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A Comparative Study of Segregation Patterns
in Belgium, Denmark, the Netherlands and Sweden:
Neighbourhood Concentration and Representation
of Non-European Migrants
Eva K. Andersson
1
•Bo Malmberg
1
•Rafael Costa
2
•Bart Sleutjes
3
•
Marcin Jan Stonawski
4,5
•Helga A. G. de Valk
3
Published online: 21 March 2018
The Author(s) 2018
Abstract In this paper, we use geo-coded, individual-level register data on four
European countries to compute comparative measures of segregation that are
independent of existing geographical sub-divisions. The focus is on non-European
migrants, for whom aggregates of egocentric neighbourhoods (with different pop-
ulation counts) are used to assess small-scale, medium-scale, and large-scale seg-
regation patterns. At the smallest scale level, corresponding to neighbourhoods with
200 persons, patterns of over- and under-representation are strikingly similar. At
larger-scale levels, Belgium stands out as having relatively strong over- and under-
representation. More than 55% of the Belgian population lives in large-scale
neighbourhoods with moderate under- or over-representation of non-European
migrants. In the other countries, the corresponding figures are between 30 and 40%.
Possible explanations for the variation across countries are differences in housing
policies and refugee placement policies. Sweden has the largest and Denmark the
smallest non-European migrant population, in relative terms. Thus, in both migrant-
dense and native-born-dense areas, Swedish neighbourhoods have a higher con-
centration and Denmark a lower concentration of non-European migrants than the
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10680-
018-9481-5) contains supplementary material, which is available to authorized users.
&Eva K. Andersson
eva.andersson@humangeo.su.se
1
Department of Human Geography, Stockholm University, Stockholm, Sweden
2
Faculty of Economic, Social and Political Sciences and Solvay Business School, Vrije
Universiteit Brussel, Brussels, Belgium
3
Netherlands Interdisciplinary Demographic Institute (NIDI)/KNAW/University of Groningen,
the Hague, the Netherlands
4
Department of Sociology and Human Geography, University of Oslo, Oslo, Norway
5
Department of Demography, Cracow University of Economics, Krako
´w, Poland
123
Eur J Population (2018) 34:251–275
https://doi.org/10.1007/s10680-018-9481-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
other countries. For large-scale, migrant-dense neighbourhoods, however, levels of
concentration are similar in Belgium, the Netherlands, and Sweden. Thus, to the
extent that such concentrations contribute to spatial inequalities, these countries are
facing similar policy challenges.
Keywords Segregation Comparison Non-European immigrants Concentration
Representation Belgium Denmark The Netherlands Sweden
1 Introduction
The aim of this paper is to compare levels and patterns of non-European migrant
segregation in four different countries: the Netherlands, Denmark, Sweden and
Belgium. Comparing residential segregation between countries helps scholars to
analyse the causes and consequences of segregation, and also helps them to suggest
policy measures against segregation. Three questions will be in focus. (1) To what
extent are there differences in the concentration and representation of non-EU
migrants across various spatial scales? (2) How can differences in segregation
patterns across four national contexts be explained by differences in structural
factors? (3) To what extent has spatial segregation reached such levels that the
social cohesion of different member states is endangered?
Residential segregation implies the relatively strong presence of a specific group
in some spatial units combined with a relatively low presence in others (Massey and
Denton 1988). In particular, the spatial segregation of non-European migrants is a
topic of academic and policy debate in several countries across Europe. However,
the levels of segregation and their specific spatial patterns are different across
countries, as well as across cities and regions within countries. These differences
may be explained by the large variation in the size and composition of the non-
European migrant population across countries, as well as by cross-national variety
in structural conditions. For example, a recent comparative study identified
globalisation, social inequalities, welfare regimes, housing systems and occupa-
tional structures as the main structural factors shaping socioeconomic segregation
(Tammaru et al. 2016).
Making country comparisons is important since this makes it possible to discuss
the influence of national policies on segregation patterns. Although the countries we
study are similar in terms of welfare policies, there are large differences in terms of
the housing market, placement policies and in the composition of non-European
migrants, which may be reflected in different spatial segregation patterns.
Earlier comparative studies on residential segregation usually were carried out on
the geographical level of cities or city regions. Examples include the study
by Musterd and van Kempen (2009) on ethnic segregation, the recent study by
Tammaru et al. (2016) on socioeconomic segregation and the work of Musterd and
Ostendorf (1998) and Musterd (2005) on both types of segregation. However, some
of the factors that previous studies identified as crucial for shaping spatial
segregation-social inequality, namely welfare regimes and housing market policies
252 E. K. Andersson et al.
123
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(Tammaru et al. 2016), play a role at the national scale and are just as likely to
influence segregation both within and across non-metropolitan and metropolitan
areas. Segregation is a phenomenon that is present wherever there is population and
is thereby not limited to large metropolitan areas or big cities. In fact, patterns of
segregation are also visible in small towns and cities. Segregation can even exist
between rural settlements. Indeed, a number of recent studies have highlighted
residential segregation in non-metropolitan areas (Lichter et al. 2012;O
¨sth et al.
2014). It is therefore interesting to compare differences in segregation patterns
between different countries while focusing on urban, suburban and rural areas.
One important finding in our study that focuses on conditions in 2011 is that
segregation patterns at the lowest geographical scale are remarkably similar, almost
identical in fact, across the four countries we study. This is a finding that we have
not seen reported before, and it points to a strong need to consider how such similar
outcomes can result in different countries. At larger scales, we find that the level of
segregation is markedly higher in Belgium compared to Denmark, Sweden, and the
Netherlands. This points to the possibility that differences in policy are more
important for large-scale patterns than for small-scale patterns of segregation. Our
analysis also shows that the proportion of the population that lives in migrant-dense
neighbourhoods is higher in countries with a large non-European migrant
population. This is not unexpected but underlines that segregation can become a
greater challenge in such countries. On the other hand, in Sweden and the
Netherlands, relatively high proportions of non-European migrants are also found in
neighbourhoods dominated by natives and European-born persons, a pattern
suggesting that policies that are in place in these two countries have had some
success in preventing segregation. Overall, the results we present show that using
methods that ensure comparability and a multiscalar approach opens up possibilities
for evaluating competing theories of segregation and assessing the impact of
different policies.
2 Previous Comparative Studies on Segregation Patterns
Several previous studies made cross-country comparisons of residential segregation.
Some studies compared segregation in Europe to that in the USA. The overall
conclusion of these studies is that segregation levels in Europe are more modest
(Friedrichs et al. 2003; Musterd and Ostendorf 1998) than in the USA. Musterd and
Ostendorf (1998) studied segregation in different Western cities, including
Stockholm, Amsterdam and Brussels. They concluded that segregation is stronger
in Brussels than in Amsterdam and Stockholm, but levels are low compared to the
American and South African contexts they covered in their work.
A number of studies compared segregation across metropolitan regions in
Europe. Musterd (2005) compared scores on the dissimilarity index (DI) for ethnic
minorities between different European cities based on results published from the
late 1990s to the early 2000s. His work showed that German cities, as well as Oslo
and Vienna, have the lowest segregation levels (DI), followed by Dutch cities with
the exception of Rotterdam. Belgian cities generally have higher segregation levels.
A Comparative Study of Segregation Patterns in Belgium…253
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In a later, similar study, Musterd and van Kempen (2009) again collected DI-scores
from European cities for several years and stated that the dissimilarity indices were
stable over time, and even decreased somewhat.
In a study of segregation patterns including 16 European countries clustered into
different welfare regimes (social-democratic, corporatist, liberal or Latin Rim) until
the mid-1990s, Arbaci (2007) concluded that ‘‘welfare arrangements are critically
important’’ (p. 429). Generally, cities in corporatist welfare systems have the lowest
levels of spatial segregation because of their ‘unitary’ or ‘integrated’ rental systems,
and cities in liberal welfare states have the highest degree of segregation due to a
‘dualist’ rental system (Arbaci 2007). In dualist systems, public housing is a
restricted sector for low-income households, whereas social housing is competing
on even terms with private renting in unitary rental systems (Kemeny 2006; Skifter
Andersen et al. 2016).
In the literature, the availability of housing and the opportunities for different
ethnic minority groups to gain access to housing has been mentioned as an
important driver of ethnic segregation in both the USA (South et al. 2011) and in
Europe (Musterd and van Kempen 2009; Skifter Andersen et al. 2016). One reason
is that the social rented sector is not equally distributed across urban space
(Friedrichs et al. 2003; Tammaru et al. 2016). This translates into ethnic segregation
since non-Western migrants are over-represented in lower socioeconomic strata and
groups with a low socioeconomic status are over-represented in the social rented
sector. It also implies that ethnic segregation will overlap closely with socioeco-
nomic segregation.
Skifter Andersen et al. (2016) compared ethnic segregation in four Nordic
capitals (Stockholm, Copenhagen, Helsinki and Oslo) with similar social-demo-
cratic welfare state systems but different housing markets and spatial distributions
of housing tenures. They found that generally, the degree of ethnic segregation
increases with the size of the immigrant population: the strongest segregation was
found in Stockholm, with the largest immigrant population. A lack of local mixing
of tenure types, however, also influenced the level of segregation. Still, there is no
straightforward relationship between the housing system and the level of
segregation. Ethnic segregation is stronger in a restricted social sector, but if
housing policies guarantee a mixture of tenure in neighbourhoods, the level of
segregation for the neighbourhood as a whole may be lower, as the example of
Helsinki showed (Skifter Andersen et al. 2016).
Avoidance or flight by natives also influences the degree of segregation. In the
European context, some evidence of a higher likelihood for natives to leave
migrant-dense neighbourhoods was found for the UK (van Ham and Manley 2009),
Denmark and Sweden (Skifter Andersen et al. 2016). Several studies suggest that
natives tend to avoid such areas (Bra
˚ma
˚2006; Zorlu and Latten 2009). Examples of
constraints that lead to the concentration of migrant groups in certain neighbour-
hoods are restrictive housing allocation systems and welfare state mechanisms
(Musterd and van Kempen 2009; van Ham and Manley 2009).
These previous studies lead us to expect differences in segregation patterns
between Belgium, Denmark, the Netherlands and Sweden essentially depending on
how factors such as the housing market, the welfare state and spatial planning are
254 E. K. Andersson et al.
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arranged in each national context. Regarding the welfare state system, Denmark,
Sweden and the Netherlands fall into a social-democratic welfare cluster, whereas
Belgium can be characterised as a corporatist welfare state (Arbaci 2007). This is
also reflected in the housing market structure. Belgium has a dualist rental system
with a limited social rental sector that accounts for only 6.9% of the total housing
stock (Vanneste et al. 2008; de Decker 2008; Kesteloot and Cortie 1998), which is
considerably smaller than the number of families eligible for public rented housing
(de Decker 2008; Kesteloot and Cortie 1998). As early as 1998, different
segregation outcomes between Belgium and Sweden were said to be ‘‘in part based
on the different nature of the housing system’’ (van der Wusten and Musterd 1998,
p. 240). The same study also concluded that an important public housing component
can have ‘‘a softening effect on segregation levels’’ (p. 246).
Although the Netherlands, Sweden and Denmark have unitary rental systems
with a large stock of public housing, there are still considerable differences between
the four housing markets. In 2011, 44% of the total housing stock in the Netherlands
belonged to the public rented sector: subsidised dwellings offered by housing
corporations, with rents below approximately 600 euros. In the cities, this
proportion has always been higher: in Amsterdam, it was approximately 67% in
2011 (Statistics Netherlands 2011). The Dutch social rented sector is considered
attractive because of the large proportion of single-family housing and the often
good state of dwellings. Although units are generally allocated to low-income
households, tenants cannot be evicted when their income increases. As a result, the
tenants are mixed in terms of income (Bolt et al. 2008). In recent years, however,
urban policies have sought to reduce the stock of social rented dwellings (Savini
et al. 2016). The large and diverse Dutch social rented sector explains why
segregation levels in Dutch cities are much more modest compared to cities in the
UK (Murie and Musterd 1996), where the social rented sector is smaller and
spatially more concentrated. Kesteloot and Cortie (1998) compared ethnic
segregation in Amsterdam and Brussels for Turkish and Moroccan migrants and
concluded that these groups are more strongly segregated in Brussels. Due to the
small social rented sector in Brussels and their generally lower incomes, Turkish
and Moroccan migrants in Belgium depend on the private residual rented sector,
which is mainly found in a restricted number of working-class neighbourhoods.
Another factor influencing segregation patterns is differing policies that regulate
the number of people entering the country, when they can enter, who can enter,
where they can settle, and the kind of support offered to immigrants living in the
country. Sweden, Denmark and the Netherlands have had policies seeking the
dispersal of refugee populations (Andersson 2003; Robinson et al. 2004).
Concerning the number of entering non-European migrants, before 2015, Denmark
was renowned for its low levels of immigration, whereas Sweden, the Netherlands
and Belgium had accepted larger numbers. In earlier studies, Scandinavian countries
together with the Netherlands were portrayed as similar concerning the organisation
of housing (van der Wusten and Musterd 1998). In terms of welfare systems,
Denmark and Sweden are considered as belonging to the social-democratic model,
whereas Netherlands and Belgium have been classified as hybrids between the
conservative and social-democratic model (Kammer et al. 2012). This welfare state
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classification, thus, to some extent points to similarities rather than differences
between the countries in our study.
3 The Role of Spatial Scale for Segregation Measurement
Over the past two decades, an increasing number of studies have addressed how
residential segregation relates to spatial scale. van der Wusten and Musterd (1998)
concluded that their study on segregation in Western cities cannot be considered
truly comparative due to barriers related to data acquisition, differences in spatial
units and different definitions of ethnic categories. In relation to the scale-dependent
nature of segregation effects, they observed that:
When either income or ethnic status differences are very pronounced,
exclusion is supposed to follow. In the case of segregation, opinions are more
divided. It depends on the spatial scales involved: the larger the units, the
higher the probability of exclusion. A major reason why this seems convincing
is the provision of a self-sufficient environment within larger units with no
incentives to use urban space at large (van der Wusten and Musterd 1998,
p. 241).
Similar conclusions were drawn by Musterd (2005), whose study compared levels
of ethnic segregation across metropolitan areas in different European countries and
acknowledged both conceptual issues and problems related to the scale of
measurement and time points in measurement. The different ways in which
statistical units are constituted across different areas is referred to as the Modifiable
Areal Unit Problem (MAUP) (Openshaw 1984;O
¨sth et al. 2014). MAUP prevents
reliable comparisons of segregation levels and patterns between areas of different
sizes, as well as between different countries, while ideally, comparative studies use
uniform units of measurement (Musterd 2005).
Thus far, most studies on residential segregation have focused on patterns in
metropolitan areas and have used administrative (neighbourhoods) and statistical
(census tracts) units for segregation measurement. Krupka (2007) argued that the
often found relationship between high segregation levels and large city size is
spurious and caused by differences in neighbourhood size between larger cities and
smaller towns. Census tracts in metropolitan areas generally consist of a single
neighbourhood, while neighbourhoods in smaller towns have fewer inhabitants and
must be combined in order to fill a census tract of comparable size. Krupka (2007)
measured segregation at different spatial scales and found that using smaller areas of
analysis diminished the differences between large cities and small ones.
The plea for multiscalar measurements of segregation has intensified over the
past few years. Fowler (2016) recently argued that there is no ‘correct’ scale for
measuring segregation: it is continuous across scales and should be measured
accordingly. Single scalar measurements may also ignore the fact that smaller units
are embedded in larger spatial contexts. Within larger entities with low or moderate
segregation levels, strong concentrations may exist at smaller spatial scales, or vice
versa. Focusing on only one spatial scale may overlook specific ethnic
256 E. K. Andersson et al.
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concentrations (Fowler 2016). Furthermore, fixed borders may lead to over- or
underestimations of very specific concentrations that occur at the border of two
administrative or statistical districts (Clark et al. 2015).
The increased availability of geo-coded individual data offers opportunities for
solving boundary and scale issues by constructing scalable individualised neigh-
bourhoods. These districts are ‘egocentric’: the exact residential location of an
individual is taken as the centroid, from which point a buffer is constructed that
consists of a predefined distance radius (Reardon et al. 2008) or a k-number of
nearest neighbours (k-levels) (Andersson and Malmberg 2015;O
¨sth et al. 2014).
The resulting sample of individuals is then used to compute aggregate statistics,
such as the share of people within a buffer belonging to a certain migrant group
(Clark et al. 2015). Since the distance radius or the number of nearest neighbours
within the buffer can vary, individualised neighbourhoods of different sizes seen
from the same location can be studied, enabling the analysis of residential
segregation from a multiscalar perspective.
Scale can be of importance both with respect to concentration and representation.
If there is a high concentration of non-European migrants at small neighbourhood
scales (for example, among the nearest 200 neighbours) but not on a larger scale (for
example among the 1000 or 10,000 nearest neighbours), this can, as suggested by
van der Wusten and Musterd (1998), have consequences for the way people living in
the neighbourhood interact with different groups. Even if interactions with the
closest neighbours will be mostly with non-European migrants, interactions in
workplaces, at shopping centres, cafe
´s, etc., can occur with a more mixed
population. If, on the other hand, the concentration of non-European migrants is also
high for larger-scale neighbourhoods including the 50,000 nearest neighbours, this
can result in much larger proportions of individuals’ daily and weekly activities
taking place in a context where the concentration of non-European migrants is high.
In the literature, this has been related to mechanisms of role modelling and networks
that may influence people’s norms and behaviours in these neighbourhoods.
Swedish studies using multiscalar measures of neighbourhood contexts suggest that
this reasoning is valid. Large-scale elite environments and large-scale deprived
areas tend to have a more pronounced effect on individual-level outcomes compared
to small-scale contexts (Andersson and Malmberg 2015,2016).
A similar argument can be made with respect to the representation of non-
European migrants. Certainly, high levels of small-scale, under- and over-
representation of non-European migrants are problematic from a social cohesion
perspective since they can signal a principle of separateness on the part of the native
population and may also reflect a negative attitude towards mixing in the local area.
Still, if the under- and over-representation of non-European migrants at higher scale
levels is less strong, this can signal preparedness for sharing a broader urban
environment with groups of different origins. Strong under- and over-representation
at larger scales, for example, among the nearest 50,000 neighbours, could make
under-represented groups feel unwelcome. Living in an area where large-scale
under-representation is strong also implies that there is little opportunity, even by
moving outside your immediate neighbourhood, of getting to know the under-
represented group.
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4 Data and Methods
The empirical analysis of this paper focuses on non-European migrants, that is,
foreign-born individuals with a country of birth outside Europe (defined as
consisting of the 28 European Union member states plus the four EFTA countries,
Norway, Switzerland, Iceland and Liechtenstein). It is important to realise that this
definition implies that we study only first-generation immigrants and not their
descendants. In all the countries, data on country of birth are from population
registers (the central population register in Denmark, the national register of natural
persons in Belgium, municipal population registers in the Netherlands, and the total
population register in Sweden).
In Denmark, Belgium, and the Netherlands, residential coordinates for individ-
uals in the population registers were obtained by matching addresses in the
population registers with addresses in building or land registers (Den Offentlige
Informationsserver [OIS] in Denmark, Land Registry of the General Administration
of Patrimonial Documentation in Belgium, Basisregistratie Adressen en Gebouwen
in the Netherlands). In Sweden, permanent residents are registered to specific real
estate properties and residential coordinates were obtained by matching with the
land registry on the basis of the property registration numbers. For all four countries,
data from 2011 were used.
In Sweden, the individual geo-coded data were made available to researchers
through an online database created for the Department of Human Geography by
Statistics Sweden (Geostar). In the Netherlands, the data are part of the System of
Social Statistical Datasets (SSD) created by Statistics Netherlands. In Belgium, the
linkage between the population register and the geo-coordinates is administered as a
part of the 2011 register-based census. In each country, the total population was
taken into account (Nielsen et al. 2017).
The processing of the register data was carried out in the same way in all four
countries. In the first step, the individual-level data were aggregated to a
geographical grid of 100 by 100 m (in Denmark, the Netherlands, and Belgium)
and, for Sweden, to a geographical grid of 250 by 250 m (in densely populated
areas, due to data restrictions), or 1000 by 1000 m (in sparsely populated areas); see
Table 1 The gridded population, descriptive statistics, 2011. Source: Authors’ calculations based on
register data from statistics Belgium, statistics Denmark, statistics Netherlands, and statistics Sweden
Number of
populated
grid squares
Median
population
Maximum
population
Median
number of non-
European
migrants
Maximum
number of non-
European
migrants
Total
population
Belgium 608,850 9 1753 0 516 11,000,638
Denmark 421,365 5 1129 0 275 5,566,100
Netherlands 559,504 11 1105 0 771 16,727,659
Sweden 202,067 157 4114 7 1345 9,466,727
258 E. K. Andersson et al.
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Table 1. For each square, the number of non-European migrants and the total
population number were computed.
In the second step, Equipop software (O
¨sth et al. 2014) was used to process this
gridded population data. Equipop expands a buffer around each populated grid cell
until the total population count in the buffer reaches a threshold level of knearest
neighbours. When this threshold is reached, Equipop computes the proportion of
non-European migrants in the buffer population. If calculations for multiple k-levels
are requested, the software then continues to expand the buffer until the next
threshold is reached, computes the proportion of non-European migrants, and
continues to expand the buffer and calculate proportions until values for all the
requested k-values are obtained. In the current study, we focus proportions of non-
European migrants computed for the 200, 1600, 12,800 and 51,200 nearest
neighbours.
As the individualised neighbourhoods are expanded until a specific population
threshold is reached, their geographical size/radius in metres will vary depending on
population density. Table 2provides information about this variation. Some
neighbourhoods will be very large, much larger than the areas one conventionally
regards as neighbourhoods. Thus, our concept of neighbourhood is stretched in its
meaning. They are neighbourhoods by virtue of being areas that reach a predefined
number of closest neighbours.
One important conclusion from Table 2is that in spite of large differences in
overall population density, people in the four countries live in local neighbourhoods
that are similarly structured. Fifty per cent of the population have their closest 200
neighbours within approximately 200 m from their dwelling or less, and 90% of the
Table 2 Size of individualised
neighbourhoods in Belgium,
Denmark, the Netherlands, and
Sweden, radius in metres
(percentiles based on population
count), 2011. Source: Authors’
calculations based on register
data from statistics Belgium,
statistics Denmark, statistics
Netherlands and statistics
Sweden
Percentile Belgium Denmark Netherlands Sweden
k=200 k=200 k=200 k=200
10 100 100 100 0
25 100 100 100 0
50 141 141 100 250
75 224 224 141 250
90 424 1000 224 1414
95 608 1513 500 2236
99 1105 2200 1265 5000
k=51,200 k=51,200 k=51,200 k=51,200
10 1500 1664 1712 2000
25 2865 3354 2302 3162
50 5049 7912 3612 10,050
75 7200 15,008 6379 22,472
90 9411 20,132 9080 35,609
95 12,394 23,308 10,515 44,294
99 20,096 36,111 14,091 104,346
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population have their closest 200 neighbours within approximately 1000–1500 m or
less, and in Belgium, as close as within a 400-m radius. Only approximately 1% of
the population in Sweden lives in a location where the distance to the closest 200
neighbours is much greater than in Denmark, Belgium, or the Netherlands.
If, however, the neighbourhood scale is expanded to encompass the closest
51,200 neighbours, the picture changes somewhat. For 25% of the population, there
is essentially no difference. Their distance to the nearest 51,200 neighbours is
approximately 3 km or less in all four countries. Instead, the largest differences in
population density are found for the 25% of the population that live in the sparsely
populated areas. In Sweden, for this population, the neighbourhood area must be
given a radius of at least 22.5 km in order to encompass 51,200 neighbours, whereas
11 km or more will suffice to reach 51,200 for the Dutch and Belgians living in the
25% of the most sparsely populated neighbourhoods. Furthermore, the differences
are even larger for the 1% most sparsely populated individualised neighbourhoods.
In Sweden, this group would need to attract all neighbours within a radius of at least
100 km in order to reach 51,200 people, whereas in the Netherlands, a radius of
16 km would suffice. Thus, in spite of large differences in population density,
national differences in the geographical structure of the neighbourhoods are unlikely
to influence segregation patterns at small neighbourhood scales. On the other hand,
it could be argued that in sparsely populated parts of Sweden, the presence of non-
European migrants among the closest 51,200 neighbours implies that there is not
even cycling distance between them and individuals from the majority population.
4.1 Concentration
The values obtained as output from the Equipop processing correspond to the
concentration measure of segregation: the proportion of the local neighbourhood’s
population that consists of non-European migrants. In order to compare differences
in concentration across countries, we look at the percentile values of this proportion.
One important point to remember here is that when neighbourhood values are
computed for individualised neighbourhoods, the number of neighbourhoods will
correspond to the number of individuals in the population. Each individual has its
own neighbourhood. Thus, if the 10
th
percentile of the local neighbourhoods’
population that consists of non-European migrants is 1%, this signifies that 10% of
the population lives in neighbourhoods with less than 1% non-European migrants.
Percentile values across different countries and across k-values will therefore
provide a detailed and comparable picture of how segregation in terms of the local
concentration of non-European migrants varies between countries.
4.2 Representation of Non-European Migrants
Representation is measured as the proportion of the total non-European migrant
population that lives in a neighbourhood. If this proportion is lower than that
neighbourhood’s proportion of the overall total population, then non-European
migrants are under-represented. To assess this representation of non-European
migrants in different neighbourhoods, the data resulting from the Equipop
260 E. K. Andersson et al.
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processing were aggregated into 100 different neighbourhood types, or bins, based
on the proportion of non-European migrants, with each bin representing 1% of the
total population. For details, see Andersson et al. (2017).
Based on these grouped neighbourhoods, the representation of non-European
migrants in different types of neighbourhoods was computed as:
Non Europeansi
P99
i¼0Non Europeansi
ð1Þ
where Non Europeans
i
is the number of non-European migrants living in bin
i. Expression (1) is thus the proportion of the total non-European migrant popula-
tion, P
i=0
99
Non Europeans
i
, that is living in bin i. As each bin contains 1% of the
total population, equal representation is achieved when the value of expression (1)is
1%. If the proportion is lower than 1%, non-Europeans are under-represented. If the
proportion is higher than 1%, non-Europeans are over-represented.
The definitions of concentration and representation used in this paper are given in
Table 3. Here, it is clarified that the concentration measure relates the size of any given
neighbourhood’s non-European migrant population to the size of the total neighbour-
hood population, whereas the representation measure relates the size of any given
neighbourhood’s non-European migrant population to the total non-European migrant
population in the country (Hennerdal and Nielsen 2017). Note also the different
probability interpretations of concentration and representation that are given in Table 3.
4.3 The Dissimilarity Index: An Aggregate Measure of Over- and Under-
Representation
The most widely used aggregate measure of segregation is the dissimilarity index
(Duncan and Duncan 1955; Massey and Denton 1988). With two population groups,
NE, non-European migrants, and E, European-born persons, the dissimilarity index
can be defined as:
DI ¼1
2X
99
i¼0
nei
NE ei
E
ð2Þ
where ne
i
is the number of non-European migrants living in neighbourhood bin i,e
i
is the number of European-born persons living in neighbourhood bin i, NE is the
Table 3 Concentration and representation explained
Concentration Representation
Non European migrants in neighborhood
Total neighborhood population
Non European migrants in neighborhood
Total Non European migrant population
Probability interpretation: Selecting one
individual randomly from the neighbourhood,
what is the probability that the individual will be
a non-European migrant?
Probability interpretation: Selecting one individual
randomly from the non-European migrant
population, what is the probability that the
individual will live in this specific neighbourhood?
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total non-European migrant population, and Eis the total European-born population.
An inspection of (2) shows that nei
NE is our measure of representation for non-Euro-
pean migrants. In the same way, ei
Eis the representation of European-born persons.
This implies that the dissimilarity index, being the sum of the absolute difference
between non-European migrant representation and European-born person repre-
sentation divided by two is an aggregate measure of over- and under-representation.
DI will be zero if both groups are equally represented in all neighbourhoods, and
one if non-European migrants have zero representation in neighbourhoods where
European-born persons live, whereas European-born persons have zero represen-
tation where non-European migrants live. Note that in this formula, NE, ne
i
,E, and
e
i
are not defined in the same way as when DI is computed for fixed geographical
areas. Yet, the standard interpretation of DI as the share of the minority population
that must move in order to arrive at an even distribution still applies.
Using the approach described above, we computed dissimilarity indices for the
four countries under study and for different k-values. A comparison of these indices
will complement the analysis of over under- and over-representation based on
percentile plots and help us to assess the extent to which segregation patterns in
these countries are similar or dissimilar.
5 Results
The first point of interest is how the inflow of non-European migrants has shaped the
population composition of neighbourhoods in the four countries under study. This
concentration can be analysed using bin plots for the proportion of the neighbour-
hood population that consists of non-European migrants. Another point of interest is
the extent to which the non-European migrant population is evenly distributed
across neighbourhoods. In other words, the analysis focuses on the degree to which
non-European migrants are over- and under-represented in neighbourhoods.
5.1 The Non-European Migrant Population in Belgium, Denmark,
the Netherlands, and Sweden
There are few studies that have systematically studied the extent to which
segregation levels differ between countries. Therefore, the aim of this study is to
compare levels and patterns of non-European migrant segregation in the four
different countries.
Non-European migrants
1
in the four countries under study are more similar in
terms of their origins compared to European migrants, whose origins are more
diverse and distance-related. If there is a crisis, e.g., in a Middle Eastern country,
immigrants will enter all countries examined in this study. Differences between the
European migrant populations also occur because European immigrants are
1
We subsequently use the definition EU28?EFTA. This does not include all European countries. In the
text, we consider people from Andorra or Serbia as non-Europeans.
262 E. K. Andersson et al.
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dominated by the closest neighbouring countries, like Finns and Norwegians in
Sweden, for example. These immigrants often dominate in border regions.
The total population of Denmark in 1990 was 5,135,409, and in 2011, it was
5,560,628 and the proportion of immigrants increased over the same period. Among
all immigrants, the proportion of non-EU immigrants has risen the fastest, from 1.9
in 1990 to 4.8 in 2011 (and 5.7 in 2016).
The total Swedish population increased from 8,590,701 in 1990 to 9,555,892 in
2012. Compared to Denmark, Sweden had a higher proportion of both European and
non-European migrants at the beginning and end of the period. An important
difference is that in Sweden during this period, the proportion of European migrants
was higher compared to Denmark, where non-European migrants represented the
majority of migrants.
In the Netherlands, the composition of migrants is similar to Denmark in that the
proportion of non-European migrants is larger than the proportion of European
migrants. The four largest non-Western groups in the Netherlands are Moroccans,
Antilleans, Surinamese and Turks (Hartog and Zorlu 2009).
In Belgium, Europeans make up the majority of migrants, coming mostly from
neighbouring countries as well as from Italy. Nevertheless, the largest increase in
numbers in recent years was Eastern Europeans after the successive enlargements of
the EU since the 1990s. The largest groups of non-European migrants in Belgium
are of Turkish, Moroccan, and to a lesser extent, Congolese origin (Phalet et al.
2007).
5.2 Neighbourhood-Level Concentration of Non-European Population: By
Percentiles
In Fig. 1, percentile plots for different scales (k-levels) show how the concentration
of non-European migrants varies between neighbourhoods across countries. The
percentile plots are split into two parts: one plot showing percentiles 0–80
(presented in the left-hand column of Fig. 1) and one plot showing percentiles
70–99 (presented in the right-hand column). By splitting the plots, different scaling
of the vertical axes can be used, making it easier to read the concentration values for
low percentiles. In the discussion below, we first review percentiles with the highest
proportions of non-European migrants, especially the 10% most immigrant-dense
areas. Thereafter, we look at percentiles consisting of neighbourhoods with low
proportions of non-European migrants.
Concerning the rank of the proportions in the various countries, see the right
column, first diagram in Fig. 1, for k=200 and the 90–99% of the population
living in the most non-European-dense areas. The line for Sweden is above the
others, showing the highest concentrations of immigrants in these immigrant-dense
neighbourhoods. The reason for this is based on the fact that Sweden has the highest
proportion of non-European migrants overall, while the Netherlands, followed by
Belgium and then Denmark, have lower levels (Table 4). The most important
message, however, is that the patterns of the proportions across neighbourhoods of
non-European migrants are very similar across countries; it is almost exclusively the
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difference between the overall proportions that makes the difference between lines
in the graphs.
In Denmark, in the 10% most migrant-dense areas, proportions vary from 13% up
to the value of 36% (at the k=200 level) for the 90th to 99th percentiles (Table 5).
These proportions are lower compared to the most immigrant-dense areas in the
other countries. In Belgium, the proportions are a little higher: 21–44% non-
European migrants in the densest areas. For the Netherlands, the proportions are
higher again, with equivalent numbers of 21–47% non-European migrants in the
10% most immigrant-dense neighbourhoods. In Sweden, the percentage for 2011 is
the highest and varies between 26 and 55% non-European migrants in the most
immigrant-dense areas; see Table 5and Fig. 1. Thus, concentrations of non-
European migrants in the Netherlands and Sweden are high in certain
neighbourhoods.
The left-hand column in Fig. 1shows the neighbourhoods with low proportions
of non-European migrants, which is where natives and European migrants
dominate. Continuing from the discussion above, the overall rank of countries’
proportions in neighbourhoods shows only a few deviations from this pattern,
reflecting the national proportions. There are also two groups of countries, one
including Sweden and the Netherlands and another including Denmark and
Belgium. Seventy per cent of the population (percentiles [30) in Sweden live in
diversifying neighbourhoods, that is, neighbourhoods containing more than 5% non-
European migrants (k= 12,800, in Fig. 1). The situation in Sweden can be
considered as one in which there is a move towards spatial assimilation with regard
to having diverse neighbourhoods (even if this is only a cross-sectional measure-
ment). Non-European migrants are living in the majority of neighbourhoods.
Belgium, on the other hand, shows a pattern of low proportions in neighbour-
hoods of approximately 50% of its population, but then there is an abrupt change to
high proportions of non-European migrants in the remaining 50% of the population
(k= 12,800 in Fig. 1). Hence, even if Belgium and Denmark have similar low
levels of non-European migrants in low migrant-dense areas, the lines depart from
50% of neighbourhoods. This is especially clear at higher scale levels of k=12,800
bFig. 1 Concentration of non-European migrants in individualised neighbourhoods in Belgium, Denmark,
the Netherlands and Sweden, 2011. Percentile values for k-levels 200, 1600, 12,800 and 51,200. Lower
percentiles in column one and percentiles above 70 in column two
Table 4 Population share of non-European migrants in Belgium, Denmark, the Netherlands and Swe-
den, 2011, per cent. Source: Authors data and Eurostat
Country 2011 (%) 2015 (Jan., 1) born in non-member state (Eurostat) (%)
Denmark 4.8 6.9
Belgium 7.3 8.5
Netherlands 8.0 8.7
Sweden 9.1 11.1
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and 51,200. In Belgium, the pattern indicates that large areas of the housing stock
are inaccessible to non-European migrants. This may be due to the fact that many
non-European newcomers are concentrated in central urban neighbourhoods with
low-quality dwellings and are excluded from suburban areas with better housing
Table 5 Concentration of non-European immigrants in Belgium, Denmark, Netherlands and Sweden,
percentiles for different scales (k-levels), 2011. Source: Authors’ calculations based on register data from
statistics Belgium, statistics Denmark, statistics Netherlands and statistics Sweden
Percentile Belgium Denmark Netherlands Sweden
k= 200 k= 200 k= 200 k= 200
10 0.5% 0.5% 0.5% 0.9%
25 1.3% 1.0% 1.4% 2.0%
50 3.4% 2.5% 4.0% 4.8%
75 9.4% 5.8% 10.0% 10.8%
90 21.1% 12.6% 20.7% 25.5%
95 30.3% 19.6% 30.2% 38.3%
99 44.4% 36.1% 46.8% 54.6%
k= 1600 k= 1600 k= 1600 k= 1600
10 1.1% 1.0% 1.2% 1.7%
25 1.8% 1.6% 2.1% 2.8%
50 3.6% 3.0% 4.6% 5.7%
75 9.4% 6.2% 10.1% 11.4%
90 20.3% 11.7% 19.5% 24.9%
95 28.2% 17.5% 27.7% 36.2%
99 42.1% 31.0% 43.4% 52.6%
k= 12,800 k= 12,800 k= 12,800 k= 12,800
10 1.5% 1.6% 1.7% 2.6%
25 2.2% 2.1% 2.9% 4.1%
50 4.2% 4.0% 5.4% 7.1%
75 9.7% 6.7% 10.2% 12.0%
90 19.4% 10.3% 17.3% 21.2%
95 26.6% 13.9% 25.1% 29.9%
99 40.6% 22.2% 39.8% 46.7%
k= 51,200 k= 51,200 k= 51,200 k= 51,200
10 1.9% 2.1% 2.4% 3.6%
25 2.7% 2.7% 3.7% 5.0%
50 4.5% 4.4% 6.2% 7.8%
75 9.9% 6.5% 10.1% 13.2%
90 18.8% 9.8% 16.8% 17.7%
95 25.6% 12.5% 23.6% 26.5%
99 39.3% 16.1% 37.8% 40.4%
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conditions. The analogous line for Belgium is at the same low proportion level as
for Denmark, which has a much lower overall proportion of non-European migrants.
Nevertheless, the line for Denmark in Fig. 1continues at a lower level of non-
European migrants in neighbourhoods for the entire population.
5.3 Representation of Non-European Migrants Across Neighbourhoods
Figure 2shows, for different k-values, the representation of non-European migrants
across neighbourhood types in Belgium, Denmark, the Netherlands, and Sweden.
Again, in order to facilitate the analysis, the left-hand column shows this proportion
for the 71 bins that have the lowest proportions of non-European migrants. The
right-hand column shows the proportion of non-European migrants living in the 41
bins with the highest proportion of non-European migrants. Remember also that
each bin represents 1% of the total population in each country; that is why there
should be 1% of non-European immigrants if they were evenly distributed across
neighbourhoods.
First examining the left-hand column Fig. 2, these graphs show how large parts
of the population in the four countries live in neighbourhoods with a lower
proportion of non-European migrants than one would expect if every bin had the
same proportion of non-European migrants (top of graph is equal to 1%).
If the proportion of the total population of non-European migrants in the different
bins had been close to 1%, this would indicate low levels of segregation. In contrast,
the graphs show that large parts of the population in all these four countries live in
neighbourhoods where the proportion of non-European migrants is much lower than
would be expected if there was no segregation. This is especially true for low k-
levels. For k= 200, approximately 50% of the population in Belgium, Denmark, the
Netherlands and Sweden live in neighbourhoods whose proportion of non-
Europeans is less than half (0.5%) of what would be expected with an equal
distribution of non-Europeans across neighbourhoods. Along these lines, 20% of the
population in all Denmark, the Netherlands and Sweden, and close to 30% of the
population in Belgium live in neighbourhoods whose proportion of non-European
migrants is less than one-fifth of what would be expected with an equal distribution.
What is striking here is that the figures are very similar across Denmark, the
Netherlands and Sweden, Fig. 2. Belgium has an even stronger under-
representation.
For larger k-values, segregation levels are less strong, at least in Denmark, the
Netherlands and Sweden. Thus, for k= 51,200, less than 25% of the population in
Denmark, the Netherlands and Sweden live in neighbourhoods whose proportion of
non-European migrants is less than half of what would be expected with an equal
distribution. Furthermore, almost no one lives in areas where the proportion of non-
Europeans is less than one-fifth of what would be expected with an equal
distribution. Again, Belgium differs. Here, almost 45% of the population lives in
neighbourhoods whose proportion of non-Europeans is less than half of what would
be expected with an equal distribution. Moreover, approximately 5% of the Belgian
population lives in neighbourhoods where the representation of non-European
migrants is less than one-fifth of what would be expected with an equal distribution.
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Turning to the right-hand column, we focus instead on segregation patterns in
areas where there is over-representation of non-European migrants. As shown in
these graphs, this over-representation starts close to the 70th percentile for low
k-values and at around the 60th percentile for the highest k-values. For k= 200,
however, only approximately 15% of the population in Denmark, the Netherlands
and Sweden (and somewhat more in Belgium) lives in neighbourhoods whose
proportion of the total non-European population is twice as high as it would be with
no segregation. For higher k-values, this percentage is even lower, at least if
Belgium is excluded. In Sweden, for k= 51,200, about 7.5% of the population lives
in neighbourhoods where the representation of non-Europeans is twice the level of
equal representation. In Denmark and the Netherlands, 10% of the population lives
in such neighbourhoods, whereas in Belgium, approximately 15% of the population
lives in areas with such over-representation. Moreover, in Belgium, 2% of the
population lives in k= 51,200 neighbourhoods where the over-representation of
non-European migrants is even more extreme, 7.5 times the level that would
correspond to equal representation.
5.4 Dissimilarity Index
The third indicator we use in our study is the dissimilarity index (DI) showing the
extent to which the spatial sorting of the non-European migrant population is
stronger in some of our studied countries than in others. The smaller the number of
nearest neighbours, the larger the measured dissimilarity index. This is the regular
consequence of small populations becoming homogenous more easily than large
populations and areas. The larger the population, the more likely there will be a
larger mix and a lower dissimilarity index. This is undoubtedly the most obvious
result when measuring dissimilarity at several scales, and points to the importance
of having detailed multiscalar data when measuring segregation, as shown in
Table 6.
Starting with the 200 closest neighbours, i.e., rather small local areas, the
dissimilarity index ranges from Denmark’s 0.475 to the value for the Netherlands of
0.487, to Sweden’s value of 0.489 and the highest dissimilarity for Belgium of
0.512. The same order, with Denmark having the lowest, the Netherlands second
lowest, Sweden third lowest and Belgium the highest measured DI is the rule
throughout k=200, 400, 800, 1600, 3200 and 6400. At the large-scale level of
neighbourhoods with 12,800 closest neighbours, the order is changed so that the
Netherlands has the second highest value followed by Sweden and Denmark, and
the same goes for k=51,200. In all cases, Belgium has the highest value for DI.
While Sweden and the Netherlands by scale change from the second to third highest
index value, Denmark increases the relative difference in DI value compared to
bFig. 2 Representation of non-European migrants in 1% population bins, 2011. Population bins sorted
according to proportion of non-European migrants and diagrams showing different k-values. Left column
showing under-representation (below 1%, which is at the top of the diagram) and moderate and strong
under-representation with 0.5 and 0.2%. Right column illustrating over-representation above 1% and
moderate and strong over-representation at 2.0 and 5.0% non-European migrants in a bin. See online
appendix for a discussion of these values
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Sweden and the Netherlands as the scale of neighbourhoods increases. In
conclusion, Denmark has the lowest segregation of non-European migrants at all
scale levels as measured by the dissimilarity index.
6 Discussion and Conclusions
The aim of this paper has been to compare levels and patterns of non-European
migrant segregation in four different countries. We have analysed the extent to
which there are differences in the concentration and representation of non-EU
migrants across various spatial scales.
Our first finding is that the differences we find between countries are small, and
moreover, that the similarities are especially pronounced at the lowest scale level.
This is in sharp contrast to earlier studies that reported large differences in the
dissimilarity index between urban areas, e.g., Skifter Andersen et al. (2016),
Musterd (2005) and Arbaci (2007). One reason for this could be that we focus on
entire national areas, but it can also reflect that we have tried to measure segregation
for comparable migrant groups in a way that avoids the MAUP. Again, our
contribution is that the results we present show that use methods ensuring
comparability and multiscalability to provide possibilities for evaluating competing
theories of segregation and assessing the impact of different policies.
If one focuses on larger neighbourhood scales, however, we do find differences.
Here, as is clearly evidenced by the dissimilarity index, Belgium stands out as the
exception, whereas Sweden, Denmark, and the Netherlands are remarkably similar.
The fact that the similarities are stronger for small-scale than for large-scale patterns
underlines the importance of using a multiscalar approach to segregation
measurement.
A possible explanation for the similarities in large-scale segregation patterns
between Sweden, Denmark, and the Netherlands, as well as the contrast with
Belgium, is how the housing sectors in these countries have developed since the
1950s. For an extended period, Sweden, Denmark, and the Netherlands put strong
emphasis on the construction of relatively large housing estates that have provided
relatively low cost and accessible housing options. With increasing incomes, these
housing options often became a secondary option for middle-income households
Table 6 Dissimilarity index in Belgium, Denmark, Netherlands and Sweden, 2011. Source: Authors’
calculations based on register data from statistics Belgium, statistics Denmark, statistics Netherlands and
statistics Sweden
k-value Belgium (%) Denmark (%) Netherlands (%) Sweden (%)
200 51.2 47.5 48.7 48.9
1600 47.3 40.4 43.6 44.1
12,800 43.7 31.3 37.5 35.7
51,200 40.6 25.3 32.6 29.7
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preferring single-family housing, with the result that these dwellings have become
an important alternative for newly arrived migrants with relatively low incomes. At
the same time, the public housing sector in Sweden, Denmark, and the Netherlands
is not restricted only to the poorest groups, and this may have stimulated some
mixing. Social housing in Belgium, in contrast, is typically more exclusively for the
poor and this may have contributed to a higher degree of segregation. Another
difference is that housing allowances are much more widely available in Sweden,
Denmark, and the Netherlands compared to Belgium (Juntto and Reijo 2010). As
housing allowances also make housing options in less low-income dominated areas
more available for low-income households, this can make it possible for poor, non-
European migrant households to access neighbourhoods preferred by medium-
income European-born households.
However, it could be that differences in immigrant settlement policies have also
played a role. Such policies have often been deemed to have little effect but a more
detailed exploration of whether policies of dispersal have affected Denmark,
Sweden, and the Netherlands is required to prevent the higher segregation levels at
larger scales that characterise Belgium.
In terms of concentration (Fig. 1), the differences between the four countries
studied here are much larger than differences in the patterns of representation. This
is because the relative size of the non-European migrant population differs between
the four countries. Thus, Sweden, having the highest proportion of non-European
migrants in its population, also has the highest proportion of the population living in
migrant-dense neighbourhoods, at least for small- to medium-sized neighbourhoods.
Denmark, having the lowest proportion of non-European migrants in its population,
also has the lowest proportion of the population living in migrant-dense
neighbourhoods. The conclusion here is that by increasing the levels of concen-
tration of migrants in migrant-dense neighbourhoods, an expanding migrant
population may accentuate problems associated with segregation even in cases
where there is no change in patterns of over- and under-representation.
Analyses of segregation among foreign-born migrants should focus not only on
migrant-dense neighbourhoods, however, but also on areas where migrants are
under-represented. In many such areas, both Sweden and the Netherlands have
relatively high concentrations of non-European migrants.
In Sweden, especially for larger-scale levels, only very small parts of the
population live in neighbourhoods with low concentrations of non-European
migrants. For example, for k= 51,200, less than 20% of the population lives in
neighbourhoods with fewer than 5%. In Denmark and Belgium, more than 50% of
the population lives in such native- and European migrant-dominated neighbour-
hoods. Although spatial assimilation is a process that evolves over time, this pattern
in Sweden could be interpreted as a sign of the start of spatial assimilation. The
stronger persistence of neighbourhoods with very low concentrations of non-
European migrants in Denmark and Belgium can be interpreted as a reflection of
place stratification, namely that there are areas where non-European migrants are
more or less excluded from entry. That both Denmark and Belgium have a lower
proportion of non-European migrants certainly contributes to this pattern, but it
could also be the case that an expanding migrant population is an important factor
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affecting spatial assimilation. The expansion of a migrant population leads to a spill-
over effect when early and established migrants settle in areas with lower
concentrations. Here, it should be noted, however, that Belgium has a higher
proportion of non-European migrants in the population than Denmark. The fact
Belgium has the same high proportions of the population in neighbourhoods with
few non-European migrants is therefore the result of stronger spatial sorting in
Belgium. One interpretation of this pattern is that place stratification is stronger in
Belgium than in Denmark.
Many studies have shown that non-European migrants, especially the newly
arrived, have higher unemployment and lower income levels than the general
population (Semyonov and Gorodzeisky 2014). Because of this disadvantage, the
high concentrations of non-European migrants found in the highest percentiles in
the countries under study indicate a risk of negative neighbourhood effects. In
response to this, several countries have developed dispersal policies, although such
policies can be seen as problematic with regard to the individual’s right to decide
settlement. These policies can also be problematic if they assign individuals to areas
with few employment opportunities. In the case of Denmark, an infamous ‘ghetto’
debate is the result of such initial thoughts about high concentrations (Aner 2015).
In contrast, the present study shows no such evidence of Denmark having especially
high levels of segregation compared to the other studied countries.
Given that neighbourhood effects are probably stronger when there is strong
segregation across scale levels, living in large housing estates built during the 1960s
and 1970s might affect residents more. Large-scale segregation has the consequence
of producing environments that meet most of their residents’ needs. As mentioned
as early as 1998 in the book edited by Musterd, a self-sufficient environment with
larger units does not provide any incentive to use the wider urban space. On the
other hand, small-scale segregation, where there is a high proportion of non-
European migrants among the 200 closest neighbours, might be small and difficult
to interpret in terms of neighbourhood effects if the nearest neighbourhood has a
diverse population composition.
In the case of Belgium, where the dissimilarity index was particularly high, social
cohesion might be a concern. Nationally, Belgium shows low levels of non-
European migrants in many neighbourhoods, but the gap in the size of the
concentration is high in that there are also neighbourhoods with very high
proportions of non-European migrant residents. Compared to the other three
countries, larger-scale segregation of non-European migrants is also much stronger
in Belgium. Such pronounced residential segregation patterns provide fewer
opportunities for diversity and mixing in neighbourhoods, which in the long run
might hinder understanding between groups and social cohesion in society.
7 Conclusions
An important first conclusion of this paper is that small-scale segregation patterns of
non-European migrants are strikingly similar across the four countries studied in
this paper. To our knowledge, such a consistency has not been demonstrated in any
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earlier studies and it provides a challenge to future attempts to explain processes of
segregation. A second conclusion is that there is more variation between countries
with respect to large-scale segregation patterns. This indicates that factors
explaining large-scale segregation can be different from factors that explain
small-scale segregation. We have not been able to explicitly test different
explanations. Yet, in line with our summary of earlier studies, we find that
differences in housing policies can certainly be important. Finally, even if all four
countries under study have neighbourhoods with high concentrations of non-
European migrants, it is also the case that in Denmark, the Netherlands and Sweden,
at least, substantial numbers of non-European migrants can also be found outside
such migrant-dense areas. The popular image that non-European migrants are
concentrated only in migrant-dense areas is not consistent with the results of this
paper.
Acknowledgements We thank the two anonymous referees for help in improving our paper. We also
acknowledge funding from Urban Europe, the Joint programming initiative (JPI) to partners in Belgium,
Denmark, Norway, the Netherlands and Sweden in the project ‘Residential segregation in five European
countries a comparative study using individualized scalable neighbourhoods’, acronym ResSegr, under
the Grant Agreement 2014–1676 from the research council FORMAS. http://www.residentialsegregation.
org/.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, dis-
tribution, and reproduction in any medium, provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were
made.
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