rethinking the long-term consequences of growing up in a disadvantaged neighbourhood: lessons from...

This article was downloaded by: [University of Victoria]On: 19 November 2014, At: 02:19Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Housing StudiesPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/chos20

Rethinking the Long-TermConsequences of Growing Up in aDisadvantaged Neighbourhood: Lessonsfrom SwedenLars Brännström a & Yerko Rojas aa Swedish Institute for Social Research, Stockholm University ,Stockholm , SwedenPublished online: 30 Aug 2012.

To cite this article: Lars Brännström & Yerko Rojas (2012) Rethinking the Long-Term Consequencesof Growing Up in a Disadvantaged Neighbourhood: Lessons from Sweden, Housing Studies, 27:6,729-747, DOI: 10.1080/02673037.2012.714460

To link to this article: http://dx.doi.org/10.1080/02673037.2012.714460

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/loi/chos20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/02673037.2012.714460

http://dx.doi.org/10.1080/02673037.2012.714460

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

Rethinking the Long-Term Consequencesof Growing Up in a DisadvantagedNeighbourhood: Lessons from Sweden

LARS BRANNSTROM & YERKO ROJASSwedish Institute for Social Research, Stockholm University, Stockholm, Sweden

(Received January 2011; revised June 2011)

ABSTRACT Using extensive longitudinal register data for more than 80 000 young metropolitanSwedes, this study addresses the effect of a disadvantaged neighbourhood social context ongroupings of outcomes that are important for the living conditions of young adults. The overallresults show that growing up in a disadvantaged neighbourhood increases the risk of experiencingcomparably more unemployment, having less education and receiving more social assistance thansimilar young people from more affluent neighbourhoods. However, when the estimated effects ofneighbourhood are assessed by means of an epidemiological impact measure that takes theprevalence of the risk factor at population level into account; these effects prove to be minimal.We discuss possible drawbacks of placing too much emphasis on policies targeting disadvantagedneighbourhoods versus universal social policy measures.

KEY WORDS: Adolescents, longitudinal, neighbourhood effects, outcome profiles

Introduction

In Sweden, as in other European Union countries and the USA, the hypothesised effect of

disadvantaged neighbourhood social context on individual opportunity continues to be at

the core of the social stratification research agenda. It is also central to policymaking

aimed at combating outcomes of poverty, social exclusion and inequality (Andersson et al.,

2010; Durlauf, 2006; Friedrichs et al., 2003; Goering & Feins, 2003; Musterd et al., 2010;

Oberwittler & Wikstrom, 2009).

Although there has been a considerable effort to quantify the effects of neighbourhood

social context for a variety of populations and numerous social and economic outcomes

(Dietz, 2002; Galster, 2008; Sampson et al., 2002), comparatively less attention (if any)

has been paid to the effect of neighbourhood context on combinations of outcomes for

young people that are important for their living conditions. This is surprising given that

studying different types of outcomes in combination is usually deemed the best way to

ISSN 0267-3037 Print/1466-1810 Online/12/060729–19 q 2012 Taylor & Francis

http://dx.doi.org/10.1080/02673037.2012.714460

Correspondence Address: Lars Brannstrom, Swedish Institute for Social Research, Stockholm University, 10691

Stockholm, Sweden. Email: [email protected]

Housing Studies,Vol. 27, No. 6, 729–747, September 2012

Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

http://dx.doi.org/10.1080/02673037.2012.714460

approach the complex reality of poverty, social exclusion and inequality (Backman &

Nilsson, 2011; Ferrarini et al., 2010; Whelan et al., 2002). As a result, policy makers rarely

have sufficient information about the variety of consequences that may arise from residing

in specific social environments. Similarly, decisions about social interventions in

distressed residential districts may be made on the basis of too limited knowledge of

individual opportunity. Because the justification for area-targeted social interventions to

some extent rests on the hypothesised negative impact of disadvantaged neighbourhood

social context on individual outcomes (Lupton & Kneale, 2010; Propper et al., 2005),

estimates of the neighbourhood effect on groupings of outcomes that are significant for

people’s circumstances are of great interest.

This article looks for evidence that a disadvantaged neighbourhood social context shapes

young people’s outcome profiles, here conceptualised as combinations of outcomes related

to labour-market participation, education, welfare receipt and offending. By using

extensive longitudinal register data for more than 80 000 young people born in the late

1970s and registered as living in any of Sweden’s three largest conurbations (Stockholm,

Gothenburg, Malmo) in early adolescence, our analysis makes several important

contributions to research into neighbourhood effects on young adult outcomes. First, we

address the effect of a disadvantaged neighbourhood social context on adolescents’

outcome profiles as young adults, rather than on a variety of young adult outcomes analysed

in isolation. This person-oriented approach (Bergman et al., 2003), which emphasises that it

is the outcome pattern as a whole that carries the information rather than the parts regarded

separately, seems fruitful since it is so well established that several of the outcomes

addressed in this area of research tend to go hand in hand (Hallerod & Bask, 2008; Korpi

et al., 2007). Second, the data permit a dynamic classification of neighbourhood population

characteristics, which allows for the fact that population characteristics may have changed

over time. Third, we have a more fully specified model because the data also allow a more

rigorous control for confounding factors such as the socio-economic circumstances of the

young people’s parents. Finally, we make use of a more policy-relevant impact measure

frequently used in epidemiology—the population-attributable fraction—when discussing

the practical implications of the estimated impact of neighbourhood at population level.

The remainder of the article is structured as follows. The article proceeds with a short

summary of why the social characteristics of neighbourhoods may pattern adolescents’

outcomes as young adults; this section includes results from previous Swedish

longitudinal research. The next section describes data and methods. Finally, we present

and discuss the empirical findings of the study.

How Might Adolescents’ Neighbourhoods Shape Their Outcomes as Young Adults?

There have been many insightful discussions of the potential causal pathways through

which neighbourhood social characteristics may pattern individual conduct and they need

not be repeated here (for a recent review, see e.g. Galster, 2010, and an issue of Housing

Studies (No. 5) [Blasius et al., 2007]). This section addresses mechanisms related to social

interaction between neighbourhood residents and how this may affect young people’s

development, socialisation and careers.

The scientific literature on neighbourhood effects often picks up on the impact of the

social interaction between neighbourhood residents and how this may affect young

people’s development, socialisation and career paths (Jencks & Mayer, 1990). In simple

730 L. Brannstrom & Y. Rojas

Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

terms, the idea is that the social characteristics of peer groups influence young people’s

world orientations and thereby their choice of action. Neighbourhood environment is thus

considered an important arena for interaction with people who may exert an influence

(both positive and negative) on young people’s attitudes, norms and values (Elliott et al.,

2006). Assuming that processes related to population sorting in the housing market to

some extent determine the social characteristics of young people’s peer groups, it can be

assumed that the spatial concentration of young people from less advantaged households,

in particular neighbourhoods (among other things), gives rise to fewer opportunities for

learning, insufficient information about potential future labour-market opportunities

and/or a lack of role models who can promote choices and behaviours that contribute to

success both in school and at work (Cook et al., 2002). Thus, it is assumed that the

likelihood of deviant behaviour, negative career paths, etc. will be higher in

neighbourhoods with poor resources—characterised, for example, by high unemployment,

many poor people or high residential turnover.

It is of particular interest for this study that poor neighbourhoods with many minority

residents have been found to have the fewest resources of all kinds (Furstenberg et al.,

1999). Part of the explanation is that homophily and social position operates jointly, that is,

segregated (and hence rather closed) social networks composed by a high proportion of

others in a similar disadvantaged positions are unable to mobilise and access to

fundamental forms of resources such as job information and job opportunities (Rostila,

2010). Minority residents can therefore be said to be at a double disadvantage, first in

terms of their resource status and second in terms of ethnic status (Furstenberg et al.,

1999). Ethnicity is in itself an important basis for stratification, that is, to be classified as

part of an ethnic minority most often implies to be located in a subordinated position

(i.e. low ethnic status) in the hierarchy (in this case) of ‘Swedishness’ (Breen & Rottman,

1995; Graham, 2007).

It is not surprising that the empirical literature relevant to this article comprises a great

number of potentially useful studies (for a recent compilation of the international

empirical literature, see e.g. Bolster et al., 2007). Thus, there is a need to restrict the scope

of the summary of previous findings. It has been hypothesised that Sweden’s

institutionalised welfare policies, which involve considerable intervention in market

processes and social spheres (Abrahamson, 2003), may limit the scope and magnitude of

neighbourhood effects (Andersson, 2008). Consequently, we present the results of a

number of Swedish longitudinal studies that address sufficiently similar outcomes to those

scrutinised in this article and whose study populations comprise wholly or in part young

people.

Previous Swedish studies looking at whether neighbourhood social characteristics affect

young people’s future circumstances have reported that neighbourhood population

characteristics related to low socio-economic resources (e.g. Andersson, 2004; Andersson

& Subramanian, 2006; Bergsten, 2010) and concentrations of immigrants (Gronqvist,

2006) are negatively associated with educational outcomes. In a similar way, low socio-

economic resources in a neighbourhood increase the likelihood of a young person from

that neighbourhood experiencing unemployment compared with a comparable young

person from a residential area with higher socio-economic resources (Bergsten, 2010;

Urban, 2009). Similar results have been reported for the relationship between the

proportion of people in a neighbourhood receiving social assistance and benefit receipt at

individual level (e.g. Mood, 2010b). Other studies, however, have not been able to identify

Rethinking the Long-Term Consequences 731

Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

any clear and statistically significant differences between young people from resource-

poor neighbourhoods and comparable others in terms of incidence of benefit receipt and

labour-market participation (e.g. Brannstrom, 2004; Sundlof, 2008).

There is a shortage of Swedish longitudinal studies into whether neighbourhood social

characteristics affect young people’s criminality. Hallsten et al. (2011) have reported a

negative association between higher concentrations of socio-economic resources and

offending. Brannstrom (2004), however, has compared differences in offending between

young people who have grown up in resource-poor neighbourhoods and peers who, given

their observed characteristics, could have lived in such a neighbourhood but did not.

The findings do not indicate that level of offending differs between these groups to any

significant degree.

To sum up: in line with the existing scientific literature and popular beliefs about the

importance of social-interactive mechanisms in patterning youth behaviour it is reasonable

to hypothesise that neighbourhood social characteristics influence people’s norms,

opportunities and expectations; moreover, higher concentrations of resource-poor groups

and/or ethnic minorities in an area can be expected to have a negative impact on the

outcomes in focus in this study. However, the results of previous Swedish (and

international) research are not unambiguous (cf. Galster, 2010).

One of the reasons for this inconsistency may be related to previous studies that have

usually focused on the effect of neighbourhood on a variety of outcomes analysed in

isolation and, hence, may not have sufficiently addressed the complex reality of poverty,

social exclusion and inequality. When it is plausible that the outcomes addressed in this

area of research go hand in hand, it seems reasonable to suggest that the effect of

disadvantaged neighbourhood conditions may be more pertinent on grouping of outcomes.

The person-oriented approach applied in this study could potentially transcend this issue

by targeting the outcome pattern as a whole. However, no previous study has, to our

knowledge, addressed the effect of neighbourhood social context in adolescence on

combinations of outcomes as young adults. It is therefore difficult to a priori establish the

direction and magnitude of the effect on the identified patterns of outcome profiles detailed

below.

Data and Measurement Issues

This study uses comprehensive longitudinal register data. The database consists of

information from a large number of high-quality registers provided by Statistics Sweden

and other public authorities (including, but not limited to, the Population and Housing

Census (FoB1990), the Register of persons found guilty of offences (Lagforingsregistret),

the Register of final year classes in compulsory school (Arskurs 9-registret) and the

Longitudinal integration database for health insurance and labour-market studies (LISA)).

The data were compiled by the former Epidemiological Centre at the Swedish National

Board of Health and Welfare.

The population was limited to three birth cohorts (1977–1979) who lived for at least

three years in the same neighbourhood unit (1990–1994) in any of Sweden’s three largest

metropolitan areas (Stockholm, Gothenburg, Malmo). The exposure period is uniform, i.e.

individuals born in 1977/1978/1979 were exposed to one and the same neighbourhood unit

at least during the period 1990–1992/1991–1993/1992–1994, respectively. The study

population was consequently aged between 13 and 15 at the time in question (i.e., during


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

the calendar years when they were aged between 13 and 15), a period during which one

can expect the effects social interaction to be particularly strong, according to theory and

previous North American studies (Ellen & Turner, 2003). The period of exposure in this

study coincided with the deep economic crisis of the 1990s, which brought about dramatic

changes in the Swedish labour market. This, in turn, had a negative impact on the

economic situation of the population (Fritzell, 2001) and also resulted in a greater

geographical concentration of poverty (Kolegard Stjarne et al., 2007). Consequently,

neighbourhood effect theories are tested here on a sample of individuals who should

support the overall neighbourhood effect hypothesis.

The data comprise 80 092 young people (38 960 girls and 41 132 boys) who are nested

in 598 neighbourhoods. The number of young people per neighbourhood unit in our

sample ranges from 2 to 617 (mean 223, median 207). It is well known that socio-

economic and ethnic residential segregation in Sweden go hand in hand (National Board of

Health & Welfare, 2010). As a consequence of this, neighbourhood categorisation is here

based on both of these factors. The social characteristics of neighbourhoods are

categorised according to an existing social-class grouping based on economic and ethnic

indicators and reflect the combined development of concentrations of resource-poor

groups and visible minorities 1990–1994. Categorisation by economic status (i.e. level of

income) is based on the extent to which both the most well-off and the poorest groups are

represented in the area. Similarly, categorisation by ethnic status is based on the extent to

which both Swedes and members of the visible immigrant population are present in the

area (for details, see Biterman & Franzen, 2007a, 2007b).

The concept of ‘visible and non-visible minorities’ has recently started to be applied in

Sweden as a classification scheme for immigrants and minority groups. It is used within

the segregation research to label immigrant groups whose appearance, may it be physical,

dressing, habits, religious beliefs or way of speaking, etc. are perceived as extraneous by

the majority population. The main reason for introducing this concept was the lack of

accuracy of previous classifications based on cultural or geographical characteristics and

the disproportionate emphasis on immigrants’ behaviour or cultural background as all-

encompassing explanatory factors. Furthermore, by using this classification scheme, we

take into account how native Swedes typically perceive and treat immigrant groups,

including aspects such as discrimination and racism. In Sweden immigrants groups with

origins in south-east Europe, Asia, Africa and Latin America are considered to be

‘visibles’ (National Board of Health & Welfare, 2006, 2010).

Migration can alter the socio-economic composition of a neighbourhood as can

processes related to gentrification and urban revitalisation programmes (Jackson & Mare,

2007). Neighbourhood social composition changed during the 1990s (partly as a

consequence of the economic crisis), a factor that we have taken into account in this study.

As shown in Table 1, the sample is dominated (approximately 64 per cent) by young people

who spent at least part of their formative years in affluent residential areas with a

predominantly Swedish-born population (Neighbourhood type 0). This type of residential

area represents the majority of the classified neighbourhoods in the sample (56.5 per cent).

These are largely suburban areas of detached and semi-detached housing or inner-city

neighbourhoods. A total of 260 neighbourhoods in the data have, to varying degrees, a higher

concentration of resource-poor groups and visible minorities (Neighbourhood types 1–5).

Neighbourhoods with the highest concentrations of the latter correspond to neighbourhood

types 4 and 5 that are dominated by rental accommodation in apartment blocks. These two


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

categories together comprise approximately 12 per cent of the study population and of the total

number of classified neighbourhood units.

As noted above, this study analyses the effects of neighbourhood on youth development

with regard to accumulated patterns of labour-market participation, education, welfare

receipt and offending. There is a uniform 12-year follow-up period (ages 16–27). That is,

the outcomes for the young people born in 1977/1978/1979 were measured in 1993–

2004/1994–2005/1995–2006, respectively. Labour-market participation is reflected by

registered number of days of unemployment. Education indirectly refers to number of

formal years of education and is thereby a measure of educational attainment. Welfare

receipt is indicated by number of months in receipt of means-tested social assistance.

Offending is indicated by a number of convictions. All outcomes were measured

throughout the follow-up period. There was an internal drop-out of around 5 per cent of the

observations. Depending on scales of measure, missing data have been imputed using the

mean/median/mode in the neighbourhood.

The dependent variable here is accumulated outcome profile. A number of strategies are

available for identifying patterns in combinations of the addressed outcomes. We draw on

cluster-analytic tools related to the person-oriented approach in developmental

psychology (Bergman et al., 2003). Since we have a large sample (N . 80 000),

k-means cluster analyses are used (Wishart, 2005). Results of extensive cluster analyses

suggest that a seven-cluster solution is a sufficiently valid representation of groupings of

accumulated outcomes in the data (Figure 1). Around 31 per cent of the young people are

found in a cluster termed Average. Individuals in this cluster have—on average—mean

scores for the four variables. The More unemployment cluster (U) of young people, who—

on average—have comparably more days of unemployment than their peers in the

Average cluster, represents approximately 12 per cent of the sample. A third cluster

termed More unemployment/less education (Ue) represents individuals who have

comparably more days of unemployment and less education than young people in the

Average cluster. The cumulative incidence of this cluster is 4.5 per cent.

Two of the identified clusters relate to level of education: one indicates comparably

More education (E) and the other comparably less education (e). These two clusters

Table 1. Contextual variable: neighbourhood types.

Combined neighbourhood development1990–1994

No. of individualsin sample

No. of neighbourhoodsin sample

(0) Well-off/predominantly Swedish-bornpopulation

51 153 (63.9%) 338 (56.5%)

(1) Economically integrated–slightlyimpoverished/predominantly Swedish-bornpopulation

8966 (11.2%) 113 (18.9%)

(2) Economically integrated–slightlyimpoverished/ethnically integrated, elementsof visible minorities

6418 (8.0%) 40 (6.7%)

(3) Poor/predominantly Swedish-born population 2783 (3.5%) 37 (6.2%)(4) Poor/Predominantly visible minorities 7981 (10.0%) 57 (9.5%)(5) Very poor/almost exclusively

visible minorities2791 (3.5%) 13 (2.2%)

Total 80 092 (100%) 598 (100%)


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

represent around 38 per cent and 10 per cent, respectively, of the young people. Around 3

per cent of the sample is found in a more problem-burdened cluster defined as More

unemployment/less education/More social assistance (UeS). The seventh and final cluster

is an extended version of the previous one, since the young people found here also have

comparably more convictions (More unemployment/less education/More social

assistance/More convictions cluster, UeSC). In absolute numbers, this cluster represents

around 800 people, which corresponds to approximately 1 per cent of the sample.

To ensure that the profile pattern depicted above derives from real underlying structures

in the data, two types of sensitivity analysis were performed: replication and simulation

(cf. Bergman et al., 2003; Milligan, 1996). To save space, estimates are not shown. The

replication analyses show that the seven-cluster solution is fairly reproduced in the 10

sub-samples. The simulation analyses were structured as follows. Ten randomly altered

(‘shaked’) datasets, which matched the general characteristics of the original data

(i.e. equal with respect to means, standard deviations and min–max values for the outcome

variables), were created by means of chaos mathematics. The same cluster analysis

initially performed on the original data was then conducted on the 10 ‘shaked’ datasets.

These analyses did not reproduce the obtained seven-cluster solution. This was expected

since the ‘shaking’ has removed the relationships in the original data. If the obtained

–202468

–202468

–202468

Average (n = 24 841/31.0%) U (n = 9 884/12.3%) Ue (n = 3 623/4.5%)

E (n = 30 756/38.4%) e (n = 7 917/9.9%) UeS (n = 2 255/2.8%)

UeSC (n = 816/1.0%)Unemployment

Educational attainment

Social assistance

Convictions

U = comparably more unemployment (than young people in the Average cluster)

Ue = comparably more unemployment/less education

E = comparably more education

e = comparably less education

UeS = comparably more unemployment/less education/more social assistance

UeSC = comparably more unemployment/less education/more social assistance/more convictions

Figure 1. Dependent variable: accumulated outcome profiles (cumulative incidence withinbrackets). Results from k-means cluster analysis (means, standardised values).


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

seven-cluster solution had been reproduced, our outcome profiles had been uninteresting

in the sense that they were only what to be expected to result from analysing a random

dataset. Taken together, the results from the two sensitivity analyses do not prevent the

reported cluster solution from being sufficiently valid and thereby having some generality.

Perhaps the most serious obstacle to drawing inferences about the causal role of

neighbourhood context in individual outcomes is related to the fact that the selection of

individuals into and out of neighbourhoods is non-random (Bergstrom et al., 2010). When

selective processes are operating, we are faced with the standard problem of not knowing

whether observed differences in outcomes actually mirror the hypothesised causal forces

under consideration, or whether they merely reflect unmeasured differences between the

initial populations experiencing each condition (Lieberson, 1985). To considerably reduce

problems related to selectivity, and thereby strengthen the hypothesised causal link

between the observed neighbourhood social characteristics and the outcome profiles

addressed in this article, a number of relevant control variables are included (for details,

see Table 2). Apart from individual-level controls such as sex and immigrant status ( first

or second generation immigrant), it is important to take the observed demographic and

socio-economic circumstances of their parents/of the household into account (type of

family, form of dwelling tenancy, household income, parental educational level, parental

social class, parental welfare receipt (which indicates household poverty), parental

unemployment). All information about parents and household is for 1990, i.e. largely those

circumstances which pertained prior to the exposure period (1990–1994). Information

about the household refers for the most part to the mother.

Young people are exposed to a number of overlapping settings in their daily lives. To

reduce the risk of the contextual variables also picking up the potential influence of

extraneous factors, a series of dummy variables for birth cohort, region and municipality

are included. Young people spend much of their time at school as well as in their

residential environments. Here it is important to note that it is not common for all the

young people in a residential area to attend the same school. Just over a quarter of the 510

schools in the data had students from one single neighbourhood; the median was five

neighbourhood units per school, while at most a school had students from 151 different

neighbourhoods. The results of earlier studies indicate that estimated neighbourhood

effects on young people’s educational outcomes tend to be proxies for characteristics that

are actually related to the school environment (Brannstrom, 2008; Sykes & Musterd,

2011). This makes it desirable to try to refine the effect of neighbourhood by keeping the

unobserved characteristics related to school units fixed. We included fixed effects for

school units but this computationally intensive model did not converge. As a consequence,

we have to accept that the reported estimates of neighbourhood effect may be biased

upwards, at least where the outcome profiles related to education are concerned.

A multinomial/polytomous logistic regression model is the main tool for analysing

which type of neighbourhood predicts accumulated outcome profiles. The Average cluster

represents the base outcome and relative-risk ratios (RRRs) are used as measure of effect

(for details of the statistical model, see Long & Freese, 2006). The RRR does not address

the importance of the risk factor at population level, as its prevalence is not taken into

account. To give an indication of the practical relevance of the results, estimated RRRs of

the neighbourhood effect were converted to population-attributable fractions, a more

policy-relevant impact measure common in epidemiology. Assuming that the effect of

exposure is causal, the population-attributable fraction estimates the proportion of


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

Table

2.

Co

ntr

ol

var

iab

les

(N¼

80

09

2).

Var

iab

leD

escr

ipti

on

Pro

po

rtio

n/a

ver

age

(std

.dev

.)M

in–

max

Sex

Bo

y0

.51

40

–1

Bir

thco

ho

rt1

97

70

.33

30

–1

19

78

0.3

26

0–

11

97

90

.34

10

–1

Fir

st-g

ener

atio

nim

mig

ran

tB

orn

abro

ad0

.07

80

–1

Sec

on

d-g

ener

atio

nim

mig

ran

tB

orn

inS

wed

en,

mo

ther

bo

rnab

road

0.1

47

0–

1P

aren

t’s

edu

cati

on

alle

vel

,1

99

0E

du

cati

on

alat

tain

men

t,m

oth

erS

ho

rtco

mp

uls

ory

0.0

80

0–

1C

om

pu

lso

ry0

.14

80

–1

Sh

ort

seco

nd

ary

0.3

38

0–

1S

eco

nd

ary

0.1

02

0–

1L

ow

erte

rtia

ry0

.14

40

–1

Hig

her

tert

iary

0.1

89

0–

1P

aren

t’s

soci

alcl

ass,

19

90

Occ

up

atio

nal

clas

s(S

EI)

,fa

ther

Un

skil

led

and

sem

isk

ille

dw

ork

ers

0.2

72

0–

1S

kil

led

wo

rker

s0

.07

90

–1

Ass

ista

nt

no

n-m

anu

alem

plo

yee

s0

.19

20

–1

Inte

rmed

iate

no

n-m

anu

alem

plo

yee

s0

.26

30

–1

Hig

her

no

n-m

anu

alem

plo

yee

s0

.10

20

–1

Up

per

lev

elex

ecu

tiv

es0

.00

90

–1

Sel

fem

plo

yed

/far

mer

s0

.03

20

–1

Un

clas

sifi

edem

plo

yee

s0

.04

50

–1

No

info

rmat

ion

0.0

05

0–

1H

ou

seh

old

inco

me,

19

90

Ho

use

ho

ldd

isp

osa

ble

inco

me

(lo

g),

mo

ther

7.7

10

–1

1.8

5(0

.61

5)

Par

enta

lu

nem

plo

ym

ent,

19

90

Mo

ther

has

rece

ived

com

pen

sati

on

fro

mth

eu

nem

plo

ym

ent

insu

ran

ce0

.07

90

–1

Par

enta

lw

elfa

rere

ceip

t,1

99

0M

oth

erh

asre

ceiv

edm

ean

s-te

sted

soci

alas

sist

ance

0.0

25

0–

1T

yp

eo

ffa

mil

y,

19

90

Mar

ried

cou

ple

0.7

37

0–

1C

oh

abit

atio

n0

.06

30

–1

Sin

gle

par

ent

0.1

90

0–

1


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

Table

2.Continued

Var

iab

leD

escr

ipti

on

Pro

po

rtio

n/a

ver

age

(std

.dev

.)M

in–

max

Dw

elli

ng

ten

ancy

,1

99

0O

wn

sh

ou

se0

.57

00

–1

Ten

ant

ow

ner

ship

0.0

98

0–

1R

igh

to

fte

nan

cy/s

ub

lett

ing

0.3

12

0–

1O

ther

0.0

13

0–

1N

oin

form

atio

n0

.01

00

–1

Reg

ion

Sto

ckh

olm

0.5

80

0–

1G

oth

enb

urg

0.2

46

0–

1M

alm

o0

.17

40

–1

Mu

nic

ipal

ity

Mu

nic

ipal

ity

ID1

–4

4


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

outcome for a given population that theoretically would not have occurred if none of the

individuals had been exposed to the risk factor (Laaksonen, 2010). The resulting estimates

can guide policy makers interested in planning social interventions that aim to improve the

outcomes addressed in this study. The population-attributable fraction was calculated using

the following formula: population-attributable fraction ¼ (adj.RRR21/adj.RRR)*fraction

exposed. Fraction exposed refers here to the prevalence of the risk factor for the three birth

cohorts addressed in this study (see Table 1). Stata 10.1/SE version was used for all the

analyses, and standard errors in our regressions were computed using the cluster-robust

option to account for the clustering of individuals within neighbourhoods.

Analysis and Empirical Findings

We start by presenting crude (i.e. unadjusted) differences in accumulated outcome profiles

between the different types of neighbourhood. After that we present adjusted differences

in which selection and other confounding factors have been taken into account (Table 3).

For reasons of brevity, estimates of the intercepts and control covariates are suppressed.

The section concludes with an assessment of the practical importance of the previously

estimated impact of neighbourhood context. Extensive sensitivity analyses not reported

here indicate that the overall results are fairly robust for separate analyses of various

subgroups (e.g. boys, girls and immigrant statuses). Moreover, to ensure that the adjusted

RRRs of neighbourhood effect are not biased due to residual variation (Mood, 2010a),

a number of heterogeneous choice models were fitted (Williams, 2009). The results from

these models do not suggest that comparisons of adjusted RRRs across neighbourhood

types are invalid (estimates not shown).

Compared to young people from well-off areas with a predominantly Swedish-born

population, the crude RRRs clearly support the notion that higher concentrations of

resource-poor and visible immigrants are negatively associated with the outcome profiles

(Table 3, Model 1). For example, young people from neighbourhoods with the highest

concentrations of resource-poor and visible immigrants (Neighbourhood type 5) have a 26

per cent elevated risk of being found in the More unemployment cluster (U) rather than in

the Average cluster (RRR ¼ 1.26). The crude excess risk of being found in the Ue rather

than in the Average cluster ranges from 1.25 to 3.38 depending on neighbourhood type.

Young people from Type 5 neighbourhoods, for example, have a more than three-fold

elevated risk of being found here rather than in the Average cluster (RRR ¼ 3.38).

Furthermore, young people from neighbourhoods with higher concentrations of resource-

poor and visible immigrants (Types 4 and 5 neighbourhoods) are at considerably lower

risk of figuring in the More education cluster (E). Compared to young people from well-off

areas with a predominantly Swedish-born population, those from Type 5 neighbourhoods,

for example, have an around 66 per cent lower risk of belonging to this cluster than to the

Average cluster (RRR ¼ 0.34).

The most notable crude excess risks, however, are associated with the two most

problem-burdened clusters (UeS and UeSC). The excess risk of belonging to the UeS

rather than the Average cluster ranges from 1.67 to 7.13 depending on neighbourhood

type. Compared to young people from well-off areas with a predominantly Swedish-born

population, their peers from neighbourhoods with the highest concentrations of resource-

poor groups and visible immigrants (Type 5) have a seven-fold excess risk of being found


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

Table

3.

Eff

ect

of

nei

gh

bo

urh

oo

dty

pe

on

accu

mu

late

do

utc

om

ep

rofi

les

(N¼

80

09

2).

Res

ult

sfr

om

mu

ltin

om

ial

log

isti

cre

gre

ssio

n(r

elat

ive-

risk

rati

os,

clu

ster

-ro

bu

stst

and

ard

erro

rsw

ith

inp

aren

thes

es).

UU

eE

eU

eSU

eSC

Co

mb

ined

neig

hb

ou

r-hood

dev

elopm

ent

1990

–1994n

pro

file

Model

1(c

rude)

Model

2(a

dju

sted

)M

odel

1(c

rude)

Model

2(a

dju

sted

)M

odel

1(c

rude)

Model

2(a

dju

sted

)M

odel

1(c

rude)

Model

2(a

dju

sted

)M

odel

1(c

rude)

Mo

del

2(a

dju

sted

)M

odel

1(c

rude)

Model

2(a

dju

sted

)

(0)

Wel

l-off

/pre

dom

i-nan

tly

Sw

edis

h-b

orn

popula

tion

11

11

11

11

11

11

(1)

Eco

nom

ical

ly1.0

61.0

41.2

5***

1.0

20.6

8***

0.8

4***

1.3

0***

1.0

51.6

7***

1.0

21.5

0***

1.0

6in

tegra

ted

–sl

ightl

yim

pover

ished

/pre

dom

i-nan

tly

Sw

edis

h-b

orn

popula

tion

(0.0

7)

(0.0

5)

(0.1

2)

(0.0

8)

(0.0

5)

(0.0

4)

(0.0

8)

(0.0

6)

(0.1

8)

(0.0

8)

(0.2

7)

(0.1

6)

(2)

Eco

nom

ical

ly1.1

11.0

61.7

6***

1.2

2***

0.5

6***

0.8

5***

1.3

0***

1.0

22.7

4***

1.3

6***

2.7

5***

1.4

4***

inte

gra

ted

–sl

ightl

yim

pover

ished

/eth

nic

ally

inte

gra

ted,

elem

ents

of

vis

ible

min

ori

ties

(0.1

0)

(0.0

6)

(0.1

6)

(0.0

9)

(0.0

5)

(0.0

4)

(0.0

9)

(0.0

6)

(0.3

6)

(0.1

4)

(0.3

8)

(0.1

9)

(3)

Poor/

pre

dom

inan

tly

0.8

5**

0.9

71.2

8*

1.2

00.4

6***

0.6

8***

1.4

7***

1.0

52.1

2***

1.3

1**

2.3

7***

1.3

2S

wed

ish-b

orn

popu-

lati

on

(0.0

7)

(0.0

7)

(0.1

6)

(0.1

4)

(0.0

4)

(0.0

5)

(0.1

4)

(0.0

7)

(0.3

4)

(0.1

5)

(0.6

2)

(0.3

0)

(4)

Poor/

pre

dom

inan

tly

1.2

2***

1.1

2**

2.3

9***

1.3

4***

0.4

2***

0.8

0***

1.6

0***

1.1

2**

3.9

2**

1.4

2***

3.6

9***

1.4

9***

vis

ible

min

ori

ties

(0.0

8)

(0.0

5)

(0.2

2)

(0.0

9)

(0.0

3)

(0.0

4)

(0.1

1)

(0.0

6)

(0.4

8)

(0.1

1)

(0.4

6)

(0.1

8)

(5)

Ver

ypoor/

alm

ost

1.2

6***

1.0

43.3

8***

1.4

7***

0.3

4***

0.7

1***

2.1

6***

1.1

17.1

3***

1.7

6***

3.5

5***

0.8

5ex

clusi

vel

yvis

ible

min

-ori

ties

(0.1

1)

(0.0

8)

(0.4

5)

(0.1

3)

(0.0

3)

(0.0

6)

(0.2

6)

(0.1

2)

(1.3

1)

(0.2

2)

(0.5

6)

(0.1

5)

Note

:*

**

/**

/*S

tati

stic

alsi

gn

ifica

nce

atth

e1

/5/1

0%

lev

els,

resp

ecti

vel

y.

Inte

rcep

tsan

dco

ntr

ol

cov

aria

tes

sup

pre

ssed

.B

ase

ou

tco

me¼

aver

age.


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

here rather than in the Average cluster (RRR ¼ 7.13). Similar sizeable crude excess risks

(RRR ¼ 1.50–3.55 depending on neighbourhood type) are also associated with the UeSC.

Because selection and other confounding factors were not taken into account, it is not

surprising that the crude excess risks reported above are biased upwards. The excess risks

are reduced considerably when these factors have been controlled for (Table 3, Model 2).

The adjusted excess risk of belonging to the More unemployment cluster (U) rather than

the Average cluster, for example, is more or less zero regardless of neighbourhood type

(RRR ¼ 0.97–1.12). Given that much of the follow-up period coincided with the labour-

market crisis of the 1990s, in which more young people experienced unemployment than

any previous generation in the history of the modern Swedish welfare state (Salonen,

2000), these results are not too surprising. Similar negligible results are also found for

being found in the less education cluster (e) rather than in the Average cluster

(RRR ¼ 1.02–1.12). These results are less expected, especially given the fact that it was

not possible to control for unobserved factors related to young people’s school

environment.

Nevertheless, young people from neighbourhoods with higher concentrations of

resource-poor groups and visible minorities were still less likely to end up in the More

education cluster (E) (RRR ¼ 0.71–0.85 depending on neighbourhood type). There are

also other adjusted excess risks that do not contradict the idea that neighbourhood social

context may pattern youth development. For example, if all other variables are constant,

there is still a notable and statistically significant excess risk of young people from

neighbourhoods with the highest concentrations of resource-poor groups and visible

immigrants (Neighbourhood types 4 and 5) ending up in the Ue and UeS clusters

(RRR ¼ 1.34–1.76 depending on outcome profile). The adjusted likelihood of belonging

to the UeSC rather than the Average cluster is more ambiguous. While the adjusted and

statistically significant excess risk of those youngsters from Neighbourhood type 4 ending

up in this profile remains (RRR ¼ 1.49), the risk appears to be 15 per cent lower for young

people from Neighbourhood type 5 (RRR ¼ 0.85). This contradictory pattern may suggest

that the effects of a concentration of resource-poor groups and visible immigrants on

groupings of young adult outcomes that include convictions are not well understood.

How much of the outcome burden in the current population might be eliminated if the

effect of neighbourhood social context could be eliminated? To address this question, we

have converted the estimated RRRs into an epidemiological impact measure: the

population-attributable fraction. The population-attributable fraction for each neighbour-

hood type (1–5) and outcome profiles ranges from approximately 0.1 to 3 per cent (Table 4).

When these are added together, the total attributable fraction is around 2–7 per cent

depending on outcome profile. This reduces the total number of people in the study

population who have comparably More unemployment (U) by around 2 per cent. Utilising

the information on cumulative incidence for this outcome profile (Figure 1), in absolute

numbers this corresponds to a hypothetical reduction of around 200 people. A further

example is those with Ue, for whom the potential reduction is around 6 per cent. This

corresponds to approximately 210 people. The number of people who only have less

education (e) would be reduced by around 4 per cent if the effect of the concentration of

resource-poor groups and visible minorities in a neighbourhood were removed entirely.

In absolute numbers, this corresponds to a reduction of some 300 people. Analogous

reductions for those with comparably UeS are around 7.5 per cent and 170 people. Finally,

the hypothetical reduction of people ending up in the UeSC cluster is approximately 7 per


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

Table

4.

Pra

ctic

alsi

gn

ifica

nce

:p

op

ula

tio

n-a

ttri

bu

tab

lefr

acti

on

s.

Co

mb

ined

nei

gh

bo

urh

oo

dd

evel

op

men

t1

99

0–

19

94n

pro

file

UUe

Ee

UeS

UeS

C

(1)

Eco

no

mic

ally

inte

gra

ted

–sl

igh

tly

imp

ov

eris

hed

/pre

do

min

antl

yS

wed

ish

-bo

rnp

op

ula

tio

n0

.4%

0.2

%†

0.5

%0

.2%

0.6

%(2

)E

con

om

ical

lyin

teg

rate

d–

slig

htl

yim

po

ver

ish

ed/e

thn

ical

lyin

teg

rate

d,

elem

ents

of

vis

ible

min

ori

ties

0.4

%1

.4%

†0

.1%

2.1

%2

.5%

(3)

Poor/

pre

dom

inan

tly

Sw

edis

h-b

orn

popula

tion

†0.6

%†

0.2

%0.8

%0.8

%(4

)P

oor/

pre

dom

inan

tly

vis

ible

min

ori

ties

1.1

%2.5

%†

2.5

%2.9

%3.3

%(5

)V

ery

po

or/

alm

ost

excl

usi

vel

yv

isib

lem

ino

riti

es0

.1%

1.1

%†

0.4

%1

.5%

†T

ota

lp

op

ula

tio

n-a

ttri

bu

tab

lefr

acti

on

2.0

%5

.8%

3.7

%7

.5%

7.2

%A

pp

rox

.im

pro

vem

ent

(ab

solu

ten

um

ber

s)1

98

21

02

93

16

95

9P

aren

tal

wel

fare

rece

ipt,

19

90

1.7

%3

.3%

†2

.7%

5.2

%5

.2%

Ap

pro

x.

imp

rov

emen

t(a

bso

lute

nu

mb

ers)

16

81

20

21

41

17

42

Notes:

Po

pu

lati

on

-att

rib

uta

ble

frac

tio

n¼

(ad

j.R

RR

21

/ad

j.R

RR

)*fr

acti

on

exp

ose

d.

†N

ot

app

rop

riat

e(a

dj.

RR

R,

1.0

).A

pp

rox

.im

pro

vem

ent

(ab

solu

ten

um

ber

s)¼

PA

F*

cum

ula

tiv

ein

cid

ence

inab

solu

ten

um

ber

s.


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

cent. Since the cumulative incidence of this outcome profile is comparably low (around 1

per cent), the elimination of the risk factor corresponds to a reduction of less than 60 people.

The theoretical improvements outlined above must be deemed modest, irrespective of

outcome profile and neighbourhood type. Even if one assumes that these hypothetical

improvements are desirable and feasible they must be seen in relation to the social and

economic costs involved in changing the social composition of the population of the 260

neighbourhoods which were not classified as well-off with a predominantly Swedish-born

population. They must also be seen in relation to the fact that the potential improvement

achieved by eliminating the risk factor ‘parental welfare receipt’ (which indicates

household poverty) is of similar magnitude to the total neighbourhood effect (Table 4).

The fact that we were not able to fully control for unobserved factors related to school

environment and/or the parents of the young people in the study population must also be

taken into consideration when looking at potential theoretical improvements.

Concluding Discussion

There can be little doubt that the issue of neighbourhood effects on individual outcomes

continues to be at the core of the social stratification research agenda. It is also central to

policies aiming to combat outcomes of poverty, social exclusion and inequality. By means

of extensive analyses of comprehensive longitudinal register data, this study has looked

for evidence that a disadvantaged neighbourhood social context influences Swedish

adolescents’ groupings of outcomes as young adults. To our knowledge, this article

represents the first attempt to quantify the effect of neighbourhood on combinations of

outcomes rather than on a variety of outcomes analysed in isolation. As a result, this

person-oriented study provides policy makers with better information about the potential

consequences of residing in disadvantaged social environments. It may also enable

decisions about social interventions aimed at the outcomes addressed in this study to be

based on broader aspects of individual opportunity.

This study presents mixed evidence about whether a disadvantaged neighbourhood

social context during adolescence patterns groupings of young adult outcomes net of

observed individual-level background characteristics. Young people from neighbourhoods

with higher concentrations of resource-poor groups and visible minorities were less likely

to be found in the cluster with comparably more education. Interestingly, young people

from more disadvantaged neighbourhoods were not more likely to figure in the

comparably less education cluster or the comparably more unemployment cluster. It was

the case, however, that young people from neighbourhoods with the highest

concentrations of resource-poor groups and visible immigrants were at statistically

significant excess risk of being found in the cluster in which unemployment, less education

and social assistance go hand in hand. Although these findings are theoretically important,

and may indicate that much closer attention needs to be paid to the explanatory scope of

theories of neighbourhood effects on young adult outcomes, the chief message is that the

estimated effect of disadvantaged neighbourhood context at population level must be

deemed modest. Furthermore, the results must be seen in relation to the fact that the

reported effect sizes may be biased upwards since it was not possible to control for

unobserved factors related to the young people’s school environment. So, where do the

results found in this study leave us if we are interested in combating outcomes related to


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

poverty, social exclusion and inequality and improving individual opportunity? A number

of suggestions can be put forward.

To some extent, a government’s welfare policy determines people’s strategies for

making a living; it may thus be considered an intervening variable between market

distribution and outcomes related to poverty and inequality. According to Ostendorf et al.

(2001), the relationship between having a job and social indicators such as income,

education and quality of housing are stronger in market-oriented welfare states such as the

USA. In such situations, they argue, unemployment often results in low incomes and poor

housing. In contrast to the USA, it is more common for European Union welfare states to

intervene in market processes.

Comparative studies of income poverty have shown that poverty levels are more

moderate in Western Europe than in the USA. In Sweden, relative poverty rates are

fundamentally lower than in market-oriented welfare states (Kangas & Palme, 2000).

The latter tend to favour targeting and flat-rate benefits rather than universal and earnings-

related benefits (Nelson, 2004). As shown by Korpi & Palme (1998), comparative analyses

of the effects of different types of welfare state on poverty and inequality indicate that

institutional differences lead to unexpected outcomes and contribute to a paradox of

redistribution: the more we target benefits at the poor, the less likely we are to reduce

poverty and inequality. This indicates that the relationships between neighbourhood

disadvantage and the outcomes addressed in this study could well be weaker in welfare

states where policy makers are more concerned with creating equal public transfers to all.

We also know from previous studies that social policy generosity mitigates the

prevalence of multiple welfare problems (Ferrarini et al., 2010; Korpi et al., 2007).

Multiple welfare problems are much more common in Europe outside the Nordic

countries. The proportion of the population with multiple welfare problems is generally

twice as large in Europe as in Sweden. For specific countries in eastern and southern

Europe, the differences are even greater (Ferrarini et al., 2010). Seen in this light, our

results are less surprising because they do not contradict the notion that a society with a

strong egalitarian thrust may reduce the scope and magnitude of neighbourhood effects.

Another issue we would like to draw attention to is that of labelling. It is not surprising

that the media’s traditional portrayal of residential areas with a high concentration of

immigrants as problematic has also helped to create a negative reputation for the residents

of such neighbourhoods (Burns et al., 2007). Swedish public authorities’ introduction of

the term ‘visible and non-visible minorities’, in the classification of residential areas to

highlight that visibility is a key dimension in how native Swedes and the Swedish society

in general perceive and treat immigrant groups, is a big departure from earlier practice.

There is, in other words, a powerful imagery in Swedish society that divides residential

areas according to a scale of ‘Swedishness’ in terms of appearance (including aspects such

as colour of the skin, habits, religious beliefs or not speaking Swedish as the majority

population), with complete visibility at the bottom end of the continuum, that is, complete

deviance from this appearance norm, denoting disadvantage.

One of several problems with this type of imagery is that it tends to be based on

stereotypes propagated by powerful institutions or private actors about these

neighbourhoods’ current residents, history, environmental or topographical disamenities,

style, scale and type of dwelling, or condition of commercial districts and public spaces

(Galster, 2010). Public health scientists have warned against policy initiatives aimed

specifically at foreign-born people, or groups of foreign-born people with a specific


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

background, as they involve a significant risk of stigmatisation and greater discrimination.

Initiatives of this kind need a very robust scientific foundation, and they should also be

specifically requested by the immediate minority group in question. Initiatives against

infectious diseases, such as tuberculosis and hepatitis B, are examples of measures that

often fulfil these criteria (Hjern, 2009).

So, do our results give us reason to believe that our fears about the hypothesised

negative consequences of growing up in a disadvantaged neighbourhood are exaggerated?

We do not think one should jump to such a conclusion. In addition to the fact that

neighbourhood population characteristics may affect (combinations of) outcomes that we

have not addressed here (e.g. subjective well-being, income to name but a few), it is

important to remember that we have looked at the effect of neighbourhood during

adolescence on young adult outcomes. It is reasonable to assume that the effect of

disadvantaged neighbourhood conditions may be cumulative over the life course

(Wheaton & Clarke, 2003) and may even extend across generations (Sharkey & Elwert,

2011). Even if the persistence of neighbourhood disadvantage across generations is less

pertinent in Sweden (Andersson, 2008), further comprehensive longitudinal studies are

needed. However, despite the limits of the design of this study, its findings challenge those

scholars and policy makers who contend that a disadvantaged neighbourhood social

context plays a major role in young people’s development and that social interventions

directed at distressed residential districts are a pathway for improving individual

opportunity.

References

Abrahamson, P. (2003) The end of the Scandinavian model? Welfare reform in the nordic countries, Journal of

Societal and Social Policy, 2(2), pp. 19–36.

Andersson, E. (2004) From valley of sadness to hill of happiness: The significance of surroundings for

socioeconomic career, Urban Studies, 41(3), pp. 641–659.

Andersson, E. & Subramanian, S. V. (2006) Explorations of neighborhood and educational outcomes for young

Swedes, Urban Studies, 43(11), pp. 2013–2025.

Andersson, R. (2008) Neighbourhood effects and the welfare state. Towards a European research agenda?

Schmollers Jahrbuch, 128, pp. 149–163.

Andersson, R., Brama, A. & Holmqvist, E. (2010) Counteracting segregation: Swedish policies and experiences,

Housing Studies, 25(2), pp. 237–256.

Bergman, L., Magnusson, D. & El-Khouri, B. M. (2003) Studying Individual Development in an Interindividual

Context: A Person-Oriented Approach (London: Lawrence Erlbaum Associates).

Bergsten, Z. (2010) Battre framtidsutsikter? Blandade bostadsomraden och grannskapseffekter: En analys av

visioner och effekter av blandat boende (Uppsala: Uppsala universitet).

Bergstrom, L., van Ham, M. & Manley, D. (2010) Neighbourhood Choice and Neighbourhood Reproduction,

IZA DP No. 5238 (Bonn: The Institute for the Study of Labor).

Biterman, D. & Franzen, E. (2007a) Appendix 1 – Classifications, International Journal of Social Welfare, 16,

pp. S233–S240.

Biterman, D. & Franzen, E. (2007b) Residential segregation (Chapter 6), International Journal of Social Welfare,

6, pp. S127–S162.

Blasius, J., Friedrichs, J. & Galster, G. (2007) Introduction: Frontiers of quantifying neighbourhood effects,

Housing Studies, 22(5), pp. 627–636.

Bolster, A., Burgess, S., Johnston, R., Jones, K., Propper, C. & Sarker, R. (2007) Neighbourhoods, households

and income dynamics: A semi-parametric investigation of neighbourhood effects, Journal of Economic

Geography, 7, pp. 1–38.

Breen, R. & Rottman, D. (1995)Class Stratification: AComparative Perspective (London: Harvester Wheatsheaf).


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

Brannstrom, L. (2004) Poor places, poor prospects? Counterfactual models of neighbourhood effects on social

exclusion in Stockholm, Sweden, Urban Studies, 41(13), pp. 2515–2537.

Brannstrom, L. (2008) Making their mark: The effects of neighbourhood and upper secondary school on

educational achievement, European Sociological Review, 24(4), pp. 463–478.

Burns, T. R., MacHado, N., Hellgren, Z. & Brodin, G. (2007) Makt, kultur och kontroll over invandrares

livsvillkor (Uppsala: Acta Uppsaliensis).

Backman, O. & Nilsson, A. (2011) Pathways to social exclusion – A life-course study, European Sociological

Review, 27(1), pp. 107–123.

Cook, T. D., Herman, M., Phillips, M. & Setterston, R. J. Jr (2002) Some ways in which neighborhoods, nuclear

families, friendship groups and schools jointly affect changes in early adolescent development, Child

Development, 73(4), pp. 1283–1309.

Dietz, R. D. (2002) The estimation of neighborhood effects in the social sciences: An interdisciplinary approach,

Social Science Research, 31(4), pp. 539–575.

Durlauf, S. N. (2006) Groups, social influence, and inequality, in: S. Bowles, S. N. Durlauf & K. Hoff (Eds)

Poverty Traps, pp. 141–175 (New York: Russell Sage Foundation).

Ellen, I. G. & Turner, M. A. (2003) Do Neighborhoods Matter and Why? in: J. Goering & J. D. Feins (Eds)

Choosing a Better Life? Evaluating the Moving to Opportunity Social Experiment, pp. 313–338

(Washington, DC: The Urban Institute Press).

Elliott, D. S., Menard, S., Rankin, B., Elliott, A., Wilson, W. J. & Huizinga, D. (2006) Good Kids from Bad

Neighborhoods. Successful Development in Social Context (Cambridge: Cambridge University Press).

Ferrarini, T., Nelson, K. & Sjoberg, O. (2010) Multipla sociala problem: Sverige i jamforande belysning, Social

rapport 2010, pp. 128–148 (Stockholm: Socialstyrelsen).

Friedrichs, J., Galster, G. & Musterd, S. (2003) Neighbourhood effects on social opportunities: The European and

American research and policy context, Housing Studies, 18(6), pp. 797–806.

Fritzell, J. (2001) Inkomstfordelningens trender under 1990-talet, in: J. Fritzell & J. Palme (Eds) Valfardens

finansiering och fordelning – SOU 2001:57, pp. 131–187 (Stockholm: Fritzes).

Furstenberg, F. F. Jr, Cook, T. D., Eccles, J., Elder, G. H. Jr & Sameroff, A. (1999) Managing to Make It: Urban

Families and Adolescent Success (Chicago, IL: The University of Chicago Press).

Galster, G. C. (2008) Quantifying the effect of neighbourhood on individuals: Challenges, alternative approaches,

and promising directions Schmollers Jahrbuch, 128, pp. 7–48.

Galster, G. C. (2010) The mechanism(s) of neighborhood effects: Theory, evidence, and policy implications.

Paper presented at the ESRC Seminar on Neighborhood effects: Theory & evidence, St. Andrews University,

Scotland, UK, 4–5 February.

Goering, J. & Feins, J. D. (Eds) (2003) Choosing a Better Life? Evaluating the Moving to Opportunity Social

Experiment (Washington, DC: The Urban Institute Press).

Graham, H. (2007) Unequal Lives: Health and Socioeconomic Inequalities (Buckingham: Open University

Press).

Gronqvist, H. (2006) Ethnic enclaves and the attainments of immigrant children, European Sociological Review,

22(4), pp. 369–382.

Hallerod, B. & Bask, M. (2008) Accumulation of welfare problems in a longitudinal perspective, Social

Indicators Research, 88, pp. 311–327.

Hjern, A. (2009) Migration och halsa, Folkhalsorapport 2009, pp. 373–390 (Stockholm: Socialstyrelsen).

Hallsten, M., Sarnecki, J. & Szulkin, R. (2011) Crime as a Price for Inequality? The Delinquency Gap between

Children of Immigrants and Children of Native Swedes, Working Paper 2011:2 (Stockholm: Stockholm

University, Linnaeus Center for Integration Studies).

Jackson, M. & Mare, R. (2007) Cross-sectional and longitudinal measurements of neighborhood experience and

their effects on children, Social Science Research, 36, pp. 590–610.

Jencks, C. & Mayer, S. (1990) The social consequences of growing up in a poor neighbourhood, in: L. Lynn &

M. McGreary (Eds) Inner-City Poverty in the United States, pp. 111–186 (Washington, DC: National

Academic Press).

Kangas, O. & Palme, J. (2000) Does social policy matter? Poverty cycles in OECD countries, International

Journal of Health Services, 30, pp. 335–352.

Korpi, T., Nelson, K. & Stenberg, S. -A. (2007) The accumulation of social problems 1974–2000 (Chapter 4),

International Journal of Social Welfare, 16, pp. S91–S104.

Korpi, W. & Palme, J. (1998) The paradox of redistribution and strategies of equality: Welfare state institutions,

inequality, and poverty in the Western countries, American Sociological Review, 63, pp. 661–687.


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

Kolegard Stjarne, M., Fritzell, J., Brannstrom, L., Estrada, F. & Nilsson, A. (2007) Boendesegregationens

utveckling och konsekvenser, Socialvetenskaplig Tidskrift, 14(2–3), pp. 153–178.

Laaksonen, M. A. (2010) Population Attributable Fractions (PAF) in Epidemiologic Follow-Up Studies

(Helsinki: Finnish National Institute for Health and Welfare & Tampere School of Public Health, University

of Tampere).

Lieberson, S. (1985) Making It Count: The Improvement of Social Research and Theory (Berkeley: University of

California Press).

Long, S. & Freese, J. (2006) Regression Models for Categorical Dependent Variables Using Stata, 2nd ed.

(College Station, TX: Stata Press).

Lupton, R. & Kneale, D. (2010) Are There Neighbourhood Effects on Teenage Parenthood in the UK, and does it

Matter for Policy? A Review of Theory and Evidence, CASE Paper No. 141. (London: Centre for Analysis of

Social Exclusion, London School of Economics).

Milligan, G. W. (1996) Clustering validation: Results and implications for applied research, in: P. Arabie,

L. J. Hubert & G. De Soete (Eds) Clustering and Classification, pp. 341–375 (London: World Scientific).

Mood, C. (2010a) Logistic regression: Why we cannot do what we think we can do, and what we can do about it,

European Sociological Review, 26(1), pp. 67–82.

Mood, C. (2010b) Neighborhood social influence and welfare receipt in Sweden: A panel data analysis, Social

Forces, 88(3), pp. 1331–1356.

Musterd, S., Ostendorf, W. & de Vos, S. (2010) Environmental effects and social mobility, in: R. Paddison &

W. Ostendorf (Eds) Urban Studies – Society, Volume 1, Cities as Social Spaces, pp. 291–305 (London:

Sage).

National Board of Health and Welfare (2006) Social Rapport 2006 (Stockholm: Socialstyrelsen).

National Board of Health and Welfare (2010) Social Rapport 2010 (Stockholm: Socialstyrelsen).

Nelson, K. (2004) Mechanisms of poverty alleviation in the welfare state. A comparative study of anti-poverty

effects of non means-tested and means-tested benefits in five countries in the 1990, Journal of European

Social Policy, 14(4), pp. 371–390.

Oberwittler, D. & Wikstrom, P. -O. (2009) Why small is better: Advancing the study of the role of behavioral

contexts in crime causation, in: D. Weisburd, W. Bernasko & G. J. N. Bruinsma (Eds) Putting Crime in its

Place: Units of Analysis Issues in Geographical Criminology, pp. 35–60 (Dordrecht: Springer).

Ostendorf, W., Musterd, S. & de Vos, S. (2001) Social mix and the neighbourhood effect. Policy ambitions and

empirical evidence, Housing Studies, 16, pp. 371–380.

Propper, C., Jones, K., Bolster, A., Burgess, S., Johnston, R. & Sarker, R. (2005) Local neighbourhood and mental

health: evidence from the UK, Social Science & Medicine, 61, pp. 2065–2083.

Rostila, M. (2010) Birds of a feather flock together – And fall ill? Migrant homophily and health in Sweden,

Sociology of Health & Illness, 32, pp. 382–399.

Salonen, T. (2000) Ungdomars socialbidragstagande och forsorjningssvarigheter under 1990-talet, in:

A. Bergmark (Ed.) Valfard och forsorjning, SOU 2000:40, pp. 171–206 (Stockholm: Fritzes).

Sampson, R. J., Morenoff, J. D. & Gannon-Rowley, T. (2002) Assessing “neighborhood effects”: Social

processes and new directions in research, Annual Review of Sociology, 28, pp. 443–478.

Sharkey, P. & Elwert, F. (2011) The Legacy of Disadvantage: Multigenerational Neighborhood Effects on

Cognitive Ability. American Journal of Sociology, 116(6), pp. 1934–1981.

Sundlof, P. (2008) Segregation och karriarposition. En studie av bostadsomgivningens betydelse for utbildning,

sysselsattning och inkomst bland yngre i Stockholmsregionen (Uppsala: Uppsala universitet).

Sykes, B. & Musterd, S. (2011) Examining neighbourhood and school effects simultaneously: What does the

Dutch evidence show? Urban Studies, 48(7), pp. 1307–1331.

Urban, S. (2009) Is the neighbourhood effect an economic or an immigrant issue? A study of the importance of

the childhood neighbourhood for future integration into the labour market, Urban Studies, 46(3),

pp. 583–603.

Wheaton, B. & Clarke, P. (2003) Space meets time: Integrating temporal and contextual influences on mental

health in early adulthood, American Sociological Review, 68(5), pp. 680–706.

Whelan, C. T., Layte, R. & Maıtre, B. (2002) Multiple deprivation and persistent poverty in the European Union,

Journal of European Social Policy, 12(2), pp. 91–105.

Williams, R. (2009) Using heterogeneous choice models to compare logit and probit coefficients across groups,

Sociological Methods & Research, 37, pp. 531–559.

Wishart, D. (2005) k-Means Analysis, in: B. S. Everitt & D. C. Howell (Eds) Encyclopedia of Statistics in the

Behavorial Sciences, pp. 1017–1022 (Chichester: Wiley).


Dow

nloa

ded

by [

Uni

vers

ity o

f V

icto

ria]

at 0

2:19

19

Nov

embe

r 20

14

rethinking the long-term consequences of growing up in a disadvantaged neighbourhood: lessons from...

Documents