Analysis of Differential

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Theories of Delinquency / 753

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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754 /Social Forces 81:3, March 2003

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


15/34

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)


16/34


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)*


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)*


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)*



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.



17/34

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


18/34


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)*


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)



Percent female head .65 (.35)

Percent jobless males 1.04 (.81)


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)


19/34


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


Longitudinal Study, 1990 (Continued)


Level-1 error .49 (.02)*

AIC 2.21

R2 (level 1) .39

(N = 10,860)


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)


20/34


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)*


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)*



School involvement .04 (.02)* .04 (.02)*




Percent jobless males 1.60 (.73)*


Random effects

Intercept .04 (.01)* .03 (.01)* .02 (.01)* .03 (.01)*



Conventional definitions .001 (.000)* .001 (.000)*Peer expectations .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)


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.



21/34

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


22/34



Longitudinal Study, 1990 (Urban Areas Only)


Intercept .86 (.24)*


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



Percent jobless males 1.72 (.77)*


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)


23/34


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


Longitudinal Study, 1990 (Urban Areas Only) (Continued)


Level-1 error .48 (.02)*

AIC 2.22

R2 (level 1) .29

(N = 2,061)


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.



24/34


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


25/34

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


26/34


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).


27/34

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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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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