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Examining the Neighborhood Context of the Violent Offending-Victimization Relationship: A Prospective Investigation

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Abstract

The persistent link between offending and victimization is one of the most robust empirical findings in criminological research. Despite important efforts to isolate the sources of this phenomenon, it is not fully understood. Much attention has been paid to the role of individual-level factors; however, few studies have systematically integrated neighborhood conditions. Using prospective data from the Pittsburgh Youth Study the current research examines a set of hypotheses regarding the interplay of neighborhood structural conditions and the victim-offender overlap. A multilevel analytical technique is applied to the data which purges time-varying covariates of all time-stable unobserved heterogeneity. Results indicate that the relationship between offending and victimization is pronounced in disadvantaged neighborhoods, while offending is not significantly related to victimization risk in contexts marked by lower levels of disadvantage. The implications of the results for theory are discussed, along with recommendations for future research.

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Notes

  1. A study participant reflecting on his justification for routinely victimizing other criminals, quoted in Wright and Decker (1997):71

  2. Strands of lifestyles/routine activities theory also argue that socioeconomic characteristics foster “subcultural adaptations” which encourage individuals to “precipitate or escalate violent victimization” (Hindelang et al. 1978:268).

  3. For the sake of theoretical clarity, it is important to highlight an alternative perspective of the lifestyles/routine activities framework. Osgood et al. (1996) model proposes that offending itself is not a routine activity (e.g. Jensen and Brownfield 1986) but an outcome of routine activities, including unstructured socializing.

  4. Prior research has found no evidence of selective attrition in the PYS with regard to socioeconomic status, race, initial risk rates, non-lethal victimization, or involvement in criminal behavior (see Loeber et al. 2008). Another wave of data collection began in the latter portion of 2008, but this data is not yet available for analyses.

  5. Additional waves of data are available in the PYS that were gathered prior to the waves used in the current study; however, as noted above, the earlier waves do not contain questions regarding victimization.

  6. The SRD also asks respondents how often in the past year they had been involved in a “physical fight.” An analogous item is not available on the PYS victimization questionnaire. Results with the fighting item included in the offending measure are very similar to those reported here. These results are available from the authors upon request.

  7. Taylor et al. (2007) remark that any person "experiencing 12 robberies or 12 aggravated assaults during a 12 month period constitutes a high rate victim" (F.N. #5). Likewise, they assert that an individual who commits up to 12 robberies or 12 aggravated assaults in a 1 year time-frame is conceivably a high rate offender (for related, see DeLisi 2005). Respondents who report experiencing more than 12 incidents of victimization during the recall period receive a score of 12 for that period due to truncation (see Taylor et al. 2007: F.N. # 5). The same strategy is applied to counts of violent offending. Thus, the truncating procedure permits a maximum of 24 reported incidents of violent offending and 24 incidents of violent victimization per respondent at each available wave of data. Approximately one percent of respondents reported committing more than twelve incidents of violence and roughly one percent reported being victimized more than twelve times prior to truncating the scores. Conveying the rarity of serious violence, Wright and Decker’s (1997) study of active armed robbers found that the vast of majority committed, on average, less than one robbery per month.

  8. The alcohol and drug use variables were both capped at 365 and transformed into their square roots in order to reduce outliers and manage skew.

  9. The inter reliability coefficient (α) for the attitudes towards violence item is stable across waves, ranging from .78 at wave the first wave to .90 at wave seven.

  10. The following items are included in the distress index: (1) Fear you may think or do something bad, (2) Fear you have to be perfect, (3) Feel nervous, high-strung or tense, (4) Feel too dependent on others, (5) Fears certain animals, situations or places, (6) Cried frequently. Potential responses to each item range from (0) never to (5) always.

  11. The delinquent peers item has a stable inter reliability coefficient (α) across the study waves. It ranges from .74 at wave one to .88 at wave seven.

  12. On average there are roughly 1.50 tracts within a neighborhood, while the modal number of tracks is 1.

  13. Across the neighborhoods the average proportion of persons living below the poverty line was 20%. Roughly 29% of neighborhoods fit a “high poverty” classification. By contrast, in approximately one quarter of the neighborhoods the poverty rate is around 10%. On average, the median family income is $18,500; in approximately 30% of neighborhoods the median income level is less than $12, 000 and is above $40,000 in roughly four percent of neighborhoods. On average, the unemployment rate is 13.9% and in roughly one-quarter of the neighborhoods it is above 26%. The average proportion of African-Americans is 32.5%. Across the neighborhoods the average proportion of female headed households with children is 46%, and this proportion breaches 90% in approximately 13% of the neighborhoods.

  14. To calculate the person-specific mean of a time-varying predictor we use: \( \overline{X} = \frac{1}{T}\sum\nolimits_{i = T}^{T} {X_{{_{it} }} } \); the formula for the group-mean centered aspect of the predictor is expressed as: \( \Updelta x_{{_{it} }} = X_{it} - \overline{X}_{{_{i} }} \).

  15. While the model effectively reduces the potential influence of time-stable heterogeneity, it does not sweep out time-varying unobserved heterogeneity from the estimates (Osgood 2010).

  16. The variables included at level-2 allow us to compare why one person versus another is more likely to be victimized based on a given predictor. As noted, level-2 estimates are analogous to estimates generated in cross-sectional studies. However, for the sake of parsimony, these measures are conceptualized in the current study as control variables and do not bear directly on our hypotheses (see, for instance, Bellair and McNulty 2009).

  17. A body of existing research suggests that prior victimization is a predictor of subsequent victimization, implying that perhaps violent offending channels this effect (Lauritsen and Davis-Quinet 1995; Tseloni and Pease 2004). In theory, offending may, therefore, be insignificant in the presence of prior values of y. To ensure that the influence of offending on victimization is not a product of prior values of victimization we conducted specification checks in a supplementary set of analyses. We re-estimated the models using lagged values of y along with the full array of variables. The results were largely unchanged relative to those reported in Table 2; specifically, the lagged measure of victimization was positive and significant, while the measure of offending also retained a strong significant influence on victimization risk.

    In prospective panel designs the process of obtaining valid statistical estimates of “dynamic causal relationships” (see Halaby 2004)—where prior values of y influence y—is sensitive to the form of estimator employed in the analyses. Statistical routines for panel data such as the random effects (RE) and fixed effects (FE) frameworks, including the three-level Poisson model adopted here, may produce biased and inconsistent estimates of the extent to which prior victimization influences subsequent victimization (for a thorough review, see Halaby 2004; Kiviet 1995). To adjust for these issues, we applied the Anderson and Hsiao (1982, 1991) 2SLS-IV model to the data. This model provides an asymptotically consistent estimator of the effects of a lagged y (Halaby 2004), making it a more suitable method to examine dynamic systems within the current context. The model transforms the focal data in dynamic equations into first-differences and then implements twice lagged levels of y or twice lagged differences of y as instruments. The twice lagged levels and differences of y are related to the one-period lag of y, but not to the first differenced error term (Bond 2002). To fit the structural demands of the 2SLS-IV model we added a constant (+0.5) to the outcome measure of victimization and transformed it into its logarithm, thus making it linear. We used a 2 year lagged level of victimization (y t-2) as an instrument along with time (see Kiviet 1995). Also, included in the model was the full array of time-varying variables, including violent offending. We were unable to estimate the influence of the time-stable variables due to the fixed-effects properties of the estimator inherent to the 2SLS-IV model.

    We applied the Sargan test (in STATA 11.0) to the data and found no evidence that it violated conditions of orthogonality (see Baum and Schaffer 2003). Estimates generated in the 2SLS-IV analyses showed that violent offending had a significant positive influence on victimization; moreover the lag of victimization had a significant positive effect on subsequent victimization, though the magnitude of this effect was weaker than that of offending. Although the 2SLS-IV technique is relatively reliable in the context of available methods, it is unable to control for observed time-stable variables, plus it does not currently permit estimation of a three-level hierarchical framework. Notwithstanding these caveats, the findings uncovered using the 2SLS-IV estimator are similar to those reported elsewhere with regard to the mutual effects of offending and past victimization on risk for violent victimization (Lauritsen et al. 1991; Lauritsen and Davis-Quinet 1995; Ousey et al. 2008). In sum, based on this specification check we are confident that the causal effect of offending on victimization reported here is not an artifact of prior values of y.

  18. We estimated an additional model containing all of the study predictors with the exception of neighborhood disadvantage. Overall, the within-person and between-person estimates from this model are very similar to those reported in model 3. The magnitude of the violent offending coefficient was slightly greater relative to its value in model 3 due to the absence of the disadvantage variable (results not shown).

  19. In a separate analysis we probed how much of an effect time-stable unobserved heterogeneity has on the relationship between offending and victimization in model 3 of Table 2 (see Gottfredson 1984). We discovered that if the offending variable was not adjusted for time-stable unobserved heterogeneity, then the size of the coefficient was inflated relative to its magnitude in model 3. Consistent with theoretical claims, these findings appear to suggest that a portion of the offending-victimization relationship is explained by both time-stable unobserved and time-varying observed heterogeneity.

  20. We classify neighborhoods as having "high" levels of disadvantage if they fall 1 standard deviation above the mean on the disadvantage scale, and neighborhoods that are 1 standard deviation below the mean on the scale are classified as having "low" levels of disadvantage.

  21. Under Meier and Miethe's (1993) multilevel formulation, emphasis is placed on the interplay of "macrodynamic forces [e.g., proximity and exposure] that contribute to a criminal opportunity structure" and micro-level characteristics [e.g., guardianship and attractiveness] which influence whether targets are selected by would-be offenders (for a comprehensive overview, see Meier and Miethe 1993:475–476).

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Acknowledgments

This research was supported in part by a dissertation fellowship granted to the first author by the Harry Frank Guggenheim Foundation. We are grateful to Finn Esbensen, Janet Lauritsen, D. Wayne Osgood, Richard Rosenfeld, Lee Ann Slocum, Michelle N. Berg and T.J. Taylor for their helpful comments on earlier versions of this manuscript. We also wish to thank the editors of the Journal of Quantitative Criminology and three anonymous reviewers for their valuable feedback.

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  1. Department of Criminal Justice, Indiana University, Sycamore Hall, Bloomington, IN, USA

    Mark T. Berg

  2. School of Medicine and Western Psychiatric Institute, University of Pittsburgh, Pittsburgh, PA, USA

    Rolf Loeber

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Correspondence to Mark T. Berg.

Appendix

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Table 3 Multilevel count models predicting violent victimization: between-person estimates (person mean of time-varying variables)
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Berg, M.T., Loeber, R. Examining the Neighborhood Context of the Violent Offending-Victimization Relationship: A Prospective Investigation. J Quant Criminol 27, 427–451 (2011). https://doi.org/10.1007/s10940-011-9129-7

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