Gang Organization and Gang Identity: An Investigation of Enduring Gang Membership

Abstract

Objectives

Motivated by recent advances in the study of disengagement from street gangs, this research develops a theoretical framework of enduring gang membership based on gang organization and gang identity. Using multivariate data, this research tests the theoretical framework against a competing theory derived from the general theory of crime where gang organization and gang identity are non-existent or unimportant in producing enduring gang membership.

Methods

Eight waves of panel data on high-risk youth from the Denver Youth Survey and discrete-time event-history models are used to investigate enduring gang membership.

Results

The length of time an individual spends in a gang is associated with the perceived organization of the gang and an individual’s gang identity. In a hazard model, accounting for right censoring, low self-control, and contextual time-varying gang related variables, increases in gang identity were associated with (on average) a 26% lower rate of reporting no longer being a gang member. Increases in perceived gang organization were associated with (on average) a 12% lower rate of reporting no longer being a gang member. Surprisingly however, no association was found between gang organization and gang identity.

Conclusions

This research finds support for using a theoretical framework based on gang organization and gang identity to understand enduring gang membership. Both gang identity and gang organization exert independent effects on the length of time an individual spends in a gang.

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Fig. 1

Notes

  1. 1.

    In the gang literature, some researchers use the term, “desistance from gangs” to describe the process of leaving gangs. To avoid confusion, and following previous research (e.g., Sweeten et al. 2013) we use the term “disengagement from gangs” to refer to the process of leaving gangs, and reserve the term, “desistance,” to refer to desistance from criminal activity.

  2. 2.

    Our data allow us to test the primary hypothesis that reductions in gang identity are associated with an increased probability of leaving a gang. We are unable—because of data limitations—to test a secondary hypothesis that the reductions of gang identities are caused by concomitant increases in conventional role-identities, and thus, may operate directly or indirectly through gang identities. This secondary hypothesis is supported by studies of delinquency, such as Giordano et al. (2002) study of conventional role-identities as “hooks for change” producing desistance from crime.

  3. 3.

    We use the hazard of leaving the gang to measure enduring gang membership, and note that they are statistically equivalent: If p is the probability of leaving the gang, 1 − p is the probability of remaining in the gang.

  4. 4.

    Our hazard models estimate the key substantive parameters regressing the hazard of leaving a gang on demographic, self-control, and gang variables. We do not present estimates of parameters predicting gang variables, as they do not alter our substantive conclusions.

  5. 5.

    Because of issues with funding, interviews were not conducted in 1992 or 1993. Thus after Wave 5 interviews were completed in 1991 Wave 6 interviews were not completed until 1994.

  6. 6.

    Gang scholars have argued that leaving a gang is more of a process than a discrete event (Decker and Lauritsen 2002). Recent research has found that modeling leaving the gang as an event is a valid and reliable proxy for modeling leaving as a process (Decker et al. 2014b).

  7. 7.

    We attempted to assess within gang reports of gang organization i.e., did respondents in the same gang report the same organizational characteristics. Respondents reported being members of what appear to be over 70 distinct gangs including 14 variations of Crips gangs and 3 variations of Bloods gangs. This is problematic as different types of Crips can be subsets of one another, or distinct entities entirely (Howell 2007). Due to limitations of the data we lack the ability to parse this out.

  8. 8.

    This procedure will yield unbiased estimates of measurement properties (reliability) under the assumption of no age and cohort effects on measurement properties and change in true scores. Departures from zero age and cohort effects will result in downward-biased estimates of reliability. Therefore, our estimates should be considered lower-bound estimates of true reliability.

  9. 9.

    Recent research questions the stability of self-control through adolescence (e.g., Burt et al. 2014; Na and Paternoster 2012). Therefore, we use measures of self-control that are lagged one period in addition to time stable measures of self- control.

  10. 10.

    We also tested for non-proportionality by interacting key covariates with duration and failed to reject the proportionality assumption.

  11. 11.

    It was necessary to collapse the 3-year, 4-year, 5-year, and 6-year gang members into one category due to the small N in the latter categories. Results from the reported models do not differ significantly from models ran with the linear term.

  12. 12.

    As noted above, duration was found to be non-linear therefore we estimated duration dependence, using three dummy variables.

  13. 13.

    We estimated a model for the first event of leaving a gang and found similar substantive results, but larger estimated standard errors. We also tested for interaction effects for key substantive variables by first versus second events, and failed to find significant interactions. Furthermore, we found that robust standard errors for clustering were similar in magnitude to our classical standard errors, suggesting that parameter estimates of our nonlinear model are not biased due to clustering (see King and Roberts 2015).

  14. 14.

    We examined the distribution of missing data on the key variables in our event history analysis (total of 35% compared to the full sample of gang members wave 1–10). We compared the distribution of 20 key variables for included observations against the excluded observations. Out of 20 total tests, only three showed statistically significant differences: included observations showed slightly higher scores on the gang providing protection, lower scores on childhood delinquency, and slightly higher scores on beer drinking. Moreover, our demographic variables, offending variables, gang organization, and gang identity are not significantly different between included and excluded observations (table available upon request).

  15. 15.

    Here we report the full sample of gang members in the Denver Youth Survey not just our analytic sample.

  16. 16.

    Tests were conducted to assess whether multicollinearity was an issue. A correlation matrix indicated no extremely high correlations and variance inflation factors (VIFs) indicated no issues with multicollinearity.

  17. 17.

    All results are reported in the text use the formula (β − 1) × 100 so results indicate the percentage change in the probability of exiting the gang given the respondent has not yet left.

  18. 18.

    We purposely used all available observations for each model to increase power. We conducted sensitivity checks using a listwise approach restricting the sample size of all three models to the smallest model (Model 3, N = 225) and results were consistent with reported models.

  19. 19.

    Recent research by Pyrooz et al. (2017) has found some support for parenthood as a turning point or pull factor in the life course of gang members.

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Acknowledgements

We thank Callie Burt, Jerry Herting, Katherine O’Neill, and Lynette Hoelter for helpful comments on an earlier draft, Aimée Dechter for helpful advice, and the Center for Social Science Computation and Research at the University of Washington and Charles Lanfear for computing assistance.

Funding

This research was supported by grants from the Blumstein-Jordan Endowed Professorship in Sociology, University of Washington, the National Institute of Justice (2014-R2-CX-0018), and the National Science Foundation Graduate Research Fellowship Program (1256082). Partial support for this research came from a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant to the Center for Studies in Demography and Ecology at the University of Washington (R24 HD042828).

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Appendix

Appendix

Here we describe in more detail our event history analysis, including treatment of repeated events, treatment of left-hand censoring, and treatment of missing data. We also report results from confirmatory factor analyses for gang organization and gang identity.

Event History Model Specifications

Our event history models use time since entering a gang as the clock governing enduring gang membership.

Treatment of Repeated Events

A few respondents left the risk set (self-reported that they were no longer in the gang) and then in later waves returned to the gang. Recall that the hazard of enduring gang membership is governed by a clock that measures duration of time in the gang. After an individual desists from the gang, their clock is reset to 0 and their second event is treated as separate from the first. In addition, a dummy variable (Episode) is added to the model indicating whether it was a respondent’s second event (Allison 1982). We found that robust standard errors clustered on individuals were similar to classical standard errors, suggesting that our non-linear parameter estimates are not biased due to clustering (King and Roberts 2015).

Left-Hand Censoring

Observations are left-censored when the start of the event time is unknown. This occurs in our event histories in a few cases in which respondents reported being in a gang at the first wave, and we do not know precisely when they joined. It is often recommended that left-censored cases be removed from analysis to reduce bias (Singer and Willett 2003). Nevertheless, when the number of left-censored cases is substantial and are correlated with duration, bias will remain. A key question here is at what age to most youth join gangs? Howell and Griffiths (2015) reported that the typical age ranges youth members join a gang is between 11 and 15 years old. Two recent studies using the National Longitudinal Survey of Youth found similar results (Pyrooz 2014; Pyrooz and Sweeten 2015). Thus, previous research indicates that most youth join gangs in their early teenage years, with a few outliers joining at a younger age. The DYS captures not only those at prime gang-joining age, but the majority of early-onset outliers. DYS respondents were given their first youth survey at approximately age 11 and were asked: “in the past year were you a member of a street gang?” This covers gang membership beginning at age 10, the low end of the age spectrum for joining a gang. Thus, while it is possible that a youth could have joined and left a gang by age 10, it is extremely unlikely. Therefore, it is reasonable to assume that the DYS captured the majority, and possibly all, gang-involved youth in the sample and left censoring is negligible (Table 3).

Table 3 Variable descriptions and descriptive statistics, denver youth survey waves 3–10. N = 159

Treatment of Missing Data

Missing data arises from a number of sources, including (1) the time-lagging of covariates; (2) key predictor variables not being asked until Wave 3; and (3) respondents not being interviewed in the person-year before the spell (left censoring) or in the person year after the spell (right censoring). The first two sources of missing data are unlikely to be systematic or biasing; they led to dropping 52 person-years from analysis. Individuals with data missing in the person-year before the spell began were left-censored and eliminated from the study (N = 26) (Singer and Willett 2003). Right censoring (when the event occurrence is unknown because the study ended or the person was not sampled in the subsequent wave) of 66 person-years is handled by our event history models (Allison 2013).

Confirmatory Factor Analyses

To investigate the dimensionality of gang organization and gang identity we estimated confirmatory factory models for ordinal and dichotomous indicators using one time point for each respondent. These models using scale appropriate polychoric correlations under the assumption that underlying each ordinal indicator is a continuous latent construct. We found similar results using diagonally-weighted least squares and maximum likelihood with Satorra-Bentler corrections for test statistics. We observed high factor loadings for both gang organization (range 0.44–0.92) and gang identity (range 0.66–0.86) indicating each observed variable is strongly related to the corresponding latent construct (see Table 4). Moreover, fit statistics for both gang organization (χ2 = 10.59, df = 20, p < .96) and gang identity (χ2 = 10.33, df = 6, p < .11) indicate one-factor solutions.

Table 4 Confirmatory factor analyses: gang identity and gang organization

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Leverso, J., Matsueda, R.L. Gang Organization and Gang Identity: An Investigation of Enduring Gang Membership. J Quant Criminol 35, 797–829 (2019). https://doi.org/10.1007/s10940-019-09408-x

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Keywords

  • Gangs
  • Disengagement from gangs
  • Enduring gang membership
  • Gang organization
  • Gang identity