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Violence in Urban Neighborhoods: A Longitudinal Study of Collective Efficacy and Violent Crime

Abstract

Objectives

Cross-sectional studies consistently find that neighborhoods with higher levels of collective efficacy experience fewer social problems. Particularly robust is the relationship between collective efficacy and violent crime, which holds regardless of the socio-structural conditions of neighborhoods. Yet due to the limited availability of neighborhood panel data, the temporal relationship between neighborhood structure, collective efficacy and crime is less well understood.

Methods

In this paper, we provide an empirical test of the collective efficacy-crime association over time by bringing together multiple waves of survey and census data and counts of violent crime incident data collected across 148 neighborhoods in Brisbane, Australia. Utilizing three different longitudinal models that make different assumptions about the temporal nature of these relationships, we examine the reciprocal relationships between neighborhood features and collective efficacy with violent crime. We also consider the spatial embeddedness of these neighborhood characteristics and their association with collective efficacy and the concentration of violence longitudinally.

Results

Notably, our findings reveal no direct relationship between collective efficacy and violent crime over time. However, we find a strong reciprocal relationship between collective efficacy and disadvantage and between disadvantage and violence, indicating an indirect relationship between collective efficacy and violence.

Conclusions

The null direct effects for collective efficacy on crime in a longitudinal design suggest that this relationship may not be as straightforward as presumed in the literature. More longitudinal research is needed to understand the dynamics of disadvantage, collective efficacy, and violence in neighborhoods.

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Fig. 1

Notes

  1. At the time of writing, only the Project for Human Development in Chicago Neighborhoods (PHDCN) and LA FANS in Los Angeles have collected more than one wave of neighborhood survey data that specifically captures collective efficacy (Sampson 2012).

  2. The PHDCN is a longitudinal project sponsored by the MacArthur Foundation in partnership with the National Institutes of Justice and Mental Health, Harvard School of Public Health, the Administration on Children, Youth and Families of the U.S. Department of Health and Human Services and the U.S. Department of Education. It is a multi-million dollar project examining the social, criminological, economic, organizational, political and cultural structures of Chicago’s communities.

  3. In Australia, the term “suburb” is used to refer to a feature that in the U.S. would be referred to as a “neighborhood”. Throughout, we use the more familiar term “neighborhood” to refer to these. The suburbs in the ACCS sample include those that are adjacent to the main city center and those located in peri-urban areas which have experienced large increases in population growth.

  4. In Australia, the number of mobile phone only users has only increased recently. 90 % of the population was covered by landline phones in 2008, and in 2011 (the last wave of our sample) the number of mobile phone-only users was estimated to still be just 19 % (Australian Communications and Media Authority 2012). By comparison, in the US there were over 45 % mobile only users in 2014 (Blumberg and Luke, 2015).

  5. In contrast to face to face surveys like those used in the PHDCN or the Los Angeles Family and Neighborhood Study, phone response rates tend to be lower. This is true for the ACCS survey’s response rate. Yet the response rates for ACCS are on par with or indeed higher than other studies in Australia and the United States using phone contact (Duncan and Mummery 2005; Pickett et al. 2012; Lai, Zhao and Longmire, 2012; Larsen et al., 2004; Wood et al. 2012.

  6. Factor analysis provides specific weights to each of the variables that compose the measure, which are analogous to an item response theory (IRT) approach; see Kamata and Bauer (2008) for the analytical proof that these approaches are identical.

  7. This equation is: y ij  = α + N j ΓN + X ij ΓX + ε ij ; where y ij is the factor score of collective efficacy as reported by the i-th respondent of I respondents in the j-th neighborhood, α is an intercept, N j is an indicator of the neighborhood in which the respondent lives, ΓN is a vector of the effects of these neighborhoods on collective efficacy, X is a matrix of the exogenous household-level predictors, ΓX is a vector of the effects of these predictors on the subjective assessment, and ε ij is a disturbance term. The following individual level characteristics are included in the model: household income, education level, length of residence in the neighborhood, female, age, homeowner, marital status (single, widowed, divorced, and married as the reference category), presence of children, and speaking only English in the home. Previous research found very high correlations between measures using a frequentist approach, as we do here, and those using a Bayesian approach (see Steenbeek and Hipp, 2011, footnote 12 on page 846).

  8. Note that using factor scores for some of our measures that are standardized to a mean value of 0 at each time point is not problematic given that we are not estimating latent trajectory models attempting to capture change over time. Instead, our models are interested in marginal change in the outcome variable at a point in time given a marginal change in the covariate. Thus, the centering of the variables does not impact the substantive interpretation of our results, and is captured in the intercept terms estimated at each time point.

  9. This measure was based on the following language groups: indigenous; East Asian; South-central Asian; Southeast Asian; Southern Asian; Eastern European; Northern European; Southern European; other languages.

  10. The 2006 census measures are included as covariates in each of these equations, as they are clearly temporally prior to the 2010 and 2012 survey waves.

  11. Regarding model fit, for the two-year lag model (Table 2), the χ2 of 173.2 on 73 df (p < 0.01) implies a nonperfect fit, although the RMSEA of 0.098 and the CFI of 0.945 suggest a reasonable approximate fit for this model. Simulation studies have shown that model fit can be impacted by various characteristics of the model, and therefore strictly employing cutoff values is not wise; nonetheless, rough guidelines includes RMSEA values below 0.08 and CFI values above 0.95 (Hu and Bentler 1999). Inspection of modification indices suggested only that estimating the effect of lagged violence on current violence should be freed over the two waves; when freeing this path, or constraining the error covariances at the same time point to be zero (given their nonsignificance), the model fit only improved somewhat (RMSEA = 0.081, CFI = 0.958), and, most importantly, the substantive results remained unchanged. It is important to recall the insights of Browne and colleagues (Browne, MacCallum, Kim, Andersen, and Glaser 2002) that model fit will be negatively impacted due to high statistical power when the R-squares of the equations are quite high, as they are here, ranging from 0.56 to 0.90. In the simultaneous effects model (Table 3) the fit was similar: χ2 of 174.1 on 72 df (p < 0.01) implies a nonperfect fit, although the RMSEA of 0.10 and the CFI of 0.944 suggest a reasonable approximate fit for this model. Freeing the lagged violence effect and constraining the error covariances at the same timepoint to zero again resulted in modest improvement to model fit, but with the substantive results remaining unchanged. The 5-year lag model (Table 4) was exactly identified, and therefore model fit could not be assessed.

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Acknowledgments

This work was supported by the Australian Research Council (LP0453763; fDP0771785; RO700002; DP1093960; DP1094589 and DE130100958).

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Correspondence to John R. Hipp.

Appendices

Appendix 1

See Table 5 and Fig. 2.

Table 5 Resident population size for ACCS suburbs and sample size by wave and cohort
Fig. 2
figure 2

Full path model showing crime (c), disadvantage (d), and collective efficacy (ce)

Appendix 2

See Table 6.

Table 6 ABS census and ACCS sample demographics

Appendix 3

See Table 7.

Table 7 Comparison of neighborhood characteristics for ACCS suburbs compared to state and national averages

Appendix 4

See Table 8.

Table 8 Items from the collective efficacy scale from waves 2, 3 and 4 of the ACCS

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Hipp, J.R., Wickes, R. Violence in Urban Neighborhoods: A Longitudinal Study of Collective Efficacy and Violent Crime. J Quant Criminol 33, 783–808 (2017). https://doi.org/10.1007/s10940-016-9311-z

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  • DOI: https://doi.org/10.1007/s10940-016-9311-z

Keywords

  • Collective efficacy
  • Violence
  • Disadvantage
  • Neighborhood