The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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notes that concentrating on the ‘power few’ 9 may at the same time be considered “perfectly rational and morally reprehensible.” Although only a small proportion <strong>of</strong> probation clients are at the highest risk <strong>of</strong> involvement in serious crime (as <strong>of</strong>fenders or victims), why should those deemed to be at low risk <strong>of</strong> serious re<strong>of</strong>fending be denied the same level <strong>of</strong> attention from a probation <strong>of</strong>ficer if they need it? Not only could low-risk <strong>of</strong>fenders be denied help and services they still need, but they may still pose a serious threat in the future. As one anonymous Philadelphia probation <strong>of</strong>ficer told a local newspaper after the restructuring <strong>of</strong> Philadelphia APPD: “Anybody is capable <strong>of</strong> anything. You can’t just assume [low-risk <strong>of</strong>fenders] won’t pose a problem.” Another stated: “We don’t want to give people a chance to go out and commit more crimes” (Gambacorta, 2009). <strong>The</strong>se concerns cannot be discounted if Philadelphia’s model <strong>of</strong> low-intensity supervision is to become a viable policy beyond the RCT. However rigorous the experimental design, the main outcomes may mask subtle variations in effects. Different conceptualizations <strong>of</strong> the outcome measure, differential treatment take-up, and differential subgroup effects may all affect our conclusions about the efficacy <strong>of</strong> the policy. This paper examines the extent to which different outcome measures and heterogeneity in <strong>of</strong>fender characteristics explain any differences in recidivism outcomes in the sample <strong>of</strong> probationers assigned to LIS, compared to the control group who received ‘SAU.’ Our analysis also extends the main results discussed above by increasing the follow-up period to two years post-random assignment, to examine whether the null findings are sensitive to a longer follow-up period. <strong>The</strong> specific research questions addressed in this paper are: 73
1. Does the effect <strong>of</strong> LIS on recidivism (new charged <strong>of</strong>fenses) differ if we consider recidivism frequency as well as participation? 2. Does assignment to LIS affect the time to failure? 3. Does differential treatment take-up affect the probability <strong>of</strong> recidivism? 4. Do the effects <strong>of</strong> LIS differ across <strong>of</strong>fender subgroups? 5. Do the outcomes for the above research questions hold across specific <strong>of</strong>fense types? Analytic Strategy Offending participation versus frequency A common concern in criminal career research is the distinction between <strong>of</strong>fending participation and <strong>of</strong>fending frequency. Participation refers to whether or not a person was involved in criminal behavior (a dichotomous outcome), while frequency refers to the number <strong>of</strong> crimes committed within a certain period <strong>of</strong> time. <strong>The</strong>se two conceptualizations <strong>of</strong> crime outcomes are open to different interpretations. As Blumstein et al. (1988) note in the context <strong>of</strong> lifetime <strong>of</strong>fending: “Participation distinguishes active <strong>of</strong>fenders from non-<strong>of</strong>fenders within a population; frequency is a reflection <strong>of</strong> the degree <strong>of</strong> individual criminal activity by those who are active <strong>of</strong>fenders” (p. 4). Thus, an experimental intervention could produce differential effects on outcomes depending on how the outcome is measured before and after treatment. Ideally, we want an intervention that reduces participation and frequency <strong>of</strong> recidivism, resulting in some <strong>of</strong>fenders desisting from crime completely and those that do not at least re<strong>of</strong>fending less <strong>of</strong>ten. However, in practice we may see an impact on one but 74
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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
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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 and 86: Interventions and Follow-Up Probati
Page 87: officers more <str
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
Page 137 and 138: 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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