Being Poorer Than the Rest of the Neighborhood: Relative Deprivation and Problem Behavior of Youth
Being Poorer Than the Rest of the Neighborhood: Relative Deprivation and Problem Behavior of Youth
Jaap Nieuwenhuis 0 1 2 3 4 5
● Maarten van Ham 0 1 2 3 4 5
● Rongqin Yu 0 1 2 3 4 5
● Susan Branje 0 1 2 3 4 5
● Wim Meeus 0 1 2 3 4 5
● Pieter Hooimeijer 0 1 2 3 4 5
0 Department of Psychiatry, University of Oxford , Oxford , UK
1 School of Geography & Sustainable Development, University of St Andrews , St Andrews , UK
2 OTB - Research for the Built Environment, Faculty of Architecture and the Built Environment, Delft University of Technology , P.O. box 5043, 2600GA Delft , The Netherlands
3 Urban and Regional Research Centre Utrecht (URU), Utrecht University , Utrecht , The Netherlands
4 Department of Developmental Psychology, Tilburg University , Tilburg , The Netherlands
5 Research Centre Adolescent Development, Utrecht University , Utrecht , The Netherlands
According to the neighborhood effects hypothesis, there is a negative relation between neighborhood wealth and youth's problem behavior. It is often assumed that there are more problems in deprived neighborhoods, but there are also reports of higher rates of behavioral problems in more affluent neighborhoods. Much of this literature does not take into account relative wealth. Our central question was whether the economic position of adolescents' families, relative to the neighborhood in which they lived, was related to adolescents' internalizing and externalizing problem behavior. We used longitudinal data for youth between 12-16 and 16-20 years of age, combined with population register data (N = 926; 55% females). We employ between-within models to account for timeinvariant confounders, including parental background characteristics. Our findings show that, for adolescents, moving to a more affluent neighborhood was related to increased levels of depression, social phobia, aggression, and conflict with fathers and mothers. This could be indirect evidence for the relative deprivation mechanism, but we could not confirm this, and we did not find any gender differences. The results do suggest that future research should further investigate the role of individuals' relative position in their neighborhood in order not to overgeneralize neighborhood effects and to find out for whom neighborhoods matter.
Neighborhood effects ● Externalizing problems ● Internalizing problems ● Parent-adolescent conflict ● Relative deprivation ● Residential mobility
Growing up in neighborhoods with higher rates of poverty
has been shown to be related to higher rates of problem
behavior in youth (Leventhal et al. 2009; Xue et al. 2005).
Popular policy responses to such research finding are to
create more socio-economically mixed neighborhoods, with
the idea that poor families could benefit from the presence
of, and interaction with more affluent families (Galster and
Friedrichs 2015). Despite the popularity of neighborhood
mix policies, there is no convincing evidence that such
policies have the desired effects (van Ham and Manley
2010). Contrasting with the idea that richer neighborhoods
are better places to grow up in, there has also been report of
higher rates of behavioral problems in more affluent
neighborhoods and for children from poor families moving
from poor to more affluent neighborhoods (for a review,
see Leventhal and Brooks-Gunn 2000). These conflicting
findings might indicate that moving to a neighborhood with
a different level of wealth may not influence the problem
behavior of all adolescents in the same manner.
The absolute level of wealth in a neighborhood may be
less important than the relative level of wealth. According
to the relative deprivation theory (Walker and Pettigrew
1984), people compare their own situation to that of a
relevant reference group, in this case, their neighbors. Thus,
people’s perception of their own wealth will be influenced
by the comparison with the wealth of their neighbors, or, as
illustrated by Karl Marx (1847): “A house may be large or
small; as long as the neighboring houses are likewise small,
it satisfies all social requirements for a residence. But let
there arise next to the little house a palace, and the little
house shrinks to a hut.” Moving from a relatively poor to a
more affluent neighborhood might strengthen the perception
of (relative) poverty, because in their new neighborhood
people might feel poorer than the rest of the neighborhood.
When this relatively disadvantaged situation is seen as
unfair, this may subsequently lead to more problem
behavior (Pettigrew 2016).
In our study, we will examine the relative deprivation
theory in the neighborhood context in two steps. Firstly, we
focus on how moving to a neighborhood with a different
level of wealth is related to psychosocial problem behavior
outcomes in adolescents in the Netherlands. Secondly, we
focus on how the relation between neighborhood wealth and
problem behavior might differ between adolescents
depending on their family’s income. More specifically, we
focus on both internalizing (depression and social phobia)
and externalizing psychosocial problem behavior outcomes
and family dynamics (aggression and conflict with parents).
We included these different outcomes, because relative
deprivation may trigger different responses in people.
Adolescents may turn inward and have increased levels of
depression and social phobia, because they feel inept to
compare positively with their neighbors. Another response
can be to turn outwards and have increased levels of
aggression and conflict, because they feel frustrated with
and deprived by their relatively disadvantaged position. By
combining longitudinal survey data with population
register data from the Netherlands, we can use repeated
measures of problem behaviors, and detailed measures of
changes in neighborhood wealth and family income.
Combined, these data are a strong tool to test the relative
The sizeable literature on neighborhood effects suggests
that growing up in affluent neighborhoods leads to better
life chances compared to growing up in poorer
neighborhoods (see e.g., Dietz 2002; Ellen and Turner 1997; Galster
2002; Nieuwenhuis and Hooimeijer 2016), although the
causal pathway and direction is not clear. Neighborhood
effects have been found for outcomes such as education,
unemployment, health, and deviant behavior. A variety of
social-interactive mechanisms are suggested to explain this
relationship. For example, more affluent neighborhoods may
comprise more positive role models, showing the merits of
education for youth, who, consequently, may internalize the
pro-schooling norms that exist among the neighborhood
population (Ainsworth 2002; Wilson 1996). Furthermore, in
more affluent neighborhoods, residents might be better able
to enforce social control over the neighborhood youth, that
way minimizing the risk of deviant behavior (Akers et al.
1979; Sampson and Raudenbush 1999). Through these
mechanisms, residents in affluent neighborhoods who are
relatively less affluent than the rest of the neighborhood
might profit from the overall neighborhood affluence as well
(Galster and Friedrichs 2015). These ideas, however, hinge
on the assumption that when families of different classes live
side-by-side, they will also interact (positively) with each
other. This assumption though, is often contested in the
literature (Atkinson and Kintrea 2000; Blokland and
van Eijk 2010; Karsten and Felder 2015; Kleinhans 2004;
Nieuwenhuis et al. 2013a).
Although most research into neighborhood effects seems
to suggest positive effects of neighborhood affluence on
individual outcomes, some studies seem to find the opposite
(for a review, see Galster 2011). More affluent
neighborhoods have been linked to lower educational attainment for
a sample of white US kids (Ginther et al. 2000), negative
socio-economic outcomes for disadvantaged British women
(McCulloch 2001), and poorer health outcomes (Duncan
and Jones 1995; Shouls et al. 1996). Although some of
these results are counter-intuitive, these findings suggest
that neighborhood effects are more ambiguous than they are
often suggested to be.
A possible explanation for such negative externalities from
living in affluent neighborhoods is the relative deprivation
mechanism. This mechanism suggests that individuals who
are relatively poor in an affluent neighborhood might
perceive their disadvantaged situation as a psychological strain.
Because youth from relatively disadvantaged families
perceive neighbors who have more resources and opportunities
than they have themselves, they might create unrealistic
expectations that cannot be attained with their current
socioeconomic position. Moreover, growing up in a family that is
relatively worse off than the neighboring families, and the
inability to imitate the life-style of the better-off neighbors,
confirms someone’s relatively lower position in the social
hierarchy (Bourdieu 1984), which may be seen as unfair
(Pettigrew 2016). This may lead to feelings of inferiority
and shame, loss of self-esteem, dissatisfaction with their
own situation and envy for their better-off neighbors
(Galster 2011; Honneth 2007; McCulloch 2001; Oberwittler
2007; Sayer 2007). These negative attitudes may be related
to youth’s development. Studies that find support for the
relative deprivation mechanism have thus far linked
neighborhood affluence to poorer educational,
socio-economic, and health outcomes for the relatively poor (Duncan
and Jones 1995; Ginther et al. 2000; McCulloch 2001;
Shouls et al. 1996). However, the theory suggests that
individuals might experience their relatively disadvantaged
situation as a psychological strain, possibly leading to
psychosocial problems. And conversely, people who are
relatively richer than their neighbors might find it easier to
achieve social recognition (Bacqué et al. 2014), which
might positively influence their psychosocial state. In order
to examine this, we study whether there is a relation
between moving between neighborhoods with different
levels of wealth and changes in adolescents’ psychosocial
problem behavior, and whether this relation is moderated by
the income levels of the adolescents’ families. With this
strategy we can study whether families’ relative position in
relation to the neighborhood wealth is important in
predicting adolescents’ problem behavior outcomes.
Relative Deprivation and Adolescent Problem Behavior
Studies that have thus far examined the relation between
individuals’ relative deprivation compared to their
neighbors and psychosocial problem behavior are scarce and
report mixed results for boys and girls. A twin study
looking specifically at the effect of relatively worse
economic positions compared to neighbors found that the effect
of growing up among more affluent neighbors led to higher
rates of antisocial behavior for boys from low-income
families compared to boys from low-income families
growing up among poorer neighbors. For girls this effect
was not found (Odgers et al. 2015). Furthermore, a study
examining affluent youth found that for affluent boys and
girl, levels of delinquency and anxiety-depression,
respectively, were lower in middle-class neighborhoods compared
to affluent neighborhoods (Lund and Dearing 2012). This
study suggests that, when youth are better off compared to
their neighbors, they exhibit less problem behavior. Another
study of youth from England found that children in families
that live in socially rented dwellings (i.e., subsidized public
housing) in neighborhoods with low proportions of social
renters experience higher rates of internalizing problem
behavior than those living in neighborhoods with high
proportions of social renters (Flouri et al. 2015). This is also
supports the idea that being poor among more affluent
neighbors has a negative impact on psychosocial problem
Examples can also be drawn from the US Moving to
Opportunity (MTO) program, where randomly assigned
families in deprived neighborhoods received vouchers to
move to low-poverty neighborhoods or an unrestricted
voucher with which people were free to choose to move to any
type of neighborhood. The move to a low-poverty
neighborhood results in a worsened relative economical position
compared to their neighbors, and could therefore, following
the logic of the relative deprivation hypothesis, lead to more
problem behaviors. However, these studies have to be
interpreted with caution, because the effect of the
neighborhood could be confounded with the effect of moving, since
the control group did not move. The MTO results are very
distinct for boys and girls: boys who moved with their
families to low-poverty neighborhoods compared to those
who did not move showed increased rates of major
depression, PTSD, conduct disorder (Kessler et al. 2014),
psychological distress and behavioral problems (Osypuk et al.
2012a, b). However, contrasting with aforementioned
findings, Leventhal and Brooks-Gunn (2003) found a positive
effect for boys who moved with their families to a
lowpoverty neighborhood; they showed lower rates of anxiety
and depressive problems compared to boys who stayed in
their poor neighborhood. Girls who moved with their
families to low-poverty neighborhoods compared to those
who did not move showed decreased rates of depression,
conduct disorder (Kessler et al. 2014), psychological distress,
major depressive disorder, and behavioral problems (Osypuk
et al. 2012a, b). The MTO studies, but also Odgers and
colleagues (2015), found important differences between boys
and girls, and even though the differences are inconsistent,
they seem to suggest that boys experienced more detrimental
results from relative deprivation than girls.
The Current Study
Above mentioned studies on psychosocial problem
behavior supported the relative deprivation mechanism.
However, all but one study (i.e., Lund and Dearing 2012)
examined samples of children from low-income families.
A sample of low-income people is likely to be more socially
isolated (Wilson 1987), and more often stigmatized by
society (Wacquant 2008). Because of social isolation and
stigmatization, low-income residents may be more bound to
and limited by their neighborhood, and therefore more
likely to be influenced by it. In contrast, a general
population sample is likely to have more resources and therefore
more opportunities to go beyond the own living
environment, potentially leading to a weaker relation between
neighborhood characteristics and individual outcomes. We
contribute to this literature by using an average sample of
youth to study whether the relative deprivation thesis still
holds for the general population. And by combining survey
data with population register data, we had access to very
reliable and direct measures of the income levels of the
youth’s families and of the affluence of their neighborhoods.
To test the relative deprivation mechanism, we studied both
internalizing (depression and social phobia) and
externalizing problem behaviors and family dynamics (aggression
and conflict with parents) in adolescents. We first examined
the extent to which moving between neighborhoods with
different levels of wealth was related to changes in
adolescents’ psychosocial problem behavior, and second
whether this relation was different for adolescents from families
with different income levels. In line with the relative
deprivation thesis we hypothesize that, conditioned on
changes in families’ income, moving to a wealthier
neighborhood will be related to an increase in psychosocial
problem behaviors for adolescents, because moving to a
wealthier neighborhood means that individuals’ relative
position compared to their neighborhood will get worse.
Furthermore, we hypothesize that this relation will be
stronger for adolescents from lower income families,
compared to adolescents from higher income families.
Additionally, we looked into gender differences. It was
argued that boys have a higher propensity to be exposed to
risk behaviors and situations than girls, possibly resulting in
higher levels of problem behavior for boys compared to girls.
If they move to low-poverty neighborhoods, boys may bring
these behaviors and see more opportunities for them than
girls, and therefore more often experience negative influences
from moving to more affluent neighborhoods. Because girls’
leisure activity patterns are more restricted to the vicinity of
the home, they are less likely to change their behavior based
on changes in the neighborhood environment, and therefore
less likely to experience negative influences from moving to
low-poverty neighborhoods (Clampet-Lundquist et al. 2011).
However, in the above mentioned research that studied
relative deprivation in a neighborhood context, the results
pertaining to differences between boys and girls are mixed.
One study suggests effects for boys, but not for girls (Odgers
et al. 2015), whereas others show opposite negative effects
for boys and positive effects for girls (Kessler et al. 2014;
Osypuk et al. 2012a, b). Because mixed findings on gender
differences were found, we explored these differences in the
links, on the one hand, neighborhood wealth and relative
deprivation and, on the other hand, problem behavior.
Data and Methods
Our sample consisted of 926 Dutch youth who were part of
the Conflict and Management of Relationships (Conamore)
panel dataset (Meeus et al. 2010). Conamore consisted of
1313 respondents recruited from various high schools in the
province of Utrecht, the Netherlands. Conamore consisted
of two cohorts: early-to-middle adolescents (n = 923;
70.3%) who were on average 12.4 years of age at the first
wave, and middle-to-late adolescents (n = 390; 29.7%) with
an average age of 16.7 years at the first wave. Six waves of
survey data were collected, the first five waves annually
between 2001/02 and 2005/06 and the sixth wave in 2009/
10. The sixth wave included an additional Life History
Calendar (LHC; Caspi et al. 1996) with retrospective
questions from the age of 12 until the sixth wave. We used
survey data from the first five waves. Of the sixth wave we
only used the LHC in order to obtain residential histories.
For waves 1, 2, 3, 4, 5, and 6, the number of respondents
was 1313, 1313, 1293, 1292, 1275, and 1026, respectively.
For the first five waves, sample attrition was very low (7%
from wave 1 to 5). Attrition from the fifth to the sixth wave
was higher (20%), because of the 5-year time gap between
wave five and six, compared to the 1-year gaps between
waves one through five. In order to obtain parental income
data, we combined Conamore with Dutch register data by
matching respondents on address and date of birth (see
Measurements section below for more information). Of the
1026 respondents, we lost 40 respondents that we were not
able to match to the register data. After listwise deletion of
cases with missing values, our sample consisted of 926
respondents, with on average between 4.6 and 4.9
observations per respondent over the first five waves, depending
on the model. Total observations were 4410.
We compared the respondents in our sample with the
respondents before listwise deletion, which showed we had
a higher attrition of respondents from foreign born parents
than respondents with at least one Dutch born parent
(18.8% before listwise deletion vs. 10.4% in our sample;
χ2(1) = 35.42; p = .00). Furthermore, comparing our
sample to the sample before listwise deletion, there were no
significant differences in the share of girls (χ2(1) = 2.09;
p = .15) and the share of the young-to-middle adolescents
cohort (χ2(1) = .34; p = .56). Also, there were no
differences between the two samples in the share of respondents
who score 1 on depression or aggression, and in the mean
values of social phobia, conflict with father, and conflict
We combined three data sources for the analyses: the first
five waves and the LHC of the Conamore panel dataset,
postcode area characteristics from Statistics Netherlands
(2006, 2011), and population register data from the
tics Netherlands System of Social Statistical Datasets (SSD). The SSD is an extensive system of longitudinal
datasets, combining, among other, population, tax, and
housing registers, covering the full population of the
Netherlands since 1999 (Bakker et al. 2014). Most measures
described below were measured at five points in time
(i.e., the first five waves of Conamore). Only the four
time-invariant control variables (cohort, gender, parents
foreign born, and parental education) did not vary
over the five waves. Descriptive statistics can be found in
Depression was measured using the Children’s Depression
Inventory (CDI) intended to capture depressive symptoms
in children and adolescents (Craighead et al. 1998). The
scale consisted of 27 items such as: “I am sad all the time”,
“I hate myself”, and “Nobody really loves me”. The items
had a 3-point response scale, ranging from “false”, “a bit
true” to “very true”. The Cronbach’s alpha of the scale was
Table 1 Descriptive statistics
.90. Depression was non-normally distributed, with a
skewness of 2.48 and kurtosis of 10.85, and was therefore
converted into a dummy variable, where 0 means not
depressed, and 1 means depressed. We constructed our
dichotomous variable as such that at least 30% of the
sample scored 1, which meant that on a scale from 0–2,
respondents scored .1851851 or higher.
Social phobia was measured with a subscale of the
SCARED (Hale et al. 2005). The social phobia scale
consists of 4 items: “I don’t like to be with people I don’t know”,
“I feel nervous among people I don’t know very well”,
“I find it difficult to talk to people I don’t know”, and “I’m
shy among people I don’t know very well”. The items had
3 response categories: “almost never”, “sometimes”, and
“often”. The scale ranged from 0–2. Internal consistency for
the scale was good (Cronbach’s alpha: .86).
a The Ns of the time-varying variables (incl. the dependent variables) are based on observations within
individuals. The Ns of the time-invariant variables is based on individual respondents
Aggression was measured using the Direct and Indirect
Aggression Scales (Björkqvist et al. 1992). We used 17
items to measure two types of aggression: indirect and
direct. This scale has good reliability (Björkqvist et al.
1992). Example items for the two types are respectively the
following answers to the question “If you’re mad or angry
with someone, what do you do?”: “I try to annoy the other
so much that he/she will lose his/her patience” and “I hit or
kick”. The items had 4 response categories: “never”,
“sometimes”, “often”, and “very often”. The scale’s
Cronbach’s alpha was .82. Aggression was non-normally
distributed with a skewness of 1.58 and kurtosis of 6.63,
therefore we made aggression into a dummy, where 0
means not aggressive, and 1 means aggressive. We
constructed our dichotomous variable as such that at least 30%
of the sample scored 1, which meant that on a scale from
0–3, respondents scored .5294117 or higher.
Conflict with parents
Conflict with parents was measured using the Interpersonal
Conflict Questionnaire, and consists of two scales: conflict
with father and conflict with mother, both consisting 35
items (Laursen 1993). Respondents were asked how often
they have conflict in the last week with their father/mother
about, for example: “money or things of value”, “dating”,
and “grades in school”. They were given 5 response
categories: “never”, “almost never”, “sometimes”, “frequently”,
and “often”. The scales ranged from 0 to 4 and the
Cronbach’s alphas were .95 for the father and .95 for the mother.
Time-varying control variables
With the Conamore dataset, we constructed two
timevarying control variables relating to the family, because the
parental home is an important context for adolescent
development. Changes in parenting strategies and family
structure may relate to changes in problem behavior in
adolescents. First, we assessed whether respondents still
lived in the parental home (0), or whether they had moved
out (1). And second, we assessed how supportive the
parental home was using the Network of Relationship
Inventory (NRI; Furman and Buhrmester 1985), which has
adequate validity (Edens et al. 1999). The parental support
scale consisted of 12 items and was asked separately about
the father and the mother. Example items are: “Do you share
secrets or personal feelings with you father/mother?” and
“Does your father/mother appreciate the things you do?”
The items had five answering categories ranging from “little
or not at all” to “more is not possible”. Cronbach’s alphas
were .92 for fathers and .91 for mothers. We combined the
scales for fathers and mothers into one scale measuring
parental support, which ranged from 0 to 4.
Time-invariant control variables
Additionally, we constructed four time-invariant control
variables: cohort, gender, parents foreign born, and parental
education. Cohort was measured as a dummy indicating
whether a respondent belonged to the group
young-tomiddle adolescents (0; average age of 12.4 at the first
wave), or middle-to-late adolescents (1; average age of 16.7
at the first wave). Gender was coded male (0) and female
(1). The parents foreign born dummy measured whether
both parents were born outside of the Netherlands (1), or
not (0). And parental education was measured using six
dummy variables: lower vocational education or lower (1);
preparatory middle-level vocational education (2);
middlelevel vocational education (3); higher general continued
education or preparatory scientific education (4); higher
vocational education (5); and scientific education (6;
The Life History Calendar in the Conamore dataset was
geocoded, and included all addresses, including six-digit
postcodes (areas containing, on average, 17 households)
where respondents had lived between the age of 12 and the
time of the sixth wave data collection. Respondents did not
cluster in postcode areas. The average number of
respondents per postcode area across waves was 1.13, with a
maximum of 3 respondents in one postcode area for 1.2% of
the sample. Using postcodes we were able to link the
Conamore data with six-digit postcode-level data provided
by Statistics Netherlands (2006, 2011). From the Statistics
Netherlands (2006) data we used the average property value
as measured in 2004 as a proxy to measure neighborhood
wealth, which we imposed over all five measurement
points. This was assessed to be a good indicator of
neighborhood wealth (Visser et al. 2008). The six-digit postcode
areas consist of, on average, 17 households, and capture the
average wealth in the proximate surrounding of the
adolescents’ homes. In order to control for demographic
characteristics that may be related to the dynamics of the
neighborhood, we used the Statistics Netherlands (2011)
data, and constructed a control variable which measures the
neighborhoods’ proportion of non-Western immigrants with
2010 information. Neighborhoods’ ethnic composition was
found to be related to problem behavior in the Netherlands
(Flink et al. 2013). Both variables were standardized.
Neighborhood-level variables can only change when
individuals move between different postcode areas.
Statistics Netherlands used a combination of address
information and dates of birth to link Conamore data to
register data provided by Statistics Netherlands: the System
of Social Statistical Datasets (SSD; Bakker et al. 2014).
After data linkage we could only access the data in a secure
environment controlled by Statistics Netherlands. Through
linkage we derived the income of the adolescents’ core
family from the SSD by taking the income of the two
highest earners in the household, when adolescents were
registered as ‘children living at home’. When adolescents
were not registered as living at home, we took their personal
income. The income variable was standardized.
We employed hybrid random-effects models, also called
between-within (BW) models, over the first five waves of
the Conamore study. The BW model is a hybrid model that
combines the advantages of both fixed- and random-effects
models, allowing for both within-individual effects and
between-individual effects (Bell and Jones 2015; for
examples, see Hedman et al. 2015; Nieuwenhuis et al.
2016a). It can be written as:
where β1 is the within effect and β2 is the between effect of
a series of time-variant variables xij (Bell and Jones 2015).
The time-varying dependent variables were transformed
into deviations from their person-specific means in order to
create estimators equal to those in fixed-effects models. And
in addition to the time-invariant variables, we included the
person-specific means for the time-varying variables, which
are time-invariant. As fixed-effects models, the BW model
regresses the within-person change in the dependent
variables (problem behaviors) on the within-person change in
the independent variables. For the neighborhood wealth
variable this meant that we estimated how moving to a
neighborhood with a different level of wealth was related to
changes in problem behavior. We consider this a test for
relative deprivation, because, conditioned on changes in
adolescents’ families’ income, moving to a wealthier
neighborhood will increase adolescents’ relative deprivation
compared to the neighborhood. Observed and unobserved
time-invariant characteristics are automatically controlled
for, as the sum of their change is always zero. The
coefficients and standard errors of time-varying variables in BW
models are therefore identical to those in fixed-effects
models. Additionally, as random-effects models, a BW
model allows the inclusion of time-invariant variables (β3),
providing additional information on differences between
individuals that is not available in fixed-effects models.
For the neighborhood wealth variable this meant that we
estimated how differences between people in the wealth
levels of the neighborhood in which they grew up was
related to levels of problem behavior. To test for the
moderating effects of parental income and gender, we made
separate models including interactions between
withinindividual neighborhood wealth and within-individual
parental income or gender. For depression and aggression
we ran logistic BW models, and for social phobia and
conflict with parents we ran linear BW models. For the
linear BW models we reported robust standard errors.
To investigate whether the BW models were preferred
over random-effects models, we used the Wald test to test
the equality of the pairs of coefficients (Allison 2009). The
results indicated that the BW model was clearly preferred
over the random-effects model for depression (χ2(5) =
36.90, p = .0000), social phobia (χ2(5) = 46.75, p = .0000),
aggression (χ2(5) = 25.12, p = .0001), and conflict with
mother (χ2(5) = 11.79, p = .0377). The model for conflict
with father did not show that the BW model was preferred
(χ2(5) = 8.40, p = .1354), but for the sake of consistency,
we use the BW model for conflict with father as well.
By design, the BW model removed potential selection
bias from observed and unobserved time-invariant
characteristics that influence both neighborhood selection and
internalizing and externalizing problem behavior and family
dynamics (Galster 2008). Because time-varying
characteristics were not automatically controlled for, we linked in
parental income from the register data of Statistics
Netherlands. We expected this to be the most important
confounder, because changes in parental income may lead to a
residential move, but also to changed relations within the
family (Davis-Kean 2005; Hanson et al. 1997; Nieuwenhuis
et al. 2013b), possibly influencing adolescents’ psychosocial
adjustment. By controlling for parental income, in addition
to parental support, parental country of birth, parental
education and family structure, we attempted to control for
a good portion of the potential selection bias emerging
through family environments.
Out of the 926 respondents, 152 moved once, 25 moved
twice, and 2 moved between neighborhoods three times.
There were 120 moves to neighborhoods with lower wealth,
and 88 moves to neighborhoods with higher wealth.
Correlations between our main variables (see Table 2)
revealed that all problem behavior outcomes were
significantly correlated to each other, with the exception of
social phobia and aggression. Neighborhood wealth was
Table 2 Correlations between
variables (N = 4028)
2. Social phobia
4. Conflict with father
5. Conflict with mother
6. Neighborhood wealth
7. Parental income
*p < .05; **p < .01; ***p < .001
negatively correlated with all problem behavior outcomes,
with the exception of conflict with father. Finally,
neighborhood wealth and parental income were positively
The results of the BW models were presented in Table 3.
All models were significant with a significance level lower
than 0.0001. The within-individual results showed that, after
controlling for several individual, parental and neighborhood
characteristics, moving to a more affluent neighborhood was
related to an increase in adolescents’ levels of depression,
social phobia, aggression, and conflict with fathers and
mothers. This can possibly be explained by the relative
deprivation mechanism: when adolescents move from a
neighborhood where they were relatively rich to a
neighborhood where they were relatively poor, this might explain
the associated increase in psychosocial problem behaviors.
In order to test the relative deprivation hypothesis more
directly, we interacted parental income with neighborhood
wealth. We expected that an increase in neighborhood
wealth would be stronger related to an increase in
psychosocial problem behaviors for adolescents from families with
lower income compared to families with higher income.
Our results show that there was no difference between
adolescents from families with lower and higher income
levels. None of the interaction terms was significant
(depression: b = −.57, s.e. = .34, p = .09; social phobia: b
= −.01, s.e. = .02, p = .57; aggression: b = .21, s.e. = .17,
p = .22; conflict with father: b = −.01, s.e. = .01, p = .68;
conflict with mother: b = −.00, s.e. = .01, p = .88). Thus,
we could not confirm the relative deprivation mechanism by
interacting neighborhood wealth with parental income.
Additionally, we studied differences between boys and
girls. Main effects of sex on psychosocial problem behavior
showed that girls were more prone for depression and social
phobia, and less prone for aggression and conflict with their
father than boys were (see Table 3 and Fig. 1). There was
no difference between boys and girls in the model for
conflict with mother. We tested for differences between
boys and girls in how susceptible they were to changes in
neighborhood wealth by including interaction effects
between within-individual changes in neighborhoods
wealth and gender. None of the interaction terms was
significant (depression: b = −.24, s.e. = .36, p = .51; social
phobia: b = −.02, s.e. = .03, p = .60; aggression: b = .50, s.
e. = .42, p = .23; conflict with father: b = .05, s.e. = .04, p
= .18; conflict with mother: b = .04, s.e. = .05, p = .36).
Thus, we could not replicate the gender differences of the
effect of relative deprivation on psychosocial problem
behavior found by previous studies.
Further examining the results, the between-individual
models showed that only in the social phobia model, there
was a significant coefficient for neighborhood wealth,
indicating that adolescents living in wealthier
neighborhoods have lower levels of social phobia than adolescents
living in poorer neighborhoods.
Examining the within-individual control variables,
several variables seemed to be related to psychosocial problem
behavior. First, on the family level, we found that increases
in parental income were related to increases in aggression.
Furthermore, increases in parental support were associated
with decreases in depression and conflict with parents. It
was not related to social phobia and aggression. Moving out
of the home was only related to a decrease in conflict with
both parents, which seems logical with the accompanying
decrease in proximity.
Third, on the neighborhood level, an increase in the
proportion of non-Western immigrants was only associated
with an increase in aggression, not with depression, social
phobia and conflict with parents.
Finally, examining the time-invariant control variables,
the young-to-middle adolescent cohort was more likely to
have conflict with their parents and have aggressive
behavior than the middle-to-late adolescent cohort. Adolescents
from foreign born parents only scored higher on aggressive
behavior, for the rest, they did not differ from native Dutch
adolescents in their problem behavior. And parental
education did not have a clear effect.
In order to assess the robustness of our findings, we tested
the models using different scales and we tested differences
Table 3 Between-within models of problem behavior
Note: M1 and M3 are logistic between-within regressions; M2, M4 and M5 are linear between-within regressions
*p < .05; **p < .01; ***p < .001
between the early-to-middle and middle-to-late cohorts in
our sample. We transformed the five outcome scales into
intensity scales, assigning to each item scores from 1 to n in
case of n response categories. These scores were added to
create the intensity scales. We were able to replicate similar
results for our main independent variable ‘Neighborhood
wealth’ in the models for social phobia (b = .15, s.e. = .07,
p = .04) and conflict with father (b = 1.46, s.e. = .68,
p = .03); the coefficient in the model for conflict with
mother was just on the border of the conventional
significance threshold (b = 1.48, s.e. = .76, p = .05). We were
not able to replicate the results in the models for depression
(b = .31, s.e. = .21, p = .13) and aggression (b = .34, s.e.
= .29, p = .23). Using alternative logistic models with
different cut-off points we were able to replicate the results
for depression (cut-off .2: b = .43, s.e. = .22, p = .05) and
aggression (cut-off .5: b = .42, s.e., = .20, p = .04).
Additionally, we included the interaction between the
variables ‘Neighborhood wealth’ and ‘Cohort’ to test for
differences between the early-to-middle and middle-to-late
cohorts. We did not find significant interactions in the
models for depression (b = .35, s.e. = .38, p = .35), social
phobia (b = −.03, s.e. = .04, p = .46) and conflict with
father (b = .05, s.e. = .04, p = .16). We did find significant
interactions in the models for aggression (b = −.98, s.e.
= .50, p = .05) and conflict with mother (b = .11, s.e. = .05,
p = .03). The older cohort was less likely to have a relation
between changes in neighborhood wealth and changes in
Internalizing problem behavior
−.14 (.11) −.04 (.02)** .04 (.10) .01 (.02) .00 (.02)
−.23 (.13) −.03 (.02) −.28 (.12)* −.01 (.02) .00 (.02)
−1.37 (.18)*** −.15 (.03)*** −.76 (.17)*** −.17 (.03)*** −.24 (.03)***
−1.30 (1.33) −.47 (.19)* .11 (1.33) .05 (.20) −.18 (.19)
.06 (.11) .03 (.02) −.14 (.11) .03 (.02) .03 (.02)
1.71 (.46)*** .85 (.08)*** 1.40 (.44)*** 1.08 (.07)*** 1.32 (.08)***
122.82 (18)*** 140.10 (18)*** 176.15 (18)*** 254.05 (18)*** 255.47 (18)***
4388 4219 4380 4265 4381
920 920 920 903 918
Fig. 1 Mean values of problem
behavior for boys and girls.
Note: scale anchors: depression
(0–1), social phobia (0–2),
aggression (0–1), conflict with
father (0–3.38), conflict with
mother (0–3.39). Note 2: t-tests
for differences between boys
and girls: depression: t(4386) =
−8.18, p < 0.001; social phobia:
t(4218) = −11.05, p < 0.001;
aggression: t(4378) = 17.60,
p < 0.001; conflict with father:
t(4263) = 7.11, p < 0.001;
conflict with mother: t(4379) =
5.69, p < 0.001
aggression compared to the younger cohort, and more likely
to have a relation between changes in neighborhood wealth
and changes in conflict with mother.
We studied the relative deprivation hypothesis by
researching whether adolescents who are poorer than their
neighbors are more likely to show problem behavior than
adolescents who are richer than their neighbors. The
rationale to pose this question was that people compare their
own situation to that of the people around them and judge it
by that standard. Therefore two individuals with the same
income may have a very different perception of their own
income based on the wealth of their neighbors. When
adolescents perceive their neighbors to be wealthier, they
may judge this situation as unfair, possibly leading to
increases in problem behavior (Pettigrew 2016; Walker and
Pettigrew 1984). We found indications for the relative
deprivation hypothesis: conditioned on changes in the
income of adolescents’ family, moving to a wealthier
neighborhood was related to increased levels of depression,
social phobia, aggression, and conflict with parents. These
findings were in line with earlier studies of psychological
functioning and relative deprivation in neighborhood
contexts (Flouri et al. 2015; Kessler et al. 2014; Lund and
Dearing 2012; Odgers et al. 2015; Osypuk et al. 2012a, b).
However, we did not find support for the relative
deprivation hypothesis with our more direct measure that
interacts parental income changes with neighborhood
Our results lead to doubt on the effectiveness of urban
renewal policies or housing voucher policies aimed at
mixing lower class households with middle class
households where the latter are supposed to serve as
positive role models. The idea that the behavior of affluent
neighbors will simply rub off on their less advantaged
neighbors, who consequently will experience all kinds of
positive outcomes, seems far from reality. Many studies
have linked neighborhood affluence to advantageous
outcomes for individuals, however, this is not universally true
for all outcomes, and might be highly dependent on
individuals relative status in the neighborhood. Social mixing
literature that links neighborhood inequality to positive
individual outcomes might be picking up the positive
effects for the relatively wealthy in the neighborhood, who
have more positive outcomes from social comparisons with
their relatively poorer neighbors. The people at risk, that is,
those at the bottom of the social hierarchy, might mainly
experience negative outcomes of social mix.
We could not replicate the earlier found differences between
boys and girls (Odgers et al. 2015; Osypuk et al. 2012a, b).
The reason we found no differences between boys and girls
in our analyses might be because our sample is from a
general Dutch population. The studies that did find clear
gender differences mainly use US samples of youth that
were at high financial risk. It might be that boys and girls
indeed react differently when they are in very demanding
and stressful environments, or move from such
environments to more affluent environments. However, in our data,
we did not specifically target an at-risk population, but
rather a general population, that includes youth from all
socio-economic backgrounds. In general our respondents
did not grow up under extreme circumstances like those in
the US samples. Previous studies found that with higher
social class, adolescents are more resilient, receive higher
rates of maternal warmth and responsiveness, and have
better problem solving capabilities (Klebanov et al. 1994;
Starfield et al. 2002). It may be that youth from lower social
classes are less equipped to deal with neighborhood
deprivation and climbing up from their disadvantaged situation.
We speculate that gender differences in the reaction to
changes in neighborhood wealth become most pronounced
under extreme circumstances, when people are more
strongly bounded to their neighborhood due to limited
resources or through social network and are less well
equipped to deal with their deprived circumstances. Perhaps
there is even a threshold effect, and that the levels of
poverty needed for a gender difference to become apparent, are
only reached when specifically targeting at-risk youth.
Objective vs. Subjective Relative Deprivation
We focused on objective relative deprivation with our
interaction between changes in neighborhood wealth and
changes in parental income. However, the reason we do not
find significant interactions might be because this measure
can possibly be considered a proxy for what is actually
causing psychosocial problem behavior, that is, subjective
relative deprivation. It is likely that feelings of relative
deprivation have a stronger relation with psychosocial
problem behavior than objectively measured relative
deprivation. Individuals might objectively be relatively
deprived, but when they do not experience it like that, it is
unlikely to have an effect on their behavior. A meta-analysis
over 26 studies that include both objective and subjective
relative deprivation measures revealed that subjective
relative deprivation yields larger effects which are more often
statistically significant (Smith et al. 2012). This might
explain why we do find an effect from moving to a
wealthier neighborhood (after controlling for changes in parental
income), because relative to their old neighborhood,
adolescents might feel an increase in relative deprivation when
moving to a wealthier neighborhood. Subsequent studies
might benefit from looking at people’s perceptions about
their relative status in order to grasp more directly what
relative deprivation does to an individual’s psychosocial
We employed between-within (BW) models to overcome
selection bias. BW models control for all time-invariant
unobserved characteristics that could potentially influence
both neighborhood choice and psychosocial problem
behavior. BW models do not control for time-varying
characteristics, so there is still a possibility of selection bias
through time-varying characteristics. However, it is likely
that most selection effects depend on family characteristics,
because adolescents usually do not choose where to live,
but rather their parents; and adolescents’ psychosocial
problem behavior is likely related to family characteristics and
parental child rearing strategies. Hypothetically, it might be
that parenting strategies that influence psychosocial
problem behavior are related to parental considerations when
choosing a neighborhood for their child to grow up in. In
that case, if we would not sufficiently control for parental
characteristics, then our neighborhood effects could merely
be reflecting a family effect. In an attempt to control for a
good portion of selection bias stemming from time-varying
family characteristics, we controlled for parental income,
parental support, and living arrangement.
Although our study adds to the understanding of how
neighborhoods are related to adolescents’ problem behavior,
it also has several limitations. First, our data spans five
waves over 5 years, however, we were not able to test
whether the relations we found were long-lasting, that is,
whether moving to a relatively wealthier neighborhood had
a lasting effect on increased problem behavior in
adolescents, or whether this effect was a temporary shock that
would fade away over time. Besides, stronger measures of
neighborhood wealth that take into account in situ changes
for people who do not move, may capture better the changes
that adolescents go through and therefore shed more light
on the persistence neighborhood effects. Further research
should look into the longevity of the found relations.
Second, under some circumstances the results were
vulnerable to change. Most prominently in the case of
depression and aggression, for which the results obtained
with logistic regression could not be reproduced with linear
functions, only with different cut-off points. Alternative
model checks for social phobia and conflict with father and
mother did show robust results. Furthermore, heterogeneity
in the sample could lead to different interpretations of the
results. For example, the older cohort was less likely to have
a relation between changes in neighborhood wealth and
changes in aggression than the younger cohort, and more
likely to have a relation between changes in neighborhood
wealth and changes in conflict with mother. Future research
could examine how different age groups react differently to
changes in neighborhood wealth.
The conclusion that moving to a wealthier neighborhood is
related to increased psychosocial problem behavior has
various implications for both research and policy. First, for
researchers interested in neighborhood effects and
neighborhood mix it is crucial not to overgeneralize the influence
of the neighborhood, but to relate individuals to their
neighborhood, for example by using person-context
interaction designs (see also Nieuwenhuis 2016; Nieuwenhuis
et al. 2015, 2016b; Tuvblad et al. 2006; van Ham and
Manley 2012; Yu et al. 2016). This way, research can tease
out the differential effects of neighborhood characteristics
for different people. It is essential for our understanding of
neighborhood effects to study for whom neighborhoods
matter and for whom they do not. Furthermore, this study
confirms that to understand adolescent psychosocial
functioning, the residential environment is an important factor to
take into account. Although this is not a new conclusion,
still, neighborhood environments are often overlooked in
research on problem behavior. And second, the widespread
belief among policy makers that social mixing of
neighborhoods is a panacea for all kinds of social problems
should be reconsidered. For the poor, living among wealthy
neighbors is unlikely to result in more socially mixed
networks and more individual opportunities that are assumed
to come along with more mixed networks (Musterd and
Andersson 2005). And from our analyses, it even seems that
moving to wealthier neighborhoods is related to increased
psychosocial distress and parent–adolescent conflict for
youth from relatively poor families. More research is
needed to be conclusive, but our preliminary recommendations
are that when targeting social problems, such as, in this
case, psychosocial problem behavior and conflict with
parents among adolescents, an environmental policy such as
social mixing will not necessarily sort the desired effect.
Potential solutions can be found in policy that is better
informed about differences between people who live in
neighborhoods. Knowledge about the neighborhood’s
residents can inform policy makers whether area-based policies
aimed at social mixing will reach the right population and
desired effect, or whether targeted policies that provide
equal opportunities to youth from poor households might be
Acknowledgements We are grateful to Dario Diodato for
discussions about the underlying ideas behind this article, to Merle Zwiers
for methodological discussions, and to Reinout Kleinhans for
comments on an earlier draft of this article. The research leading to these
results has received funding from the European Research Council
under the European Union’s Seventh Framework Programme (FP/
2007-2013)/ERC Grant Agreement n. 615159 (ERC Consolidator
Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived
neighbourhoods, and neighbourhood effects), from the Marie Curie
programme under the European Union’s Seventh Framework Programme
(FP/2007-2013)/Career Integration Grant n.
PCIG10-GA-2011303728 (CIG Grant NBHCHOICE, Neighbourhood choice,
neighbourhood sorting, and neighbourhood effects), and seed money from
Utrecht University’s strategic theme Dynamics of Youth.
Author Contributions J.N. conceived of the study, performed the
statistical analyses and drafted the manuscript; M.H. helped with the
interpretation of the results, and helped to refine the manuscript; R.Y.
helped with the interpretation of the results, and helped to refine the
manuscript; S.B. helped with the interpretation of the data; W.M.
conceived of the original data collection and helped with the
interpretation of the data; P.H. helped with the interpretation of the data.
All authors read and approved the final manuscript.
Compliance with Ethical Standards Treatment of participants was
in accordance with the ethical standards of the APA and this study was
reviewed and approved by the ethical-medical committee of University
Medical Centre Utrecht, the Netherlands.
Informed Consent For participation in the present study, written
informed consent was obtained from adolescents and their parents, and
also from all the participating schools.
Jaap Nieuwenhuis is a postdoctoral researcher at OTB—Research for
the Built Environment at Delft University of Technology, the
Netherlands. He received his Ph.D. in 2014 from Utrecht University,
the Netherlands. His major research interests include understanding
whether neighborhoods matter for adolescents’ developmental
outcomes, and, by studying this within a person-context framework,
understanding whether this relation differs for adolescents with
different personalities, genetic characteristics, or family background.
Maarten van Ham is professor of Urban Renewal and Housing at
Delft University of Technology, and Professor of Geography at the
University of St Andrews. He studied economic geography at Utrecht
University, where he obtained his PhD with honors in 2002. His major
research interests include urban poverty and inequality, segregation,
residential mobility and migration, and neighborhood effects.
Rongqin Yu is a postdoctoral researcher at the Department of
Psychiatry, University of Oxford, United Kingdom. She did her PhD
in adolescent development at the Utrecht University, The Netherlands.
Her major research interests include person-environment interactive
processes, mental illness and violent behaviors, and adolescent
personality and social development.
Dr. S. J. T. Branje is full Professor of Adolescent Development and
chair of the Research Center Adolescent Development at Utrecht
University, the Netherlands. She received her Ph.D. in 2003 from the
Radboud University Nijmegen, the Netherlands. Her work generally
focuses on understanding the developmental changes in adolescents’
relationships with parents, siblings, friends, and romantic partners and
their associations with the development of adolescent adjustment.
Wim Meeus Ph.D is professor of Adolescent Development at Utrecht
University, and professor of Developmental Psychology at Tilburg
University, The Netherlands. He received his doctorate in Social
Psychology from Utrecht University, The Netherlands. His major
research interests include identity and personality development,
personal relationships, and psychosocial problems in adolescence.
Pieter Hooimeijer is professor of Human Geography and
Demography at Utrecht University, where he also received his PhD
degree. His main research interest is the recursive relation between
population change and the dynamics of housing and labor markets at a
variety of spatial scales, from neighborhoods to metropolitan areas.
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