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Refugee flows, rivalry, and how the Arab Spring changed the international system: A multi-layer

network analysis

Justin Schon

Jeffrey C. Johnson

Draft as of September 2019. The work reported here was funded by the Army Research Office under the

Multidisciplinary University Research Initiative (Grant #W911NF1810267). The views and

interpretations expressed in this document are those of the authors and should not be attributed to the

US Army.

Abstract

What drives the formation and evolution of the global refugee flow network over time? Existing

research devotes substantial effort to explain migration behavior in specific cases, but macro-level

global cross-national patterns receive far less attention. Research that has delved into this area does not

typically account for interdependencies between actors in its analysis. We fill this gap with a dyadic

hypothesis testing method—Multiple Regression- Quadratic Assignment Procedure (MR-QAP)—that

explicitly accounts for interdependencies and actor-level relationships in order to identify significant

dyad-level correlations. We argue that the post-Cold War period must be disaggregated in order to

properly examine patterns in refugee flows. Then, we estimate a series of MR-QAPs for the 2012-2016

time period, due to the strong correlations between refugee flows during those years. We find that

increasing amounts of onward migration decreased the influence of contiguity on refugee flows, while

strategic rivalry shifted from being positively correlated with refugee flows to being negatively

correlated. Meanwhile, this trend masks fluctuation in the correlations of specific types of strategic

rivalry: ideological, positional, and spatial. Our findings contribute to the study of refugee flows,

international migration, alliance and rivalry relationships, and the application of social network analysis

to international relations.

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Introduction

Refugee flows have become a source of global concern. Origin countries fear lost human capital,

lost legitimacy for their governments, and the possibility that political opponents will establish bases

outside their reach (Beine, Docquier & Rapoport, 2001; Betts & Loescher, 2011; Salehyan & Gleditsch,

2006). Destination countries fear the tensions that come with shifts in their demographic balances and

the diffusion of conflict and terrorism (Choi & Salehyan, 2013; Rüegger, 2019). Governments in many

destination countries have therefore implemented measures to secure their borders and prevent

immigration (Longo, 2017; Triandafyllidou & Maroukis, 2012). Yet, refugee flows may follow specific

patterns due to networks that exist within the international system (Czaika & Haas, 2014; Windzio,

2018). These networks may be too powerful for individual countries to effectively block refugee flows.

This leads to the question: What drives the formation and evolution of the global refugee flow network

over time?

To answer this question, we contend that it is valuable to examine the many overlapping

networks that form the international system (Cranmer, Menninga & Mucha, 2015; Maoz, 2010; Zhukov

& Stewart, 2013). Networks of international trade, international migration, alliances, rivalry, physical

distance, remittance flows, and other factors combine to form the structure of motivations and

opportunities that drive global refugee flows (Boulding, 1962; Czaika & Haas, 2014; Fagiolo &

Mastrorillo, 2014; Leblang, 2017; Oatley et al., 2013; Schon, 2019; Thompson, 2001).

Our analysis focuses on the 2012-2016 time period because this period forms an important and

distinct phase of refugee flows since the end of the Cold War. As migration research such as Orchard

(2014) observes, the post-Cold War time period has experienced multiple phases in refugee flow

patterns. Large refugee flows during the early 1990s that resulted from episodes such as the breakup of

Yugoslavia, Siad Barre’s removal from power in Somalia, the Taliban’s takeover of Afghanistan, and mass

killing and genocide in Rwanda and Burundi were met with the exhaustion of American and other

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Western governments from hosting refugees during the Cold War (Orchard, 2014). Neighboring

countries such as Kenya, Iran, Pakistan, Tanzania, and the Democratic Republic of the Congo welcomed

refugees, but their hospitality progressively waned by the end of the 1990s (Landau, 2008; Verdirame,

1999; Whitaker, 2003). In the early 2000s, governments around the world rapidly increased the amount

of border walls and obstacles to refugee flows and international migration (Avdan & Gelpi, 2016; Carter

& Poast, 2015; Hassner & Wittenberg, 2015; Longo, 2017). By the end of the 2000s, new refugee flows

had fallen to relatively low levels. In 2011, the Arab Spring sparked renewed surges of refugees.

Governments fell and civil wars began across Tunisia, Libya, Egypt, Syria, Yemen, and Iraq (Lynch, 2014).

These developments destabilized other countries as well. Libya’s collapse contributed to flows of

weapons and fighters into Mali, Niger, Chad, Sudan, and other countries in the African Sahel (Lacher,

2013). While these developments primarily led to internal displacement in 2011, refugee flows

increased substantially starting in 2012. The 2012-2016 time period marks the clearest coherent phase

of refugee flows after the Cold War, so we believe that this is a particularly valuable time period to

analyze.

We hypothesize that power competition and physical distance are the most important factors

for explaining refugee flows. While the growing field of refugee studies frequently attempts to apply

insights from the study of labor migration, we believe that refugee flows and labor migration require

fundamentally different explanations. Moorthy and Brathwaite (2016) identified power competition in

the form of rivalry as an important variable. We add a disaggregation of the rivalry category into

ideological, positional, and spatial rivalry. Jackson and Atkinson (2019) use pooled time series cross-

national dyadic regressions that do not account for interdependencies between dyads and find that the

effect of rivalry comes specifically from ideological rivalry. We therefore believe that this issue merits

additional consideration.

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Quantitative network analysis can help disentangle which of these networks are important

drivers of refugee flows. This is an important step in developing an understanding of the model that

researchers should use for causal inference. To this end, we estimate a series of Multiple Regression-

Quadratic Assignment Procedure (MR-QAP) models in order to identify the variables that significantly

effect refugee flows. MR-QAP is a dyadic hypothesis testing method that accounts for actor-level

correlations and interdependencies across dyads. It allows the researcher to consider the role of several

types of relationship characteristics in forming some other relationship characteristic that is of interest.

In our case, we treat refugee flows as one type of relationship characteristic between countries, and we

examine the role of other types of relationship characteristics in shaping the global refugee flow

network.

Our network approach with MR-QAPs detects a much more complicated role for each type of

strategic rivalry. Ideological, positional, and spatial rivalry all matter in different ways. Consistent with

existing research, we also find that refugees are more likely to move to neighboring countries.

Intuitively, countries are generally more likely to interact with neighbors than non-neighbors, so this

finding is not surprising (Boulding, 1962), but it is essential to confirm in order to ensure that it is

included in modelling efforts.

Meanwhile, the role of rivalry and contiguity in shaping refugee flows shifted from 2012-2016.

Contiguity progressively declined in importance, while rivalry shifted from a positive to a negative

relationship with refugee flows. The overall trend in the relationship between the aggregated rivalry

category and refugee flows appears to be driven by increasing amounts of onward migration. This

onward migration involves movement from contiguous countries to non-contiguous countries. Refugees

did not typically move through new directed dyads, but it does appear that the proportion of refugees

moving through non-contiguous dyads increased from 2012-2016. For example, Syrian refugees who

had initially moved to Jordan, Lebanon, or Turkey in 2012 or 2013 were increasingly moving to Germany

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or Sweden by 2016. The Syria-Germany dyad was not new for refugee flows, but the magnitude

increased substantially (Ragab, Rahmeier & Siegel, 2017). As hard as the United States and Western

European governments have tried to keep refugees within their origin regions in Africa, the Middle East,

and Central and South Asia, increasing numbers of people are finding ways to exit their origin regions.

We find additional evidence for this argument when we disaggregate the rivalry category into

spatial, ideological, and positional rivalry. Spatial rivalry is the form of rivalry that is most closely linked

to contiguity, and it shifts from having a positive to a negative relationship with refugee flows.

Ideological rivalry, which is the least related to contiguity, shifted from having a negative to a positive

relationship with refugee flows. Positional rivalry, meanwhile, has a consistently positive relationship

with refugee flows. Throughout this period of onward migration, therefore, power competition for

status in the international system appears to have continued including refugee admissions.

We contribute to existing research in several ways. First, we show that refugee flows require

fundamentally different explanations than international migration that is not driven by war and

persecution. Second, we build upon Maoz (2010) and demonstrate that the international system can be

productively studied as a multi-layer network. Third, we build upon recent research that examines the

relationship between rivalry and refugee flows. Our results suggest that the relationship between rivalry

and refugee flows is more complex than originally conceived. Fourth, we unpack multiple phases of

global refugee flow patterns since the end of the Cold War. Future research on refugee flows will benefit

from comparing patterns across these phases.

Our paper proceeds as follows. We begin by describing patterns in global refugee flows since the

end of the Cold War. This description highlights the observation that the Arab Spring marks a clearly

defined new phase in refugee flows from 2012-2016. Then, we discuss arguments on how different

types of international networks may affect refugee flows. After that, we discuss the data that we use in

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our MR-QAP models. Next, we share the results of our analysis and discuss those results. We conclude

with a summary of our argument and suggest options for future research.

How explanations of labor migration might inform the study of refugee

flows

To explain refugee flow patterns, many researchers start with insights from existing research on

migration, especially labor migration (FitzGerald & Arar, 2018). This branch of research tends to focus on

economic factors. If these factors explain refugee flows and labor migration, then there is justification to

argue that economics explains migration regardless of primary reported reason.

The old “neoclassical economics of migration” approach treated people as simple rational actors

who were more likely to migrate as wage gaps between origin and destination locations increased

(Massey et al., 1993). From this view, individuals migrate in order to reach countries that offer wage

gains.

More recently, the “new economics of migration” approach disputes the primacy of the wage

gap in explaining migration decisions. First, it argues that there is too much emphasis on the individual

from the neoclassical research program. Instead, it views households as the more appropriate unit of

analysis (Stark & Bloom, 1985). Then, rather than responding to wage gaps, households seek to

maximize economic benefits for all of their members, minimize risks, and loosen constraints associated

with market failures (Stark & Taylor, 1991). This approach can account for many circumstances where

migration does not follow wage gaps and where people may not respond to wage gaps with migration.

For starters, the new economics of migration approach considers household networks. It

recognizes that households often select specific members to migrate, while other household members

stay home. The household members who stay home may join the members who migrated at a later

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point in time. This staggered migration approach yields the expectation that previous migration will

produce future migration.

As households strategize their migration, remittances are a crucial economic mechanism.

Remittances may motivate migration by providing incentive to households to send members overseas

(Lindley, 2010). Remittance flows can also facilitate return migration (Leblang, 2017). Lillo, García and

Santander (2017) add that remittance flows tend to cluster within particular remittance-sending and

remittance-receiving communities. These clusters of remittance flows arguably correspond with labor

migration flows. If refugee flows align with labor migration flows, then the remittance flow network and

the refugee flow network should correspond.

Another potential source of overlap between economic networks and the global refugee flow

network is international trade. Peres, Xu and Wu (2016) analyze the top origin and destination countries

of migration for each country and find that developed countries, especially the United States, are

popular destinations for international migration. China and India, meanwhile, are major sources of

emigration. Fagiolo and Mastrorillo (2014) find that trade and migration networks are strongly

correlated, and that greater embeddedness within international migration networks has larger effects

upon trade. If refugee flows follow similar patterns as labor migration, then we should find support for

the following hypotheses:

H1a: Refugees tend to move toward countries that offer larger wage gains. H1b: Refugees from a given origin country tend to move towards destination countries that already host

large numbers of immigrants from that country. H1c: Refugee flows in a given year tend to occur within the same dyads as in the previous year. H1d: Country dyads with larger remittance flows have larger refugee flows. H1e: Country dyads with more trade have larger refugee flows.

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How relative safety may drive the selection of refugee destinations

There remains, however, an open question as to whether refugee flows can be explained

through the same factors as labor migration. In a phenomenon that is most commonly caused by

violence and insecurity, refugee destinations may also be chosen based on security concerns (Adamson,

2006; Adhikari, 2013; Moore & Shellman, 2007). Since violence is the most common cause of internal

displacement and refugee flows during conflict, it is intuitive that violence, particularly the absence of

violence, would also influence the selection of refugee destinations (Adhikari, 2013; Schon, 2019).

This expectation implies that civilians are making security comparisons between their origin

country and intended destination country (Schon, 2018). If it is the comparison that matters, then

security conditions in the origin or destination location alone are not the most relevant factor. This is an

important adjustment to the two-stage model that Davenport, Moore and Poe (2003) propose. In their

two-stage model, the first stage results in migration if perceived threat surpasses some threat threshold.

The second stage involves the selection of a destination location. Their analysis does not specify

whether considerations of safety in the destination are based on absolute safety in the potential

destination or relative safety between the origin and potential destination. We argue that the relative

safety calculation should be tested, which yields the following dyadic hypothesis:

H2: Refugees tend to move to safer countries.

How alliances may drive the selection of refugee destinations

Rather than the relative safety calculation, refugees may be driven by the desire to find friendly

countries. There are several possible indicators of when a country will be perceived as relatively more

friendly. First, countries that are more democratic may seem more desirable due to benefits like rule of

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law and human rights (Moore & Shellman, 2007). The democratic peace research program developed

the logic for this perspective:

… liberalism is not inherently 'peace-loving'; nor is it consistently restrained or peaceful in intent." It has, however, "strengthened the prospects for a world peace established by the steady expansion of a separate peace among liberal societies. (Doyle, 1983: p. 206)

If people themselves share this view, then countries that are more democratic than one’s origin country

should be desirable destinations. Democratic peace arguments have been criticized on numerous

grounds, but their persistence suggests that regime type remains important to incorporate in analysis

(Rasler & Thompson, 2016; Rosato, 2003). In particular, democratic peace proponents contend that it is

relationships between democracies that are peaceful (Kinsella, 2005). Regime type is therefore

important to consider as a dyadic measure of relative regime type.

Second, countries tend to accommodate non-core groups from allies, so they may be perceived

as welcoming countries for refugees (Mylonas, 2012). For origin country governments, non-refugee

migration occurs in sufficiently large numbers to motivate over half of all United Nations member states

to establish diaspora institutions that maintain connections with their emigrants (Gamlen, 2014). Jordan

provides an example of this behavior. There are some estimates that remittances from Jordanian

migrants in oil-producing Arab states contribute roughly 20 percent of the country’s GDP. This has led to

migration treaties with the Gulf Cooperation Council (GCC) that secure the well-being of Jordanian

citizens in those countries (Adamson & Tsourapas, 2018). These kinds of institutions encourage positive

relationships between origin and destination countries, as well as between the origin country emigrants

and destination country citizens (Fitzgerald, 2009). A similar logic may hold for refugees. For destination

country governments, immigrants from allied countries may be particularly welcome because their

presence may facilitate new opportunities for international trade (Fagiolo & Mastrorillo, 2014). They

may welcome refugees from allied countries through a similar logic, or they may welcome refugees if

those refugees are fleeing rebel and terrorist groups.

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Third, countries that send weapons between each other may be perceived as allies who will be

friendlier toward each other’s refugees. Arms flows signal a military alliance relationship in particular

(Muggah & Sang, 2013). Alliance relationships in one domain often translate into alliance relationships

in other domains, so arms flows could serve as a valuable indicator of a friendly government (Gartzke &

Lindsay, 2019). These insights yield the following hypotheses:

H3a: Refugees tend to move to more democratic countries. H3b: Refugees tend to move between countries with alliances of mutual defense. H3c: Refugees tend to move between countries with higher levels of arms flows.

How rivalry may drive the selection of refugee destinations

Alternatively, it may be animosity—not amity—that drives refugee flows. A strong case exists for

the argument that amicable relationships between governments facilitate non-refugee movement. On

the other hand, animosity between governments may facilitate refugee movement. This case draws

from incentives that exist for origin and destination country governments and for refugees themselves.

Since governments tend to be responsible for the worst atrocities and the largest share of

violence, civilians may simply view a rival government as the most reasonable source of protection. For

example, South African refugees during the 1980s knew that Robert Mugabe’s government in Zimbabwe

opposed the South African apartheid government, so they had confidence that Zimbabwe would provide

them with sanctuary (Minter, 1994; Mufson, 1990). Refugee movement between countries has even

received the label of “voting with their feet” from scholars observing the tendency of refugees to move

between countries with opposing governments (Betts & Loescher, 2011).

It is also possible that governments, not civilians, are the true drivers of refugee flows because

they are the ones who set the structural conditions of migration. In this case, refugees could merely be

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pawns in a larger game of “migration diplomacy” (Adamson & Tsourapas, 2018). Migration diplomacy

becomes increasingly salient as migration flows between country dyads increase their “migration

interdependence” (Oyen, 2015; Thiollet, 2011; Tsourapas, 2018).

Origin country governments may intentionally create large refugee flows as a tool to extract

foreign policy concessions from more powerful countries. This tactic, sometimes referred to as “coercive

engineered migration,” can be an important foreign policy strategy (Adamson, 2006; Greenhill, 2010).

Destination countries may accept refugees in order to undermine the legitimacy or status of an

adversary (Betts & Loescher, 2011; Jackson & Atkinson, 2019; Moorthy & Brathwaite, 2016). We

consider adversaries in the form of strategic rivals. Strategic rivals mutually perceive each other as

threats. Strategic rivals may compete for status in the international system (positional rivals), ideological

dominance (ideological rivals), or for territorial control (spatial rivals) (Thompson & Dreyer, 2011).

Meanwhile, countries are unlikely to have strong sentiments toward each other unless they are

physically close enough to have frequent interactions (Boulding, 1962). This is why rivalry between

contiguous countries is more likely to start, persist, and escalate (Rasler & Thompson, 2000; Vasquez,

1996; Wiegand, 2011). This yields the following hypotheses about rivalry and contiguity:

H4a: Strategic rivals tend to have higher refugee flows. H4b: Ideological rivals tend to have higher refugee flows. H4c: Positional rivals tend to have higher refugee flows. H4d: Spatial rivals tend to have higher refugee flows. H4e: Refugees tend to move to neighboring countries.

Refugee flows since the end of the Cold War

These four sets of explanations are not necessarily contradictory. They may combine and jointly

explain which country dyads have refugee flows. Our analysis is exploratory, so we do not begin with

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firm prior beliefs. Instead, we intend for our analysis to help researchers develop the best possible

model for refugee flows.

We do, however, have more existing evidence that relative safety and inter-state rivalry

influence refugee flows than for extending explanations of labor migration to refugee flows or for

alliance relationships to drive refugee flows. There is evidence that the economic factors that are central

to explanations of labor migration might be relevant for refugee flows, but they are not as important as

violence (Adhikari, 2013; Schon, 2019). Researchers have repeatedly shown that conflict-related

violence in residential areas is the most important motivating factor for migration during conflict (Schon,

2016). This statement applies to both internal displacement and refugee flows, so we contend that it is

important to recognize that violence generally increases the amount of people who want to migrate

somewhere. We extend this argument to assert that people should be more likely to move to locations

where they experience the greatest gains in safety. With policy playing an important role in creating

opportunities for migration, it is then important for a potential host government to want to host

refugees (Orchard, 2014). A government that is allied with the government of a country that is

producing refugees is unlikely to want to host those refugees because doing so would impose costs in

reputation and legitimacy on their ally. Rivals, on the other hand, may view hosting each other’s

refugees as a valuable foreign policy tactic (Jackson & Atkinson, 2019).

In order for us to examine these explanations, we believe that it is important to focus on one

distinct time period. Many researchers might perform this analysis with time series cross-national data

and then use a pooled regression approach. This approach requires an assumption that independent

variables have constant effects. Yet, substantial changes in international alliance and rivalry

relationships, conflict patterns, and migration patterns over time suggest that we may not want to

assume homogenous effects over time (Maoz, 2010). Our analysis focuses specifically on the 2012-2016

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time period, due to the similarity of its refugee flow patterns. We understand this period through the

lens of evolving conflict, refugee policy, and refugee behavior since the end of the Cold War.

Many conflicts progressively ended after the Cold War as the United States and the Soviet Union

stopped funding armed groups in many cases (Kalyvas & Balcells, 2010). The end of conflicts coincided

with a decline in the world’s refugee population. Figure 1 below shows that the global refugee

population progressively fell from 1990 to 2005.

Figure 1: World refugee population by year 1990-2016, excluding Palestinian refugees

Refugee flows tend to occur between specific sets of country-to-country directed dyads, so it is

also valuable to consider whether the set of directed dyads with refugee flows remains similar across

years. We therefore examine correlations of refugee flows over time. To prevent particularly large

refugee flows from biasing our understanding, we dichotomize refugee flows. This produces a

correlation network where edge weights are based on the magnitude of the Pearson’s correlation

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coefficient. For clarity in our visualization, we exclude ties between years that have a correlation

coefficient less than 0.2.1 Our resultant correlation network is Figure 2 below.

Figure 2 suggests that there may be several phases of refugee flows since the end of the Cold

War. We find that the 1990s had moderately inter-correlated refugee flows, with 1994 being a transition

point. This is most likely due to the Rwandan Genocide that occurred from April-July 1994 (Des Forges,

1999; Mamdani, 2001). This suggests that the early 1990s formed a distinct phase of refugee flows.

Then, the mid-to-late 1990s appear to have been a transition phase. Building on Figure 1, refugee flows

were occurring through changing sets of directed dyads at the same time that the global refugee

population was shrinking. Since 2000, refugee flows were highly inter-correlated, leading to a dense

cluster.

1 All correlations were positive. Correlation magnitudes are what varied.

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Figure 2: Dichotomized refugee flows, correlated across years

Meanwhile, global powers shifted their refugee policies after the Cold War. During the Cold

War, refugees had become associated with people who were white, male, and anti-communist (Chimni,

1998: p. 357; Zolberg, Suhrke & Aguayo, 1989). When the Cold War ended, refugee identities shifted

toward higher proportions of non-white women, children, and LGBT people. The United States and

other Western countries faced high levels of asylum applications in the early 1990s, but they no longer

felt incentivized to host refugees. This motivated an effort to keep refugees within their home region

(Orchard, 2014). As long as world refugee populations kept falling, this approach seemed stable.

Yet, large numbers of refugees refused to return to their origin country, even after the conflicts

that initially caused their movement had ended. Figure 3 below shows that both the mean and median

refugee exile durations progressively rose until 2005, despite the world’s total refugee population falling

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during that period.2 This group of refugees had worn out their welcome by the end of the 1990s.

Tanzania, a country that had developed an international reputation for hospitality to refugees under

Julius Nyerere’s policy of “ubuntu,” became increasingly hostile to Rwandan and Burundian refugees by

the mid-1990s. Tanzanian political campaigns in 1995 scapegoated refugees for social problems, and in

1996 Tanzania engineered the return of Rwandan refugees under circumstances that raised accusations

of forcible return (Whitaker, 2003). In Kenya, Somali refugees that had initially been welcomed in the

early 1990s and allowed to settle in special economic zones were pushed to re-locate to camps at

Kakuma and Dadaab by the end of the decade (Verdirame, 1999). After accepting Afghan refugees as

prima facie refugees (automatic refugee status without the need for evaluation of individual cases),

Pakistan chose in 1998 to start classifying Afghans as illegal immigrants. On November 9, 2000, Pakistan

officially closed its border with Afghanistan (United States Committee for Refugees and Immigrants,

2002).

Figure 3: Mean and median refugee exile durations

By the early 2000s, refugee-hosting countries had large groups that were opposed to hosting

additional refugees. This contributed to a surge in construction of border walls and other securitization

2 This data comes from (Accessed May 30, 2019): https://blogs.worldbank.org/developmenttalk/refugees-average-duration-exile-going-down-why-bad-news

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measures worldwide (Longo, 2017; Massey, Pren & Durand, 2016; Triandafyllidou & Maroukis, 2012).

The world refugee population hit its lowest level after the Cold War in 2005. Then, after an increase in

2006 and 2007, the world refugee population remained stable until 2012. Relatively few people were

becoming refugees, and existing refugees were generally remaining refugees.

From 2012-2016, the world refugee population under UNHCR’s mandate increased substantially,

from roughly 10.5 million to 17 million refugees.3 This increased activity in the global refugee flow

network created substantial international concern. Below, Figure 4 shows the correlation network based

on actual refugee flow magnitudes. As Figure 4 shows, refugee flows from 2012-2016 were highly

correlated at the directed dyad level. This apparent trend in the aggregate and at the directed dyad level

suggests that something new was taking place. The 2012-2016 time period was a new phase in global

refugee flow patterns. This new phase merits close analysis.

3 As of January 1, 2017, the United Nations Relief and Works Agency (UNRWA) estimated that it had 5.3 million Palestinian refugees under its mandate. These refugees were spread across Jordan, Lebanon, Syria, and the West Bank and Gaza Strip. For more, see (Accessed May 29, 2019): https://www.unrwa.org/sites/default/files/content/resources/unrwa_in_figures_2017_english.pdf

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Figure 4: Refugee flows, correlated across years

For global refugee flows to be correlated across years during the 2012-2016 time period, we

contend that some international process was likely occurring. Domestic processes are always related to

armed conflict and refugee flows, but domestic processes would not explain correlations across years in

global refugee flows. International factors, however, can shape global trends in civil war (Kalyvas &

Balcells, 2010). They are suspected to also play a role in shaping global migration trends (Czaika & Haas,

2014; Orchard, 2014).

We argue that the new phase in refugee flows from 2012-2016 was a product of events in 2011.

This is when the Arab Spring took place. The Arab Spring included the fall of governments in Tunisia,

Egypt, Yemen, and Libya, as well as civil wars in Syria, Libya, and Yemen (Lynch, 2014). These

developments destabilized other countries not directly involved in the Arab Spring as well. Libya’s

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collapse flooded the African Sahel with fighters and weapons (Conflict Armament Research, 2016).

Syria’s civil war contributed to civil war in Iraq (Lister, 2016). These challenges compounded existing

issues with the founding of South Sudan in 2011, Somalia’s 2011 drought that triggered more refugee

flows than Somalia had produced since the early 1990s, and the straining of host communities in Iran

and Pakistan to host millions of Afghan refugees.

The mix of existing challenges and stressors from the Arab Spring initially fueled more internal

displacement in 2011. In 2012, however, the new phase of conflict and migration activity had their

effect on refugee flows. With the world refugee population rising, efforts to restrict refugee flows to

origin regions increasingly failed. Refugees found ways to exit their origin regions, including the

establishment of smuggling networks that could circumvent European border security initiatives (Achilli,

2018; Majidi, 2018). In our analysis, we will monitor whether the Arab Spring caused new factors to

influence refugee flows, had no effect on existing refugee flow influences, or changed how existing

factors influence refugee flows. The following sections articulate four categories of explanations—and

their associated testable hypotheses—for refugee flows.

Data

Given the salience of the 2012-2016 time period, we test our argument with data from 2012-

2016. Since data on refugees and other relevant socio-economic data has increased in quality and

quantity over time, we thereby benefit from the expansion of available data resources. We display these

variables in Table I below and then discuss them.

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Table I: Variable descriptions

Variable Description Source

Dependent Variable

Refugee flows First difference in dyadic refugee stocks

(Flows from country i to country j)

UNHCR

Networks of relative safety

Relative safety Difference in political terror between

destination and origin country (Gradient)

Political Terror Scale

Economic Networks

Wage gaps Destination country GDP per capita minus

origin country GDP per capita (Gradient)

World Development Indicators

Remittances Remittance flows

(Flows from country i to country j)

World Bank

Total migration Total migrant stock in 2010

(Flows from country i to country j)

World Bank Bilateral Migration Matrix

Prior refugee flows One year lag of refugee flows

(Flows from country i to country j)

UNHCR

International trade Magnitude of international trade

(Flows from country i to country j)

Correlates of War

Networks of amity

Regime type difference Difference between destination and

origin country regime type (Gradient)

V-DEM

Alliances of mutual defense Existence of an alliance of mutual defense

(Undirected dyad)

ATOP version 4.01

Arms flows Quantity of arms flows in SIPRI units

(Flows from country i to country j)

SIPRI Arms Transfers Database

Networks of animosity

Strategic rivalry Existence of strategic rivalry

(Undirected dyad)

Thompson & Dreyer (2011)

Positional rivalry Existence of positional rivalry

(Undirected dyad)

Thompson & Dreyer (2011)

Ideological rivalry Existence of ideological rivalry

(Undirected dyad)

Thompson & Dreyer (2011)

Spatial rivalry Existence of spatial rivalry

(Undirected dyad)

Thompson & Dreyer (2011)

Contiguity Dichotomous indicator of land or water

contiguity

(Undirected dyad)

Correlates of War

Our dependent variable, Refugee Flows, comes from refugee data from the United Nations High

Commissioner for Refugees (UNHCR).4 This resource includes dyadic refugee stocks from 1975-2016,

4 http://data.un.org/Data.aspx?d=UNHCR&f=indID%3AType-Ref

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excluding Palestinian refugees since they fall under the mandate of the United Nations Relief and Works

Agency (UNRWA). Before 1990, a large portion of UNHCR’s refugee data is not actually dyadic, so we

would advise researchers to only use UNHCR’s monadic data if they wish to conduct analysis on refugee

population data pre-1990. We use data for the 2012-2016 time period and calculate the first difference

of refugee stocks to obtain net refugee flows. We then replaced missing values with zero. We also

followed existing standard practice and replaced negative net refugee flow values with zero.

Next, we add our independent variables. Our variable Prior Refugee Flows is a measurement of

net refugee flows in the previous year. Our measurement of immigration, Total Immigration 2010,

comes from the World Bank’s data on bilateral international migration. It measures dyadic immigrant

stocks as of 2010. We then used Benjamin Graham’s and Jacob Tucker’s data repository to obtain data

on GDP per capita from the World Bank’s World Development Indicators database (Income Gradient),

the Varieties of Democracy (V-DEM) additive polyarchy measure of regime type (Regime Type Gradient),

and the Political Terror Scale (PTS) index value for each country (Safety Gradient) (Graham & Tucker,

2019). From these monadic variables, we created gradient matrices to capture the difference in these

values between destination and origin countries.5 Our Safety Gradient and Income Gradient variables

only go through 2015, so we used those values for our 2016 models as well. Our measurement of

remittance flows, Remittance Flows, comes from annual dyadic data from the World Bank.6 Our

measurement of dyadic arms flows, Arms Flows, comes from annual dyadic data from the Stockholm

International Peace Research Institute.7 We replaced missing values of Remittance Flows and Arms

5 For Safety Gradient, we used values coded from Amnesty International reports. When those were missing, we used values coded from State Department reports. This yielded a variable with zero missing values for 2015. For 2011-2014, we used values coded from Human Rights Watch reports when there were missings for Amnesty International and the State Department. For remaining missing values, we imputed a value of zero. For Regime Type Gradient, we replaced missing dyad gradient values with zero. For Income Gradient, we also replaced missing dyad gradient values with zero. 6 http://www.worldbank.org/en/topic/migrationremittancesdiasporaissues/brief/migration-remittances-data 7 https://www.sipri.org/databases/armstransfers

22

Flows with zero. Our measure of international trade, Trade, comes from the Correlates of War project

(Barbieri, Keshk & Pollins, 2009).8 This variable only goes through 2014, so we used the 2014 values for

models of refugee flows in 2015 and 2016. Our measure of contiguity (Contiguity) also comes from the

Correlates of War project (Douglas et al., 2002).9 Our measure of alliances of mutual defense (Alliance of

Defense) comes from the Alliance Treaty Obligations and Provisions Project (ATOP) (Leeds et al., 2002).

Our rivalry measures, Strategic Rivalry, Ideological Rivalry, Positional Rivalry, and Spatial Rivalry, come

from the rivalry dataset created by William R. Thompson (Thompson, 2001; Thompson & Dreyer, 2011).

This dataset codes rivalries through 2010, so we use the rivalry dyads that existed as of 2010 for our

analysis. Given the tendency for rivalries to persist, we believe that this is a reasonable choice (Colaresi,

Rasler & Thompson, 2008: p. 77).10

Using MR-QAP to analyze the global refugee flow network

In a 2016 online symposium, International Studies Quarterly hosted discussion of strengths and

weaknesses of analyzing dyadic relationships through standard regression-based approaches or network

analysis (Wolford, 2016). There was broad consensus that researchers must choose the methods that

best fit their research design. In addition, there was consensus that when there is concern about

interdependencies between dyads, network approaches are often far superior.

Quantitative network analysis is a category of methods, not a single unitary method. Cranmer et

al. (2017) provide a valuable overview of three of these methods—Exponential Random Graph Models

(ERGMs), latent space network models, and multiple regression with a quadratic assignment procedure

8 Barbieri, Katherine and Omar M. G. Omar Keshk. 2016. Correlates of War Project Trade Data Set Codebook, Version 4.0. Online: http://correlatesofwar.org. 9 Correlates of War Project. Direct Contiguity Data, 1816-2016. Version 3.2. 10 The end of the Cold War ushered an end to 21 rivalries and a beginning for 11 rivalries in the 1990s. In the 2000s, nine more rivalries ended and only one rivalry began. While the 2010s have certainly contained domestic instability in many countries, the inter-state security system has been relatively stable. If we are missing rivalry initiation or termination, then we expect to be missing few of these events.

23

(MR-QAP). ERGMs are able to model a wide variety of relational, positional, and structural network

interdependencies. For example, migration scholars have used ERGMs to examine the aggregate

international migrant stock network and the internal displacement flow network in Somalia (Schon,

2018; Windzio, 2018). These studies coalesce around several insights—regardless of whether the

migration is international migration or internal displacement. Migration is directional, does not form

triangles, occurs between locations of similar ethnic, religious, or linguistic characteristics (homophily),

and it has the structural characteristics of preferential attachment, preferential dis-attachment, and

transitivity. Unfortunately, ERGMs are primarily useful for modelling the network interdependencies of a

single type of characteristic. Latent space network models and MR-QAP both allow the user to test

hypotheses and include several independent variables in their analysis of some dependent variable. MR-

QAP, however, does not require the user to specify a set of network dependencies (which is highly

prone to user error), is very good at dealing with dense overlapping networks of relationships, and it is

the most straightforward model to interpret. This led us to use MR-QAP for our analysis.

We estimate two MR-QAPs for each year from 2012-2016: one with the aggregate strategic

rivalry category and one with the rivalry categories of ideological, positional, and spatial rivalry. This

method allows us to account for network interdependencies between dyads, which is a common

critique of traditional regression analysis (Cranmer et al., 2017; Oatley et al., 2013). Our quadratic

assignment procedure uses the double semi-partialing permutation method, which is the most robust

permutation method (Dekker, Krackhardt & Snijders, 2007). Political scientists have begun to apply MR-

QAPs to a variety of research areas, such as sub-national governance, political behavior and

communication, alliances, and trade (Eveland & Kleinman, 2013; Haim, 2016; Lazer et al., 2010; Shrestha

& Feiock, 2009). We extend the method’s application to the study of refugee flows.

MR-QAP models require input variables to be in the form of square matrices. For the 2012-2016

time period, we therefore use 195 x 195 square matrices for each variable. With these matrices, the first

24

step is to estimate an ordinary least squares (OLS) regression. Then, we use the estimated t-statistic and

compare it to a distribution of t-statistics. That distribution is calculated through a series of Monte Carlo

simulations where rows and columns are shuffled, while maintaining row-column combinations. This

process breaks node-level correlations. It is then possible to estimate a p-value comparing the observed

t-statistic with the simulated distribution of t-statistics (Krackardt, 1987; Krackhardt, 1988). For our

analysis, we use 1,000 simulations to calculate our distributions of t-statistics. Due to concerns about

the sensitivity of MR-QAP to collinearity, we checked Variance Inflation Factors (VIFs) for all of our

models (Dekker, Krackhardt & Snijders, 2007). In all cases, our models did not show evidence of

collinearity.

Results

Tables II, III, and IV show the results of our analysis. These results broadly show that economic

networks do not facilitate the formation and evolution of refugee flow networks. Networks of animosity,

especially strategic rivalry and contiguity, are key factors for explaining the global refugee flow network.

From 2012-2016, Income Gradient is not a significant influence on refugee flows. This means

that there is no support for H1a. The neoclassical economics of migration approach therefore does not

appear to be appropriate for explaining refugee flows.

Meanwhile, it is also unclear whether the new economics of migration approach is appropriate

to explain refugee flow destinations. Refugee flows from 2012-2016 tend to be significantly influenced

by the total immigrant stock in 2010, but the relationship appears to be of a very small magnitude. That

relationship also has inconsistent signs. As a result, there is a lack of evidence for H1b. Prior Refugee

Flows, on the other hand, have a clear positive relationship with refugee flows, supporting H1c.

Remittance Flows does not have a clear relationship with refugee flows either. It only has a significant

coefficient for models of 2012 and 2014 refugee flows, and the signs on those coefficients are different.

25

The results, therefore, do not support H1d. Trade also does not consistently have a significant

relationship with refugee flows, so there is no support for H1e. Overall, these results primarily indicate

that there is just temporal autocorrelation in refugee flows.

Networks of amity and relative safety also do not appear to be important drivers of refugee

flows. Relative Safety is only significant in 2012 and 2016. In those years, fewer refugees tend to move

to countries that are more dangerous, but the lack of significance in 2013, 2014, and 2015 casts doubt

upon H2. Regime Type Gradient is never significant, so there is no support for H3a. Alliance of Mutual

Defense is only significant in 2014 and 2015, and its sign is negative when it is significant, so there is also

no support for H3b. Arms Flows is only significant in one model in 2016, so there is also not support for

H3c. Our results thereby tell us that refugee flows are not clearly influenced by considerations of relative

safety or amicable relations between origin and potential destination countries.

Networks of animosity, on the other hand, do appear to influence refugee flows, but the

influence shifted from 2012-2016. Strategic Rivalry was significant from 2012-2015, although it shifted

from having a positive coefficient in 2012 to a negative coefficient from 2013-2015. Contiguity, however,

was significant and positive from 2012-2015. It was then significant and positive only when the rivalry

category was disaggregated in 2016. This supports H4e, but the decreasing magnitude of the coefficient

and more tenuous significance in 2016 suggests that refugees were moving further and further away

from their origin countries during this time period. Since contiguity is related to the existence and

escalation of rivalry, there is reason to believe that the coefficient on Strategic Rivalry shifted from

positive to negative because refugees increasingly moved past the rivals of their origin country during

the 2012-2016 time period. This story would be consistent with H4a.

The disaggregation of Strategic Rivalry provides a complex picture. Ideological Rivalry was

negative and significant in 2012 and 2013, and then switched to become positive and significant in 2014

and 2015. In 2016, Ideological Rivalry did not have a significant coefficient. These results support H4b if

26

the trend of refugees moving further away from their origin countries includes increasing movement

toward ideological rivals. Ideological rivals are more likely to be non-contiguous than positional or

spatial rivals, so this explanation is plausible. Positional Rivalry had a positive and significant coefficient

in 2012, 2013, 2015, and 2016. Its coefficient was negative and significant in 2014. Spatial Rivalry had a

positive and significant coefficient in 2012 and 2014, whereas its coefficient was negative and significant

in 2013, 2015, and 2016. The results on Spatial Rivalry thereby support the broader story about H4d.

The results on Positional Rivalry, meanwhile, suggest that its effects are less sensitive to refugees

moving further away from their origin countries. It provides more straightforward support for H4c.

Table II: MR-QAP results 2012 and 2013

2012 Refugee Flows 2012 Refugee Flows 2013 Refugee Flows 2013 Refugee Flows

Coefficients p-value Coefficients p-value Coefficients p-value Coefficients p-value

Intercept 8.650440956 0.670 8.287825308 0.694 -3.383203249 0.314 -3.827650385 0.275

Prior Refugee Flows 0.075331212 0.001 0.080767785 0.000 1.747413143 0.000 1.748742317 0.000

Regime Type 15.456058 0.602 15.26778365 0.639 23.59910413 0.460 23.51895186 0.441

Remittances 0.052436224 0.027 0.070427354 0.018 -0.022040303 0.384 0.068923592 0.108

Arms Flows 0.155865771 0.089 0.198783241 0.072 0.037299883 0.418 0.025211317 0.578

Strategic Rivalry 3964.378236 0.000 -884.6407191 0.011

Ideological Rivalry -3595.58114 0.001 -537.199306 0.023

Positional Rivalry 5717.618971 0.002 746.5149237 0.023

Spatial Rivalry 2866.551787 0.003 -2837.207261 0.004

Contiguity 723.9909965 0.000 734.4324867 0.000 178.4953106 0.027 230.5338372 0.024

Income -0.000594787 0.280 -0.000590274 0.296 -0.000268402 0.594 -0.000266881 0.638

Relative Safety -35.48313105 0.003 -35.46607177 0.006 -7.01802676 0.502 -6.998891349 0.491

Alliance of Mutual Defense -120.0319896 0.055 -105.8939277 0.070 34.60876072 0.273 34.31071829 0.307

Trade -0.00903963 0.007 -0.010742072 0.006 0.000953238 0.137 0.000837859 0.146

Total immigrant population in 2010 0.000312245 0.024 0.000285284 0.012 -0.000494487 0.013 -0.000483198 0.019

27

Table III: MR-QAP results 2014 and 2015

2014 Refugee Flows 2014 Refugee Flows 2015 Refugee Flows 2015 Refugee Flows

Coefficients p-value Coefficients p-value Coefficients p-value Coefficients p-value

Intercept 9.829025 0.647 12.50009 0.623 4.796545 0.520 4.236103 0.565

Prior Refugee Flows 0.704987 0.000 0.708221 0.000 0.766808 0.000 0.767305 0.000

Regime Type 10.62715 0.827 10.59146 0.813 11.71734 0.702 11.73148 0.658

Remittances -0.57795 0.016 -0.56013 0.011 -0.17227 0.072 -0.11802 0.109

Arms Flows -0.11444 0.359 -0.32935 0.149 0.217679 0.058 0.252873 0.060

Strategic Rivalry -1455.17 0.026 -2260.61 0.004

Ideological Rivalry 1349.527 0.009 1299.905 0.011

Positional Rivalry -10894 0.000 1369.944 0.019

Spatial Rivalry 5458.895 0.003 -4335.93 0.001

Contiguity 966.5088 0.000 911.1866 0.001 217.6153 0.034 213.2622 0.043

Income -0.00146 0.120 -0.00145 0.104 0.00054 0.304 0.000565 0.256

Relative Safety -22.0186 0.207 -21.8214 0.217 -0.80677 0.920 1.230937 0.905

Alliance of Mutual Defense -293.293 0.010 -314.376 0.008 104.0131 0.046 111.8998 0.031

Trade -0.0145 0.006 -0.00969 0.007 0.008842 0.010 0.007275 0.009

Total immigrant population in 2010 0.00812 0.000 0.008139 0.000 -0.00413 0.000 -0.00414 0.000

Table IV: MR-QAP results 2016

2016 Refugee Flows 2016 Refugee Flows

Coefficients p-value Coefficients p-value

Intercept 0.721493 0.169 -9.21032 0.397

Prior Refugee Flows -0.00022 0.030 0.36819 0.000

Regime Type -0.65352 0.557 50.56075 0.216

Remittances 5.29E-05 0.928 0.012361 0.377

Arms Flows 0.031113 0.037 -0.0049 0.995

Strategic Rivalry -2.82063 0.144

Ideological Rivalry -41.6167 0.347

Positional Rivalry 458.9031 0.022

Spatial Rivalry -743.256 0.008

Contiguity 1.008875 0.175 481.4276 0.001

Income 2.33E-05 0.090 -0.00038 0.470

Relative Safety -0.59812 0.025 -53.6612 0.000

Alliance of Mutual Defense -0.70763 0.324 90.74471 0.074

Trade -7.14E-06 0.337 -0.00093 0.152

Total immigrant population in 2010 4.14E-06 0.042 -0.0009 0.007

28

Discussion

In general, geographic contiguity and inter-state competition for power are the two most

important drivers of refugee flows. This security explanation—highlighting rivalry networks and

contiguity—contrasts with other types of explanations that are discussed more widely, such as

neoclassical and new economics of migration, relative safety, and Democratic Peace. Other research

using dyadic regression analysis also finds statistically significant relationships between rivalry and

contiguity, but our findings diverge on many other variables.

We believe that our application of MR-QAP to the analysis of refugee flows is an important

reason for the divergence between our findings and the findings in other research. Like other critics of

Democratic Peace explanations, we believe that the overlap between political regime type and

economic factors like GDP per capita makes it difficult to identify a specific role for regime type in

influencing refugee flows. It is also unclear whether and how countries that are more democratic would

actually be more welcoming for refugees. Broadening our economic considerations from income to

remittances and total migration, we suspect that labor migration and refugee flows require

fundamentally different sets of explanations. Neoclassical economics and now the new economics of

migration were developed largely through studies of migration that was not motivated by armed

conflict. Analyses of refugee flows that attempt to apply these lenses may suffer because these

economic explanations might not be appropriate for explaining refugee flows.

Our findings in support of explaining refugee flows with rivalry and contiguity offer important

nuances. For starters, relative safety calculations are not consistently important drivers of refugee flows.

This finding merits additional research, particularly because it is so intuitive that refugees would move

to safer countries. Future research may wish to use alternative measures to our comparison of index

values on the Political Terror Scale. For example, sub-national indicators of relative safety, such as

comparisons between violence levels in each origin country province to violence levels in each

29

destination country province, might detect larger and more consistent effects. Our findings may also be

a result of governments making conscious choices about which people should receive refugee status

(FitzGerald & Arar, 2018). Many undocumented immigrants in the United States, for instance, might

receive refugee status through the policies that other countries use. Alternatively, relative safety may

not be consistently relevant to refugee flows, regardless of how safety and refugee flows are measured.

Instead, relative safety may really matter for influencing the destinations for internal migration. When

people choose to leave their home country, their calculations may shift. The value in safety may really

come from escaping the specific violence that targets them. A destination country with violence may still

be safer because that violence involves different actors than those that threaten them in their origin

country.

Instead, people may really be seeking countries that will welcome them. We believe that this is

why networks of animosity, specifically rivalry, is significantly related to refugee flows. Rivals know that

refugee admissions can embarrass origin countries, so they have geopolitical incentives to admit

refugees from each other. Our added nuance comes through our consideration of how refugee flows

shifted from 2012-2016. In 2012, strategic rivalry had the expected positive relationship with refugee

flows. This relationship switched from positive to negative in subsequent years, but we believe that this

is due to refugees moving increasingly far away from their origin countries. Rivals tend to share a

border, or when they do not share a border they are located in the same geographic region, so the

increasing numbers of refugees from countries like Syria, Afghanistan, Somalia, Nigeria, and Eritrea that

reached distant countries in Europe or South Africa (in several African cases) are likely driving this shift.

As a result, our findings allow us to confirm some existing expectations and show what changes

occurred during the dynamic post-Arab Spring time period.

Another contribution of our analysis is that we are able to show that different types of rivalry –

ideological, positional, and spatial rivalry—may have different relationships with refugee flows. These

30

patterns shift during time periods when refugees are moving further and further away from their origin

countries. From 2012-2016, this trend occurred as the Arab Spring saturated neighboring countries

within origin regions with refugees and those refugees increasingly chose onward migration.

Conclusion

In this paper, we have demonstrated the value of explaining refugee flows as a network. An

analysis that incorporates multiple overlapping networks allows us to account for many kinds of network

dependencies. Our analysis describes the evolution of the global refugee flow network since the end of

the Cold War. Considering correlations between the sets of existing refugee flow directed dyads in each

year, we observe a clear pattern that refugee flows occurred through very different sets of dyads during

the 1990s and after the year 2000. When we examined correlations between the magnitudes of

refugees flowing through each directed dyad in each year, we observe a noisier process from year to

year. The 2012-2016 time period, however, stands out as a period with highly inter-correlated refugee

flows across years. With this periodization in mind, we applied MR-QAP and learned that competition

for power and geographic contiguity were the most important drivers of refugee flows during this time

period.

There are several possibilities for future research. It would be valuable to use these macro-level

findings to guide further research on refugee flow ego networks (e.g., focus on a single country’s flows).

This could help explain variation in the importance of various factors over space and time. In addition,

research that further unpacks inter-state rivalry and its interactions with geographic proximity may help

explain the fluctuation in the effects of ideological, positional, and spatial rivalry on refugee flows.

Finally, the importance that we ascribe to the Arab Spring suggests that additional research on the Arab

Spring could contribute to broad understandings of changes in the international system, beyond

changes in the Middle East alone.

31

Global refugee flows sparked massive public policy concern in the 2010s. With a strongly

correlated phase in refugee flows forming from 2012-2016, it is important to understand whether and

why refugee flow patterns will change. This understanding will carry substantial policy and scholarly

value.

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