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https://doi.org/10.1177/00031224231212679
American Sociological Review
1 –35
© The Author(s) 2023
DOI: 10.1177/00031224231212679
journals.sagepub.com/home/asr
The active drivers of migration have been
extensively studied, yet less attention has been
paid to the factors that hinder migration—a
research gap that has been called the “mobil-
ity bias” within the migration literature
(Schewel 2020). The relatively overlooked
phenomenon of immobility is important in its
own right, having substantial consequences
for the social world. Migration influences the
functioning of the labor market (Hyatt et al.
2018), the landscape of stratification and
social mobility (Jasso 2011), and the socio-
cultural meanings in everyday lives (Bauman
2000; Mata-Codesal 2015); mechanisms that
impede migration can thus have outcomes
that extend far beyond the migration system
itself.
Understanding immobility is an especially
apt challenge in the context of the modern
United States. Long thought of as a “rootless
society” (Fischer 2002) with high geographic
mobility (Long 1991; Steinbeck 1939), the
United States has arguably turned into a
1212679ASRXXX10.1177/00031224231212679American Sociological ReviewHuang and Butts
research-article2023
aUniversity of California-Irvine
Corresponding Author:
Carter T. Butts, University of California-Irvine,
SSPA 2145, Irvine, CA 92697-5100, USA
Email: buttsc@uci.edu
Rooted America: Immobility
and Segregation of the
Intercounty Migration
Network
Peng Huanga and Carter T. Buttsa
Abstract
Despite the popular narrative that the United States is a “land of mobility,” the country
may have become a “rooted America” after a decades-long decline in migration rates. This
article interrogates the lingering question about the social forces that limit migration, with an
empirical focus on internal migration in the United States. We propose a systemic, network
model of migration flows, combining demographic, economic, political, and geographic
factors and network dependence structures that reflect the internal dynamics of migration
systems. Using valued temporal exponential-family random graph models, we model the
network of intercounty migration flows from 2011 to 2015. Our analysis reveals a pattern
of segmented immobility, where fewer people migrate between counties with dissimilar
political contexts, levels of urbanization, and racial compositions. Probing our model using
“knockout experiments” suggests one would have observed approximately 4.6 million (27
percent) more intercounty migrants each year were the segmented immobility mechanisms
inoperative. This article offers a systemic view of internal migration and reveals the social and
political cleavages that underlie geographic immobility in the United States.
Keywords
migration, social networks, political polarization, immobility, segregation
2 American Sociological Review 00(0)
“rooted America” after a decades-long decline
in migration rates (DeWaard et al. 2020; Frey
2009). The reality of low migration rates is
clear, but explanations for current population
immobility are less well-developed. Macro-
economic studies have found that demographic
and socioeconomic structures are not sufficient
to explain observed levels of immobility, and
neither are the business composition of labor
markets nor properties of housing markets
(Hyatt et al. 2018; Hyatt and Spletzer 2013;
Molloy, Smith, and Wozniak 2011, 2017). A
broader sociological view suggests the poten-
tial for cultural, political, and other social
forces as possible explanatory factors (Gimpel
and Hui 2015; Massey and Denton 1993;
Stockdale and Haartsen 2018; Tiebout 1956).
Moreover, the migration system has its own
intrinsic feedback mechanisms that could
endogenously sustain or undermine further
migration (Bakewell 2014; de Haas 2010),
which may also play a role in population
immobility. Probing the combined influence of
these myriad factors requires a systemic treat-
ment of U.S. internal migration, allowing us
to simultaneously examine the joint effects of
social, economic, political, and demographic
mechanisms on flows of migrants throughout
the country. This article pursues such an analy-
sis, with the objective of identifying the factors
associated with both mobility and immobility
in the contemporary United States.
Broadly, extant research on drivers of U.S.
migration and immobility shares two character-
istics. First, most research examines migration
from an economic perspective, assuming that
most, if not all, migration is labor migra-
tion, driven by economic incentives.1 Yet, deci-
sions regarding residential settlement are not
purely economic (Ryo 2013): political climate,
racial composition, and urbanization of local
communities are potential contributors to the
phenomenon (Brown and Enos 2021; Cramer
2016; Massey and Tannen 2018). This article
incorporates the sociocultural and political per-
spectives into an analysis of U.S. immobility.
A second dominant characteristic of the
extant literature on U.S. migration treats
migration as a feature of geographic areas,
examining correlates between net migration
rates into or out of states or counties and
their demographic or economic character-
istics. Although convenient, this practice of
reducing the interconnected migration system
into local features of areal units (geographic
segments such as counties or states) intro-
duces two limitations. First, by aggregating
across origins and destinations for migrants
emigrating from or immigrating into a given
area, it obscures the interactive effects from
the sending and receiving areas, such as their
political or cultural similarity and differences
in employment rates. Second, it does not
allow for treatment of the internal dynam-
ics of the migration system (de Haas 2010).
In particular, research in this area typically
does not address the presence of mecha-
nisms such as return or stepwise migration,
where people migrate further after reaching
their first destination; the flow of migrants
from one place to another can thus affect
the flow of migrants from that destination
to others. Because migration is a relational
process between places of origin and destina-
tion, and migration flows can influence each
other, this article takes a systemic, network
approach that shifts analysis from the migra-
tion rates of areal units to the migration flows
between areal units. By leveraging migration
systems theory and social network methods,
we show that dissimilarities between counties
are important contributors to the immobility
of U.S. society.
To advance our understanding of the social
forces behind geographic immobility in the
modern United States, we adopt a compre-
hensive theoretical framework incorporat-
ing geographic, demographic, economic,
political, and social influences on migration
and perform a systemic analysis of inter-
nal migration as an evolving valued network
of migration flows.2 Using valued tempo-
ral exponential-family random graph models
(valued TERGMs), we analyze the network
of intercounty migration flows in the United
States from 2011 to 2015. We identify a pat-
tern of segmented immobility, where, net of
other factors, less migration happens between
Huang and Butts 3
counties with dissimilar political contexts,
levels of urbanization, and racial composi-
tions. We probe this mechanism using an
in silico “knockout experiment,” which sug-
gests that in a counterfactual world without
segmented immobility (but holding all other
factors constant), we would expect to have
seen approximately 4.6 million (27 percent)
more intercounty migrants in the United
States each year. This implies that social and
political cleavages in the United States are
substantial contributors to immobility, and
potentially exacerbate growing trends toward
geographic segregation.
We also examine the relationship between
internal and international migration flows,
showing that, contrary to the balkanization
thesis (Frey 1995a, 1995b), international
migration into a county is positively associ-
ated with its overall domestic mobility and
does not promote net outflows of residents.
The model also identifies the internal dynam-
ics of migration systems (de Haas 2010),
including a suppression of what we dub “way-
point” flows (i.e., balanced in- and outflows
of an areal unit) alongside strong patterns of
reciprocity and perpetuation. Data availabil-
ity limits our focus to population immobility
in the 2010s, but the empirical evidence,
together with our proposed theoretical and
methodological frameworks, opens the door
to unpack the long-term phenomenon of pop-
ulation immobility. This article thus joins the
growing literature that grapples with mobility
bias in migration studies (Schewel 2020),
demonstrating how a comprehensive analytic
framework and a systemic, network approach
offer new insights about immobility, and more
broadly, the dynamics of population move-
ment among social and geographic spaces.
THEORY
Existing literature defines immobility as “con-
tinuity in an individual’s place of residence
over a period of time” (Schewel 2020:344).
Because immobility is not only an individu-
alistic phenomenon but also a population and
social one, we offer a macrosociological
definition of immobility: a lack of popula-
tion exchange between localities. Drivers
of immobility, in terms of this framework,
are defined as factors that reduce migration
rates relative to what would be expected in
their absence. The scarcity of migration in
an immobile society has substantial effects.
Because migration is a critical channel for
people to respond to fluctuations of the local
economy, population immobility implies a
rigid labor market with lower productiv-
ity, higher unemployment rates, and more
prolonged recession when experiencing eco-
nomic shocks (Hyatt et al. 2018). Migration
can also improve one’s life chances (Jasso
2011; Weber 1922) and help one cope with
adverse events (Spring et al. 2021). Popula-
tion immobility thus has important ramifica-
tions for social mobility, stratification, and
poverty (Briggs, Popkin, and Goering 2010;
Clark 2008; Jasso 2011).
Immobility is not merely the flip side
of mobility; it carries its own sociocultural
meanings. As the aspiration–ability model
argues, migration requires both the aspira-
tion to migrate and the ability to realize that
aspiration (Carling and Schewel 2018). This
means that immobility is not necessarily a
passive outcome of simply staying in place,
but can be a conscious choice to remain. In
line with this view, recent literature has begun
augmenting the widely discussed notion of
“cultures of migration” with the notion of
“cultures of staying” that facilitate and main-
tain immobility (Stockdale and Haartsen
2018). The level of population (im)mobility
can affect the broader social norms of a soci-
ety; a mobile society may have a prevailing
nomadic culture, whereas the dominant cul-
ture of an immobile society may be sedentary
(Bauman 2000; Mata-Codesal 2015).
Understanding immobility is especially
relevant in the U.S. case. From the earli-
est observations of Tocqueville (1834) and
Ravenstein (1885) to Steinbeck (1939),
America has long been considered a “restless”
or “rootless” society with high geographic
mobility. Yet, after a decades-long decline in
its migration rate, the contemporary United
4 American Sociological Review 00(0)
States has arguably become a “rooted” soci-
ety with considerable population immobility.
However, as Herting, Grusky, and Van Rom-
paey (1997:267) note, sociological research
on U.S. mobility has “narrowed and now
focused almost exclusively on mobility of
a purely economic or occupational variety,”
with much less focus on mobility across geo-
graphic space. Research in migration studies
has historically focused on the social forces
that lead to migration, but it has largely
neglected the counter forces that inhibit peo-
ple from moving, a tendency Schewel (2020)
describes as the “mobility bias.” A lack of
research on geographic mobility in Ameri-
can sociology, together with the scarcity of
theoretical and empirical work on immobility
in migration studies, has led to a gap in our
knowledge regarding the mechanisms behind
population immobility in contemporary U.S.
society.
Culture and Politics of Immobility
Although the immobility of the U.S. popula-
tion has received less sociological attention,
economists and geographers have conducted
empirical analyses on this matter (e.g.,
Cooke 2013; Jia et al. 2022; Kaplan and
Schulhofer-Wohl 2017; Treyz et al. 1993).
These studies have identified important con-
nections between the labor market and migra-
tion rates, but their findings largely rely on
the assumption that most, if not all, migra-
tion is labor migration, driven by economic
incentives. The economic perspective has
a fundamental role in explaining migration
and immobility; the relative gains in moving,
transaction costs, and loss of specialized local
investments are factors that shape migra-
tion. But other factors, such as regionally
specific cultural values, locally conventional
ways of understanding opportunity (Carling
2002; Carling and Schewel 2018), and prefer-
ences for particular local policies or political
regimes (Tiebout 1956) also shape migra-
tion. Indeed, recent research on the U.S.
economy shows that over the past several
decades, migration has not been effective in
responding to fluctuations and shocks in labor
markets (Dao, Furceri, and Loungani 2017;
Jia et al. 2022). Relatedly, macroeconomic
factors do not have a strong correlation with
migration rates in the United States (Hyatt
et al. 2018; Hyatt and Spletzer 2013; Molloy
et al. 2017). Economic forces are important
ingredients in a viable model of the migra-
tion system, but a comprehensive analysis of
immobility demands considerations of other
social institutions.3
Thinking on internal migration is generally
dominated by labor market considerations, but
sociologists have given considerable attention
to other factors when studying migration at
smaller scales (e.g., across neighborhoods).
For instance, research on residential segre-
gation has long identified how people with
different racial identities and political beliefs
become segregated from each other (Bishop
and Cushing 2009; Krysan and Crowder
2017; Massey and Denton 1993), including
the accumulated influence of even relatively
weak preferences for same-group interactions
(Sakoda 1971; Schelling 1969); the latter can
act as a powerful macro-level sorting force,
even in the presence of economic or other
factors (Butts 2007).
Much of this work focuses on racial segre-
gation, but more recent work has also probed
segregation along political or cultural axes.
For instance, Brown and Enos (2021) found
that a large proportion of U.S. adults live in
neighborhoods where most residents share
the same partisanship. Gimpel and Hui (2015)
used a survey experiment to show that people
more favorably evaluate properties in areas
with predominantly co-partisan neighbor-
hoods. Social cleavages might deter people
from settling in places with distinct identi-
ties and beliefs, so the social gaps between
rural and urban areas and among different
parts of the continent (e.g., the South versus
coastal regions) (Cramer 2016; Hochschild
2018) may also contribute to the inhibition
of geographic movement. At another scale, in
the context of international migration, migra-
tion studies have long stressed the roles of
culture and politics in shaping population
Huang and Butts 5
mobility (Castles, de Haas, and Miller 2013;
Cohen and Sirkeci 2011; Jennissen 2007;
Massey et al. 1999; Vögtle and Windzio
2022; Waldinger and Fitzgerald 2004). Fol-
lowing this thread, this article incorporates
political, racial, and rural-urban structures in
investigating U.S. immobility.
Systemic Theories of Migration
The second characteristic of the extant lit-
erature on U.S. immobility is that studies
usually view migration as a feature of geo-
graphic areas. This approach examines the
characteristics of an areal unit that influence
its net immigration and emigration rates,
such as percentages of current residents who
are immigrants or emigrants. This is a mar-
ginal approach that sums up (i.e., marginal-
izes) migration flows from/to each areal unit
across all destinations/origins to describe the
overall mobility of each place. The marginal
approach is empirically straightforward and
has unquestionably contributed to our under-
standing of the driving forces of migration, by
identifying the associations between demo-
graphic and economic features of an areal
unit and the scale of its population inflows
or outflows (e.g., Partridge et al. 2012; Treyz
et al. 1993). Yet, migration—by definition, pop-
ulation moving from one place to another—is
inherently relational, having properties that
cannot be reduced to the features of individ-
ual areal units. For instance, studies consider-
ing net in- or out-migration rates in isolation
must choose either the sending or receiving
area as the focus of analysis (thereby obscur-
ing the joint roles of origin and destination
areas), or must merge in- and out-migration to
obtain a net migration rate (which confounds
inflows and outflows). Beyond the fact that
every pairwise migration flow among send-
ing and receiving areas depends on the prop-
erties of the sender and the receiver, such
studies are unable to account for relational
factors, such as geographic proximity and
political difference between areal units. Nor
can this approach consider the interactions
among migration flows, such as reciprocal
population exchange (A → B & B → A)
arising from return migration. Probing such
mechanisms requires a different theorization
of the migration process, a systems theory of
migration.
Such systemic thinking has a long tradition
in migration studies under the umbrella of
migration systems theory (MST) (Bakewell
2014; Fawcett 1989; Kritz, Lim, and Zlot-
nik 1992; Mabogunje 1970; Massey et al.
1999). A comprehensive theory that concerns
the complex interactions among various ele-
ments related to migration, such as flows of
people, information, (formal and informal)
institutions, and strategies (Bakewell 2014),
MST identifies interconnectivity as a key
feature of migration. As de Haas (2010:1593)
summarizes, a migration system is “a set of
places linked by flows and counter-flows
of people, goods, services and information,
which tend to facilitate further exchange,
including migration, between the places.”
The theoretical focus on flows between ori-
gin and destination suggests a relational
analysis of migration, integrating push and
pull factors in one single analytic framework
(Lee 1966). Fawcett (1989) demonstrates this
with a theoretical framework of “linkages”
in MST, focusing on how various linkages
between origin and destination shape migra-
tion in between. Among the linkages Fawcett
(1989:677) discusses, we focus on relational
linkages, “derived from comparison of two
places.” Instead of studying how a state’s or
a county’s political climate influences its net
marginal migration rate (e.g., Charyyev and
Gunes 2019; Preuhs 1999), an analysis of
relational linkages examines how the differ-
ence in political climates between counties
influences the number of people migrating
from one to the other.
Another critical implication from the inter-
connectivity feature of migration systems is
the presence of internal dynamics of migra-
tion (Bakewell, Kubal, and Pereira 2016; de
Haas 2010; Mabogunje 1970). As Mabogunje
(1970:16) put it, the migration system is “a
circular, interdependent, progressively com-
plex, and self-modifying system in which the
6 American Sociological Review 00(0)
effect of changes in one part can be traced
through the whole of the system.” Similarly,
Fawcett (1989:673) argued that the migration
systems framework “brings into focus the
interconnectedness of the system, in which
one part is sensitive to changes in other
parts.” This means migration is not a pure
product of exogenous social forces. It forms
a system with endogenous processes, where
one migration flow can promote or suppress
another migration flow. For example, because
migrants transmit information and social con-
nections when they move, the migration flow
from Arizona to Texas brings job informa-
tion and personal contacts along, potentially
inspiring migration in the opposite direction.
Internal dynamics like this can lead to an
endogenous accumulation of migration net of
exogenous social and economic influences.
Migration Systems through
a Network Lens
The insight of interconnectivity from MST
resonates with that of social network analy-
sis. Indeed, past research has used social
network analysis to study migration sys-
tems (Charyyev and Gunes 2019; Desmarais
and Cranmer 2012; DeWaard et al. 2020;
DeWaard and Ha 2019; DeWaard, Kim, and
Raymer 2012; Hauer 2017; Leal 2021; Liu,
Andris, and Desmaris 2019; Nogle 1994;
Vögtle and Windzio 2022; Windzio 2018;
Windzio, Teney, and Lenkewitz 2019). This
school of MST, which Bakewell (2014) calls
the “abstract system,” interrogates macro-
level migration patterns by analyzing migra-
tion networks consisting of localities (in
network terms, nodes) and migration flows
between each directed pairs of localities (in
network terms, edges).4 Network analysis
effectively captures the two critical implica-
tions of MST, relational linkages and inter-
nal dynamics of migration systems, bringing
new perspectives compared to the marginal
approach of migration, which is commonly
used in studies of U.S. immobility. Rather
than viewing localities/places as units of anal-
ysis, the network approach takes migration
flows between places as analytic units. This
perspective preserves information regard-
ing emigration and immigration processes,
enabling analysis of how characteristics of
origin and destination areas interact to influ-
ence migration flows, a relational account of
linkages in migration systems.
The network approach also examines the
internal dynamics of migration systems, by
studying the dependence structure among
migration flows. The dependence structure
identifies how migration flows are associated
with each other, net of exogenous contexts
such as the economic and political environ-
ments. Taking the example of reciprocity,
the network approach measures whether, and
to what extent, an increase of one migra-
tion flow (e.g., Los Angeles to Baltimore)
is associated an increase in its opposite flow
(Baltimore to Los Angeles), net of other fac-
tors. The dependence structure can go beyond
a pair of places and describe how the whole
network system of migration flows are inter-
connected, such as how the migration inflows
of Denver are associated with its outflows,
which in turn serve as the inflows of other
places, such as Dallas or Atlanta. Figure 1
illustrates the network approach versus the
marginal approach.
The network approach introduces unique
perspectives overlooked by the marginal
approach, but its insights have not yet been
fully appreciated. One notable characteristic
of prior research on migration networks is
the focus on the “diversity” rather than the
“intensity” of migration flows (DeWaard and
Ha 2019; Leal 2021; Vögtle and Windzio
2022; Windzio 2018; Windzio et al. 2019).
In other words, extant research examines
the number of migration flows rather than
their magnitude. This is associated with the
practice of dichotomizing migration flows
into two statuses, either no migrants versus
at least one migrant, or few migrants versus
many migrants (although Windzio [2018] and
Windzio et al. [2019] divide them into five
statuses in some parts of their research).
This approach is compatible with the com-
mon practice in social network research of
Huang and Butts 7
approximating social relations with a binary
form, facilitating the use of existing network
theories and methods to describe the migra-
tion system.
Analyzing the “diversity” of migration
flows offers useful knowledge about the
migration system, but it ignores the rich infor-
mation about variation in migration magni-
tudes. The intensity of migration flows is a
critical question in understanding population
immobility. In particular, DeWaard and col-
leagues (2020) find that the decline of U.S.
migration is not due to a decline in the diver-
sity of migration flows (the number of county
pairs with population exchange), but a decline
in the intensity of migration flows (their aver-
age count of migrants). Studying the inten-
sity of migration flows requires describing
migration networks in a valued form, where
the edges are not binary but take quantita-
tive values. Because the quantitative feature
of migration intensity is critical in grappling
with the question of population immobil-
ity, this article bridges migration systems
theory and recent advances in statistical and
computational methods for valued network
analysis (Huang and Butts 2024; Krivitsky
2012). We formally theorize the relational
linkages and internal dynamics in the expres-
sions of valued networks, developing a road-
map to quantitatively describe and test the
interconnectivity of population flows.
Regarding migration systems, new the-
oretical insights are needed for studies of
immobility. MST is not an exception from
the mobility bias critique of migration the-
ories (Schewel 2020). As de Haas (2010)
argues, MST has historically focused on
migration-facilitating mechanisms that lead
to the perpetuation of migration flows, but
largely overlooked the migration-undermin-
ing mechanisms that lead to the decline of
migration flows. Building on this critique,
a line of theoretical and empirical research
studies why some instances of pioneer migra-
tion drive the formation of migration systems
while others do not, as well as the endog-
enous mechanisms that can undermine migra-
tion systems (Bakewell, de Haas, and Kubal
2012; Bakewell, Engbersen, et al. 2016;
C
D
A
B
C
D
A
B
C
D
A
B
A
B
C
D
A
B
C
D
+2
1
0
1
BA
CA
C B
B C
DC
Network
Approach
Marginal
Approach
Net count
Figure 1. Schematic Illustration of the Marginal Approach versus the Network Approach
Note: The marginal approach takes geographic areas as units of analysis and tends to condense the
in- and out-migration flows into a single number about net migration rate/count of a geographic area.
The network approach takes each migration flow between a directed pair of geographic areas as an
analytic unit. This approach incorporates origin and destination in understanding their joint influence
on migration flows; it also preserves the local structural properties of migration flows, allowing one to
examine systemic patterns.
8 American Sociological Review 00(0)
de Haas 2010). Bakewell, Engbersen, and
colleagues (2016) go beyond the MST frame-
work, as they incorporate scenarios where
migration systems fail to form or perpetuate.
Unquestionably, this is a promising direc-
tion to further the theorization of migra-
tion dynamics. Yet, for our focus on internal
migration in the contemporary United States,
the migration system has existed for gen-
erations and is unlikely to vanish in the near
future. Therefore, the migration system is
still a useful research subject and perspective,
where we explore the social mechanisms that
immobilize populations from migrating.
The network approach inspires us to con-
sider population immobility from a relational
perspective. We conceptualize the pattern of
segmented immobility, that is, in a society
where people cluster in geographic segments
based on their cultural and political traits,
immobility can occur due to people’s ten-
dency to avoid migrating toward places with
divergent environments. By jointly incorpo-
rating origin and destination in an analytic
framework, the relational perspective allows
us to examine the influence of dissimilar-
ity between counties on the magnitude of
migrant populations moving between them,
connecting population immobility with seg-
regation and polarization.
Along with examining migration pat-
terns via a hypothesis testing lens, we utilize
“knockout experiments” to directly quantify
the contribution of segregation and polariza-
tion to immobility. Originating in biomedical
research, a knockout experiment probes the
functional role of a system component by
removing or inactivating it, comparing nor-
mal system behavior with behavior when the
component is “knocked out” (Hall, Limaye,
and Kulkarni 2009; Vogel 2007). In social sci-
ences, knockout experiments are performed
in silico, where researchers simulate the
potential social outcomes when certain social
forces are removed. The knockout experiment
can be considered a model-based thought
experiment (Gedankenexperiment, Einstein,
Podolsky, and Rosen 1935), in which we
predict the social outcomes of interest under
a counterfactual scenario where certain social
effects are inoperative. In our case, we com-
pare the total number of migrants observed
in the real world to that simulated when seg-
mented immobility mechanisms are knocked
out. This theoretical exercise allows us to
leverage the power of modern, generative
network models to gain insights into the func-
tioning of migration systems.
HYPOTHESES
Relational Linkages: Political
Segregation and Segmented
Immobility
Decisions about migration, a behavior aimed
at improving life chances (Jasso 2011), typi-
cally come out of a comparison between
place of departure and place of destination.
One critical dimension in migration decisions
is the political environment of the origin
and putative destination communities. Rising
political polarization has divided Americans
along party lines (Levendusky 2009); social
cleavages by political ideology extend to a
growing array of public opinions (Baldassarri
and Gelman 2008; DellaPosta 2020) and life-
styles (DellaPosta, Shi, and Macy 2015) and
have led to segregated social networks and
tensions in relationships, including family
interactions (Chen and Rohla 2018; DiPrete
et al. 2011). This political alignment also hap-
pens across space, with distinct political con-
sciousness across geographic regions, rural
and urban lands, and local neighborhoods
(Bishop and Cushing 2009; Cramer 2016;
Hochschild 2018).
Recent spatial analysis on partisan isola-
tion reveals that a large fraction of U.S. adults
live in places where almost no one in their
neighborhood votes in a manner opposed
to their own (Brown and Enos 2021). This
pattern is prevalent nationwide and is dis-
tinct from other types of segregation, such
as across racial lines. This state of affairs is
overtly recognized within U.S. political dis-
course, where media outlets routinely make
distinctions between “red” (conservative) and
Huang and Butts 9
“blue” (liberal) regions and ascribe (correctly
or not) a large body of cultural and politi-
cal traits to the regions and their inhabitants
(Badger, Bui, and Katz 2018; Wallace and
Karra 2020).
To the extent that individuals are likely
to both affiliate with the political culture of
their area and regard their opposites on the
political spectrum with suspicion and even
hostility (Iyengar et al. 2019; Iyengar, Sood,
and Lelkes 2012), people may be unwilling to
migrate between regions with differing politi-
cal cultures. Even setting aside motivations
arising from political culture, according to
the public choice theory and the consumer-
voter model, people should be more willing to
migrate to regions whose governments most
closely match their own policy preferences
(Dye 1990; Tiebout 1956), with individuals
from solidly “red” areas preferring to move
to other “red” areas, and likewise for people
from “blue” areas. Empirical analyses using
various data and methods generally confirm
the existence of migration preference toward
co-partisanship (Gimpel and Hui 2015; Liu
et al. 2019; Tam Cho, Gimpel, and Hui 2013),
although with some counter evidence (Mum-
molo and Nall 2016). Together, this work
motivates the following hypothesis:
Hypothesis 1.1: The more dissimilar counties
are in their average political orientation, the
lower the migration flow between them.
The limited population exchange between
geographic segments with dissimilar social
environments, or what we call segmented
immobility, may not be unique to the political
dimension, but could be a pervasive pattern
arising from people’s evaluation of places
along multiple dimensions. One underlying
mechanism that can lead to such a pattern
is homophily. Homophily refers to people’s
tendency to be connected to and interact with
others similar to themselves based on char-
acteristics such as racial and ethnic identity,
religious belief, political ideology, personal-
ity, or normative inclinations like altruism
(DiPrete et al. 2011; Leszczensky and Pink
2019; McPherson, Smith-Lovin, and Cook
2001; Moody 2001; Smith, McPherson, and
Smith-Lovin 2014; Wilson, O’Brien, and
Sesma 2009). Homophily occurs not only
within personal networks but also as a spatial
phenomenon: people tend to live close to
others with similar racial identity, economic
background, and political ideas (Bishop and
Cushing 2009; Intrator, Tannen, and Massey
2016; Massey and Denton 1993). Residents
choose to migrate toward places where peo-
ple similar to them concentrate, and they
avoid destinations with different identities
from their own (Crowder, Pais, and South
2012; Massey, Gross, and Shibuya 1994;
Schelling 1969), giving rise to this spatial
pattern. Literature about this residential sort-
ing process focuses primarily on mobility
among neighborhoods in urban areas, but we
argue that a similar process may also work
at a larger scale. When choosing a county to
reside in, people may favor places with a sig-
nificant presence of their co-ethnics and like-
minded residents. Likewise, opportunities to
migrate may be turned down if they would
lead movers to find themselves socially iso-
lated or targets of discrimination.
Segmented immobility can also arise in
more subtle ways: even if individuals do
not avoid living with dissimilar others, they
may exclude potential migration destinations
that cannot offer the lifestyle and cultural
consumption they are used to. Moving from
Manhattan to rural Texas, the New Yorker
would miss the coffee shop at the street
corner, while a Texan migrating in reverse
might feel nostalgia for the country music
scene back home. Hence, migration between
rural and urban areas, and across culturally
different states, is likely to be disfavored.
Racial demographics can also be a determi-
nant of the cultural and economic conditions
of a place; a racially diversified area not only
offers a diversity of cultural affordances (e.g.,
as reflected by cuisines and music genres),
but also provides vital economic opportuni-
ties and ethnic capital for ethnic minorities
(Fernandez-Kelly 2008; Lee and Zhou 2017;
Zhou 1992).
10 American Sociological Review 00(0)
Similarly, migrants from rural counties
might find themselves excluded from jobs
in urban areas because these jobs demand
skills hard to obtain in their rural hometown,
potentially leading to circulation of poor rural
migrants among non-metropolitan counties
(Lichter, Parisi, and Taquino 2022). This sug-
gests an economic dimension to segmented
immobility, in which migration between
dissimilar places is suppressed when these
places have different economic structures,
making it difficult for migrants to utilize
human capital accumulated in their place of
origin. As services, cultural activities, and
modes of production become specialized to a
local social ecology, individuals who adapted
to producing and consuming within that ecol-
ogy will find it increasingly difficult to utilize
opportunities in ecologically distinct locali-
ties. These mechanisms lead to the following
hypotheses:
Hypothesis 1.2: The more dissimilar counties
are in their levels of urbanization, the lower
the migration flow between them.
Hypothesis 1.3: The more dissimilar counties
are in their racial compositions, the lower
the migration flow between them.
The hypothesis of segmented immobility
is based on the assumption that most residents
and migrants identify with their current resi-
dence, or the place of departure. However,
if we were to suppose that the majority of
the migrating population moves to escape
their current residence in favor of one more
to their liking, then migration flows would
occur between dissimilar areas; this would
lead to “mobility across segments,” in con-
trast to “segmented immobility.” Tiebout
(1956) proposed this type of process as a
mechanism of political sorting; at the micro
level, similar processes occur in personnel
turnover (Krackhardt and Porter 1986) and
cascade-like relocation phenomena (Schell-
ing 1978). We contend that such sorting flows
are unlikely to be the major force of contem-
porary internal migration in the United States.
Research has not documented substantive
social changes that drove massive redistribu-
tion of the U.S. population since the fading
of the Great Migration of Black Americans
in the 1970s (Sharkey 2015; Tolnay 2003).
The continuing decline of internal migration
for the past decades suggests a scenario of
equilibrium, or “an inflection point” (Molloy
et al. 2011:173). Moreover, analyses of vot-
ing behaviors reveal that internal migrants
tend to hold political orientations consistent
with those of their origins (Preuhs 2020).
Nevertheless, we consider this as a compet-
ing hypothesis to the segmented immobility
hypotheses, and will directly test it in our
analysis.
Internal Dynamics: Reciprocity
and Perpetuation
The network approach also brings the oppor-
tunity to formally examine the interrelation-
ships among migration flows themselves,
thereby revealing the internal dynamics of
the migration system. This is particularly true
for the valued network models used here,
which allow us to examine quantitative ques-
tions that go beyond the simple presence or
absence of migration. Here, we focus on sev-
eral mechanisms motivated by prior theory on
migration behavior at the micro level, which
lead to hypotheses regarding interdependence
among macroscopic migration flows.
We begin by considering the relationship
between one migration flow (e.g., from Seat-
tle to Austin) and its opposite flow (e.g., from
Austin to Seattle). As the transnationalism
school has argued in the context of interna-
tional migration, migration is not a one-way
process, but an enduring reciprocal exchange
of people, goods, and cultures between
sending and receiving countries (Schiller,
Basch, and Blanc 1995; Waldinger 2013).
These same mechanisms could also apply
to movement within countries: in his classic
work, Ravenstein (1885:187) documented the
“universal existence” of “counter-currents of
migration” between counties in the United
Kingdom, where populations not only moved
from agricultural areas to commercial and
Huang and Butts 11
industrial areas, but each of these migration
currents corresponded to a current running in
the reverse direction. Considering that migra-
tion control policies suppress the circulation of
international migrants between nation-states
(Czaika and de Haas 2017; Massey, Durand,
and Pren 2016), we expect even stronger reci-
procity of migration flows in the context of
internal migration in the United States, where
there is no state control over migration. Reci-
procity can arise from sharing exogenous prop-
erties of the bidirectional flow; for example,
geographic proximity is a driver of reciprocal
population exchange, as it facilitates migration
in both directions. Nevertheless, we argue that
reciprocity is also an internal dynamic of the
migration flow system, such that net of exog-
enous factors, a larger migration flow in one
direction is still associated with a larger migra-
tion flow in the opposite direction.
The endogenous, systemic pattern of reci-
procity could result from at least two micro-
mechanisms in the U.S. migration system.
First, migration in one direction actively
motivates the flow in the opposite direc-
tion. Migrants bring information and social
connections from their origin to destination,
inspiring and facilitating migration in the
opposite direction. Second, return migrants
participate in flows in both directions, con-
tributing to the positive association between
the pair of flows. For example, Spring and
colleagues (2021) find family ties are a deci-
sive factor for people separated from their
spouses or cohabiting partners when deciding
to return to their hometowns. Von Reichert,
Cromartie, and Arthun (2014a, 2014b) show
that migrants returning from urban to rural
areas are mainly driven by social connections
rather than economic opportunities, and they
usually bring people in their family network
along when they return. Given the plausibility
of both mechanisms, we posit the following
macro-level hypothesis:
Hypothesis 2.1: The flow of migration from
county A to county B increases with the flow
of migration from county B to county A.
An important feature underlying the
macro-level pattern of reciprocity is the pres-
ence of (interpersonal) migrant networks that
link persons in the sending and receiving
regions, as literature on transnationalism
points out (Lubbers, Verdery, and Molina
2020; Mouw et al. 2014; Verdery et al.
2018). Migrant networks, according to Mas-
sey and colleagues’ (1993:448) definition,
“are sets of interpersonal ties that connect
migrants, former migrants, and nonmigrants
in origin and destination areas through ties
of kinship, friendship, and shared commu-
nity origin.” We have argued that, theoreti-
cally, migrant networks should contribute to
the reciprocity of migration-flow networks
via migrants bringing resources to destina-
tions and triggering populations moving in
the opposite direction, and by motivating
return migrants moving between regions in
both directions. Yet, reciprocity is not the
only pattern that emerges from migrant net-
works. As the cumulative causation theory
argues, the formation and development of
migrant networks are a key contributor to
the perpetuation of migration flows, which
suggests inertia (i.e., a positive association)
of the same migration flow over time (Mas-
sey 1990; Massey et al. 1993). Specifically,
migrants not only bring information and
social connections from their origin to their
destination, triggering migration in reverse,
but they also take resources from their desti-
nation back to their origin, by returning home
or via communication with nonmigrants back
home. This lowers the costs and potentially
raises aspirations of migrating to the same
destination, making future migration more
likely (Garip 2008; Garip and Asad 2016;
Liang and Chunyu 2013; Liang et al. 2008;
Lu, Liang, and Chunyu 2013; Massey, Gold-
ring, and Durand 1994; Palloni et al. 2001).
Therefore, we hypothesize the perpetuation
of migration flows in the system:
Hypothesis 2.2: The flow of migration from
county A to B increases with the past flow
of migration from county A to county B.
12 American Sociological Review 00(0)
Waypoint Flows
We now turn to the internal dynamics at the
level of triads, that is, among three localities
(Davis and Leinhardt 1972). Specifically, we
examine the waypoint structure in migration
flow networks. Similar to a layover airport
that mainly serves connecting flights, a “way-
point” is a place where the scales of migrant
inflows and outflows are similar to each
other. Demonstrated in Figure 2, counties A,
B, and C have the same amount of associated
migration events in total (six), but their dis-
tributions of immigration and emigration are
different. County A is an example of a way-
point, where inflows and outflows are evenly
distributed, county C is a counter-example
that has few inflows but many outflows, and
county B is in between. The difference can be
represented by the measure of waypoint flow,
which is the total amount of migration flows
moving in and out of a focal place. When
we hold constant the total number of migra-
tion events, a high volume of waypoint flow
represents a high level of equality between
inflows and outflows. In Figure 2, the volume
of waypoint flows for counties A, B, and C
are three, two, and one, respectively, indi-
cating that county A has the most balanced
inflows and outflows, followed by B and C.
Waypoint flows can arise from chain-like
migration processes (Leal 2021), such as step-
wise migration and relay migration. Stepwise
migration refers to movements of migrants
who pass through at least one waypoint before
reaching their final destination (Conway
1980). Originally theorized in Ravenstein’s
(1885) classic article, stepwise migration
has been widely documented under various
social contexts (Freier and Holloway 2019;
Paul 2011, 2017; Riddell and Harvey 1972),
including internal migration in the United
States (DeWaard, Curtis, and Fussell 2016).
Stepwise migration usually happens when the
final destination is not directly reachable due
to high financial burdens or hardship in acquir-
ing visas for international migration. Migrants
respond to this challenge by first migrating to
waypoints that facilitate their accumulation of
various kinds of capital before moving to their
ultimate stop (Paul 2011).
Another migration process that gives rise
to waypoints is relay migration, where the
exodus of local residents leaves vacancies
in the labor market that attract inflows of
migrants (Durand and Massey 2010). Relay
migration can also happen in the reverse
order, where an influx of migrants triggers
outflows of local residents (Leal 2021). The
key difference between stepwise migration
and relay migration is that the former is
about the same migrant taking a multiple-step
move, whereas the latter involves different
populations participating in inflows and out-
flows of waypoints.5 The two processes are
not distinguishable in aggregate migration
flows, but both reflect the interconnectedness
of the migration system, where the change
of one migration flow could alter another via
their shared connection at the waypoint.
Existing literature has studied the migra-
tion processes that can generate waypoint
flows, but less is known about their preva-
lence in migration systems. This knowledge
gap drives us to further theorize chain-like
migration processes by considering them
against other migration processes. Because
migration is an arduous undertaking with sub-
stantial risks, costs, and barriers (Carling and
Schewel 2018; Liang et al. 2008; Schewel
2020), prolonging one-step migration into
AB C
Figure 2. Waypoint Flows
Note: Counties A, B, and C have the same number (six) of associated migration events, but their levels of
equality for in- and out-migration flows vary. This is reflected in their volumes of waypoint flow, three
for the most equal county A, two for the medium equal county B, and one for the least equal county C.
Huang and Butts 13
stepwise migration is not a desirable choice.
Compared to international migration, inter-
nal migration in the United States is usually
more affordable and less constrained by state
regulations; an internal migrant in the United
States is thus less likely to opt for stepwise
migration than is a migrant from the Philip-
pines who wishes to settle in Spain. Relay
migration is not a universal pattern, either. It
requires substantial inflows or outflows that
can alter the local labor and housing market
or socio-political contexts to trigger further
migration flows. This means waypoint flows
arising from relay migration are conditioned
on uncommon incidents, such as major eco-
nomic shocks or environmental disasters that
bring mass population movements.
Moreover, a deficit in waypoint flows can
also be a structural signature of inequality in
migration flow networks, where the major-
ity of counties either receive many migrants
but send few, or send many migrants but
receive few. This imbalance between in- and
out-migration flows can arise when the dif-
ference in the level of attractiveness across
places remains unaccounted for; in this case,
a county is either popular so as to attract
and retain migrants, or the reverse. A lack of
waypoint flows can also occur endogenously.
For instance, potential migrants may take
current migration rates as social or economic
signals about an area’s long-term desirability
and adjust their decisions accordingly. This
tendency creates a feedback loop in which an
influx of migrants to an area leads potential
out-migrants from the area to instead remain,
which in turn feeds an imbalance between
in- and out-migration (in > out) that moti-
vates yet more potential migrants to move
in. The same mechanism may also lead to a
Schelling-like exit cascade (Schelling 1978),
in which an initial out-migration shock both
encourages further exit from those now in the
location and makes the location appear less
desirable to potential in-migrants, thus lead-
ing to a poorer in/out balance (in < out) and
further net out-migration.
Clearly, there are interesting and plausible
hypotheses in both directions. For simplicity,
we hypothesize a high-waypoint scenario,
reflected by a balanced distribution of inflows
and outflows:
Hypothesis 3: Inflows of migration to a county
increase with its outflows.
The waypoint flow is a network structure
related to but distinct from the transitive hier-
archy studied in some international migra-
tion network research (Leal 2021; Windzio
2018). Both are triadic structures concerning
migration flow among three places (i, j, k).
The waypoint flow is the backbone of the
transitive hierarchy, as the former considers
migration flows of i → j and j → k, whereas
the latter involves the co-presence of i → k
flow. This means that networks with a lack
of waypoint flow will have few closed tran-
sitive triads (i → j, j → k, i → k).6 We thus
focus on the more fundamental waypoint
flow structure to explore the more basic form
of the endogenous mechanism in the migra-
tion network.
Internal Migratory Response
to Immigration
Finally, this article considers the relationship
between international migrant (i.e., immi-
grant) inflows and internal migrant flows in
the United States. Debates about the effects of
immigration on internal migration prompted
much research in the 1990s, which provided
insights about the demographic and eco-
nomic influence of immigration, the structure
of labor markets, and the social cohesion
of U.S. society. Frey (1995a) hypothesized
that immigration to the United States would
lead to demographic balkanization, in which
immigrant inflows trigger outflows of internal
migrants and deter their inflows. Figure 3
depicts this hypothesis from the perspective
of internal migration flows, where larger
populations are expected to migrate from
county A, which has high immigrant inflows,
toward county B, which has low immigrant
inflows, and a smaller population would leave
14 American Sociological Review 00(0)
county C, which has low immigrant inflows,
toward county D, which has high immigrant
inflows, net of other factors. Prior work sug-
gests this mechanism leads to a “balkanized”
regionalization of the United States, with
immigrants and natives increasingly segre-
gated in different regions.7 Empirical find-
ings were inconclusive about the relationship
between internal and international migration
flows, with some supporting evidence for
Frey’s (1995a) hypothesis (Borjas 2006; Frey
1995a, 1995b; White and Liang 1998), and
others finding opposing evidence (Card 2001;
Kritz and Gurak 2001; Wright, Ellis, and
Reibel 1997). We revisit this debate with new
data about migration in the 2010s for all U.S.
counties. Following Frey’s (1995a) proposal,
we hypothesize the following, from the per-
spective of internal migration flows:
Hypothesis 4: The flow of migration from
county A to B increases with international
immigration into county A, but decreases
with international immigration into county B.
DATA AND METHODS
V alued TERGMs
We use valued temporal exponential-family
random graph models (valued TERGMs) to
study the intercounty migration-flow network
within the United States. Exponential-family
random graph models (ERGM) offer a flex-
ible framework that describes the probability
of observing certain network structures as
a function of their nodes’ covariates, edges’
covariates, and the dependence structure
among edges (Hunter et al. 2008; Wasser-
man and Pattison 1996). This empowers
us to simultaneously model the character-
istics of areal units (nodes’ covariates), the
relational linkages (edges’ covariates), and
the internal dynamics (dependence structure)
hypothesized to characterize migration-flow
networks.
Previous research has used ERGMs in a
wide range of social network settings, includ-
ing friendship networks in schools (Goodreau,
Kitts, and Morris 2009; McFarland et al.
2014; McMillan 2019), inmate power rela-
tionships in prison (Kreager et al. 2017), col-
laboration networks in firms (Srivastava and
Banaji 2011), online social networks (Lewis
2013, 2016; Wimmer and Lewis 2010),
and various types of gang networks (Lewis and
Papachristos 2019; Papachristos, Hureau, and
Braga 2013; Smith and Papachristos 2016).
Most studies model social relations as binary
networks (i.e., encoding only whether or not
relationships exist), but it is more accurate
and informative to model migration-flow
systems as valued networks, where edges
represent the size of populations migrat-
ing between county pairs. Although valued
ERGMs (VERGMs) are to date less well-
studied than binary ERGMs, we use Krivit-
sky’s (2012) count-data ERGM framework to
capture migration rates in a quantitative fash-
ion. Our model also incorporates temporal
Figure 3. Hypothesized Relation between Internal and International Migration
Note: Vertical gray arrows denote international immigration flows; horizontal dark arrows denote
internal migration flows. Arrow width denotes the magnitude of migration flows. According to
Frey’s (1995a) hypothesis, larger populations are expected to migrate from county A, which has high
immigrant inflows, toward county B, which has low immigrant inflows, and smaller populations would
leave county C, which has low immigrant inflows, toward county D, which has high immigrant inflows,
net of other factors.
Huang and Butts 15
effects (the perpetuation pattern), making it a
valued temporal ERGM, or valued TERGM.
We detail the model setup, computation
methods, and procedures in Part B of the
online supplement. We also develop and
report a model adequacy check for Valued
ERGMs, detailed in Part D of the online
supplement.
Knockout Experiments
Exploiting our ability to quantitatively model
the magnitude of migration flows using
VTERGMs, we perform in silico “knockout
experiments” to show the effect of modeled
social mechanisms in influencing the size of
the migrant population, tackling the question
of how particular social forces give rise to
immobility. Originating and widely used in
the experimental sciences (Hall et al. 2009;
Vogel 2007), this way of thinking has also
been applied in the social sciences (e.g., Han
et al. 2021; Lakon et al. 2015; Xie and Zhang
2019), especially in the context of agent-
based modeling (Miller and Page 2009).
For social science research, the knockout
experiment can be considered a model-based
thought experiment (Gedankenexperiment,
Einstein et al. 1935), where we use models to
predict social outcomes of interest (e.g., total
number of migrants) under a counterfactual
scenario where certain social mechanisms
are removed (e.g., the political segmentation
effect) while other factors are held constant.
This approach is particularly powerful for
nonlinear, systemic models like those used
here, where seemingly small, local effects can
have global consequences.
We perform our knockout experiments
as follows. Starting with a VTERGM cali-
brated using empirical migration data, we
compute the total expected number of inter-
county migrants when either the political
segmentation mechanism or all three seg-
mentation mechanisms (jointly) are knocked
out, and we compare this number with the
observed migrant population size. The dif-
ferences in total migrant population between
these scenarios offer insight into the scale of
mobility suppression from these segmenta-
tion mechanisms; that is, if we could “turn
them off,” what would we expect to see? The
counterfactual scenario was simulated by the
Markov chain Monte Carlo (MCMC) algo-
rithm based on the network model with zero
coefficient values for the specified knockout
social effects.8 Because the network model
specifies the dependence structure between
migration flows, it accounts for both direct
effects of the segmentation between each
county pair on their own migration flows, and
the indirect effect arising from the internal
dynamics of migration systems that spillover
from this exogenous effect. This knockout
experiment thus offers a systemic depiction
of the segmented immobility pattern.
Data
We analyze the intercounty migration flow
data from the American Community Sur-
vey (ACS). As a political unit with reliable
demographic and economic data, counties
serve as a geographic area that effectively
describe residents’ social contexts, such as
political environments and rural versus urban
centers (Lobao and Kelly 2019; Mueller and
Gasteyer 2023; Schroeder and Pacas 2021).
Movement across a county boundary is a
frequently-used definition of internal migra-
tion in the literature (Brown and Bean 2016;
DeWaard et al. 2020; Hauer 2017; Partridge
et al. 2012). Administered by the U.S. Census
Bureau, ACS surveys respondents’ location
of residence one year ago and estimates the
population size that migrated between each
pair of counties each year.9 Their released
data report the average annual migrant counts
in a five-year time window in order to have
enough monthly samples for reliable estima-
tion at the intercounty level. The outcome of
interest is the count of migrant population
flowing between 3,142 counties in the United
States from 2011 to 2015.
The explanatory variables are from the
2010 U.S. Census and ACS 2006 to 2010.
Specifically, the intercounty distance was
calculated based on the 2010 Census by the
16 American Sociological Review 00(0)
National Bureau of Economic Research
(2016). We use presidential election turn-
out in 2008 to indicate the political climate
of each county (MIT Election Data and
Science Lab 2018). Data sources for each
covariate are listed in Part A of the online
supplement.
Variables
Dependent edge variable. The model pre-
dicts the count of migrants moving between
each directed pair of counties from 2011 to
2015 from the American Community Survey.
Because the count-valued ERGM effectively
operates through a logarithmic link (see Kriv-
itsky 2012), we can directly predict untrans-
formed migrant counts in the model.
Dissimilarity score for segmented
immobility. The segmented immobility
thesis contends that less migration happens
between places with different political cli-
mates, levels of urbanization, and racial com-
positions. To test the hypotheses, we measure
the dissimilarity within each pair of counties
along these dimensions as edge covariates
for migration flows. For difference in politi-
cal climates, we follow Liu and colleagues
(2019) and calculate the absolute difference
in percentage of votes for the Democratic
candidate in the 2008 presidential election, a
behavioral measure of partisanship.10 For lev-
els of urbanization, we calculate the absolute
difference in percentage of population resid-
ing in rural areas, a standard urbanization
measurement reported in the 2010 Census.
For racial/ethnic composition, we use the L1
Euclidean distance measure, or what is called
the dissimilarity score in social segregation
literature (Massey and Denton 1988). For-
mally, we describe the relationship between
counties A and B by
R
PA
PA
P
P
B
B
AB
i
n
ii
=
()
()
−
()
()
=
∑
1
21
where RAB is the dissimilarity score of racial
composition between county A and county
B, P(A) is the total population size of county
A, and P(A)i is the population size of the
i-th racial group in county A. We follow the
census to consider the following five racial/
ethnic categories: Hispanic or Latino, non-
Hispanic Black or African American, non-
Hispanic Asian, non-Hispanic White, and
population with other racial identifications.
The difference is divided by two to make the
theoretical value of the score range from 0 to
1. The higher the dissimilarity score, the more
different the two counties are in the measured
dimension, and the less migration is expected
according to the hypotheses.
Network covariates. We utilize the
mutuality statistic in the ergm.count R pack-
age to measure reciprocity in migration flows
(Krivitsky and Butts 2013).11 A positive coef-
ficient indicates reciprocity within the net-
work, such that a large migration flow is more
likely to have a larger counter current rather
than a smaller one, ceteris paribus.
The model also includes the number of
migrants in the past five-year window from
2006 to 2010 in log scale from ACS as an
edgewise covariate, to account for the asso-
ciation of migration flows over time, utilizing
the temporal feature of TERGMs. A positive
coefficient for this term suggests the perpetu-
ation of migration flows over time; a nega-
tive coefficient suggests negative dependence
between past and present flows.
Waypoint flow is captured by summation
of the volumetric flow for each county in
the network. Intuitively similar to the notion
of the flow volume “through” or “across”
an areal unit in the field of fluid mechanics,
the flow associated with a given unit is the
minimum of its total inflows and total out-
flows.12 A positive coefficient for the flow
term indicates that the observed network has
larger volumes of waypoint flows than would
be expected given all other mechanisms and
covariates specified in the model, suggesting
a relatively equal distribution of in- and out-
migration flows across counties; a negative
coefficient would indicate otherwise.
To examine the relationship between inter-
nal and international migration flows, for
Huang and Butts 17
each intercounty migration flow, the model
measures its associations with the total immi-
grant inflows of its sending and receiving
counties in the same time window (2011 to
2015). The international immigrant popula-
tion is transformed by taking the natural
logarithm.
Demographic covariates. The model
also accounts for areal characteristics that
might influence intercounty migration. These
include demographic characteristics of the
sending and receiving counties, from basic
geo-demographic statistics to demographic
compositions.
Classic models from spatial economet-
rics (i.e., the gravity model) suggest migra-
tion rates are positively associated with the
population sizes of the sending and receiving
regions, but negatively associated with their
distance, with a general power law form
(Boyle, Halfacree, and Robinson 2014; Poot
et al. 2016; Zipf 1946, 1949). Such models
can be expressed by a linear combination
of population and distance in the log space.
Formally,
log Mlog Plog P
logD
AB
AB
AB
()
=+
()
+
()
+
()
+
ββ β
βε
01 2
3
where MAB is the migration volume from A
to B, P is the regional population, D is the
inter-regional distance, β is a covariate vector,
and
ε
is the residual. Almquist and Butts
(2015) suggest this may arise from the volume
of interpersonal contacts between regions,
which also frequently scales in power law
form. Although we do not use a regression
model of this type here, we emulate this class
of effects within our model by incorporating
(1) the log populations for the sending and
receiving counties, and (2) the log distance
between counties (in kilometers) as predictors
of intercounty migration rates; this means our
models can be considered an extension of the
gravity model. We also include population
densities of sending and receiving counties
(in thousand people per squared-kilometer),
because Cohen and colleagues (2008) show
that population density is a critical factor in
predicting international migration flows. We
use data from the 2010 Census for the covari-
ates listed above.
For demographic composition, the model
first considers the age structure of sending
and receiving counties, as Kim and Cohen
(2010) found that migrants are more likely to
leave younger countries for older countries in
the context of international migration. Using
the 2010 Census, the potential support ratio
(PSR) equals the ratio of population age 15
to 64 over the population age 65+, which
is the inverse of the dependency ratio in
demography literature; the higher the PSR,
the younger the population.
Racial composition could also influence a
population’s mobility, as extant literature has
found different patterns of internal migration
between racial groups (Crowder et al. 2012;
Sharkey 2015). Hence, besides the dissimilar-
ity of racial composition between counties,
we also consider the racial composition of the
sending county to account for different groups’
varying mobility, as measured by the propor-
tion of each racial category in the population.
Economic covariates. Economic struc-
tures of origins and destinations could
potentially influence their migration flows.
Because renters, on average, are more mobile
than house owners (Frey 2009; Molloy et al.
2011) even after controlling for demographic
and socioeconomic factors (Jia et al. 2022),
the model includes the percentages of hous-
ing units occupied by renters for both origin
and destination, using 2010 Census data. The
model also controls for the percentage of
the population with a college degree using
the 2006 to 2010 ACS. Human capital may
offer greater ability and opportunities for
migration, and previous analyses have found
that populations with higher education attain-
ments have higher migration rates in the
United States (Frey 2009).
Neoclassical economic theory predicts that
people migrate toward economic opportu-
nities (Massey et al. 1993; Todaro 1976).
The theory also predicts that regions with
18 American Sociological Review 00(0)
more economic opportunities will send more
migrants, as their population has more capi-
tal to finance their migration (Massey and
Espinosa 1997). We thus include the unem-
ployment rate of the origin county, and the
difference in the unemployment rate between
the destination and origin counties. Based
on neoclassical economic theory and the
aspiration–ability model (Carling 2002), we
hypothesize that more migration will come
from counties with lower unemployment
rates, given their population’s greater abil-
ity to move, and more migration will happen
when the destination has lower unemploy-
ment rates than the origin, offering more
economic opportunities and higher aspiration
for migration. Similarly, the models incorpo-
rate the logarithm of median monthly housing
costs of the origin county and the difference
in log housing costs between the destination
and origin counties.
Geographic covariates. Besides dis-
tance between counties, the model also con-
trols for regional differences in mobility.
Previous research has found that migration
rates and their trends in different parts of the
United States vary significantly (Frey 2009).
We believe regional differences may not be
fully explained by differences in social con-
texts indicated by the covariates above. We
created dummy variables to indicate whether
the origin and destination counties are in the
West, the Midwest, or the South, with the
Northeast as the reference group, based on
the U.S. Census Bureau’s (2013) definitions.
Administrative boundaries are also likely
to influence migration flows. Charyyev and
Gunes (2019) found that, marginally speak-
ing, the majority of intercounty migration in
the United States happens within a state, and
we further examine whether state boundaries
influence migration flows after controlling
for distance and dissimilarity between coun-
ties. Intrastate intercounty migration could be
more prominent than cross-state migration
because, compared to intrastate migration,
cross-state migration creates extra burdens,
ranging from adaptation to unfamiliar legal
and cultural environments, to navigation of
administrative procedures such as change in
occupational licensing for workers in certain
occupations (Johnson and Kleiner 2020). Yet,
the opposite hypothesis is plausible under the
consumer-voter model, which contends that
people vote with their feet (Dye 1990; Tie-
bout 1956); as a means of pursuing favorable
policies, cross-state migration is more effec-
tive if people migrate to seek lower tax rates
or more welcoming policies and climates
for immigrants (Preuhs 1999; Schildkraut
et al. 2019). The model creates a dummy
variable indicating whether the two counties
are affiliated with the same state. A posi-
tive coefficient suggests intrastate intercounty
migration is more prominent, and a negative
coefficient suggests interstate migration is
more prominent.
Variable setup. We report two models
in the Results section. The first model con-
tains every covariate except the rural dissimi-
larity score, which is included in the second
model, the full model. Because the level
of urbanization is strongly associated with
political environment, comparison between
the two models could reveal how much of
the total effect of political dissimilarity might
be explained by their difference in level of
urbanization. Besides the sum term serving
as an intercept, we add to the models a term
that counts the number of nonzero dyads of
the network to account for the zero-inflation
of migration flow data (Krivitsky and Butts
2013). Its negative coefficients in Table 1
indicate the sparsity of a migration flow net-
work, that is, a county pair is more likely to
have no migrants moving between them, even
after controlling for all the covariates in the
model. Summaries of descriptive statistics
and data sources are in Part A of the online
supplement.
RESULTS
Bivariate Analyses of Migration
and Political Division
To explore the pattern of segmented immobil-
ity by political orientation, we first perform
Huang and Butts 19
bivariate analyses between intercounty migra-
tion and political division, as depicted in
Figure 4. We divide counties into two broad
groups, Democratic counties and Republican
counties. Democratic counties are counties
where the Democratic candidate (Obama)
received more votes than the Republican
candidate (McCain) in the 2008 presidential
election, and vice versa for the Republican
counties.
The sociogram in Panel A of Figure 4
shows the magnitude of migration within and
between Democratic and Republican coun-
ties, which is proportional to the width of
edges. Migration flows within each group
have thicker edges than flows between, sug-
gesting that more migration happens from one
Democratic county to another, or from one
Republican county to another, than between a
Democratic county and a Republican county.
The spineplot in Panel B represents the mag-
nitude of migration flow within and between
groups by the area of each block. The shaded
blocks are migration happening within Dem-
ocratic or Republican county groups, sug-
gesting again that more migration happens
on either side of the party line than across it.
The color of each block indicates whether the
origin of the migration flow is from a Demo-
cratic (blue) county or a Republican (red)
county (see the online version of the article
for color figures). The spineplot indicates
that only 31 percent of migrants moving into
a Democratic county come from a Republican
county, and just 44 percent of migrants mov-
ing into a Republican county come from a
Democratic county.
Panel C of Figure 4 shows the relation-
ship between the percentage of Democratic
votes in the 2008 election and the compo-
sition of in-migrants and out-migrants for
each county. The upper-left panel shows that
the higher the Democratic vote in 2008, the
larger the proportion of migrants coming
from a Democratic county, and the smaller
the proportion of migrants coming from a
Republican county, as shown in the lower-left
panel. Similarly, the right-hand column sug-
gests that a larger share of 2008 Democratic
votes within a county is associated with a
larger proportion of out-migrants moving to a
Democratic county, and a smaller proportion
Figure 4. Immobility from Political Division
Note: The sociogram (A) represents the magnitude of migration flow within and between Republican
counties (node R in red) and Democratic counties (node D in blue), which is proportional to the
width of the edge (see the online version of the article for color figures). The spineplot (B) represents
the magnitude of migration flow within and between the two groups by the area of each block. The
shaded blocks represent migration within each group. Scatterplots (C) show the relationship between
percentage of Democratic votes in a county in 2008 and the composition of its in-migrants and out-
migrants. The lines are fitted bivariate linear regression lines.
20 American Sociological Review 00(0)
to a Republican county. Overall, the panels
reveal a clear and strong pattern of political
sorting, where fewer people migrate between
counties with distinct political environments
than between counties with similar political
environments.
Segmented Immobility
The bivariate analysis suggests that inter-
county migration is immobilized by political
divisions in the United States. We further
examine this using VTERGMs that incorpo-
rate demographic, economic, geographic, and
political factors at the county and intercounty
levels, together with explicit specifications
of internal dynamics of migration systems.
Table 1 displays the results. Model 1 sug-
gests that, holding all other factors constant,
a larger difference in political environments
between counties predicts less migration
between them. Because the political environ-
ment is associated with a county’s level of
urbanization (Cramer 2016), Model 2 further
includes the dissimilarity of urbanization
between counties. From Model 1 to Model
2, the effect size of political dissimilarity
becomes modestly smaller, suggesting the
effect of political difference can be partly
(but not completely) explained by the dif-
ference in level of urbanization. The smaller
BIC of Model 2 further indicates that differ-
ence in the level of urbanization is effectively
explaining the variation in the magnitude of
migration flows. Nonetheless, in Model 2,
larger political dissimilarity is still a statis-
tically significant predictor of less migra-
tion between counties, offering empirical
evidence for Hypothesis 1.1. Holding other
factors constant, a pair of counties with a
10 percent larger difference in the 2008 vot-
ing outcome is expected to have 2.5 percent
(i.e., [1−exp(−0.256×10%)]) fewer migrants
than another county pair. Similar to political
segmentation, Model 2 also reveals that larger
differences in levels of urbanization and
racial compositions of two counties predict
fewer migrants moving between them, hold-
ing other factors constant, lending support
for Hypotheses 1.2 and 1.3. The VTERGM
results do suggest that migration is inhib-
ited between places with dissimilar political
contexts, levels of urbanization, and racial
compositions.
To quantify the contribution of segmented
effects to immobility, we perform knockout
experiments to compute the total migrant
population under counterfactual scenarios
where these effects are inoperative, and com-
pare that with the observed scenario. Table 2
shows that when the political segregation
effects on migration flows are knocked out,
the expected intercounty migrant population
each year would increase by 788,661, which
is 4.6 percent higher than the observed. With
the absence of all three segmentation patterns,
we would expect 26.6 percent more internal
migrants in the United States, that is, 4.56
million more people moving from one county
to another each year.13
Results of the VTERGMs and knockout
experiments suggest that segmented immobil-
ity serves as a critical and substantial social
mechanism behind the immobility of contem-
porary U.S. society. These social mechanisms
may be partly driven by economic forces
(although supplementary analysis shows that
dual labor and housing markets have little
effect on the described segmentation pat-
tern, see Part C of the online supplement);
they may also reflect people’s preference for
residing in culturally and politically familiar
environments. This tendency not only implies
social cleavages along party lines, between
urban and rural lands, and across communi-
ties with varying racial demographics, but it
could also contribute to growing geographic
segmentation along those lines. As has been
known since the classic works of Sakoda
(1971) and Schelling (1969), even a small
preference for homophily can lead to sub-
stantial segregation in residential settlement
patterns (see also Fossett 2006).
Network Dynamics Influencing
Migration Flows
The VTERGMs also consider the network
patterns of the migration flow system. That
all coefficients are significant in the network
Huang and Butts 21
Table 1. Valued TERGMs for Intercounty Migration Flows, 2011 to 2015
Model 1 Model 2
Estimate SE Estimate SE
Segmented Immobility
Political dissimilarity –.368*** .007 –.256*** .007
Rural dissimilarity –.399*** .004
Racial dissimilarity –.361*** .006 –.217*** .006
Network Patterns
Mutuality .054*** .002 .045*** .002
Log (past migrant flow) .303*** <.001 .300*** <.001
Waypoint flow –.014*** .001 –.015*** .001
Destin.log (immigrant inflow) .062*** .001 .056*** .001
Origin.log (immigrant inflow) .040*** .001 .035*** .001
Demographics
Destin.log (population size) .351*** .002 .351*** .002
Origin.log (population size) .370*** .002 .373*** .002
Destin.log (population density) –.077*** .001 –.083*** .001
Origin.log (population density) –.062*** .001 –.069*** .001
Destin.PSR .018*** .001 .017*** .001
Origin.PSR .013*** .001 .013*** .001
Origin.P (White) (reference group)
Origin.P (Hispanic) –.012 .007 –.064*** .007
Origin.P (Black) .147*** .008 .117*** .008
Origin.P (Asian) .408*** .020 .467*** .020
Origin.P (other race) 1.031*** .015 .993*** .015
Economics
Destin.P (renter) .405*** .011 .348*** .011
Origin.P (renter) .507*** .012 .476*** .012
Destin.P (higher education) .327*** .011 .359*** .011
Origin.P (higher education) .157*** .012 .153*** .012
Difference.log (housing costs) –.135*** .004 –.153*** .004
Origin.log (housing costs) –.248*** .005 –.277*** .005
Difference.P (unemployment) –1.305*** .040 –1.300*** .040
Origin.P (unemployment) –3.039*** .052 –3.012*** .052
Geographics
Log (distance) –.563*** .001 –.568*** .001
Same state .501*** .002 .510*** .002
Northeast (reference group)
Destin.South .258*** .003 .253*** .003
Origin.South .047*** .003 .046*** .003
Destin.West .384*** .004 .374*** .004
Origin.West .193*** .004 .184*** .004
Destin.Midwest .203*** .003 .197*** .003
Origin.Midwest .085*** .003 .080*** .003
Baseline
Sum –1.609*** .040 –1.193*** .040
Nonzero –13.966*** .028 –13.917*** .028
BIC 2,221,363 2,210,125
*p < .05; **p < .01; ***p < .001 (two-tailed tests).
22 American Sociological Review 00(0)
patterns section in Model 2 of Table 1 confirms
that they play a significant role in determining
the directions and magnitudes of intercounty
migration flow. In Model 2, the positively sig-
nificant mutuality term confirms Hypothesis
2.1, that reciprocity is present in the migration-
flow networks: a larger flow from county A to
B is positively associated with a larger flow
from county B to A, holding other effects
constant. Joining research on global migra-
tion and intercounty migration in the U.K.
(Ravenstein 1885; Windzio 2018), we show
that reciprocity is also a network pattern found
within U.S. migration. Some prior studies do
not observe reciprocity effects in their analy-
ses (Desmarais and Cranmer 2012; Windzio
et al. 2019), which might be due to omission
of some regional characteristics that influ-
ence the attractiveness of regions to migrants,
or their operation of data transformation for
the migrant count variable. Future research
might replicate the analysis of reciprocity
using count-data network models under vari-
ous social contexts to understand whether
reciprocity is a prevalent phenomenon or can
be suppressed by some social forces.
Model 2 also reveals that a larger migra-
tion flow during 2006 to 2010 is significantly
associated with a larger migration flow dur-
ing 2011 to 2015, even after holding all
exogenous and endogenous factors constant.
This confirms Hypothesis 2.2 regarding the
perpetuation of the migration flow system,
showing that migration-facilitating mecha-
nisms offer the system its own momentum,
promoting future migration net of exogenous
factors such as a region’s demographic struc-
tures (de Haas 2010).
The significantly negative coefficient of
the flow term indicates a lack of waypoint
structures of intercounty migration, refuting
Hypothesis 3. The negative waypoint flow
effect implies that relatively little migration
is proceeding in a chain-like manner, such as
by stepwise or relay migration. After hold-
ing other factors constant, counties generally
have an imbalance or inequality in the scales
of their migration inflows and outflows,
either sending many migrants but receiving
few, or receiving many migrants but sending
few. This may represent emergent attractive-
ness effects, in which in-migration makes a
county seem more attractive to other possible
migrants, and out-migration makes a county
seem less attractive. It may also reflect unob-
served heterogeneity in attractiveness arising
from other factors; the specification of way-
point flows in the model thus controls for this
possible source of autocorrelation, beyond its
substantive interest.
Note that the inequality identified by a lack
of waypoint flows in this intercounty migra-
tion network is different from the inequal-
ity captured by an abundance of transitive
hierarchy in other cross-national migration
networks (e.g., Leal 2021). Transitive hier-
archy requires many waypoints serving as
the “mildly structurally attractive position,”
between the highly and the minimally “struc-
turally attractive positions” (Leal 2021:1086).
In the multilayer hierarchy of the global sys-
tem, this implies countries are positioned in
the core, the semi-periphery, or the periphery
(Wallerstein 2011). In contrast, in this net-
work with a lack of waypoint flows, there
is an absence of semi-periphery areas serv-
ing as waypoints between the core and the
periphery. Compared with the international
migration system, the U.S. migration system
is relatively bipolar, with counties tending to
be either structurally attractive or unattrac-
tive, with few in the middle ground.
Table 2. Migrant Population Sizes under Observed and Knockout Scenarios
Total Migrants Increment in Count and Rate
Observed 17,176,675
Remove political segmentation 17,965,336 788,661 4.6%
Remove all segmentation 21,741,021 4,564,346 26.6%
Huang and Butts 23
The model also examines the relationship
between internal and international migration.
It shows that larger immigrant inflows from
2011 to 2015 are positively associated with
larger intercounty inflows and outflows in
the same period. This finding does not corre-
spond to either side in the debate about inter-
nal migratory response to immigration, which
contends that large immigrant inflows are
either associated with small internal migrant
inflow and large outflows, or not associ-
ated with internal migrant flows. Rather, the
results suggest that counties with large immi-
grant inflows are active in both sending and
receiving intercounty migrants. Furthermore,
the larger coefficient of destination effect
than origin effect suggests that increasing
immigrant inflows to a county is associated
with a larger increase of internal inflow than
internal outflow. In other words, immigration
is actually associated with a net population
increase from internal migration. Overall, the
finding shows a common mobility pattern for
internal and international migration, wherein
counties popular among international immi-
grants are also popular and active in both
receiving and sending internal migrants.14
Demographic, Economic, and
Geographic Determinants of
Migration
Alongside segmented immobility and network
patterns, the models also consider other fac-
tors that could influence intercounty migra-
tion. For demographic characteristics, Model
2 confirms findings from spatial econometrics
(gravity) models that population sizes in both
sending and receiving regions are positively
associated with migrant flow (Boyle et al.
2014; Zipf 1946, 1949). A 10 percent increase
in a destination’s population size is associated
with a 3.4 percent (i.e., [1.10.351−1]) increase
in the number of migrants, and a 10 percent
increase in an origin’s population size is asso-
ciated with a 3.6 percent (i.e., [1.10.373−1])
increase in the number of migrants, holding
other factors constant. Population density has
a significantly negative effect for the number
of in-migrants and out-migrants, holding pop-
ulation size and other factors constant. One
possible mechanism is that higher population
density leads to larger shares of local connec-
tions for residents (Butts et al. 2012; Hipp
et al. 2013; Thomas et al. 2022), where more
job transitions and housing transactions can
happen locally thanks to these connections,
reducing migration across county borders.
With respect to demographic composition,
larger migration flows are significantly more
likely to be observed between counties with
younger populations, in line with the migra-
tion schedule literature that finds younger
adults are more mobile than older adults
(Raymer and Rogers 2007; Rogers and Castro
1981). The model also shows that counties
with larger shares of Hispanic population
tend to send fewer migrants, but counties
with larger shares of non-Hispanic Black,
non-Hispanic Asian, and other races popula-
tions tend to send more intercounty migrants.
These effects do not directly describe the
mobility of each racial/ethnic population, as
they are predicting the magnitude of migra-
tion flow for all racial and ethnic populations.
Decomposing migration flows into migrants
of each racial/ethnic population is necessary
to reveal the variation of mobility between
people with different racial/ethnic identities.
Economic covariates in Model 2 show that
larger migration flows exist between counties
with higher shares of renters and people with
college degrees, consistent with previous liter-
ature that finds renters and people with higher
education credentials are more mobile than
their counterparts (Frey 2009). We also see
that larger migration flows happen when the
route offers greater declines in housing costs,
indicating a tendency to move toward cheaper
housing (Plantinga et al. 2013). Holding other
factors constant, counties with lower housing
costs have higher out-migration. This might
be due to the better financial conditions rent-
ers have in low housing cost areas, enabling
them to move and relocate. It is also compat-
ible with previous findings that lower housing
equity is associated with higher mobility rates
(Coulson and Grieco 2013).
24 American Sociological Review 00(0)
For unemployment rates, the model sug-
gests the lower the unemployment rate at the
origin, and the larger the decline in unem-
ployment rate from origin to destination, the
more intercounty migration. These results are
compatible with the cost-benefit model of
the neoclassical economic theory of migra-
tion, that populations move toward economic
opportunities (Todaro 1976), and that more
economic opportunities financing migra-
tion makes migration more likely to happen
(Massey and Espinosa 1997). The relational
approach used here enables empirical analy-
sis of the aspiration–ability model (Carling
2002; Carling and Schewel 2018), revealing
that both aspiration, as influenced by the
relative economic conditions of origin and
destination, and ability, as influenced by the
economic conditions of the origin, matter to
migration behaviors.
In terms of geographic factors, the model
suggests a negative association between
distance and number of migrants flowing
between two counties, as the gravity model
predicts (Zipf 1946, 1949). A 10 percent
increase in the log distance between two
counties is associated with a 5.6 percent (i.e.,
[1−1.1−0.568]) decrease in intercounty migra-
tion. Administrative boundaries also influ-
ence migration flows; migration flows within
the same state are expected to be larger
than those across states, holding other factors
constant. Additionally, different U.S. regions
have varying mobilities. The model indicates
that compared to the Northeast, every other
region receives and sends more intercounty
migrants. This suggests the existence of some
latent characteristics inhibiting the mobility
of people in the Northeast, which deserves
more examination in future work.
Finally, to check the model adequacy, we
simulate networks based on Model 2 (the
full model) in Table 1 using MCMC algo-
rithms. We then calculate the total in-migrant
and out-migrant count for each county, and
compare the observed distribution with the
simulated distribution. We find that the fit-
ted model recapitulates the county-level
migration data (see Part D of the online
supplement). We also calculate the Pearson’s
correlation between observed and simulated
distributions, which are all above .95. We
conclude that the model effectively repro-
duces the quantitative features of observed
migration flow networks.
DISCUSSION AND
CONCLUSIONS
This article offers a comprehensive analysis
of the intercounty migration structure in
the United States, encompassing not only
economic, demographic, and geographic fac-
tors, but also political and cultural factors
and internal dynamics of the migration sys-
tem. Network models reveal a pattern of
segmented immobility in the United States,
in which less migration happens between
counties with dissimilar political environ-
ments, levels of urbanization, and ethnic/
racial compositions. Yet, we do not observe
segmentation between internal migrants and
international immigrants; rather, the model
shows that counties active in receiving many
international immigrants are active in sending
and receiving many internal migrants as well.
Our analysis also suggests the significance
of internal dynamics of the migration flow
system; we see strong patterns of reciprocity
and perpetuation, along with a suppression of
waypoint structure. These results lend empiri-
cal evidence to the systemic theory of migra-
tion (Bakewell 2014; de Haas 2010; Fawcett
1989; Mabogunje 1970), showing that popu-
lation flows assemble an interdependent net-
work system that carries its own momentum.
We identify segmentation as a critical
mechanism behind population immobility
in contemporary U.S. society, which could
potentially have deterred millions of people
from migrating each year, as suggested by
the knockout experiments. This finding sug-
gests a tendency for individuals to choose
residency in localities that match their politi-
cal affiliations and sociocultural attributes,
which can lead to geographic segmentation
between people with different political identi-
ties (Brown and Enos 2021) and increase the
Huang and Butts 25
homogeneity of their social relations (DiPrete
et al. 2011). Such sorting could reinforce
political polarization (DellaPosta and Macy
2015) and also serve as a mechanism that
maintains and even exacerbates residential
segregation along other dimensions (Fossett
2006; Sakoda 1971; Schelling 1969). While
classic analyses of segregation have focused
on local communities within urban areas
(Bishop and Cushing 2009), the effects seen
here could potentially contribute to macro-
level segmentation across the whole country
(Liu et al. 2019). From a migration per-
spective, although internal migration in the
United States does not involve international
border-crossing or other forms of government
restrictions (e.g., the household registration
system in China, hukou), population move-
ment is never free of constraints. Rather,
as our analysis shows, Americans today are
separated by the invisible borders and walls
constructed by party lines, at the midway
between rural and urban landscapes, and over
the gap across communities with varying
racial demographics.
Our analytic framework provides an
example of structural and systemic analysis
of mobility and immobility, broadly defined.
The relational approach connects the per-
spectives of emigration and immigration to
examine how characteristics of origin and
destination jointly influence migration, which
enables us to see the segmented immobility
in the U.S. migration system. The formal
specification of the interdependence between
migration flows under the ERGM framework
identifies the structural signature of networks,
reflecting the internal dynamics of migra-
tion systems. The knockout experiment offers
model-based insights into how the system
might react to social change. Finally, leverag-
ing advances in scalable VERGM estimation
and simulation allows quantitative analysis
of the magnitude of population flows and
their determinants in large social systems.
The applicability of this framework extends
beyond population movement between geo-
graphic areas, encompassing mobility in the
occupational system for the study of social
stratification and mobility (Cheng and Park
2020), the exchange of personnel between
organizations (Sparrowe and Liden 1997),
and the migration of scholars between institu-
tions and research domains in the sociology
of knowledge (Burris 2004; Gondal 2018;
McMahan and McFarland 2021).
Our study enables a much richer examina-
tion of the mechanisms driving or inhibiting
internal migration at a larger scale than what
has been possible in extant literature, but it
is not without its own limitations. First, as a
macrosociological study about the “function-
ing of a social system” (Coleman 1986:1312),
this article examines an aggregate-level social
phenomenon, that is, population immobility.
Analysis of the migration flow network facili-
tates a systemic understanding of migration
and its relation to segmentation from a holis-
tic viewpoint, but it does not directly describe
the patterns of individual migration behavior.
Although we can test for the structural signa-
tures of such micro-level processes, unpack-
ing those fine details requires information on
decision-making and behavior patterns at the
individual level. For example, distinguish-
ing stepwise migration and relay migration
requires data about the migration trajectories
of individual migrants. Studies like this are
hence complementary to micro-level analyses
(both quantitative and qualitative) that could
shed further light on processes at the indi-
vidual and household levels (e.g., DeLuca,
Wood, and Rosenblatt 2019; Fitchen 1994;
Lichter et al. 2022; Quillian 2015). Another
promising research direction is to pursue
studies that directly bridge individual behav-
iors and aggregate social outcomes, which is
still an open problem in sociology (Cetina
and Cicourel 2014; Coleman 1986).
Second, because the American Commu-
nity Survey did not start collecting data until
2005, our analysis only includes migration-
flow networks for two time points (2006–
2010 and 2011–2015). This data limitation
prevents us from conducting dynamic analy-
sis about changes in intercounty migration
patterns throughout the past decades, and
therefore, our findings do not speak directly
26 American Sociological Review 00(0)
to the reasons behind the long-term decline of
migration. Yet, our identification of drivers,
and especially inhibitors, behind migration
flows could serve as a starting point for this
inquiry. For example, because political divi-
sion across geographic areas deters migra-
tion, future research might examine how
the geography of politics and preferences
regarding political homophily have changed
over time, and how the evolution of politi-
cal landscapes and polarization relates to the
long-term decline of migration. Studies of
the changing patterns of immigrant inflows
and the relationship between internal and
international migration flows can illuminate
the change of population dynamics over time.
Applying knockout experiments via network
simulation to historical data about political
climate and migration/immigration flows
might be one approach to advance inquiry
into the social forces behind the growing
immobility in the United States. In addition,
future research might benefit from exploring
the changing balance of forces of the com-
peting internal dynamics of the migration
system over the past decades. Given that the
VTERGM framework we use here is capa-
ble of handling networks with multiple time
steps, our analytic framework could be used
for dynamic analysis once migration-flow
data for more time points become available.
In a similar vein, the time period we ana-
lyzed covers the Great Recession (Grusky,
Western, and Wimer 2011). Despite our
controls for various economic factors, some
aspects of our findings may be particular to
this period, as economic shocks can influence
migration patterns (Monras 2018; cf. Molloy
et al. 2011). Specifically, because economic
recession can suppress migration, it is pos-
sible that fewer waypoint flows are a conse-
quence of the period effect that temporarily
suppresses stepwise migration. Nevertheless,
the formal expressions of relational linkages
and network patterns, and the modeling of
migration-flow networks using ERGMs, are
generally applicable to study migration flows
of different periods and regions at different
scales. Future research may compare analyses
of relational and network patterns of migra-
tion flows in different times and space using
similar frameworks; this work could reveal
what patterns are context-specific in certain
spatial-temporal settings, and which are gen-
eralizable to migration in other societies.
Another fruitful direction for future work
is to complicate the analysis of internal
dynamics of migration systems by examining
the higher-order dependence structure of (val-
ued) networks. One example is network tran-
sitivity, a structural feature associated with
hierarchy within the migration system (Leal
2021). We do not find a strong transitive hier-
archy in the U.S. internal migration system,
as indicated by the lack of waypoint flows.15
Nevertheless, transitivity is a theoretically-
interesting dependence structure for the study
of mobility networks, and it would ideally
be examined in valued networks to consider
the quantitative feature of migration flows.
This requires theoretical and methodologi-
cal developments in formal specification of
dependence terms in the valued network set-
ting, for example, clarifying the properties of
different definitions of transitivity and their
relationship to network degeneracy (Krivit-
sky 2012). It also demands further advance-
ments in computational methods for valued
network models to allow for evaluation of
more complicated dependence structures in
large networks.
Last but not least, as population immobil-
ity has become a long-term phenomenon in
the United States, it poses important ques-
tions about its broader social implications.
Future research could explore the relation-
ship between geographic mobility and social
mobility, and how divergent geographic
mobility patterns across various social groups
may influence their life chances and well-
being. Furthermore, a lack of population
exchange, especially between localities with
different cultural and political climates, could
have ramifications for social divisions in the
country. Two decades ago, Putnam’s (2000)
Bowling Alone sparked great debates about
the “collapse of American communities,”
marked by individuals’ detachment from
Huang and Butts 27
and disengagement with local communities.
The observed population segmentation and
immobility raises the question of whether we
are witnessing the “tribalization of Ameri-
can communities,” where local communities
diverge in their demographics, culture, and
policy, with limited interaction, communi-
cation, and cooperation among people and
organizations from dissimilar areas.
In conclusion, grappling with the mobility
bias in migration studies, this article utilizes
migration systems theory and network meth-
ods to study the mechanisms behind popu-
lation immobility in the United States. We
identify segmentation as a significant feature
of the U.S. migration landscape, which poten-
tially immobilized millions of intercounty
migrants each year in the 2010s. We demon-
strated how network and simulation methods
can contribute to a systemic understanding
of mobility and population dynamics. We
also call for more theoretical and empirical
research about the interrelationships between
migration, segregation, and polarization, and
how they shape the foundation of social lives
in the United States and beyond.
Acknowledgments
We thank Stanley Bailey, Susan Brown, Katherine Faust,
David Grusky, David Schaefer, Jacob Thomas, Ashton
Verdery, Feng Wang, Wendy Zhou, and attendees of the
Social Networks Research Group at UC-Irvine for their
comments and suggestions at various stages of this proj-
ect. We thank the election offices of Hawaii and Alaska
for the useful information and archives.
Funding
This research was supported by the National Science
Foundation (SES-1826589).
ORCID iDs
Peng Huang https://orcid.org/0000-0001-5614-786X
Carter T. Butts https://orcid.org/0000-0002-7911-9834
Data Note
Replication data and code can be found at https://doi
.org/10.7910/DVN/I7HT9T.
Notes
1. As an example, Eeckhout (2004:1431) writes, “the
central thesis in this paper: population mobility is
driven by economic forces.”
2. By valued network (or weighted network), we refer
to networks whose ties are not binary (present or
absent), but are associated with a quantitative value;
specifically, tie values in this study indicate the vol-
ume of migration flows between directed pairs of
U.S. counties.
3. One example is the study of migration and family.
The role of family in migration processes is beyond
an economic unit that makes collective decisions
(Mincer 1978; Stark and Bloom 1985); it is a socio-
cultural organization that interplays with gender
norms (Abrego 2014) and state regulation (Chavez
2013).
4. Bakewell (2010, 2014) and DeWaard and Ha (2019)
have debated whether and how studies of migration
networks contribute to MST. Echoing Leal (2021),
we agree with DeWaard and Ha (2019) that network
analysis is an effective way of theorizing and test-
ing the structures and dynamics of migration across
geography. We also recognize Bakewell’s critique
that network analysis of migration flows is one of
many approaches to study migration systems, and
students of MST should beware the pitfall of exces-
sively stylized and static descriptions of migration
systems that are not empirically realistic. In this
regard, we leverage theories and empirical findings
in migration studies to motivate tests about struc-
tures and patterns of migration networks. We also
call for more research with different levels of analy-
sis to triangulate our findings for a comprehensive
understanding of migration and immobility.
5. We thank an anonymous reviewer for pointing out
this distinction.
6. Transitive hierarchy is a network structure built on
waypoint flow, and an underrepresentation of the
former necessarily implies an underrepresentation
of the latter. In this circumstance, there could be a
net tendency for waypoint flows to be transitively
rather than cyclically closed where they occur. But
one will still see fewer transitive closures (as there
are fewer paths to close in the first place) than one
would expect by chance. Put another way, standard
transitivity effects measure the overrepresentation
of both waypoint flow and transitive closure, not
merely the latter.
7. The phrase “balkanization” can be construed to
carry certain normative connotations regarding
immigration, so we follow Kritz and Gurak (2001)
and describe the phenomenon as the internal migra-
tory response to immigration.
8. We also simulated networks using the full model
(without knockouts), and calculated the difference
in the total migrant size between the full-model
simulation and the observed, as a measurement
28 American Sociological Review 00(0)
of bias introduced in the procedure. We then
corrected the total population sizes in knockout
scenarios by extracting that difference. As the dif-
ference is 0.7 percent of the observed migration
volume, corrected and uncorrected estimates are
nearly identical.
9. The Internal Revenue Service (IRS) provides
another dataset that reports counts of county-to-
county migration flows (Hauer and Byars 2019).
Whereas ACS is a nationally representative demo-
graphic survey, representativeness is a potential
concern for the IRS dataset, as it only contains
people filing tax returns, and therefore is not rep-
resentative of elder, low-income, and immigrant
populations. Furthermore, IRS data post 2011–2012
currently suffer from systemic problems that are not
yet resolved (DeWaard et al. 2022). Nonetheless,
the IRS reports migration data annually, and these
data can be useful for fine-grained dynamic analysis
of migration before 2011.
10. Given how Hawaii and Alaska calculate their elec-
tion results, we conducted the following operations
to map their local election data to counties. Kala-
wao County, HI, is regarded as part of Maui County,
HI, for election purposes, so we input the election
results of both counties with their pooled results.
Election results in Alaska were reported by election
districts rather than counties. We used the map to
match election results of the 40 districts with the 28
counties. A county’s result was input with that of its
district if the county was affiliated with one single
district. We take the mean of the results of the dis-
tricts a county spans if the county is affiliated with
multiple districts. The approximation would under-
estimate the political difference between counties,
but the bias should be minor, as the affected county
takes less than 1 percent of the sample. We thank
the election offices of Hawaii and Alaska for clari-
fication and maps of the election districts from 2002
to 2013 in Alaska.
11. The reciprocity statistic calculates the summa-
tion of minimum value of each pair of edges by
dyad. Formally, gy miny y
mi
jijj
i
() (, ),
(, )
=∑ ∈
where denotes the set of all i, j pairs.
12. Formally,
gminy
fi jjiijk
ki
()
{,
,,
y=∑
∑∑
∈∈
≠∈
≠
yki}, where is the set of all vertices/nodes (coun-
ties), and yij,yki are values of the edge from county
i to j and k to i, respectively. The term is similar
to the 2-paths or mixed-2-stars in binary ERGMs,
which is the number of times a node receives an
edge and sends another (Morris, Handcock, and
Hunter 2008).
13. This conclusion depends on the assumption that the
context dissimilarity influences people’s decisions of
whether or not to migrate, not merely their choice of
destination. We would thus not expect this model to
accurately predict involuntary migration in response
to events like political turmoil or natural disasters,
which dominate people’s migration decisions under
those circumstances. However, such events seem
unlikely to have been significant drivers of inter-
nal migration in the United States during the study
period. We thank an anonymous reviewer for point-
ing out this assumption.
14. Because this is an aggregate-level analysis of
population flows, the finding does not distinguish
the characteristics of internal migrants, such as
their race and ethnicity or socioeconomic status.
Hence, we do not directly engage with more fine-
grained debates about whether immigration deters
in-migration and promotes out-migration of certain
population categories, as predicted by some of the
literature (Frey 1995a). Such an analysis would
require more detailed data.
15. As discussed in the Hypotheses section, both way-
point flow and transitivity are triadic features that
concern edge structure in an (i, j, k) triple; way-
point flow captures the “backbone” of flow within
the triple (i → j → k), and transitive triads involve
the co-presence of waypoint flow and a direct i →
k flow. The negative effect for waypoint flow in
our models means triples with strong i → j → k
paths are suppressed, which also necessarily sup-
presses transitive triples net of other effects in the
model. Interestingly, while the waypoint flow (and
its binary-network version, two-paths) is a more
basic lower-level dependence structure, which car-
ries motivations from social behavior patterns such
as those detailed in this article, it receives relatively
less examination in the network literature. We hope
this article helps draw more attention to waypoint
flow and other triadic network structures of poten-
tial substantive importance for flow networks.
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Peng Huang is a Sociology PhD candidate and a
Statistics MS candidate at the University of California-
Irvine. He develops and applies statistical and compu-
tational methods to explore migration dynamics, social
interaction patterns, population health, neighborhood
effects, and inequality. His current project investigates
the social and political forces that drive or inhibit
migration. He also studies the spatial distribution of
interpersonal networks to reveal the disease diffusion
process and associated health disparities. His work has
appeared in American Sociological Review, Journal of
Mathematical Sociology, Proceedings of the National
Academy of Sciences, Social Networks, and Sociologi-
cal Methodology.
Carter T. Butts is a Chancellor’s Professor at the Uni-
versity of California-Irvine. His work involves the devel-
opment and application of mathematical, computational,
and statistical techniques to complex relational systems,
including social networks, patterns of social micro-
interaction, trajectory data, and spatial processes. His
work has appeared in Science, the Proceedings of the
National Academy of Sciences, the American Sociologi-
cal Review, Social Networks, and the Journal of Math-
ematical Sociology.