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Gangs and Violence: Disentangling the Impact of Gang Membership on the Level and Nature of Offending

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Abstract

Objectives

To determine whether membership in youth gangs provides a unique social forum for violence amplification. This study examines whether gang membership increases the odds of violent offending over and above involvement in general delinquent and criminal behavior.

Methods

Five waves of data from a multi-site (seven cities) panel study of over 3,700 youth originally nested within 31 schools are analyzed. We estimate four level repeated measures item response theory models, which include a parameter to differentiate the difference in the log of the expected event-rate for violent offense items to the log of the expected event-rate for nonviolent offense items.

Results

Depending on the comparison group (gang youth, overall sample), periods of active gang membership were associated with a 10 or 21% increase in the odds of involvement in violent incidents. When the sample is restricted to youth who report gang membership during the study, the proportionate increase in the odds of violence associated with gangs is statistically similar for males and females. After youth reported leaving the gang their propensity for violence was not significantly different than comparison group observations, although levels of general offending remain elevated.

Conclusions

While results are limited by the school-based sampling strategy, the importance of gang prevention and intervention programming for violence reduction is highlighted. Preventing youth from gang membership or shortening the length of gang careers through interventions may reduce absolute levels of violence.

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Notes

  1. This figure represents the number of schools at the recruitment stage of the evaluation.

  2. Other studies have also utilized the method to investigate community-level variation in violence (Schreck et al. 2009) and individual tendencies to be a victim and/or offender in violent incidents (Schreck et al. 2008).

  3. Length of time the locale had operated the program and the extent to which school students had been exposed to the program were assessed when sites were selected for the national evaluation. Sites where the program was just beginning were excluded because they were deemed likely to have had less time to “work out the kinks” associated with delivering the program with fidelity. Conversely, some sites with a long history of delivering the program were excluded from consideration because it was deemed to be likely that the program had saturated the entire school and/or community context.

  4. For a more thorough description of the site selection process, consult Esbensen et al. (2012).

  5. There were a total of 480 respondents who reported gang membership at some point during the study, but two of these cases lacked basic information necessary for inclusion in the current study.

  6. The question concerning “gang fights” was included in the analysis because the majority (between 58 and 69% depending on wave) of respondents who reported being involved in such incidents were non-gang members. Further, while reported analyses classified “weapon carrying” as a violent offense, consistent with Osgood and Schreck (2007), supplementary analyses that classified such incidents as non-violent produced substantively similar results.

  7. In the style of notation used by Raudenbush and Bryk (2002), the Level 1 regression equation is: \( \ln (\lambda ij) = \psi_{0tij} + \psi_{1tij} \,{\text{Violence}} + \sum\limits_{i = 2}^{I - 1} {\psi_{xtij} D_{ij} } \quad (1) \)

    The Level 2 regression equations are: \( \psi_{0tij} = \pi_{00ij} + \pi_{01ij} \times \left( {X_{1tij} } \right) + \pi_{02ij} \times \left( {X_{2tij} } \right) + \cdots + e_{0tij} \quad (2) \)

    \( \psi_{1tij} = \pi_{10ij} + \pi_{11ij} \times \left( {X_{1tij} } \right) + \pi_{12ij} \times \left( {X_{2tij} } \right) + \cdots + e_{1tij} \quad (3) \)

    \( \psi_{xtij} = \pi_{x0ij} \quad (4) \)

    The Level 3 regression equations are:

    \( \pi_{00ij} = \beta_{000j} + \beta_{001j} \times \left( {X_{1ij} } \right) + \beta_{002j} \times \left( {X_{2ij} } \right) \, + \cdots + r_{00ij} \quad (5) \)

    \( \pi_{10ij} = \beta_{100j} + \beta_{101j} \times \left( {X_{1ij} } \right) + \beta_{102j} \times \left( {X_{2ij} } \right) \, + \cdots + r_{10ij} \quad (6) \)

    \( \pi_{xj} = \beta_{j} \quad (7) \)

    The Level 4 regression equations are:

    \( \beta_{000j} = \gamma_{0000} + \gamma_{0001} \times \left( {X_{1j} } \right) + u_{000j} \quad (8) \)

    \( \beta_{100j} = \gamma_{1000} + \gamma_{1001} \times \left( {X_{1j} } \right) + u_{100j} \quad (9) \)

    \( \beta_{xj} = \gamma_{xj} \quad (10) \)

  8. A cubic term for age was included in supplemental analyses predicting both general offending propensity and violence specialization, but was not significant and thus not included in reported analyses.

  9. There was significant variation in overall delinquency at the individual and school level, however, so control for school-level effects was still necessary.

  10. Given the nature of the dependent variable as a count of delinquent acts, an over-dispersed poisson regression model with a log link function is utilized. By default with four level models in HLM 7.00 for Windows (Raudenbush et al. 2010), results are based upon penalized quasi-likelihood estimation.

  11. Event rate ratios can also be interpreted as the percentage change in the expected outcome for a one-unit change in the independent variable, holding other variables constant, by using the following formula: (100 × [exp(B) − 1]) (Long 1997).

  12. We thank an anonymous reviewer for highlighting Sullivan’s (1989) research as an example of comparative research on group delinquency.

  13. We thank an anonymous reviewer for suggesting that such processes may help explain the current findings.

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Acknowledgments

This research was made possible, in part, by the support and participation of seven school districts, including the School District of Philadelphia. This project was supported by the National Institute of Justice, Office of Justice Programs, US Department of Justice, award No. 2006-JV-FX-0011.

Disclaimer

The points of view in this document are those of the authors and do not necessarily represent the official position of the US Department of Justice.

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Authors and Affiliations

  1. School of Criminal Justice, Michigan State University, 560 Baker Hall, East Lansing, MI, 48824, USA

    Chris Melde

  2. Department of Criminology and Criminal Justice, University of Missouri-St. Louis, St. Louis, MO, 63121, USA

    Finn-Aage Esbensen

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  1. Chris Melde
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  2. Finn-Aage Esbensen
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Correspondence to Chris Melde.

Appendices

Appendix 1

See Table 5.

Table 5 Correlations between change in property and violent offending across waves
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Appendix 2

See Table 6.

Table 6 Delinquency frequency descriptive statistics and estimated item severity
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Melde, C., Esbensen, FA. Gangs and Violence: Disentangling the Impact of Gang Membership on the Level and Nature of Offending. J Quant Criminol 29, 143–166 (2013). https://doi.org/10.1007/s10940-012-9164-z

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