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Instability, informal control, and criminogenic situations: community effects of returning prisoners

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Abstract

Incarceration, whose putative goal is the reduction of crime, may at higher concentrations actually increase crime by overwhelming neighborhoods with limited resources. The present research poses and provides initial support for an explanation of this paradoxical consequence of a crime control strategy. Specifically, we draw on two different lines of theoretical work to suggest that large numbers of returning prisoners may negatively impact a community’s economic and residential stability, limiting a community’s capacity for informal social control and resulting in labor market conditions conducive to criminal behavior. This study combines data on local social organization processes from a large survey of Seattle residents with contextual, crime, and incarceration data from the US Census, Seattle Police Department, and Washington State Department of Corrections. The results suggest that high concentrations of returning prisoners are associated with a reduced capacity for collective efficacy; the fostering of social situations conducive to criminal behavior; and higher levels of violent crime. The impact of incarceration on these neighborhood processes, however, appears to be largely indirect through the turmoil that concentrations of incarceration create in a neighborhood’s labor and housing markets. We conclude with a call for greater scrutiny of the goals and actual outcomes of incarceration policy.

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Notes

  1. http://bjs.ojp.usdoj.gov/content/glance/tables/corr2tab.cfm, population estimates from www.census.gov, both accessed on 8/25/2011.

  2. http://www.pewcenteronthestates.org/uploadedFiles/8015PCTS_Prison08_FINAL_2-1-1_FORWEB.pdf, accessed on 8/25/2010.

  3. Incarceration estimates from http://bjs.ojp.usdoj.gov/content/pub/pdf/pim09st.pdf; population estimates from http://www.census.gov/popest/national/asrh/NC-EST2009-asrh.html, both accessed on 8/25/2010.

  4. The implications of focusing on releases over admissions for the present analysis are elaborated in the Results and Discussion section, where we also raise questions for future research.

  5. In this sense Crutchfield et al. [20] avoid the “materialistic fallacy” that economic strife is limited to the production of economic motivations for crime [70], instead drawing on control [27] and routine activities [17] perspectives to argue that such economic conditions cluster those with reduced attachments and commitments to conventional labor market institutions, and that such clusters provide ample supplies of offenders with diminished constraints as well as suitable victims in environments likely bereft of capable guardians.

  6. For a longer discussion of issues related to simultaneity and endogeneity, see chapter 7 of Clear’s book [12].

  7. One census tract, entirely composed of the University of Washington campus, was not included in the survey. For this tract, census data are unreliable (the population turns over yearly), Seattle Police data are incomplete (the University’s police force handles most of the crime), and we collected no survey information.

  8. The cooperation rate for the survey was over 97 %. The AAPOR response rate was over 50 %.

  9. Miethe targeted 600 city blocks of 100 of Seattle’s then 121 census tracts. In each tract, three blocks with at least one burglary reported to the police in 1989 were selected, along with one adjoining “control” block for each [42]. The SNCS targeted these same street segments, but in many cases had to extend the block in one direction or the other to achieve the requisite number of completed surveys.

  10. Comparison of responses from phone surveys, mail-back surveys, and questionnaires from households with unlisted telephone numbers revealed similar distributions on key variables.

  11. At the individual level, the models ask how personal characteristics are associated with perceptions of collective efficacy and situations of company. This paper, however, is interested in relative differences between neighborhoods in the overall presence of collective efficacy and situations of company. If, for instance, female respondents are more likely to report the presence of collective efficacy than their male counterparts in the same neighborhood, then regardless of who is “more correct” about the presence of collective efficacy, a neighborhood where we happen to interview a larger number of men will appear to have less collective efficacy than a neighborhood where we happen to interview a large number of females even if the “true” levels of collective efficacy are identical. Thus, we control for individual characteristics not because we are interested substantively in the influence of these characteristics on perceptions of collective efficacy or situations of company, but to create comparable measures of neighborhood-level values of these qualities to assess whether neighborhood characteristics are associated with relative differences in collective efficacy and informal social control.

  12. Proximity is measured using a “queen” type contiguity-based spatial weight matrix, which defines all tracts sharing a common border or at least a common corner with a tract as neighbors. Contiguity was restricted across major waterways separating census tracts in Seattle (the Duwamish River and the Lake Washington Ship Canal).

  13. Ideally labor and housing instability would be examined separately, as the expectation is that collective efficacy will be particularly strongly related to residential instability while criminogenic situations will be particularly strongly related to labor instability. In practice, however, the two sources of instability overlap so highly across Seattle neighborhoods—a trend that should not be surprising as they are likely both products of similar macro-social forces [53, 84]—that it is not possible to empirically distinguish their effects. In the presentation of results I also report on additional exploratory models in which only labor or residential instability is included.

  14. A 3-year period is used to reduce the influence of single-year aberrations in the number of crimes in a neighborhood.

  15. Exploratory analyses in which residential and labor instability were considered independently in consecutive models suggest that residential instability plays a much more important role in the production of collective efficacy than labor instability—at least in models that also control for affluence, the presence or absence of which appears to be the more relevant economic dimension for this social process.

  16. Or, more precisely: perceptions of collective efficacy—controlling for differences based on individual race, education and other characteristics—are lower in neighborhoods with greater numbers of African-Americans. It is possible that some form of racial stigma drives respondents to perceive neighborhoods with greater numbers of African-Americans as having lower capacities for collective action than they truly do—prior work has suggested that residents perceive the presence of local disorder [71] and safety more generally [59] through a racial lens. If this stigma is not captured by the individual-level controls then the association between the racial composition and collective efficacy may reflect perceptual rather than true differences. This caveat also applies to the interpretation of the association between the racial composition and situations of company.

  17. Exploratory analyses in which residential and labor instability were considered independently in consecutive models suggest that both residential and labor instability are strongly related to criminogenic conditions when considered on their own.

  18. Though the present model is not able to identify the source of this dependence as being a substantive process like diffusion versus measurement error resulting from a mismatch between administratively-defined census tract boundaries and the behavioral boundaries in which individuals truly experience their local area. At minimum, accounting for spatial dependence addresses the omitted variable problem which exists in models that do not account for existing spatial dependence.

  19. These models employ empirical Bayes residuals derived from a multi-level model which includes controls for the various sources of potential response bias at the person-level. Spatial dependence is assessed using a Moran’s I test for spatial autocorrelation in residuals from a linear model predicting the empirical Bayes residuals.

  20. Variance inflation factors are quite high when instability is included and its removal results in a larger significant negative role for concentrated affluence.

  21. A Poisson model, also used for counts, assumes that the mean and variance are equal. However, as the likelihood ratio tests for dispersion (included in Table 6) confirm, a negative binomial model which accounts for overdispersion (where the variance exceeds the mean) is more appropriate.

  22. Exploratory analyses in which residential and labor instability were considered independently in consecutive models suggest that both residential and labor instability each are strongly related to crime when considered on their own.

  23. The measures of both criminogenic conditions and collective efficacy are empirical Bayes residuals derived from a multi-level model that includes controls for the various sources of potential response bias at the person-level. Empirical Bayes residuals are equal to the least-squares residuals shrunk towards zero by a factor equal to their unreliability ([60], p. 48).

  24. Controlling for prior crime is also conservative in the sense that some of the observed correlation between prior and future crime is likely the product of a strong error correlation resulting from similar systematic biases in the reporting of crime in each time period. Exploratory analyses suggest evidence for this in a weaker effect for a different measure of prior crime constructed from survey victimization reports—though such a measure is subject to its own measurement issues (e.g. [23, 32]). In fact, a logged version of prior crime (which matches the functional form of the crime outcome induced by the log-link function) is so strongly related to future crime that other substantive predictors do not have significant effects. Alternatively, column 4 can be viewed as a basic cross-sectional model.

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Acknowledgements

This research is supported by grants from the Open Society Institute, the Jeht Foundation, the National Science Foundation (SES-0004324, SES-0966662), and the National Consortium on Violence Research (SBR-9513040). An early version of this work was presented at the American Society of Criminology Annual Meeting in Los Angeles, November 2006. Thanks to Northeastern University’s School of Criminology and Criminal Justice Faculty Writing Group as well as the anonymous reviewers for comments and guidance on an earlier draft of this paper.

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  1. School of Criminology and Criminal Justice, Northeastern University, 433 Churchill Hall, 360 Huntington Avenue, Boston, MA, 02115, USA

    Kevin M. Drakulich & Kristin Rose

  2. Department of Sociology, University of Washington, Seattle, WA, USA

    Robert D. Crutchfield & Ross L. Matsueda

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Drakulich, K.M., Crutchfield, R.D., Matsueda, R.L. et al. Instability, informal control, and criminogenic situations: community effects of returning prisoners. Crime Law Soc Change 57, 493–519 (2012). https://doi.org/10.1007/s10611-012-9375-0

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