The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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From a theoretical standpoint, Chapters 2 and 3 also provide an insight into the nature <strong>of</strong> low-risk <strong>of</strong>fenders and the types <strong>of</strong> crime they commit. Understandably, a great deal <strong>of</strong> attention is paid in the criminological literature to unpacking the characteristics <strong>of</strong> more serious <strong>of</strong>fenders, but in developing low-intensity supervision the Philadelphia APPD hypothesized that the majority <strong>of</strong> its caseload could be classified as low risk. If it is true that the majority <strong>of</strong> <strong>of</strong>fenders pose little risk <strong>of</strong> serious recidivism, it is important to learn how they compare to the minority who pose a greater threat. In Chapter 2 we utilize a range <strong>of</strong> statistical procedures to break down the main results <strong>of</strong> the low-intensity supervision experiment, in order to ensure that there are no circumstances under which reduced supervision increases recidivism. We find no evidence that this was the case. We use instrumental variables techniques to model the characteristics <strong>of</strong> low-risk <strong>of</strong>fenders and their relationship to take-up and outcomes <strong>of</strong> low-intensity probation. We find that low-risk <strong>of</strong>fenders represent a much broader range <strong>of</strong> society than the traditional ‘young male’ <strong>of</strong>fender. However, they have an extremely low propensity to re<strong>of</strong>fend and appear to perform well regardless <strong>of</strong> the degree <strong>of</strong> supervision they receive. Thus, we conclude that it is not necessary to treat all <strong>of</strong>fenders equally when it comes to probation supervision. Standards can be relaxed for most <strong>of</strong>fenders to allow probation <strong>of</strong>ficers to spend more time with higher-risk clients, working to identify and address their needs. For such a model to work in practice, a good prediction model is needed to identify who can safely be diverted to the low-intensity unit. Chapter 3 examines whether the prediction model used in the low-intensity supervision experiment, which classifies <strong>of</strong>fenders as low and non-low risk based on their xiii
isk <strong>of</strong> committing just the most serious crimes, is an effective tool for the type <strong>of</strong> risk management strategy employed by Philadelphia APPD. Using methods from the epidemiology field, we assess the sensitivity <strong>of</strong> the model to the patterns <strong>of</strong> <strong>of</strong>fending severity exhibited in a sample <strong>of</strong> probationers. We find that low-risk <strong>of</strong>fenders have a very low propensity for serious <strong>of</strong>fending, while those receiving a non-low risk prediction are much more likely to engage in these <strong>of</strong>fense types. <strong>The</strong> model appears to successfully recognize this over several different definitions <strong>of</strong> severity. Furthermore, there is no change in <strong>of</strong>fending severity when supervision is reduced for predicted lowrisk <strong>of</strong>fenders. We find further evidence <strong>of</strong> the homogeneity in this sample’s propensity to re<strong>of</strong>fend regardless <strong>of</strong> supervision intensity. Predicted low-risk <strong>of</strong>fenders with a history <strong>of</strong> serious <strong>of</strong>fending respond just the same to reduced supervision as they do to regular supervision. For those without a serious <strong>of</strong>fending history, reduced supervision is even associated with reductions in recidivism compared to the status quo. Thus, we conclude that low-intensity supervision is a safe and effective strategy that works particularly well for a probation agency’s lowest-level <strong>of</strong>fenders. xiv
Page 1 and 2: University of Penn
Page 3 and 4: In memory of Edwar
Page 5 and 6: Janel McCaffrey and Knakiya Hagans,
Page 7 and 8: traditional practice, we find no ev
Page 9 and 10: Methodology .......................
Page 11 and 12: LIST OF FIGURES Figure 1.1: Effect
Page 13: intensity makes no difference to re
Page 17 and 18: impact of probatio
Page 19 and 20: which perhaps suggests that ISP is
Page 21 and 22: program intended to reduce the stra
Page 23 and 24: supervision with probation
Page 25 and 26: 3. To what extent do the risk/need
Page 27 and 28: Methods Scale (SMS: Farrington et a
Page 29 and 30: Types of Outcomes
Page 31 and 32: Description of met
Page 33 and 34: actual behavior. We analyze technic
Page 35 and 36: Treatment of quali
Page 37 and 38: treatment group, and those to the r
Page 39 and 40: Discussion of Resu
Page 41 and 42: were evaluated in the same report (
Page 43 and 44: direction of effec
Page 45 and 46: To what extent does program philoso
Page 47 and 48: Which other components of</
Page 49 and 50: .450; technical violations: Q B < .
Page 51 and 52: it should be noted that those progr
Page 53 and 54: up in unpublished reports than thos
Page 55 and 56: violations first went before a judg
Page 57 and 58: 2 One U.S. estimate indicates that
Page 59 and 60: Tables Table 1.1: Characteristics <
Page 61 and 62: Table 1.3: Moderator Variable <stro
Page 63 and 64: Table 1.4: Moderator Variable <stro
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Figures Figure 1.1: Effect
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Figure 1.5: Effect of</stro
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CHAPTER 2. ‘Low-Intensity
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What Works in Probation Supervision
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to do with it. Furthermore, the mor
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to the New York City and Oregon stu
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Barnes et al. (forthcoming) have su
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paradox.” He suggests that ‘not
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fatal shootings in Philadelphia are
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the proportions of
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Interventions and Follow-Up Probati
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officers more <str
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1. Does the effect of</stro
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all, resulting in a large number <s
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Differential treatment take-up and
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control group members who received
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effect of LIS on r
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incarcerated must also be removed f
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offenders in our s
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ecidivists in the sample, 77 commit
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(IRR = 1.41, p ≤ .107). SES did n
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probability of fai
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The count model ou
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estimate is also practically identi
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twice the risk of
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an end. On the other hand, research
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low-intensity supervision would imp
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Conclusion The aim
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We found that low-risk of</
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7 LIS participants who were transfe
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Tables Table 2.1: Sample Characteri
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Table 2.3: Frequency of</st
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Table 2.5: Instrumental Variables M
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Table 2.7: Prevalence of</s
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Table 2.9: Time to Failure (Violent
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Table 2.12: Treatment Effect by Sub
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Table 2.14: Frequency of</s
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Table 2.16: Instrumental Variables
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Figures Figure 2.1: Risk-Based Allo
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Figure 2.5: Survival Time for LIS E
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CHAPTER 3. Risk Prediction for Effe
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sensitivity of the
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Programs should be designed to adhe
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weight information in an appropriat
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statistical model that would predic
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The Philadelphia m
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assignment pool. Backfill probation
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Little research has been done on th
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already predicted to be at low risk
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It assumes that if of</stro
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involved. Crimes that obviously met
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We examine the potential interactio
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age is based on this date regardles
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obberies. The only
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of low-intensity s
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classified as low risk. The
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All the risk ratios in Table 3.9 in
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0.55. Raising the threshold results
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are partly selected on the basis <s
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make up the majority of</st
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strategy allows probation o
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Notes 1 In this study, murder, atte
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Table 3.2: Sample Characteristics (
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Table 3.6: Predictive Ability at Al
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Table 3.9: Post-Random Assignment O
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APPENDICES Appendix A: Systematic R
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combination. The s
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B. ELIGIBILITY CHECKLIST B1. First
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Other: txcxloth C6d. Additional pro
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D. SAMPLE LEVEL CODING SHEET Instru
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E. DEPENDENT VARIABLE CODING SHEET
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F14. Comparison group sample size f
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weight w is then recalculated (6),
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Study Sheffield (Males) 1976 (Folka
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Study Dallas TX 1992 (Turner & Pete
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Study Minnesota ISR 1995 (Deschenes
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Study Clark WA 2003 (Barnoski, 2003
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Study Whatcom WA 2003 (Barnoski, 20
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Study Los Angeles CA 2005 (Zhang &
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Fed. Prob., 56, 12-17. Turner, Pete
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Study Reference Benekos & Sonnenber
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Study Reference Giblin (2002) Using
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Study Reference Lasater et al. (n.d
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Study Reference Robertson (2000) Co
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Study Reference Turner & Greene (19
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Appendix E: Philadelphia APPD Low-<
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Prevalence of Reci
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Frequency of Recid
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Instrumental Variables Model: Preva
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Instrumental Variables Model: Preva
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Post-RA Months in Jail and Probabil
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Scaled Schoenfeld Residuals Time =
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BIBLIOGRAPHY Chapter 1 ** Denotes t
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Cook, T. D., & Campbell, D. T. (197
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Glaze, L. E, & Bonczar, T. P. (2009
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MacKenzie, D. L. (2006a). What work
Page 257 and 258:
Petersilia, J., Turner, S., & Desch
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safety first: 13 strategies for suc
Page 261 and 262:
Wilson, D. B. (2010). Meta-analysis
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Angrist, J. D. (2006). Instrumental
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Clear, T. R., & Hardyman, P. L. (19
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period266.pdf Lowenkamp, C. T., Lat
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Rosch, J. (2006). Deviant peer cont
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Taxman, F. S., & Thanner, M. (2006)
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statistical learning. Journal <stro
Page 275 and 276:
Federal Bureau of
Page 277 and 278:
Marsh, K., & Fox, C., (2008). <stro
Page 279:
Sherman, L. W. (1993). Defiance, de
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