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Refugee Geography and theDiffusion of Armed Conflict inAfricaKerstin Fiska

a Department of Political Science, Loyola MarymountUniversity, University Hall 4203, 1 LMU Drive, LosAngeles, CA 90045, USAPublished online: 19 Jan 2015.

To cite this article: Kerstin Fisk (2014) Refugee Geography and the Diffusion of ArmedConflict in Africa, Civil Wars, 16:3, 255-275, DOI: 10.1080/13698249.2014.979019

To link to this article: http://dx.doi.org/10.1080/13698249.2014.979019

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Refugee Geography and the Diffusion of ArmedConflict in Africa

KERSTIN FISKDepartment of Political Science, Loyola Marymount University, University Hall

4203, 1 LMU Drive, Los Angeles, CA 90045, USA

Why are refugee populations associated with the spread of conflict? Do

refugees upset local dynamics by increasing the mobilization opportunities of

rebels? Work on rebel motivation predicts that the strategic impact of a

location influences armed actors’ decisions to fight there; thus, I identify two

strategic aspects of refugee geography which may influence where conflict

takes place in the host country – refugee mass and refugee accommodation

type. I examine the influence of these factors using a new, disaggregated

dataset on refugees in 26 African countries engaged in armed conflict during

the period 2000–2010.

INTRODUCTION

A longstanding research tradition explores the diffusion patterns of international

wars, or ‘how wars grow,’1 by focusing on the interdependence rather than the

independence of war – how a country’s borders impact its likelihood of

experiencing interstate conflict.2 Most and Starr, for example, show how states’

geographic proximity affects the occurrence of war based on the heightened

interaction opportunities created by shared borders. The authors find that a state with

a neighbor at war is three to five times more likely to experience an interstate war

compared to a state that does not have a neighbor at war.3 Later work by Siverson

and Starr asserts that war spreads via a disease-like process and geographical

proximity to a warring neighbor determines a state’s likelihood of becoming

infected. Their findings demonstrate that a country’s risk of experiencing

international war is positively related to the location of the state in a war-affected

region.4 This basic conflict diffusion/contagion logic has more recently been applied

in studies of civil war, in order to account for how spatial proximity, ‘transborder

linkages and processes,’ and domestic phenomena concurrently influence the

likelihood of intrastate conflict.5

While transnational linkages have been explored in a number of conflict studies,6

this article focuses on the role of forced migration. Refugee populations have

previously been linked to violence and conflict in a number of qualitative studies7

and in a country-level, quantitative analysis carried out by Salehyan and Gleditsch.8

q 2014 Taylor & Francis

Civil Wars, 2014

Vol. 16, No. 3, 255–275, http://dx.doi.org/10.1080/13698249.2014.979019

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However, to my knowledge, a disaggregated analysis of the relationship between

refugees and armed conflict has not yet been carried out. A disaggregated study is

necessary in order to achieve a stronger match between theory and data – to avoid

studying at the national-level phenomena which are better understood within a sub-

national context.9 In order to test this relationship at the sub-country level, I created

Geo-Refugee, a new dataset that accounts for the geographical locations and

population sizes of refugees10 in Africa (refugees in camps as well as self-settled

refugees) designated within a particular first level administrative unit (region,

province, or state) for the period 2000–2010.11 This study captures the size of camp-

based refugee populations within sub-national units, and then links this measure to

the occurrence of conflict between host governments and formally organized rebel

groups. Figure 1 below illustrates the spatial clustering of refugee populations and

armed conflict events. Evaluating the effect of refugees on host country conflict

propensity in a disaggregated study is a valuable undertaking, as refugee crises

continue to increase both in number and in geographical scope. For instance, the

year 2010 reached a 15-year high in the total refugee population12 – followed by a

20 percent increase in armed conflicts, the largest single-year increase since 1990 –

in 2011.13

FIGURE 1

REFUGEE AND STATE-BASED CONFLICT CLUSTERING, 2000 – 2010.

Sources: Geo-Refugee and Uppsala Conflict Data Program, Georeferenced Event Dataset

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The article proceeds as follows. I begin by briefly reviewing the conflict

geography literature and then outline my assumptions with regard to how refugee

populations can alter the incentive structures of armed actors engaged in ‘state-

based,’ traditional armed conflicts involving the government of a state and formally

organized rebels.14 Next, I test the proposition that refugees contribute to armed

conflict in receiving countries by examining the propensity for host governments

and rebels to fight in regions that host larger camp-based refugee populations. I also

investigate whether this effect is amplified when the refugees are located in the host

country’s border region. Contrary to the theoretical arguments linking ‘refugee

resources’ to armed conflict, I do not find evidence that armed conflict events are

more frequent in regions that host larger populations of refugees. I conclude by

discussing possible directions for future research.

LITERATURE REVIEW

As mentioned above, the study of spatial processes in civil war extends earlier

research dealing with the diffusion patterns of international wars.15 Specifically, it is a

continuation of international war diffusion research which focused on the

interdependence rather than the independence of war – how a country’s borders

impact its opportunity for experiencing interstate conflict. Two main ‘types’ of

geography, physical and human, help to illuminate conflict diffusion in this line of

scholarship. In terms of physical geography, spatial proximity to conflict is often

associated with civil war onset in the conflict literature. For instance, Salehyan and

Gleditsch find that countries which have neighbors experiencing civil war are more

likely to suffer war themselves.16 Likewise, in a broader examination of the domestic

and international determinants of civil conflict onset from 1950 to 2001, Buhaug and

Gleditsch find that irrespective of similar domestic characteristics, countries

‘infected’ with civil conflict are hazardous to the health of their neighbors.17 Some of

the most widely tested predictors of civil war are also based on physical geography,

including proximity to natural resources,18 proximity to borders,19 proximity to the

capital city,20 and proximity to rugged or mountainous terrain,21 among others.

The theoretical link between physical geography and armed conflict is based on the

motivation and opportunity conferred by each of these factors: geography (e.g.,

diamond deposits or proximity to a border) motivates fighting via the material or

strategic value of a location and also shapes the opportunity for rebellion. For

instance, Le Billon argues that ‘lootable’ diamonds can motivate armed conflict

because they are ‘. . . often easily accessible to governments and rebels alike with

minimal bureaucratic infrastructure.’22 Groups able to fight and gain control over

natural resources can, by extension, tax and loot those resources. In Angola, the

Angolan Armed Forces (FAA) and the National Union for the Total Independence of

Angola (UNITA) would frequently ‘. . . clash and fight brief and fierce battles’ over

mining zones.23 Natural resources are generally fixed, so firms extracting the

resources and interested in protected their investments will pay off whoever has

control over that location, be it the state or a rebel organization. Capturing resource

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wealth provides opportunities for rebellion by financing the war effort and helping to

solve recruitment and retention problems, because enlistees and fighters recognize a

potential for access to it.24

While theoretical connections between human geography (e.g., population

distributions) and war fighting have been made for centuries,25 scholars have only

recently begun refining the study of the relationship between human and physical

geography as they relate to civil war. Researchers have, for instance, related

elements of human geography including population movements, population size,

and population concentration to armed conflict in several studies.26 Salehyan and

Gleditsch present refugee populations as one causal mechanism explaining the

observed spatial diffusion of armed conflict.27 A handful of analyses also uncover

evidence of relationships between population size and concentration and conflict

location. Raleigh and Hegre theorize that armed groups target more populous

locations because they are richer – they possess more food and other supplies. The

authors further suggest that concentrated locations are conflict prone because they

are easier to organize, relatively homogenous, and more autonomous. Their findings

support these arguments – both larger population mass and greater population

density contribute to conflict events.28 Using new spatial data on ethnic groups,

Weidmann similarly finds that high levels of ethnic group concentration are

positively and significantly related to ethnic conflict.29

To further evaluate the impact of human geography variables on conflict, this

article re-focuses on the interaction effects caused by the spillover of conflict and

refugees. Refugees are a byproduct of insecurity across borders (accounting for the

role of exposure to conflict in a ‘bad neighborhood’), and like conflict, refugee

populations are geographically clustered within countries. But do refugees

themselves pose real threats to national and international security by contributing

to the spread of armed conflict?

Salehyan and Gleditsch contend that larger refugee flows increase the likelihood

of civil war in the host country ‘. . . by altering the incentives and opportunities of

actors in receiving areas to engage in violence.’30 They advance several processes

through which refugees theoretically increase the risk of conflict. In some cases, host

governments may fight refugees directly. For example, direct conflict between the

host government and refugees can ensue when hosts take action to stop refugee

combatants from engaging in militant activity against the sending state, and these

‘refugee warriors’ fight back. Refugees may also form new rebel organizations in

receiving areas and seek to ‘. . . influence the domestic political process in ways that

are not welcome by the state.’31 Refugees can increase the probability of host state

civil war in a less straightforward manner as well. Refugees might, for instance,

strengthen the motivation and capability of would-be rebels in the host by increasing

the available conflict-inducing resources, including militant ideology, recruits, and

weapons brought in from the sending country. Or refugees may exacerbate local-

level grievances by generating ‘negative externalities.’ For example, refugees can

increase competition for jobs and other resources, spread disease, and/or degrade the

environment.32

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Examining a global sample of countries during the period 1951–2001, Salehyan

and Gleditsch find a positive and significant link between receiving large refugee

inflows from neighbors and the likelihood of civil war onset in the host country.

The authors conclude that ‘local integration efforts’ and the demilitarization of

refugee camps can help to prevent refugee-related conflict. Notably, these policy

recommendations suggest that the manner in which refugees are accommodated

(e.g., camp settlement versus self-settlement) in the host country influences the

propensity for conflict.33 To my knowledge, however, a large-N assessment of the

effects of alternative accommodation types on host country conflict has not been

conducted.

THEORY

As discussed above, scholars have already advanced a host of explanations for the

demonstrated refugee–conflict connection, linking larger refugee inflows from

neighbors to civil war onset in the host country. Following the logic of Most and

Starr34 and Siverson and Starr,35 refugee populations are theorized to generate

interaction opportunities and increase motivations to rebel, thereby increasing the

likelihood of civil war in receiving states.36 However, the previous focus on the total

size and origin of the refugee population overlooks variation in refugee settlement

within receiving states.37 This is potentially problematic because the effects of

refugee populations on the host are expected to be most acute in closer proximity to

the refugees,38 but refugee populations may not be located where the violence

actually takes place. Accordingly, Salehyan and Gleditsch acknowledge that ‘These

aggregate figures for a country may often mask or understate the relevant impact of

refugees as felt on the ground by local communities. Refugees are not distributed

equally across the territory of a country, and their effects may be particularly acute

within particular regions or areas.’39 Thus, to begin to address the well-recognized

issue of over-aggregation, I first examine the effects of refugee mass, the total

population size of a refugee population, on the occurrence of armed conflict events

at the sub-national level.

Hypothesis 1: Regions hosting larger numbers of refugees are more likely to

experience armed conflict events involving the host government and

rebels.

Next, I consider a specific condition under which refugees can become part of the

opportunity and willingness-related40 incentive structures of armed groups and

thereby influence the dynamics of armed conflict. I begin with the assumption that

the relative strategic and material value of refugee-populated areas41 can incentivize

targeting these areas by increasing rebels’ ability to mobilize fighters and by

providing the resources rebel groups need to sustain themselves. This logic is similar

to that of several studies demonstrating that natural resources increase the

probability of violent conflict in a location.42 Combatants need to acquire resource

wealth in order to solve the recruitment and retention problems that inhibit collective

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action given incentives to free ride. If an armed group controls territory that has

valuable resources, it is easier to motivate individual fighters.43 If these resources

facilitate armed conflict, I expect battles between the host government and rebels to

be more frequent in areas where the refugee presence is more valuable – where

‘refugee resources’ are more abundant.

Along these lines, several qualitative studies assert that refugee aid explains the

link between refugees and conflict.44 Both scholars and practitioners have argued

that the presence of international refugee regimes in a country provides combatants

with valuable economic resources.45 For instance, Zolberg et al. argue that ‘refugee

warrior communities’ are the direct result of ‘relief assistance for the refugees’

coming from the international refugee regime.46 Fiona Terry, formerly of Doctors

without Borders and the International Committee of the Red Cross, contends that

there is a ‘paradox’ of humanitarian aid because assistance provided by the

international refugee regime provides a ‘renewable source of exploitation’ and

finances rebels’ war economies.47 Sarah Kenyon Lischer likewise maintains that the

aid provided by humanitarian agencies provides food, medical supplies, and

sanctuary to militants, and ultimately funds a war economy. According to Lischer,

combatants steal food and other supplies to the tune of thousands and possibly even

millions of dollars per year.48

Given that refugee aid is considered one of the primary explanations for the

refugee–conflict relationship, I argue that locations hosting larger numbers of camp-

based refugees should be more likely to be targeted by combatants. This is because

refugee camps and centers ‘easily qualify as the most conspicuous element of

refugee assistance,’ whereas self-settled or dispersed refugees ‘tend to live outside

of the assistance circuit of international agencies.’49 Camp locations reliably possess

international aid – food, medicine, and other supplies that can enhance their relative

worth. This resource wealth may be used to decrease the cost of engaging in violent

conflict as well as to attenuate recruitment and allegiance problems, as individual

fighters can take advantage of refugee resources. Accordingly, reports of rebel

attacks on refugee camps and aid convoys are not uncommon. For instance, in July

2014, an estimated 150 rebel fighters in the Central African Republic raided a camp

for Sudanese refugees and took everything of value, including money and cell

phones.50

Camp-based refugees can also affect the incentive structures of domestic armed

groups by increasing opportunities for both voluntary and forced conscription.

As Lichbach argues, rebels can overcome barriers to collective action and retention

(the ‘rebel’s dilemma’) in environments that make it easier for them to enforce

allegiance.51 Recruitment of camp-based refugees is likely easier because camps

provide avenues for controlling refugee civilians.52 Their condensed and uniform

nature help reduce coordination (collective action and organizational) costs.

Monitoring for compliance is easier in spatially concentrated groups because it is

easier for rebel commanders to impart rewards and punish defectors. A further

incentive for rebels looking to recruit fighters (forcibly or otherwise) is the relative

physical and economic vulnerability of the camp-based refugees. For instance,

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Achvarina and Reich argue that ‘the degree to which children are protected in

refugee camps is the primary determinant of child soldier recruitment rates.’53

Vulnerable individuals are generally considered ripe for rebellion because they have

a lower opportunity cost for fighting.54 If refugee populations dependent on the aid

community for their livelihoods and protection are easier to conscript, armed groups

may be more likely to target refugee camps based on their relative ability to acquire

recruits there. If rebel groups target camp-based refugees based on the value they

confer, host governments may clash with the rebels to prevent the region from being

exploited.

Another reason camp locations may be considered more valuable to combatants

is their relative visibility to the international community. Camp-based refugees are

assisted by the UNHCR as well as non-governmental aid organizations. They are

distinct population centers situated on a negotiated piece of land allocated by

the host government. The strategic value of controlling these locations may thus

be greater – the visibility of control is clearer, as is the reputational cost for the

government if a rebel group has control and/or operates with impunity.55 In other

words, host country rebel groups might seek target camp locations in part to appear

stronger relative to the government, and to embarrass the government by creating or

spreading the perception that it lacks the capacity to control and protect refugee-

populated locations. The enhanced visibility of refugee-hosting location may,

therefore, shed light on the armed struggle.

By extension, I expect that administrative regions hosting self-settled refugee

populations will be less likely to experience armed conflict events compared to those

hosting organized, camp-based refugee populations. Self-settled refugee popu-

lations should be a diminished strategic and material resource for at least two

reasons: first, dispersed populations should be difficult for rebels to exploit and

control; second, self-settled refugees have less access to humanitarian aid and

therefore are less likely to be valuable to combatants.

To sum up the discussion thus far, I have argued that regions hosting larger

camp-based refugee populations are more likely to experience armed conflict events

due to the strategic and material value they represent to combatants. Rebel groups

are likely to target these locations because controlling valuable territory increases

their chances of survival, compared to seeking to control locations with smaller and/

or more dispersed and unaided populations. For governments attempting to curtail

the appropriation of refugees and associated resources by rebel groups, the most

effective points of attack are locations that are heavily populated by camp-based

refugees. When these locations attract rebels, governments will likely place a high

strategic value on putting a stop to it.56 Thus, targeting refugee camp locations may

be a calculated choice intended to undermine the rebel capability. Based on this

discussion, I present the following hypotheses:

Hypothesis 2: Regions hosting larger numbers of camp-based refugees are more

likely to experience armed conflict events involving the host

government and rebels.

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Hypothesis 3: Regions hosting larger numbers of self-settled refugees are less

likely to experience armed conflict events involving the host

government and rebels.

Interaction Effects

There are also theory-based reasons to interact human and physical geography

variables, in particular the size of a camp-based population with border locations.

If domestic rebels target camp-based refugees for resources, as they did in the

Central African Republic case mentioned above, they may be more likely to do

so in border areas more difficult for the host government to control. As Mogire

observes, rebel activity is more common ‘. . . where the refugee camps are located

in traditionally insecure and lawless areas because the government has little

control of territory, thus enabling militias and fighters to entrench themselves in

order to operate.’57 This logic applies to rebel groups from the sending country as

well as to domestic rebel groups. Battles between sending country rebels and

the host government might ensue if the host government attempts to prevent

the foreign rebels from using refugee camps as a base of operations and/or

for resources. Since foreign rebels are likely to operate in border locations

where they can more easily reach the sending state, conflict may be more likely

in these locations. I include the following hypothesis to capture these interactive

effects:

Hypothesis 4: Regions that host more camp-based refugees and are located on a

border are more likely to experience armed conflict events involving

the host government and rebels.

RESEARCH DESIGN

Data and Methods

In order to locate refugees at the sub-national level, I rely on the United Nations’

Second Administrative Level Boundaries data set project (SALB) for identification

of the majority of first-level administrative units within each country. The SALB

data are preferable mainly because they are reliable and generally uncontested, as

they are coded for historic changes and are ‘developed in collaboration with and

validated by the National Mapping Agencies (NMA) of each UN member state.’58

The temporal range for this analysis includes the years 2000 to 2010, as these are the

years for which the UNHCR has systematically collected data on the location and

population size of refugees within host countries. Since the focus of this study is the

impact of refugees on the dynamics of armed conflict, I examine only the countries

in Africa that experienced an armed conflict during the analysis period. This results

in a sample of 466 first-level administrative units across 26 countries. The study of

Africa is particularly relevant because the continent hosts more refugee camps than

any other region in the world.59

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The vast majority of disaggregated studies conducted thus far have been

conducted using geographical squares as the units of analysis, usually 100-km2 grid

cells. The use of grid cells as the unit of analysis allows researchers to test constant

(homogenously sized) cross-sections, but these are relatively non-intuitive and

generally arbitrarily determined.60 The preference for uniform grid cells is often

related to the concern that the use of more intuitive yet heterogeneous political units

is problematic because political boundaries can shift over time, particularly in the

developing world. While these changes have gone largely unexplained in the

scholarly literature, work in development studies indicates that new districts are

created primarily to generate patronage (e.g., new jobs) for the purpose of winning

elections.61 Yet this study is conducted within a limited time frame, diminishing the

likelihood of observing non-constancy, which occurs when boundary shifts lead to

changes in the landmass of administrative units.

Dependent Variable

I test for the effects of refugee populations using a disaggregated conflict dataset,

Uppsala Conflict Data Program’s Georeferenced Event Dataset (GED). GED

includes event data for armed conflicts that, by definition, result in at least 25 annual

battle-related deaths. The data in this analysis account for battles between host

governments and formally organized rebel groups (termed state-based conflicts in

GED). State-based conflicts fit the traditional definition of armed conflict; they

involve ‘a contested incompatibility that concerns government and/or territory

where the use of armed force between two parties, of which at least one is the

government of a state, results in at least 25 battle-related deaths.’62 State-based

conflict events are therefore all events leading to at least one battle-related death in

dyad years that cross the 25-death threshold. Events within each of these conflict

types are recorded if they result in at least one death that is directly related to combat

between the warring parties. I aggregate them into yearly counts within each first-

level administrative unit for the period 2000–2010.

Explanatory Variables

The variable refugee mass represents the total size of the refugee population at the

first administrative level (province, state, region, district, etc.). These data were

obtained from the statistics office at the UNHCR. Importantly for my purposes,

UNHCR also categorizes refugees’ accommodation types as either ‘camp/center’ or

‘rural/dispersed’.63 I have argued that these forms of accommodation theoretically

indicate the relative strategic and material value of a refugee-hosting location. The

additional independent predictors in the statistical models are therefore camp

refugees, the total number of ‘camp/center’ refugees in an administrative unit per

year, and dispersed refugees, the total number of ‘dispersed’ (or self-settled)

refugees in an administrative unit per year. These variables are intended to capture

the effects of refugees in line with their accommodation type and the resources

associated with their presence. In the sample used here, camp-based refugees

constitute slightly more than half of the total refugee population. The variable

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generated to capture the interaction effects between physical and human geography

– borders and camps – is camp £ on border.

The original data for the category refugees and people in a refugee-like situation

by accommodation type and location name come directly from the UNHCR.64 The

category refugees includes: ‘. . . individuals recognized under the 1951 Convention

relating to the Status of Refugees and its 1967 Protocol; the 1969 OAU Convention

Governing the Specific Aspects of Refugee Problems in Africa; those recognized in

accordance with the UNHCR statute; individuals granted complementary forms of

protection and those enjoying “temporary protection”.’ The category people in a

refugee-like situation ‘. . . is descriptive in nature and includes groups of people who

are outside their country of origin and who face protection risks similar to those of

refugees, but for whom refugee status has, for practical or other reasons, not been

ascertained.’65 A refugee population measure accounting for both refugees and those

undocumented but living in a refugee-like situation is important because the number

of unregistered refugees is typically much higher than numbers of registered

refugees. Attaining refugee status from the UNHCR is not an automatic process, as it

entails receiving dedicated (rather than ad hoc) institutional support from the host

government and/or the UNHCR.66

The data, however, are limited: first, UNHCR systematically collected location,

accommodation, and demographic data for refugee populations beginning in 2000,

which limits the scope of analysis considerably; second, the available data are in

end-year figures, which means that they are not representative of the yearly average

population in each location; third, data are available only for locations in which the

population numbers more than 100 persons. Each location type/name was attributed

to a first-level administrative unit using additional UNHCR sources due to a lack of

reliable information on locations at a lower level of analysis.

Control Variables

In addition to the main explanatory variables discussed above, I include expectations

for several geographical, sub-national unit level control variables that are likely to

influence the dependent variables, particularly spatial dependence.

Proximate conflict. Taking into account the earlier discussion regarding the

geography of conflict diffusion, conflict events are likely to be spatially dependent,

meaning that conflicts in an administrative region are likely to influence conflict

events in proximate regions. Positive spatial clustering in the dependent variable is

addressed using the spatial lag conflict adjacent, a count variables capturing all

conflict events that took place in adjacent administrative regions (within the host

state as well as in adjacent units that lie across international borders) within the

calendar year.

Distance of the location from the capital. Several disaggregated studies have

linked conflict events in a location to where the location is positioned relative to the

capital. Regions located at farther distances from the capital region are more likely

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to be outside of the government’s military reach, thus non-state war events may be

more common in them.67 The combination of distance, unawareness of local

conditions, and lack of local support all diminish the state’s control over (and ability

to provide security in) the periphery. The hypothesized direction of the relationship

between distance to the capital and conflict varies, however. Herbst postulates that

the proximity of the location to the capital matters less than whether or not the

location itself is valuable. He argues that battles occur closer to areas where there is a

much to be gained or lost – closer to capital cities.68 I therefore control for capital

distance, which is a continuous measure of the distance in kilometers between the

administrative region and the capital region.

Locations adjacent to a border. Sub-national units located in the periphery are

relatively less likely to be under the full control of government forces.69 Thus, rebels

may be more likely to wage battles in peripheral (border) areas, where the

probability of success is relatively high. The propensity for conflict in that region

might increase due to the opportunity to secure (in the case of insurgents) or prevent

(in the case of the government) access to cross-border safe havens. Border locations

may also possess strategic value if they make it easier to smuggle weapons and other

resources into proximate units.70 As Buhaug and Gates note, ‘Control of

international borders thus ensures that the rebel army will fight another day.’71

I therefore control for on border, a dummy variable capturing whether or not the

administrative region abuts the country’s border.

Diamond-rich locations. Sub-national units with greater resource wealth should

increase that location’s target value. Secondary diamonds, because they are a

lootable resource, present an immediate opportunity for financial gain for both the

government and rebels. As the expected payoffs are higher, there is also a greater

strategic value to be gained from controlling these areas. Diamonds is

operationalized as a dummy variable capturing whether there is a secondary

(lootable) diamond deposit in the unit. The data are derived from Gilmore, Lujala,

Gleditsch, and Rod.72

Location size. Beyond factors like proximity to the capital and abutting an

international border, the overall land area of a location may have an impact on its

risk of experiencing armed conflicts. As Herbst argues, just by the virtue of their size

alone, smaller states present advantages for governments in their exercise of control

over territories.73 Regardless of whether a region is dominated by the state or a rebel

group, controlling a larger administrative region is likely to be more difficult than

controlling a smaller one. Thus, government forces and rebel groups may be more

likely to battle one another, and battles between rival rebel groups may also be more

common in larger regions. The variable unit size is a calculation of the total land area

of the administrative unit.74

Temporal dependence. Time-series cross-sectional data often display problems of

autocorrelation in the form of temporal dependence. For instance, repeated events in

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a location are likely, because reprisals often follow an initial attack.75 Observations

in one year may depend to a large degree on observations from the previous year.

The lagged dependent variable is commonly offered as a solution to the problem of

serial correlation. Although the lagged dependent variable remedy has generated a

fair amount of debate, Beck and Katz conclude that there is ‘nothing pernicious’

about them. They perform as well as or better than the more complicated estimators,

including instrumental variable alternatives.76 Lagged dependent variables

essentially bias findings conservatively, against rejecting the null hypothesis.

Thus, in order to account for temporal dependence, I control for prior conflict, which

is a measure of the total number of armed conflict events which took place in the

region in the preceding year.

Statistical Method

Since the dependent variables in my analyses are the cumulative number of conflict

and war events which occur in a first-level administrative region in a calendar year,

an event count model is the most appropriate statistical method. Another

characteristic of the data influencing model selection is the small chance that event

counts within these units are completely independent.77 Since non-independence is

likely an issue and positive contagion increases the variance of the observed counts,

the negative binomial model is preferred. Negative binomial models are more

appropriate for handling over-dispersed count data because they have an extra

parameter to handle over-dispersion.78 Thus, I estimate panel negative binomial

event count regression models,79 which make sense theoretically because they

control for non-independence among event counts.80 It is also important to point out

that the model controls for a temporal baseline or counterfactual for what would

have happened if refugees had not arrived at a given time by generating a ‘baseline’

model automatically. All models are population averaged and report robust standard

errors clustered by sub-national administrative unit.81 I also estimate fixed effects

models clustered on countries in order to address the issue of heterogeneity across

units. These models are included in the Appendix.

EMPIRICAL RESULTS

In Table 1, Models 1–7 provide tests of hypotheses regarding the determinants of

battle locations in armed conflicts. Models 1–3 test the effects of refugee mass,

camp-based refugees, and self-settled refugees controlling only for temporal and

spatial dependence; Models 4–6 introduce the additional geographical controls

expected to condition actors’ involvement in conflict; finally, Model 7 estimates the

interactive effect of camp-based refugees located on a border. Models 8–14, which

include country fixed effects, are reported in Table 2.

Notably, the empirical models presented in Table 1 do not support the hypothesis

relating refugee resources to armed conflict dynamics. The simple models (Models

1–3) demonstrate that refugee mass and the overall size of refugee camps are

negatively related to armed conflict events, contrary to the expectations of

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Hypotheses 1 and 2 (H1 and H2). While regions hosting larger numbers of self-

settled refugees are less likely to experience conflict in line with the expectations of

H3, the effect is not significant at conventional levels. Introducing the control

variables into the models changes the direction of the relationship between refugee

mass and camp-based refugee size and conflict events from negative to positive, but

this positive effect remains statistically insignificant. The relationship between self-

settled refugees and conflict events remains negative but insignificant. Accounting

for the interaction between refugee accommodation and internal structural

conditions is also important to the study of armed conflict diffusion; however, the

model containing multiplicative effects (corresponding to H4) also fails to find

evidence of a moderating relationship between camp-based refugees and conflict,

even in seemingly volatile border locations. The effect of camp refugees moderated

by their presence on an international border is calculated by adding together the

camp refugees coefficient with the camp £ on border coefficient. The results

indicate that the coefficient on the interaction term is close to zero and not significant

(0.018); therefore, H4 is not supported.

TABLE 1

ESTIMATION OF STATE-BASED CONFLICT EVENT LOCATIONS, 2000 – 2010

Variables (1) (2) (3) (4) (5) (6) (7)

Conflictadjacent

0.592*** 0.592*** 0.586*** 0.683*** 0.679*** 0.673*** 0.680***(0.0422) (0.0421) (0.0434) (0.0451) (0.0442) (0.0468) (0.0443)

Prior conflict 0.936*** 0.941*** 0.956*** 0.719*** 0.729*** 0.726*** 0.725***(0.0637) (0.0629) (0.0666) (0.0888) (0.0883) (0.0975) (0.0888)

Capitaldistance

20.255** 20.251** 20.253* 20.255**(0.129) (0.118) (0.151) (0.118)

On border 20.294** 20.302** 20.317** 20.319**(0.139) (0.147) (0.142) (0.156)

Diamonds 20.128 20.112 20.131 20.112(0.210) (0.227) (0.226) (0.227)

Unit size 0.136** 0.129** 0.144** 0.131**(0.0647) (0.0624) (0.0694) (0.0624)

Refugeemass

20.00914 0.00170(0.0215) (0.0231)

Camprefugees

20.00154 0.0142 20.0185(0.0225) (0.0203) (0.0547)

Dispersedrefugees

20.0233 20.0160(0.0250) (0.0361)

Camp £on border

0.0365(0.0593)

Constant 22.351*** 22.370*** 22.371*** 22.135*** 22.101*** 22.194*** 22.083***(0.235) (0.226) (0.238) (0.471) (0.445) (0.540) (0.448)

Observations 4,931 5,007 4,683 4,832 4,908 4,584 4,908Number ofcode

466 466 462 457 457 453 457

Note: Robust standard errors in parentheses.***p , 0.01, **p , 0.05, *p , 0.1.

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The geographical controls demonstrate that a few physical geographical features

are stronger predictors of conflict involving the host government and rebels. In all of

the models, administrative units located farther from the capital and on borders

experience significantly fewer battles, not more. Larger administrative units suffer a

higher rate of conflict, and this effect is also statistically significant. Diamonds are

negatively related to armed conflict events. The direction of this relationship does

not follow in line with the hypothesized link between natural resource wealth and

conflict. Consistent with previous literature, the two strongest predictors of events

are battles in a neighboring administrative unit and the severity of fighting in the

previous year. Proximity thus predicts conflict spillover – regions proximate to

conflict are significantly more likely to experience a conflict events.82 Regions that

experience more conflict events in the previous year also have significantly higher

rates of conflict. The spatial and temporal controls are consistently positive and

significant at the 0.01 percent level.

Results are similar in the models that report country-level fixed effects, with the

exception that some of the effects become stronger in a few models (Table 2). The

relationship between refugee mass and conflict events remains negative but becomes

TABLE 2

ESTIMATION OF STATE-BASED CONFLICT EVENT LOCATIONS, 2000 – 2010

Variables (8) (9) (10) (11) (12) (13) (14)

Conflictadjacent

0.417*** 0.418*** 0.401*** 0.433*** 0.436*** 0.421*** 0.435***(0.0256) (0.0256) (0.0263) (0.0258) (0.0258) (0.0265) (0.0258)

Priorconflict

0.955*** 0.952*** 0.964*** 0.920*** 0.918*** 0.916*** 0.911***(0.0242) (0.0241) (0.0243) (0.0257) (0.0255) (0.0261) (0.0256)

Capitaldistance

20.0613** 20.0596** 20.0877*** 20.0701**(0.0290) (0.0286) (0.0305) (0.0289)

On border 20.407*** 20.450*** 20.394*** 20.495***(0.0826) (0.0803) (0.0841) (0.0823)

Diamonds 20.181 20.252* 20.0981 20.270*(0.143) (0.144) (0.150) (0.144)

Unit size 0.0900*** 0.0924*** 0.100*** 0.100***(0.0313) (0.0315) (0.0323) (0.0318)

Refugeemass

20.0229** 20.0125(0.0101) (0.0106)

Camprefugees

0.00105 0.0131 20.100**(0.0108) (0.0112) (0.0488)

Dispersedrefugees

20.0429*** 20.0384***(0.0137) (0.0143)

Camp £on border

0.124**(0.0501)

Constant 23.656*** 23.698*** 23.572*** 23.936*** 23.982*** 23.794*** 23.955***(0.0887) (0.0879) (0.0899) (0.232) (0.230) (0.236) (0.229)

Observations 4,410 4,484 4,181 4,311 4,385 4,082 4,385Number ofcountry

21 21 21 21 21 21 21

Note: Robust standard errors in parentheses.***p , 0.01, **p , 0.05, *p , 0.1.

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statistically significant at the 0.05 level in the simple model (Model 8). However,

this variable loses its significance once the controls are introduced (Model 11). Thus,

there H1 is again not supported. H2, which concerns the relationship between camp-

based refugees and armed conflict dynamics, is again positive but statistically

insignificant. H3 is supported in Models 10 and 13; regions hosting larger numbers

of dispersed refugees experience significantly fewer conflict events. Model 14

indicates that in non-border regions, the size of camp-based refugee populations is

negatively and significantly related to conflict events at the 0.05 level. What about in

border areas? When the camp refugees variable is interacted with the on border

variable, we can see that there is a significant moderating relationship between the

two, as expected. However, once the effect of camp refugees moderated by their

location on the border is calculated (by adding together the camp refugees

coefficient with the camp £ on border coefficient), the results indicate that the

coefficient on the interaction term is close to zero and not significant (0.024). Thus,

H4 is not supported in the fixed effects model.

To sum up these results, I do not find evidence that larger camp-based refugee

populations, as an indicator of the relative abundance of refugee resources, are related

to armed conflict events. At the same time, several of the geographical features tested

here are significantly related to where battles most frequently take place.

DISCUSSION AND CONCLUSION

The results presented above demonstrate that the diffusion of armed conflict is

determined largely by spatial and temporal proximity to conflict. This effect holds

controlling for demographic and geographic factors previously related to where civil

conflicts occur within states. Spatial proximity to conflict is consistently found to

influence where conflicts occur in quantitative, sub-country studies. For instance,

Ostby et al., Hegre and Raleigh, Buhaug and Rod, and O’Loughlin and Witmer all

show that proximity to a conflict zone positively and significantly influences conflict

propensity.

At the same time, factors often associated with the value of a location, and

therefore combatants’ incentives to fight, are found to be unrelated to conflict. This

is not particularly surprising once I examine a sampling of findings in disaggregated

studies of civil war. In fact, the findings for several alternative geographical

predictors (e.g., lootable diamonds) in disaggregated studies vary greatly.83 The

likely explanation for these inconsistent results is that resources are associated with

conflict in some cases but not others. For instance, in a recent study, Koos and

Basedau test for whether locations in Africa with uranium mines are more likely to

experience armed conflict. They find that the relationship appears to exist in only a

subset of the countries in the study, and argue that the strategic value of controlling

the uranium mines is attractive to rebels, but this value also means that governments

are more likely to secure these areas, which has a deterrent effect. The implication is

that ‘a number of further specific circumstances have to be additionally present for

uranium to contribute to conflict.’84

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Along these lines, the results presented above do not find evidence in favor of a

link between ‘refugee resources’ and the location of fighting within armed conflicts.

Specifically, these results do not uncover a relationship between camp-based

refugees and armed conflict dynamics that involve host governments and formally

organized, non-state actors. The findings thus do not follow in line with the

expectations of this article, nor with some of the previous theorizing on the refugee–

conflict connection. One possible reason for the divergent findings of this study

compared to Salehyan and Gleditsch’s findings at the country level is the difference

in geographical and temporal scope. Whereas Salehyan and Gleditsch analyzed a

global sample of countries during the period 1951–2001, a lack of available sub-

country data limits this study to Africa between the years 2000 and 2010. Another

explanation may be that host governments have learned from past incidents of

refugee militarization, and now prioritize securing areas that host refugee camps,

and/or ensure that refugee camps are located in secure locations. Nevertheless, the

findings of this article suggest that a link between refugees and armed conflict in host

countries should not be overgeneralized.

The one finding that supports the predictions of this article is the demonstrated

negative relationship between dispersed (or self-settled) refugees and armed conflict

dynamics. While more work on the role of refugee accommodation type needs to be

done, this result suggests that refugee integration may dampen the conflict-inducing,

negative impacts of refugees as felt by host communities. This initial finding is also

an important one given that countries sometimes force refugees into camps citing

security risks. In Kenya, for instance, authorities recently announced a plan to gather

50,000 refugees living in urban areas and force them into camps in response to

‘emergency security challenges.’85

While this article does not find a relationship between camp-based refugees and

armed conflict that is specific to the targeting decisions of formally organized armed

groups (rebels or governments), conflicts other than those involving formally organized

armed actorsmay be related to the refugee presence. Rather than insurgency, the impact

of refugees on the security of receiving areas may be more closely related to

intercommunal violence (for instance, due to effects on local economic competition).

Futurework should therefore explorewhether refugee geography related to conflict and

violence perpetrated by actors beyond those formally organized for combat, e.g.,

communal violence committed by informal groups.

ACKNOWLEDGEMENTS

The author would like to thank Jacek Kugler, Jennifer Merolla, Hal Nelson, and Melissa Rogers for theirvaluable feedback and suggestions.

NOTES ON CONTRIBUTOR

Kerstin Fisk is a Visiting Assistant Professor of Political Science at Loyola

Marymount University. Her current research explores the transnational determinants

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of conflict diffusion, the dynamics of violence in intrastate wars, and the emergence

and diffusion of a preventive self-defense norm. Email: kerstin.fisk@lmu.edu

NOTES

1. Randolph Siverson and Harvey Starr, The Diffusion of War: A Study of Opportunity and Willingness(Ann Arbor, MI: U of Michigan P 1991) p. 3.

2. Lewis F. Richardson, Statistics of Deadly Quarrels (Chicago, IL: Quadrangle Books 1960); BenjaminA. Most and Harvey Starr, Inquiry, Logic and International Politics (Columbia, SC: U of SouthCarolina P 1989); Stuart A. Bremer, ‘The Contagiousness of Coercion: The Spread of SeriousInternational Disputes 1900–1976’, International Interactions 9/1 (1982) pp.29–55; Harvey Starr &Benjamin A. Most, ‘Contagion and Border Effects on Contemporary African Conflicts’, ComparativePolitical Studies 16/1 (1983) pp.92–117; Henk W. Houweling and Jan G. Siccama, ‘TheEpidemiology of War, 1816–1980’, Journal of Conflict Resolution 29/4(1985) pp.641–63; PaulDiehl, ‘Geography and War: A Review and Assessment of the Empirical Literature’, InternationalInteractions 17/1(1991) pp.11–27.

3. Benjamin A. Most and Harvey Starr, Inquiry, Logic and International Politics (Columbia, SC: U ofSouth Carolina P 1989).

4. Siverson and Starr (note 1).5. Lars-Erik Cederman, Luc Girardin, and Kristian Gleditsch, ‘Ethnonationalist Triads: Assessing the

Influence of Kin Groups on Civil Wars’, World Politics 61/3 (2009) pp.403–37, p. 404; see also,Nicholas Sambanis, ‘A Review of Recent Advances and Future Directions in the QuantitativeLiterature on Civil War’, Defence and Peace Economics 13/3 (2002) pp.215–43; Monty G. Marshalland Ted Robert Gurr, Peace and Conflict 2003: A Global Survey of Armed Conflicts, Self-determination Movements, and Democracy (Center for International Development and ConflictManagement, U of Maryland 2003); Paul Collier and Nicholas Sambanis (eds), Understanding CivilWar: Evidence and Analysis, Vol. 1 – Africa (World Bank 2005); Havard Hegre and NicholasSambanis, ‘Sensitivity Analysis of Empirical Results on Civil War Onset’, Journal of ConflictResolution 50/4 (2006) pp.508–35; Alex Braithwaite, ‘The Geographic Spread of MilitarizedDisputes’, Journal of Peace Research 43/5 (2006) pp.507–22; Idean Salehyan and Kristian SkredeGleditsch, ‘Refugees and the Spread of Civil War’, International Organization 60/2 (2006) pp.335–66; Kristian Skrede Gleditsch, ‘Transnational Dimensions of Civil War’, Journal of Peace Research44/3 (2007) pp.293–309; Halvard Buhaug and Kristian Gleditsch, ‘Contagion or Confusion? WhyConflicts Cluster in Space’, International Studies Quarterly 52/2 (2008) pp.215–33; Idean Salehyan,‘Transnational Rebels: Neighboring States as a Sanctuary for Rebel Groups’, World Politics 59/2(2007) pp.217–42; Idean Salehyan, Rebels without Borders: Transnational Insurgencies in WorldPolitics (Ithaca, NY: Cornell UP 2009).

6. See e.g. Gleditsch (note 5); Buhaug and Gleditsch (note 5).7. See e.g. Gil Loescher, Refugee Movements and International Security (London: Brassey’s for the

International Institute for Strategic Studies 1992); Myron Weiner, ‘Security, Stability andInternational Migration’, International Security 17/3 (1992/1993) pp.91–126; Sarah Kenyon Lischer,Dangerous Sanctuaries: Refugee Camps, Civil War and the Dilemma of Humanitarian Aid (Ithaca,NY: Cornell UP 2005); Edward Mogire, Victims as Security Threats: Refugee Impact on Host StateSecurity in Africa (Burlington, VT: Ashgate 2011).

8. Salehyan and Gleditsch (note 5).9. Halvard Buhaug and J. K. Rød, ‘Local Determinants of African Civil Wars, 1970–2001’, Political

Geography 25/3 (2006) pp.315–35; Lars-Erik Cederman and Kristian Skrede Gleditsch,‘Introduction to Special Issue on “Disaggregating Civil War”’, Journal of Conflict Resolution 53/4(2009) pp.487–95; Havard Hegre, Gudrun Ostby, and Clionadh Raleigh, ‘Poverty and Civil WarEvents: A Disaggregated Study of Liberia’, Journal of Conflict Resolution 53/4 (2009) pp.598–623;Clionadh Raleigh, Frank D. W. Witmer, and John O’Loughlin, ‘A Review and Assessment of SpatialAnalysis and Conflict: The Geography of War’ in R. Denemark (ed.) The International StudiesEncyclopedia Vol X (Oxford: Wiley-Blackwell 2010), among others.

10. UNHCR identifies a refugee as a person who, ‘owing to a well-founded fear of being persecuted forreasons of race, religion, nationality, membership of a particular social group or political opinion, isoutside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail

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himself of the protection of that country.’ Available from http://www.unhcr.org/pages/49c3646c125.html

11. The United Nations High Commissioner for Refugees (UNHCR) began systematically collectingdemographic and location data for refugees in 2000.

12. ‘UNHCR Global Trends 2010’, available from http://www.unhcr.org/4dfa11499.html13. Uppsala University Press Release, ‘The Number of Armed Conflicts Increased Strongly in 2011’,

Published 7 Jul. 2012. Available from http://www.uu.se/en/media/pressreleasedocument/?id¼1724&area ¼ 2,3,16&typ ¼ pm&na ¼ &lang ¼ en

14. UCDP/PRIO Armed Conflict Dataset Codebook, Version 4 (2012).15. Siverson and Starr (note 1).16. Salehyan and Gleditsch (note 5) p.356.17. Buhaug and Gleditsch (note 5) p.230.18. For example, Indra de Soysa, ‘The Resource Curse: Are Civil Wars Driven by Rapacity or Paucity?’

in Mats Berdal and David Malone (eds) Greed & Grievance: Economic Agendas in Civil War(Boulder, CO: Lynne Rienner 2000) pp.113–35; Phillipe Le Billon, ‘The Political Economy ofResource Wars’ in Jakkie Cilliers and Christian Dietrich (eds) Angola’s War Economy (Pretoria,South Africa: Institute for Security Studies 2000) pp.21–42; Phillipe Le Billon, ‘The PoliticalEcology of War: Natural Resources and Armed Conflicts’, Political Geography 20/5 (2001) pp.561–84; Halvard Buhaug and Scott Gates, ‘The Geography of Civil War’, Journal of Peace Research 39/4(2002) pp.417–33; Michael L. Ross, ‘What Do We Know about Natural Resources and Civil War?Journal of Peace Research 41/3 (2004) pp.337–56; Carlo Koos and Matthias Basedau, ‘DoesUranium Mining Increase Civil Conflict Risk? Evidence from a Spatiotemporal Analysis of Africafrom 1960 to 2008’, Civil Wars 15/3 (2013), pp.306–31; Michael Burch and Elise Pizzi, ‘LocalFights in National Conflicts: Understanding the Location of Conflict Events during InterstateConflict’, Civil Wars 16/1 (2014) pp.24–45.

19. Buhaug and Gates (note 18) p.422.20. Halvard Buhaug, ‘Dude, Where’s My Conflict?: LSG, Relative Strength, and the Location of Civil

War’, Conflict Management and Peace Science 27/2 (2010) pp.107–28; Hanne Fjelde and DesireeNilsson, ‘Rebels against Rebels: Explaining Violence between Rebel Groups’, Journal of ConflictResolution 00/0 (2012) pp.1–25; Jeffrey Herbst, States and Power in Africa: Comparative Lessons inAuthority and Control (Princeton, NJ: Princeton UP 2000) pp.152–54.

21. Halvard Buhaug, Scott Gates, and Pavi Lujala, ‘Geography, Rebel Capability, and the Duration ofCivil War’, Journal of Conflict Resolution 53/4 (2009) pp.544–69.

22. Le Billon (note 18) p.569.23. Christian Dietrich, ‘Inventory of Formal Diamond Mining in Angola’ in Jakkie Cilliers and Christian

Dietrich (eds) Angola’s War Economy (Pretoria, South Africa: Institute for Security Studies 2000)pp.141–172.

24. Paul Collier, ‘Rebellion as a Quasi-Criminal Activity’, Journal of Conflict Resolution 44 (2000)pp.839–53.

25. Victor Mair, The Art of War: Sun Tzu’s Military Methods (New York: Columbia University Press2005).

26. Paul Collier andAnkeHoeffler, ‘Aid, Policy and Peace: Reducing the Risks of Civil Conflict’,Defenseand Peace Economics 13 (2002) pp.435–50; Monica Duffy Toft, The Geography of Ethnic Violence:Identity, Interests and the Indivisibility of Territory (Princeton, NJ: Princeton UP 2003); Salehyan andGleditsch (note 5); Nils B. Weidmann, ‘Geography as Motivation and Opportunity: GroupConcentration and Ethnic Conflict’, Journal of Conflict Resolution 53/4 (2009) pp.526–43; ElliottGreen, ‘The Political Demography of Conflict in Modern Africa’, Civil Wars 14/4 (2012) pp.477–98.

27. Salehyan and Gleditsch (note 5).28. Clionadh Raleigh and Havard Hegre, ‘Population Size, Concentration, and Civil

War: A Geographically Disaggregated Analysis’, Political Geography 28 (2009) pp.224–38,pp.225–26.

29. Weidmann (note 26).30. Salehyan and Gleditsch (note 5) p.341.31. Ibid., pp.342–43.32. Ibid., pp.343–44.33. Ibid., p.361.34. Most and Starr (note 2).35. Siverson and Starr (note 1).

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36. Interaction opportunities are ‘the possibilities that are available to any entity within any environment,representing the total set of environmental constraints and possibilities’ (Siverson and Starr, note 1,p. 48).

37. Buhaug and Rød, for instance, argue that ‘. . . any statistical study of civil war that uses country-levelapproximations of local phenomena is potentially flawed.’ See Buhaug and Rød (note 9) p.315.

38. The localized nature of refugee populations is further underscored by the fact that refugees in manycountries face containment, or severe restrictions on their freedom of movement. The US Committeefor Refugees and Immigrants, which tracks the states that ‘warehouse’ refugees by depriving them oftheir freedom of movement for five consecutive years or more, shows that, in 2008, 19 Africancountries had systematically deprived refugees of their right to freedom of movement under Article26 of the 1951 Convention. See Warehousing Map, World Refugee Survey 2009, US Committee forRefugees and Immigrants. Available from http://www.uscrirefugees.org/2010Website/3_Our%20Work/3_2_2_Warehousing_Campaign/WarehousingMap.pdf

39. Salehyan and Gleditsch (note 5) p. 349. Likewise, past studies have shown that the majority of armedconflicts are regional (see Buhaug and Gates [note 18]) – and do not diffuse beyond a quarter of acountry’s territory (see Raleigh et al. [note 9] p.1).

40. Willingness is based on ‘a decision maker’s calculations of advantage and disadvantage, cost andbenefit, considered on both conscious and unconscious levels.’ See Siverson and Starr (note 1) p.49.Willingness and opportunity interact: ‘Anything that affects the structural possibilities of theenvironment or environments within which decision makers must act also affects the incentivestructures of those decision makers’ (p.49).

41. Hegre, Ostby, and Raleigh (note 9) introduced the theoretical framework for ‘target value,’ anddescribe in detail the distinction between determinants of support level and target value. The authorsargue that conflict events are more likely to occur where support is strong and target value is high.

42. Buhaug and Rød (note 9); John Bellows and Edward Miguel, ‘War and Local Collective Action inSierra Leone’, Journal of Public Economics 93/11–12 (2009) pp.1144–57; Hanne Fjelde, Sub-national Determinants of Non-state Conflicts in Nigeria, 1991–2006 (Uppsala University and Centrefor the Study of Civil War, PRIO 2009); Gudrun Ostby, Ragnhild Nordas, and Jan Ketil Rod,‘Regional Inequalities and Civil Conflict in Sub-Saharan Africa’, International Studies Quarterly 53(2009) pp.301–24.

43. Mark Irving Lichbach, The Rebel’s Dilemma (Ann Arbor, MI: U of Michigan P 1995).44. Barry Munslow and Christopher Brown, ‘Complex Emergencies: The Institutional Impasse’, Third

World Quarterly 20/1 (1999): 207–21; Mary B. Anderson, Do No Harm: How Aid Can SupportPeace – Or War (Boulder, CO: Lynne Rienner 1999); Fiona Terry, Condemned to Repeat? TheParadox of Humanitarian Action (Ithaca, NY: Cornell UP 2002); Stephen John Stedman and FredTanner (eds), ‘Refugees as Resources in War’ in Refugee Manipulation: War, Politics and the Abuseof Human Suffering (Washington, DC: Brookings 2003); Sarah Kenyon Lischer, ‘Collateral Damage:Humanitarian Assistance as a Cause of Conflict’, International Security 28/1 (2003) pp.79–109;Guglielmo Verdirame and Barbara Harrell-Bond, Rights in Exile: Janus-Faced Humanitarianism(Studies in Forced Migration, Vol. 17) (New York and Oxford: Berghahan Books 2005); Eric Morrisand Stephen John Stedman, ‘Protracted Refugee Situations, Conflict and Security: The Need forBetter Diagnosis and Prescription’, in Gil Loescher (ed.) Protracted Refugee Situations: Political,Human Rights and Security Implications (Tokyo: United Nations UP 2008).

45. Loescher (note 7); Terry (note 44); Lischer (note 44); Verdirame and Bond (note 44).46. Aristide Zolberg, Astri Suhrke, and Sergio Aguayo, Escape from Violence: Conflict and Refugee

Crisis in the Developing World (New York, NY: Oxford UP 1989), pp.276–77.47. Terry (note 44) p.13.48. Lischer (note 7) p.8.49. Anna Schmidt, Forced Migration Online Thematic Guide: Camps versus Settlements (Oxford: U of

Oxford 2003) pp.1–2.50. ‘Sudan: CAR Forces Attack, Loot Sudan Refugee Camp’, All Africa, 24 Jul. 2014. Available at http://

allafrica.com/stories/201407040364.html51. Lichbach (note 43).52. Stedman and Tanner (note 44); Terry 2002 (note 44).53. Vera Achvarina and Simon F. Reich, ‘No Place to Hide: Refugees, Displaced Persons, and the

Recruitment of Child Soldiers’, International Security 31/1 (2006) pp.127–164.54. See e.g. Patricia Justino, ‘Poverty and Violent Conflict: AMicro-Level Perspective on the Causes and

Duration of Warfare’, Journal of Peace Research 46/3 (2009) pp.315–33.

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55. That governments suffer both psychological and reputational costs due to inability to control an areais brought to light by Raleigh and Hegre (note 28) p.225.

56. Zolberg et al. (note 49).57. Mogire (note 7) p.45.58. UN Geographic Information Working Group, Second Administrative Level Boundaries (SALB)

Dataset. Available at http://www.unsalb.org/59. Schmidt (note 49).60. Ostby et al. (note 42) p.309.61. Elliott Green, ‘Patronage, District Creation, and Reform in Uganda’, Studies in Comparative

International Development 45/1 (2010) pp.83–103.62. Lotta Themner, UCDP/PRIO Armed Conflict Dataset Codebook, Version 4 (Center for the Study of

Civil Wars, International Peace Research Institute of Oslo [PRIO] 2011).63. A number of factors can influence a host government’s choice of refugee settlement policy. For

instance, governments often reference security concerns in order to justify confining refugees tocamps. Another reason cited is the relative ease of targeting and distributing refugee aid in camps.Further, host governments prefer camps based on the view that they minimize refugees’ negativeeffects on host communities.

64. The UNHCR’s Division of Programme Support and Management kindly shared the original data onthe locations and demographic composition for the category ‘refugees’ and ‘people in a refugee-likesituation’ for the years 2000–2010. Missing data were addressed using additional sources compiledby the UNHCR and contained in the Refworld database.

65. UNHCR Statistical Yearbook (2010) p.15.66. The original data from UNHCR contained location information for refugees in the form of camp and

village names. In most cases, the camp name did not provide an indication of the camp location. Theauthor therefore consulted supplementary resources in order to ascertain these locations.

67. In line with Boulding’s loss of strength gradient applied to civil wars, Buhaug (note 20) argues thatlocations farther from the national capital should be more difficult for the state to control, thus moreconflict prone. Kenneth Boulding, Conflict and Defense (New York, NY: Harper & Row 1962).

68. Herbst (note 20) pp.152–54.69. Buhaug et al. (note 21).70. Hegre et al. (note 9) p.614.71. Buhaug and Gates (note 18) p.422.72. Elisabeth Gilmore, Nils Petter Gleditsch, Paivi Lujala, and Jan Ketil Rod, ‘Conflict Diamonds:

A New Dataset’, Conflict Management and Peace Science 22/3 (2005) pp.257–72.73. Herbst (note 20).74. Data for this measure came from Gwillim Law’s Statoids, available at http://www.statoids.com/75. Hegre et al. (note 9) p.614.76. Nathaniel Beck and Jonathan N. Katz,Modeling Dynamics in Time-Series – Cross-Section Political

Economy Data, Social Science Working Paper 1304, California Institute of Technology, Jun. 2009,p.24.

77. Hegre et al. (note 9) p.614.78. The results of a likelihood ratio test for whether the negative binomial model is preferable to a

standard Poisson model show that alpha is not equal to zero; therefore, the negative binomial model ispreferred. I also test for whether a zero-inflated model is appropriate for the data, yet a Vuong testshows that a negative binomial regression model is preferred to a zero-inflated model.

79. Additional information on the negative binomial regression can be found in the appendix.80. J. Scott Long, Regression Models for Categorical and Limited Dependent Variables (London: Sage

1997).81. Hubbard et al. argue in favor of population average models, and claim that mixed models lead to

potentially misleading estimates and biased inference. Alan E. Hubbard, Jennifer Ahern, NancyFleischer, Mark Van der Laan, Sheri A. Lippman, Nicholas Jewell, Tim Bruckner, and WilliamSatariano, ‘To GEE or Not to GEE: Comparing Population Average and Mixed Models forEstimating the Associations between Neighborhood Risk Factors and Health’, Epidemiology 21/4(2010) pp.467–74.

82. Spatial proximity to conflict is consistently found to influence where conflicts occur in quantitative,sub-country studies. For instance, Ostby et al. (note 42), Hegre et al. (note 9), Buhaug and Rød (note9), and O’Loughlin and Witmer (2011) all show that proximity to a conflict zone positively andsignificantly influences conflict propensity. John O’Loughlin and Frank D.W. Witmer, ‘The

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Localized Geographies of Violence in the North Caucasus of Russia’, Annals of the Association ofAmerican Geographers 101/1 (2011) pp.178–201.

83. A sampling of findings shows that the results for several alternative geographical predictors (e.g.,diamonds, borders, and distance from the capital) in disaggregated studies vary greatly. Hegre et al.(note 9) find that diamond deposit locations are negatively associated with conflict events during thecivil war in Liberia. Yet, Ostby et al. (note 42) show that secondary diamond locations are positivelyand significantly related to conflict events at the one percent level in their study of 22 Africancountries from 1986 to 2004. Ostby et al. demonstrate that the location of a unit on an internationalborder is negatively and in some models negatively and significantly related to conflict risk. However,Buhaug, Gleditsch, Holtermann, Ostby, and Tollefsen (2009) find that locations nearer to borders aremore likely to experience conflict onset. Buhaug and Rød (note 9), Weidmann (note 26) as well asBuhaug, Cederman, and Rod (2008) show that conflict events are significantly more likely atdistances farther from the capital. Conversely, Buhaug et al. (note 21) find that conflict is not relatedto distance from the capital. Halvard Buhaug, Kristian Skrede Gleditsch, Helge Holtermann, GudrunØstby, and Andreas Forø Tollefsen, ‘It’s the Local Economy, Stupid! Geographic Wealth Dispersionand Conflict Outbreak Location’, Journal of Conflict Resolution 55/5 (2011) pp.814–840; HalvardBuhaug, Lars-Erik Cederman, and Jan Ketil Rød, ‘Disaggregating Ethno-Nationalist Civil Wars: ADyadic Test of Exclusion Theory’, International Organization 62/3 (2008) pp.531–551.

84. Koos and Basedau (note 18).85. ‘Kenya: Plan to Force 50,000 Refugees Into Camps’ Human Rights Watch, 26 Mar. 2014. Available

at http://www.hrw.org/news/2014/03/26/kenya-plan-force-50000-refugees-camps

APPENDIX. FULL LIST OF COUNTRIES INCLUDED IN THE ANALYSES

Algeria, Angola, Burundi, Central African Republic, Chad, Congo, Cote d’Ivoire,

Democratic Republic of Congo, Eritrea, Ethiopia, Ghana, Guinea, Guinea-Bissau,

Kenya, Liberia, Mali, Mauritania, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra

Leone, Somalia, Sudan, and Uganda.

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