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.

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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)