Using ERGMs to Disaggregate Displacement
Cascades*Justin Schon
Indiana University, 1100 E Seventh Street, 210 Woodburn Hall, Bloomington, IN 47405
jschon@indiana.edu*

Abstract
How do civilians select internal displacement destinations during conflict? Existing researchemphasizes the value of cascades as a guide to making these difficult decisions. Cascades may
involve civilians following people in their social networks (community cascades), people withsimilar characteristics (co-ethnic cascades), or the crowd in general (herd cascades). Analyses
relying upon interview or regression-based methodological approaches face substantial challenges
in identifying the prevalence of, and relationship between, each type of cascade. While interviewbased approaches can incorporate location characteristics and movement patterns, they struggle
with assessing aggregate trends. Meanwhile, regression-based approaches can assess aggregate
trends, but they struggle with incorporating location characteristics and movement patterns.
Exponential Random Graph Models (ERGMs) that conceive of locations as nodes in a networkand movements between those locations as ties can overcome these challenges and assess
aggregate trends while incorporating location characteristics and movement patterns. This paper
demonstrates the utility of this approach using data from UNHCR on internal displacement in
Somalia from 2007-2013. Results reveal that herd cascades only form at high displacement levels,
co-ethnic cascades form at medium and high displacement levels, and community cascades form
at all displacement levels. Therefore, cascades provide stronger guides for displacement-related
decisions as civilians switch from following the crowd in general to following those with similar
characteristics to following social ties.
*

Acknowledgements: Many people contributed to the development of this research. William Winecoff and Karen
Rasler provided extensive comments on the paper. Graduate students in the Department of Political Science havealso read and commented upon previous drafts, most notably Kirk Harris, Yehuda Magid, and Alison Carden.
Brandon Kendhammer provided the idea to interview Somalis living in Columbus, Ohio, which turned out to be avery fruitful endeavor. Comments from my presentation for the Conflict Consortium Virtual Workshop in Spring2015 motivated me to consider the role of information in displacement. Finally, I thank the anonymous reviewers fortheir comments. All errors and omissions are my own.

I.

Introduction

As refugee crises, most notably the Syrian refugee crisis, have captured the concern ofgovernments worldwide, internal displacement is often overlooked. Yet, according to estimatesfrom the International Organization of Migration (IOM), there are now approximately 20 million
refugees (external displacement) worldwide, while there are approximately 40 million internallydisplaced people (IDPs) (UNHCR, 2015c). These internally displaced people concern hostcommunities due to their increased likelihood of engaging in violent collective action, aggravating foodinsecurities, increasing housing prices in host communities, and facilitating conflict diffusion(Corps, 2012; FSNAU, 2015; Salehyan, 2011). Syria is currently the most infamous example ofthese dynamics. While its 4 million refugees are widely covered in international media, its 8
million IDPs receive far less coverage, despite facing conditions that are arguably more severe(Martinez and Eng, 2016). Large-scale internal displacement also affects many African countries,
such as Libya, South Sudan, Central African Republic, Democratic Republic of Congo, andSomalia.
As observers attempt to understand the processes driving internal displacement, there is a
popular image of civilians fleeing for their lives from massacre. However, recent research showsthat civilian decision-making incorporates considerations of a broad array of social, economic,
geographic, and political factors, as well as expectations about conflict dynamics and potential
threats (Schon, 2015; Rubin and Moore, 2007; Uzonyi, 2014). This decision-making produces
patterns in displacement destinations. There is a wealth of scholarship examining patterns in
refugee movement (Ruegger and Bohnet, 2015; Moore and Shellman, 2006; Moore and Shellman,2007). To improve explanations of these patterns for IDPs, this paper asks how civilians selecttheir internal displacement destinations.
Civilians weigh many factors while making decisions about their displacement whilenavigating a confusing information environment, in which it is difficult to distinguish fact fromfiction (Schelling, 1981). This makes the act of interpreting existing information crucial, as well
as extremely difficult. Facing this challenge, people often follow the behavior of others and form
cascades, especially when the decision is risky (Göbel, 1998). Cascades result from thetransformation of heterogeneous individual preferences and characteristics into group action(Granovetter, 1978). Civilians observing the formation of cascades can then use them as guides tointerpreting existing information, thereby shaping their displacement decision-making. Suchbehavior is frequently documented in contexts with large-scale population movements, leading to
the development of labels such as ‘chain migration’ (Davenport et al., 2003; Moore and Shellman,
2006; Ruegger and Bohnet, 2015; MacDonald and MacDonald, 1974).
Cascades can involve different types of groups. Some argue that civilians move in herdcascades, converging on the same locations as other civilians that moved before them, regardlessof whether these civilians have personal ties (Banerjee, 1992; Bauer et al., 2002; Epstein, 2008).
Other scholars argue that civilians move in co-ethnic cascades, converging on co-ethnic locations(Ruegger and Bohnet, 2015; Steinberg, 2015; Bartel, 1989). Still, other scholars argue thatcivilians move in community cascades, converging on the same locations as their friends andfamily members (Rawlence, 2016; Epstein, 2008). However, we researchers often lack the ability1to directly compare the patterns in how these types of cascades influence displacement
destinations.
This challenge comes from the inherent limitations of interview and regression-based
approaches to the study of migration. Interview-based approaches can effectively incorporate
location characteristics, including origin, transit, and destination locations, and movement patterns
into nuanced analyses. Such analyses have been crucial for explanations of the transnational
dynamics of migration (Horst, 2006; Whitehouse, 2012), remittance behavior (Lindley, 2010a),and for uncovering important mechanisms in the process of migration (Kane, 2013). However,they are often unable to assess broader correlations and aggregate migration trends. Regressionbased approaches can effectively assess aggregate migration trends (Czaika and Haas, 2014), but
their requirement of using variables from a single level of analysis forces them to focus on location
characteristics or movement patterns. They cannot fully incorporate both.
Social network analysis, therefore, presents a valuable alternative. Migration scholars
usually use social network analysis to examine networks of individuals and their relationships witheach other. I apply social network analysis to examine networks of locations and internal
displacement flows between those locations. This shift allows me to examine how people
incorporate information about locations, and movements between locations, into their decisions of
where to move.
After conceiving of locations as nodes in a network and displacement movements between
those locations as ties, Exponential Random Graph Models (ERGMs) can incorporate both of theseelements into a single quantitative analysis to consider aggregate trends in displacement. Internal
displacement data from Somalia that tracks the origin and destination locations of each internal
displacement movement facilitates this analysis (UNHCR, 2007). Network covariates that can bemodeled with Exponential Random Graph Models (ERGMs) can then directly measure eachcascade type: ethnic homophily for co-ethnic cascades, preferential attachment for herd cascades,
and transitivity for community cascades (Cranmer and Desmarais, 2011; Wasserman and Faust,
1994; Maoz, 2010).
Through ERGMs with thresholds for ties set at 1 person (Unweighted), 324 people (25thpercentile of displacement movement magnitudes), 835 people (50th percentile of displacement
movement magnitudes), and 2622 people (75th percentile of displacement movement magnitudes),it is then possible to detect patterns in the presence of each cascade type. 1 These thresholds arearbitrary. The primary goal was to set several different threshold levels based on clear, objective
criteria.
Findings indicate that each cascade type forms at different thresholds. Herd cascades form
at the 50th and 75th percentile of displacement movement magnitudes. Co-ethnic cascades form at
the 25th percentile and above. Community cascades form at all displacement levels. This provides
evidence as to when civilians use each cascade type as a guide. It supports the view that, whenpossible, civilians use the behavior of their social ties as guides. As displacement levels increase,
In this paper’s analysis, these thresholds will be referred to as Unweighted, 25th percentile, 50th
percentile, and 75th percentile.
1

2

the resultant confusion can make social ties difficult to track, so civilians resort to shared ethnicity
as a Plan B. Even higher displacement levels can make the actions of others with shared ethnicity
difficult to track, so civilians resort to following the crowd as a Plan C. This preference order can
only be detected by disaggregating cascade models, and it reveals a key civilian preference orderfor following social ties, then co-ethnics, and then the crowd in general.
This paper will begin by explaining the complexity involved in civilian displacement
decision-making. This complexity arises from the many factors civilians must weigh, as well as
the uncertainty regarding the accuracy of available information about those factors. Responding tocomplexity, civilians use displacement cascades as a guide to interpreting available information.
Then, this paper will specify three types of cascades: herd cascades, co-ethnic cascades, andcommunity cascades. ERGMs using UNHCR data on Somali internal displacement movements
facilitate an analysis of the patterns of each cascade type. Finally, the paper concludes withdirections for future research on displacement and how other areas of contentious politics research
may evaluate the roles of each cascade type.

II.

The Complexity of Civilian Decision-Making about
Displacement

Civilian decision-making about displacement weighs many social, economic, geographic,
and political factors. Socially, civilians must consider the needs of their own families andhouseholds. Personal networks can provide the necessary resources for civilians to avoid
movement, or they can make movement easier. They can also make certain displacement
destinations more desirable (Harpvicken, 2009). Economically, civilians need the financial
resources to move. At the same time, they must also weigh the economic conditions of their origin
location and the economic conditions of their destination location (Adhikari, 2013).
Geographically, civilians must consider the distances that they must travel and the types of terrainthat they must navigate (Moore and Shellman, 2006). Politically, civilians must consider the levelof physical threat that they face (Davenport et al., 2003). This combination of considerations canbe extremely difficult to weigh.
Thus, during conflict, civilians find it valuable to monitor as much information as possible
about violence and wider conflict dynamics. This information can take many forms, such as media
reports, rumors, conspiracy theories, signals, and experiences. Often, this information is unverified
and may be distorted in numerous ways (Allport and Postman, 1947; Shibutani, 1966). The sumof all of this information forms a confusing information environment, in which it is difficult todistinguish fact from fiction.
This confusing information environment requires civilians to find ways to interpret
information, so that they may weigh the social, economic, geographic, and political factors that
affect their decision-making about displacement. Facing this challenge, people often follow the
behavior of others and form cascades. Cascades form when people with heterogeneous individual
preferences and information choose to engage in group action (Granovetter, 1978). This isparticularly common when civilians have to make risky decisions (Göbel, 1998). Cascades form3

because they offer civilians a guide with which to interpret information and make decisions(Banerjee, 1992).2
Cascades do not necessarily drive each movement during the displacement process.
Instead, they drive the selection of final destination locations. This is important, as displacement
often includes multiple movements. Each movement can involve new decisions (Karen Jacobsen
and IDMC, 2008; Triandafyllidou and Maroukis, 2012). Several factors make displacement a
process that involves multiple movements. First, checkpoints and violence along roads motivate
civilians to use different routes (Lubkemann, 2008; Singer and Massey, 1998; Lombard, 2013).
Second, varying levels of access to transportation lead to variation in when and where civilians areable to move (Brachet, 2009; Steinberg, 2015; Triandafyllidou and Maroukis, 2012). Third,
households divide during the displacement process, motivating civilians to reconnect as soon as
possible (Horst, 2006).
Moreover, the desire for physical safety tends to drive the first movement in thedisplacement process. Civilians pursue other concerns, including economic prosperity, after
physical safety is achieved (Metcalfe et al., 2012; Lauten and Kesmaecker-Wissing, 2015; Ferris,
2014; Drumtra, 2014). As one Somali refugee comments, “When you flee your home, the only
thing on your mind is safety. You keep moving until you find some place that is safe. Only when
you have safety do you think about other factors, such as employment, health care, and food”(Author interview in Columbus, Ohio, in May 2015).
This statement reveals another important reason for multiple movements. Displacement
motivations change from one movement to another. In the common scenario that this Somali
refugee describes, civilians often initially flee violence. Then, they realize that they need to keep
moving in order to find a location where they can access necessities, such as jobs, health care, andfood. This view of displacement asserts that individual movements have a primary motivation, but
the complete journey from origin to final destination can involve many motivations. Such amultitude of motivations adds even more reasons that civilians can benefit from guides that help
them interpret existing information.

III.

Cascade Models

During armed conflict, cascades are arguably one of the most valuable guides for civilians.
The following sections suggest a division of cascade models into three types: herd cascades,
community cascades, and co-ethnic cascades. Each cascade type involves different kinds ofconnections between individuals involved in the cascade and different conditions under which
individuals join each cascade type. Thus, rather than challenging information cascade models as a2

Cascades matter for both refugees and IDPs, but for different reasons. The challenges of crossing
country borders often mean that refugees face greater uncertainty in selecting destination locations
and how to reach those locations. IDPs are arguably more vulnerable than refugees though, as they
have failed to escape the country experiencing conflict and often lack access to humanitarian aid.
4

whole, this paper disaggregates cascade models in order to develop more precise explanations of
how civilians select internal displacement destinations.
a. Herd Cascades
Cascade models were developed primarily as models of herd behavior (Banerjee, 1992;
Lohmann, 1993; Lohmann, 1994; Kuran, 1989; Ellis and Fender, 2011). When individuals are
uncertain about the best course of action, they follow the behavior of others, regardless of whethersocial ties exist between them. As more people mobilize to participate in a given course of action,
the herd behavior becomes a clearer guide about the desirability and potential for effectiveness of
the action. Different types of individuals may wait for different thresholds of mobilization before
choosing to participate, but observed group behavior should nevertheless increase their chances ofparticipation. Furthermore, there may be advantages in conforming to group behavior, such as‘strength in numbers.’ These mechanisms of herd behavior are commonly asserted to exist in
migration processes (MacDonald and MacDonald, 1974; Schelling, 1981; Epstein, 2008). Herd
behavior could, therefore, be a key driver of the selection of displacement destinations. This leads
to the following hypothesis.
H1: IDPs are more likely to move to the same locations as other IDPs, regardlessof the types of social ties that exist between them.
b. Community Cascades
Alternatively, social ties may be an important driver of whether individuals choose toparticipate in mobilization. In particular, civilians may take their cues from community members.
There are many ways to define community (Agruwal and Gibson, 1999). In this paper, community
is defined as a group of people who reside in close spatial and social proximity. Friends, family
members, and neighbors are the key groups within an individual’s community. Community
members live near each other, and they have regular social interactions. These elements of spatialand social proximity reinforce each other, as people that live close together naturally have more
opportunities for social interaction (McPherson et al., 2001).
Community cascades involve civilians moving to the same destination locations as othercivilians from their origin location. In particular, it involves civilians converging on the same
locations as friends, family members, and neighbors from their origin location. Civilians converge
by communicating as much as possible with other displaced community members (Harpvicken,2009).
The argument that origin communities converge on the same destination locations isdifferent from arguing that all civilians move together. As civilians become separated from eachother, they often attempt to reunite. This eventually produces a convergence upon certain
destination locations. If all civilians from an origin community moved together, then there would
not be any need to reunite later.
Community connections can provide many important ‘network externalities’ to facilitate
mobilization. The information, support, and resources from friendships can convince people tojoin rebel groups (Parkinson, 2013). Friendships in rebel groups can even provide fun, as in the5case of the Muhajir Qaumi Movement (MQM) in Pakistan (Verkaaik, 2004). This insight has been
applied even more broadly to protest movements and other kinds of mobilization (McAdam, 1986).
Migration and displacement also draw upon these externalities from friends, family members,
neighbors, and other community members (Epstein, 2008). With these considerations, this paper
will test the following hypothesis.
H2: IDPs are more likely to move to the same locations as other community
members from their origin location.
c. Co-ethnic Cascades
While seeking community members, civilians may not be able to reconstitute their origin
communities. Many civilians simply lose track of their origin communities as they leave theirhomes. This fragmentation of social ties motivates civilians to replace the fragmented social ties
(Harpvicken, 2009). Such replacement social ties need to be durable and trustworthy. Short-term
disposable ties may briefly provide necessary information, but they are not trustworthy enough todrive the selection of displacement destinations (Desmond, 2012).
Instead, in the midst of the instability and uncertainty of conflict, civilians often turn to coethnics (Habyarimana et al., 2007). In fact, a growing body of research argues that during conflict,intra-ethnic trust and generosity tend to increase (Bauer et al., 2011; Voors et al., 2012). Thesechanges occur as conflicts instigate demographic changes, whereby areas that were ethnicallyheterogeneous pre-conflict become ethnically homogenous during conflict (Balcells and Steele,2016). This is especially likely in cases of ethnic cleansing (Kapteijns, 2013). Therefore, civilians
who are unable to maintain their origin communities may move to locations inhabited by coethnics (Whitehouse, 2012; Zilberg, 2011). This motivates the development of a third type ofcascade: co-ethnic cascades.
Recent studies provide additional evidence on the role of co-ethnic cascades. In theiranalysis of external displacement destinations, Ruegger and Bohnet (2015) find that refugees tend
to flee to co-ethnic regions. While there is insufficient data to determine whether this research on
external displacement can also explain internal displacement, it does at least suggest that co-ethnic
cascades merit consideration in an analysis of internal displacement. This leads to the following
hypothesis:
H3: IDPs are more likely to move to co-ethnic locations.

IV.

Testing Cascade Models in the Context of SomaliInternal Displacement 2007-2013

To evaluate these cascade models, I will focus my analysis on internal displacement in
Somalia from 2007-2013. This time period begins with the dramatic escalation of violence
resulting from the Ethiopian invasion at the end of 2006. This invasion provoked the rise of Al
Shabaab in 2007 (Hansen, 2012). From that point, through 2013, conflict in Somalia has produced
some of the largest internal displacement flows in the world. Between Ethiopia’s invasion at theend of 2006 and the end of 2008, roughly two-thirds of Mogadishu’s population fled the city6

(Lindley, 2010b). In total, over a million people left their homes from 2007-2013, with most people
moving several times (Drumtra, 2014). As one of the world’s largest producers of internallydisplaced people, Somalia presents an important context in which to analyze how cascades
influence the selection of internal displacement destinations.
Admittedly, despite the magnitude of its internal displacement crisis, Somalia contains a
relatively extreme combination of characteristics. These characteristics include Somalia’s nomadic
traditions, state failure, foreign intervention, extreme poverty, environmental challenges,
prolonged conflict, proliferation of armed groups, and clan divisions (Lewis, 2008; Aristide
Zolberg et al., 1989).3 All of these characteristics influence displacement patterns (Massey andBohra-Misha, 2011; Massey and Silva, 2014; Balcells and Steele, 2016).
As a result, this paper’s primary goal of explaining the role of cascades in Somali internal
displacement has inherent policy importance. The unique combination of characteristics in the casemotivates the recognition of clear scope conditions (Beach and Pedersen, 2013). Somalis, as awhole, have grown accustomed to adapting to hard times. Drought, violence, or a variety of othersources of insecurity have a history of displacing Somali civilians (Horst, 2006). In addition,
nomadic traditions within Somalia help civilians respond and adapt to insecurity (Lewis, 2008).
Furthermore, Somalia’s weak state apparatus has provided additional impetus for Somali civilians
to become adaptable and ready to respond to insecurity (Lewis, 1994). This adaptability creates
the possibility that Somalis may be more adept at responding to insecurity than civilians of othernationalities. Their established methods of responding to insecurity may thus be different from themethods civilians use in other contexts. However, cascades are consistently observed throughout
cases of migration and displacement. Variation between cases should at most cause variation in
the magnitude of different types of displacement cascades, not whether displacement cascadesform.
By focusing on the analysis of this single case, this paper benefits from the strengths ofwithin-case analysis to build a nuanced explanation of how herd cascades, community cascades,
and co-ethnic cascades influence the selection of internal displacement destinations in Somalia(Snyder, 2001). Future research could then make valuable contributions by extending and testing
this explanation in other contexts.

V.

Analyzing Somali Internal Displacement as a Network

To observe displacement cascades, it is important to observe civilian movements. In theaggregate, civilian movements provide the opportunity to assess the types of cascades that civilians
form. These movements reveal connections between locations. This means that, by treating
locations as nodes and movements as connections or ties between nodes, the system of population
movements can be analyzed as a network. Social network analysis then provides a wealth of tools
with which to analyze displacement patterns.

Somalia’s clan structure has driven clan politics to function comparably to ethnic politics in therest of Africa (Zolberg, Suhrke, & Aguayo, 1989).
37

Such an analysis begins with a descriptive assessment of displacement patterns. Social
network analysis provides powerful tools to track the direction of movement, locations civilians
most commonly leave, locations civilians most commonly seek, and the characteristics of origin
and destination locations. These components of the displacement network provide important
insights into which factors civilians consider during their displacement, and thereby how cascades
can guide civilians through their decision-making about displacement.
Next, Exponential Random Graph Models (ERGMs) provide the opportunity to directly
measure each cascade type through network covariates and measures of different kinds of network
structures. By varying the thresholds for defining a connection between locations, ERGMs canthen identify whether each cascade type exists at all displacement levels.
The general equation for ERGMs can be expressed as:,
where Y is the random variable for the network state (with realization y), g(y) is a vector of model
statistics for network y, θ is the vector of coefficients for those statistics, and k(θ) represents thequantity in the numerator summed over all possible networks (Wasserman and Faust, 1994).
ERGMs simultaneously allow both inference on covariates and for arbitrarily complex
network structures to be modeled (Cranmer and Desmarais, 2011). Moreover, ERGMs account for
dependencies between locations (nodes) and connections (ties). There are several ways to interpret
ERGMs. One way involves interpretation at the network level. At the network level, the network
statistics directly condition the probability of the entire network—allowing us to estimate the effectof within-network configurations on the predicted probability of observing a particular instance ofthe network (Cranmer and Desmarais, 2011). Simulating a large number of networks based on the
ERGM parameters yields the opportunity to approximate the characteristics of the distribution of
network statistics (Morris et al., 2008; Handcock and Gile, 2010). If the coefficient on a network
statistic in ERGM is positive, then the observed network is likely to possess the given network
characteristic.
Another way involves interpretation at the dyad level. At the dyad level, the quantity ofprimary interest is the likelihood of tie formation between any two nodes. In the displacement
context, this means that ERGMs are interested in the likelihood of civilians moving between any
two locations (Cranmer and Desmarais, 2011). If the coefficient on a dyad level statistic is positive,then that characteristic increases the likelihood of tie formation.
Finally, at the monadic level, node characteristics affect the likelihood of tie formation withspecific nodes. For undirected networks, node characteristics can affect the likelihood of the node
forming ties with other nodes. For directed networks, in-degree node characteristics affect thelikelihood of nodes receiving ties and out-degree node characteristics affect the likelihood of nodes
sending ties. This paper is interested in monadic, dyadic, and network interpretations in directed
networks.
8

Figure 1. Transitivity

For the unweighted networks, connections between locations, or network ties, will bedefined by whether at least one person has moved from one location to the other during the 20072013 time period in Somalia. This movement produces a directed tie between those locations.
There are three weighted displacement networks, specifically 25th Percentile, 50th Percentile, and75th Percentile displacement networks. Each displacement network requires a progressively higher
threshold to constitute the formation of a tie. This threshold increases to at least 324 people for the
25th Percentile displacement network, at least 835 people for the 50th Percentile displacement
network, and at least 2622 people for the 75th Percentile displacement network. Varying thresholdlevels provides the opportunity to assess whether expected relationships hold across all
displacement flow magnitudes. It also accounts for the reality that displacement flows of 10 people
between two locations are very different from flows of 1,000 people.
There are two within-network configurations of particular interest for this analysis:
preferential attachment and transitivity. Preferential attachment means that certain nodes are more
likely to receive ties (Wasserman and Faust, 1994). This configuration in a network would indicate
convergence towards specific locations, making preferential attachment a valid proxy for herdcascades. Transitivity means that civilians fleeing location A are divided between locations B andC with movements A B and AC. Then, civilians in location B reconstitute their community
by joining their fellow community members in location C with a movement BC, as is displayedin Figure 1 (Wasserman and Faust, 1994; Papachristos et al., 2013). If transitivity is driving
convergence in displacement destinations, then there is evidence that civilians select displacement
destinations based on which destination best facilitates the reconstituting of their communities.
Rather than following the actions of any member of their society, civilians follow the actions of
their own community members. This configuration is thereby a valid proxy for community
cascades.
Ethnic homophily can then serve as a measure for co-ethnic cascades. Homophily refers to
the expectation that actors with similar characteristics are more likely to form connections witheach other (McPherson et al., 2001). In particular, co-ethnic locations are likely to be desirable
9displacement destinations. This is because as displacement divides origin communities, civilians
are likely to look to co-ethnics to help create a new, trustworthy community (Steinberg, 2015;
Zilberg, 2011; Whitehouse, 2012; Horst, 2006). Ultimately, homophily exists because people trustand interact with those who are similar to them more than those who are dissimilar to them. Coethnics provide durable ties that are valuable during times of instability, including displacement(Habyarimana et al., 2007). The incentive to draw upon the durable ties of co-ethnics increases as
displacement flow magnitudes increase. Large displacement flows indicate that there is extremechaos, thereby increasing the desire to find co-ethnics.

VI.

Data on Network Ties

The UNHCR Population Movement Tracking (PMT) initiative provides the displacement
data for this analysis. This data tracks internal displacement in Somalia from 2007-2013 on a daily
basis through coordination with 48 NGOs in Somalia and the UNHCR office in Nairobi. Internal
displacement origins, destinations, dates, and primary reason for displacement are all included inthis data source. The data tracks displacement movements, so it is not useful for counting the total
number of IDPs. Instead, it is a valuable tool for understanding when people move, where they
move to and from, and why they move.
For each displacement movement, the data records one reason for the movement. Rather
than dismissing the possibility that multiple factors may motivate displacement, the data simply
focuses on the primary reason for each displacement movement. These reasons include insecurity,
clan conflict, drought, flood, famine, fire, lack of livelihood, eviction, relocation, IDP return, andforced return (UNHCR, 2007). Insecurity accounts for the overwhelming majority of Somaliinternal displacement.
However, this data source is not perfect. International NGOs have a very limited presencein Somalia, which minimizes the oversight for local NGOs. In addition, it is very difficult to track
all displacement movements. Large movements may overwhelm those attempting to monitor them(Crisp, 1999). Within-city displacement is often missed by monitors (Karen Jacobsen and IDMC,
2008). Civilians moving to stay with family members or friends may not always be counted as
displacement. Displacement in rural areas is also difficult to monitor due to the lack of people andinfrastructure (Brookings, 2013). Moreover, there are incentives to inflate estimates of internal
displacement flows (Watch, 2013).
Despite these concerns, UNHCR’s PMT initiative is the first and only publicly available
data source that captures internal displacement with such detail. UNHCR has invested substantial
resources in collaborating with so many local NGOs, and they are surely able to capture theimportant displacement trends in Somalia (Lindley 2010b; Schon 2015).

VII.

Data on Network Node Attributes

Network nodes were created with four attributes. These attributes include clan, urban, road,and control changes. The clan attribute refers to the largest clan in each location. There isdisagreement on the number and structure of clan groups in Somalia, but this paper uses Hawiye,
Darod, Dir, Rahanweyn, and Bantu (Lewis, 1994). Urban refers to whether the location is one of10Somalia’s district capitals.4 Road refers to whether the location is within 10 kilometers of a roadused by the World Food Program (WFP) on its aid distribution routes. WFP roads are a subset of
all roads in Somalia, but it is reasonable to expect that they are the largest and most salient roadsin Somalia. Major roads provide opportunities for access to transportation, information, andnecessary resources (Joireman et al., 2012; Klaeger, 2013). The control changes attribute measures
whether there have been any battles where territorial control changed hands within 10 kilometersof a given location. This attribute approximates the level of competition for each location. All ofthese attributes are displayed on a map in the appendix.
The urban attribute is a dichotomous variable. It is coded as 1 when the location is a district
capital and 0 otherwise. Some district capitals in Somalia contain small populations, so they are
not all equally important. Still, district capitals are most likely to be the locations where civilians
can find humanitarian aid, jobs, and other resources when they are in need. Out of 296 locations,
65 of them are urban.
Road is also a dichotomous variable. It is coded as 1 when the location is within 10
kilometers of a road used by the WFP and 0 otherwise. Locations close to roads are likely to beeasier to leave and easier to reach. They are also more likely to have access to humanitarianassistance. There are 170 locations near WFP roads. From these 170 locations, 120 of them are notnear urban areas. On the other hand, 15 urban areas are not near WFP roads.
Clan is a factor variable. It codes each node based on the largest clan in each location.
While the proper manner in which to disaggregate the Somali population into clans is an open
debate, this paper uses Hawiye, Darod, Dir, Rahanweyn, and Bantu. In order to code each location,a map image of the distribution of clans in Somalia was georeferenced using 8 reference points.
Locations within a clan zone are coded as being populated by that clan. This variable does not
capture the distribution of clans within each location, so it is unable to distinguish whether there
is a neighborhood within a location that is heavily populated by a minority clan. However, it isable to provide information about the main clan in each location. Out of the 296 locations, 3 are
Bantu, 65 are Darod, 22 are Dir, 159 are Hawiye, and 47 are Rahanweyn.
This coding neglects the distribution of clans in each location, as well as whether there areprominent neighborhoods of minority clans in a given location. Research on Somali refugees often
notes how they cluster with their clans and sub-clans within cities, such as Nairobi andJohannesburg (Thompson, 2015; Horst, 2006; Ndzovu, 2014). Future research could make avaluable contribution by providing more fine-grained data on the distributions of clans within
Somalia.
The control changes attribute is another dichotomous variable. It is coded as 1 when there
was at least one battle involving a change in territorial control from 2007-2013 within 10
kilometers of the location and 0 otherwise (Clionadh Raleigh et al., 2010). This variable is more
4

This study does not use population data due to the unreliability of population data in Somalia.
Population estimates that exist can vary dramatically between sources. See the variation between
Lindley (2010) and 2006 UNDP data for an example.
11

Figure 2. Somalia Displacement Flow Network**Label size is based on weighted in-degree centrality. Edges are weighted based on IDP flows.
appropriate than a count of violent events because it can show whether one armed group was firmly
in control of a location. Unstable territorial control could increase the likelihood of displacementfrom locations and decrease the desirability of a location as a displacement destination. There are
64 locations with at least one control change.
Variables involving these node attributes, as well as configurations of network ties, reveal
a great deal of information about displacement patterns. While ERGMs are needed to properly
assess the role of cascades, descriptive information about the network can provide important
context and information about the kinds of factors that may be motivating displacement cascades.

VIII.

Descriptive Information about Internal Displacement in
Somalia

In order to illustrate the displacement flow network in Somalia from 2007-2013, there are
six plots of the internal displacement network. All of these plots display the magnitude of thedisplacement flow between locations by varying line thickness. Thicker lines indicate larger
displacement flow magnitudes. All nodes are represented by dots, and the location name labels
each node. The label size represents the weighted in-degree in Figure 2 and weighted out-degree
in Figure 3. Additional figures that are displayed in color in the online appendix use label size torepresent the weighted degree of each location. These figures represent node and edge colors basedon attribute characteristics.
These plots indicate that there are some locations with particularly notable displacement
activity. Mogadishu is clearly the key city. As Somalia’s capital and largest city, this is12

Figure 3. Somalia Displacement Flow Network** Label size is based on weighted out-degree centrality. Edges are weighted based on IDP flows.
understandable. Mogadishu is a hotly contested city by Somalia’s armed groups, territorial control
changes frequently, and humanitarian aid organizations cluster in Mogadishu and its airport. Withlarge displacement magnitudes to and from Mogadishu, it would be worthwhile to unpack thenumerous factors at play within the city. While this in-depth analysis is beyond the scope of thispaper, Figure 4 shows only the displacement flows to and from Mogadishu for descriptive benefit.
Figure 4. Mogadishu Section of Somalia Displacement Flow Network**Label size is based on weighted degree centrality. Edges are weighted based on IDP flows.

13

Table 1. Weighted Degree Centrality Measures

#1
#2
#3
#4
#5

Weighted Degree
Mogadishu (1115853)
Afgooye (311639)
Guri-Ceel (199090)
Belet Weyne (118231)
Gaalkacyo (89804)

Weighted In-Degree
Afgooye (255709)
Gaalkacyo (66879)
Xaawo-Cabdi (61631)
Dhobley (59326)
Mogadishu (52650)

Weighted OutDegree
Mogadishu (1063203)
Guri-Ceel (157391)
Belet Weyne (95047)
Afgooye (55930)
Laas Caanood (53759)

To highlight important locations beyond Mogadishu, I turn to weighted in-degree and outdegree centrality measures in Table 1. These measures indicate which locations receive and send
the most displaced civilians respectively. While Mogadishu is by far the most common
displacement origin and destination location, Afgooye and Dhuusamarreeb are also common
origin and destination locations. Guri-Ceel and Belet Weyne are common origin locations, whereas
Gaalkacyo and Dhobley are common destination locations. Afgooye is the second most importantcity in this network. Much of the displacement to and from Afgooye involves Mogadishu,
highlighting that the Mogadishu-Afgooye dyad is also the most important dyad for displacement.
Afgooye is a common first destination for civilians fleeing Mogadishu. In addition, civilians often
travel through Afgooye on the way to Mogadishu. These centrality measures indicate that
displaced Somali civilians do converge on specific locations.
Displacement behavior also varies by clan as Table 2 displays. The Hawiye and Darod are
the two largest clans, so, understandably, their locations have the largest in-flows and out-flows.
On average, Hawiye locations send and receive the largest amounts of displaced people. Darod
locations also send and receive large numbers of displaced persons, albeit less than Hawiye
locations. Rahanweyn and Dir locations receive far more displaced people than they send, on
average. Bantu locations, due to some extent by the relatively small Bantu population in Somalia,
do not send nor receive large numbers of displaced people.
Table 2. Displacement by Clan
Average Displacement InFlow
609
Bantu
5486
Darod
7407
Dir
8993
Hawiye7289
Rahanweyn.
14

Average Displacement OutFlow
0
4801
3723
11647
1030

Table 3. Displacement Out-Flow by Attribute Traits

Urban
Road
ControlChangesALL
ATTRIBUTES

Control
Urban Road
Changes
11,880156,86642
156,866 1,821,790
7,672
42

7,672

ALL
ATTRIBUTES

4,886
90,105

Table 4. Displacement In-Flow by Attribute Traits

Urban
Road
ControlChangesALL
ATTRIBUTES

Control
Urban Road
Changes
28,459311,491
8,063
311,491 1,066,419
44,814
8,063

44,814ALL
ATTRIBUTES

59,389380,013

There are also important dynamics to note with the urban, road, and control changes
attributes as shown in Table 3. Tables of total displacement flows to and from locations highlightimportant trends. Locations along roads send the most displaced people, followed by urban
locations, and then locations with control changes. This shows that transportation options facilitate
displacement. Urban locations with control changes and no major roads send almost no displaced
people. This reveals just how vital roads are for displacement. It also shows that locations withchanges in territorial control deter displacement.
There are additional observations to make regarding the characteristics of locations
receiving displaced people as shown in Table 4. Again, locations with roads receive the mostdisplaced people, followed by urban locations and then locations with control changes. Also, urban
locations with control changes and without roads receive the fewest displaced people. It also adds
to the argument that roads facilitate displacement. Also, urban areas receive far more displaced
people than they send.
It is also clear that displacement tends to occur in specific directions. While there are 507
edges in the unweighted network, only 37 dyads had reciprocal ties. When the threshold for a tie
is set at the 25th percentile, 28 dyads have reciprocal ties out of 363 edges. At the 50th percentile,
17 dyads have reciprocal ties out of 239 edges. At the 75th percentile, 6 dyads have reciprocal tiesout of 123 edges. So, when civilians move between two locations during displacement, they tend
to move in one direction. Reciprocity is very uncommon.

15

Table 5. Network Node Summary Statistics

Bantu
Dir
Rahanweyn
Darod
HawiyeUrban
ControlChanges
Road
Total

IX.

25
Unweighted Percentile
3
3
22
14
47
27
65
50
159
127
65
58
64
170
296

54
131
221

50 Percentile
1
10
15
37
95
53

75 Percentile
0
7
9
19
64
43

46
99
158

32
67
99

Consideration of Sparse Networks

The unweighted network is a very sparse network, with a density of 0.006. This means that
only 0.6% of possible ties between nodes are realized. As thresholds are adjusted to the 25th, 50th,and 75th percentiles, many nodes become isolates, and the density decreases to even smaller levels.
This creates problems with convergence for the ERGM models. ERGM models are already known
to be unstable models that can have challenges with model degeneracy, so this is a practicalconcern that must be overcome (Wasserman and Faust, 1994). To overcome this obstacle, Idisregard isolates from all weighted network models.
Nodes that are disregarded at each threshold no longer have a sufficient displacement flow
either to or from the location to have any ties to other nodes. Based on Table 5, the locations with
control changes and Rahanweyn clan members are disregarded at the fastest rate due to the small
displacement flows to or from them.
The small displacement flows to and from locations with Rahanweyn clan members offers
an interesting observation about conflict dynamics in Somalia. While many clans and sub-clansfight each other in Somalia, some tensions are more serious than others. Darod and Hawiye clantensions are arguably the most important clan tensions in south and central Somalia (Kapteijns,2013). Also, as the map in the Appendix illustrates, Rahanweyn clans are not the largest group in
many major Somali cities like Mogadishu or Kismayo. Thus, while there are many Rahanweyn
locations with displacement, the magnitude of Rahanweyn displacement appears smaller than themagnitude of displacement to and from locations inhabited by other clans.

X. ERGM Results
With this understanding of the descriptive characteristics of the Somali displacement
network, we can proceed to the ERGMs. The ERGMs yield the opportunity for rigorous analysis
16

Table 6. Unweighted Directed Network

Edges

Estimate-5.678***SE
0.1112Estimate-6.333***SE
0.4820Estimate-5.603***SE
0.1195Main Effects
In-Degree Urban
Out-Degree Urban
In-Degree Control Changes
Out-Degree Control Changes
In-Degree Road
Out-Degree Road

-0.322*-0.440-0.059-0.4520.350*
0.759

0.12350.12600.11520.12440.09910.1029-0.033-0.1910.161*-0.242*-0.0400.349***

0.06390.10380.0802
0.0951
0.05810.0573-0.291*-0.444***-0.062-0.452***0.320*
0.758***

0.12270.12800.11110.1241
0.09830.1025

HomophilyClan0.100*

0.09330.016

0.05970.101

0.09282.616***

0.4458-0.2690.19005912

Network Structure
Transitivity
Preferential Attachment
AIC
Number of Nodes
Displacement Threshold

1209805519
296
1

of varying cascade types. In this analysis of cascade types, observed trends exist while controlling
for the monadic effects of transportation costs, violence, and urban areas.
Goodness of fit tests show that dyad dependence models are the most appropriate at all
threshold levels. At the unweighted, 25th, and 50th percentiles, the best model specifications at eachthreshold level include transitivity and not preferential attachment. At the 75th percentile, the bestmodel specification includes both transitivity and preferential attachment.5 Model results in thetables below show the Akaike Information Criteria (AIC) statistic for each model. They also show
the number of network nodes and the displacement flow threshold required for a tie to exist.
Additional goodness of fit results are included in the appendix.
a. How Herd Cascades Influence Internal Displacement Destinations
Model results reveal important nuances in how herd cascades, measured as preferential
attachment, influence internal displacement destinations. Table 6 and Table 7 show that herdcascades are not statistically significant for the unweighted network or for the 25th percentile

5

Models including both the transitivity and preferential attachment terms do not converge withthe unweighted and 50th percentile networks. This indicates that these model specifications do
not fit the data well.
17

Table 7. 25th Percentile Directed Network
Estimate
Edges

Main Effects
In-Degree
Urban
Out-Degree
Urban
In-Degree
ControlChanges
Out-Degree
ControlChanges
In-DegreeRoad
Out-Degree
Road

SE

Estimate

SE

Estimate

SE

Estimate

SE-5.257*** 0.1240-6.108***0.2097-5.487***0.1285-5.223***0.13750.089 0.1340

0.201***

0.04070.199

0.13890.08650.1328-0.268 0.1468-0.313***0.0391-0.0560.1329-0.2674

0.1496-0.093 0.1377-0.0480.03910.127

0.1080-0.09130.1323-0.527*** 0.1536-0.527***0.0481-0.2430.1564-0.528***0.15330.157 0.1184

0.037

0.0288-0.0560.12110.14990.11540.427*** 0.1177

0.335***

0.03640.120

0.08440.429***

0.11850.275* 0.1102

0.278***

0.03050.149

0.12080.272*

0.10842.952***

0.1792

1.819***

0.0646-0.1192

0.2166

Homophily
Clan
Network
Structure
Transitivity
Preferential
Attachment
AIC
Number of
Nodes
Displacement
Threshold0.845 1857.1505
63357

3833

3761

4048

221
324

network. Instead, Table 8 and Table 9 show that herd cascades are statistically significant at the50th and 75th percentile. In short, herd cascades matter only at high displacement levels.
a. How Co-ethnic Cascades Influence Internal Displacement Destinations
Co-ethnic cascades follow a similar pattern as herd cascades, except they begin to matter atlower displacement levels. Table 6 shows that co-ethnic cascades did not occur for the18

Table 8. 50th Percentile Directed Network

Edges

Estimate-5.782***SE
0.1796

Estimate-5.290***SE
0.1335

Estimate-6.029***SE
0.2074Main Effects
In-Degree Urban
Out-Degree Urban
In-Degree Control Changes
Out-Degree Control Changes
In-Degree Road
Out-Degree Road

0.232
0.948***
0.115
1.138***
0.088-0.0510.16120.14500.16420.14960.15180.1683-0.1570.340***-0.241**0.430***
0.021
0.003

0.08850.08660.08630.06630.08130.05910.290
0.951***
0.151
1.139***
0.111-0.0540.18300.14640.18240.15100.17350.1690

HomophilyClan0.473***

0.13670.167**

0.0631

0.489***

0.1362

1.901***

0.1152

Network Structure
Transitivity
Preferential Attachment
AIC
Number of Nodes
Displacement Threshold

34255

2296
158
835

0.733**
0.2764
2387

unweighted network. Table 7 shows that when the percentile is set at the 25th percentile, three outof the four models yield a significant coefficient for co-ethnic homophily. This provides strong
evidence that co-ethnic cascades begin at the 25th percentile. Then, Table 8 and Table 9 show thatco-ethnic homophily is significant across all models for thresholds at the 50th percentile and at the75th percentile. Therefore, while co-ethnic cascades do not matter at low displacement levels, they
become important at lower levels than herd cascades. This supports the view that civilians seekout people with similar characteristics when possible, rather than simply ‘following the crowd’ as
herd cascades would suggest.
a. How Community Cascades Influence Internal Displacement Destinations
Meanwhile, community cascades influence internal displacement destinations at all
thresholds of displacement magnitudes. Tables 6-9 show that transitivity is significant across allmodels. Regardless of whether displacement levels are high or low, civilians follow their origin
community members.
This discrepancy between the results for community cascades versus the results for herdcascades and co-ethnic cascades support the assertion that displacement cascades function
differently depending upon whether they are following personal connections. Herd cascades
involve people ‘following the crowd’. Co-ethnic cascades involve people following others withsimilar characteristics. Community cascades involve people following specific people from their
19

Table 9. 75th Percentile Directed Network

Edges
Main Effects
In-Degree
Urban
Out-Degree
Urban
In-Degree
ControlChanges
Out-Degree
ControlChanges
In-Degree Road
Out-Degree
Road
Homophily
Clan
Network
Structure
Transitivity
Preferential
Attachment
AIC
Number of
Nodes
Displacement
Threshold

EstimateSE-5.738*** 0.3220

Estimate
SE-6.857*** 0.4232

EstimateSE-5.726*** 0.3049

Estimate-6.517***SE
0.41090.2234-0.011 0.3143

0.051 0.2094-0.1340.3086-0.882** 0.2742-0.715** 0.2639-0.722** 0.2639-0.876**

0.27730.018 0.2228

0.240 0.2955

0.127 0.2008

0.067

0.3094-1.012*** 0.2675
0.240 0.2151-0.824** 0.2537
0.189 0.3122-0.860*** 0.2571
0.043 0.2053-1.005***0.413

0.27020.30801.723*** 0.2820

1.457*** 0.2764

1.481*** 0.2875

1.719***

0.28390.503** 0.1921

0.434* 0.1810

0.399* 0.1661

0.552**

0.20222.099*** 0.1924

1.507*** 0.1496-0.05313392

2.393*** 0.4084
1114

1152

1.774*** 0.3853
1160

99
2622

personal networks. This distinction reveals a critical hierarchy governing the importance of eachcascade type, in which civilians prioritize their social networks, then groups with similar
characteristics, and then general crowds. Thus, rather than classifying cascades as a single categoryof behavior, it really is important to disaggregate displacement cascades into multiple typologies.
Jonny Steinberg describes this hierarchy in practice in his biographical account A Man ofGood Hope. This account tells the story of a Somali man’s displacement from Mogadishu as achild in 1991, life in Kenya and Ethiopia, moving down to South Africa, and then life in South
Africa. Every time Somalis reach a new and unfamiliar place, their first response is to inquire
about the whereabouts of other Somalis. This process begins with a search for close family
members. If they cannot be found, then people move to more and more distant sub-clan or clan
ties.
20

Within sub-clans and clans, Somalis are noted for their generosity, but this generosity isunreliable across clan lines (Menkhaus, 2003; Lewis, 1994). This dynamic makes the distinction
between herd cascades, co-ethnic cascades, and community cascades particularly critical in theSomali context. However, Somalia is far from the only country with ethnic divisions. Especiallyduring the polarizing conditions of armed conflict, distinctions in the patterns of different cascade
types are likely to appear in many contexts.

XI. Applications for Future Research
These findings, drawn from the context of Somalia, would be valuable to test in othercontexts. Like Somalia, African countries such as Libya, Central African Republic, South Sudan,
and the Democratic Republic of the Congo all face the challenges of conflict and large-scale
displacement. Their civilians must also deal with challenging ethnic, tribal, or clan politics, andweak states. It is therefore likely that patterns in the formation of herd cascades, co-ethnic cascades,
and community cascades would be similar between these cases. If subsequent analyses contradict
this expectation, they would have the potential to contribute valuable nuances from the perspectiveof their own unique contexts.
These cascade models can help explain a variety of other mobilization activities as well.
Protest mobilization may involve a combination of herd cascades, community cascades, and coethnic cascades. Rebel groups and terrorist organizations also may employ all three of thesecascade models. Studying cascade types during other mobilization activities can reveal whether
they demonstrate the same kinds of patterns. Cascade models have been a powerful tool for social
scientists attempting to explain political mobilization. They will become even more powerful as
we disaggregate and develop additional nuance.

21

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Appendix
Below is the map created in ArcGIS showing the variables used to create the node attributes.

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Below are the network plots with colors based on node attributes that are mentioned in the maintext.
Somalia Displacement Flow Network by Clan

Note: Label size is based on weighted degree centrality. Edges are weighted
based on IDP flows. Red nodes are Hawiye. Green nodes are Darod. Purple
nodes are Dir. Blue nodes are Rahanweyn. Yellow nodes are Bantu.

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Somalia Displacement Flow Network by Control Changes

Note: Label size is based on weighted degree centrality. Edges are weighted
based on IDP flows. Red nodes have control changes. Blue nodes do not havecontrol changes.

29Somalia Displacement Flow Network by Road

Note: Label size is based on weighted degree centrality. Edges are weighted
based on IDP flows. Blue nodes have roads. Orange nodes do not have roads.

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Somalia Displacement Flow Network by Urban

Note: Label size is based on weighted degree centrality. Edges are weighted
based on IDP flows. Red nodes are urban. Green nodes are rural.

Below are the goodness of fit plots for each ERGM model. These plots informed the selection of
the best fitting models. In the main text, the best fitting model at each threshold level turns out tobe the model with the lowest AIC statistic.

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Note: These diagnostics produced some error messages, which is why there are thick solid blacklines on two of the plots.

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