A Contextual Analysis of Differential Association, Social Control, and Strain Theories of Delinquency*

Theories of Delinquency / 753 A Contextual Analysis of Differential Association, Social Control, and Strain Theories of Delinquency*

JOHN P. H OFFMANN, Brigham Young University

Abstract

The history of criminological thought has seen several theories that attempt to link community conditions and individual-level processes. However, a comparative analysis of contextual effects has not been undertaken. This article estimates a multilevel model that examines the effects of variables derived from three delinquency theories. The results indicate that youths residing in areas of high male joblessness who experience stressful life events or little parental supervision are especially likely to be involved in delinquent behavior. The attenuating impact of school involvement on delinquency is more pronounced in urban environments low in male joblessness. These results suggest that examining the contextual implications of delinquency theories is important, but theories need to be developed with more attention to specific contextual processes.

The search for macro-micro linkages and how they affect deviant and criminal behavior has a substantial and notable history (Coleman 1990; Durkheim 1951 [1897]; Stark 1987). The history of criminological thought has seen Shaw and McKay’s 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 grant 11293. An earlier version of this article was presented at the 2000 annual meeting of the American Society of Criminology, San Francisco, Calif. I thank Bob Bursik, Frank Cullen, Bob Agnew, David Greenberg, and an anonymous Social Forces reviewer for helpful suggestions on earlier drafts. I also appreciate the assistance and advice provided by Bob Johnson, Harvey Goldstein, Jon Rasbash, Ken Rasinski, Shaun Koch, and Jing Zhou. Please address all correspondence to John P. Hoffmann, Department of Sociology, 844 SWKT, Brigham Young University, Provo, UT 84602. E-mail: John- [email protected].

© The University of North Carolina Press Social Forces, March 2002, 81(3):753-785 754 / Social Forces 81:3, March 2003 that they collected (Bursik & Grasmick 1993; Shaw & McKay 1931, 1969); Merton’s discussions of opportunity structures and strain (Merton 1968, 1995); and Sutherland’s 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 theoretical and empirical dormancy, the last ten to fifteen years or so has seen a resurgence of interest in how macroprocesses affect microlevel social relationships. At least two motivating factors underlie this resurgence. First, Shaw and McKay’s (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 Theories of Delinquency / 755 at 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 elaborate 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 motivation 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 analysis 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 and criminal 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 theory’s macro-micro linkages are demonstrated in early criminological work (Kornhauser 1978; 756 / Social Forces 81:3, March 2003 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. Attachment to parents and peers, for instance, has a differential impact on delinquent behavior 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 community 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 means on 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. Theories of Delinquency / 757 Agnew’s (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 Agnew’s 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 Agnew’s 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 Sutherland’s differential association theory addressed explicitly 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 one’s own delinquent behavior (Krohn, Lanza-Kaduce & Akers 1984; Matsueda 1988). This macro-micro link has been described, albeit rather 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 758 / Social Forces 81:3, March 2003 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 learning’s 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. Theories of Delinquency / 759 Data and Methods

The data used to examine the contextual variation of delinquency theories are drawn 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 census tract 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 760 / Social Forces 81:3, March 2003 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 one’s 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 one’s 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 Agnew’s 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- Theories of Delinquency / 761 ment 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 one’s 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 examined. The analysis is restricted, however, to four variables that previous research suggests are important for understanding delinquent and other deviant behaviors (Chase-Lansdale & Gordon 1996; Hoffmann 2002; Sampson & Groves 1989). The variables are often used as indicators of community disorganization, 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 unemployed 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 762 / Social Forces 81:3, March 2003 that make the impact of individual-level characteristics on delinquent behavior 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 code–level data (Pearson’s 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:

1 2 2 ÿþ3 nn 11×−×ppii Segregation index = ln ln (1) nqnq== = ji11ii i 1

The ratios of pi /qi indicate 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 Theories of Delinquency / 763 models. 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 community j, then the variance components model may be expressed as β Level 1 (respondents): yij = 0j + eij (2) βγ=+ Level 2 (community): 00jju

The second level of equation 2 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 β β 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 level 1 and community-level variables at level 2. Assuming there are p level-1 and q level-2 explanatory variables, the random coefficients model may be written as =+ββ ++ β + Level 1 (respondents): yxxeij011 j j ijÿ pj pij ij 764 / Social Forces 81:3, March 2003 βγγ=+ ++ γ + Level 2 (community): 000011jjqqjjwwuÿ 0 0 βγγ=+ ++ γ + (3) 110111jjqqjjwwuÿ 1 1 ÿ βγ=+ γ ++ γ + Pj P011 Pwwu jÿ Pq qj Pj

The first level of equation 3 is the same as in equation 2, except that yij depends β not only on the community-level intercept, 0j, but also on the community-specific β β 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 =+γγ + γ + γ ++ + yxwxwuxueij00 10 1 ij 01 1 j 11() 1 ij 1 j 0 j 1 ij 1 j ij (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 equation 4 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 level 2. 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 be estimated 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). Theories of Delinquency / 765 Results

Table 1 provides a crude assessment of the cross-level conditional effects that are 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 Table 1 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 expectations vary 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. Model 1 exhibits the variance components model. Exponentiating the fixed effects intercept term provides the expected value of delinquency among the adolescents (e1.16 – 1 = 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. 766 / Social Forces 81:3, March 2003 9.9* 4.1* High 60.7* 34.4* 52.1* 23.0* 45.1* 10.2 Medium 73.9 66.0 81.1 74.9 10.1 8.0 51.3 46.8 ales Poverty Percent 4.0* High Low 63.5* 66.9* 13.2* 5.2 44.3* 12.9 Medium 70.4 66.3 74.0 71.2 11.2 7.5 50.0 47.8 1.0* .3 .6 .5 .6 .5 .6 High Low 10.0 10.0 9.9 9.9 10.0 9.8 60.8* 46.5* 22.0* 9.9 45.3* 10.4 Medium Percent Female-Percent or Unemployed Percent Headed Households Out-of-Workforce M 69.4 68.1 85.9 75.3 1.0* 1.1 1.0 .9* .9 1.0 1.0 1.1 .9 .9* 9.8* 9.8 9.9 4.3*7.6* 2.0 5.9 5.8 24.5* 5.5 8.4 15.9* 3.6 6.9 20.8* 2.5* 6.2 8.5 7.0* High Low 68.4* 85.6* 10.0 Medium 12.9 10.4 13.2 7.2 Total Segregation Index 19.111.3 4.5 3.4 18.9 11.3 19.0 11.4 19.3* 11.1* 19.2 11.3 19.1 11.4 18.8 11.2 19.1 11.4 19.0 11.3 19.1 11.1 19.1 11.4 19.0 11.3 19.1 11.1* 34.2 2.9 34.2 34.2 34.1 34.1 34.2 34.2 34.2 34.2 34.2 34.0 34.1 66.6 63.1 67.5 70.8 49.4 74.1 12.2 24.5 8.3 47.5 45.7 48.1 48.1 49.1 47.8 Longitudinal Study, 1990 Study, Longitudinal definitions biological mother and father (percent yes) .6 .7 .5 .5 .3 .4 Pacific Islander 7.5 Conventional Peer expectations 9.9 1.9 9.9 Percent living w/ Stressful life eventsMonetary strain 1.0 attachment Parental supervision Parental 1.2 1.1 1.0 1.0 1.0 1.0 1.1 1.0 1.0 1.0 .9 1.0 1.1* School involvement 1.0 1.1 .9 1.0 Percent white Percent Hispanic Percent black 9.5 Percent Asian/ Percent male TABLE 1: Distribution of Individual-Level Variables, by Community-Level Characteristics, National Educational 1:National Characteristics, TABLE Community-Level by Variables, Individual-Level of Distribution Variable Mean S.D. Low Individual-level variables Theories of Delinquency / 767 1.18* High estfall et al. 1999) et al. estfall Medium 1.14 1.17 scales; standardized scales tribution. Kruskal-Wallis tests Kruskal-Wallis tribution. ales Poverty Percent 1.18* High Low Medium High Low Medium Percent Female-Percent or Unemployed Percent Headed Households Out-of-Workforce M High Low Medium Total Segregation Index 1.16 .9 1.14 1.17 1.15 1.14 1.15 1.20 1.10 1.16 5.92 3.3 10.84 3.5 12.68 9.3 Longitudinal Study, 1990 (Continued) Study, Longitudinal “Low” refers to the lowest quartile, “medium” to the second and third quartiles, and “high” to the highest quartile of to the dis “high” and quartiles, and third the second to “medium” the lowest quartile, refers to “Low” (0-3.18) (natural logarithm) households or out-of-workforce males threshold Past-year delinquency Segregation indexPercent female-headed Percent unemployed .13 .5 Percent below poverty TABLE 1: Distribution of Individual-Level Variables, by Community-Level Characteristics, National Educational 1:National Characteristics, TABLE Community-Level by Variables, Individual-Level of Distribution Variable Mean S.D. Low Note: * p < .05 (two-tailed) with Dunn’s multiple comparison adjustments (Daniel 1990) and a step-down bootstrap comparison multiple (Daniel adjustments for mean comparisons multiple (W adjustment with Dunn’s were used to determine significant differences across community types. The numbers shown are means based primarily on additive are used in subsequent analyses. (N = 10,860 observations and 1,612 communities) Outcome variable Community-level variables 768 / Social Forces 81:3, March 2003 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 Γ–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) Theories of Delinquency / 769 Model 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 equation. 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 involved 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 Table 3) 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 events × percent jobless males (β = .46, p ≅ .11). It suggests that in communities with a high proportion of jobless males the impact of stressful life events on delinquency is particularly consequential. Nevertheless, the p-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. Table 2, model 4). No other cross-level interaction approached significance.10

ARE CONDITIONING EFFECTS OF COMMUNITY VARIABLES SPECIFIC TO 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 770 / Social Forces 81:3, March 2003 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) Theories of Delinquency / 771 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 model 4 of Table 2), 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 Γ–1 priors. The table shows coefficients with standard errors in parentheses. * p < .05 (2-tailed) 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 Table 4. The initial two models, model 1 and model 2, were quite similar to those shown in Table 2. 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 significantly across 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 more conventional definitions, peer expectations, parental attachment, and school involvement are less likely to be involved in delinquent behavior, but the impact 772 / Social Forces 81:3, March 2003 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 Γ–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) Theories of Delinquency / 773 of 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. Model 4 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 Table 5) indicate that the percent of jobless males in a community interacts significantly with stressful life events (β = 1.12, p < .05), school involvement (β = 1.11, p < .05), and parental supervision (β = –.48, p < .05) to affect delinquency. These interaction terms indicate that the positive impact of stressful life events and the negative impact of parental supervision on delinquent behavior is much more substantial in communities that include a high proportion of males who are unemployed or out of the workforce. For example, the expected delinquency among youths with a high degree of stressful life events (one standard deviation above the mean) in high jobless areas (15%) is 6.4, whereas the expected delinquency in these areas among youths with a low degree of stressful life events (one standard deviation below the mean) is 1.2. In low jobless areas this difference is much less dramatic (3.2 vs. 1.6). Similarly, in low jobless areas (4%) the difference in expected delinquency between youths with high parental supervision and those with low parental supervision is quite modest (2.0 vs. 2.5); in high jobless areas (15%) the expected difference is much more substantial (1.2 vs. 6.6). Hence, in areas with low rates of joblessness, the expected effects of stressful life events and parental supervision are much more modest. On the other hand, the attenuating impact of school involvement on delinquency is more substantial in urban environments that have low rates of male joblessness. In fact, using the results to estimate the expected value of delinquency suggests that there is a positive relationship between school involvement and delinquency in areas of high joblessness; the anticipated negative relationship occurs only in areas of low joblessness. School activities may offer conventional alternatives for youth only in areas that are able to support complementary activities and involvement (Hoffmann & Xu 2002). 774 / Social Forces 81:3, March 2003 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) Theories of Delinquency / 775 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 model 4 of Table 4), 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 Γ–1 priors. The table shows coefficients with standard errors in parentheses. * p < .05 (two-tailed)

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 — affect important 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 support recent work that indicates that poverty and joblessness at the community level are associated with more delinquency (Sampson 1987; Short 1997). The value 776 / Social Forces 81:3, March 2003 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 adolescent 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 occur (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 McKay’s (1969) seminal work on social disorganization theory, for example, developed from observations restricted to Chicago’s inner-city areas, which they subsequently broadened by examining other urban areas in the U.S. (Shaw & McKay 1931). Sutherland’s 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, Fischer’s (1984) ideas about how urbanism affects Theories of Delinquency / 777 deviant behavior draws partly from Sutherland by stressing the opportunities and social supports for these behaviors (Stark 1987). And while Merton’s (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 778 / Social Forces 81:3, March 2003 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). Theories of Delinquency / 779 In 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 tracts in the U.S. and about 20,000 zip codes used. The zip code–level 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). 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 or antidelinquent 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-unit ratio measure. 6. I also computed a monetary strain measure that used a variable that asked about the respondent’s “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 the models (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 community characteristics 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 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 are designed 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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