The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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<strong>The</strong> collapse <strong>of</strong> the rehabilitative ideal and the subsequent ‘nothing works’ paradigm <strong>of</strong> the 1970s, along with a sharp rise in crime, led to an exponential increase in prison growth (and the cost <strong>of</strong> corrections) that has persisted ever since (e.g., Ruth & Reitz, 2003). <strong>The</strong> probation population was also growing at a similar pace, and probation <strong>of</strong>ficer caseloads were becoming too large to allow them to serve the increasing number <strong>of</strong> serious and high-need <strong>of</strong>fenders being granted probation or parole (Petersilia & Turner, 1993). By the 1980s there was renewed interest in ISP as part <strong>of</strong> a battery <strong>of</strong> ‘intermediate sanctions’ that sought to alleviate prison overcrowding and save money, while maintaining the appearance <strong>of</strong> being tough on <strong>of</strong>fenders who would otherwise have been incarcerated. <strong>The</strong> focus was on surveillance and control <strong>of</strong> the <strong>of</strong>fender through small caseloads, frequent contacts, increased drug testing, and mandatory employment. <strong>The</strong> new ISP was rooted in the classical theory <strong>of</strong> deterrence through swift, certain punishment, effected by close supervision (Petersilia & Turner, 1990). Georgia was the first state in the U.S.A. to implement this new generation <strong>of</strong> ISP program. Participants had very low recidivism rates, maintained employment, and paid probation fees that helped <strong>of</strong>fset the cost <strong>of</strong> supervision. <strong>The</strong> Georgia model was subsequently adopted elsewhere in the United States, with mixed results. <strong>The</strong> Bureau <strong>of</strong> Justice Assistance (BJA) responded to the interest in and uncertainty about the Georgia model by funding a large, multi-site randomized controlled trial in the mid-1980s, which was evaluated by the RAND Corporation. Twelve <strong>of</strong> the fourteen experiments compared ISP to routine supervision, while two compared ISP to incarceration. By and large, the results <strong>of</strong> the evaluations were disappointing, again showing little impact on new crimes and an increase in technical violations compared to usual practice. Furthermore, a 5
program intended to reduce the strain on the prison system actually resulted in more incarcerations, as increased surveillance and drug testing raised the likelihood <strong>of</strong> probation failure (Petersilia & Turner, 1993). <strong>The</strong> inability <strong>of</strong> ISP to demonstrate potential as a crime prevention program under the scrutiny <strong>of</strong> a rigorous research design largely killed <strong>of</strong>f interest in the surveillance/control model <strong>of</strong> probation supervision by the 1990s. ISP was listed in the influential University <strong>of</strong> Maryland report to the United States Congress, Preventing Crime: What Works, What Doesn’t, What’s Promising, as a program that did not work (Sherman et al., 1997; MacKenzie, 2006b). However, the ‘what works’ movement also led to an increased focus on the factors that influence successful programming. Andrews, Bonta, and Hoge (1990) introduced what are now commonly described as the ‘principles <strong>of</strong> effective intervention’ (PEI), which posit that programs should be designed to be responsive to <strong>of</strong>fenders’ specific risk and need levels (the risk-need-responsivity, or RNR, model: see also Taxman & Thanner, 2006). <strong>The</strong> risk principle in particular suggests that more intensive supervision and treatment should be targeted at higher-risk <strong>of</strong>fenders, an idea that is strongly supported by empirical research (see Lowenkamp, Latessa, & Holsinger, 2006, for a summary). <strong>The</strong> PEI suggest that ISP might be more effective if, through increased contact and control, the probation <strong>of</strong>ficer were able to establish <strong>of</strong>fenders’ risk and need levels and direct them into appropriate treatment. Treatment provision was not a priority <strong>of</strong> the BJA/RAND-evaluated programs, and few participants received such services (Latessa et al., 1998). However, results from some <strong>of</strong> the study sites indicated that intensive supervision combined with treatment might have a positive effect on crime, which led the evaluators to call for more research 6
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 and 14: intensity makes no difference to re
Page 15 and 16: isk of committing
Page 17 and 18: impact of probatio
Page 19: which perhaps suggests that ISP is
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
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
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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
Page 259 and 260:
safety first: 13 strategies for suc
Page 261 and 262:
Wilson, D. B. (2010). Meta-analysis
Page 263 and 264:
Angrist, J. D. (2006). Instrumental
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Clear, T. R., & Hardyman, P. L. (19
Page 267 and 268:
period266.pdf Lowenkamp, C. T., Lat
Page 269 and 270:
Rosch, J. (2006). Deviant peer cont
Page 271 and 272:
Taxman, F. S., & Thanner, M. (2006)
Page 273 and 274:
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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