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The University of North Carolina Press Social Forces, March 2002, 81(3):753-785
A Contextual Analysis of Differential
Association, Social Control, and StrainTheories of Delinquency*
JOHN P. HOFFMANN,Brigham Young University
Abstract
The history of criminological thought has seen several theories that attempt to linkcommunity conditions and individual-level processes. However, a comparative analysisof contextual effects has not been undertaken. This article estimates a multilevel modelthat examines the effects of variables derived from three delinquency theories. The resultsindicate that youths residing in areas of high male joblessness who experience stressfullife events or little parental supervision are especially likely to be involved in delinquentbehavior. The attenuating impact of school involvement on delinquency is morepronounced in urban environments low in male joblessness. These results suggest thatexamining the contextual implications of delinquency theories is important, but theoriesneed to be developed with more attention to specific contextual processes.
The search for macro-micro linkages and how they affect deviant and crimi-nal behavior has a substantial and notable history (Coleman 1990; Durkheim
1951[1897]; Stark 1987). The history of criminological thought has seen Shaw
and McKays seminal work on how social disorganization affects behavior at
the individual level, especially with reference to the qualitative life histories
* Support for this research was provided by National Institute on Drug Abuse grant11293. An earlier version of this article was presented at the 2000 annual meeting ofthe American Society of Criminology, San Francisco, Calif. I thank Bob Bursik, FrankCullen, Bob Agnew, David Greenberg, and an anonymous Social Forces reviewer forhelpful suggestions on earlier drafts. I also appreciate the assistance and advice providedby Bob Johnson, Harvey Goldstein, Jon Rasbash, Ken Rasinski, Shaun Koch, and JingZhou. Please address all correspondence to JohnP. Hoffmann, Department of Sociology,844 SWKT, Brigham Young University, Provo, UT84602. E-mail: [email protected].
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that they collected (Bursik& Grasmick 1993; Shaw& McKay 1931, 1969);
Mertons discussions of opportunity structures and strain (Merton 1968, 1995);
and Sutherlands discourse on the links between differential association and
differential social organization (Reinarman& Fagan 1988; Sutherland 1939,
1973[1942]). Although attention to these processes suffered a period of theo-
retical and empirical dormancy, the last ten to fifteen years or so has seen a
resurgence of interest in how macroprocesses affect microlevel social relation-
ships.
At least two motivating factors underlie this resurgence. First, Shaw and
McKays (1969) social disorganization theory has been revisited and found to
have merit. A number of studies indicate that aspects of social or community
disorganization, a macrolevel construct, either affect individual behavior indirectly
through micro relations or condition the impact of individual-level factors on
delinquent and criminal behavior (Bursik& Grasmick 1993; Elliott et al. 1996;
Sampson& Groves 1989; Taylor 1997; Veysey& Messner 1999; Yang& Hoffmann
1998). A key theoretical proposition is that socially disorganized communities are
less able to control the general behavior of residents, thus affecting delinquent and
criminal behavior via attenuated social control processes (Kornhauser 1978; Shaw&
McKay 1931).
The resurgence of social disorganization theory has prompted others to describe
potential macro-micro linkages that elaborate several important theories of
delinquency. These include elaborations of conflict and control processes in the
development of delinquent behavior (Colvin& Pauly 1983; Hagan 1989),
differential association and social learning theory to account for structural
influences on learning and peer affiliations (Akers 1998; Reinarman& Fagan 1988),
and the variable distribution of strains across types of communities (Agnew 1999).
Second, recently developed statistical models, drawn primarily from educational
research, now allow precise empirical attention to how macrolevel (contextual)
variables condition the impact of explanatory variables on a variety of outcomes
of interest to the criminological community. Recent studies have examined whether
school- and community-level factors affect the relationship between demographic,
family, and peer factors and various measures of delinquent behavior, drug use,
violence, victimization, and fear of crime (Elliott et al. 1996; Hoffmann 2002;
Perkins& Taylor 1996; Rountree, Land& Miethe 1994; Sampson, Raudenbush&
Earls 1997). For instance, research suggests that community disorganization
attenuates informal social control, which is then negatively related to adolescent
deviant behavior (Elliott et al. 1997). Community disorganization may also have a
direct impact on individual-level deviant behavior, even net of the effects of
individual-level control mechanisms (Gottfredson, McNeil& Gottfredson 1991;
Simcha-Fagan& Schwartz 1986; Taylor 1997).
A limitation of this research has been its conceptual focus on linking social
disorganization at the contextual level and social control or bonding mechanisms
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Theories of Delinquency / 755at the individual level (Bursik& Grasmick 1993; Elliott et al. 1997; Sampson,
Raudenbush& Earls 1997; Yang& Hoffmann 1998). Although the links between
social disorganization and individual-level bonds are appealing and
theoretically elegant, recent discussions of how other delinquency theories may
be elaborated to include macro-micro connections offer a promising avenue
for research (cf. Agnew 1999; Akers 1998; Reinarman& Fagan 1988; Simcha-
Fagan& Schwartz 1986).
In this article, I draw upon three major theories of delinquent behavior
social control, strain, and differential association/social learning to elabo-
rate the community context of adolescent involvement in delinquency.1 The
goal is to determine whether some of the key individual-level relationships
expressed by these theories vary across U.S. communities and, if so, whether
community characteristics condition these relationships. To provide motiva-
tion for this goal, the following section reviews these three theories with a clear
eye toward discussing how their implied relationships might be conditioned
by community characteristics. This discussion is followed by an empirical analy-
sis designed to test hypotheses concerning the contextual effects of delinquency
theories.
Macro-Micro Context of Delinquency Theories
A key goal of the sociological enterprise, and the criminological initiatives that
it engendered, has been to describe how group processes and environmental
conditions affect individual-level behavior (Durkheim 1982[1895]; Hechter 1987).
Important criminological inquiries drawn from this interest include the following:
Why do residents of certain urban regions tend to engage in more delinquent andcriminal behavior than residents of other areas? (Shaw& McKay 1931, 1969; Stark
1987). What ecological characteristics affect the probability of gang formation or
individual delinquent behavior? (Short 1997). What community factors affect the
fear of victimization or actual victimization? (Perkins& Taylor 1996; Rountree,
Land& Miethe 1994). A variety of explanations have been proposed to answer
questions such as these. The following discussion addresses three of these
explanations: social control (bonding) theory, strain theory, and differential
association theory. Although these theories focus primarily on individual-level
processes, all are amenable to contextual elaboration.
SOCIAL CONTROL THEORY
Although its individual-level processes are well known due to the work of
Hirschi (1969), several observers argue that social control theorys macro-micro
linkages are demonstrated in early criminological work (Kornhauser 1978;
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Sampson& Groves 1989). Community disorganization, for instance, is thought
to attenuate bonding mechanisms by making supervision and interpersonal
attachments more tenuous (Elliott et al. 1997; Shaw& McKay 1931; Simcha-
Fagan& Schwartz 1986). One might also ask whether community
disorganization weakens the ability of social bonds to circumscribe delinquent
behavior:
In communities characterized by residential instability and heterogeneity and
a high proportion of broken and/or single parent families [i.e., community
disorganization], the likelihood of effective socialization and supervision is reduced
and it becomes difficult to link youths to the wider society through institutional
means. (Bursik& Grasmick 1983:37)
Empirical research supports the notion that the impact of social bonds
varies by type of community and that disorganized communities negatively
affect the ability of social bonds to reduce delinquent behavior. Attachmentto parents and peers, for instance, has a differential impact on delinquent be-
havior that depends on the type of community within which it occurs (Krohn,
Lanza-Kaduce& Akers 1984; see, however, Reinarman& Fagan 1988). More-
over, community disorganization reduces social support structures and thus
attenuates effective parenting, an important source of successful socialization
and conventional bonding (Peeples& Loeber 1994; Sampson& Laub 1994;
Simons et al. 1997; Yang& Hoffmann 1998). In general, social bonds such as
attachment and involvement in conventional activities may have significant
countervailing forces in disorganized communities characterized by poor com-
munity supervision and control (Sampson 1987); hence their effectiveness at
preventing delinquency is diminished.
STRAIN THEORY
The initial development of strain theory had both macro and micro roots
(Agnew 1987; Bernard 1987; Bernard& Snipes 1996; Merton 1995). Merton
(1968) posited that opportunity structures affect the ability to realize common
cultural goals, such as the quest for monetary gain. This has primarily a
structural component that affects deviant behavior in the aggregate. But it also
has an individual-level component: The strain of pursuing goals within diverse
opportunity structures may lead to adaptations such as crime, delinquency, and
other deviant behavior (Cullen 1984). However, assuming that opportunity
structures vary by community (Cloward& Ohlin 1960), it is reasonable to posit
that the effects of strains caused by the disjunction between goals and meanson deviant behavior will vary by community. One might hypothesize, for
instance, that strained youths in disorganized communities have a more realistic
picture of their plight, so deviant adaptations become more likely.
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Theories of Delinquency / 757Agnews (1992) recent elaboration of this theoretical tradition broadens the
notion of strain considerably by conceptualizing it as coming from a variety of
sources, including families, schools, and cognitive skills. Moreover, he has
recently proposed an elaboration of general strain theory to encompass
community effects (Agnew 1999). In general, Agnew posits that deprived
communities are more likely to be populated by strained individuals and that
these communities will suffer from more blocked opportunity structures.
Hence these communities tend to create an atmosphere conducive to anger
and frustration, key antecedents to delinquent behavior. Community
characteristics produce environments that condition the effect of strain on...
crime (Agnew 1999:128). Since Agnews definition of a deprived community
includes many of the same characteristics that delineate disorganized
communities (e.g., economic deprivation, percent minority), it seems clear that
he is proposing that community disorganization either indirectly or
conditionally affects deviant behavior via straining mechanisms (for a review
of the empirical support for these points, see Agnew 1999:130-45).
Similarly, recent studies suggest that stressful life events, an important
straining mechanism under Agnews scheme (cf. Hoffmann& Cerbone 1999),
vary by communities. Community disadvantage (an aggregate of poverty,
unemployment, and low education) is associated directly with more stressful
life events (Simons et al. 1997), and the impact of life events on various
outcomes is conditioned by community contexts (Aneshensel& Sucoff 1996;
Takeuchi& Adair 1992).
DIFFERENTIAL ASSOCIATION/SOCIAL LEARNING THEORY
Early versions of Sutherlands differential association theory addressedexplicitly its broader structural implications. Under the term differential social
organization (Akers 1998; Cressey 1960; Matsueda 1988; Reinarman& Fagan
1988; Sutherland 1973[1942]), this macro analogue to differential association
proposes that criminal associations and normative conflict vary across
community types; it is this variation that explains the distribution of crime
rates (Cressey 1960; Reinarman& Fagan 1988). Individuals embedded within
structural units are differentially exposed to definitions in favor of or opposed
to delinquent and criminal behavior; these definitions directly affect ones own
delinquent behavior (Krohn, Lanza-Kaduce& Akers 1984; Matsueda 1988).
This macro-micro link has been described, albeit r ather vaguely, but it has been
ignored in most empirical examinations (Reinarman& Fagan 1988).
Akers (1998) has recently elaborated his social learning theory to expressly
link macrolevel processes with individual-level learning structures. A key issue
for this elaboration is describing the source of prodeviant definitions and
effectiveness of differential reinforcement across social groups. Akers (1998)
sees the source of these differences in whether or not a social system is organized
or cohesive: The less solidarity, cohesion, or integration there is within a
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group... the higher will be the rate of crime and deviance (334). This
macrostructure then determines whether an individual will be exposed to
various associations and definitions conducive to delinquency. Akers proposes
that social structural influences on delinquency and other deviant behaviors
are mediated fully by social learning processes.
A social learning model of structural influences has not been tested
explicitly, although several studies support its basic precepts. For example, social
learning variables such as deviant peer relations and differential reinforcement
may mediate community influences on deviant behavior (Krohn, Lanza-
Kaduce& Akers 1984; Simcha-Fagan& Schwartz 1986), although some studies
indicate little variation of social learnings effects on delinquency (Reinarman&
Fagan 1988).
Each of these theories of delinquency offers avenues that link community
characteristics and individual-level behavior. Each assumes that there is significant
variation in individual-level correlates of delinquent behavior: bonds, strain, and
differential associations and reinforcements depend, in part, on macro contexts.
Nevertheless, if one is to adopt a social or community disorganization framework
(cf. Agnew 1999; Akers 1998; Sampson& Groves 1989), then, in addition to
searching for mediating effects, it is also essential that we ask how community
characteristics condition the impact of various individual-level attributes on
delinquent behavior. If various straining mechanisms lead to delinquent
adaptations, then areas that allow fewer opportunities to escape strain should see a
stronger link between strain and delinquent behavior (Agnew 1999). Similarly,
community disorganization makes the social bonds that restrain delinquent
behavior less effective, especially since such communities are less able to provide
sufficiently broad control over residents behaviors. Differential associations and
reinforcements conducive to delinquent behavior are more likely in certain social
environments, and they may be more effective in disorganized environments since
prosocial definitions and reinforcements are concomitantly less frequent.
Unfortunately, these propositions remain largely untested except by
inappropriate statistical models. Whether attention has focused on mediating effects
or conditional effects, studies have relied primarily on single-level regression
models. These models are inappropriate since observations are not independent
within social contextual units; hence variance estimates from these models are
biased (Goldstein 1995).2
The following analysis improves upon previous research by (1)using a
multilevel model that allows for the correct specification of the error structure when
examining macro-micro links, (2)employing nationally representative data from
a large sample of adolescents from the U.S., (3)incorporating key variables from
three common theories of delinquency, and (4)addressing directly the question of
whether community characteristics condition the impact of these variables on
delinquent behavior. Furthermore, it explores potential indirect effects that are
implied by these three theories.
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Theories of Delinquency / 759Data and Methods
The data used to examine the contextual variation of delinquency theories aredrawn from the National Educational Longitudinal Study (NELS), a longitudinal
study designed to explore the impact of families and schools on a variety of
educational, vocational, and behavioral outcomes. The initial wave of NELS drew
a representative sample of 24,599 eighth-grade students from U.S. schools in 1988.
A subsample of this original group was also interviewed in 1990, when most of the
students were in tenth grade. The sample was also refreshed by drawing a
supplemental sample of tenth-grade students. Therefore, the tenth-grade sample is
representative of tenth-grade students in the U.S. in 1990 (N=20,706) (NCES
1992). Details of the sample selection procedures, interview format, and sample
attrition are provided in NCES (1992). The analysis relies on the tenth-grade sample
for two reasons. First, a larger number of questions about delinquent behavior were
administered to the tenth-grade participants than to participants in other years.Second, the analysis uses a special NELS data file that has been linked to decennial
census data at the zip code level. These census data are most appropriate for the
tenth-grade data since they were collected in 1990. Thus, the community
characteristics that may condition the impact of relevant variables on deviant
behavior are contemporary in the lives of the adolescents.
NELS used a randomly rotating panel of questions, so that some sets were asked
only of a subset of the sample. This reduces the sample size used in the analysis to
10,860 adolescents who were in tenth grade in 1990 and, assuming a typical life
course trajectory, were scheduled to graduate from high school in 1992.
A special supplemental file was prepared for the National Center of Education
Statistics (NCES) that matches the students residential addresses to census tract
identifiers. It was recognized early in the file preparation stage that the typical censustract did not contain a sufficient number of subjects to permit statistical analyses.
Therefore, census tract data were aggregated to the zip code level. Census tracts are
often used in studies that examine the impact of neighborhoods on various
outcomes (Sucoff& Upchurch 1998). Zip codes generally cover a geographic area
that is two to three times the size of a census tract,3 so I do not claim to be examining
neighborhood effects; rather, I use the zip code area as a proxy for a geographically
bounded community (cf. Arora& Cason 1998; Corcoran et al. 1992; Hoffmann
2002). In the following analysis, the 10,860 adolescents are nested in 1,612
communities identified by zip code. Hence, there is an average of about 6.7
adolescents per zip code in the applicable NELS data.4
MEASURES
The key explanatory variables in this analysis are conventional definitions, peer
expectations, stressful life events, monetary strain, parental attachment, parental
supervision, and school involvement. The first two variables are drawn from
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differential association/social learning; the next two are used to examine strain
theory; and the final three are common measures from social control theory.
Conventional definitions are constructed from a set of nine questions that asked
respondents whether it is OK to engage in a variety of deviant activities such as
fighting, belonging to a gang, destroying school property, bringing weapons to school,
or using illegal drugs. The response categories are (1)often OK, (2)sometimes OK,
(3)rarely OK, and (4)never OK. Each variable was standardized prior to computing
an additive score, higher values of which indicate that it is rarely acceptable to engage
in these types of activities.5 The alpha reliability for this scale is .81.
A limitation of the NELS data set is that it does not ask any direct questions
about peer behavior, a staple of differential association and social learning theory
(Akers 1998; Akers et al. 1979; Matsueda 1982; Mears, Ploeger& Warr 1998; Warr
2002). However, there are a set of questions that inquire about ones friends
expectations concerning behavior and life goals. Hence the measurement of one
aspect of differential reinforcement is feasible (Akers 1998; Akers et al. 1979).
Interactions with peers who see the importance of conventional behaviors and goals
provide reinforcement for those behaviors and goals. The questions that gauge these
reinforcement patterns ask respondents whether, among their friends, the following
activities are (1)not important, (2)somewhat important, or (3)very important:
getting good grades, finishing high school, continuing ones education past high
school, and studying. After standardizing each item, an additive scale was computed.
The alpha reliability for this scale is .81.
To measure strain theory, I draw upon two sets of items. First, continuing a
trend that began about ten years ago (Burton et al. 1994; Farnworth& Lieber 1989),
traditional individual-level strain is operationalized as the disjunction between the
following two items: How important is it to you to have a lot of money? and
What are the chances that you will graduate from high school? Monetary strain
is a binary indicator coded 1 if money is very important yet the respondent said
there is a low chance that he or she would graduate from high school, and 0
otherwise.6
Second, a scale of stressful life events is included to gauge one important aspect
of Agnews general strain theory: the presentation of noxious stimuli (Agnew 1992;
Hoffmann& Cerbone 1999). Previous studies indicate that stressful life events are
a consistent predictor of various delinquent and other deviant activities (for a review,
see Hoffmann& Su 1998). The scale is conceptualized as a count variable of the
number of activities experienced over the past year. These fourteen activities include
family moves, parental divorce or remarriage, job loss among parents, and serious
illness or death among family members. The alpha reliability for this scale is .44,
reflecting, not surprisingly, some independence among the items. Since stress
provides cumulative stimuli, however, it is reasonable to represent it as a count
variable (Agnew 1992; Hoffmann& Cerbone 1999).
Social control theory is assessed by three commonly used scales: attachment
to parents, parental supervision, and involvement in school activities. Attach-
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Theories of Delinquency / 761ment to parents is measured by four questions that ask respondents about lik-
ing parents, getting along with parents, being understood by parents, and
disappointing parents. The items were coded so that higher values indicated
a better relationship with ones parents. The items were standardized and used
to create a summated scale. The alpha reliability for this scale is .80.
Parental supervision is based on a set of five questions that asked if the
respondents parents know their friends, know where they go at night and after
school, know how they spend money, and know what they do with their free time.
The alpha coefficient for this standardized additive scale is .84.
School involvement is gauged by questions that asked about participation
in seven different types of activities, including honor society, cheerleading,
music/theater, hobby clubs, academic clubs, yearbook or school newspaper, and
student council (cf. Hoffmann& Xu 2002). The variable is coded to count the
number of activities respondents are involved in, so it ranges from 0 to 7. The
alpha coefficient is .42, thus reflecting some independence in school activities.
As with stressful life events, the key is the cumulative impact of school
involvement as a mechanism for attenuating delinquent behavior.
Several additional variables are included in the model as control variables. Since
there are clearly differences demonstrated in the literature between males and
females in general delinquency involvement (Mears, Ploeger& Warr 1998) and
race/ethnicity affects involvement in delinquent behavior, I include variables
indexing these demographic characteristics. A set of dummy variables gauges race/
ethnicity, with white adolescents representing the omitted reference group. I also
include a dummy variable that measures family structure (0=living without two
biological parents; 1=living with two biological parents). Finally, family income
was included in the model as a set of three dummy variables, with the highest
quartile serving as the omitted reference category. Although a number of other
control variables were considered, a preliminary analysis examining the impact of
urban/suburban/rural residence and region (North, South, Midwest, West) showed
no significant effects. However, as shown in the analysis section, urban residence
emerged as an important consideration.
There are numerous community-level characteristics that might be exam-
ined. The analysis is restricted, however, to four variables that previous research
suggests are important for understanding delinquent and other deviant behav-
iors (Chase-Lansdale& Gordon 1996; Hoffmann 2002; Sampson& Groves
1989). The variables are often used as indicators of community disorganiza-
tion, disadvantage, or economic viability (Elliott et al. 1997; Sampson,
Raudenbush& Earls 1997). They are based on data from the 1990 decennial
census aggregated to the zip code level. Percent female-headed households in
the community ranges from 0% to 24.3%, with a mean of 5.9%; percent un-
employed or out-of-workforce males ranges from 0% to 67.8%, with a mean
of 10.8%; and percent below the poverty threshold ranges from 0% to 68.3%,
with a mean of 12.7%. These variables are assumed to regulate macroprocesses
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that make the impact of individual-level characteristics on delinquent behav-
ior more or less probable.
The fourth community-level variable used in the analysis is a racial segregation
index. Several studies consider percent black, percent white, or some index of
dissimilarity to gauge the effects of community segregation on behavioral outcomes
(Brooks-Gunn et al. 1993; Krivo& Peterson 1996). A common finding is that
percent black has a curvilinear relationship with community problems, with the
lowest prevalence of problems occurring when blacks are a small proportion or a
large proportion of the population (e.g., Messner& South 1992). These measures
suffer from at least two drawbacks for the present study. First, if percent black has
a curvilinear effect on crime and delinquency, then it forces one to introduce
nonlinear effects in the model. Second, percent black or percent white fails to
address the role of Hispanics, a large and rapidly growing minority group. In order
to overcome these deficiencies, I considered three alternatives for a racial
segregation index: an entropy-based measure (Theil 1972), a proportion-based
heterogeneity measure (Blau 1977), and a log-linear index derived from work on
occupational sex segregation (Weeden 1998). These measures are free of marginal
dependencies and allow one to consider the distribution of three or more groups.
They also assess the segregation-integration continuum in a linear fashion. Although
the three measures are highly correlated in the NELS zip codelevel data (Pearsons
r.80), I use the log-linear-based index because a series of simulations indicated
that it was less skewed than the entropy-based or the heterogeneity measures. The
log-linear-based segregation index is given as follows:
Segregation index =
12 2
3
1 1 1
1 1ln ln
n ni i
j i ii i
p p
n q n q= = =
(1)
The ratios ofpi/q
iindicate the three racial/ethnic comparisons within each zip
code.7 The letter i indexes the numbers in the subsamples, and the summation of
j=1 to 3 indicates that the equation sums the three difference measures to the
right (cf. Weeden 1998). The index has a minimum value of 0 that implies that
non-Hispanic whites, non-Hispanic blacks, and Hispanics are equally represented
in the community. The maximum value of about .30 is attained in those
communities that are almost fully racially segregated.
The outcome variable, delinquency, is based on six questions that ask about
past-year involvement in fighting, getting suspended or expelled from school, and
being arrested by the police. The response categories for these questions are
never(0), 1-2 times(1), 3-6 times(2), 7-9 times(3), and 10 or more times(4).
As is common for this type of variable, a raw additive frequency measure based
on these questions results in a highly skewed outcome variable. Hence the
natural logarithm of this scale (+1) is used as the endogenous variable in the
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Theories of Delinquency / 763models. Mean involvement in delinquency is 1.16, with a standard deviation
of .86, a minimum of 0, and a maximum of 3.18. The alpha coefficient for the
delinquency scale is .78.
METHODS
Since the data consist of a two-level hierarchy with respondents nested within
geographically bounded communities, a multilevel statistical model is used to
estimate the direct and conditional effects of the key explanatory variables on
delinquency. Unlike traditional single-level models, multilevel models allow one
to estimate the variance of some outcome at the individual level and the community
level (Goldstein 1995). This is important since we wish to determine whether the
presumed effects drawn from theories of delinquent behavior vary by community.
These models also allow the unbiased estimation of cross-level effects, such as those
examined between the individual-level variables and community characteristics.Since the outcome variable is a continuous measure of involvement in
delinquency, the model is estimated with a linear regression approach. A Q-Q plot
demonstrates that the logged version of delinquency follows a normal distribution.
Multilevel modeling normally follows a two-step process (Bryk& Raudenbush
1992). First, a variance components model is estimated to determine whether the
variance in the outcome of interest differs by the level-2 unit of analysis. If we let
yij
denote the delinquency score reported by respondent i in communityj, then
the variance components model may be expressed as
Level 1 (respondents): yij
= 0j
+ eij
(2)
Level 2 (community): 0 0j ju = +
The second level of equation2 consists of a single equation: The community-
specific intercept of the j-th community is set equal to the sum of an overall
intercept and a level-2 random error term.
The presence of two random error terms, eij
and u0j, distinguishes the multilevel
model from the standard linear regression model. The level-1 error term, eij, varies
among respondents, while the level-2 error term, u0j
, varies across communities.
The presence of level-2 error implies that there are unmeasured community-level
characteristics that affect 0j
. Thus,0j
varies depending upon the community, rather
than remaining constant across all communities.
Second, a random coefficients model extends the variance components
model by adding individual-level variables at level1 and community-level
variables at level2. Assuming there are p level-1 and q level-2 explanatory
variables, the random coefficients model may be written as
Level 1 (respondents): 0 1 1ij j j ij pj pij ij y x x e = + + + +
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Level 2 (community): 0 00 01 1 0 0 j j q qj jw w u = + + + +
1 1 0 11 1 1 1 j j q qj jw w u = + + + +
(3)
0 1 1Pj P P j Pq qj Pjw w u = + + + +
The first level of equation3 is the same as in equation2, except thatyij
depends
not only on the community-level intercept, 0j
, but also on the community-specific
regression slopes denoted by 1j
through Pj
. Each of the regression parameters
has a subscript j that denotes that each of these parameters varies across
communities. When these parameters are specified as random, they are treated as
response variables in the model. Each may be regressed on the community-level
explanatory variables. An alternative specification that yields the same results is to
estimate a series of cross-level interactions, such as
( )00 10 1 01 1 11 1 1 0 1 1ij ij j ij j j ij j ij y x w x w u x u e = + + + + + + (4)
This model specification is useful for determining whether the community-level
variables amplify or dampen the effects of the individual-level explanatory variables
on the outcome variable (Goldstein 1995).
Although the most general formulation of equation4 could include a large
number of parameters, we specify only the level-1 intercept and the key explanatory
variables drawn from the theories of interest as random at level2. This is a practical
constraint for two reasons: the first is that one of our goals is to determine whether
these effects on the outcome vary across communities; the second is that the sparse
community subsamples limit the number of random coefficients that may beestimated in the model (Goldstein 1995). Hence we specify the level-1
demographic variables (sex, race/ethnicity, family structure, family income) as fixed
effects in the model.8
The models shown below were estimated using a restricted interactive
generalized least squares (RIGLS) approach and validated using a Monte Carlo
Markov Chain (MCMC)Gibbs sampling estimation method (Browne& Draper
n.d.; Gilks, Richardson& Spiegelhalter 1996) available in the software package
MLwiN (Goldstein et al. 1998).9 In order to guard against capitalizing on chance
to obtain significant results when examining the models with cross-level interaction
terms, model fit is determined by the AIC statistic. The AIC statistic is sensitive to
sample size and penalizes models that simply include additional parameters yet
provide no additional statistical information about the outcome variable (Heck&Thomas 2000). An R2 measure, based on the proportional reduction in error
for predicting the individual-level delinquency measure, is also used to
determine model fit (Snijders& Bosker 1999).
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Theories of Delinquency / 765Results
Table 1 provides a crude assessment of the cross-level conditional effects thatare examined in this study. Along with the means and standard deviations
overall, the table presents the mean values of the level-1 variables at three
categories of each level-2 variable based on their quartiles. Post hoc multiple
comparison tests are used to determine whether there are significant differences
across the categories (Westfall et al. 1999). Assuming that recent cross-level
theorizing is correct, one might expect youth from disorganized communities
to experience more stress, fewer positive roles and relationships, and more
involvement in delinquent activities. The crude results shown in Table1 do not
consistently support such hypotheses. As generally expected, there is slightly
more delinquency in areas with a higher proportion of jobless males or
residents living below the poverty threshold. The other results provide no
consistent picture, however. Conventional definitions and peer expectationsvary little across communities, except in high poverty areas. There is slightly
less parental supervision in high poverty areas, and there is less school
involvement in areas high in poverty or female-headed households.
Table 2 shows the initial multilevel models. Model1 exhibits the variance
components model. Exponentiating the fixed effects intercept term provides the
expected value of delinquency among the adolescents (e1.161=2.19). More
important for this analysis, though, is the random effects intercept. This term
indicates that the frequency of delinquency varies significantly across the level-2
communities. Average expected delinquency varies across communities from a
low of about 1.9 to a high of about 2.5 (95% confidence intervals). This significant
effect coupled with an intraclass correlation of .05 suggests that modeling the
proposed effects with a single-level regression model would lead to biased estimates.Model 2 includes the control variables and random intercept only. The random
effect for the intercept remains significant. The coefficients for the control variables
indicate that males and adolescents who do not live with both biological parents
are more likely to be involved in delinquency. Moreover, blacks and Asian/Pacific
Islanders are less likely than whites to be involved in delinquent behavior.
Model 3 provides an assessment of the fixed and random effects of the key
individual-level explanatory variables. Most of the variables demonstrate their
expected fixed effects: Adolescents who report more stressful life events, fewer
conventional definitions, lower peer expectations, poor parental attachment, less
parental supervision, or involvement in fewer school activities are more likely than
other adolescents to be involved in delinquent activities. Monetary strain does
not significantly affect delinquency in general (cf. Farnworth& Lieber 1989).A further exploration of the effects of monetary strain suggests that its
significant effects dissipate once parental attachment and supervision are added
to the equation.
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TABLE 1: Distribution of Individual-Level Variables, by Community-Level Characteristics, National EdLongitudinal Study, 1990
Percent Female- Percent Unemployed or
Total Segregation Index Headed Households Out-of-Workforce Males
Variable Mean S.D. Low Medium High Low Medium High Low Medium High
Individual-level variables
Percent male 47.5 45.7 48.1 48.1 49.1 47.8 45.3* 50.0 47.8 44.3*
Percent Asian/
Pacific Islander 7.5 13.2 7.2 2.5* 6.2 8.5 7.0* 11.2 7.5 4.0*
Percent black 9.5 12.9 10.4 4.3* 2.0 5.8 24.5* 5.5 8.4 15.9*
Percent Hispanic 12.2 24.5 8.3 7.6* 5.9 10.4 22.0* 9.9 12.9 13.2*
Percent white 70.8 49.4 74.1 85.6* 85.9 75.3 46.5* 74.0 71.2 66.9*
Percent living w/
biological motherand father 66.6 63.1 67.5 68.4* 69.4 68.1 60.8* 70.4 66.3 63.5*
Conventional
definitions 34.2 2.9 34.2 34.2 34.1 34.1 34.2 34.2 34.2 34.2 34.2
Peer expectations 9.9 1.9 9.9 10.0 9.8* 9.8 9.9 10.0 10.0 9.9 9.9
Stressful life events 1.0 1.2 1.1 1.0 1.0 1.0 1.0 1.1 1.0 1.0 1.0
Monetary strain
(percent yes) .6 .7 .5 .5 .3 .4 1.0* .3 .6 .5
Parental attachment 19.1 4.5 18.9 19.0 19.3* 19.2 19.1 18.8 19.1 19.0 19.1
Parental supervision 11.3 3.4 11.3 11.4 11.1* 11.3 11.4 11.2 11.4 11.3 11.1
School involvement 1.0 1.1 .9 1.0 1.0* 1.1 1.0 .9* .9 1.0 1.0
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TABLE 1: Distribution of Individual-Level Variables, by Community-Level Characteristics, National EdLongitudinal Study, 1990 (Continued)
Percent Female- Percent Unemployed or
Total Segregation Index Headed Households Out-of-Workforce Males
Variable Mean S.D. Low Medium High Low Medium High Low Medium High
Community-level variables
Segregation index .13 .5
Percent female-headed
households 5.92 3.3
Percent unemployed
or out-of-workforce
males 10.84 3.5
Percent below poverty
threshold 12.68 9.3
Outcome variable
Past-year delinquency
(0-3.18) (natural
logarithm) 1.16 .9 1.14 1.17 1.15 1.14 1.15 1.20 1.10 1.16 1.18*
(N = 10,860 observations and 1,612 communities)
Note:Low refers to the lowest quartile, medium to the second and third quartiles, and high to the highest quart ile of the distrib
with Dunns multiple comparison adjustments (Daniel 1990) and a step-down bootstrap adjustment for multiple mean compar
were used to determine significant differences across community types. The numbers shown are means based primarily on additive
are used in subsequent analyses.
* p < .05 (two-tailed)
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TABLE 2: Multilevel Linear Regression Models of Delinquent Behavior,National Educational Longitudinal Study, 1990
Parameter Model 1 Model 2 Model 3 Model 4
Fixed effects
Intercept 1.16 (.01)* 1.23 (.02)* 1.37 (.04)* 1.29 (.05)*
Individual-level variables
Male .21 (.02)* .04 (.01)* .04 (.02)*
Asian/Pacific Islandera .33 (.03)* .26 (.03)* .27 (.03)*
Blacka .23 (.03)* .13 (.03)* .15 (.03)*
Hispanica .01 (.03) .03 (.03) .03 (.03)
Biological mother and father .23 (.02)* .14 (.02)* .14 (.02)*
Stressful life events .05 (.01)* .05 (.01)*
Monetary strain .13 (.11) .13 (.11)
Conventional definitions .07 (.00)* .05 (.00)*
Peer expectations .03 (.00)* .04 (.00)*
Parental attachment .04 (.00)* .04 (.00)*
Parental supervision .01 (.00)* .01 (.00)*
School involvement .05 (.01)* .05 (.01)*
Community-level variables
Segregation index .14 (.18)
Percent female head .78 (.31)*
Percent jobless males .93 (.27)*
Percent poverty .41 (.12)*
Random effects
Intercept .04 (.01)* .03 (.01)* .02 (.01)* .02 (.01)*
Stressful life events .01 (.00)* .01 (.00)*
Monetary strain .13 (.12) .12 (.12)
Conventional definitions .00 (.00) .00 (.00)Peer expectations .00 (.00) .00 (.00)
Parental attachment .00 (.00) .00 (.00)
Parental supervision .00 (.00) .00 (.00)
School involvement .00 (.00) .00 (.00)
Level-1 error .78 (.01)* .68 (.01)* .49 (.01)* .48 (.01)*
AIC 2.53 2.49 2.23 2.21
R2 (level 1) .13 .38 .39
(N = 10,860)
Note:The outcome variable is a logged frequency measure that gauges involvement in six types of
delinquent behavior in the past year. The random effects were estimated in piecemeal fashion by
estimating a random intercept and then adding the relevant groups of variables in three separate
models. The final models were validated with an MCMC-Gibbs sampling approach using (Baye-sian) diffuse 1 priors. The table shows coefficients with standard errors in parentheses. Family
income effects are not shown.
a The comparison group is white adolescents.
* p < .05 (two-tailed)
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Theories of Delinquency / 769Model 3 also indicates that the notion that the effects of the key explanatory
variables vary across communities is not supported. With but one exception, the
effects of variables drawn from differential association/social learning, strain, and
social control theory are invariant across a range of communities (cf. Krohn,
Lanza-Kaduce& Akers 1984; Reinarman& Fagan 1988). The one exception
involves stressful life events: Their effects vary significantly, yet quite modestly,
across communities. The random effect suggests that in certain communities
they have a stronger impact on delinquency than in other communities. The
expected range of this effect is from .03 to .07 (95% confidence intervals), thus
indicating a modest significant difference across the set of communities.
Model 4 adds the community-level characteristics to the multilevel equa-
tion. The inclusion of these variables has little effect on the other coefficients
in the model. However, three out of the four community-level variables are
associated significantly with delinquency. Adolescents living in communities
with more male joblessness, a higher percentage of female-headed households,
and more poverty are more likely than adolescents living elsewhere to be in-
volved in delinquent behavior, even after controlling for the effects of a host
of individual-level variables, including several drawn from important theories
of delinquency.
As a final modeling exercise, I computed a series of cross-level interaction terms
to determine whether, even in the absence of significant random coefficients, there
might be some conditional effects based on community characteristics. Most
relevant for this exercise are the interactions between the community-level variables
and stressful life events. The results of this model (see Table3) indicate that the
random effects of stressful life events on delinquency are not conditioned by
community characteristics. The only cross-level interaction that approached
significance was stressful life eventspercent jobless males (=.46,p.11). Itsuggests that in communities with a high proportion of jobless males the impact
of stressful life events on delinquency is particularly consequential. Nevertheless,
thep-value must make one suspicious of this interpretation. Moreover, the AIC
(2.21) indicates that including the interaction terms does not improve the model
(cf. Table2, model4). No other cross-level interaction approached significance.10
ARE CONDITIONING EFFECTSOF COMMUNITY VARIABLES SPECIFICTO URBAN AREAS?
Although the lack of varying effects of the individual-level variables on
delinquency may seem disheartening to those who advocate a contextual
approach for delinquency theories, one should recall that many of the seminal
arguments that informed criminological theory emerged from studies of urban
areas (e.g., Cloward& Ohlin 1960; Shaw& McKay 1931, 1969; Stark 1987;
Sutherland 1973[1942]). Hence it is not unreasonable to ask whether the
impacts of strain, definitions, social reinforcement, or social bonds on
delinquent behavior are variable within urban areas. To examine this issue, I
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TABLE 3: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National Educational
Longitudinal Study, 1990
Parameter Coefficient
Intercept 1.28 (.09)*
Individual-level variables
Stressful life events .02 (.02)
Monetary strain .33 (.43)
Conventional definitions .05 (.01)*
Peer expectations .04 (.00)*
Parental attachment .04 (.00)*
Parental supervision .01 (.01)
School involvement .10 (.02)
Community-level variables
Segregation index .14 (.18)
Percent female head .65 (.35)
Percent jobless males 1.04 (.81)
Percent poverty .79 (.35)*
Interaction terms
Stressful life events percent female head .23 (.22)
Monetary strain percent female head .25 (.23)
Conventional definitions percent female head .03 (.06)
Peer expectations percent female head .13 (.09)
Parental attachment percent female head .12 (.09)
Parental supervision percent female head .09 (.08)
School involvement percent female head .45 (.38)
Stressful life events percent jobless males .46 (.28)
Monetary strain percent jobless males .74 (.73)
Conventional definitions percent jobless males .04 (.05)
Peer expectations percent jobless males .08 (.10)
Parental attachment percent jobless males .00 (.07)
Parental supervision percent jobless males .08 (.07)
School involvement percent jobless males .30 (.24)
Stressful life events percent poverty .07 (.09)
Monetary strain percent poverty .61 (.98)
Conventional definitions percent poverty .08 (.08)
Peer expectations percent poverty .03 (.04)
Parental attachment percent poverty .05 (.04)
Parental supervision percent poverty .04 (.03)
School involvement percent poverty .03 (.10)
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Theories of Delinquency / 771
restricted the sample to adolescents residing in urban areas only. The NELS
sample contains 2,061 adolescents residing in urban areas nested within 266
geographically bounded communities. The results of fitting identical multilevel
models are presented in Table4.
The initial two models, model1 and model2, were quite similar to those
shown in Table2. In other words, males were more involved, and blacks, Asian/
Pacific Islanders, and those living with both biological parents were less involved
in delinquency. Moreover, the mean level of delinquency varied significantlyacross urban communities by approximately the same degree as in the
unrestricted sample.
Model 3 includes the effects of the key explanatory variables. It appears that
in urban communities, stressful life events do not affect delinquency whereas
monetary strain does. This supports the notion that a traditional measure of
strain has its most consequential impact on urban environments (cf.
Farnworth& Lieber 1989). However, it should be noted that while the mean
effect of stressful life events on delinquency is not significant, their effect does
vary across urban communities. Hence they may affect delinquency in some
types of urban areas.
It is also interesting to compare the impact of items drawn from differential
association/social learning and social control theory. Those who report moreconventional definitions, peer expectations, parental attachment, and school
involvement are less likely to be involved in delinquent behavior, but the impact
TABLE 3: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National Educational
Longitudinal Study, 1990 (Continued)
Parameter Coefficient
Level-1 error .49 (.02)*
AIC 2.21
R2 (level 1) .39
(N = 10,860)
Note:The outcome variable is a logged frequency measure that gauges involvement in six types of
delinquent behavior in the past year. Although the full model was included (see model4 of
Table2), only the fixed effects interaction terms and their constituent variables are shown for ease
of presentation. The interactions that involved the segregation index were omitted from the final
model since none approached significance. The final model was validated with an MCMC-Gibbs
sampling approach using (Bayesian) diffuse 1priors. The table shows coefficients with standarderrors in parentheses.
* p < .05 (2-tailed)
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TABLE 4: Multilevel Linear Regression Models of Delinquent Behavior,National Educational Longitudinal Study, 1990 (Urban Areas Only)
Parameter Model 1 Model 2 Model 3 Model 4
Fixed effects
Intercept 1.12 (.02)* 1.18 (.05)* 1.31 (.08)* 1.22 (.11)*
Individual-level variables
Male .19 (.04)* .04 (.03) .05 (.03)
Asian/Pacific Islandera .30 (.06)* .27 (.06)* .27 (.06)*
Blacka .19 (.06)* .09 (.06) .07 (.06)
Hispanica .01 (.05) .01 (.04) .04 (.05)
Biological mother-father .18 (.04)* .12 (.04)* .11 (.04)*
Stressful life events .02 (.01) .03 (.02)
Monetary strain .45 (.21)* .43 (.21)*
Conventional definitions .05 (.00)* .04 (.00)*
Peer expectations .04 (.01)* .04 (.01)*
Parental attachment .04 (.01)* .04 (.01)*
Parental supervision .01 (.01) .01 (.01)
School involvement .04 (.02)* .04 (.02)*
Community-level variables
Segregation index .32 (.44)
Percent female head .32 (.69)
Percent jobless males 1.60 (.73)*
Percent poverty .68 (.29)*
Random effects
Intercept .04 (.01)* .03 (.01)* .02 (.01)* .03 (.01)*
Stressful life events .01 (.00)* .01 (.00)*
Monetary strain .00 (.00) .00 (.00)
Conventional definitions .001 (.000)* .001 (.000)*Peer expectations .00 (.00) .00 (.00)
Parental attachment .001 (.000)* .001 (.000)*
Parental supervision .00 (.00) .00 (.00)
School involvement .00 (.00) .00 (.00)
Level-1 error .67 (.02)* .64 (.02)* .54 (.03)* .51 (.04)*
AIC 2.49 2.46 2.25 2.23
R2 (level 1) .05 .25 .27
(N = 2,061)
Note:The outcome variable is a logged frequency measure that gauges involvement in six types of
delinquent behavior in the past year. The random effects were estimated in piecemeal fashion by
estimating a random intercept and then adding the relevant groups of variables in three separate
models. The final models were validated with an MCMC-Gibbs sampling approach using (Baye-sian) diffuse 1 priors. The table shows coefficients with standard errors in parentheses. Family
income effects are not shown.
a The comparison group is white adolescents.
* p < .05 (two-tailed)
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Theories of Delinquency / 773of definitions and parental attachment vary across the urban communities
sampled in NELS. Hence conclusions drawn from the entire NELS sample
which include many diverse communities from throughout the U.S. may
be hasty. Consistent with the seminal descriptions of two of these theories, there
is variability across urban communities.
The next step is to determine whether the community characteristics
assessed in this study condition the variable impact of the individual-level
constructs. Model4 provides the first model designed to examine this issue.
Note first that, among the community-level variables, both percent jobless males
and percent poverty are significantly associated with delinquency. These results
suggest that involvement in delinquent behavior is especially likely in urban
areas with a large proportion of unemployed or out-of-workforce males or a
high percentage of residents living below the poverty threshold.
A series of cross-level interaction terms (see Table5) indicate that the
percent of jobless males in a community interacts significantly with stressful
life events (=1.12, p
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TABLE 5: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National Educational
Longitudinal Study, 1990 (Urban Areas Only)
Parameter Coefficient
Intercept .86 (.24)*
Individual-level variables
Stressful life events .11 (.06)
Monetary strain .92 (.81)
Conventional definitions .05 (.02)*
Peer expectations .02 (.01)
Parental attachment .05 (.01)*
Parental supervision .01 (.01)
School involvement .19 (.06)*
Community variables
Segregation index .28 (.45)
Percent female head .58 (.94)
Percent jobless males 1.72 (.77)*
Percent poverty .38 (.19)*
Interaction terms
Stressful life events percent female head .00 (.61)
Monetary strain percent female head .89 (.76)
Conventional definitions percent female head .21 (.19)
Peer expectations percent female head .38 (.29)
Parental attachment percent female head .51 (.29)
Parental supervision percent female head .04 (.08)
School involvement percent female head .13 (.51)
Stressful life events percent jobless males 1.12 (.54)*
Monetary strain percent jobless males .71 (.77)
Conventional definitions percent jobless males .10 (.21)
Peer expectations percent jobless males .30 (.24)
Parental attachment percent jobless males .37 (.23)
Parental supervision percent jobless males .48 (.18)*
School involvement percent jobless males 1.11 (.54)*
Stressful life events percent poverty .21 (.23)
Monetary strain percent poverty .70 (.93)
Conventional definitions percent poverty .13 (.08)
Peer expectations percent poverty .08 (.08)
Parental attachment percent poverty .12 (.08)
Parental supervision percent poverty .08 (.07)
School involvement percent poverty .29 (.23)
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Theories of Delinquency / 775
Discussion
Recent theoretical activity in criminology has adopted the notion that macro
conditions affect the relationship between individual-level variables and delinquent
behavior. The history of sociological thought, in fact, almost requires the existence
of these indirect or conditional relationships. Social control theory, strain theory,
and differential association/social learning theory have each been elaborated to posit
that community characteristics a key macrolevel construct affectimportant aspects of their theoretical structure. For instance, disorganized
communities are thought to weaken social bonds, expose residents to more
stressful environments which offer little chance of escape and reinforce
perceived blocks to opportunity, and provide deviant learning opportunities
and reinforcements (Agnew 1999; Akers 1998; Elliott et al. 1996; Fischer 1984;
Sampson& Groves 1989). Each of these conditional characteristics is deemed
to increase the risk of individual-level involvement in delinquent behavior.
Using data from a large, nationally representative survey of U.S. adolescents,
there is little evidence, in general, that these indirect or conditional relationships
exist. Rather, if one uses models that observe a range of diverse communities across
the United States, key variables drawn from three major theories of delinquency
are equally predictive of delinquent behavior. Moreover, the results supportrecent work that indicates that poverty and joblessness at the community level
are associated with more delinquency (Sampson 1987; Short 1997). The value
TABLE 5: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National Educational
Longitudinal Study, 1990 (Urban Areas Only) (Continued)
Parameter Coefficient
Level-1 error .48 (.02)*
AIC 2.22
R2 (level 1) .29
(N = 2,061)
Note:The outcome variable is a logged frequency measure that gauges involvement in six types of
delinquent behavior in the past year. Although the full model was included (see model4 of
Table4), only the fixed effects interaction terms and their constituent variables are shown for ease
of presentation. The interactions that involved the segregation index were omitted from the final
model since none approached significance. The final model was validated with an MCMC-Gibbs
sampling approach using (Bayesian) diffuse 1priors. The table shows coefficients with standarderrors in parentheses.
* p < .05 (two-tailed)
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of the current study is that it shows, in one sense, the unique impact of these
individual-level and macrolevel variables on delinquency.
At first glance, one might contend that the results cast serious doubt on
the utility of recent macro-micro theorizing in criminology. Taking a more
optimistic view, one might argue that these three theories of delinquency (or
at least key variables drawn from each) offer general explanations of adoles-
cent behavior that transcend broader structural conditions. Hence, when one
considers attempts by various criminologists to develop general theories of
criminal and delinquent behavior, the results of this study are promising. They
suggest that definitions that oppose delinquent behavior, peer reinforcement
of prosocial activities, absence of stress, solid attachment to parents, sufficient
parental supervision, and involvement in conventional activities all serve to
diminish the likelihood of delinquent behavior, regardless of where they oc-
cur (Akers 1998; Reinarman& Fagan 1988).
Moreover, the results using the full sample indicate that, consistent with
previous studies, the percentage of unemployed or out-of-workforce males, the
proportion of female-headed households, and the percent living below the poverty
line significantly affect delinquent behavior. These relationships are not mediated
or moderated by individual-level variables (cf. Akers 1998; Chase-Lansdale&
Gordon 1996). Therefore, the explanation for these effects is elusive, although several
observers have pointed out the pernicious role that male joblessness and other
neighborhood characteristics play in communities (Sampson 1987; Wilson 1996).
As Shaw and McKay (1931) described several decades ago, communities that are
impoverished economically and socially may have particular difficulties controlling
the behavior of residents. Community supervision is inadequate, organizations that
offer alternative resources and activities find it difficult to thrive, and residents do
not perceive that they have the ability or support to affect community change
(Bursik& Grasmick 1993; Sampson, Raudenbush& Earls 1997; Simcha-Fagan&
Schwartz 1986). These communities may also provide substantial opportunities
for delinquent and criminal behavior (Cloward 1959; Felson 1998; Stark 1987).
Without additional information not available in this study, however, any
interpretation of these direct community-level effects must be tentative.
Nevertheless, a key drawback of such a broad macro-micro test is that it
ignores an important issue. That is, the major sociological theories of
delinquency emerged from research on urban areas. Shaw and McKays (1969)
seminal work on social disorganization theory, for example, developed from
observations restricted to Chicagos inner-city areas, which they subsequently
broadened by examining other urban areas in the U.S. (Shaw& McKay 1931).
Sutherlands macrolevel notions about differential social organization were
motivated by a concern about why so much crime and deviance seemed to
occur in urban areas, especially among urban minorities (Sutherland
1973[1942]). Similarly, Fischers (1984) ideas about how urbanism affects
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Theories of Delinquency / 777deviant behavior draws partly from Sutherland by stressing the opportunities
and social supports for these behaviors (Stark 1987). And while Mertons (1968,
1995) proposed links between anomie and deviant behavior were concerned
primarily with broad cultural and social processes (Bernard 1987; Bernard&
Snipes 1996), the main uses of his theory have concerned the etiology of serious
offending among inner-city youth (e.g., Cloward& Ohlin 1960). It is thus
reasonable to ask whether the most popular theories of delinquency are
actually theories of urban adolescent behavior.
In response to this line of reasoning, the multilevel models were reestimated
using a subsample restricted to adolescents residing in urban areas. With respect
to the main effects of the individual-level explanatory variables, the results of
the models using the full and urban samples were roughly similar. The only
difference involved the role of strain: Stressful life events significantly affect
delinquency in the general population, while monetary strain significantly
affects delinquency in urban communities. In addition, the rates of male
joblessness and poverty have similar positive relationships with delinquency
in both models (although the size of these relationships is larger in the urban
model). Consistent with the ideas that motivated this study, however, the impact
of several of the individual-level explanatory variables on delinquent behavior
varies significantly across urban communities. In particular, the effects of
stressful life events, conventional definitions, and parental attachment depend
upon the types of urban communities in which they are observed. Although it
is difficult with these limited data on community characteristics to pinpoint
the types of communities in which these variables had stronger or weaker
effects, one important cross-level interaction emerges. This interaction
indicates that stressful life events are more consequential in communities
suffering from high rates of male joblessness. In these communities, adolescents
who are exposed to more stressful life events are highly likely to report
involvement in delinquent behavior, perhaps because they are more likely to
associate with other strained individuals and perceive fewer opportunities
to escape their plight (Agnew 1999). Hence, as hypothesized by Agnew (1992,
1999), they are likely to react to strain with anger and thus engage in delinquent
behavior. Moreover, although there is no evidence that the impact of school
participation or parental supervision on delinquency varies randomly, the
effects of both of these individual-level variables on delinquency depends, in
part, on community-level rates of male joblessness. It seems that parental
supervision has a more important effect on delinquency in areas where male
joblessness is high.
Although these results appear inconsistent with recent theorizing that
posits that disorganized communities are less able to take advantage of family
resources to control adolescent behavior (Furstenberg 1993; Peeples& Loeber
1994; Sampson& Laub 1994; Simons et al. 1997; Yang& Hoffmann 1998), they
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are compatible with recent research on the pernicious role that male joblessness
plays in communities (Almgren et al. 1998; Short 1997; Wilson 1996). Wilson
(1996) argues, for example, that joblessness is a key ingredient to social
disorganization in a community, along with crime and drug abuse. Poverty is
less likely to result in disorganization if residents hold jobs, although, as we see
above, poverty is positively related to delinquency even after controlling for
male joblessness. Following this line of reasoning, adolescents from more
disorganized communities benefit substantially more than adolescents from
organized communities when they are supervised by parents. Although
supervision may be difficult in these communities as parents are pulled away
from their families by other financial and social concerns (Furstenberg 1993),
it clearly serves as an important mechanism through which the likelihood of
involvement in delinquency is diminished.
Similarly, recent research on the disintegration of community resources in
many urban areas indicates that this trend has affected disorganized
communities more than others (Furstenberg 1993; Furstenberg et al. 1999).
Hence parents in these communities have few extrafamilial resources to draw
upon in raising children. The families that successfully dissuade adolescents
from participating in delinquent activities, therefore, are those that depend on
closely supervising and restricting activities (Furstenberg et al. 1999). In areas
where raising children is more of a collective enterprise, there is less need for
parental supervision to affect involvement in delinquency.
Moreover, the finding that areas of high joblessness have more delinquency,
even after controlling for individual-level processes and other community
characteristics, helps elaborate criminological theorizing about opportunities and
routine activities (Cook 1986; Felson 1998). A debate in the criminology literature
is that unemployment has countervailing effects on crime and delinquency: It may
increase the motivation to commit crime (Kohfeld& Sprague 1988) or it may
decrease crime because of increased guardianship (Cantor& Land 1985; Cook
1986). The results of the present study suggest that, if there is a guardianship effect
that is linked to unemployment patterns, it is outweighed substantially by other
factors (e.g., community stress due to high poverty or joblessness; lack of access to
legitimate opportunities; lack of collective supervision of adolescent activities).11
Although the results support at least two conditional effects of variables
drawn from major theories of delinquent behavior, there is an important
limitation that recommends further research on this topic. That is, the outcome
measure admittedly focuses on relatively minor forms of delinquency. The
NELS data set is limited in the number of questions that address delinquent
behavior. It does not include measures of more serious forms of delinquency
(e.g., robbery, sexual assault, or other forms of violent behavior), yet it is these
behaviors that may be affected most by community characteristics (Sampson
1987; Sampson, Raudenbush& Earls 1997; Short 1997).
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Theories of Delinquency / 779In sum, although much recent effort has been expended to describe the
contextual effects of some common theories of delinquency, the results of this
research suggest that these efforts may be slightly misdirected. Variables drawn from
social control, general strain, and social learning theory might actually offer
compelling and quite general predictions of delinquent behavior in broadly
inclusive general samples. In a practical sense, this should serve as a positive
outcome. If one goal of research on delinquency is to prevent its negative
consequences, then an understanding of the general individual-level processes that
affect it is needed. However, the implicit grounding of these theories in urban
environments should also be considered and examined carefully. The evidence
presented here indicates that the effects of at least two variables drawn from social
control theory and strain theory namely, parental supervision and stressful life
events on delinquency are conditioned by the rate of male joblessness in the
surrounding urban area. However, contrary to the suggestions of some, these
variables are more consequential in communities that appear less organized;
communities embedded in urban areas that garnered most of the attention of the
originators of criminological thought.
Notes
1. These three theories were not chosen simply for convenience. Rather, as demonstrated
in the next section, they were chosen because each has been discussed in the context of
how community factors might condition the implied relationships of these theories. There
are certainly other delinquency theories that might be broadened to focus on contextual
factors (e.g., labeling, various integrated theories, rational choice; Braithwaite 1989;
Hechter 1987); there are a number of theories designed explicitly to address broader
structural processes (e.g., conflict, radical; Lynch& Groves 1991); and several conceptual
models have been introduced that expressly link macro-micro processes (power-control,
integrated Marxist; Colvin& Pauly 1983; Hagan 1989). Nevertheless, since social control,
strain, and differential association represent the most widely tested microlevel delinquency
theories and each has affected policies designed to prevent delinquency and other deviant
behavior (Akers 1998; Vold, Bernard& Snipes 1998), concentrating on their tacit
contextual variation is warranted.
2. Assuming a positive correlation of observations within contextual units, the direction
of the bias is typically downward. Thus, standard errors from these single-level models
tend to be too small, and significant findings are more likely to emerge.
3. There are about 51,000 census tr acts in the U.S. and about 20,000 zip codes used. The
zip codelevel file was constructed by the National Opinion Research Center under
contract to the National Center for Education Statistics.
4. Although one would prefer to have more respondents sampled per community unit,
power analyses of multilevel models suggest that having a large number of level-2
(community) units is more important than the number of level-1 units (respondents)
(Cohen 1998).
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780 /Social Forces 81:3, March 2003
5. There is some controversy over whether questions such as these measure an aspect
of differential association (i.e., definitions) or a component of social bonding theory
(beliefs). In this article, I take the position that these are a direct measure of negative orantidelinquent definitions (Akers 1998; Matsueda 1998). Of course, one might reverse-
code this variable to compute a measure of positive definitions of delinquency, or attempt
some within-un it ratio measure.
6. I also computed a monetary strain measure that used a variable that asked about the
respondents chances of having a job that will pay well. There was considerable overlap
between these two indicators of monetary strain, so I used the Farnworth and Lieber
approach.
7. The supplemental file from which the census measures were drawn did not include
the number of Asian and Pacific Islanders in the communities. Hence they could not be
considered in the construction of the segregation index.
8. Another practical constraint resulting from the sparse within-unit sample sizes is the
inability to include all the random coefficients in one model. As an alternative, I examined
a series of piecemeal models that included three sets of random coefficients denoting
differential association/social learning, strain, and social control theory, respectively. As
shown in the results section, few of the parameters significantly varied across
communities. This strongly suggests that even if all the random parameters could be
estimated in a single model, the results would not differ from those presented.
9. A substantial amount of research has been conducted in the past few years to determine
the best approaches for analyzing multilevel data. An MCMC-Gibbs sampler approach
with diffuse priors is recommended to validate models (Browne& Draper n.d.). MCMC
takes a Bayesian approach to estimating parameters by way of a resampling procedure.
Hence it reduces the potential biases in standard errors (similar to a bootstrap) and
makes chance findings less likely. Mathematical details are provided in Gilks, Richardson,
and Spiegelhalter (1996). I allowed 10,000 iterations of the Gibbs sampler to validate themodels (Goldstein et al. 1998).
10. Although community characteristics do not condition the individual-level relationships
in the model, it is feasible that there may be some indirect effects of community
characteristics on delinquency that are routed through differential association/social
learning, strain, or social control variables (cf. Akers 1998; Sampson& Groves 1989;
Veysey& Messner 1999). In order to explore this possibility, I estimated a series of
structural equation models designed to assess potential indirect effects (Hox 2000; Krull&
MacKinnon 2001; Raudenbush& Sampson 1999). The results are not promising for those
who would advocate such an approach. The community characteristics do not indirectly
explain the variability in delinquency via the individual-level explanatory variables.
Moreover, the direct effects of the community-level variables on delinquency are
unchanged when one adds the individual-level variables to the model. Taken together,
these results strongly suggest that any potential indirect effects of communitycharacteristics on delinquency are not routed through key variables drawn from theories
of delinquency.
11. It is noteworthy that the zero-order correlation between community characteristics,
in particular male joblessness, and parental supervision is negative, but minimal
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Theories of Delinquency / 781(r=.02). One may infer that this questions the assumption of routine activities theory
that unemployment increases guardianship. Nevertheless, without substantially more
information about the urban communities in question or longitudinal data that aredesigned to examine changes in the macro and micro characteristics of communities, it
is overly speculative at this point to draw inferences from this analysis that are germane
to the debate about unemployment, routine activities, and crime. I thank David F.
Greenberg and an anonymous Social Forces reviewer for helping me see the connection
between the effects of joblessness found in the analysis and research on unemployment
and crime.
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