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Residential segregation and ‘ethnic flight’ vs. ‘ethnic avoidance’ in SwedenTim S. Müller, Humboldt University Berlin and Linköping UniversityThomas U. Grund, University College Dublin and Linköping UniversityJohan Koskinen, University of Manchester and Linköping University
Tim Müller (corresponding author)Humboldt University BerlinBerlin Institute for Integration and Migration Research (BIM)Unter den Linden 610099 Berlin, Germanyt.mueller@hu-berlin.de
Thomas GrundUniversity College DublinSchool Of SociologyNewman BuildingBelfieldDublin 4, Irelandthomas.grund@ucd.ie
Johan KoskinenSocial Statistics Discipline AreaSchool of Social SciencesHumanities Bridgeford StreetUniversity of ManchesterMANCHESTERM13 9PL, United KingdomJohan.Koskinen@manchester.ac.uk
Word count: 8224First submitted on 12/08/2015First revision submitted on 30/10/2016Second revision submitted on 28/04/2017Third revision submitted on 29/11/2017Final revision submitted on 23/03/2018
Funding: This research has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no 324233, Riksbankens Jubileumsfond (DNR M12-0301:1), and the Swedish Research Council (DNR 445-2013-7681) and (DNR 340-2013-5460).
Residential segregation and ‘ethnic flight’ vs. ‘ethnic avoidance’ in Sweden
Abstract
Residential segregation along ethnic categories has been associated with social
disadvantages of minority group members. It is considered a driving factor in the
reproduction of social inequalities and a pressing issue in many societies. While
most research focuses on neighbourhood segregation in the United States, less is
known about the origins of ethnic enclaves in European cities. We use complete data
of residential moves within Stockholm municipality between 1990-2003 to test
whether “ethnic flight” or “ethnic avoidance” drives segregation dynamics. On the
macro-level, we analyse the binary infrastructure of natives’ and immigrants’
movement flows between 128 neighbourhoods with exponential random graph
models, which account for systemic dependencies in the structure of the housing
market. On the micro-level, we analyse individual-level panel data to account for
differences between native and immigrant in- and out-movers. Our results show
strong support for “ethnic avoidance” on both levels – native Swedes avoid moving
into neighbourhoods where ethnic minorities live. This is even more pronounced
when controlling for socio-economic factors. At the same time, there is only little
support for “ethnic flight” on the micro-level – native Swedes are only marginally
more likely to move out of neighbourhoods where many immigrants live.
Introduction
Residential segregation along ethnic categories is one of the most pressing
problems in contemporary societies (Massey and Denton, 1993). Ethnic minorities
tend to live in neighbourhoods where other ethnic minority members live, whereas
natives form own communities within city boundaries. Such separation of ethnic
groups into distinct neighbourhoods is considered to be a driving factor in the
reproduction of social inequalities (Wilson, 1987), including wage differentials,
differing job opportunities, social contacts, health status or even cognitive abilities of
individuals (see e.g. Groshen, 1991; Bygren and Kumlin, 2005; Sharkey and Elwert,
2011).
While most existing research focuses on the United States (e.g. Crowder, 2000;
Crowder et al., 2011; Galster, 1990; Massey and Denton 1993), it remains an open
question what drives segregation dynamics in European countries (see for an
exception Bråmå, 2006 and Aldén et al., 2015). This article contributes to current
debate by investigating the origins of neighbourhood segregation in what is typically
conceived of as a highly meritocratic and egalitarian European country – Sweden.
On the macro-level, we investigate the pattern of movement flows between
neighbourhoods. These patterns can be revealed from studying the topology
(Fagiolo and Mastrorillo, 2013) of the binary network underlying the flows,
representing the infrastructure of migration. We analyse the network topology of
migratory flows with exponential random graph models (ERGM) (Lusher et al.,
2013), which are capable of taking into account systemic dependencies and
underlying status hierarchy. Systemic dependencies might be created through
“vacancy chains” (White 1970): Flows into one part of the city might structure the
opportunities for flows into other parts of the city. Furthermore, an underlying status
hierarchy of neighbourhoods (i.e. some neighbourhoods are systematically more
popular than others) might structure observable flows. EGRMs are capable of
capturing both kinds of effects. Our findings show no support for “ethnic flight”, but
strong support for “ethnic avoidance”, even after controlling for socio-economic
differences and geographic proximity of neighbourhoods.
On the micro-level, we relax some of the systemic constraints to consider individual-
level moves between neighbourhoods. We account for neighbourhood properties
and temporal variation using various multilevel regression techniques. This approach
controls for a wide range of individual socio-economic and demographic
characteristics. In terms of moving-out, native Swedes are slightly more likely to
leave a neighbourhood where many immigrants live. But the effect size is small and
not in stark contrast with our macro-level result. Looking at relative propensities for
being a native Swede vs. immigrant when moving into a neighbourhood, we find
strong support for “ethnic avoidance”. Native Swedes avoid neighbourhoods where
many immigrants live. Together, our findings indicate that “ethnic avoidance” and not
“ethnic-flight” is the main driver for segregation in Sweden.
Theoretical background
“Ethnic flight” and “ethnic avoidance” (also known as “white flight” and “white
avoidance” in the context of the United States) refer to different mechanisms leading
to neighbourhood segregation (Crowder, 2000; Pais, South, and Crowder, 2009;
Quilian, 2002). “Ethnic flight” describes the selective out-migration of white residents
due to in-movement of black/ethnic minority residents. In contrast, “ethnic avoidance”
refers to the avoidance of whites to move into neighbourhoods with a large share of
black/ethnic minority residents. Both mechanisms lead to segregation on the macro-
level, but imply vastly different intervention strategies.
According to Schelling (1971), segregation is the macro-level outcome of (mild)
micro-level preferences to live around (ethnically) similar individuals. Small and
seemingly unimportant differences in individuals’ preferences can lead to huge
differences at the population level due to segregation dynamics (see also Aldén et
al., 2015). In that context, various scholars investigated the idea of a threshold,
where migration (especially “ethnic flight”) sets in when the number of minority
members exceeds a certain level (Card et al. 2008; Aldén et al., 2015). Additional
work even shows that neighbourhood segregation can result when all actors have a
preference for diversity (van de Rijt, Siegel and Macy, 2009), while preferring “all-
similar” over “all-different” neighbourhoods. Research on stated preferences
consistently reveals that whites prefer neighbourhoods with fewer minority
populations (Emerson et al., 2001; Bruch and Mare, 2006).
At the same time, structural or systemic effects might play an important role as well.
For example, ethnic minorities face discrimination in the housing market. Findings
show that non-natives are less likely to get rental housing or property in
neighbourhoods dominated by natives (Ellen, 2000; Ondrich et al. 1999; Ross and
Turner, 2005). In Sweden, Ahmed and Hammarstedt (2008) showed with a field
experiment that landlords are more likely to give call-backs to individuals with
Swedish than with Arabic/Muslim sounding names who applied for vacant rental
apartments (see also Ahmed et al., 2010). Furthermore, socio-economic differences
between natives and ethnic minority group members limit the set of affordable
housing. Rental prices in desired neighbourhoods, dwelling sizes, distance to work,
available vacancies and other factors might decrease chances for individuals with an
immigrant background to move to majority dominated neighbourhoods (Hedman and
Ham, 2011, 2012; Quilian, 2002; Andersson and Bråmå, 2004).
Lastly, decisions against ethnically mixed neighbourhood might reflect a desire “to
avoid residence in neighbourhoods with unstable populations, large numbers of poor
residents, weak ties between neighbours, or other deleterious social and economic
conditions, rather than an aversion to living near minority group members per se”
(Crowder, 2000: 226). From this perspective, stereotypes about the socio-economic
instability and safety of minority neighbourhoods would be a main driver behind flight
and avoidance behaviour.
“Transient” neighbourhoods that serve as “ports of arrival” to new immigrants play an
important role for segregation in the Chicago school (Andersson and Bråmå, 2004;
Bråmå, 2008). High turnover rates and ethnic concentration create neighbourhood
instability and possibly distressed social conditions. Non-minority residents and
socio-economically better off immigrants are more likely to leave these “ports of
arrival”, thus creating vacancies for newcomers, which possess fewer language and
labour market skills.
Previous research
While most previous research focuses on neighbourhood segregation in the United
States, much less is known about the origins of ethnic enclaves in European cities
(see Bråmå 2006; Andersson 2013 and Aldén, et al. 2015 for exceptions). Studies
usually focus on segregation between blacks and whites, but more recently a multi-
ethnic perspective has been taken as well (Pais et al., 2009).
South and Crowder (1998) observe that high socio-economic status increases the
chances for moving into neighbourhoods with a large white population. Additionally,
blacks are less likely to move in, but more likely to move out of neighbourhoods
where many whites live. Racial differences persist even after controlling for socio-
economic characteristics, life-cycle effects and geographic location. Crowder (2000)
also finds that whites have higher chances to move out of multi-ethnically mixed
neighbourhoods. They also react to the increases of inflow from black in-movers.
Surprisingly, changes in socio-economic conditions of neighbourhoods do not
matter. “Ethnic flight” behaviour seems to be motivated by racial or ethnic
preferences and not socio-economic conditions. Generally, however, effect sizes are
rather small. Only large changes in the ethnic composition show any sizeable effect
(increases in minority population have to be in the range of over 15% and result only
in up to 2% higher probability for leaving).
More recently, the scope of selective movement patterns has been broadened and
the set of methods refined. Crowder and South (2008) and Crowder, Hall and Tolnay
(2011) show that the composition and housing vacancies in nearby neighbourhoods
have an impact on majority groups’ decisions to move out. Socio-economic factors
matter as well. While blacks are more likely to move out of wealthy neighbourhoods,
whites’ chances are unaffected by average wealth (Crowder et al., 2011). Pais,
South and Crowder (2009) show that out-movement differs quite strongly for different
ethnic groups, which are affected differently by changes in the ethnic composition of
neighbourhoods.
Concerning “ethnic avoidance” Quilian (2002) finds that whites are more likely to
move into predominantly white neighbourhoods. Furthermore, socio-economic
conditions cannot account for this.
Bråmå’s (2006) and Andersson’s (2013) results show that changes in neighbourhood
composition are not due to selective out-movement of native Swedes (the out-
movement rates from immigrant dense areas are only slightly higher for native
Swedes), but rather due to “ethnic avoidance”. Native Swedes are less likely to move
towards neighbourhoods where many immigrants live (Bråmå, 2006). Looking at
Stockholm county during the period 2005-2008, Andersson (2013) also finds mostly
evidence for “ethnic avoidance”. A more detailed study of segregation dynamics in
Gothenburg (Bråmå 2008) shows that different migration dynamics for different
ethnic subgroups exist. Bråmå observes that immigrants often start in “ghettoized”
neighbourhoods, acting as “ports of arrival”, but steadily work their way up into more
integrated and affluent areas (cf. Sampson and Sharkey 2008 for similar results in
the US)
Aldén et al. (2015) focus on tipping behaviour in aggregate growth-rates in Sweden
and find different responses of natives to inflows of European and non-European
migrants. Furthermore, their analysis shows evidence for “ethnic avoidance” before
2000, but more evidence for “ethnic flight” in the years after 2000.
Data
We use Swedish register data to obtain information about residential moves within
Stockholm municipality between 1990-2003. Data contain information about
residence (SAMS neighbourhood area), socio-demographic characteristics and
income of all individuals living in the Stockholm Metropolitan Area between the years
1990-2003. Stockholm’s 128 SAMS boundaries follow traditional neighbourhood
definitions, hence, they incorporate local characteristics and make a meaningful unit
of analysis.
Figure 1 shows the change in the proportion of immigrants, the proportion of movers,
the dissimilarity index for immigrants and the Gini-coefficient for income from 1990 to
2003 for the whole of Stockholm municipality. In total, there have been 41,578
moves (between 1990 and 2003). The rate of moving remained stable at around 5%
of the population per year. The proportion of immigrants has risen steadily from 1990
to 2003. There is also an increase in segregation as depicted by the dissimilarity
index, i.e. Swedes and immigrants seem to be more clustered residentially in 2003
than in 1990.
FIGURE 1 ABOUT HERE
Neighbourhood-level analysis
While every movement decision, on which movement flows are based, can arguably
be modelled as an individual choice driven by opportunities and constraints, the
aggregated structure of flows reveals systematic patterns and dependencies of the
underlying topology, which might be overlooked in a strictly individual perspective.
Network techniques have been employed successfully before to study the structure
of flows, for example to understand the structure of the world trade (e.g. Fagiolo and
Mastorillo, 2013; de Benedictis and Tajoli, 2011; Koskinen and Lomi, 2013) or
international migration flows (Slater 2008).
We investigate the migratory movement flows of immigrants and natives in
Stockholm by constructing directed and binary networks (each represented by an
adjacency matrix ), where network nodes are the 128 neighborhoods in Stockholm
municipality and network ties represent flows from one neighborhood to another. We
apply a threshold and create a network tie between two neighborhoods and
when the aggregate number of moves between them is greater than 1.96
standard deviation units above the average (aggregate) number of moves (the mean
and standard deviations are calculated separately for natives and immigrants).
Figure 2 and 3 present the time-aggregated networks for natives and immigrants
within the geographic context of Stockholm. Larger node sizes refer to more densely
populated neighborhoods while darker shaded nodes refer to neighborhoods with a
higher share of those with an immigrant background.
Both networks are spatially clustered; most ties are between neighborhoods that are
physically close to each other. While the natives’ network (Figure 2) has more ties in
the city center of Stockholm, the immigrants’ network (Figure 3) has more ties in the
geographic periphery, where also more immigrants live.
FIGURE 2 ABOUT HERE
FIGURE 3 ABOUT HERE
Modelling neighbourhood-level migration flows
We use exponential random graph models (ERGM) (Wasserman and Pattison, 1996;
Lusher et al., 2013) to model the network ties of our networks. ERGMs are
particularly useful to find out about which structural patterns are over-represented in
a network compared to what one would expect by chance. They indicate in which
ways the network tie-formation process that generated a network deviates from
independence. The general form of the model formulation is detailed in the online-
supplement (cf. Appendix A1 “ERGM model formulation”).
Structural effects
We include a reciprocity effect that models if flows between neighbourhoods go in
both directions. The underlying network statistic is the count of the number of
reciprocated ties , i.e. corresponding to the prevalence of ties from to being
reciprocated by ties from to . Furthermore, we include a two-path effect (the
underlying network statistic is the sum of over all ties). This essentially models
the correlation between the number of ties a neighbourhood sends and receives. To
model heterogeneity in the in- and out-degree distributions, i.e. the marginal returns
of additional ties (neighbourhoods that already have a high number of flows directed
to them might reach a threshold at which the probability to receive further inflows
declines), we include alternating in- and out-star statistics. Positive effects for these
statistics mean that many ties are concentrated on a few nodes and that few ties are
concentrated on many nodes – some neighbourhoods are more popular than others
(the so-called ‘Matthew effect’, Merton, 1968). Negative effects mean that the
distribution of ties (sent/received) are evenly distributed. To capture local hierarchy
and balance, we include configurations of transitive ties (whether the tie A->B is
embedded in the path A->C->B) and cyclic ties (A->C->B->A) (Holland and
Leinhardt, 1970). These configurations allow us to infer whether moving patterns
point towards more or less popular neighbourhoods (cf. Bråmå 2008). For these triad
configurations, we use the alternating form of the statistic proposed by Snijders et al.
(2006) and elaborated in Robins et al. (2009).
Controls
To model the dependence of the network structure on properties of the
neighbourhoods, we include a number of social selection effects (Robins et al.,
2001). Here, let represent a generic variable for neighbourhood . Sender
effects capture properties of neighbourhoods that are associated with sending more
ties. This is naturally modelled though including sums of as statistics – if the
parameter is positive, networks where neighhoods with large values on will send
more ties. Receiver effects capture properties of neighbourhoods that are associated
with receiving more ties. Similar to the sender effect, the statistic is the sum .
Homophily is the tendency for nodes with similar properties to be more likely to be
connected than nodes with different properties (McPherson et al., 2001). Homophily,
or its converse – heterophily – in ERGMs, is captured by statistics that count (or
measure) the number of same (different) category ties. Here we use a heterophily
statistic that adds for all pairs and . If the corresponding parameter is
negative it means that ties between different nodes are less prevalent.
Large neighbourhoods can sustain more ties, both in terms of receiving and sending
ties: a higher population makes it more likely that moves occur. Therefore, we
include the population of each neighbourhood as a covariate both for sending and
receiving ties. A heterophily statistic considers whether ties might be more likely
between neighbourhoods of the same size. To consider potential effects of the
income-level in a neighbourhood, we include similar effects, namely the sender,
receiver, and heterophily statistics (flows might be more likely to happen between
neighbourhoods with similar income-levels). We use the average income of a
neighbourhood as the nodal value. i Furthermore, we include the log-transformed
Euclidean distance in physical space between neighbourhoods as a dyadic
covariate (Daraganova et al., 2012). This reflects the fact that people might be
more likely to move in proximity to their original neighbourhoods (cf. Crowder and
South, 2008 and Crowder et al., 2011 for proximate neighbourhood effects)
Effects for ethnic neighbourhood composition
For each neighbourhood, we calculate the share of immigrants averaged over the
complete period 1990-2003. We count those individuals as having a foreign or
immigration background that either immigrated to Sweden themselves, or have at
least one parent that migrated from a foreign country to Sweden in the past.
Therefore, we count all first and second generation immigrants as having an
“immigrant background”. The rationale is that discrimination, should it be present in
any of the processes described, is usually not limited to first generation immigrants.
We are not making further distinctions about the country of origin in this part of our
analysis, but we will take European/non-European backgrounds into account when
modelling individual chances of moving. Our model includes sender, receiver and
heterophily effects with respect to the share of immigrants, which can be interpreted
in the same manner as the heterophily effects included for population size and
average income. Together, these statistics allow us to unpack whether there are
migration corridors depending on immigrant share. For example, we can differentiate
(natives’) “ethnic avoidance” from immigrants’ preference to move to same ethnicity
neighbourhoods, if (a) immigrants are happy to move between neighbourhoods that
have similar composition, over and above their preference for moving to same
ethnicity neighbourhoods but, (b) natives have a preference against high immigrant
neighbourhoods and this cannot be explained by them moving between
neighbourhoods with similar composition.
We estimate the parameters of the model using maximum likelihood with the
constraints that the total number of ties is fixed (Snijders and van Duijn, 2002).
Furthermore, all nodal variables are standardized (mean=0, standard deviation=1).
The goodness-of-fit for Model 2 were satisfactory (Robins and Lusher, 2013).
Neighbourhood-level results
Table 1 shows ERGM results for both the network of natives Swedes (Models 1 and
2) and the network of immigrants (Models 3 and 4). For each network, we present
two models, one where we include structural effects and the ethnic composition
(Models 1 and 3) and another one where we also include income (Models 3 and 4).
Coefficients can be interpreted like normal logistic regression coefficients, with the
exception that dependencies between network ties are modelled explicitly.
Overall, there is strong evidence for local hierarchy among neighbourhoods. The
two-path effect is negative and significant in all models ( = -0.101 in Model 1 and
= -0.139 in Model 3), indicating a negative correlation between in- and outdegree for
each node: neighbourhoods that are more likely to send ties are somewhat less
likely to receive ties to the same extent. The effect for transitive triads is positive ( =
1.024 in Model 1 and = 1.271 in Model 4) and the effect for cyclical triads is
negative ( = -0.327 in Model 1 and = -0.468 in Model 3). Taken together, this
suggests the presence of network structures corresponding to hierarchy. Some
neighbourhoods are more desirable than others, even after taking into account the
share of persons with an immigrant background in the neighbourhood. Moreover, we
find that the omission of these structural dependencies between neighbourhoods
leads to a non-trivial bias among the other effects estimates, as additional models
reveal.ii
Concerning “ethnic flight” our results show no positive sender effect for %Immigrant (
= -0.044; n.s in Model 1). Neighbourhoods with a high share of immigrants are not
more likely to have outflow of native Swedes. This is in line with previous findings
(Bråmå, 2006, 2008). Looking at the network of immigrants, however, reveals a
different pattern. Immigrants are more likely to leave neighbourhoods where many
immigrants live ( = 1.217 in Model 3).
Focusing on the characteristics of the destination neighbourhoods, we do find a
%Immigrant receiver effect for native Swedes ( = 0-.515 in Model 1). This suggests
that movement flows of Swedes are less likely towards neighbourhoods where many
immigrants live. In contrast, immigrants are more likely to move into neighbourhoods
where already many immigrants live ( = 1.089 in Model 3).
The %Immigrant difference effect is non-significant for the native Swedes ( = -
0.304; n.s. in Model 1), but significant for immigrants ( = -0.522 in Model 3).
Immigrants are more likely to move into neighbourhoods that have a similar share of
immigrants as the neighbourhoods where they come from. Notice that all these
effects control for each other.
In Model 2 and 4, we include income effects. High income neighbourhoods are less
likely to have out-migration of Swedes (Avg. income sender; = -0.424 in Model 2),
but they are also less likely to receive in-migration ( = -0.447 in Model 2). The same
pattern holds for the network of immigrants as well. Immigrants are also less likely to
move out of high income neighbourhoods ( = -0.471 in Model 4) and less likely to
move into high income neighbourhoods ( = -0.581 in Model 4).
Furthermore, moves between neighbourhoods with different socio-economic status
are unlikely for both native Swedes ( = -0.603 in Model 2) and immigrants ( = -
0.242 in Model 4), probably due to strong differences in rent and property prices. It is
notable that hierarchy effects persist, even after accounting for socio-economic
effects. This suggests that neighbourhood popularity is reflected by factors other
than income-levels and share of immigrants in a neighbourhood. Most prominently,
however, findings for presence of “ethnic avoidance” and lack of “ethnic flight”
persist. “Ethnic avoidance” is accentuated when we control for average income in
neighbourhoods.iii The %Immigrant receiver effect for native Swedes increases from
= -0.515 (in Model 1) to = 0-.732 (in Model 2). Effects for the immigrant network
are slightly less pronounced. “Ethnic avoidance” cannot be explained by socio-
economic conditions. Contrary to common belief, it might not be sufficient to reduce
economic inequalities in order to counter tendencies of “ethnic avoidance”.
TABLE 1 ABOUT HERE
Individual-level analysis
The analysis of movement flows between neighbourhoods as a network with ERGMs
allows discovering (and controlling for) neighbourhood-level patterns (e.g. hierarchy)
that could not be discerned from the individual level. Therefore, we apply several
different modelling strategies to explain individual moving-out and moving-in
behaviour. Firstly, we use a two-step hierarchical estimation technique, suggested by
Achen (2005) and Lewis and Linzer (2005), which allows controlling for individual
and neighbourhood characteristics. Secondly, we use multilevel logistic and
multinomial logistic regression models to allow for the comparison of more fine-
grained categories of immigrant background and their different reactions towards
changes in the ethnic neighbourhood composition, as previous research hints at
more differentiated inter-ethnic preferences (cf. Crowder, 2000; Pais, South, and
Crowder, 2009).
Modelling individual moves
For the two-step hierarchical regression analysis first individual-level logistic
regression models predicting the decision to leave (or stay) are performed for each
neighbourhood and each year. We control for a broad range of possible individual
confounding variablesiv and include a variable that distinguishes between native
Swedish vs. immigrant background of a person. The logit coefficient for this covariate
becomes the dependent variable in the second step of the analysis, which performs
neighbourhood-level regressions to account for variation in coefficients between
neighbourhoods and over time.v We apply Fixed Effects WLS panel regressions with
the following explanatory variables on the neighbourhood level: %Immigrant, logged
population size of the neighbourhood, and the median disposable household income
of each neighbourhood.
TABLE 2 ABOUT HERE
Moving-out results
Figure 4 displays the bivariate relationship between the logarithmic odds for leaving
a neighbourhood for immigrant vs native from the first step of the two-step
hierarchical estimation (individual-level). Logistic regression coefficients for each
neighbourhood are plotted against the share of residents with immigrant background
in each neighbourhood. Positive values indicate that immigrants are more likely to
leave, while negative values indicate that Swedish natives are more likely to leave a
neighbourhood. There exists a small effect of neighbourhood ethnic composition on
individuals’ chances to leave, even after controlling for individual-level
characteristics. Native Swedes are slightly more likely to leave a neighbourhood
where many immigrants live compared to immigrants. However, for most
neighbourhoods the effect is close to 0.
FIGURE 4 ABOUT HERE
Looking at the distribution of coefficients (see Figure 4), we cannot discern
discontinuities which would indicate tipping points. Hence, our findings do not align
with Aldén et al. (2015), who found a sudden increase in moving-out behaviour once
the share of immigrants reached 4.5 to 9% in a neighbourhood.
Table 2 shows results from the second step (neighbourhood-level) of the two-step
hierarchical estimation for various model specifications. Results indicate that
immigrants are less likely to leave a neighbourhood compared to native Swedes
when the share of immigrants goes up. Similarly, immigrants are less likely to leave
than native Swedes when the neighbourhood median income increases.
Relaxing the dichotomous distinction between immigrants and native Swedes, we
ran multilevel logistic regression analyses with similar controls for three ethnic
groups: native Swedish, European Union (EU) and non-European Union (non-EU)
background. These analyses included interaction terms between the individual ethnic
group and the neighbourhood-level ethnic composition.vi To address the possibility
that individuals might base their moving out decision not on the static neighbourhood
composition but on the change in composition between t-2 and t-1, we also
calculated models with the respective difference in composition.
FIGURE 5 ABOUT HERE
Figure 5 shows the odds-ratio (leaving vs. staying) for Swedish natives (left),
European Union immigrants (middle) and non-European Union immigrants (right)
when either the share of EU or non-EU immigrants increases at the neighbourhood
level for each year. Coefficients represent the effect strength of an increase in group
size by one standard deviation in a given year (increase in the difference of group
size between t-1 and t0) compared within each group.vii For the Swedish native
population, we find significant ethnic flight effects with regard to the non-EU
population, but only for the first three years under examination (upper panel, left plot
in Figure 5). For the next few years, the effect is essentially zero and even becomes
negative in the years 1998-2002. Increases in the EU population seem to lead to a
slightly higher chance of leaving from 1995 onwards. There is some evidence for
ethnic flight behaviour for the Swedish population, but the pattern is not clear over
time. We find similar results for the moving-out chances of EU migrants (upper
panel, middle plot in Figure 5). There are no more flight effects from 1994 onwards.
For non-EU immigrants (upper panel, right plot in Figure 5), there is also no time-
consistent moving-out pattern.
While the pattern for changes in composition between t0 and t-1 is slightly different
(lower panel in Figure 5), the overall picture does not change substantially. The
compositional change in EU or non-EU immigrants in a neighbourhood does
basically not affect the moving-out chances for a Swedish native individual (lower
panel, first plot in Figure 5). There are also only small and inconsistent effects on the
moving-out chances for the other groups. Overall, moving-out patterns do not seem
to point to any significant “ethnic flight” behaviour on part of any of the groups.
FIGURE 6 ABOUT HERE
Moving-in results
To test, whether Swedes avoid moving into neighbourhoods where many immigrants
live (“ethnic avoidance”), we use a similar design as before. But this time the
population of interest comprises those individuals who move into a new
neighbourhood.viii The dependent variable in the first step regression is “immigrant
vs. native”. We control for the same set of individual-level variables as before.
Figure 6 shows the log-odds for being an immigrant vs. native Swedish among the
population of individuals who move towards a new neighbourhood against the
proportion of immigrants living in these new neighbourhoods. Results suggest
“ethnic avoidance” behaviour; in neighbourhoods with small shares of immigrants,
natives have higher chances to be among the in-moving population compared to
immigrants. In neighbourhoods where many immigrants live, immigrants are more
likely to be among the in-movers. The second step neighbourhood-level regressions
confirm these findings controlling for all other individual and neighbourhood
characteristics (Table 2).
FIGURE 7 ABOUT HERE
Relaxing our distinction between natives and immigrants, the additional multinomial
regressionsix explore if it is undifferentiated avoidance behaviour (native vs.
immigrant) or whether different preferences between more fine-grained categories
(native Swedish, EU, non-EU) exist. On the macro-level we controlled for median
neighbourhood income and the share of EU- and non-EU residents in the destination
neighbourhoods in the year prior to the move. The results in Figure 7 present
comparisons with different baseline categories, which can be easily derived from the
original model results. In the left plot (upper panel, Figure 7) we find the results of a
native Swede being an in-mover compared to a person with non-EU background.
The different symbols refer to the effects that a one standard deviation increase in
either the EU or non-EU population have on moving into the neighbourhood. The
pattern is clear and consistent. Most of the time, the chances are halved for a
Swedish native compared to a non-EU immigrant to be found among the in-mover
population as the share of non-EU residents increases by one standard deviation.
However, Swedes have about the same chance as non-EU immigrants to move into
neighbourhoods with an increase in EU migrant population by one standard
deviation.
The second plot (upper panel) of Figure 7 shows that EU-immigrants consistently
have a higher chance to move into neighbourhoods with a higher share of either EU-
or non-EU population than native Swedes. And lastly the third plot (upper panel) of
Figure 7 shows that as the share of non-EU residents in a destination neighbourhood
increases by one standard deviation, the odds are almost doubled for a non-EU
individual to be an in-mover compared to Swedish natives. But native Swedes and
non-EU immigrants have about the same chance to move into a neighbourhood with
a higher share of EU-residents. The results for the compositional changes between t-
2 and t-1 (lower panel) generally confirm this picture. Swedish natives (left plot, lower
panel) avoid moving into neighbourhoods that experienced an increase of either EU
or non-EU populations in the previous period. EU and non-EU immigrants are more
likely to move into neighbourhoods that experienced an increase in EU or non-EU
populations respectively (middle and right plots, lower panel).
Conclusion
This article aims to investigate the origins of segregation in Sweden (see also
Bråmå, 2006, 2008). Based on the most common explanations for selective in- and
out-movement patterns, ethnic preferences (Schelling, 1971; Emerson et al., 2001),
discrimination (Massey and Denton, 1993; Zubrinsky and Bobo, 1996), socio-
economic differences (Crowder, 2000) and previous research we investigate two
mechanisms in detail: 1) “ethnic flight”, which refers to the selective out-movement of
natives from neighbourhoods where many immigrants live, and 2) “ethnic
avoidance”, which refers to selective in-movement of natives to neighbourhoods
where only few immigrants live. We apply a two-pronged strategy. First, we
conceptualise the flows of movement of Swedes and immigrants between Stockholm
neighbourhoods between 1990 and 2003 as a network and apply exponential
random graph models. This macro-level approach allows us to account for hierarchy
between neighbourhoods as well as spatial dependence of moves, which go
unnoticed at the individual level. While residential moves between neighbourhoods
aggregate into flows of stocks from one location to another, aggregate flows reveal
repeated structural patterns of exchange, much like roads may be considered
aggregates of traffic and ant paths are emergent highways. The most travelled paths
also are indicative of systemic constraints – not everyone can live in the same
neighbourhood. Second, we complement these analyses with micro-level analyses
at the individual level. These analyses cannot account for complex
interdependencies between moves, but they allow for the inclusion of characteristics
of individuals and neighbourhoods. It is clear that both levels of analysis are
important but a new modelling framework to combine both would be required.x
On the macro-level, we find clear evidence for “ethnic avoidance”. Swedes are more
likely to move towards neighbourhoods where fewer immigrants live. Surprisingly,
this effect is even more pronounced when controlling for socio-economic conditions
at the neighbourhood level. There is no evidence for “ethnic flight”. On the micro-
level, we also find support for “ethnic avoidance”. Individuals who move to a new
neighbourhood are more likely to be immigrants than Swedes when the share of
immigrants is high in the destination neighbourhood. Looking at the total population,
Swedes are slightly more likely to leave neighbourhoods where many immigrants
live. Hence, we only find scant evidence for “ethnic flight” at the individual-level. To
summarise, our findings suggest that “ethnic avoidance” and not “ethnic flight” is the
main driver behind segregation in Sweden.
The application of the network approach is adding to the existing literature in the field
by explicitly taking into account (1) that a popularity hierarchy between
neighbourhoods might exist, which structures movement decisions, but which is
usually not considered in models of individual movement decisions. Such a hierarchy
might not accurately be reflected by observable variables, but it can be inferred from
the topography of the network of flows; (2) that movements in one part of the city
might structure the alternatives of movers in other parts of the city (similar to
Harrison White’s, 1970, argument of vacation chains); (3) that movements are
strongly dependent on spatial proximity. Generally, we find that ignoring these
effects leads to a biased estimation of the other effects.
How do our results line up with previous research? First, the effects of “ethnic flight”
are very small or hardly detectable in the analysis of movement flows. This is not an
unusual finding. Even in the United States, where segregation is more pronounced,
the observed effect for “ethnic flight” is very small (Crowder, 2000; Quilian, 2002;
Crowder et al. 2011). The results of our individual-level analyses show that “ethnic
flight” is detectable in Stockholm, but the effects are far too small to exert a
meaningful influence on the segregation process. This also holds true if one looks at
compositional differences rather than static neighbourhood compositions. The effects
of selective in-movement and “ethnic avoidance” seem much more important. This is
also in line with previous research (Andersson, 2013; Quilian, 2002; Hedman and
Ham, 2011; Bråmå, 2006; Simpson and Finney 2009). More recently, Aldén et al.
(2015) suggested that “ethnic flight” and not “ethnic avoidance” drives segregation in
Sweden after 2000. This inconsistency with our results could be due to different time
periods or our focus on Stockholm municipality, which largely omits urban/rural
differences. Most remarkably, our analyses indicate that the avoidance effects
increase after controlling for the income in neighbourhoods. While more research is
certainly needed, previous studies also show that ethnic or racial preferences persist
after taking socio-economic conditions into account (Crowder, 2000; Crowder et al.,
2011; Emerson et al., 2001). In consequence, the observed levels of segregation
would not be reduced by policies that strictly aim at the reduction of poverty or
neighbourhood distress (Andersson and Bråmå, 2004; Andersson, 2006). It would
need a change in “ethnic preferences” to reduce ethnic avoidance behaviour. A
further explanation for large differences in moving-in patterns might be found by
taking into account the properties of the Swedish housing system, which allocates
rental housing according to waiting time and therefore might put newcomers to the
Stockholm housing market at a disadvantage (cf. Özüekren and van Kempen 2003;
Andersen et al. 2013). The market might then be divided between native Swedish
tenants with longer queue waiting times, which can get easier access to the inner
central neighbourhoods (also by exercising their option to buy apartments) and
newcomers (many of them immigrants) that due to their shorter waiting times might
be confined to the more peripheral neighbourhoods, resulting in the moving patterns
that we have observed in this study.
ReferencesAchen, C. H. (2005). Two-Step Hierarchical Estimation: Beyond Regression Analysis, Political Analysis, 13(4), 447-456.
Ahmed, A., Andersson, L. and Hammarstedt, M. (2010) Can ethnic discrimination in the housing market be reduced by increasing the information about the applicants? Land Economics, 86(1), 79–90.
Ahmed, A. and Hammarstedt, M. (2008) Discrimination in the Rental Housing Market: A Field Experiment on the Internet. Journal of Urban Economics, 64(2), 362-372.
Aldén, L., Hammarstedt, M. and Neuman, E. (2015) Ethnic Segregation. Tippingf Behavior, and Native Residential Mobility, International Migration Review, 49(1), 36-69.
Andersen, H. S., Turner, L. M., & Søholt, S. (2013). The special importance of housing policy for ethnic minorities: evidence from a comparison of four Nordic countries. International Journal of Housing Policy, 13(1), 20-44.
Andersson, R. (2013) Reproducing and reshaping ethnic residential segregation in Stockholm: the role of selective migration moves. Geografiska Annaler: Series B, Human Geography, 95(2), 163-187
Andersson, R. and Bråmå, Å. (2004). Selective migration in Swedish distressed neighbourhoods: can area-based urban policies counteract segregation processes? Housing Studies, 19(4), 517-539.
Andersson, R. (2006). ‘Breaking Segregation’—Rhetorical Construct or Effective Policy? The Case of the Metropolitan Development Initiative in Sweden, Urban Studies, 43(4), 787–799.
Bråmå, Å. (2006). ’White Flight’? The Production and Reproduction of Immigrant Concentration Areas in Swedish Cities, 1990-2000, Urban Studies, 43(7), 1127-1146.
Bråmå, Å. (2008). Dynamics of ethnic residential segregation in Göteborg, Sweden, 1995–2000. Population, Space and Place, 14(2), 101-117.
Bruch, E. and Mare, R. D. (2006). Neighborhood Choice and Neighborhood Change, American Journal of Sociology, 112(3), 667-709.
Butts, C.T. (2007). Models for Generalised Location Systems. Sociological Methodology 37, 283– 348.
Bygren, M. and Kumlin, J. (2005). Mechanisms of organizational sex segregation: organizational characteristics and the sex of newly recruited employees, Work and Occupations, 32, 39-65.
Card, D., Mas, A., and Rothstein, J. (2008). Tipping and the Dynamics of Segregation. The Quarterly Journal of Economics, 123(1), 177-218.
Crowder, K. (2000). The Racial Context of White Mobility: An Individual-Level Assessment of the White Flight Hypothesis, Social Science Research, 29, 223–257.
Crowder, K., Hall, M. and Tolnay, S. E. (2011). Neighborhood Immigration and Native Out-Migration, American Sociological Review, 76(1), 25-47.
Crowder, K. and South, S. J. (2008). Spatial Dynamics of White Flight: The Effects of Local and Extralocal Racial Conditions on Neighborhood Outmigration, American Sociological Review, 73(5), 792-812.
Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., Baum, S. (2012). Networks and geography: modelling community network structures as the outcome of both spatial and network processes, Social Networks, 34(1), 6-17.
De Benedictis, L., Tajoli, L. (2011) The world trade network. World Econ 34:1417–1454
Ellen, I. G. (2000). Sharing America’s neighborhoods. Cambridge, MA: Harvard University Press.
Emerson, M. O., Chai, K. J. and Yancey, G. (2001). Does race matter in residential segregation? Exploring the preferences of White Americans, American Sociological Review, 66(6), 922–935.
Fagiolo, G., Mastrorillo, M. (2013). International migration network: Topology and modeling, Physical Review E, 88, 012812.
Galster, G. (1988). Residential segregation in American cities: A contrary Review, Population Research and Policy Review, 7(2), 93-112.
Galster, G. (1990). White flight from racially integrated neighborhoods in the 1970s: The Cleveland experience, Urban Studies, 27, 385-399.
Goldstein, H., & Noden, P. (2003). Modelling social segregation. Oxford Review of Education, 29(2), 225-237.
Groshen, E. L. (1991). The structure of the female/male wage differential. Is it who you are, what you do, or where you work? Journal of Human Resources, 26(3), 457-472.
Hedman, Lina and Ham, M. (2011). Neighbourhood Choice and neighbourhood reproduction, Environment and Planning A, 43, 1381-1399.
Holland, P.W. and Leinhardt, S.(1970). A Method for Detecting Structure in Sociometric Data. American Journal of Sociology, 76(3):492-513
Hunter, D. R.,Goodreau, S. M., and Handcock, M. S. (2008). Goodness of fit of social network models. Journal of the American Statistical Association, 103, 248–258.
Koskinen, J., and Lomi, A. (2013). The Local Structure of Globalization: The Network Dynamics of Foreign Direct Investments in the International Electricity Industry. Journal of Statistical Physics. Vol. 151, (3), 523-548.
Koskinen, J., Mueller, T., Grund, T. (in press). A dynamic discrete-choice model for movement flows. In Perna, C., Pratesi, M. & Ruiz-Gazen, A. (eds.), Studies in Theoretical and Applied Statistics. Springer
Leckie, G., & Goldstein, H. (2015). A multilevel modelling approach to measuring changing patterns of ethnic composition and segregation among London secondary schools, 2001–2010. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(2), 405-424.
Lewis, J. B. and Linzer, D. A. (2005). Estimating Regression Models in Which the Dependent Variable Is Based on Estimates, Political Analysis, 13, 345-364.
Lusher, D., Koskinen, J. and Robins, G. (2013) (Eds.). Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, Cambridge University Press: New York.
Massey, D. S. and Denton, N. A (1993). American Apartheid. Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press.
McPherson, M., Smith-Lovin, L., and Cook, J. M. (2001). Birds of a feather: Homophily in social networks, Annual Review of Sociology, 27, 415–444.
Merton,R.K. (1968) The Matthew effect in science. Science, 159, 56–63.
Özüekren, A. S., & Van Kempen, R. (2002). Housing careers of minority ethnic groups: Experiences, explanations and prospects. Housing studies, 17(3), 365-379.
Ondrich, J., Stricker, A. and Yinger, J. (1999). Do Landlords Discriminate? The Incidence and Causes of Racial Discrimination in Rental Housing Markets, Journal of Housing Economics, 8(3), 185-220.
Pais, J. F., South, S. J. and Crowder, K. (2009). White Flight Revisited: A Multiethnic Perspective on Neighborhood Out-Migration, Population and Residential Policy Review, 28, 321-346.
Quilian, L. (2002). Why Is Black–White Residential Segregation So Persistent? Evidence on Three Theories from Migration Data, Social Science Research, 31, 197-229.
Robins, G.L., Elliott, P., & Pattison, P.E. (2001). Network models for social selection processes, Social networks, 23, 1–30.
Robins, G., Pattison, P., & Wang, P. (2009). Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks. Social Networks, 31(2), 105-117.
Robins, G. L., Lusher, D., 2013. Illustrations: Simulation, Estimation, and Goodness of Fit. In: Lusher, D., Koskinen, J. H., Robins, G. L. (Eds.), Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press, Cambridge, UK, pp. 167–185.
Ross, S. L. and Austin Turner, M. (2005). Housing discrimination in metropolitan America: Explaining changes between 1989 and 2000, Social Problems, 52(2), 152-180.
Sampson, R. J. and Sharkey, P. (2008). Neighborhood Selection and the Social Reproduction of Concentrated Racial Inequality, Demography, 45(1), 1- 29.
Schelling, T. C. (1971). Dynamic models of segregation, Journal of Mathematical Sociology, 1(2), 143-186.
Schweinberger, M., Krivitsky, P. N., and Butts, C. T. (2017). Foundations of finite-, super-, and infinite-population random graph inference. https://arxiv.org/abs/1707.04800.
Sharkey, P., & Elwert, F. (2011). The legacy of disadvantage: Multigenerational neighborhood effects on cognitive ability. American Journal of Sociology, 116(6), 1934-81.
Simpson, L. and Finney, N. (2009). Spatial patterns of internal migration: evidence for ethnic groups in Britain, Population, Space and Place 15 (1), 37– 56.
Slater, P. B. (2008). Hubs and clusters in the evolving us internal migration network. arXiv preprint arXiv:0809.2768.
Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. Socio- logical Methodology, 361–395, Vol 31 31.
Snijders, T.A.B., and van Duijn, M.A.J. (2002). Conditional Maximum Likelihood Estimation under Various Specifications of Exponential Random Graph Models. Iin Hagberg, J. (ed.), Contributions to Social Network Analysis, Information Theory, and Other Topics in Statistics; A Festschrift in honour of Ove Frank. University of Stockholm, Department of Statistics, 117-134.
Snijders, T. A. B., Pattison, P., Robins, G. and Handcock. M. (2006). New specifications for exponential random graph models, Sociological Methodology, 36(1), 99-153.
South, S. J. and Crowder, K. (1998) Leaving the 'Hood: Residential Mobility between Black, White, and Integrated Neighborhoods, American Sociological Review, 63(1), 17-26.
Van de Rijt, A., Siegel, D. and Macy, M. (2009). Neighborhood Chance and Neighborhood Change: A Comment on Bruch and Mare, American Journal of Sociology, 114(4), 1166-1180.
Wasserman, S. and Pattison, P. (1996). Logit Models and Logistic Regressions for Social Networks. An Introduction to Markov Graphs and p*, Psychometrika, 61(3), 401-42.
White, H. (1970). Chains of Opportunity. System Models of Mobility in Organizations. Cambridge, MA: Harvard University Press.
Wilson, W.J. 1987. The Truly Disadvantaged: Essays on Inner City Woes and Public Policy. Chicago: University of Chicago Press.
Wimmer, A. and Lewis, K. (2010). Beyond and Below Racial Homophily: ERG Models of a Friendship Network Documented on Facebook, American Journal of Sociology, 116(2), 583-642.
Zubrinsky C. and Bobo, L. (1996). Prismatic Metropolis: Race and Residential Segregation in the City of the Angels, Social Science Research, 25: 335-374.
i Average disposable household income (from all income sources after taxes) is calculated over the complete
period in our data and standardized by household size. We are using the component that is calculated for
each household member and take the arithmetic mean for each neighbourhood. This measure serves as a
proxy for neighbourhood socio-economic conditions, but could also reflect rent prices
ii We also ran additional models, which omitted the structural network effects for systemic dependencies in
the network of movement flows (two-paths, transitive and cyclical triads, which are used to model the
underlying popularity hierarchy among neighbourhoods). (See Table A2 in the online supplement.)
Noteworthy differences emerge, when these effects are not explicitly taken into account: (1) for the immigrant
population the hierarchy effect is erroneously attributed to homophily on income (Model 6 vs. Model 8); (2)
the effect of nbhd. population size (i.e. vacancies) is underestimated; (3) the results point towards a (non-
significant) ethnic flight effect among the native population; (4) the effect of nbhd. income as a negative
predictor of outflows is attenuated while income in receiving nbhds. as a negative predictor of inflow is
inflated; (5) generally, the effects of the ethnic nbhd. composition as a characteristic of receiving nbhds. are
attenuated for native and immigrant movers. These differences are also reflected in the goodness-of-fit. (For
a discussion of GOFs see: Hunter et al., 2008.) The model without hierarchy produces networks that are not
as clustered as the observed network and where the ties of the immigrant population are much more evenly
spread out across neighbourhoods than they actually are. Furthermore, where M6 neither captures the
degree distribution nor the clustering coefficients, M8 replicates all of these.
iii In general, the fit for the models including the additional structural effects was better in comparison to the
models that omitted them and the inclusion of nbhd. income improved the model fit compared to the models
omitting nbhd. Income, following the criteria of Robins and Lusher (2013:184-185). A comparison of model fit
by more conventional global measures (e.g. AIC) is difficult and currently subject to debate (Schweinberger
et al., 2017).
iv The nesting is individuals in neighborhoods. We control for a range of socio-demographic characteristics
that have been found to be important in this context (South and Crowder, 1998): age, disposable household
income adjusted for household size, sex, number of children below 18, marital status and immigrant
background. The online supplement (sections A4 and A5) contains further information on the models and
descriptive statistics.
v We are using Achen’s (2005) and Lewis and Linzer’s (2005) approach to assure consistency and efficiency
from the second step regression by weighting for the sampling error of the first stage. Further panel model
specifications with very similar results are presented in Tables A3.1 and A3.2 in the appendix. They also
include models with time-lagged variables.vi This means the effect sizes of the compositional variables are plotted. For Swedish natives the main effect is plotted, for the other groups the interaction terms of the compositional variable with the group variable (EU/non-EU) are plotted. On the neighbourhood-level we accounted for the share of EU and non-EU immigrants in the year of moving, the neighbourhood median HH income and the population size. Individual-level controls are the same as in the two-step procedure (see online supplement A4 and A5).
vii E.g. a unit increase in %non-EU on the neighbourhood-level increases the odds of leaving for Swedish
residents by the factor 1.1 in 1991 in comparison to Swedish residents that do not experience the increase. A
unit increase in %non-EU in 1991 increases the odds of leaving for EU immigrant residents by the factor 1.2
in comparison to EU residents that do not experience the increase.
viii We ran individual-level regressions at each time point, when individuals had already moved to the new
neighbourhood (i.e. for movers between 1991 and 1992 in 1992).
ix A multinomial multilevel model is appropriate here because we want to model the composition of the in-
mover population. The nesting is individuals in neighbourhoods. The use of multilevel models, taking group
indicators as dependent variables to model segregation processes, has been applied by Goldstein and
Noden (2003) and Leckie and Goldstein (2015) before. Further information on the modelling exercise are
given in the appendix, section A4.
x Butts (2007) proposes a model for allocation of people that respects some of the systemic constraints;
Koskinen, Müller, and Grund (2017) propose to achieve this through extending the stochastic actor-oriented
family of models of Snijders (2001)
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