The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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groups. However, control group <strong>of</strong>fenders saw their probation <strong>of</strong>ficers more frequently. Control group <strong>of</strong>fenders continued their regular appointments with their usual probation <strong>of</strong>ficer and were not informed <strong>of</strong> their low-risk prediction or experimental status. Re<strong>of</strong>fending data, including charges for new <strong>of</strong>fenses, technical violations, and wanted card issuances, were collected for each participant. New charge data are available for the two years post-random assignment. For the low-intensity model to be considered a success, failure rates in the treatment group had to be at least the same, if not lower, than in the control group. As long as recidivism was not worse in the treatment group, the APPD deemed low-intensity supervision an acceptable policy. Main results Barnes et al. (forthcoming) report experimental outcomes one year post-random assignment. <strong>The</strong>y note that treatment group cases received approximately 45 per cent fewer contacts than they had in the year prior to random assignment, while the amount <strong>of</strong> contact in the control group did not change. Assuming control group <strong>of</strong>fenders had a face-to-face meeting with their probation <strong>of</strong>ficers once a month, treatment group <strong>of</strong>fenders were expected to receive one face-to-face contact for every six control group contacts, or one contact <strong>of</strong> any type (face-to-face or telephone) for every three control group contacts. This standard was not quite met, but LIS participants still received a lower-intensity intervention. <strong>The</strong>y received about half the number <strong>of</strong> contacts as the control group overall. In practice, some control group members met their probation <strong>of</strong>ficers less than once a month, and some treatment group members saw their probation 71
<strong>of</strong>ficers more <strong>of</strong>ten than the experimental protocol required. 8 Nonetheless, as Barnes et al. note: “<strong>The</strong> two groups were clearly subjected to different numbers <strong>of</strong> contacts with their probation <strong>of</strong>ficers, and the experimental treatment appears to have been delivered as designed in strategy, if not in dosage.” No significant differences in new <strong>of</strong>fending were found between the treatment and control groups after one year. Sixteen per cent <strong>of</strong> the treatment group and 15 per cent <strong>of</strong> the control group were charged with a new <strong>of</strong>fense <strong>of</strong> any type (p ≤ .593). Similarly, 15 per cent <strong>of</strong> treatment group <strong>of</strong>fenders and 16.5 per cent <strong>of</strong> the control group were incarcerated during the same time period (p ≤ .426). Overall, it appeared that lowintensity probation did not lead to more crime compared to supervision as usual, and is a safe strategy for restructuring probation supervision according to APPD’s plans. Sensitivity analysis <strong>of</strong> main results <strong>The</strong> idea <strong>of</strong> reducing the amount <strong>of</strong> resources made available to low-risk <strong>of</strong>fenders may be controversial to policymakers and the public, however logical it may seem to concentrate probation efforts on the higher end <strong>of</strong> the risk spectrum. Distinctions are rarely drawn between high- and low-risk <strong>of</strong>fenders in popular dialogue. Although murderers and sex <strong>of</strong>fenders usually arouse stronger emotions than low-level thieves or drug <strong>of</strong>fenders, the idea that some people who are involved with the criminal justice system at any level may ‘get away with’ minimal supervision may <strong>of</strong>fend the public’s sense <strong>of</strong> fairness. Unequal distribution <strong>of</strong> resources may also make policymakers uneasy, despite being a somewhat obvious money-saving proposition. Sherman (2007, p. 303) 72
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University of Penn
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In memory of Edwar
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Janel McCaffrey and Knakiya Hagans,
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traditional practice, we find no ev
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Methodology .......................
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LIST OF FIGURES Figure 1.1: Effect
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intensity makes no difference to re
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isk of committing
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impact of probatio
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which perhaps suggests that ISP is
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program intended to reduce the stra
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supervision with probation
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3. To what extent do the risk/need
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Methods Scale (SMS: Farrington et a
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Types of Outcomes
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Description of met
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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
Page 65 and 66: Figures Figure 1.1: Effect
Page 67 and 68: Figure 1.5: Effect of</stro
Page 69 and 70: CHAPTER 2. ‘Low-Intensity
Page 71 and 72: What Works in Probation Supervision
Page 73 and 74: to do with it. Furthermore, the mor
Page 75 and 76: to the New York City and Oregon stu
Page 77 and 78: Barnes et al. (forthcoming) have su
Page 79 and 80: paradox.” He suggests that ‘not
Page 81 and 82: fatal shootings in Philadelphia are
Page 83 and 84: the proportions of
Page 85: Interventions and Follow-Up Probati
Page 89 and 90: 1. Does the effect of</stro
Page 91 and 92: all, resulting in a large number <s
Page 93 and 94: Differential treatment take-up and
Page 95 and 96: control group members who received
Page 97 and 98: effect of LIS on r
Page 99 and 100: incarcerated must also be removed f
Page 101 and 102: offenders in our s
Page 103 and 104: ecidivists in the sample, 77 commit
Page 105 and 106: (IRR = 1.41, p ≤ .107). SES did n
Page 107 and 108: probability of fai
Page 109 and 110: The count model ou
Page 111 and 112: estimate is also practically identi
Page 113 and 114: twice the risk of
Page 115 and 116: an end. On the other hand, research
Page 117 and 118: low-intensity supervision would imp
Page 119 and 120: Conclusion The aim
Page 121 and 122: We found that low-risk of</
Page 123 and 124: 7 LIS participants who were transfe
Page 125 and 126: Tables Table 2.1: Sample Characteri
Page 127 and 128: Table 2.3: Frequency of</st
Page 129 and 130: Table 2.5: Instrumental Variables M
Page 131 and 132: Table 2.7: Prevalence of</s
Page 133 and 134: Table 2.9: Time to Failure (Violent
Page 135 and 136: 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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