Unpacking the Relationships between Impulsivity, Neighborhood Disadvantage, and Adolescent Violence: An Application of a Neighborhood-Based Group Decomposition
Unpacking the Relationships between Impulsivity, Neighborhood Disadvantage, and Adolescent Violence: An Application of a Neighborhood-Based Group Decomposition
Matt Vogel 0 1 2
● Maarten Van Ham 0 1 2
0 School of Geography and Geosciences, University of St. Andrews , St Andrews , UK
1 Faculty of Architecture and the Built Environment, OTB- Research for the Built Environment, Delft University of Technology , Delft , The Netherlands
2 Department of Criminology and Criminal Justice, University of Missouri-St. Louis , St. Louis , USA
Scholars have become increasingly interested in how social environments condition the relationships between individual risk-factors and adolescent behavior. An appreciable portion of this literature is concerned with the relationship between impulsivity and delinquency across neighborhood settings. The present article builds upon this growing body of research by considering the more nuanced pathways through which neighborhood disadvantage shapes the development of impulsivity and provides a situational context for impulsive tendencies to manifest in violent and aggressive behaviors. Using a sample of 12,935 adolescent from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (mean age = 15.3, 51% female; 20% Black, 17% Hispanic), we demonstrate the extent to which variation in the association between impulsivity and delinquency across neighborhoods can be attributed to (1) differences in mean-levels of impulsivity and violence and (2) differences in coefficients across neighborhoods. The results of a series of multivariate regression models indicate that impulsivity is positively associated with self-reported violence, and that this relationship is strongest among youth living in disadvantaged neighborhoods. The moderating effect of neighborhood disadvantage can be attributed primarily to the stronger effect of impulsivity on violence in these areas, while differences in average levels of violence and impulsivity account for a smaller, yet nontrivial portion of the observed relationship. These results indicate that the differential effect of impulsivity on violence can be attributed to both developmental processes that lead to the greater concentration of violent and impulsive adolescents in economically deprived neighborhoods as well as the greater likelihood of impulsive adolescents engaging in violence when they reside in economically disadvantaged communities.
Person-context research ● Neighborhood effects ● Decomposition ● Delinquency
Over the past two decades, research has increasingly
highlighted the importance of social context for adolescent
development and well-being. Much of this research has
focused on the ways in which school and neighborhood
environments influence outcomes like school performance
(Dotterer and Lowe 2011; Irvin et al. 2011), mental health
(Nair et al. 2013), and delinquent behavior (Deutsch et al.
2012; Vogel et al. 2015). On the whole, findings from this
body of literature indicate that contextual risk-factors are
robust and persistent correlates of youth behavior. More
recently, scholarly attention has shifted to understanding the
ways in which social environments condition the
relationships between individual risk-factors and adolescent
behavior, especially delinquent and violent conduct. This
emerging perspective, referred to here as “person-context
research”, assumes that behavioral outcomes are not the
result of individual or environmental factors, but are
dependent on who is in what environment (Messner and
Zimmerman 2012; Wikström 2004). The general consensus
is that dispositional risk factors, such as impulsivity or
low self-control, are contingent on the characteristics of
broader ecological contexts, such as the school one attends
or neighborhood in which one resides (e.g., Fine et al.
2016; Lynam et al. 2000; Vogel and Barton 2013;
Much of the research in this area has focused on the
differential effects of impulsivity across neighborhood
settings (see Vaughan 2017 for recent overview). Although
there remains some debate as to the exact parameters of the
association, much of the empirical literature demonstrates
that structural characteristics and the social processes at
work in disadvantaged neighborhoods moderate the
influence of impulsivity on criminal behavior (c.f. Vazsonyi
et al. 2006; Zimmerman et al. 2015). These differential
effects, sometimes referred to as evidence of “contextual
moderation”, are often attributed to neighborhood features
providing greater access to criminogenic opportunities or
greater exposure to socialization processes promoting
violence over normative behaviors.
Two observations complicate the results presented in
prior research. First, a sizable body of literature suggests
that indicators of neighborhood deprivation are associated
with youth offending; adolescents who reside in
economically deprived neighborhoods typically exhibit higher
levels of problem behaviors than adolescents from more
affluent neighborhoods (see Kubrin and Weitzer 2003;
Sampson 2002 for reviews). Second, emerging research
suggests that the social processes in disadvantaged
neighborhoods may contribute to the development of undesirable
personality traits (Hart et al. 2008; Pratt et al. 2004; Turner
et al. 2005). From this vantage point, economic deprivation,
limited informal control, and socialization processes
promoting crime and delinquency may place youth from
disadvantaged neighborhoods at a higher risk of developing,
for instance, impulsive tendencies. As a result, these youth
typically exhibit higher levels of delinquency and higher
levels of impulsivity than those from more affluent
communities. It remains unclear whether evidence of contextual
moderation uncovered in prior research reflects a “true”
neighborhood effect or developmental processes that give
rise to compositional differences in both impulsivity and
delinquency across neighborhoods. In other words, whether
impulsive youth are more likely to offend when they live in
impoverished areas, or whether youth who live in
impoverished areas are more likely to display impulsive and
aggressive tendencies. As is argued below, disentangling
contextual influences from developmental processes is
critical to understanding the complex role that neighborhoods
play in adolescent behavior.
The present article attempts to bridge this gap in the
literature in several key regards. The analyses begin by
examining the relationship between impulsivity and violent
behavior among a nationally representative sample of
American adolescents. Census data are linked to the
residential tracts of survey respondents to examine whether and
how indicators of neighborhood disadvantage moderate the
relationship between impulsivity and self-reported violence.
Finally, a neighborhood-based, group decomposition
framework is used to partition the moderating effect of
neighborhood context into its constituent parts. This
technique assumes that developmental and contextual factors
lead to unique data generating processes that differentially
affect the estimation of contextual moderation. While the
discussion is framed around recent research on impulsivity
and neighborhood disadvantage, these techniques can be
applied to a broad range of topics linking individuals to
broader ecological contexts. And, although the procedures
presented here have been used in most social science
disciplines, with a few exceptions, they have yet to be
embraced in person-context research.
Person-context research assumes that behavioral risk
factors are more strongly associated with criminal behavior
in particular social contexts (for a comprehensive overview
see Messner and Zimmerman 2012). An appreciable portion
of this research has focused on identifying
neighborhoodlevel mechanisms that either mitigate or exacerbate the
association between impulsivity and delinquency (see
Vaughan 2017 for a comprehensive review). The
overwhelming focus on impulsivity, or low self-control, is likely
attributed to the fact it is one of the most robust and
wellstudied dispositional correlates of delinquency (Gottfredson
and Hirschi 1990; Pratt and Cullen 2000). Several studies
have reported the effect of impulsivity on offending to be
strongest among adolescents living in economically
disadvantaged neighborhoods and neighborhoods
characterized by low levels of adult supervision (Jones and Lynam
2009; Lynam et al. 2000; Meier et al. 2008; Vogel 2016).
Other studies have uncovered the opposite—that the effect
of impulsivity is stronger in relatively affluent
neighborhoods with high levels of collective efficacy (Fine et al.
2016; Wikström and Loeber 2000; Zimmerman 2010). And,
at least two studies have failed to detect any evidence of
contextual moderation (Vazsonyi et al. 2006; Zimmerman
et al. 2015).
While research in this vein has produced somewhat
equivocal findings, each of these studies is grounded within
a similar theoretical framework and each points to similar
mechanisms purported to underlie the stronger effect of
impulsivity on delinquency in certain neighborhoods—
namely, the differential distribution of contextual
riskfactors. For instance, Lynam and colleagues (2000) draw
from routine activities theory (Cohen and Felson 1979;
Osgood et al. 1996; Osgood and Anderson 2004) and posit
that the lack of informal social control in disadvantaged
neighborhoods provides greater opportunity for impulsive
individuals to engage in rule violating behavior.
Zimmerman (2010), on the other hand, argues that disadvantaged
neighborhoods present a variety of risk-factors for
delinquency that suppress the influence of dispositional risk
factors. When these external factors are removed, the
association between impulsivity and delinquency emerges
more clearly. In this case, contextual risk-factors in the most
disadvantaged areas may push all youth to engage in
criminal conduct. In relatively low-risk contexts, youth with
the strongest internal controls may benefit the most from the
resources available to them (see also, Fine et al. 2016;
Vaughan 2017). Finally, Vazsonyi and colleagues (2006)
draw from Gottfredson and Hirschi (1990) and argue that
opportunities for crime are ubiquitous, thus explaining the
lack of moderation uncovered in their analysis.
Figure 1 presents a theoretical diagram outlining the
hypothesized moderating relationship of neighborhood
disadvantage on the association between impulsivity and
delinquency typically explored in person-context research.
Path A represents the direct effect of impulsivity on
offending, and Path B represents the moderating role of
neighborhood disadvantage. The dotted line differentiates
processes hypothesized to occur at the individual level from
those at the neighborhood level—in this case, path A reflects
an individual-level relationship, while path B reflects the
moderating role of neighborhood-level processes.
Drawing from the neighborhood effects literature, an
alternative explanation for a stronger effect of impulsivity,
or dispositional risk factors more generally, in economically
deprived neighborhoods can be attributed to the fact that
high-risk individuals are often overrepresented in high-risk
environments. In traditional thinking on selection effects,
this means that people with particular background
characteristics differentially select certain types of
neighborhoods; for instance, poor people may be more likely to live
in economically deprived neighborhoods as housing prices
are lower (van Ham and Manley 2012; van Ham et al.
2012). However, much of the person-context literature
focuses on adolescents and it bears to reason that the
nonFig. 1 Hypothesized moderation association neighborhood
disadvantage on impulsivity-delinquency. a Direct effects of impulsivity
on delinquency. b Moderation effect of disadvantage on the
random distribution of children and adolescents across
neighborhoods overwhelmingly reflects the decisions of
parents. While it is unlikely that impulsive youth choose to
live in neighborhoods with high levels of socioeconomic
disadvantage, as it is their parents who make residential
decisions, it is not unreasonable to assume that family and
broader community characteristics associated with
neighborhood disadvantage contribute to the greater likelihood
that these children develop impulsive traits.
Building from Wikstrom and Sampson (2003),
community context may contribute to adolescent behavior through
two complimentary processes: (1) neighborhoods can affect
the presence of situational opportunities in which crime is
considered a reasonable option and (2) neighborhoods,
through both direct and indirect means, can influence the
development of criminal predispositions, such as low
selfcontrol or impulsivity. In regards to the former (presence of
situational opportunities), neighborhood disadvantage may
diminish informal social control and provide greater
opportunity for adolescents to engage in unstructured
activities with their peers, away from adult chaperones—
prime conditions for delinquency (Bernasco et al. 2013;
Hoeben and Weerman 2014; Weerman et al. 2015;
Wikström and Butterworth 2006). In this sense, neighborhoods
can be seen as having a direct influence on individual
behavior—sometimes referred to as a “neighborhood” or
In regards to the latter (development of criminal
dispositions), neighborhoods can be thought of as a collective form
of socialization, whereby the shared monitoring and
supervision of youth behavior within the larger community
framework helps shape healthy child development (Leventhal
and Brooks-Gunn 2000; Pratt et al. 2004; Sampson 2002;
Shaw and McKay 1942). Disadvantaged neighborhoods,
characterized by low levels of cohesion and limited
communication among neighbors, may be less adept at creating
self-control in children. Moreover, families living in
economically deprived communities may face a number of
disadvantages, such as single-earner families, unemployment,
and poverty, which detract from their ability to adequately
socialize their children. Coupled with the absence of
community resources to alleviate the burden, children growing up
in these areas may experience inconsistent supervision,
inconsistent rule enforcement, and inconsistent discipline
when they misbehave. As a result of both community and
family socialization practices, children may not develop the
same executive functions (e.g., the ability to delay
gratification) as children from more affluent communities (Hart et al.
2008). Indeed, several studies have reported an inverse
relationship between neighborhood disadvantage and levels
of self-control, in some cases rivaling the effects of family
socialization (Pratt et al. 2004; Turner et al. 2005; but see
Gibson et al. 2010). In this sense, neighborhood disadvantage
Fig. 2 Hypothesized direct and moderation effects of neighborhood
disadvantage on impulsivity, delinquency, and the relationship
between impulsivity and delinquency. a Direct effects of impulsivity
on delinquency. b Moderation effect of disadvantage on the
impulsivity-delinquency association. c Direct effect of neighborhood
disadvantage on delinquency. d Direct effect neighborhood
disadvantage on impulsivity
may contribute to the development of criminogenic traits like
impulsivity. It bears to reason that these developmental
processes will be stratified by place, leading to a greater
concentration of impulsive adolescents in certain areas than
others. These differences are likely to be differentially
distributed across levels of socioeconomic disadvantage, such
that the most high-risk youth are disproportionately clustered
into the most high-risk environments.
Figure 2 presents an expanded theoretical model of the
moderating role of neighborhood disadvantage on the
association between impulsivity and delinquency, incorporating
the role of developmental and contextual influences. In this
figure, Path C represents the direct effect of
neighborhoodlevel disadvantage on offending. This pathway is assumed in
most person-context research and can be directly assessed
through the main effect of neighborhood disadvantage in
standard regression models. Path D reflects the
developmental processes that may lead to higher levels of impulsivity
among adolescents who grow up in disadvantaged
communities. Unlike the direct effect of neighborhood disadvantage,
this pathway is rarely considered and its influence cannot be
gleaned from a standard regression model. Thus, to truly
understand the moderating role of neighborhood context on
the association between impulsivity and offending,
researchers need not only examine paths A and B, but also
need to carefully consider the role of C and D.
The purpose of the current study is to examine the more
nuanced model of impulsivity, neighborhood disadvantage,
and self-reported violence presented in Fig. 2 among a
nationally representative sample of American adolescents.
Drawing from the theoretical processes highlighted in Fig. 2,
the analyses begin by examining the independent associations
between impulsivity and neighborhood disadvantage on
selfreported violence. We hypothesize that impulsivity
(Hypothesis 1) and neighborhood disadvantage (Hypothesis
2) will be positively associated with self-reported violence.
The analyses next assess whether the relationship between
impulsivity and self-reported violence is contingent on levels
of neighborhood disadvantage. Consistent with the work of
Vogel (2016) using the same data, we anticipate that
neighborhood disadvantage will strengthen the relationship
between impulsivity and violence, implying a positive
interaction effect (Hypothesis 3). The final set of analyses examine
whether differences in average levels of violence and
impulsivity across communities can help explain variation in the
effect of impulsivity on violence across neighborhoods with
varying levels of socioeconomic disadvantage. While it is
more difficult to anticipate the exact nature of the mechanisms
driving the hypothesized interaction, the theoretical processes
presented in Fig. 2 portend that average levels of both
violence and impulsivity will be higher in disadvantaged
communities. These compositional differences should then
partially explain the moderating effect of neighborhood
disadvantage on the association between neighborhood
disadvantage and self-reported violence (Hypothesis 4).
Data for the analyses were drawn from the National
Longitudinal Study of Adolescent to Adult Health (Add
Health), a nationally representative survey of adolescents
enrolled in high school during the 1994–95 academic year
and followed through early adulthood (with data collection
ongoing). The original survey design included a sample of
80 high schools and 52 middle schools from the United
States with an unequal probability of selection, ensuring
representativeness with respect to region of country,
urbanicity, school size, school type, and ethnicity. In the first
phase of data collection, a brief questionnaire was
administered to all youth enrolled in grades 7–12 in each of the
132 schools with no make-up given for absent students. The
in-school survey covered topics such as socio-demographic
characteristics, risk behaviors, future expectations, health
status, perceived school climate, and household structure. In
addition to these data, school administrators provided
information on characteristics such as graduation rate,
retention rate, and class size.
From the initial in-school survey, over 20,000 students
were selected to participate in the first wave of the
longitudinal follow-up study. The Wave I data included 39
selfreport questionnaires on topics covering general health,
romantic relationships and contraception, employment and
income, as well as personality characteristics and delinquent
behavior. Additionally, respondents’ home addresses were
geocoded, and geographic information from the 1990
census is available at the block group, tract, and county level
for each respondent. During the following year (1995–96),
respondents who were still in high school were asked to
complete a second wave of questionnaires. These data
included information from roughly 14,000 respondents
(excluding those who were high school seniors in Wave I).
The present analysis draws on a sample of 12,935
respondents who participated in the first two waves of the survey,
spanning the years 1994–1996.
Violence, the primary dependent variable, is a count-based
measure of the number of the following acts the respondent
in which the respondent engaged during the 12 months prior
to the Wave 2 interview: (1) injuring someone badly enough
to need medical attention, (2) shooting or stabbing
someone, (3) using or threatening to use a weapon to get
something from someone (4) participating in a group fight,
(5) using a weapon in a fight, (6) pulling a knife or gun on
someone, (7) getting into a serious physical fight. The scale
was constructed by first dichotomizing each of these seven
items, then summing across items to generate count-based
measure that captures the variety of violent offenses
endorsed by Add Health respondents (alpha = 0.93).
Impulsivity is measured by the extent to which respondents
agreed with the following statement: when making decisions,
you usually go with your “gut feeling” without thinking too
much about the consequences of each alternative. Responses
to this item are arranged along a five item Likert scale
ranging from strongly disagree (low impulsivity) to strongly
agree (high impulsivity). This item closely resembles (a lack
of) premeditation, one of the four key facets of impulsivity
proposed by Whiteside and Lynam (2001).1
1 Unfortunately the Add Health study does not include a compre
hensive psychometric inventory and therefore we are limited in our
ability to use a comprehensive measure of impulsivity. We should note
that this item closely parallels a lack of premeditation, or an inability to
think through the consequences of ones’ action, which has been
established as a robust correlate of criminal offending (e.g.,
Gottfredson and Hirschi 1990). As a sensitivity analysis, we also
reestimated our models with alterative measures of impulsivity used in
prior Add Health research (Vazsonyi et al. 2006; Perrone et al. 2004;
McGloin and O’Neill Shermer 2009). The results of these
supplemental models are available upon request from the corresponding
Neighborhood Socioeconomic Disadvantage
Neighborhood disadvantage is measured as a standardized
index of the percent of a respondents neighborhood
receiving welfare, the percent living at or below poverty, the
percent unemployed, and percent of female headed
households (alpha = 0.923). It is coded such that higher values
reflect a greater degree of socioeconomic disadvantage.
Race differentiates respondents who identified as
nonHispanic white (55%), non-Hispanic black (20%), Hispanic
(17%), and non-Hispanic other race (8%).
Age is measured in years at the time of the Wave 1
interview (Mean = 15.1).
Sex is a dichotomous variable differentiating males from
female (female = 1; 51%).
Two Parent Household
Two parent household is a dichotomous variable
differentiating respondents living with both biological parents
from any other family configuration (both parents = 1; 55%).
The conventional approach to assessing neighborhood
moderation in person-context research involves estimating a
regression model in which neighborhood characteristics,
impulsivity, and their product term are included alongside a
series of control variables to predict some form of
delinquency. The regression equation takes on the basic form:2
2 Some studies employ multilevel or hierarchical linear models
(HLMs) to partition the variance in the dependent variable between
individuals and neighborhoods. Unlike the methods presented here,
HLM models require (1) nested data—that is, respondents clearly
situated within broader neighborhood units, (2) a large number of
neighborhoods, and a (3) large number of respondents in each
neighborhood. Few extant data sources meet this requirement.
Moreover, the models require that variance can be specified on both L1 and
L2, which is not possible with count or categorical models—those
most commonly employed in criminological research (Sweeten 2012).
Thus, the strategy presented here is an alternative to the HLM
framework when these computational requirements cannot be met.
Where Y refers to a scale of delinquency, b1 refers to the
slope of impulsivity (denoted by Path A in Fig. 2), b2 refers
is the slope of neighborhood disadvantage (Path C), and b3
is the product term for the neighborhood disadvantage *
impulsivity (Path B). A significant coefficient for b3 is
usually considered sufficient evidence of contextual
moderation, and is often interpreted as the expected change in
the slope of b2 across levels of neighborhood disadvantage
—in other words, how neighborhood context mitigates or
exacerbates the effect of impulsivity on offending.
This interpretation is problematic, as the interaction term,
in part, reflects compositional differences in impulsivity
across neighborhoods (due to relationship D in Fig. 2). For
the reasons outlined above, we might expect average levels
of both impulsivity and violence to be higher among youth
residing in economically disadvantaged neighborhoods.
This suggests that Y and X1 will increase with
neighborhood disadvantage. In the traditional regression framework,
these higher averages can generate a statistically significant
coefficient for b3 without any true difference in the slope of
b1 across neighborhoods. Thus, to sufficiently make claims
about contextual moderation, we need to rule out the
possibility that the observed coefficient is not driven by higher
averages levels of impulsivity and delinquency alone (as
demarcated in paths C and D in Fig. 2).
The issue of developmental vs. contextual effects can be
viewed as a special version of a more general problem
identified in the social science literature: identifying the
extent to which differences in rates across groups reflects
differences in group composition. While these techniques
have been utilized in other disciplines, they have yet to be
employed in person-context research. Oaxaca (1973) and
Blinder (1973) independently proposed a relatively
straightforward means to address this problem, as it applied
to gender differences in earnings. The same basic approach
also applies here. In the standard framework, group-based
differences can be attributed to two factors—differences in
levels and differences in slopes. In the earnings
nomenclature, this means that a difference in income between
males and females could reflect, in part, higher average
education among males (levels) and the portion that cannot
be explained by educational differences (e.g., the
unexplained portion) would then be attributed to a true
interaction effect.3 In the case of neighborhood context and
impulsivity, the differences in levels can be viewed as
analogous to the differential distribution of individual
riskfactors across neighborhoods (Paths C and D), while the
3 Notably, similar strategies have also been applied in the crimin
ological literature to examine differences in offending between males
and females (e.g., Botchkovar and Broidy 2013; Botchkovar et al.
2015), and racial/ethnic differences in arrest (Kirk 2008) and
incarceration (Vogel and Porter 2016).
“unexplained” part of the interaction could be interpreted as
“neighborhood moderation” (Path B).
In its simplest application, the decomposition involves a
four-step process. In the first step, we followed prior
research in this area (e.g. Farrington and Loeber 2000; Fine
et al. 2016; Graif 2015; Vogel 2016) and collapsed the
neighborhood disadvantage index at the 75th percentile to
create a dichotomy differentiating “disadvantaged”
neighborhoods from all other neighborhoods. Second, differences
in average levels of impulsivity and offending were
assessed by comparing means across neighborhood groupings.
Third, two separate regression equations were estimated,
one for respondents living in disadvantaged neighborhoods,
and the second for respondents living in all other
neighborhoods such that:
In these equations ŷ is the predicted level of self-reported
violence, a is the regression constant, X is the mean level of
impulsivity, and b is the regression coefficient. The
subscript H refers to respondents living in neighborhoods with
high levels of disadvantage and L refers to respondents
living in neighborhoods with low levels of disadvantage.
Similar to Eq. 1, contextual moderation can be assessed by
comparing bH and bL, in this case, the Clogg Test for the
equality of coefficients can be used to assess statistical
significance (Paternoster et al. 1998).4 The difference in
average levels of self-reported delinquency can then be
expressed as the difference in predicted levels of
delinquency between Eqs. 2 and 3:
Which can be expanded into the Blinder (1973) and
Oaxaca (1973) decomposition such that:
In this equation (ŷH–ŷL) is the expected difference in
self-reported violence between adolescents living in
disadvantaged and non-disadvantaged neighborhoods.
bH(x̅H–x̅L) represents the portion of the difference in
violence across neighborhoods that can be attributed to
compositional differences—in this case, higher average levels of
impulsivity in disadvantaged neighborhoods. The final
component, x̅L(bH–bL), is the “unexplained” part of the
interaction effect, in this case, the portion of the interaction
that can be attributed to a stronger effect of impulsivity on
violence in disadvantaged neighborhoods.
4 Z = (bH−bL)/√(seH2 + seL2)
The two component model can be expanded slightly
In this case, the difference in violence across
neighborhoods is decomposed into three components, the difference
in mean levels of impulsivity [bL(x̅H–x̅L)], the difference in
coefficients [x̅L(bH–bL)], and a third component that
accounts for the part of the difference that can be attributed
to the interaction between levels and coefficients [(bH–bL)
(x̅H–x̅L)] (Daymont and Andrisani 1984). This third
component, as discussed in greater detail below, overcomes
scaling issues in X.
Finally, the equations presented in Eqs. 5 and 6 can be
expanded slightly to determine the extent to which to which
differences in Y (violence), in addition to differences in x̅
(impulsivity), affect the observed interaction. This yields a
four component solution initially proposed by Jones and
Here [(aH–aL)] is the difference in the adjusted intercepts
of the two groups or the proportion of the observed
interaction that can be attributed to variation in mean levels of
violence (Y) across neighborhoods. [bL(x̅H–x̅L)] is the
component attributed to differences in levels of impulsivity,
or how the impact of impulsivity on violence for someone
living in a high disadvantage neighborhood would change if
they were living in a low disadvantage neighborhood.
x̅L(bH–bL) is the difference in the effect on impulsivity on
violence across neighborhoods and [(bH–bL) (x̅H–x̅L)] is the
residual component interpreted as the difference in the
interaction between mean levels of impulsivity and
coefficients across neighborhoods.5,6 Examining each of these
5 This example applies to a regression model in which neighborhood
disadvantage is collapsed into a dichotomy. The same general
framework can be applied to ordinal or continuous measures of
neighborhood disadvantage. This extension would require expanding the
number of regression equations to equal the number of categories, K,
of the neighborhood measure. The decomposition could then be
replicated by comparing Y and X for each of the possible pairwise
comparisons, selecting the neighborhood with the highest level of
disadvantage as the referent.
6 The decomposition presented here assumes the parameter estimates
are generated from an OLS regression. This approach has also been
expanded to apply to binary outcomes (Fairlie 1999, 2005), quantile
regression, and counterfactual models (Machado and Mata 2005).
Bauer and Sinning (2008) provide a generalized decomposition for
non-linear models with discrete and limited dependent variables. The
NLDECOMPOSE routine in Stata can handle these more complex
modeling strategies (Sinning et al. 2008; Jann 2008).
components as a proportion of the raw difference in
predicted levels of violence, (ŷH−ŷL), provides a means to
quantify the contribution of compositional differences in
violence and impulsivity to the observed moderating effect
of neighborhood disadvantage on the association between
the two. Thus, the third component provides the estimate of
neighborhood moderation (Path B in Fig. 2) and the first
and second components provide the estimates of
developmental processes that give rise to compositional differences
across neighborhoods (paths C and D).
Table 1 presents the descriptive statistics for the variables
included in the analysis. The analytic sample was
approximately 51% female, 20% non-Hispanic black, 17%
Hispanic, and 8% non-Hispanic other race. The average age
was 15.3 years, and 55% of respondents reported living
with both biological parents. Respondents reported an
average level of impulsivity of 3.02 on a five-point scale.
Table 2 presents the results from a regression model in
which self-reported violence was regressed on the control
variables, impulsivity, neighborhood disadvantage, and the
interaction of impulsivity*neighborhood disadvantage.
Consistent with some prior research (e.g., Jones and Lynam
2009; Lynam et al. 2000; Meier et al. 2008; Vogel 2016),
impulsivity was associated with higher levels of offending,
and neighborhood disadvantage moderated this association,
such that effect of impulsivity on offending was amplified at
higher levels of neighborhood disadvantage. This provides
confirmation Hypotheses 1 and 3.
Table 3 presents the results of the regression models
estimated separately for respondents living in
neighborhoods at the top-quartile of socioeconomic disadvantage
and respondents living in all other neighborhoods.
Consistent with the models presented in Table 2, the effect of
impulsivity on self-reported violence is stronger among
Table 1 Descriptive statistics (N = 12,935)
Mean (Prop) SD
respondents living in disadvantaged neighborhoods relative
to those living in more affluent neighborhoods (relationship
B in Fig. 2; [Z = 1.92; one-tailed test]). Predicted levels of
self-reported violence in disadvantaged and
nondisadvantaged neighborhoods were next generated by
substituting the mean level of impulsivity into Eqs. 2 and 3,
respectively. This yields the constituent terms for Eq. 7. For
instance, the mean level of impulsivity in disadvantaged
neighborhoods was 3.09; substituting this value into the
regression equation (holding the other covariates constant)
Table 2 Regression of self-reported violence on impulsivity,
neighborhood disadvantage, and interaction (N = 12,935)
*p < 0.05; **p < 0.01; ***p < 0.001
Table 3 Regression of self-reported violence on impulsivity across
neighborhood type (N = 12,935)
provides a ŷ of 1.689 [ŷ = a + bx̅]. Table 4 presents the
summary statistics from these subsequent regressions.
The results of the decomposition analysis indicate that
roughly 81% of the observed interaction between
impulsivity and neighborhood disadvantage can be attributed to
differences in the slope of impulsivity across neighborhoods
(Table 5). Compositional differences in self-reported
violence (14%—Path C in Fig. 2) and impulsivity (2%—Path
D in Fig. 2) make up the remainder of the observed
interaction. Collectively, these findings suggest that much of the
observed interaction can be attributed to contextual
moderation—that is, that the effect of impulsivity on violence is
stronger in socioeconomically disadvantaged
neighborhoods. However, a non-trivial proportion, just under 16%,
can be attributed to the differential concentration of
highrisk (i.e., violent and impulsive) adolescents in
While consistent with much prior research (e.g., Farrington
and Loeber 2000; Fine et al. 2016; Graif 2015; Vogel 2016)
and necessary for the decomposition procedure, the decision
to dichotomize the measure of neighborhood disadvantage
at the 75th percentile may be viewed as somewhat arbitrary.
As sensitivity analyses, the regression models and
corresponding decomposition analyses were re-estimated by
shifting the designation of ‘disadvantaged neighborhoods’ to
the 90 percentile. The results of these supplemental analyses
can be found in Tables 6 and 7. Although the parameter
estimates and corresponding components vary from those
Table 5 Decomposition of difference in impulsivity—violence
association across neighborhood type
*p < 0.05; **p < 0.01; ***p < 0.001
Table 4 Summary statistics of
impulsivity and violence across
presented in the preceding tables, the general conclusions
remain the same. The majority of the interaction between
impulsivity and violence can be attributed to a true
difference in slopes; however, an appreciable portion of the
interaction can be attributed to higher levels of impulsivity
and violence among adolescents residing in the most
disadvantaged areas. Thus, the cut-point at which
neighborhoods were deemed “disadvantaged” had little substantive
bearing on the results presented here.
Over the past several decades, scholars have become
increasingly attuned to the importance of social context in
the etiology of adolescent development and behavior. This
research has highlighted how chacteristics of schools and
neighborhoods contribute to, for instance, educational
achievement (Irvin et al. 2011; Dotterer and Lowe 2011),
relationship conflict (Foshee et al. 2013), mental health
(Nair et al. 2013), and prosocial adjustment (Riina et al.
2013). A parallel body of literature has emphasized how
social and spatial environments condition the relationships
between individual risk-factors and adolescent behavior
(e.g., Deutsch et al. 2012; Zimmerman 2010). This research
demonstrates that adolescent behavior cannot be attributed
to dispositional or contextual factors alone. Instead,
behaviors such as drug use, violence, and delinquency are best
understood through the confluence of individual and
environmental risk-factors. While recent research in this
vein continues to underscore the intricate linkages between
individual and contextual risk-factors and their attendant
consequences for adolescent behavior (e.g., Chen and
Vazsonyi 2013; Jensen et al. 2017; Vogel et al. 2015;
Zimmerman and Farrell 2017), comparatively fewer studies
have focused on the more complex pathways through which
these processes operate. The present study attempted to
bridge this gap in the literature by examining the
relationship between impulsivity (a dispositional risk-factor) and
violence among respondents living in neighborhoods
characterized by varying degree of socioeconomic disadvantage
(a contextual risk-factor). While some prior research in this
area indicates the association between impulsivity and
offending to be strongest in economically deprived
communities (e.g., Lynam et al. 2000; Vogel 2016; c.f., Fine
et al. 2016; Zimmerman 2010), scholars have yet to
consider the more nuanced processes driving these differences.
Drawing from the work of Wikstrom and Sampson
(2003), neighborhoods can be thought of as both (1)
developmental contexts that influence the formation impulsive and
violent tendencies and (2) social contexts which provide the
opportunity for impulsivity to manifest in violent behavior.
In this sense, the stronger association between impulsivity
and violence in disadvantaged neighborhoods can be
attributed to either the higher levels of violence and impulsivity
among youth who reside in economically disadvantaged
areas (a compositional effect) or the stronger effect on
impulsivity on violence in these areas (a contextual effect). In
an effort to disentangle these complimentary processes, this
article applied a neighborhood-based, group decomposition.
The results of the regression models indicate that impulsivity
was positively associated with self-reported violence and that
this association was strongest among youth residing in the
most socioeconomically disadvantaged neighborhoods. The
results of the decomposition reveal that much of the stronger
effect of impulsivity on violence in disadvantaged
neighborhoods could be attributed to contextual processes. In other
words, there is something unique about socioeconomically
disadvantaged neighborhoods that increased the effect of
impulsivity on violence. However, a nontrivial portion of the
interaction could be attributed to higher levels of impulsivity
and self-reported violence among youth residing in
disadvantaged areas, suggesting the moderating relationship
uncovered in prior research reflects more than an abundance
of opportunities for impulsive youth to offend in
socioeconomically disadvantaged areas. Instead, there is strong
evidence that both compositional and contextual processes
are at play. In this sense, the present article provides a more
nuanced framework for understanding the complex
relationships between individual risk-factors and neighborhood
features on adolescent development and behavior.
The decomposition techniques presented here provide a
relatively intuitive means to bolster claims about the
developmental and contextual underpinnings often assumed
in person-context models of behavior. As such, we
encourage researchers to consider such techniques in their own
work. Insofar as there is apriori reason to assume
compositional differences between groups, it would be useful to
demonstrate the extent to which these differences drive
interaction effects. We caution researchers from concluding
they have uncovered evidence of contextual moderation
when compositional differences account for the majority of
the observed difference across groups. However, we also
encourage researchers to present results in which
compositional factors are primarily responsible for these
differences, as the mechanisms driving compositional effects are
meaningful in and of themselves.
The techniques presented here are not limited to studies
examining the moderating role of neighborhood context on
the association between impulsivity and violence; rather,
they are a useful resource for researchers interested in
theoretical models of behavior combining individual and
contextual factors more generally. These techniques could
be used to examine, for instance, the contribution of student
composition to differences in victimization experiences, or
to partition gene X environment interactions into
compositional and environmental components. These
techniques can be applied to most analyses examining
interaction effects in which developmental processes generate
meaningful compositional differences across groups.
It is important to note that such decomposition
procedures reflect an exercise in variance partitioning. While
these analyses provide some insight into the structure of the
interaction effects, they do little to expound the causal
processes underlying the stronger effect of impulsivity on
violence in disadvantaged neighborhoods. Thus, these
procedures allow us to conclude with relative certainty that
the tract-level interactions are not statistical artifacts;
however, the mechanisms underlying these effects remain to be
determined. Likewise, we do not present this approach as an
alternative to correcting regression models for endogeneity.
Researchers who want to remove the confounding effects of
composition remain well-served to employ standard
counterfactual models, such as fixed-effects regressions,
propensity score models, or instrumental variable approaches.
We would be remiss not to reiterate several key
limitations of the findings reported here. The most glaring
limitation is our operationalization and measurement of
impulsivity as a single item, rather than a more
comprehensive inventory that more fully captures into the
multifaceted nature of the construct. As noted, the Add Health
study was not designed to measure complex psychological
traits. As a result, we were limited in the variables at our
disposal. The assorted issues with measuring multifactorial
constructs with national survey data are well-documented in
the empirical literature (e.g., Wolfe and Hoffmann 2016).
The use of imprecise measures from questionnaires that
were not designed capture these traits pose a limitation to
any study drawing from nationally representative survey
data. As such, we would strongly encourage future
researchers to replicate the results here with a more
comprehensive psychometric measure of impulsivity that more
closely captures each of its constituent dimensions (e.g.,
risk seeking, urgency, lack of perseverance).
The limitations of decomposition techniques have been
well documented in the econometric literature (e.g., Jones
1983; Jones and Kelley 1984; Lin 2007), but warrant some
discussion here. First, the results of these procedures are
contingent on the category chosen as the referent. In the
application presented here, disadvantaged neighborhoods.
The choice of the reference group will alter the
decomposition procedure, as the choice of the base category will
affect the estimation of the coefficients in the regression
equation (and, as a result, the relative contribution of each
component to the overall difference). Second, in the
standard 2-component decomposition, the interpretation of the
unexplained portion (e.g., the difference in slopes) is
sensitive to scaling decisions and this component only has a
meaningful interpretation for variables which have a
natural zero point (Jones and Kelley 1984). This issue,
however, is resolved in the three—and four—component
decompositions. Third, the procedure presented here
assumed a binary moderator, in this case comparing
disadvantaged and non-disadvantaged neighborhoods. Of
course, collapsing continuous variables into dichotomies
truncates meaningful variation in neighborhood
disadvantage. To address this issue, researchers could employ
the same framework and decompose the differences at
various points of the neighborhood disadvantage index
(e.g., one and two standard deviations above/below the
mean). Fourth, the decomposition utilizes point estimates,
thus ignoring the standard error of the coefficients.
Although a bit beyond the purview of the present analysis, a
handful of scholars have proposed ways to incorporate
standard errors into the traditional decomposition
framework (e.g., Lin 2007). Finally, and perhaps most
importantly, the decomposition does not provide leverage to
determine what is driving the difference in slopes, only the
extent to which mean levels of X and Y contribute to the
The results presented in this study underscore the complex
pathways through which individual and contextual factors
operate to influence adolescent behavior. This study
demonstrated the extent to which variation in the
association between impulsivity and delinquency across
neighborhoods can be attributed to (1) differences in mean-levels
of impulsivity and violence and (2) differences in
coefficients across neighborhoods. The decomposition method
showed that the differential effect of impulsivity on
violence can be attributed to both developmental processes that
lead to the greater concentration of violent and impulsive
adolescents in economically deprived neighborhoods as
well as the greater likelihood of impulsive adolescents
engaging in violence when they reside in economically
disadvantaged communities. We encourage future
researchers to consider the nuanced role of developmental
and contextual processes that link individual risk-factors to
broader contextual influences. To this end, the
neighborhood-based, group decomposition presented here is a useful
heuristic tool for researchers interested in the direct and
moderating effects of contextual influences on adolescent
behavior. While the approach is commonplace in other
social science disciplines, the decomposition framework is
rarely utilized in person-context research. Unlike many of
the methods du jour, this technique is relatively intuitive,
computationally straightforward, and does not necessitate
complex modeling strategies. In regards to the
personcontext literature in particular, we encourage researchers to
simultaneously consider developmental and contextual
influences in theoretical models linking individual behavior
to broader social ecologies, and caution readers against
placing too much stock in one mechanism without
considering the contribution of the other. The decomposition
framework provides a useful means to this goal.
Acknowledgements We would like to thank Ryan D. King, Steven
F. Messner, Jaap Nieuwenhuis, and Kyle Thomas for feedback on
earlier drafts of this paper. Claire Anderson Greene provided
invaluable assistance with this project. All errors and omissions remain our
own. This research uses data from Add Health, a program project
directed by Kathleen Mullan Harris and designed by J. Richard Udry,
Peter S. Bearman, and Kathleen Mullan Harris at the University of
North Carolina at Chapel Hill, and funded by grant P01-HD31921
from the Eunice Kennedy Shriver National Institute of Child Health
and Human Development, with cooperative funding from 23 other
federal agencies and foundations. Special acknowledgment is due to
Ronald R. Rindfuss and Barbara Entwisle for assistance in the original
design. Information on how to obtain the Add Health data files is
available on the Add Health website (http://www.cpc.unc.edu/addhea
lth). No direct support was received from grant P01- HD31921 for this
Funding 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 no. 615159 (ERC Consolidator Grant DEPRIVEDHOODS,
Socio-spatial inequality, deprived neighbourhoods, and
neighbourhood effects); and from the Marie Curie programme under the
European Union’s Seventh Framework Programme (FP/2007–2013)/
Career Integration Grant no. PCIG10-GA-2011-303728 (CIG Grant
NBHCHOICE, Neighbourhood choice, neighbourhood sorting, and
Author Contributions M.V. conceived of the study design,
performed the data analysis, interpreted the findings, and drafted the
article. M.V.H. contributed to the interpretation of the findings and the
writing of the manuscript.
Compliance with Ethical Standards
The authors delare that they have no competing
Approved by IRB review at the University of
Informed Consent Informed consent was obtained from all
individual participants included in the study.
Open Access This article is distributed under the terms of
the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided you
give appropriate credit to the original author(s) and the source, provide
a link to the Creative Commons license, and indicate if changes were
Table 6 Summary statistics of
impulsivity and violence across
neighborhood type with 90/10
Table 7 Decomposition of
difference in impulsivity—
violence association across
Portion explained by differences in mean violence
Portion explained by differences in mean impulsivity
Portion explained by differences in impulsivity slope
Matt Vogel is Assistant Professor of Criminology and Criminal
Justice at the University of Missouri—St. Louis and a researcher at
OTB—Research for the Built Environment, Technological University
of Delft. His research examines contextual influences on adolescent
behavior and the relationship between population dynamics and crime.
His research has recently appeared in Criminology, Journal of
Quantitative Criminology, and Social Forces.
Maarten Van Ham is Professor of Urban Renewal and head of the
Urban and Neighbourhood Change research group at the Department
OTB—Research for the Built Environment, Delft University of
Technology. Maarten is a population geographer with a background
in economic and urban geography; is a Research Fellow at IZA; and
Professor of Geography at the University of St Andrews.
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