Questioning Refugee Camps as Sources of Conflict∗

Andrew Shaver†

Yang-Yang Zhou‡

September 24, 2015

Abstract

A substantial body of research in political science finds that refugees increase thelikelihood of conflict within the states that host them. This literature concludes thatrefugees do so by exacerbating ethnic tensions, intensifying economic competition, andexpanding insurgent networks. Related research finds that refugees diffuse regionalcivil wars. Using precisely geo-referenced data on camps of refugees and internallydisplaced persons released by the United Nations for this study, we find no support forclaims that refugees degrade the security conditions in their host communities. Instead,our analysis suggests that areas of countries that receive refugees tend to become morestable following their arrival. Given current and unprecedented numbers of forciblydisplaced persons around the world, our findings should assuage concerns that hostingrefugees will generate instability.

∗We thank Lamis Abdelaaty, Aylin Aydin, Alexander Betts, Alexander Bollfrass, David Carter, BenjaminFifield, Kosuke Imai, Robert Keohane, Kabir Khanna, Jacob Shapiro, Yao-Yuan Yeh and participants of theAmerican Political Science Association’s 2015 Annual Meeting and the International Studies Association’s2014 Annual Convention for comments on this manuscript. We also thank Tsering Wangyal Shawa forassisting with the processing of geospatially identified data. We are grateful to the United Nations HighCommissioner for Refugees for providing the data used in this study as well as to Miguel Centeno and thePrinceton Institute for International and Regional Studies for financial support. Zhou acknowledges supportfrom the National Science Foundation (SES–1148900). Finally, we wish to thank Idean Salehyan and KristianGleditsch for making their data from their 2006 article available for replication. All errors are ours.†Woodrow Wilson School of Public and International Affairs, Princeton University. E-mail:

ashaver@princeton.edu‡Department of Politics, Princeton University. E-mail: yz3@princeton.edu

1 Introduction

The global community is presently experiencing unprecedented levels of forced migration.

According to the United Nations High Commissioner for Refugees (UNHCR), 59.5 million

individuals around the world have been displaced involuntarily, the highest level on record.

Such widespread displacement is primarily the consequence of ongoing sub-state conflicts,

including those in Syria, Iraq, Ukraine, and Afghanistan. To place this figure in perspective,

one out of every 122 humans is now either a refugee, an internally displaced person, or an

asylum seeker. Were this “nation of the displaced” a country, it would rank as the 24th

largest in the world, approximating the United Kingdom in population (UNHCR, 2014b).

Among these individuals, 18.1 million are refugees – displaced persons who have fled their

home countries.12

From the string of fatal attempted boat crossings in the Mediterranean and Southeast

Asian seas to the construction of makeshift refugee camps throughout Italy and Greece to

extensive efforts for migrants and refugees to make their way across the European continent

amidst rising anti-migrant political activity, recent international headlines make clear that

the current refugee crisis is not just a priority for aid agencies and a small number of affected

countries but for governments and their citizens worldwide.

Following these refugees is the assumption that they impact the security of their host

communities. Is that true? And if so, how? In this paper, we argue that, on the whole,

1As opposed to an internally displaced person who remains within her or his country of origin, a refugeeis someone who “owing to a well-founded fear of being persecuted for reasons of race, religion, nationality,membership of a particular social group or political opinion, is outside the country of her or his nationality,and is unable, or due to such fear, unwilling to avail him- or herself of the protection of that country.” Article1, The 1951 UN Convention Relating to the Status of Refugees.

2See Tables 2, 3 and Figures 7, 8, and 9 for additional summary statistics regarding global displacementlocations.

1

refugees do not increase the likelihood of conflict in either the countries that receive them

or within the more limited regions of states in which they settle. Instead, we find that

areas which host refugees tend to experience more stability over time, which may be due to

the influx of international humanitarian organizations like the UNHCR whose presence may

bolster security.

Our findings speak to a growing body of political science research seeking to identify

migrants’, and especially, refugees’ effects on host communities. Starting with Zolberg et al.

(1989), this literature emphasizes cases of violent conflict that are attributed to militarized

refugees such as the Banyarwanda refugees in Eastern Congo in the 1960s and South African

refugees during Apartheid (e.g. Lischer, 2005; Muggah, 2006). Generalizing these cases into a

global phenomenon, scholars such as Loescher (1992); Weiner (1992); Salehyan and Gleditsch

(2006); Salehyan (2007, 2009); Choi and Salehyan (2013) argue that refugees diffuse civil war

and other forms of sub-state violence across international borders. They suggest that refugees

exacerbate ethnic tensions, intensify economic competition with locals, and expand insurgent

social networks by transporting weapons and using camps to recruit and harbor combatants.

Our results challenge this grim link between refugees and conflict diffusion. Using original

province-year data that combines precise georeferenced panel data on displacement camps

(both for refugees and internally displaced persons), terrain, ethnicity and economic devel-

opment indicators for the period 1989 to 2008, we carry out a series of empirical tests at

both country and subnational levels. We find that refugee communities are not associated

with increased conflict likelihood in the areas in which they settle. We further find that

refugee communities do not appear to facilitate regional conflict diffusion. Finally, through

a difference-in-differences approach, we compare the likelihood of conflict onset between sub-

2

national provinces with camps and those without camps during the same timeframe. In this

last test, we find evidence that those areas of countries that receive refugees tend to experi-

ence increased stability following their arrival. While previous research identifying refugees

as sources of conflict generally conclude by recommending that host governments and the in-

ternational community work towards securing refugee environments, our last finding suggests

that they may already be doing so successfully.

Host countries will continue to confront the question of whether to allow refugee integra-

tion within their communities, and the intuitive and widespread belief in the destabilizing

effects of refugees is likely to influence policymaking, as recent statements by policymakers

make clear. The White House, for instance, has expressed unease “about the destabilizing im-

pact that significant flows of [Syrian] refugees could have on the politics of an already pretty

volatile region” (House, 2014). The UNHCR has similarly warned that “failing to provide

enough humanitarian support for Syrian refugees [could have] dramatic consequences for...

the stability of the entire region, including a serious security threat to Lebanon” (UNHCR,

2014a). For their part, media outlets also give voice to the concern that individuals seeking

refuge across international borders threaten host countries with violence (e.g. Herszenhorn,

2015; Erlanger and Smale, 2015; Shafy, 2013; Shinkman, 2013; Harrigan and Easen, 2001).

2 Re-theorizing the Link between Refugees and Conflict Diffusion

A number of scholars have sought to identify possible security consequences of refugee

flows in host countries. This literature began with general descriptions moving to empirical

analysis mostly based on comparative case studies. Three cases in particular prominently

featured in these studies; those of: 1. refugee flows from Liberia’s two civil wars from 1989

3

through 2003, which is generally believed to have destabilized the neighboring countries of

Sierra Leone, Guinea, and the Ivory Coast; 2. forced migration leading to conflict in several

Balkan states throughout the 1990s; and 3. refugees from the Rwandan genocide in 1994 who

were later involved in conflicts in the Democratic Republic of the Congo (Whitaker, 2003).

Generalizing from these cases, recent work in political science using cross-national panel

data argues that refugees’ role in spreading conflict is a global phenomenon. Nevertheless,

other scholars contend that in general refugees are overwhelmingly civilian noncombatants

and not sources of conflict (Matthews, 1972; Whitaker, 2003; Onoma, 2013). In this piece,

we present evidence that is consistent with the latter.

First, the concept of the “refugee-warrior” or militarized refugee is attributable to Zol-

berg et al. (1989). These authors caution that refugees are not only victims escaping per-

secution but also political activists who mobilize while in the host country. They describe

refugee camps as potential military bases for refugee-warriors to continue opposition activ-

ities. Next, in a sweeping effort to apply a security/stability framework to international

migration, Weiner (1992) lists a number of situations in which refugees may be seen as a

security threat by host countries. In condemning the policies of their origin country, refugees

may attempt to influence host country policies, a state of affairs made particularly volatile

if these refugees are armed. In the host country, refugees may participate in terrorist at-

tacks, ally with the domestic opposition, or engage in cross-border arms trafficking. Refugees

may also destabilize the host country indirectly, by imposing a heavy economic burden and

straining the host country’s social services, infrastructure, and ecological resources.

Similarly, Loescher (1992) considers the strategic causes, consequences, and responses to

refugee movements. He points to refugee influxes shifting the ethnic composition of host

4

countries, with especially detrimental consequences for societies with a precarious ethnic

balance or pre-existing ethnic rivalries. If they affect economic conditions, by driving wages

down and housing costs up, refugees may also foment resentment among local host popu-

lations. Host country policies on naturalization may cause refugees to feel alienated and

promote militancy rather than integration. These effects are particularly threatening when

the host country’s central government is weak or when its legitimacy is in doubt.

Using comparative case studies, Whitaker (2003) examines the empirical link between

refugee flows and the cross-border spread of conflict. In particular, she asks why the 1994

influx of Rwandan refugees sparked conflict in the Democratic Republic of the Congo but not

in Tanzania. She concludes that refugee movements are more likely to produce conflict when

the host country’s regime lacks political legitimacy, when ethnic difference is politicized in

the host country, and when host country leaders use refugees to ensure their political survival.

Lischer (2005) is also concerned with uncovering the conditions under which refugees

spread civil war. With a series of case studies on Afghan, Bosnian, and Rwandan refugees, she

argues that the original cause of flight affects refugees’ political and military organization and

their propensity for cross-border violence. Conflict is more likely when refugees constitute

a state in exile, which is a group with “a strong and politicized leadership structure” that

“challenge[s] the legitimacy of the sending state government” (25). She notes that the host

state’s willingness and ability to contain refugee militancy plays a key role, as do third-party

actors.

Turning away from militarized refugees, Onoma (2013) seeks to explain when civilian

refugees become victims of violence by the host population. By comparing instances of

violence and non-violence against refugees from Liberia and Sierra Leone in Guinea with

5

Figure 1: Map of Refugee Camps and Civil Conflict Onset Propensity

Rwandan refugees in the DRC and Uganda, he finds that rare cases of local, host populations

attacking refugees occurs only when the state is experiencing political instability and actively

encourages these attacks.

Thus, are cases in which the presence of refugee communities produces conflict relatively

common as Zolberg et al. (1989) and Weiner (1992) suggest or only under exceptional circum-

stances as scholars like Onoma (2013) point out? Figure 1 depicts the geographic distribution

of refugee camps during the period of 1989 through 2008. Camps are evidently concentrated

near conflict areas. While there is no doubt that refugees and conflict are highly correlated

as conflict is currently the greatest generator of forced migration, whether refugees in turn

cause conflict in host countries is unclear.

6

Motivating this question, research in security studies has found that countries whose

neighbors are experiencing civil conflict are themselves significantly more likely to experience

civil conflict, evidence that incidents of sub-state conflict are not independent events with

purely domestic causes (Gleditsch, 2002; Hegre and Sambanis, 2006; Gleditsch, 2007). In

addition to explanations such as cross-country ethnic solidarity, diffusion of revolutionary

ideology, and negative economic externalities (Sambanis, 2002; Fearon and Laitin, 2003;

Marshall and Gurr, 2003; Beissinger, 2007; Gleditsch, 2007), several political scientists using

cross-national panel data argue that refugees increase the likelihood of conflict and even

terrorism in their receiving countries (Salehyan and Gleditsch, 2006; Salehyan, 2007, 2009;

Choi and Salehyan, 2013).

Most notably, Salehyan and Gleditsch (2006) find that larger numbers of refugees from

neighboring counties increase the likelihood of civil war onset in the host country.3 To

explain this finding, they propose four non-exclusive mechanisms by which refugees generate

civil war in host countries. First, because refugee flows often incorporate a cross-border

transfer of combatants, weapons, and ideas, the refugees may come into conflict with the host

government. Second, refugees may provide mobilization resources to domestic opposition

groups with whom they share an ethnic or ideological affinity. Third, refugee populations

may change the ethnic balance in the host country, with this demographic shift provoking

conflict. Finally, competition between refugees and locals for jobs, housing, and resources

can generate violence. Although their data and research design cannot shed light on which

(if any) of their hypothesized mechanisms is behind this relationship.

3Note, however, that Salehyan and Gleditsch (2006) do not test the general hypothesis that refugeescommunities increase the likelihood of conflict. Instead, they test the claim that refugees from neighboringcountries engaged in conflict increase the likelihood of civil conflict in countries to which they flee.

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In response, we contend that the emergent evidence from the present body of literature

suffers to a large degree from selection on the dependent variable. Selecting cases of conflict

outbreak to identify its apparent sources can be an informative explanatory exercise. Yet,

valid inferences tying the such sources to the outcome of interest cannot be made through

this process alone (King et al., 1994; Ashworth et al., 2008). Considering cases of refugee

settlement across regions of the world where conflict has and has not broken out reveals

certain characteristics about such communities.

Since the vast majority of refugees are civilians who have not engaged in combat, either

because they are mostly incapable of and/or disinterested in fighting, we argue that refugees

are not sources of new civil conflict; in fact, their presence can even bring increased stability.

Consider, for instance, that more than half of refugees in the world are children. Given

the demographic makeup of refugee communities globally, the refugee-warrior appears more

an exceptional rather than a representative figure (UNHCR, 2014b). Additionally, (Hazlett,

2013) finds that with Darfurian refugees in Chad, exposure to violence is associated with war-

weariness and pro-peace attitudes rather than support for retribution and continued violence.

Furthermore, refugee camps are often heavily controlled by host states, the UNHCR, and

other humanitarian agencies. Even weak states have the ability to administer and run refugee

camps by deploying a mix of civilian officials such as police and gendarmes as well as military

personnel (Onoma, 2013). Such conditions would render areas with refugees relatively more

secure (e.g. Malkki, 1995; Jacobsen, 2005), which is what we find in this paper. For these

and related reasons, we theorize that although local, host populations may likely to blame

refugees for instability, it is unlikely that refugees are indeed responsible.

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3 Retesting Refugees as a Security Threat

Using original data that combines refugee locations and subnational incidence of sub-

state conflict as well as recently developed techniques in causal mediation, we test whether

the presence of refugee communities in host countries increases the likelihood of conflict in

the provinces in which they settle. We approach this question by testing the following three

hypotheses:

H1. Refugees increase the likelihood of civil conflict onset in the provinces where they settle.

H2. Refugees are a mechanism by which civil conflict diffuses regionally.

H3. Provinces that experience a new refugee influx are less secure in subsequent years

compared to non-hosting counterpart provinces.

We begin by estimating the statistical likelihood of conflict onset in areas in which refugee

communities are likely to settle. Second, we test the more narrow claim that civil war diffuses

regionally (at least in part) through refugees as a mechanism. For reasons that are articulated

below, we find that results produced by Salehyan and Gleditsch (2006) may be an artifact

of their testing strategy and not a reflection of the relationship they seek to test.

These two tests subject the claim that refugees spread conflict to varying levels of scrutiny.

The first test assesses whether refugee communities are, on average, associated with deterio-

rating security conditions in and around the areas in which they seek sanctuary. This test is

more strict in that sense that, although the presence of particular refugees communities may

have a destabilizing effect on proximate population centers, it asks whether the presence of

refugee communities, irrespective of source and makeup, tend to increase conflict likelihood.

One concern with the existing body of literature is that the cases that feature prominently in

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qualitative casework (referenced in the preceding section) may represent exceptional rather

than typical cases.

The second test assesses whether refugee communities originating in countries experi-

encing civil conflict are associated with conflict onset in the areas of neighboring countries

to which members of such communities flee. For instance, if the first mechanism proposed

by Salehyan and Gleditsch (2006) is correct, a relationship might emerge under the second

test but not the first: cross-border flows of combatants and weapons may be associated with

conflict refugees but are unlikely correlated with migrants from areas afflicted by natural

disaster or economic depression.

Lastly, we employ a difference-in-differences analysis comparing refugee hosting provinces

to their non-hosting counterparts (as controls) to show that the likelihood of civil conflict is

not only lower in hosting provinces, it actually decreases in the years subsequent to a new

refugee influx.

3.1 Data combining refugee locations and conflict onset

We combine georeferenced data on the precise location of all displacement camps with

subnational data on conflict onset, terrain, economic development indicators, and distances

from refugees to borders and country capitals for the period spanning 1989 to 2008

Independent variable: refugee locations. Drawing from a unique georeferenced

dataset of the universe of displacement locations provided by the UNHCR for the purposes

of this study, we construct an indicator variable for the existence of at least one refugee camp

within any given province-year unit.

Outcome variable: conflict onset. From the Uppsala/PRIO Conflict Data Set, we

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code all cases of conflict onset (given by at least 25 battlefield deaths) at the province level. A

“1” is assigned for the first year of a conflict; otherwise, a value of “0” is assigned. Subsequent

ongoing years of the same conflict are dropped from the estimation sample.

Next, we recognize and include a set of potentially significant variables, whose absence

from previous statistical tests may have introduced bias. Three primary sources of potential

bias include internally displaced peoples, terrain, and development levels.

Internally displaced persons. Descriptive statistics generated with data on displace-

ment locales provided by the UNHCR show that many regions with large numbers of refugee

locales also host IDPs, perhaps because displaced populations tend to seek shelter in locales

where displacement infrastructure has already been established (see Figure 9) (UNHCR,

2013). Since the internal migration of peoples could also increase civil war likelihood by

affecting economic competition or within-country ethnic tensions, the presence of IDPs may

be a crucial variable omitted in previous research.

Terrain. Existing research supports the premise that insurgent violence is more likely to

emerge or persist in environments in which terrain encumbers more powerful counterinsur-

gents. “[I]nsurgents are weak relative to the governments they [fight]... to survive, the rebels

must be able to hide from government forces” (Fearon and Laitin (2003): 80). Countries

with more mountainous terrain, for instance, are more likely to experience civil war onset

(Fearon and Laitin, 2003); “forest cover increases probability [that] wars will continue”; and

“[c]ivil wars in mountainous states have a significantly... increased probability of ending

with rebel victory or truce” (Collier et al. (2004): 266). If areas with refugee settlement

correspond with regions of the world dense in terrain types favorable to insurgency, then

results generated by Salehyan and Gleditsch (2006) may be biased.

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Economic development. Levels of economic development are also associated with

refugee migration. First, refugee populations are most prevalent in countries with low levels

of development. Although no standard procedure exists for the site selection of refugee

camps, local citizens and governments are generally unwilling to negotiate with refugees over

resource-rich land. On the other hand, refugees and host governments take into consideration

the availability of food, water, and social services. Once camps are established, an influx

of humanitarian organizations and aid also changes the development levels of the local area

(Bariagaber, 2006; Jacobsen, 1996). Lastly, refugees, especially those in inhospitable camp

conditions, may also spread infectious diseases to host areas, which impacts local health and

mortality outcomes (Ghobarah et al., 2003, 2004).

Development levels are similarly correlated with conflict likelihood (Collier, 2003). Fearon

and Laitin (2003) theorize that such relationship is, at least in part, a function of state ca-

pacity: “the most important determinants of the prospects of an insurgency are most likely

the police and military capabilities of the government, and the reach of government institu-

tions into rural areas” (80). When evaluating the relationship between refugee presence and

violence over areas that vary in their development, these correlations must be controlled for

to avoid introducing possible bias.

Distance from refugee location to the host country’s border and capital. In a

subnational analysis, refugee location within a given country becomes a potentially important

omitted variable. Fearon and Laitin (2003), for instance, theorize that the likelihood of

insurgency and civil war increases with “distance from the centers of state power...” and

is more likely where rebels have access to “foreign, cross-border sanctuaries” (80). As is

evident from Figure 1, the large majority of refugee camps that have existed over the past

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several decades are located in proximity to foreign borders. To ensure that refugee location

is not spuriously correlated with conflict, we augment St,i by including measures of each

unit’s average distance to nearest contiguous foreign country and distance to the capital city

(Tollefsen et al., 2012).

3.2 H1: Do refugees increase the likelihood of civil conflict where they settle?

If communities of refugees drive conflict as the literature suggests by altering the ethnic

balance within the areas in which they take refuge or by increasing competition for resources

in such areas, conflict should be observed with greater likelihood in areas proximate to refugee

settlements. In the absence of spatially disaggregate data, previous analysis has necessarily

adopted the country-year as the unit of statistical analysis (Salehyan and Gleditsch, 2006).

However, measuring micro-relationships at this level may result in inconclusive findings even

where relationships between variables exist due to insufficient statistical power. Worse,

coefficients generated this way may suffer from aggregation bias, a possibility when inferences

are drawn from the broader population to which the unit of interest belongs (King, 1997).

We, therefore, adopt the first-order internal administrative boundary (province, district,

governorate, etc., hereafter denoted as “province”) as the spatial unit of analysis. Violence

observed in provinces without refugees but caused by refugees would result only if refugee

mediated conflict travels over significant distances. Hypotheses of ethnic conflict and local

economic competition provide no theoretical basis to expect such pattern.

Under the causal inference framework, it is “critical that each unit be potentially expos-

able” to the cause (Holland (1986): 946); thus, a treatment group of areas – countries or

provinces – with refugees requires a control group of areas that did not but potentially could

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have refugees as the proper counterfactual (Rubin, 1974). To improve the causal validity

of our results, we avoid comparing unlike units by subsetting our data. We only include

provinces within countries from regions in which there exist some refugee presence during

our study period.4

We use Bayesian logistic regression to generate an association between annual incidence

of sub-state conflict and the existence of refugee camps for comparable provinces over the

past two decades for which camp, conflict, and covariate data is available.

Specifically, the following equation is tested:

P (Yt,i = 1|Mt,i, Nt,i, St,i, Ct,k) = logit−1(αi + βMt,i + δNt,i + ξSt,i + γCt,k + %k) (1)

where t, i, and k denote time periods 1, ...,m, provinces 1, ..., n, and countries 1, ..., p,

respectively. Prior distributions for φ (where XTt,iφ = βMt,i + δNt,i + ξSt,i + γCt,k + %k) are

generated with the bayesglm() package in R (Gelman, 2007). Incidence of sub-state violence

resulting in 25 battlefield deaths or more are denoted by Yt,i (Hallberg, 2012). Indicators

for the existence of at least one refugee camp with a province-year unit and whether a unit

belongs to a country neighboring a country in conflict on a given year are given by Mt,i and

Nt,i, respectively, both ∈ [0, 1] (UNHCR, 2013). Vectors St,i and Ct,j denote subnational and

time-varying country-level controls, respectively.

Specifically, vector St,i includes total number of ethnic groups (Wucherpfennig et al.,

2011); population (logged) (Tollefsen et al., 2012); per capita gdp (logged) (Tollefsen et al.,

4Such approach results in the exclusion of the following regions of the world: Asiatic Russia, Australi-a/New Zealand, Caribbean, Eastern Asia, European Russia, Micronesia, Northern America, Northern Eu-rope, Polynesia, and Western Europe.

14

2012); indicators for the existence of at least one IDP camp unit (separate indicators are also

included for other displacement locales including asylum-seekers and returnees) (UNHCR,

2013); land cover types (deciduous forest, evergreen forest, wetlands, croplands, barren lands,

urban lands, shrub lands, herbaceous lands, and areas of water, snow, and/or ice); average

ruggedness (relief) and absolute elevation; and province size (Shaver et al., 2013). Vector Ct,j

includes measures of government authority (Marshall and Jaggers, 2002) and proxy variables

for development levels (life expectancy at birth (total years); mortality rate under the age

of 5 (per 1,000 live births); immunizations against Tetanus, Diphtheria, and Pertussis and,

separately, Measles (% of children ages 12 - 23 months) (World Bank, 2012). Finally, %k

produces country fixed effects.

From our regression results, we calculate predicted probabilities of conflict onset as µ =

1/n∑ni=1(e

(XTt,iφ)/(e(X

Tt,iφ) + 1)) for Mt,i = 1 and Mt,i = 0. Confidence intervals at the 95%

significance level are generated using quasi-Bayesian Monte Carlo simulation.5

3.3 H2: Are refugees a mechanism by which civil conflict from neighboring

countries diffuses?

Next, we test whether refugees are at least partly responsible for empirical patterns

of civil war diffusion using formal causal mediation techniques. In attempting to answer

this question previously, Salehyan and Gleditsch (2006) carry out an elementary form of

mediation analysis that neither estimates an average casual mediation effect of refugees on

conflict likelihood nor assesses the statistical significance of such effect. We extend their

5Because the inclusion of unit fixed effects within generalized linear models, such as the logistic regressionmodels used by Salehyan and Gleditsch (2006) can (but will not necessarily) bias coefficients (Neyman andScott, 1948), we also generate unbiased results using conditional logistic regression, which we compare toour primary regression results a Hausman test. They are statistically indistinguishable (p-value < 0.89).

15

analysis by carrying a formal mediation analysis of Figure 2.

Figure 2: The causal mediation chain posited by Salehyan and Gleditsch (2006): refugees is the intermediatevariable or mediator which is both affected by the main treatment variable – civil war in neighbor, andaffects the outcome variable – conflict onset in the host country.

We proceed as follows. First, we replicate Salehyan and Gleditsch (2006)’s baseline

regression results. Because those authors base their results on pooled cross-sectional data;

compare treated units with countries that are unlikely recipients of refugees and are thus

seemingly unsuitable control comparisons; and do not control for the omitted variables we

identify in the preceding section, we suspect that their results may be biased. We then follow

the causal mediation approach introduced by Imai et al. (2010) and Imai et al. (2011) to

formally test the hypothesis that civil war diffuses regionally through refugee populations.

Specifically, we test the following equations:

P (Mt,i = 1|Nt,i, St,i, Ct,k) = logit−1(ηi + ζNt,i + ϕSt,i + ϑCt,k + νk) (2)

Equations (1) and (2) are then used to estimate P (Mt,i = 1|Nt,i = 0, St,i = s, Ct,k = c)

and P (Mt,i = 1|Nt,i = 1, St,i = s, Ct,k = c), the results of which are used to estimate the

probabilities λ and ξ, where:

λ = P (Yt,i = 1|Nt,i = 1,M = Mt,i(0), St,i = s, Ct,k = c) (3)

16

and

ξ = P (Yt,i = 1|Nt,i = 1,M = Mt,i(1), St,i = s, Ct,k = c).6 (4)

Finally, the estimated average causal mediation effect, denoted τ , of refugees is calculated:

τ = E{Yt,i(1, Mt,i(1))} − E{Yt,i(1, Mt,i(0))}. (5)

Quasi-Bayesian Monte Carlo simulation is used generate a distribution of τ ’s with which

a 95% confidence interval is generated.

3.4 H3: How does a new refugee influx affect security in the host province?

Finally, we conduct a difference-in-differences analysis over the sample period (1989 to

2008) comparing camp-hosting provinces with their identified controls in the same country-

years in the years preceding and following refugee arrival. If refugee camps harbor insurgent

activity, exacerbate ethnic conflict, and/or provoke economic competition with local citizens,

local conflict should be observed in relative proximity to camps. This approach mitigates

biases in post-refugee comparisons between the camp provinces (treatment groups) and non-

camp provinces of the same country (control groups) that may result from general differences

between the two groups, as well as biases from pre- and post-refugee comparisons in camp

provinces that may result from time trends.

Because it is possible that refugee camps were established in years immediately preceding

1989, and thus proper treatment status cannot be assigned to provinces in 1989 and the years

6Equations are expressed in potential outcomes notation.

17

immediately thereafter, as a robustness check, we adopt the conservative assumption that

every province experienced refugee arrivals immediately prior to 1989 and subset our data

to only include observations from 1994 to 2008.

4 Results

4.1 Test 1: Subnational Regression Results

The subnational regression analysis shows the presence of refugees are not significantly

correlated (see Table 1). Importantly, a model using pooled cross-sectional data is inconsis-

tent with the subnational conditional fixed effects model (p-value < 6.17e− 11). This result

persists even as country fixed effects and additional control variables are added (columns 2

and 3 of Table 1, respectively).

Figure 3 shows that province-year units with refugees are associated with an approximate

7.5% predicted probability of conflict onset. Units without refugees remain at approximately

6%. Aside from the small scientific magnitude of this difference, the quantities themselves

are statistically indistinguishable.

Even if we relax the assumption that if refugees drive conflict, we would observe conflict

precisely in the subnational provinces within which they settle, analysis at the country-year

level also supports our finding. By including country fixed effects to Salehyan and Gleditsch

(2006)’s regression analysis eliminates the statistically significant coefficients previously re-

ported (see Table 4).

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Table 1: Regression Results of Refugee Camps on Conflict Onset using Province-Year Data

Dependent variable:

Onset Onset

logistic conditionallogistic

(1) (2) (3)

Refugee Camps (binary) 0.185 0.015 0.287(0.137) (0.158) (0.165)

Civil War in Neighbor 0.496∗∗∗ 0.704∗∗∗ 0.636∗∗∗

(0.059) (0.123) (0.130)Polity −0.001 −0.040∗∗∗ 0.042∗∗

(0.005) (0.011) (0.013)Poliy Squared −0.009∗∗∗ −0.012∗∗∗ −0.013∗∗∗

(0.001) (0.002) (0.002)GDP (per capita) (log) −0.052∗∗ −0.102∗∗ −0.048

(0.019) (0.035) (0.047)Population (log) 0.075∗∗∗ 0.010 0.007

(0.010) (0.016) (0.017)Ethnic Heterogeneity 0.002 0.013∗∗∗ 0.011∗

(0.001) (0.004) (0.004)IDP Camps (binary) −14.374

(856.556)Returnee Camps (binary) −0.037

(0.498)Other Refugee Locations (binary) 0.022

(0.224)Distance to Border (km) −0.0003

(0.0003)Distance to Capital (km) −0.0004∗∗

(0.0002)Constant −3.022∗∗∗

(0.195)

Development Covariates No No Yes

Terrain Covariates No No Yes

Country FE Yes Yes Yes

Observations 23, 509 23, 509 22, 500Log likelihood −5, 119.576 −3, 366.700 −3, 087.145AIC 10, 255.150

Note: ∗p<0.05; ∗∗p<0.01; ∗∗∗p<0.001

19

Figure 3: Predicted Probabilities of Onset comparing Provinces without and with Refugee Camps

4.2 Test 2: Mediation Analysis Results

Formal mediation results using the subnational panel are also inconsistent with the claim

that refugee flows are responsible for civil war diffusion; in general, refugees are not a media-

tor between conflict in neighboring countries and conflict onset in the host country. Results

confirm previous findings that civil war diffuses regionally – provinces within countries that

border at least one country experiencing conflict are significantly more likely to experience

conflict themselves. Yet, the presence of refugee camps do not account for any of the esti-

mated increased likelihood. As Figure 4 shows, in the presence of civil war in a neighboring

country, units with and without refugees are equally likely to experience conflict.

20

Figure 4: Predicted Probabilities of Onset in Mediation Framework

4.3 Test 3: Examining Conflict Likelihood by Year of Refugee Arrival

In provinces previously unsettled by refugees, we find no evidence that the establishment

of camps triggers conflict.7 Figure 5 shows that during the year of a new refugee influx,

meaning at least one refugee camp is newly established, both the camp-hosting provinces

and their counterparts within the same country experience a slight spike in likelihood of onset.

This spike is likely due to the civil war in the neighboring country, which drives both refugee

flows and conflict onset in the affected country. However, in the years following refugee

7From an interview with a UNHCR official, refugee camps are generally established ad hoc as a “quickresponse to an overwhelming phenomenon” of displaced persons gathering at a location when they “can’twalk any further” (12/30/2013). Cases of anticipatory camps set up before displacement occurs or campstaking years to be recognized by the UNHCR are highly unlikely. Thus, we believe that in our dataset theyear a camp is created coincides with the year a group of refugees arrive and settle into the given province.

21

Figure 5: Comparing Onset Likelihood between Refugee Camps Provinces and their non-Refugee CampCounterpart Provinces (1989 - 2008)

arrival, the likelihood of onset in camp-hosting provinces declines. For their counterparts,

non-camp provinces, in those same subsequent years, onset likelihood gradually increases.

A similar story emerges from the comparison of the incidence (rather than onset) of

conflict (see Figure 6). In camp-hosting provinces, a small increase in conflict likelihood

is observed during the year of refugee arrival after which this likelihood persistently de-

creases. Collectively, control provinces experience a similar contemporaneous increase but,

unlike treated units, experience increased conflict likelihood sometime around the third year

mark. These figures suggest the opposite phenomenon: security conditions within camp-

hosting provinces may actually improve after the arrival of refugees. In response to refugee

inflows, international and regional humanitarian organizations including the UNHCR and

host-government security forces often travel to afflicted areas to provide aid and maintain

security. The presence of aid workers and (additional) security personnel and the infrastruc-

22

Figure 6: Comparing Incidence Likelihood between Refugee Camps Provinces and their non-Refugee CampCounterpart Provinces (1989 - 2008)

ture they often establish may explain actual reductions in conflict likelihood in such areas.

Finally, Figures 10 and 11 show that these general patterns are unchanged when the data is

subset to include only observations between the years of 1994 to 2008.

5 Conclusion

As the head of the International Rescue Committee recently warned, the number of

individuals “displaced by persecution and conflict is a trend and not a blip” (Gladston,

2015). Additionally, climatic variation is projected to fuel additional displacement over

coming decades (Field et al., 2014). Thus, the need for durable solutions to displacement is

urgent and critical.

There are currently three such solutions according to the UNHCR: voluntary repatriation,

local integration, or resettlement to a third country. First, even as a few refugee communi-

23

ties are able to return home, new populations of refugees continue to emerge. In 2014, for

example, while some 13.9 million people became newly displaced, just 126,800 refugees were

able to return to their countries of origin (UNHCR, 2014b). Next, for many refugees, includ-

ing some “1.5 million Afghans still living in Pakistan,” resettlement is unlikely, particularly

given the current anti-immigrant climate in U.S. and Europe (Sengupta, 2015). This leaves

local integration.

Humanitarian organizations like the UNHCR are currently pursuing alternatives to refugee

camps by advocating for physical integration into local villages; social integration, such as

giving refugees access to government social security and the right to work; and political inte-

gration such as expanding access to citizenship through naturalization campaigns (UNHCR,

2014c). Undoubtedly, these policies of local integration would need to rely on the consent

and cooperation of host governments and local communities. However, given concerns of

refugees spreading conflict in popular media, public policy, and academic scholarship, these

efforts at promoting local integration may become increasing intractable.

Ultimately, determining whether refugees tend to foment conflict is critical, both theoret-

ically and from a policy respective. By examining the effects of refugee locations nationally

and subnationally, we find no evidence that refugee communities tend to increase conflict

likelihood in those areas in which they seek safety. Much as the intuitive claim that poverty

drives terrorism received considerable attention following the September 11, 2001 terrorist

attacks but was ultimately discredited by academics including Princeton economist Alan

Krueger (Krueger and Maleckova, 2003; Krueger, 2008), we find the notion that refugees

tend to spread conflict to be similarly misleading. Individual instances can, of course, be

found in which refugee communities were responsible for the production of violence. Yet, on

24

average, the presence of refugee communities is associated with no greater conflict likelihood

in the areas in which they settle than in those they do not. These results are consistent with

positions frequently espoused by relief workers who engage directly with refugee communities

– populations they describe as consisting primarily of vulnerable individuals seeking safety

and aid and unlikely to engage directly in violence conflict with other communities of people.

This does not imply that host governments should not concern themselves with the

potentially destabilizing effect of refugee communities. In fact, our finding that provinces

which host refugees tend to be more secure in subsequent years most may reflect both

domestic and international efforts in increasing security in and around camps. Yet, if this is

the case, governments can take solace in the fact that their efforts are typically successful.

Future avenues of research on refugees and conflict dynamics might explore the the possible

stabilizing effects of aid and increased development infrastructure following refugee influxes.

25

References

Ashworth, S., Clinton, J. D., Meirowitz, A., and Ramsay, K. W. (2008). Design, infer-ence, and the strategic logic of suicide terrorism. American Political Science Review,102(02):269–273.

Bariagaber, A. (2006). Conflict and the refugee experience: flight, exile, and repatriation inthe Horn of Africa. Ashgate Publishing.

Beissinger, M. R. (2007). Structure and example in modular political phenomena: Thediffusion of bulldozer/rose/orange/tulip revolutions. Perspectives on Politics, 5(2):259.

Choi, S.-W. and Salehyan, I. (2013). No good deed goes unpunished: refugees, humanitarianaid, and terrorism. Conflict Management and Peace Science, 30(1):53–75.

Collier, P. (2003). Breaking the Conflict Trap: civul War and Developement Policy. WorldBank.

Collier, P., Hoeffler, A., and Soderbom, M. (2004). On the duration of civil war. Journal ofpeace research, 41(3):253–273.

Erlanger, S. and Smale, A. (2015). Europes halting response to migrant crisis draws criticismas toll mounts. New York Times.

Fearon, J. D. and Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. Americanpolitical science review, 97(1):75–90.

Field, C. B., Barros, V. R., Mastrandrea, M., Mach, K. J., Abdrabo, M.-K., Adger, N.,Anokhin, Y., Anisimov, O., Arent, D., Barnett, J., et al. (2014). Summary for policy-makers. Climate change 2014: impacts, adaptation, and vulnerability. Part a: global andsectoral aspects. Contribution of working group II to the fifth assessment report of theintergovernmental panel on climate change, pages 1–32.

Gelman, A. (2007). Data analysis using regression and multilevel/hierarchical models. Cam-bridge University Press.

Ghobarah, H. A., Huth, P., and Russett, B. (2003). Civil wars kill and maim peoplelongafter the shooting stops. American Political Science Review, 97(02):189–202.

Ghobarah, H. A., Huth, P., and Russett, B. (2004). The post-war public health effects ofcivil conflict. Social Science & Medicine, 59(4):869–884.

Gladston, R. (2015). Aid groups are ill equipped for refugee influx, executive says. New YorkTimes.

Gleditsch, K. S. (2002). All international politics is local: The diffusion of conflict, integra-tion, and democratization. University of Michigan Press.

Gleditsch, K. S. (2007). Transnational dimensions of civil war. Journal of Peace Research,44(3):293–309.

26

Hallberg, J. D. (2012). Prio conflict site 1989–2008: A geo-referenced dataset on armedconflict. Conflict Management and Peace Science, 29(2):219–232.

Harrigan, S. and Easen, N. (2001). Afghan refugee crisis spreads. CNN.

Hazlett, C. (2013). Angry or weary? the effect of physical violence on attitudes towardspeace in darfur. Unpublished Working Paper.

Hegre, H. and Sambanis, N. (2006). Sensitivity analysis of empirical results on civil waronset. Journal of Conflict Resolution, 50(4):508–535.

Herszenhorn, D. M. (2015). Many obstacles are seen to u.s. taking in large number of syrianrefugees. New York Times.

Holland, P. W. (1986). Statistics and causal inference. Journal of the American statisticalAssociation, 81(396):945–960.

House, W. (2014). Press briefing by josh earnest. Washington, DC: Josh Earnest., (2014).

Imai, K., Keele, L., and Tingley, D. (2010). A general approach to causal mediation analysis.Psychological methods, 15(4):309.

Imai, K., Keele, L., Tingley, D., and Yamamoto, T. (2011). Unpacking the black box ofcausality: Learning about causal mechanisms from experimental and observational studies.American Political Science Review, 105(4):765–789.

Jacobsen, K. (1996). Factors influencing the policy responses of host governments to massrefugee influxes. International migration review, pages 655–678.

Jacobsen, K. (2005). The economic life of refugees. Kumarian Press.

King, G. (1997). A solution to the ecological inference problem: Reconstructing individualbehavior from aggregate data. Princeton University Press.

King, G., Keohane, R. O., and Verba, S. (1994). Designing social inquiry: Scientific inferencein qualitative research. Princeton University Press.

Krueger, A. B. (2008). What makes a terrorist: Economics and the roots of terrorism.Princeton University Press.

Krueger, A. B. and Maleckova, J. (2003). Education, poverty and terrorism: Is there acausal connection? The Journal of Economic Perspectives, 17(4):119–144.

Lischer, S. K. (2005). Dangerous Sanctuaries: Refugee camps, Civil war, and the Dilemmasof Humanitarian aid. Cornell University Press.

Loescher, G. (1992). Refugee Movements and International Security. Brasseys for the Inter-national Institute for Strategic Studies, London.

Malkki, L. H. (1995). Purity and exile: Violence, memory, and national cosmology amongHutu refugees in Tanzania. University of Chicago Press.

27

Marshall, M. G. and Gurr, T. R. (2003). Peace and Conflict 2003: A global survey ofarmed conflicts, self-determination movements, and democracy. Center for InternationalDevelopment and Conflict Management.

Marshall, M. G. and Jaggers, K. (2002). Polity iv project: Political regime characteristicsand transitions, 1800-2002.

Matthews, R. O. (1972). Refugees and stability in africa. International Organization,26(01):62–83.

Muggah, R. (2006). No Refuge: The Crisis of Refugee Militarization in Africa. Zed Books.

Neyman, J. and Scott, E. L. (1948). Consistent estimates based on partially consistentobservations. Econometrica: Journal of the Econometric Society, pages 1–32.

Onoma, A. K. (2013). Anti-Refugee Violence and African Politics. Cambridge UniversityPress.

Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandom-ized studies. Journal of educational Psychology, 66(5):688.

Salehyan, I. (2007). Transnational rebels: Neighboring states as sanctuary for rebel groups.World Politics, 59(02):217–242.

Salehyan, I. (2009). Rebels without borders: transnational insurgencies in world politics.Cornell Univ Pr.

Salehyan, I. and Gleditsch, K. S. (2006). Refugees and the spread of civil war. InternationalOrganization, 60(2):335.

Sambanis, N. (2002). A review of recent advances and future directions in the quantitativeliterature on civil war. Defence and Peace Economics, 13(3):215–243.

Sengupta, S. (2015). 60 million people fleeing chaotic lands, u.n. says. New York Times.

Shafy, S. (2013). The wave from syria: Flow of refugees destabilizes lebanon. Spiegel Online.

Shaver, A. C., Carter, D., and Shawa, T. W. (2013). Terrain ruggedness and land cover:New data. Princeton University, unpublished manuscript.

Shinkman, P. D. (2013). Syrian refugee crisis destabilizes jordan. U.S. News & World Report.

Tollefsen, A., Strand, H., and Buhaug, H. (2012). Prio-grid: A unified spatial data structure.Journal of Peace Research, 49(2):363–374.

UNHCR (2013). Unhcr camps, all. Technical report, United Nations High Commissioner forRefugees.

UNHCR (2014a). 2014 syria regional response plan: Mid-year update. Technical report.

UNHCR (2014b). Unhcr global trends: Forced displacement in 2014. Technical report.

28

UNHCR (2014c). Unhcr policy on alternative to camps. Technical report.

Weiner, M. (1992). Security, stability, and international migration. International Security,17(3):91–126.

Whitaker, B. E. (2003). Refugees and the spread of conflict: Contrasting cases in centralafrica. Journal of Asian and African Studies, 38(2-3):211–231.

World Bank, D. D. G. (2012). World Development Indicators 2012. World Bank.

Wucherpfennig, J., Weidmann, N. B., Girardin, L., Cederman, L.-E., and Wimmer, A.(2011). Politically relevant ethnic groups across space and time: Introducing the geoeprdataset. Conflict Management and Peace Science, 28(5):423–437.

Zolberg, A. R., Suhrke, A., and Aguayo, S. (1989). Escape from violence: Conflict and therefugee crisis in the developing world. Oxford University Press.

29

6 Appendix

6.1 Summary Statistics of Displacement

Table 2: Summary of UNHCR Displacement Locales

Type of Locale CountRefugee Accomodation 584

Refugee Camp 782Refugee Center 240

Refugee Dispersed 7Refugee Location 988

Refugee Settlement 550Refugee Urban location 396

Refugee Total 3547IDP Accomodation 221

IDP Camp 282IDP Center 40

IDP Dispersed 3IDP Location 178

IDP Settlement 100IDP Urban location 1

IDP Total 825Asylum Seeker Accomodation 6

Asylum Seeker Center 32Asylum Seeker Settlement 1

Asylum Seeker Total 39Returnee Accomodation 8

Returnee Camp 23Returnee Center 52

Returnee Location 88Returnee Settlement 35

Returnee Total 206TOTAL 4617

30

Table 3: Summary of UNHCR Displacement Locations by Region

REGIONS Refugee IDP Asylum Returnee TOTALAfrica 1513 351 155 2019Central Africa and Great Lakes 579 166 119 864East and Horn of Africa 431 163 1 595Southern Africa 60 11 71West Africa 443 22 24 489Asia 590 122 1 13 726Central Asia 90 1 91East Asia and the Pacific 42 42South Asia 21 21South East Asia 230 93 323South West Asia 207 29 13 249Europe 862 334 38 7 1241Eastern Europe 134 192 7 333Northern, Western, Central Europe 120 30 150South Eastern Europe 608 142 1 7 758Middle East and North Africa 397 18 415North Africa 18 18The Middle East 379 18 397The Americas 185 31 216Latin America 183 31 214North America and the Caribbean 2 2TOTAL 3547 825 39 206 4617

Figure 7: Number of UNHCR-Recognized Displacement Locations (1989 - 2008)

31

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ent

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33

6.2 Additional Results and Robustness Checks

6.2.1 Retesting Salehyan and Gleditsch (2006) with Country FE

Table 4: S&G’s “Table 4” Regression Results Replicated w/ Country FE

Dependent variable:

Onset Onset

logistic conditionallogistic

(1) (2) (3)

REFUGEES 0.042 0.042(0.024) (0.023)

CIVIL WAR IN NEIGHBOR 0.154 0.108 0.078(0.237) (0.239) (0.232)

POLITY 0.009 0.009 0.009(0.020) (0.020) (0.020)

POLITY SQUARED −0.012∗∗∗ −0.012∗∗ −0.011∗∗

(0.004) (0.004) (0.004)GDP PER CAPITA (log) 0.204 0.153 0.153

(0.309) (0.313) (0.307)POPULATION (log) 0.747∗∗ 0.529 0.481

(0.279) (0.307) (0.299)ETHNIC HETEROGENEITY 0.027 0.038

(0.054) (0.055)PEACE YEARS −0.308∗∗∗ −0.300∗∗∗ −0.294∗∗∗

(0.066) (0.067) (0.065)Spline 1 −0.005∗∗∗ −0.005∗∗∗ −0.005∗∗∗

(0.001) (0.001) (0.001)Spline 2 0.003∗∗ 0.003∗∗ 0.003∗∗

(0.001) (0.001) (0.001)Spline 3 −0.001∗∗ −0.001∗∗ −0.001∗

(0.0003) (0.0003) (0.0003)Constant −10.599∗∗∗ −8.565∗

(3.130) (3.367)

Country FE Yes Yes Yes

Observations 5, 568 5, 568 5, 685Log likelihood −643.972 −642.462 −538.115AIC 1, 643.943 1, 642.924

Note: ∗p<0.05; ∗∗p<0.01; ∗∗∗p<0.001

34

6.2.2 Examining Conflict Likelihood by Year of Refugee Arrival

Figure 10: Comparing Onset Likelihood between Refugee Camps Provinces and their non-Refugee CampCounterpart Provinces (1994 - 2008)

Figure 11: Comparing Incidence Likelihood between Refugee Camps Provinces and their non-Refugee CampCounterpart Provinces (1994 - 2008)

35