Abstract
A small number of scholars have attempted to reorient current thinking about the way cultural effects operate in poor neighborhoods. Scholars argue that socioeconomic disadvantage fosters heterogeneity in cultural models. Moreover, cultural heterogeneity theoretically plays an important role in shaping adolescent decision-making in poor neighborhoods, including decisions related to violent behavior. We test these assumptions using multilevel data comprised of a sample of African-American adolescents. Our findings lend support to these arguments. In particular, the results suggested that neighborhood structural disadvantage increases the degree of disagreement or heterogeneity regarding the inappropriateness of violence. Further, exposure to cultural heterogeneity increased adolescents’ involvement in violent behavior and had a moderating influence on the link between individual frames and adolescent violent behavior.
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Notes
In the final formulation of their model, Shaw and McKay (1969:185–187) theorized that the heterogeneity in behavioral norms within poor communities, particularly the salience of the “criminal tradition”, may represent a collective adaptation to residents’ inability to achieve societal success goals through conventional means (Finestone 1976). Others have, in fact, noted that Shaw and McKay considered “poverty to be the most important determinant of variation in delinquency rates” (Bryne and Sampson 1986:3).
Later Wilson (1987) expanded on and refined his earlier assumptions regarding the cultural context of impoverished urban settings. He adopted notions of "cultural repertoire" and "cognitive landscapes" to explain the way culture figures into the decision-making process of residents confronted with socioeconomic adversity (Wilson 2009).
Theoretical discussions regarding the behavioral consequences of prolonged exposure to social isolation do not anticipate that the crime-related effects of non-conventional culture will vary across racial groups (Sampson and Wilson 1995); rather, similarly situated white and black youths should respond similarly to oppositional cultural processes.
Cultural models refer to “taken-for-granted models of the world that are widely shared (although not necessarily to the exclusion of other, alternative models) by the members of a society and that play an enormous role in their understanding of that world and their behavior in it” (Quinn and Dorothy 1987:4).
By contrast, adolescents exposed to a homogenous neighborhood culture find a narrower range of acceptable alternative behaviors, leaving them with “few options besides persevering according to conventional cultural models” (Harding 2010:156).
It should be noted that while our third hypothesis is partially derived from recent cognitive formulations of cultural effects (i.e., Harding 2007); it is also developed from insights found in early and contemporary criminological research. Researchers posit that heterogeneity will make a person's frames less predictive of their behaviors (Harding 2010); heretofore, these assumptions have been applied to explain variation in outcomes that are not necessarily illegal or violent (e.g., college enrollment, pregnancy). We depart somewhat from this theoretical approach and incorporate theoretical insights from criminology (e.g., Shaw and McKay 1969; Sutherland 1947). More specifically, as noted in the text, we expect that cultural heterogeneity will cause individuals who have a strong commitment to conventional frames to be less likely to behave in ways that are consistent with their frames and, moreover, heterogeneity will cause those who exhibit weak commitment to conventional frames to be more likely to follow-through with violent behavior (thus, it is more probable that the latter group will behave in ways that match their own normative orientation).
There has been debate as to whether census tracts represent neighborhoods. Census tracts generally have stable boundaries and tend to be internally homogenous with respect to a common set of population, socioeconomic, and geographic characteristics: racial composition, socioeconomic status, poverty, family organization, housing density, and employment status (Sampson et al. 2002:445).
There were a total of 94 census tracts identified. However, sixteen of the tracts had no data and seven were not residential areas, which resulted in seventy-one usable census tracts for our analyses.
In the study areas for Georgia, African American community members were hired by the University of Georgia to serve as liaisons between the research team and the communities, and the liaisons compiled rosters of children who met the sampling criteria from school districts within each tract. In Iowa, families with African American children within the age criterion were identified through the Waterloo and Des Moines public school districts, and Iowa State University hired African-American college students and community members to serve as liaisons between the research team and the communities.
This was a retention rate of 88%. Analyses indicated no significant differences in economic, neighborhood, family, educational, and school performance characteristics between the families who did and did not participate in waves 1 and 2 of FACHS.
To assess whether our results were biased, we created a count measure of violent delinquency and re-ran all analyses using an over-dispersed multilevel Poisson model. In every instance, the analyses paralleled the results presented in Table 2 where we measured violent delinquency as a dichotomous outcome.
These items were reverse coded from the original metric.
To assess the validity of our heterogeneity of violence frames construct, we re-estimated all models using primary caregivers’ responses to create the cultural heterogeneity measure. In every case, the results were virtually identical to using the construct generated from adolescents’ reports. In addition, we combined both adolescent and primary caregiver reports to form the same measure. Again, the models yielded the same pattern of results as found in the target adolescent reports. We also estimated each of the separate indicators that comprise the heterogeneity of street culture measure. When each indicator was estimated, they provided a pattern of results that were consistent with the combined construct presented in Table 2.
We apply the following formula to adjust for small sample bias: \( l_{u}^{2} = l^{2} \frac{{1 - l^{2} }}{N - 1} \).
We observed strong correlations among the seven items, all of which breached values greater than .72 (p < .05).
Generally speaking, the mean and variance of a distribution will be mathematically associated. Using multiple items to capture cultural heterogeneity, however, naturally increases the degree of variation in the measure. Nevertheless, to inspect the degree to which the mean and variance of the neighborhood frames measure were related, we examined the bivariate correlation between them. The calculations showed a modest correlation (.33, p < .05) suggesting that the two measures are related, though analytically distinct.
We re-estimated models in which we used robbery and assaults, as well as a model that combined homicide, robbery, and assaults into a single construct. In all variations of measurements to capture levels of violence that we re-estimated, a similar pattern of results emerged that was consistent with homicide coefficients presented in the tables. Thus, we chose to use only homicide because it is considered by criminologists to be the most reliable measure of crime that is least sensitive to underreporting (see Sampson and Raudenbush 1999:621; Terrill and Reisig 2003:301).
We also measured individual-level conduct frames at time 2. Regardless of whether we used the time 1 or 2 measures, the results are the same. However, we were unable to use both the time 1 and time 2 measures of individual-level conduct frames in a single model because of the strong correlation between the two constructs. We chose to use the time 1 construct to establish time-order between violent frames and the outcome.
We used the GLLAMM (Generalized Linear Latent and Mixed Models) function in STATA 10 to estimate our multilevel models.
Although not shown, we estimated an unconditional model for our dependent variable: violent delinquencyT2. The variance component was significant indicating that violent delinquency varied significantly across neighborhoods. In the analyses that follow, each of the individual-level covariates are grand mean centered.
We thank an anonymous reviewer for their careful thoughts on this matter.
For the sake of clarity it is important to note that, as opposed to social control models, a cultural heterogeneity perspective assumes that the presence of non-conventional culture, rather than only weak conventional culture (e.g., Kornhauser 1978), has a causal effect on criminal behavior.
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Acknowledgments
This research was supported by the National Institute of Mental Health (MH48165, MH62669) and the Center for Disease Control (029136-02). Additional funding for this project was provided by the National Institute on Drug Abuse, the National Institute on Alcohol Abuse and Alcoholism, and the Iowa Agriculture and Home Economics Experiment Station (Project #3320). The authors wish to thank Professor David Harding (University of Michigan) for his helpful comments on earlier drafts of this manuscript.
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Berg, M.T., Stewart, E.A., Brunson, R.K. et al. Neighborhood Cultural Heterogeneity and Adolescent Violence. J Quant Criminol 28, 411–435 (2012). https://doi.org/10.1007/s10940-011-9146-6
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DOI: https://doi.org/10.1007/s10940-011-9146-6