of the actor-based model framework in exploring the

endogenous relationship between elections and conflict. Given the flexibility of this

framework, its application to the endogenous feedback between elections and conflict in

other substantive cases is not hard to imagine. In fact, such a practice would be highly

desirable. This is because the current research on Sri Lanka faces the same issue of

external validity confronting single case studies. We may still wonder if the findings of

this research hold in other parts of the world and look forward to seeing new evidence

collected from other cases.

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Table 1 Main Results

Effect Coefficient Standard

Error

Significance

Network Dynamics

Outdegree -2.834 0.206 **

Number of actors at distance 2 -0.991 0.137 **

Reciprocity 2.789 0.262 **

Adjacency 2.397 0.191 **

Distance to town (alter) -0.019 0.540 *

Distance to town (ego) -0.048 0.012 **

Same coast -0.129 0.130

Same “outfield” -0.235 0.153

Area similarity -0.190 0.540

Population similarity 0.238 0.391

Shared polling division 0.044 0.176

Turnout similarity 1.834 0.507 **

Winner support (alter) -0.579 0.646

Winner support (ego) -1.614 0.763 *

Winner support (alter)

Winner support (ego)

-11.591 2.619 **

Behavior Dynamics

Shape: linear -0.240 0.093 **

Shape: quadratic -0.045 0.019 *

Turnout rate 0.203 0.204

Winner support similarity 13.043 2.365 **

Isolation 0.092 0.192

*p-value<0.05; **p-value<0.01

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Table 2 Structures of Network and Behavior

Structure Definition Description

Network Dynamics

Outdegree j ijX The overall tendency to

have ties amongst nodes

Actors at distance 2 (1 )max ( )j ij h ih hjX X X Tendency to keep others at

distance 2

Reciprocity j ij jiX X The tendency to have

reciprocal connections

Covariate similarity j ij ijX sim Tendency to build ties with

others that have similar

nodal properties

Behavior Dynamics

Shape: linear iZ Z

Shape: quadratic 2( )iZ Z They jointly define a

parabola shape of the

objective function

Average similarity ( ) / ( )j ij ij j ijX sim X Assimilation to network

neighbors’ behavior

Isolation ( )[1 max ( )]i j ijZ Z X How being isolated in the

rebellion network can

influence voting

Note: X represents the observed network; Z represents the observed behavior; i is the

sender of a tie; j is the recipient of it

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Table 3 Model-Based Simulated Network Autocorrelation Coefficients

Model Moran’s I

Mean (Standard Error)

Geary’s C

Mean (Standard Error)

Trend 0.123(0.231) 0.914(0.191)

Control 0.202(0.257) 0.855(0.206)

Selection 0.161(0.212) 0.882(0.180)

Influence 0.497(0.248) 0.425(0.124)

Full 0.452(0.212) 0.447(0.129)

Observed 0.6956 0.479

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Figure 1: Illustration of Selection (Left) and Influence (Right) Effects

Figure 2 District Secretariats of Sri Lanka

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Figure 3 Network Autocorrelations: The Selection Effect

Note: The asterisk represents the autocorrelation coefficients for the observed data.

Figure 4 Network Autocorrelations: The Influence Effect

Note: The asterisk represents the autocorrelation coefficients for the observed data.

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References

Bandarage, Asoka. 2009. The Separatist Conflict in Sri Lanka. London: Routledge.

Birnir, Jóhanna. 2007. Ethnicity and Electoral Politics. Cambridge: Cambridge

University Press.

Brancati, Dawn, and Jack Snyder. 2011. Rushing to the Polls: The Causes of Premature

Postconflict Elections. Journal of Conflict Resolution 55 (3):469-492.

Brancati, Dawn, and Jack Snyder. 2013. Time to Kill: The Impact of Election Timing on

Postconflict Stability. Journal of Conflict Resolution 57 (2):822-853.

Cederman, Lars-Erik, Kristian S. Gleditsch, and Simon Hug. 2013. Elections and Ethnic

Civil War. Comparative Political Studies 46 (3):387-417.

Cederman, Lars-Erik, Nils B. Weidmann, and Kristian S. Gleditsch. 2011. Horizontal

Inequalities and Ethnonationalist Civil War: A Global Comparison. American

Political Science Review 105 (3):478-495.

CGIAR. 2006. Case Studies- Poverty Mapping: Sri Lanka.

Cheibub, Jose Antonio, and Jude Hays. 2009. Can Elections Be Bad for Democracy? In

Annual Meeting of the American Political Science Association.

Collier, Paul. 2009. Wars, Guns and Votes: Democracy in Dangerous Places. London:

Harper Perennial.

Denny, Elaine K., and Barbara F. Walter. 2014. Ethnicity and Civil War. Journal of

Peace Research 51 (2):199-212.

Department of Elections, Sri Lanka. 2014. Past Elections.

DeVotta, Neil. 2004. Blowback: Linguistic Nationalism, Institutional Decay, and Ethnic

Conflict in Sri Lanka. Stanford: Stanford University Press.

DeVotta, Neil. 2009. The Liberation Tigers of Tamil Eelam and the Lost Quest for

Separatism in Sri Lanka. Asian Survey 49 (6):1021-1051.

Esman, Milton J., and Ronald J. Herring. 2003. Carrots, Sticks, and Ethnic Conflict. Ann

Arbor: University of Michigan Press.

Geary, Robert. 1954. The Contiguity Ratio and Statistical Mapping. Incorporated

Statistician 5 (3):115-146.

GeoHive. 2014. Sri Lanka Population Statistics.

Gleditsch, Nils P., Håvard Hegre, and Håvard Strand. 2009. Democracy and Civil War.

In Handbook of War Studies III, edited by M. I. Midlarsky. Ann Arbor:

University of Michigan Press.

Holtermann, Helge. 2014. Relative Capacity and the Spread of Rebellion: Insights from

Nepal. Journal of Conflict Resolution online first.

Kandel, Denise. 1978. Homophily, Selection, and Socialization in Adolescent Friendship

Pairs. American Journal of Sociology 84 (2):427-436.

Kinne, Brandon. 2013. Network Dynamics and the Evolution of International

Cooperation. American Political Science Review 107 (4):766-785.

Mampilly, Zachariah. 2011. Rebel Rulers. Ithaca: Cornell University Press.

Manger, Mark, Mark Pickup, and Tom A. B. Snijders. 2012. A Hierarchy of Preferences:

A Longitudinal Network Analysis Approach to PTA Formation. Journal of

Conflict Resolution 56 (5):853-878.

Mann, Michael. 2005. The Dark Side of Democracy: Explaining Ethnic Cleansing. New

York: Cambridge University Press.

25

Mansfield, Edward D., and Jack Snyder. 1995. Democratization and War. Foreign Affairs

74 (3):79-97.

Ministry of Defence and Urban Development, Sri Lanka. Sri Lanka Among Top 10 in

Civic Engagement 2009. http://www.defence.lk/new.asp?fname=Humanitarian.

Moran, Patrick A. P. 1948. The Interpretation of Statistical Maps. Journal of the Royal

Statistical Society, Series B 10 (2):243-251.

Parasram, Ajay. 2012. Erasing Tamil Eelam: De/Re Territorialisation in the Global War

on Terror. Geopolitics 17 (4):903-925.

Pearson, Michael, Christian Steglich, and Tom A. B. Snijders. 2006. Homophily and

Assimilation Among Sport-Active Adolescent Substance Users. Connections 27

(1):51-67.

Petersen, Roger. 2001. Resistance and Rebellion: Lessons from Eastern Europe. New

York: Cambridge University Press.

Ripley, Ruth M., Tom A. B. Snijders, and Paulina Preciado. 2013. Manual for SIENA

Version 4.0. Oxford: Department of Statistics at University of Oxford.

Snijders, Tom A. B. 2005. Models for Longitudinal Network Data. In Models and

Methods in Social Network Analysis, edited by P. J. Carrington, J. Scott and S.

Wasserman. Cambridge: Cambridge University Press.

Snijders, Tom A. B., Gerhard G. van de Bunt, and Christian Steglich. 2010. Introduction

to Stochastic Actor-Based Models for Network Dynamics. Social Networks 32

(1):44-60.

Spencer, Jonathan. 2007. Anthropology, Politics and the State: Democracy and Violence

in South Asia. New York: Cambridge University Press.

Steglich, Christian, Tom A. B. Snijders, and Michael Pearson. 2010. Dynamic Networks

and Behavior: Separating Selection from Influence. Sociological Methodology 40

(1):329-393.

Stokke, Kristian. 2006. Building the Tamil Eelam State. Third World Quarterly 27

(6):1021-1040.

Strand, Håvard. 2007. Political Regimes and Civil War Revisited.

Tollefsen, Andreas Forø, and Halvard Buhaug. 2015. Insurgency and Inaccessibility.

International Studies Review 17 (1):6-25.

Veenstra, René, and Christian Steglich. 2012. Actor-based Model for Network and

Behavior Dynamics. In Handbook of Developmental Research Methods, edited by

B. Laursen, T. D. Little and N. A. Card. New York: Guilford Press.