ChapterPDF Available

Free to Move: The Evolution of the European Migration Network, 1960–2017


Abstract and Figures

This contribution introduces a network approach to horizontal Europeanisation research and investigates the interconnectedness of European societies via cross-border migration. The main underlying assumption is that the establishment of pan-European mobility rights and their extension to ever wider shares of Europe's population has stimulated intra-European migration. Taking a social network perspective, we track the development of the European migration network over more than half a century (1960-2017). The analysis is based on dyadic migration stock data for 37 European countries, stemming from the World Bank and the United Nations. Indeed, large parts of the evidence suggest advancing horizontal Europeanisation, as the European mobility network has become more tight-knit and Europeans increasingly move within Europe rather than to countries in other parts of the world. Europe even emerged as a distinct and largely unified entity in the worldwide migration network, at least until 2010. At the same time, the shape of the European migration network reveals a strong core-periphery division. Moreover, since the dissolution of the Eastern bloc this sociometric hierarchy increasingly maps on Europe's economic core-periphery structure. Taken together, our findings suggest an advancing, yet unequal and partially challenged Europeanisation.
Content may be subject to copyright.
Chapter 4
Free to Move: The Evolution of the European
Migration Network, 1960–2017
Jan Delhey1,*, Monika Verbalyte1,2, Auke Aplowski1, Emanuel Deutschmann3
1 Otto-von-Guericke University Magdeburg, 2 Free University Berlin, 3 European University Institute,
* Corresponding author:
Abstract: This contribution introduces a network approach to horizontal Europeanisation re-
search and investigates the interconnectedness of European societies via cross-border migration.
The main underlying assumption is that the establishment of pan-European mobility rights and
their extension to ever wider shares of Europe’s population has stimulated intra-European mi-
gration. Taking a social network perspective, we track the development of the European migra-
tion network over more than half a century (1960-2017). The analysis is based on dyadic migra-
tion stock data for 37 European countries, stemming from the World Bank and the United Na-
tions. Indeed, large parts of the evidence suggest advancing horizontal Europeanisation, as the
European mobility network has become more tight-knit and Europeans increasingly move
within Europe rather than to countries in other parts of the world. Europe even emerged as a
distinct and largely unified entity in the worldwide migration network, at least until 2010. At
the same time, the shape of the European migration network reveals a strong core-periphery
division. Moreover, since the dissolution of the Eastern bloc this sociometric hierarchy increas-
ingly maps on Europe’s economic core-periphery structure. Taken together, our findings suggest
an advancing, yet unequal and partially challenged Europeanisation.
For a long time, the European Union (EU) and its predecessors have aspired to “an ever closer
union among the peoples” of Europe (first mentioned in the 1983 Solemn Declaration on the
European Union). In the most ambitious interpretation, this would mean the creation of a Eu-
ropean society. Among sociologists, however, there is widespread consensus that such an inte-
grated and institutionally unified entity does not exist (Bach, 2003; Rumford, 2001), as the
demos that typically characterizes a fully-fledged society is missing (Delanty, 1998). Scholars
Forthcoming as: Delhey, J., M. Verbalyte, A. Aplowski & E. Deutschmann (2019). Free to Move: The Evo-
lution of the European Migration Network, 1960-2017. In: M. Heidenreich (ed.), Horizontal Europeanisation:
The Transnationalisation of Daily Life and Social Fields in Europe. London/New York: Routledge.
do agree, however, on the existence of processes of growing transnationalisation, interdepend-
ence, and interconnectedness across national borders in Europe (Beck and Grande, 2008; Del-
hey, 2004; Heidenreich et al., 2012; Immerfall, 2000; Mann, 1998; Mau and Verwiebe, 2010)
trends that form part of horizontal Europeanisation as defined in the introductory chapter (Hei-
denreich in this volume).
This contribution looks at the interconnectedness of European societies established through a
specific type of cross-border exchange, namely migration. Today, to many observers migration
seems to have a predominantly conflictive connotationone of increased tensions or even dis-
integration, as in the case of the Brexit referendum (cf. Miller, 2016) or the Eastern opposition
to host refugees (Krăstev, 2017). More optimistic voices hint of the potential of immigration to
make societies more (Teney, 2012) and to strengthen feelings of transnational attachment
(Deutschmann et al., 2018). From a strictly structural perspective, however, cross-border ex-
change as such indicates transnational societalisation (Deutsch, 1957; Simmel, 1904). Adopting
this structural perspective, we study the development of cross-border migration among Euro-
pean societies since 1960 with the tools of social network analysis. Using the network notion
not only as a metaphor but as an analytical concept, we investigate the internal structure, den-
sity, and formation of the European migration network, and explore its distinctiveness in the
context of global population movements.
Migration in Europe has not been thoroughly analysed from this perspective so far. Qualitative
research has investigated the relation between migration and European identity (e.g. Recci and
Favell, 2009), with a key interest in vanguard migrants. Macro-sociological research on past
(e.g. Zimmermann, 1996) and current migration in Europe (e.g. Batsaikhan et al., 2018; Fass-
mann et al., 2009) traces the main directions, structural causes, economic consequences, and
political regulations of cross-border migration but does not pay close attention to what it means
in terms of Europeanisation. To fill this research gap, the contribution at hand addresses two
main questions: First, does the development of the European migration network suggest ad-
vancing horizontal Europeanisation? Second, what does the structure of the migration network
look like in terms of core and periphery? We explore these issues for 37 European countries,
covering the years from 1960 to 2017. This long period of investigation enables us to not only
track the evolution of the migration network over more than half a century but also to relate it
to the deepening and widening of Europe’s political integration. Our argument is that there is
at least an association between the establishment of mainly EU-induced mobility rights that
now entitle the vast majority of Europeans to move freely within EU borders and the develop-
ment of the European migration network.
In the next section we introduce in more detail our conceptual framework and the network
approach. We then present our research questions and consider contextual factors that shape
the evolution and structure of the European migration network. We then describe our data and
methods and present our empirical evidence. In the final section we summarize and discuss the
main findings, particularly against the EU’s objectives of an ever closer union of people as well
as socio-economic convergence among its member states.
Horizontal Europeanisation from a Social Network Perspective
Horizontal Europeanisation and Transnational Human Activities
The concept of horizontal Europeanisation (Heidenreich in this volume; cf. also Heidenreich
et al., 2012) has been developed in order to strengthen sociology’s capacity to grasp processes
of Europeanisation that are not immediately related to Europe’s political integration and supra-
national institution building. In this vein, horizontal Europeanisation considers the transnation-
alisation of national societies, with an eye on both social fields and the social space. Social
fields are the various systemic arenas in whichtypicallycollective actors compete strategi-
cally in pursuit of desirable resources. (Case studies on several European social fields, among
them the field of universities, are included in this volume.) The social space, in contrast, is the
space of social positions and social practices. Both social fields and the social space are influ-
enced by EU policiesthe former more directly through their strategic use (be it implementa-
tion, adjustment, or contestation) by the individual and collective actors in specific areas of
their professional activity, the latter more indirectly through the dissemination of European ref-
erence frames in personal attitudes or increasing accessibility and realisation of transnational
practices by European citizens within Europe.
While the process of Europeanisation of social fields is plumbable by observing strategic ac-
tions, decisions, institutional exchanges, and argumentation patterns of specific social fields,
capturing processes of Europeanisation of the social space requires a different approach because
its potential carriers are not specific, limited groups of actors but rather the European population
as a whole. A more subjective approach to the Europeanisation of the social space is to study
attitudes and reference frames of Europeans; a more objective approach is to study transna-
tional practices such as mobilities and communications. Here, we approach the issue of Euro-
peanisation of the social space by focussing on migration as one example of such transnational
practices. Recently, a number of studies have investigated transnational practices of Europeans,
often with an interest in individual-level differences created by class position, education, or age
(Delhey et al., 2015; Diez Medrano, 2010; Fligstein, 2008; Mau and Mewes, 2012). In contrast,
we are concerned with the aggregated macrostructure of human mobility between countries,
which puts us in the tradition of the transactionalist school of regional integration (Caporaso,
1971; Deutsch, 1957). This school gauged the status of world regional integration from statistics
about various cross-border transactions, ranging from trade and foreign direct investment to
communication and human mobility. Similarly yet more recently, the interconnectedness of
national societies via transactions and exchange has been a prominent approach in globalization
research (Held et al., 1999).
Europe as a Network: The Network Approach
While the macro-sociological interest in cross-border transactions is old, some methods to an-
alyse them properly are quite recent. We suggest treating Europe as a web of cross-border in-
terrelations created by transnational human activities, or in other words: as a network. One of
the main advantages of this concept is that it allows studying Europe as an entity, without re-
quiring or claiming the existence of a European society. Presumably for this very reason, schol-
ars from various campsthe cosmopolitan and the transactionalhave epitomized Europe as
a network (Axford and Huggins, 2000; Delanty, 1998; Mann, 1998; Roche, 2009). However,
whereas many use the term network merely as a metaphor, few capitalise on the tools of
social network analysisthe methodological advantage the network concept offers.
Network analysis is characterised by its relational perspective. Unlike other methodological
approaches, the network approach accounts for the interconnections and relational interdepend-
encies (in this case constituted by the mobility of migrants) among units (here: European coun-
tries) and allows one to study both the overall structure of the network and the position of spe-
cific entities (here: European countries as receivers and senders of migrants) in it. We use this
approach to shed new light on two questions: First, is horizontal Europeanisation advancing?
Second, how is Europe’s (emerging) social space structured? Our example is cross-border mi-
gration to which we now turn.
The European Migration Network
From Migration to the Migration Network
Migration denotes a locational change of a person’s usual place of residence; international mi-
gration, then, occurs when the movement crosses national borders. Furthermore, one can argue
that when people exit their home country and enter another one, they establish a connection
between them, or in other words, they form social bonds beyond the nationstate. In case the
two countries connected are both European, the formation of these social bonds through migra-
tion can be understood as part of the process of horizontal Europeanisation. Historically, Europe
has always been involved in international migrationtraditionally as a continent of emigration
(Altman and Horn, 1991), more recently of immigration (Özden et al., 2011: 38). Bringing
migration into focus suggests itself: It is traditionally considered to be an important indicator
of regional integration (Deutsch, 1972), and data are readily available through international
bodies like the United Nations and the World Bank. Moreover, (legal) migration heavily de-
pends on regulations and rights. A long-standing goal of the EC/EU has been to enable citizens
to move freely from one member state to another, and mobility rights were introduced and
applied to member states and affiliated countries. The issue of migration, therefore, is well
suited to investigate the impact of Europe’s political unification on its social space.
The chief attraction of studying Europe as a network is that the entire web of migration move-
ments can be taken into account. Surprisingly, the proper analytical tool, social network anal-
ysis, has been rarely utilized to study migration so far. To date, sophisticated network studies
are available for European trade (e.g. Fligstein and Merand, 2002), the Eurovision Song Contest
(e.g. Charron, 2013) and European transnational attachment (Deutschmann et al., 2018), but for
migration, few studies have used this method beyond the mere visualisation of migration move-
ments (cf. Brunarska et al., 2014 for a notable exception; also see network analysis of asylum
requests and transfers in the Dublin system by Lahusen and Wacker in this volume). Still, the
network approach is not an end in itself; rather, we want to utilize it to gain new insights into
issues that have occupied the sociology of Europe for a long time. The first of these issues is
whether there is a trend towards Europeanisation.
The Migration Network and Advancing Europeanisation: Three Criteria
How can we study horizontal Europeanisation from a network perspective looking at the case
of migration? Three criteria can be established.
Criterion 1: Europe’s interconnectedness: The first and most simple yardstick is whether over
time there is an increase in intra-European migration. The more border crossings, the more
“open” the containers of the nation–states are towards each other (see Heidenreich in this vol-
ume). Secondly, the tightness (or density) of the migration network is of interest: The more
countries in the network are connected with each other via cross-border mobility and the
stronger these ties on average are, the more consolidated the European social space is. Looking
at intra-European mobility alone is sufficient to reveal growing (or declining) interconnected-
Criterion 2: Europe’s internal closure: As Michael Mann has noted, “[h]uman history has seen
an enormous secular increase in the breadth of […] networks, moving humans away from a
predominance of local networks of interaction toward the national, the international and the
transnational—and most recently towards the global” (Mann, 1998: 187). This means that var-
ious layers of societalisation involving different spatial scales exist, ranging from the local up
to the global (cf. Heidenreich in this volume). For migration, movements within Europe consti-
tute only part of the international migration of Europeans, as popular destination countries may
lie in other world regions. The second criterion, therefore, is more demanding than the first one
in requiring the migration ties within Europe to be more extensive than those between Europe
and the outside world. From this relative perspective (cf. Delhey et al., 2014; Immerfall, 2000),
to diagnose horizontal Europeanisation is only justified if intra-European migration increases
faster than the cross-border migration of Europeans in general. To capture this empirically, we
develop a measure of internal closure, which denotes the share of intra-European migration in
all cross-border migration originating from European countries. Consequently, the European
migration network suggests advancing horizontal Europeanisation to the extent that its internal
closure increases. Empirically, this requires taking into account all migration ties in which Eu-
ropean countries are involved as “senders” of people, no matter their geographical reach.
Criterion 3: Europe’s distinctiveness: The third and final criterion is the most demanding one
in necessitating that Europe has been solidifying as a distinct migration space in the world. Put
differently, European countries should be more densely connected to each other via population
exchange than to non-European countries, and thus to have emerged as a distinguishable cluster
(a sub-network) within the global stream of migration. Whether Europe reaches such a visibility
within the global network, logically, depends not only on the amount and geographic reach of
Europeans’ cross-border migration, but also on that involving all mobile people on planet Earth.
Accordingly, for this final touchstone one has to observe the development of migration ties
among all countries worldwide.
The Migration Network and Coreperiphery Structures
The second issue of general interest we address in this contribution concerns Europe’s social
structure. Previously, scholars have mainly investigated the extent of territorial inequalities in
Europe, e.g. in terms of national or regional income levels, and how they are affected by pro-
cesses of vertical (i.e., supranational) and horizontal Europeanisation (Beckfield, 2009; Hei-
denreich and Wunder, 2007). In contrast, network analysis allows us to uncover the sociometric
structure of Europein our concrete case, the sociometry of the migration network. Inspired
by the world system’s research tradition that derives core/periphery positions of countries from
their economic exchange relations (cf. Babones, 2005; Massey et al., 1999), the migration net-
work can be described in terms of core and periphery, too. Whereas core countries tend to attract
mobile people so that on balance, more people immigrate than emigrate (resulting in a positive
net migration rate), for peripheral countries it is the other way around (a negative net migration
rate). The (changing) sociometric coreperiphery structure of migration can thus be used as a
lens that allows us to unveil unequal horizontal Europeanisation. Additionally, Europe’s soci-
ometric hierarchy in the migration network can be compared to other hierarchies established by
the more traditional comparative approache.g. the economic hierarchy in terms of national
income or levels of modernizationin order to explore how they relate to and presumably in-
fluence each other.
Factors that Shape the European Migration Network: A Parsimonious Model
For structuring the empirical analysis that follows, it is helpful to devise concrete expectations
about how the European migration network developed over the past 60 years. We highlight
three factors that hypothetically could have influenced both the network’s advancement and its
structure: legal migration regulations (mobility rights), the share of people entitled to mobility
(population coverage), and the inequality of the European social space in terms of socio-eco-
nomic and political conditions (structural heterogeneity). Our rationale for considering these
factors is as follows. The classical pushpull model of international migration (cf. Harris and
Todaro, 1970; Lee, 1966; Stark and Taylor, 1989) posits that people migrate from countries
where they lack opportunities to pursue their life goals to countries that offer better life chances.
Thus, huge between-country gaps in opportunities motivate migration. However, this motiva-
tion cannot always be actualized, and this is where mobility rights come into play: For legal
migration at least, people need both exit rights to leave their country of residence and entry
rights to settle in their preferred destination country (Cassee, 2016). Our basic assumption,
therefore, is that the more unequal a social space (here: Europe) is and the more people enjoy
encompassing mobility rights, the more migration will take place within that space. We will
now explain these factors in more detail, beginning with mobility rights.
The Deepening and Broadening of Mobility Rights
After WWII, three key developments in Europe furthered mobility rights and made them avail-
able to an ever-growing number of peopleto an extent that makes Europe (more precisely,
the EU and the member states of the European Economic Area, EEA) rather exceptional world-
wide. A first milestone was the bilateral recruitment agreements of the 1960s. To compensate
for shortages in the labour force, a number of West European countries signed contracts with
Southern and South-eastern European countries (as well as with Turkey and Morocco) to attract
so-called guest workers. Initially entailing a rotation principle, the agreements were soon re-
vised so that workers were allowed to permanently reside in and to bring their families to the
host country. After being in place for roughly one decade, recruitment agreements were sus-
pended in 1973, when the oil crisis harshly hit the West European economies. Still, millions
ofmainly Europeanworkers used the opportunity to work and live in the receiver countries
(cf. Castles, 1986).
A second milestone was the dissolution of the Eastern bloc. During the Cold War, a mixture of
legal obstacles, repression, and massive border controls kept emigration numbers in the socialist
countries generally low. Important exceptions were in place for some ethnic communities, e.g.
Germans living in the Eastern territories of the former German Empire (cf. Stola, 2005). When
the socialist regimes imploded in 19891992, Eastern Europeans gained exit rights, and with
their countries’ eventual EU accession (see below) also entry rights to other EU/EEA member
statesright away or after temporary entry limitations imposed by some EU-15 members
(Mungiu-Pippidi, 2005).
The final and most consequential milestone is closely related to the EU and its predecessors:
The free movement of workers is a fundamental EU principle, first established in the Treaty of
Rome in 1957 for the European Economic Community (EEC), later enshrined in Article 45 of
the Lisbon Treaty (Treaty on the Functioning of the European Union) and developed by EU
secondary legislation and jurisprudence of the European Court of Justice. EU citizens are enti-
tled to look for a job in another EU country, work there without needing a work permit, reside
in that country for the employment period, stay there even after employment has finished, and
enjoy equal treatment with nationals in access to employment, working conditions, and all other
social as well as tax advantages. The EU citizenship (Directive 2004/38/EC) complements these
rights insofar as EU citizens are generally allowed to stay in another member country for up to
90 daysor longer, if they have sufficient financial means and health insurance. The Schengen
Agreement and the introduction of the euro further lowered barriers for Europeans mobility
(Beine et al., 2017). Taken together, these supranational regulations imply a radical removal of
barriers for cross-border movement of EU citizens within the EU and the EEA. Practically,
fellow EU citizens are put on equal footing with national citizens.
Through the stepwise enlargements of the European Union (Vobruba, 2003), an ever-growing
percentage of the European population has become entitled to these far-reaching mobility rights
(see Table 4.1). The two-tiered Eastern enlargement was certainly most significant, as 13 new
member states accessed. Currently, 30 out of the 37 countries we consider in this study as Eu-
ropean are EU or EEA members, and their citizenries thus enjoy free movement rights, which
equals almost 90 percent of Europe’s population.1
Table 4.1. The sequence of EU enlargements
EU size
Countries involved
Founding 6
Belgium, France, Germany, Italy, Luxembourg,
Western en-
Denmark, Ireland, UK
Southern en-
*Greece, Portugal, Spain
Northern en-
Austria, Finland, Sweden
Eastern en-
largement I
Cyprus, Czech Republic, Estonia, Hungary, Lithu-
ania, Latvia, Malta, Poland, Slovakia, Slovenia
Eastern en-
largement II
Bulgaria, Romania, *Croatia
EEA members
Iceland (1970), Norway
Albania, Belarus, Bosnia & Herzegovina, Macedo-
nia, Serbia, Moldova, Switzerland (part of the sin-
gle market), Ukraine
The Changing Heterogeneity of the European Social Space
International migration surely has many reasons: job-seeking and career chances, quality-of-
life and personal development, marriage and family reunion, as well as political discontent and
wars. Consequently, the European migration history of the past 50 years knows various types
of migrants (Castles et al., 2014; King, 2002; Verwiebe et al., 2014). Although our data set of
country-to-country migration flows is silent about underlying motivations, we nevertheless can
consider Europe’s changing structural diversity, as the size and nature of between-country gaps
in political, economic, and other life conditions should affect migration decisions and thus
shape the migration network in a predictable way.
To start with, after WWII Europe developed into a remarkably peaceful continent in terms of
military conflicts. The wars in the former Yugoslavia (19912000) and the recent military con-
flict in Ukraine have been the major exceptions. In terms of political liberties and civil rights,
contemporary Europe is much more homogenous than in the past, thanks to democratic transi-
tions first in the South, later in the East. The vast majority of European countries is governed
democratically (e.g. Vanhanen's Index of Democracy; Bühlmann, 2011: 2122) and is consid-
ered “free” by Freedom House. Only Belarus is rated “not free”, and four countriesAlbania,
Bosnia and Herzegovina, Moldova, and Ukraine—“partly free” (Freedom House, 2018). While
this does not rule out that political discontent could still play a role in stimulating migration
(Bygnes & Flipo, 2017), in post-Cold-War Europe few populations suffer from political perse-
cution. Tellingly, most refugees nowadays come from outside Europe (Castles et al., 2014: 80;
Eurostat, 2018).
Figure 4.1. Drivers of European migration and their impact on the European migration network
Source: Own depiction.
Structural heterogeneities
political, economic, quality of life
migration incentives
changing structure of the European
migration network
(Expanding) pan-European
mobility rights
exit rights; entry rights
migration entitlements
growing density, closure and
distinctiveness of the European
migration network
In contrast, the diversity in levels of socio-economic development is still large. There is a five-
fold difference in GDP per capita in purchasing power parities between Luxembourg (the rich-
est EU member state) and Bulgaria (the poorest one), and a threefold difference between Ireland
(second richest) and Romania (second poorest). Moldova and Belarus, two former Soviet Re-
publics, are even poorer than Bulgaria (World Bank, 2018). Most importantly, with eastward
enlargement the lion’s share of Europe’s socio-economic heterogeneity has been incorporated
into the borders of the EUand thus into the area of application of EU citizenship and free
movement rights. Public opinion research has revealed that EU citizens are quite aware of their
country’s relative economic standing, and that GDP per country is the main yardstick people
apply when evaluating various aspects of national living conditions (Delhey and Kohler, 2008).
This suggests that Europe’s economic gradient increasingly shapes the migration network.
Deriving Expectations about the Evolution of the European Migration Network
To summarize the argument (cf. also Figure 4.1), over time an increasing proportion of Euro-
peans has been legally entitled to move freely within the EU/EEA. This dual expansion terri-
torial and substantial of mobility rights took place in a situation in which political differences
between European countries lessened while socio-economic differences were still large and
progressively incorporated into the EU. This confluence of legal and structural changes allows
us to develop a set of hypotheses.
Europe’s interconnectedness: Due to expanding mobility rights and the stepwise enlargement
of the EU, we assume more intra-European migration and a more consolidated network.
H1: Over time, the European migration network has become more extensive and
Europe’s core–periphery structure: Given Europe’s (and the EU’s) socio-economic diversity,
we expect to find a clear-cut coreperiphery structure of the European migration network.
Moreover, as political conditions converge, the economic gradient should increasingly shape
the network.
H2: Over time, there is a growing association between Europe’s sociometric core–
periphery structure and its economic hierarchy.
Europe’s internal closure: The mobility rights discussed above are specifically European; they
made it easier for Europeans to move to other European countries. Thus, their implementation
should have affected the geography of migration in making other European countries more ac-
cessible as destinations, relative to countries in other parts of the world.
H3: Over time, Europeans increasingly move to other European countries, rather
than to non-European countries.
Europe’s distinctiveness: The last hypothesis addresses the visibility of a genuinely European
migration cluster as a sub-network within the worldwide flows of migration. Assuming that
intra-European mobility has increased both in absolute (H1) and relative terms (H3), it is pos-
sible that Europe has solidified as a distinct space of migration.
H4: Over time, Europe has become more distinguishable as a regional migration
cluster within the worldwide migration network.
Data and Methods
This set of hypotheses is tested with dyadic migration stock data. Data sources are the World
Bank Global Bilateral Migration Database (19602010, with one observation every 10 years,
plus 2013) and the United Nations Population Division (19902015, with one observation every
five years, plus 2017). Both organisations provide data on worldwide migration in a dyadic and
directed format, i.e., the number of migrants from country A living in country B. The definition
of migrants according to the World Bank considers only “foreign-born” people (Özden et al.,
2011). In contrast, the UN data set comprises “foreign-born” people and, if there is no infor-
mation about the place of birth, “foreign citizens” (United Nations, 2017: 3); the UN also adds
the number of refugees provided by the UNHCR (UN High Commissioner for Refugees) for
some developing countries that are “deemed not to have included refugees in their reported
statistics on the stocks of international migrants” (Ibid: 4). For full transparency, we show trends
from both sources where possible.
After excluding countries with fewer than 100,000 inhabitants, the global data set consists of
179 sender and receiver countries (amounting to a network of 31,862 dyads). We use this data
set to test H4. For the research questions related exclusively to intra-European migration (H1
and H2), we create a reduced version of the data set that contains, following the UN M.49
GeoScheme for Europe (excluding Russia), 37 European countries, or 1,332 country pairs. To
test H3, we compare intra-European migration flows with worldwide migration from Europe,
based on these data sets.
We use network visualisations to illustrate the European migration network as well as several
social network analysis measures, including density and centrality, to describe it (cf. Wasser-
man and Faust, 1999). For the last analytical step, i.e., detecting a potential European cluster
within the global migration network, we employ a community detection algorithm, the density-
based modularity approach (Newman, 2006). This algorithm combines countries into clusters
if they are more tightly connected with each other than beyond cluster borders.
The European Migration Network in 2017
Figure 4.2 depicts the state of the European migration network in 2017, taking only mobility
between European countries into account. The thicker the arrow, the more migrants from coun-
try A (where the arrow starts) live in country B (where the arrow ends). The bigger the country
bubble, the higher the involvement of that country in intra-European migration in terms of both
immigration and emigration. The colour of the country bubble indicates whether there are more
immigrants than emigrants in that country (blue) or the other way around (red).
There are three main movement directions of migrants, mirroring both recent and more distant
flows since WWII (additional visualisations for the years 1960 to 2015 are available at First, an EastWest movement (e.g. Poland and Romania
Germany; Poland UK), which includes refugees in the wake of WWII (Castles et al.,
2014: 104), dissidents fleeing from persecution under communism (e.g. Stola, 2005), and more
recent labour migrants. Economic migration from the East to the West starkly grew after the
removal of the Iron Curtain and accelerated as the EU expanded eastward in 2004 and 2007 (cf.
Kahanec et al., 2016).
The second major direction is SouthNorth (e.g. Portugal → France; Italy → Germany), which
started in the 1960s with the recruitment of guest workers to the industrially flourishing Western
European countries (e.g. Castles, 1986). The third big population movement involves the East
South direction (e.g. Romania Italy; Romania Spain). Starting in the early 2000s, this
movement is largely a product of EU enlargements (Castles et al., 2014: 114), which gave free
movement rights to the new, and on average much poorer, EU citizens (cf. Favell, 2008). In
this context, some of the Southern countries of the EU-15 themselves became destination coun-
tries for European migrants, in particular from the new EU member states (Castles et al., 2014:
Other, smaller migration routes are from the republics of the former Yugoslavia to Greece,
Italy, Austria, and Germany; and from Ukraine to Poland, Belarus, and Western European coun-
tries. These flows may be seen as exceptions in today’s context, as they were likely induced by
recent or current military conflicts: by the Yugoslav wars in 19912000 (cf. Molnar, 1997) and
the ongoing military conflict in Ukraine (e.g. European Asylum Support Office, 2016: 4041).
Other country connections reflect labour migration between prosperous countries (e.g. Ger-
many → Switzerland; Ireland UK).
Figure 4.2: European migration network, 2017
Note: Only 40 percent of the highest migrant movements (more than 2574 migrants, which corresponds
to the 3rd quintile of the rank-ordered distribution in 2010) is depicted. Data: United Nations.
Finally, the arrow from the UK to Spain may contain “lifestyle migrants”: a growing number
of (mainly retired) British citizens who go abroad to enjoy their lives in a warmer climate with
a higher disposable income than at home (Office of National Statistics, 2017).
Overall, Europeans have predominantly moved from the poorer fringes to the rich centre of the
continent, and overwhelmingly from the new member states in the East to Western and South-
ern Europe. In recent decades, these movements of people have generated five main receiver
countries (the bluest country nodes) that attract the lion’s share of European migrants in abso-
lute numbers: Germany, the UK, Spain, Italy, and France. Switzerland is another yet much
smaller immigration magnet. In contrast, Poland, Romania, Portugal, Ukraine, Bosnia and Her-
zegovina, and Albania are the countries with the highest absolute numbers of emigrants (the
reddest nodes).
The Growth and Tightness of the European Migration Network (H1)
In our observation period, the absolute number of intra-European migrants has grown continu-
ously, from 15 million in 1960 to 27 million in 2017. But does this rise just result from increases
between a few country pairs, or has the European migration network as a whole become tighter?
The density measure informs about existing ties as a portion of all theoretically possible ties,
and thus ranges from 0 to 1. The higher the density, the higher the number of countries con-
nected to each other through “considerable”2 migration ties. Figure 4.3 (black line) shows that
the density of the European migration network has grown steadily over time, increasing from
.42 in 1960 to .70 in 2017. Put differently, while in 1960, 42 percent of the country pairs had a
considerable amount of migration occurring between them, in 2017, 70 percent did. The steady
increase suggests a robust and long-term trend towards a more comprehensive, tight-knit net-
Figure 4.3: Density of the European migration network, 1960-2017
Note: The measurement of density is based on the dichotomised data (see footnote 2). The average
degree is calculated from the weighted original data.
A second network measure we draw on is average degree, i.e., the average number of migrants
between two countries. This measure also suggests growing interconnectedness (Figure 4.3,
grey line). Whereas the average link was approximately 10,000 migrants in 1960, it reached
almost 25,000 in 2017. Like density, average degree confirms H1. European countries are today
more interrelated with each other via cross-border migration than ever since the 1960s.
The CorePeriphery Structure of the European Migration Network (H2)
What does the sociometric structure of the European migration network look like, and how
closely does it match Europe’s economic hierarchy? To answer these questions, we first need
to establish the core and periphery of the migration network. As suggested in the conceptual
part, core (peripheral) countries are defined as those with the most positive (negative) net mi-
gration rates.3 Put differently, the sociometric positions are derived from relative, not absolute
migration numbers.
Figure 4.4: Net migration rates of the European countries, 2017
Data: United Nations.
In 2017, Luxembourg (well ahead of all the others), Switzerland, Austria, Norway, and Ger-
many were the most central countries, whereas Lithuania, Romania, Moldova, Belarus, and
Albania were the most peripheral ones (Figure 4.4). This structure represents a division into
West and East, as already suggested by the migration map (Figure 4.2).
We are now able to investigate the overlap between the sociometric and the economic hierarchy,
the latter derived from countries’ GDP per capita, adjusted for purchasing power. Table 4.2
provides the respective rank correlations since 1990. Back then, there was a moderately strong
positive association between the two hierarchies (a rank correlation of .55). Since then, this
association has become much stronger, hitting a record high of .78 in 2017. There has been an
intermediate trend of a slightly weakening association during the 1990s, which possibly reflects
the large number of ex-Yugoslavians who escaped war in their countries of origin and who thus
did not move for economic reasons. Yet the general trend is unmistakable: Europe’s economic
and sociometric coreperiphery structure increasingly map on each other, confirming H2. The
time period between 2000 and 2010 has seen the biggest increase of the rank correlation. Two
rounds of eastward enlargements through which predominantly less prosperous countries ac-
cessed the EU, and the pacification of the Balkan region, explain this rise. The financial crisis
of 20072008, but not the Euro crisis of 20102014, seems to have reinforced this trend.
Table 4.2: Correlation between Europe’s economic and sociometric hierarchy
Rank correlation
Source of migration data
Note: GDP in PPP (current international $) per capita. Calculations for 2017 with GDP values of 2016.
Rank correlation entries are Spearman rank correlation coefficients. * p < .05, ** p < .01, *** p < .001.
Data: World Bank for GDP, World Bank and UN for migration. If both sources provided migration data,
separate rank correlations were calculated and then averaged.
Europeanisation or Globalization? The Network’s Internal Closure (H3)
In the conceptual part we argued for the importance of distinguishing Europeanisation specifi-
cally from transnationalisation more generally. Internal closure captures the migrants from Eu-
rope moving to other European countries as a share of all migrants from Europe. As displayed
in Figure 4.5, there is a clear trend towards internal closure. In 1960, a minority of 38 percent
of European migrants moved within Europe’s borders, whereas a majority of 62 percent moved
to the other continents. By 2017, these proportions had reversed to 59 percent moving inside
and 41 percent outside of European enormous shift in proportions that confirms H3. Much
of the change took place after 1990, i.e. after the dissolution of the Eastern bloc.
Figure 4.5: Europe’s internal closure
Europe’s Distinctiveness in the Global Migration Network (H4)
We now move on to the last question, the extent to which Europe constitutes an identifiable
cluster within the worldwide migration network. Answering this question requires the analysis
of the structure of migration ties globally (technically speaking, one has to detect the global
network’s community structure). Our answer comes in two parts: one part is based on the long-
term trend from 1960 to 2010, and the other is based on recent developments from 2010 to
2017. The results of this community detection are shown in condensed form in Figure 4.6. It
reveals that the decades 19602010 are indeed characterised by the rise of Europe as a distinct
and increasingly unified regional migration cluster. In 1960 and 1970, Europe did not exist as
a united migration cluster. Instead, European countries were part of two separate clusters: The
Eastern bloc, (depicted in red) which stretched well into the western part of Europe, including
Germany and Austria, and the large, US-dominated multiregional cluster, (depicted in ma-
genta), which included most of Western and South-eastern Europe, but also many other coun-
tries from around the globe. Things changed dramatically between 1970 and 1980, as a new
European migration cluster emerged with Western Europe as its centre of gravity: “Europe-
plus” (depicted in blue). Figure 4.6 visualises how Europe-plus originated from some countries
separating from the multiregional cluster and others from the Eastern bloc. Until 2010, Europe-
plus persisted and grew bigger over time. Although formed overwhelmingly by European coun-
tries, Europe-plus also contains several non-European countries, such as former colonies in
South America that are connected to Europe-plus via strong migration ties to Portugal and
Spain, hence the plus in the cluster name. Yet over the decades, Europe-plus has steadily be-
come more European, both in terms of countries involved and the share of intra-cluster migrants
(note: only the latter information is depicted in Figure 4.6).
As a result, in 20104 the world of migration viewed from a European perspective looked as
follows (Figure 4.7): Most European countries (27) formed part of Europe-plus; six belonged
to Eastern Europe and Central Asia; and another four countries, including the UK and Ireland,
to the global multiregional cluster. Moreover, most of the 20 non-European countries that were
part of Europe-plus were connected via one or two strong ties only, typically the former colonial
powers Spain, Portugal, or France. In terms of migration volumes, roughly 85 percent of all
migration within Europe-plus was intra-European. This leads us to conclude that by 2010 Eu-
rope had emerged and solidified as a quite distinct migration space, largely in accordance with
In 2017, however, the world looked very different. Events such as the UkraineRussian war,
the civil war in Syria, and the subsequent refugee crisis of 20152016 had considerably re-
shaped the European migration cluster vis-à-vis other clusters. Basically, Europe had disinte-
grated. As Figure 4.8 reveals, in 2017, European countries belonged to four different clusters:
(1) a considerably shrunken Europe-plus, consisting mainly of Western and Southern European
countries which, in part, constituted the former EU-15; (2) Eastern Europe and Central Asia, to
which currently the majority of European countries, including Germany, belonged; (3) the mul-
tiregional cluster, of which the UK and Ireland were part; and (4) “Levant-plus,” a new cluster
with a centre of emigration in the Middle East, to which Sweden is linked via the large number
of immigrants from these countries (The United Nations Refugee Agency, 2018) and Finland
through strong migration ties with Sweden (e.g. Hedberg, 2007: 455456). The recent and
largely crisis-driven migration flows from the former Soviet republics and from Europe’s neigh-
bouring regions particularly into Europe’s Western countries thus reshaped the global migration
network and its components. These developments appear to have interfered withand to some
extent reversed—Europe’s rise as a largely united and distinct migration cluster. In terms of
migration, the period 20052010 may represent the apex of horizontal Europeanisation.
Figure 4.6: Transitions among clusters of the global migration network, 1960-2017
Note: The thickness of cluster shows what part of the worldwide migration movement the migration movement in that cluster constitutes. Only European migration is coloured.
Colours correspond to those in figures 7 and 8. Data: World Bank (for 1960, 1970, 1980, 1990, 2000, 2010) and UN (for 2017).
Figure 4.7: Worldwide analysis of migration clusters, 2010
Note: Only the highest 40 percent of migrant movements are depicted (i.e. more than 2,574 migrants, which is equivalent to the 3rd quintile of the rank-ordered distribution in the
European network in 2010). The thickness of arrows is proportional to the number of migrants who went from the country where the arrow starts to the country where the arrow
ends. The size of country nodes is proportional to the sum of all the immigrants and emigrants in the country. The size of arrows and nodes is directly comparable across years. The
community detection algorithm used is based on modularities (with a 1.5 resolution factor). The analysis returned 7 clusters (modularity = .518, with resolution = .887), represented
by different colours: Magenta Multiregional cluster, Red Eastern Europe and Central Asia, Blue Europe Plus, Green Muslim world & South Asia, Dark yellow Western
Africa, Light yellow Eastern and Southern Africa, Grey Iran, Iraq & Afghanistan. Data: World Bank.
Figure 4.8: Worldwide analysis of migration clusters, 2017
Note: For 2017, modularity analysis returned 6 clusters (modularity = .504, with resolution = .885), represented by different colours: Magenta Multiregional cluster, Red
Eastern Europe and Central Asia, Blue Europe Plus, Green Muslim world & South Asia, Dark yellow Eastern and Southern Africa, Black Levant Plus. Data: UN.
Discussion and Conclusion
The aim of this contribution was to introduce a network approach to horizontal Europeanisation
research and to demonstrate its empirical power using the example of migration. We tracked
the development of the European migration network over more than half a century (1960
2017), prompted by two main questions: Is Europe “growing together”, in terms of migration?
And how did the social structure of the European migration network change over time?
The evidence in large part suggests a growing Europeanisation since 1960, and particularly
after the continent’s political division into “West” and “East” had been overcome. European
societies have been growing together via migration (increasing interconnectedness), and mobile
Europeans increasingly move to other European countries rather than to countries outside Eu-
rope (internal closure). When considering the worldwide movement of people, Europe emerged
and then solidified as a quite distinct and increasingly united migration cluster from 1980 on-
wards, roughly until 2010 (worldwide distinctiveness).
Which social forces are responsible for this unfolding Europeanisation? We argue that supra-
national regulations and Europe’s widening political unification are key. Comprehensive mo-
bility entitlements, and particularly the EEC/EU-related free movement regulations, greatly
simplified intra-European migration. As the EU enlarged (most relevant has been the massive
eastward enlargement of 20042007), these entitlements were granted to an ever-growing num-
ber of Europeans. While we cannot statistically prove that this confluence of deepening and
widening unification caused the Europeanisation of migration, it certainly has contributed to it.
This interpretation is in line with the idea of an indirect influence of EU policies, rules, and
resources on the European social space. More generally, our findings suggest that supranational
regulation projects (cf. Therborn, 1995) do have the means to shape transaction flows and thus
to stimulate interaction among the populations involved. Yet this is not a one-way street, and
political disintegrationmember states separating, less encompassing mobility rightscould
reverse the trend towards interconnectedness.
At the same time, our results suggest that an increasing Europeanisation of migration (a solid
trend until today) does not necessarily result in Europe being a separate and largely united clus-
ter within the worldwide network of migration. The recent and largely crisis-driven immigration
from Northern Africa and the Middle East to Europe fragmented the European migration clus-
ter, reversing its formation and solidification between ca. 1975 and 2010. It is difficult to fore-
cast whether this fragmentation is just a short-term interlude or will persist. Yet it is possible
that in terms of worldwide distinctiveness, we have seen the apex of the European migration
cluster around 2010, as Europe is now attracting more and more people from other world re-
gions, intentionally and unintentionally. In fact, since 1970 the relative mix of people migrating
to European countries has gradually shifted towards non-European migrants. Thus, only seem-
ingly paradoxical, the migration flows involving European countries are characterized by both
internal Europeanisation and external globalization.
Our research also provides new insights into the social (in the sense of sociometric) structure
of Europe’s social space. The sociometric hierarchy of countries as established by migration
flows reveals a clear WestEast division: All core countries are West European countries from
the former EU-15, while the most peripheral countries all belong to the Eastern part. Our anal-
ysis further contributes to the knowledge of how horizontal Europeanisation is interrelated with
territorial inequalities. Since the end of the Cold War, Europe’s sociometric hierarchy increas-
ingly has mapped onto its economic hierarchy. In other words, despite new and emerging types
of migrants, intra-European migration increasingly follows the economic gradient. This does
not necessarily mean that the motivation to go abroad is strictly economic; rather, the data sug-
gest that whatever motivates Europeans to emigrate, they increasingly expect to find it in the
affluent countries.
Primarily, the intensification of this tendency is a consequence of the EU’s enlargements since
2004, because free-movement rights were extended to Eastern Europeans for whom the eco-
nomic incentive to migrate was enormous (Mungiu-Pippidi, 2005). In a feedback loop, migra-
tion flows might fuel intra-European differences in economic prosperity via “brain drain” and
“brain gain” (cf. Beine et al., 2003; Docquier and Marfouk, 2006). Some emigration countries
such as Poland or the Baltic states already suffer from a shortage of qualified labour (e.g. Ka-
czmarczyk and Okólski, 2008), and even when taking emigrant remittances to the home coun-
tries into account, the general effect of migration to the sending countries is still rather negative
(e.g. Atoyan et al., 2016). Therefore, current migration patterns clearly complicate the promi-
nent EU goal of social and economic convergence among member states, as stated in Article B
of the Maastricht Treaty (Treaty on the European Union). Seen in conjunction with the well-
documented class bias in transnational activities (Delhey et al., 2015; Gerhards, 2014; Kuhn,
2011; Mau and Mewes, 2012), it appears that processes of horizontal Europeanisation amplify
rather than attenuate existing inequalities.
For other reasons, too, the growing intra-European migration might turn out to be a Pyrrhic
victory. In terms of sentiment towards migrants, there is evidence for backlash, as demonstrated
by the resistance against Polish immigrants to the UK and Romanian immigrants to France,
Italy, and Spain. Anti-migration sentiment was a key issue in the Brexit campaign that finally
won the referendum (e.g. Hobolt, 2016). If properly exploited by political parties, this sentiment
is fertile ground for Euroscepticism (Kuhn, 2011)even more so as Europe has become a pre-
ferred destination for refugees from crisis-ridden neighbouring regions. It therefore remains to
be seen whether immigration undermines citizens’ support for an achievement that proved in-
dispensable for the growing together of European societies: the free-movement rights of EU
This research is part of the project Cross-border Interactions and Transnational Identities,
which is supported by the German Research Foundation (DFG) within the framework of the
DFG research unit FOR-1539, Horizontal Europeanisation. For more information, see We are grateful to the editors of this volume, Martin
Heidenreich and Jenny Preunkert, as well as to Christian Lahusen and Christian Schneickert,
for their valuable feedback, and to Jonas Lohmüller for his helpful research assistance.
1. Even if one follows the broader definition of Europe by UN DESA, 69 percent of the Euro-
pean population are inhabitants of EU/EEA member states.
2. Density is a binary measure. To reduce our valued network to the binary one, we have set a
threshold from which we count ties as being large enough to be counted as relevant. Here, this
threshold is set to 146 migrants, which corresponds to the first quintile of the rank-ordered
distribution of the migration ties between European countries in 2010 (UN data).
3. In network terms, the balance of relative in- and out-degree centrality. In-degree centrality is
the sum of all incoming ties to a country (in this case the number of European immigrants in
that country), while out-degree centrality is the sum of the outgoing ties (i.e., the number of
emigrants from a given country who moved to other European countries). Both centrality
measures were calculated using relative numbers of migrants. For in-degree centrality, the num-
ber of immigrants was divided by the population of the receiving country. Equivalently, for out-
degree centrality the emigrant volume was put in relation to the sending country population.
4. Previous years available at
Altman I and Horn JPP (1991) Introduction. In: Altman I and Horn JPP (eds) "To Make Amer-
ica": European Emigration in the Early Modern Period: Berkeley: University of California
Press, pp. 129.
Atoyan R, Christiansen LE, Dizioli A, et al. (2016) Emigration and Its Economic Impact on
Eastern Europe. Staff Discussion Notes 16(7): 1.
Axford B and Huggins R (2000) Towards a Post-National Polity: The Emergence of the Net-
work Society in Europe. The Sociological Review 48(1_suppl): 173206.
Babones S (2005) The Country-Level Income Structure of the World-Economy. Journal of
World-Systems Research 11(1): 2955.
Bach M (2003) The Europeanization of Cleavages and the Emergence of a European Social
Space. Journal of European Social Policy 13(1): 5054.
Batsaikhan U, Darvas Z and Raposo IG (2018) People on the Move: Migration and Mobility in
the European Union. Bruegel Blueprint Series, 28.
Beck U and Grande E (2008) Cosmopolitan Europe. Cambridge: Polity Press.
Beckfield J (2009) Remapping Inequality in Europe. International Journal of Comparative So-
ciology 50(5-6): 486509.
Beine M, Bourgeon P and Bricongne J-C (2017) Aggregate Fluctuations and International Mi-
gration: CESifo working paper, 4379.
Beine M, Docquier F and Rapoport H (2003) Brain Drain and LDCs' Growth: Winners and
Losers. IZA Discussion Papers, 819.
Brunarska Z, Nestorowicz J and Markowski S (2014) Intra- vs. Extra-Regional Migration in
the Post-Soviet Space. Eurasian Geography and Economics 55(2): 133155.
Bühlmann M (2011) The Quality of Democracy: Crises and Success Stories. Paper presented
at the IPSA-ECPR joint conference, Sao Paolo, February 16-19. Available at: (accessed 21 June 2018).
Caporaso JA (1971) Theory and Method in the Study of International Integration. International
Organization 25(2): 228253.
Cassee A (2016) Globale Bewegungsfreiheit: Ein Philosophisches Plädoyer für Offene Gren-
zen. Berlin: Suhrkamp.
Castles S (1986) The Guest-Worker in Western Europe - An Obituary. International Migration
Review 20(4): 761778.
Castles S, Haas H de and Miller MJ (2014) The Age of Migration: International Population
Movements in the Modern World. Hampshire and New York: Palgrave Macmillan.
Charron N (2013) Impartiality, Friendship-Networks and Voting Behavior: Evidence from Vot-
ing Patterns in the Eurovision Song Contest. Social Networks 35(3): 484497.
Delanty G (1998) Social Theory and European Transformation: Is there a European Society?
Sociological Research Online 3(1): 115.
Delhey J (2004) European Social Integration: From Convergence of Countries to Transna-
tional Relations Between Peoples. WZB Discussion Paper 2004-201.
Delhey J, Deutschmann E and Cirlanaru K (2015) Between 'Class Project' and Individualiza-
tion: The Stratification of Europeans' Transnational Activities. International Sociology
30(3): 269293.
Delhey J, Deutschmann E, Graf T, et al. (2014) Measuring the Europeanization of Everyday
Life: Three New Indices and an Empirical Application. European Societies 16(3): 355377.
Delhey J and Kohler U (2008) Where We Stand in Europe: Citizen Perceptions of European
Country Rankings and Their Influence on Subjective Well-Being. In: Alber J, Fahey T and
Saraceno C (eds) Handbook of Quality of Life in the Enlarged European Union: London:
Routledge, pp. 385404.
Deutsch KW (1957) Political Community and the North Atlantic Area: International Organi-
zation in the Light of Historical Experience. Princeton: Princeton University Press.
Deutsch KW (1972) Nationenbildung - Nationalstaat - Integration. Düsseldorf: Bertelsmann.
Deutschmann E, Delhey J, Verbalyte M, et al. (2018) The Power of Contact: Europe as a Net-
work of Transnational Attachment. European Journal of Political Research (Online first).
Diez Medrano J (2010) A New Society in the Making: European Integration and European
Social Groups. KFG (The Transformative Power of Europe) Working Paper No. 12.
Directive 2004/38/EC. Available at: (accessed 11 October 2017).
Docquier F and Marfouk A (2006) International Migration by Education Attainment, 1990
2000. In: Özden Ç and Schiff MW (eds) International Migration, Remittances, and Brain
Drain: Washington and New York: World Bank and Palgrave Macmillan, pp. 151200.
European Asylum Support Office (2016) Annual Report on the Situation of Asylum in the Eu-
ropean Union 2015. Available at: (accessed 7 March 2018).
Eurostat (2018) Asylum Statistics. Available at: (accessed 21 June
Fassmann H, Haller M and Lane DS (eds) (2009) Migration and Mobility in Europe: Trends,
Patterns and Control. Cheltenham and Northampton: Edward Elgar.
Favell A (2008) The New Face of EastWest Migration in Europe. Journal of Ethnic and Mi-
gration Studies 34(5): 701716.
Fligstein N (2008) Euroclash: The EU, European Identity, and the Future of Europe. Oxford:
Oxford Univ. Press.
Fligstein N and Merand F (2002) Globalization or Europeanization? Evidence on the European
economy since 1980. Acta Sociologica 45(1): 722.
Freedom House (2018) Freedom in the World 2018: Democracy in Crisis. Available at:
Gerhards J (2014) Transnational Linguistic Capital: Explaining English Proficiency in 27 Eu-
ropean Countries. International Sociology 29(1): 5674.
Harris JR and Todaro MP (1970) Migration, Unemployment & Development: A Two-Sector
Analysis. American Economic Review 60(1): 126142.
Hedberg C (2007) Direction Sweden: Migration Fields and Cognitive Distances of Finland
Swedes. Population, Space and Place 13(6): 455470.
Heidenreich M, Delhey J, Lahusen C, et al. (2012) Europäische Vergesellschaftungsprozesse:
Horizontale Europäisierung zwischen Nationalstaatlicher und Globaler Vergesellschaf-
tung. Available at: (accessed 15 March 2016).
Heidenreich M and Wunder C (2007) Patterns of Regional Inequality in the Enlarged Europe.
European Sociological Review 24(1): 1936.
Held D, McGrew AG and Goldblatt D (1999) Global Transformations: Politics, Economics
and Culture. Stanford: Stanford Univ. Press.
Hobolt SB (2016) The Brexit Vote: A Divided Nation, a Divided Continent. Journal of Euro-
pean Public Policy 23(9): 12591277.
Immerfall S (2000) Fragestellungen einer Soziologie der Europäischen Integration. Kölner Zeit-
schrift für Soziologie und Sozialpsychologie, Sonderheft 40: 481503.
Kaczmarczyk P and Okólski M (2008) Economic Impacts of Migration on Poland and the Bal-
tic States. Fafo-Paper 2008(1).
Kahanec M, Pytlikova M and Zimmermann KF (2016) The Free Movement of Workers in an
Enlarged European Union: Institutional Underpinnings of Economic Adjustment. In: Ka-
hanec M and Zimmermann KF (eds) Labor Migration, EU Enlargement, and the Great
Recession: Berlin and Heidelberg: Springer, pp. 134.
King R (2002) Towards a New Map of European Migration. International Journal of Popula-
tion Geography 8(2): 89106.
Krăstev I (2017) After Europe. Philadelphia: University of Pennsylvania Press.
Kuhn T (2011) Individual Transnationalism, Globalisation and Euroscepticism: An Empirical
Test of Deutsch's Transactionalist Theory. European Journal of Political Research 50(6):
Lee ES (1966) A Theory of Migration. Demography 3(1): 4757.
Mann M (1998) Is There a Society Called Euro? In: Axtmann R (ed.) Globalization and Eu-
rope: London and Washington: Pinter, pp. 184207.
Massey DS, Arango J, Hugo G, et al. (1999) Worlds in Motion Understanding International
Migration at the End of the Millennium: Understanding International Migration at the End
of the Millennium. Oxford: Clarendon Press.
Mau S and Mewes J (2012) Horizontal Europeanisation in Contextual Perspective: What Drives
Cross-Border Activities within the European Union? European Societies 14(1): 734.
Mau S and Verwiebe R (2010) European Societies: Mapping Structure and Change. Bristol:
Policy Press.
Miller D (2016) Strangers in our Midst: The Political Philosophy of Immigration. Cambridge
and London: Harvard university press.
Molnar IG (1997) The Sociology of Migration from the Former Yugoslavia. Journal of Ethnic
and Migration Studies 23(1): 109122.
Mungiu-Pippidi A (2005) Facing the 'Desert of Tartars'. In: Zielonka J (ed.) Europe Unbound:
Enlarging and Reshaping the Boundaries of the European Union. London: Routledge, pp.
Newman MEJ (2006) Modularity and Community Structure in Networks. Proceedings of the
national academy of sciences 103(23): 85778582.
Office of National Statistics (2017) Living Abroad: Migration Between Britain and Spain.
Available at: (accessed 7 March 2018).
Özden Ç, Parsons C, Schiff M, et al. (2011) Where on Earth is Everybody? The Evolution of
Global Bilateral Migration 19602000. World Bank Economic Review 25(1): 1256.
Recci E and Favell A (eds) (2009) Pioneers of European Integration: Citizenship and Mobility
in the EU. Cheltenham: Edward Elger.
Roche M (2009) Exploring the Sociology of Europe: An Analysis of the European Social Com-
plex. London et al.: Sage.
Rumford C (2001) Social Spaces Beyond Civil Society: European Integration, Globalization
and the Sociology of European Society. Innovation: The European Journal of Social Sci-
ence Research 14(3): 205218.
Simmel G (1904) The Sociology of Conflict. I. American Journal of Sociology 9(4): 490525.
Solemn Declaration on the European Union. Available at: (accessed 13
June 2018).
Stark O and Taylor JE (1989) Relative Deprivation and International Migration. Demography
26(1): 1-14.
Stola D (2005) Das kommunistische Polen als Auswanderungsland. Zeithistorische Forschun-
gen 2(3).
Teney C (2012) Space Matters. The Group Threat Hypothesis Revisited with Geographically
Weighted Regression. The Case of the NPD 2009 Electoral Success. Zeitschrift für Soziol-
ogie 41(3): 207226.
The United Nations Refugee Agency (2018) Time Series, 1951-2016. Available at: (accessed 7 March 2018).
Therborn G (1995) European Modernity and Beyond: The Trajectory of European Societies,
1945-2000. London: Sage.
Treaty on the European Union. Available at: (accessed 6 March 2018).
Treaty on the Functioning of the European Union. 2012/C 326/01. Available at: (accessed 11 October 2017).
United Nations (2017) Trends in International Migrant Stock: The 2017 Revision. United Na-
tions Database. Available at: (accessed 6 March 2018).
Vanhanen's Index of Democracy: The Polyarchy Dataset, 1810-2000. Available at: (accessed 21 June 2018).
Verwiebe R, Wiesböck L and Teitzer R (2014) New Forms of Intra-European Migration, La-
bour Market Dynamics and Social Inequality in Europe. Migration Letters 11(2): 125136.
Vobruba G (2003) The Enlargement Crisis of the European Union: Limits of the Dialectics of
Integration and Expansion. Journal of European Social Policy 13: 3562.
Wasserman S and Faust K (1999) Social Network Analysis: Methods and Applications. Cam-
bridge: Cambridge university press.
World Bank (2018) GDP (current international $). Available at: (accessed 13
June 2018).
Zimmermann KF (1996) European Migration: Push and Pull. International Regional Science
Review 19(1-2): 95128.
... In one of the landmark applications of ABM, Schelling (1971) demonstrated, as already mentioned above, that even moderate preferences for similar neighbours at the individual level can lead to de facto segregation at the neighbourhood level. In SNA, community detection algorithms allow to automatically find meaningful structural patterns within migration networks that would otherwise remain invisible (Delhey et al. 2019;Deutschmann 2021). In spite of this theoretic potential and multiple calls to attention to complexity in migration phenomena (e.g. ...
Full-text available
Computational social science provides an innovative set of methodological tools that can help answer questions of substantive interest to migration and integration research. In this introductory article, we first provide a brief history of how computational approaches have already enriched migration and integration research. Second, we identify several key promises of computational migration research (e.g. better access to hard-to-reach populations, cost reductions and time savings, better detection of causal mechanisms, avoidance of response biases and methodological nationalism through fine-grained, time-stamped, live digital trace data) and key challenges (e.g. missing categories, sampling issues, ethical concerns). Third, we illustrate how the contributions of this special issue fulfil some of these promises-as well as deal with the challenges-to gain new insights into key questions of migration and integration research that address why people emigrate, what the evolution of structural patterns in migration networks is, whether refugee movements can be predicted, how host communities respond to the influx of refugees, how people interpret, frame, and discuss these arrivals, how migration-related discourse responds to external shocks, and how spatial segregation patterns of migrants and ethnic minorities emerge.
... 4. Importantly, the likelihood of mass tourism and business travel had no effect on explaining the variability of either the number of cases or the arrival times, which distinguishes Poland from other countries like Germany (Felbermayr et al. (2020); Kovacs et al. (2020)) or USA (Killeen et al. (2020)). This can be explained by the still low purchasing power of Poles (an average Pole travels abroad touristically 5 times less often than a German (Delhey et al. (2019))). The lack of significance of external immigration could be associated with its marginal size in 2017 (Statistics Poland does not provide newer data), and perhaps with current data the effect of the inflow of foreign immigrants (Górny &Śleszyński (2019)) would be more pronounced due to the exponential increase of immigrants in the last few years e.g. in Greater Poland (Paradowski et al. (2020)). ...
Full-text available
Our task is to examine the relationship between the SARS-CoV-2 arrival and the number of confirmed COVID-19 cases in the first wave (period from March 4 to May 22, 2020 (unofficial data)), and socio-economic variables at the powiat (county) level (NUTS-4) using simple statistical techniques such as data visualization, correlation analysis, spatial clustering and multiple linear regression. We showed that immigration and the logarithm of general mobility is the best predictor of SARS-CoV-2 arrival times, while emigration, industrialization and air quality explain the most of the size of the epidemic in poviats. On the other hand, infection dynamics is driven to a lesser extent by previously postulated variables such as population size and density, income or the size of the elderly population. Our analyses could support Polish authorities in preparation for the second wave of infections and optimal management of resources as we have provided a proposition of optimal distribution of human resources between poviats.
... Intra-EU migration does not happen in a vacuum. Instead, it takes place in a pre-existing intra-EU migration space (Delhey et al. 2019). The intra-EU migration network topology (Centola 2015) is composed of two core countries (United Kingdom and Germany) characterised by a considerably higher gravity (metaphorically speaking) in terms of their ability to attract immigrants. ...
Full-text available
Using a network approach, we investigate the determinants of intra-EU migration flows between all 28 EU member states in the years 2001, 2005, 2008 and 2013. Our descriptive analysis of the networks of intra-EU migration flows shows that the EU migration space is dominated by two core destination countries (Germany and United Kingdom). The results of our cross-sectional exponential random graph models (ERGM) reveal that the status of Germany and the United Kingdom (UK) as core destination countries remains a robust characteristic of the network of intra-EU migration flows over time, even when controlling for GDP, unemployment rates or shared geographical borders between destination and source countries. Furthermore, our results point to the differentiated effects of national economic performance on outgoing and ingoing flows: GDP per capita mainly affects intra-EU inflows, while unemployment rates tend to influence outmigration. Lastly, regulatory linkages – measured with the accession to the EU of source countries and the opening of the labour market of destination countries – exert a moderate effect on intra-EU migration flows when taking into account the national economic performances of source and destination countries, the core status of Germany and the United Kingdom, and the shared borders between destination and source countries.
... There are now many different community algorithms with different strengths and weaknesses (see Coscia et al. 2011 for an overview). Here, we draw on a modularity-based community detection algorithm (Blondel et al. 2008;Newman 2006) that seems particularly well-suited and that has also been used in past research to detect clusters of human mobility between countries (Delhey et al. 2019;Sun et al. 2016). This algorithm, also called the Louvain method, belongs to the family of density-based community detection approaches. ...
Full-text available
The Mediterranean is often portrayed as a hub of human mobility. In this article, we test this widespread view by exploring the structure of travel flows in the region over the last two decades (1995–2016). We find that mobility is much higher and increasing more strongly along the northern than along the southern shore, thus creating a growing mobility divide. South‐north and north‐south movements are even scarcer and stagnate or even decline over time. With a Gini coefficient of .87, mobility flows are distributed extremely unequally across country pairs in the Mediterranean. Community detection algorithms reconfirm that mobility predominantly takes place in disparate clusters around the Mediterranean, not across it. These findings imply that a ‘neo‐Braudelian’ view of the Mediterranean as a mobility hub is less justified than a ‘Rio Grande’ perspective that conceives of the Mediterranean as a mobility hollow. Multivariate regression models for network data suggest that geographical distance and, to a lesser extent, political visa regulations, explain the unequal mobility structure better than differences in economic well‐being.
Full-text available
Aim. Our task was to examine the relationship between the SARS–CoV–2 arrival and the number of confi rmed COVID–19 cases in the fi rst wave (period from March 4 to May 22, 2020 (unoffi cial data)), and socio–economic variables at the powiat (county) level.Methods. We were using simple statistical techniques such as data visualisation, correlation analysis, spatial clustering and multiple linear regression.Results. We showed that immigration and the logarithm of general mobility was the best predictor of SARS–CoV–2 arrival times, while emigration, industrialisation and air quality explain most of the size of the epidemic in poviats. On the other hand, infection dynamics is driven to a lesser extent by previously postulated variables such as population size and density, income or the size of the elderly population.Conclusions. Our analyses could support Polish authorities in preparation for the second wave of infections and optimal management of resources as we have provided a proposition of optimal distribution of human resources between poviats. Although this isa retrospective analysis of the initial phase of the epidemic, similar patterns could repeat in case of new variants of SARS–CoV–2 or new respiratory disease for immunologically naive populations.
Full-text available
For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.
Full-text available
In times of multiple crises and a looming partial breakup of the European Union, the question of what binds Europeans together appears more relevant than ever. In this article, we propose transnational attachment as a novel indicator of sense of community in Europe, arguing that this hitherto neglected dimension is substantially and structurally different from alternative ones such as cross-border trust and identification. Combining Eurobarometer 73.3 data on ties between all EU-27 countries with further dyadic data, we show empirically that the European network of transnational attachment has an asymmetric core-periphery structure centered around five extremely popular countries (the UK, France, Germany, Italy, and Spain). In line with transactionalist theory, cross-border mobility and communication are strongly related to transnational attachment. Furthermore, we demonstrate that the network of transnational attachment is much denser among those with a higher than among those with a lower level of education. Our results suggest that offering European citizens incentives to travel to peripheral countries may help counterbalance the current asymmetric structure of transnational attachment, thereby increasing Europe’s social cohesion.
Full-text available
The outcome of the British referendum on EU membership sent shockwaves through Europe. While Britain is an outlier when it comes to the strength of Euroscepticism, the anti-immigration and anti-establishment sentiments that produced the referendum outcome are gaining strength across Europe. Analysing campaign and survey data, this article shows that the divide between winners and losers of globalization was a key driver of the vote. Favouring British EU exit, or ‘Brexit’, was particularly common among less educated, poorer and older voters, and those who expressed concerns about immigration and multi-culturalism. While there is no evidence of a short-term contagion effect with similar membership referendums in other countries, the Brexit vote nonetheless poses a serious challenge to the political establishment across Europe.
This paper studies the role of short‐run factors such as business cycles or changes in employment rates in explaining international migration flows. We first derive a model of optimal migration choice predicting that short‐run economic fluctuations may trigger migration flows on top of the impact exerted by long‐run factors. Second, we empirically test the magnitude of the effect of these short‐run factors on migration flows. Our results indicate that aggregate fluctuations and employment rates both affect migration flows. Third, we provide evidence that the Schengen Agreement and the euro significantly raised the international mobility of workers between the member countries. This article is protected by copyright. All rights reserved.
This paper analyses the impact of large and persistent emigration from Eastern European countries over the past 25 years on these countries’ growth and income convergence to advanced Europe. While emigration has likely benefited migrants themselves, the receiving countries and the EU as a whole, its impact on sending countries’ economies has been largely negative. The analysis suggests that labor outflows, particularly of skilled workers, lowered productivity growth, pushed up wages, and slowed growth and income convergence. At the same time, while remittance inflows supported financial deepening, consumption and investment in some countries, they also reduced incentives to work and led to exchange rate appreciations, eroding competiveness. The departure of the young also added to the fiscal pressures of already aging populations in Eastern Europe. The paper concludes with policy recommendations for sending countries to mitigate the negative impact of emigration on their economies, and the EU-wide initiatives that could support these efforts.
The eastern enlargements of the European Union (EU) in 2004, 2007 and 2013 created a labor market with more than half a billion people, third only to India and China in terms of population size and matched only by the United States in economic size. Along with the free movement of capital, goods and services, the acquis communautaire, basic legislation of the EU, also legally guarantee the free movement of people within the EU’s vast internal market. Owing to these liberalizations, and despite temporary transitional arrangements applied by some old member states towards citizens from new member states (NMSs), the EU witnessed a substantial east-west movement of people in the years following the eastern enlargements. The number of citizens in the old member states from the member states that joined the EU in 2004 and 2007 grew from about two million in 2004 to almost five million in 2009, signifying an increase from less than 0.5 to 1.2 % of the EU15 total population in just 5 years (Holland et al. 2011).