Skip to main content

Advertisement

Log in

Low-intensity community supervision for low-risk offenders: a randomized, controlled trial

  • Published:
Journal of Experimental Criminology Aims and scope Submit manuscript

Abstract

The Philadelphia Low-Intensity Community Supervision Experiment provides evidence on the effects of lowering the intensity of community supervision with low-risk offenders in an urban, US county community corrections agency. Using a random forests forecasting model for serious crime based on Berk et al. Journal of the Royal Statistical Society, Series A, 172(Part 1), 191–211, 2009, 1,559 low-risk offenders were identified and randomly assigned to either standard or reduced frequency of mandatory office visits. Treatment as assigned was substantially delivered at 4.5 probation visits per year versus 2.4, for as long as offenders remained on active probation or parole. In a one-year follow-up for all cases, outcomes examined were the prevalence, frequency, seriousness and time-to-failure of arrests for new crimes committed after random assignment was implemented. No significant differences (p = .05) in outcomes were found between standard and low-intensity groups. Non-significant differences for offense seriousness favored the low-intensity group. We conclude that lower-intensity supervision at the tested level of dosage can allow fewer officers to supervise low-risk offenders in the community without evidence of increased volume or seriousness of crime.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. The operating practices of the APPD do not distinguish between probationers and parolees, largely because many offenders have multiple cases simultaneously at different stages of the system. It is possible, for example, to be on probation for one offense at the same time as being on parole for another offense.

  2. APPD already had a low-risk caseload outside the regional units before the experiment was implemented. However, assignment to the caseload was based on a different risk tool that predicted arrest for any new offense. Offenders assigned to this caseload had reduced reporting requirements, so had already experienced supervision levels similar to those being tested in the experiment.

  3. One example of these unanticipated conditions was the court-ordered FIR (Forensic Intensive Recovery) program, a drug evaluation and treatment regime. Within weeks of the experiment’s start date, APPD administrators decided that the intensive monitoring required for offenders in drug treatment was impossible to provide within the experimental officers’ large caseloads. As a result, 108 offenders (58 experimental, 50 control) with FIR conditions became ineligible for low-intensity supervision.

  4. Two experimental offenders appear to have had their prior criminal records expunged from the court database, and now have no previous criminal history, despite the fact that both of them were on probation and were enrolled into the RCT.

  5. That fact makes this experiment most useful in the short run, when the APPD’s caseload is in transition for a gradual shift of existing cases from OSFA to risk-based treatment unique to that risk level. The present experiment is perhaps less valid as an assessment of differences from the initiation of probation or parole sentence, as will become the case in the long run.

  6. As before, “active supervision” excludes any time when the offender had absconded from supervision and had been placed into one of the “Wanted Card” caseloads. The same pattern of results, however, is found when this “Wanted Card” time is included in the calculations.

References

  • Anderson, E. (1999). Code of the street. Boston: Norton.

    Google Scholar 

  • Ahlman, L. C. & Kurtz, E. M. (2009). The APPD Randomized Controlled Trial in Low Risk Supervision: The Effects of Low Risk Supervision on Rearrest. Philadelphia: Adult Probation and Parole Department.

  • Berk, R. A., Sherman, L. W., Barnes, G. C., Ahlman, L., & Kurtz, E. (2009). Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. Journal of the Royal Statistical Society, Series A, 172(Part 1), 191–211.

    Google Scholar 

  • Braga, A., & Weisburd, D. (2010). Policing problem places. NY: Oxford University Press.

    Google Scholar 

  • Coalition for Evidence-Based Policy (2010). Home Page. Downloaded February 19 from http://coalition4evidence.org/wordpress/.

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Earlbaum Associates.

    Google Scholar 

  • Dodge, K. A., & Dishion, T. J. (2006). Deviant peer contagion in interventions and programs: an ecological framework for understanding influence mechanisms. In K. A. Dodge, T. J. Dishion, & J. E. Landsford (Eds.), Deviant peer influences in programs for youth (pp. 14–43). New York: Guilford Press.

    Google Scholar 

  • Dodge, K. A., Dishion, T. J., & Lansford, J. E. (2006). Deviant peer influences in programs for youth. New York: Guilford Press.

    Google Scholar 

  • Gill, C. E. (In press). “Intensity of probation supervision: A systematic review.” Jerry Lee Center for Criminology, University of Pennsylvania.

  • Goffman, A. (2009). On the run: Wanted men in a Philadelphia Ghetto. American Sociological Review, 74, 339–357.

    Article  Google Scholar 

  • Erwin, B. S. (1986). Turning up the heat on probationers in Georgia. Federal Probation, 50, 17–24.

    Google Scholar 

  • Farrington, D., Coid, J. W., Harnett, L., Jolliffe, D., Soteriou, N., Turner, R., et al. (2006). Criminal careers and life success: new findings from the Cambridge Study in Delinquent Development. London: Home Office Findings #281.

    Google Scholar 

  • Federal Judicial Center. (1981). Experimentation in the Law. Washington: Federal Judicial Center, Administrative Office of the U.S. Courts.

    Google Scholar 

  • Gibbs, J. D. (1975). Crime, punishment and deterrence. NY: Elsevier.

    Google Scholar 

  • Hanley, D. (2006). Appropriate services: examining the case classification principle. Journal of Offender Rehabilitation, 42, 1–22.

    Article  Google Scholar 

  • Jacobson, M. (2005). Downsizing prisons. NY: NYU Press.

    Google Scholar 

  • Lipsey, M. (2006). The effects of community based group treatment for delinquency: A meta-analytic search for cross-study generalizations. In K. A. Dodge, T. J. Dishion, & J. E. Landsford (Eds.), Deviant peer influences in programs for youth (pp. 162–184). New York: Guilford Press.

    Google Scholar 

  • MacKenzie, D. (2006). What works in corrections: Reducing the criminal activities of offenders and delinquents. New York: Cambridge University Press.

    Book  Google Scholar 

  • O’Connell, M. E., Boat, T., Warner, K. E. (Eds.) (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, D.C.: Committee on the Prevention of Mental Disorders and Substance Abuse Among Children, Youth and Young Adults: Research Advances and Promising Interventions. Institute of Medicine; National Research Council, National Academies Press.

  • Petersilia, J., & Turner, S. (1993). Intensive probation and parole. In M. Tonry (Ed.), Crime and Justice: A Review of Research, 17, 281–335.

  • Pew Center on the States. (2009). One in 31: The long reach of American corrections. Washington: The Pew Charitable Trusts.

    Google Scholar 

  • Rosch, J. (2006). Deviant peer contagion: Findings from the Duke executive sessions on deviant peer contagion. The Link 5, 1–17. Child Welfare League. Downloaded on May 4, 2009 from http://www.cwla.org/programs/juvenilejustice/thelink2006fall.pdf.

  • Sherman, L. W. (1993). Defiance, deterrence and irrelevance: A theory of the criminal sanction. Journal of Research in Crime and Delinquency, 30, 445–473.

    Article  Google Scholar 

  • Sherman, L. W. (2007). Use probation to prevent murder. Criminology and Public Policy, 6, 843–849.

    Google Scholar 

  • Sherman, L. W., Gottfredson, D., MacKenzie, D., Eck, J., Reuter, P., & Bushway, S. (1997). Preventing crime: What works, what doesn’t, what’s promising. Washington: U.S. Department of Justice.

    Google Scholar 

  • Weisburd, D., Lum, C., & Yang, S. M. (2003). “When can we conclude that treatments or programs “Don’t Work”? Annals of the American Academy of Political and Social Science, 587, 31–48.

    Article  Google Scholar 

  • Wilson, J. A., Naro, W., & Austin, J. F. (2007). Innovations in probation: Assessing New York City’s automated reporting system. Washington: JFA Associates.

    Google Scholar 

  • Worrall, J. L., Schram, P., Hays, E., & Newman, M. (2004). An analysis of the relationship between probation caseloads and property crime rates in California counties. Journal of Criminal Justice, 32, 231–241.

    Article  Google Scholar 

  • Zimring, F. E., & Hawkins, G. (1973). Deterrence: The legal threat in crime control. Chicago: University of Chicago Press.

    Google Scholar 

Download references

Acknowledgment

The Regulatory Institutions Network at the Australian National University is hereby acknowledged for its support of the writing and revision of this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Geoffrey C. Barnes.

Appendix

Appendix

Table 8 Additional measures of baseline equivalence

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barnes, G.C., Ahlman, L., Gill, C. et al. Low-intensity community supervision for low-risk offenders: a randomized, controlled trial. J Exp Criminol 6, 159–189 (2010). https://doi.org/10.1007/s11292-010-9094-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11292-010-9094-4

Keywords

Navigation