The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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PREFACE This dissertation presents an examination <strong>of</strong> the impact <strong>of</strong> changing the intensity <strong>of</strong> the probation sanction on the recidivism <strong>of</strong> adjudicated probation clients. Historically, research on probation supervision has revolved around the assumption that more intensive (increased) supervision is the best way to ensure public safety and prevent crime, while saving money relative to incarceration. However, that assumption was not, for the most part, evidence-based. Rigorous research has shown that intensive probation programs yield mixed results at best, and may be even less effective for the least serious <strong>of</strong>fenders. This dissertation addresses two broad questions: What scientific evidence is there for the effectiveness <strong>of</strong> probation supervision? Does variation in its intensity affect crime outcomes? <strong>The</strong> following three chapters represent three stand-alone papers looking at different aspects <strong>of</strong> these questions: a review <strong>of</strong> the existing evidence; a randomized controlled trial <strong>of</strong> low-intensity supervision; and a method for predicting low-risk <strong>of</strong>fenders suitable for minimal supervision. Chapter 1 reports the results <strong>of</strong> a rigorous search and synthesis <strong>of</strong> the existing literature on intensive supervision probation (ISP). Although ISP is one <strong>of</strong> the most thoroughly tested criminal justice interventions, the evaluations have never been identified and synthesized in a methodologically rigorous way. We examine forty-seven experimental and quasi-experimental studies conducted over the last fifty years using meta-analytic techniques to investigate whether and under what circumstances ISP might be effective. We find no evidence that the mostly high-risk probationers on ISP fare any better than their counterparts who receive traditional supervision. Increased supervision xi
intensity makes no difference to recidivism, and tends to increase the rate <strong>of</strong> technical violations (which can lead to returns to jail and further criminalization) due to the increased surveillance inherent in the process. However, we find several more recent studies that show more promising reductions in recidivism. <strong>The</strong>se programs tend to focus more closely on the content <strong>of</strong> supervision, which remains a largely neglected aspect <strong>of</strong> probation, rather than contact frequency and caseload size. We conclude that the assumption that “more is better” does not necessarily hold true, and that it is more important to ask what probation <strong>of</strong>ficers are expected to achieve during supervision. Chapters 2 and 3 build on the findings from an experiment conducted with the Philadelphia Adult Probation and Parole Department (APPD), in which the agency restructured supervision activities along risk-based lines. Sixty per cent <strong>of</strong> APPD’s caseload was filtered into reduced-intensity supervision based on a statistical prediction that they were at low risk <strong>of</strong> serious recidivism. <strong>The</strong> removal <strong>of</strong> resources from some probationers has met with criticism, despite the longer-term goal <strong>of</strong> the project: to free up staff to work more closely with the dangerous <strong>of</strong>fenders who increase the public’s fear <strong>of</strong> crime. <strong>The</strong> goal <strong>of</strong> these chapters is to provide a rigorous evaluation <strong>of</strong> low-intensity supervision and the suitability <strong>of</strong> the prediction model, to ensure first that no serious <strong>of</strong>fenders inadvertently receive too little supervision, and second that reduced supervision in itself is not criminogenic. While Chapter 1 shows no evidence that increased supervision prevents serious <strong>of</strong>fenders from committing crimes, it remains important to show that reduced supervision does not lead to unfavorable outcomes. <strong>The</strong> policy cannot work as a resource-saving strategy if agencies view it as too politically risky. xii
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: LIST OF FIGURES Figure 1.1: Effect
Page 15 and 16: isk of committing
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
Page 65 and 66:
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
Page 81 and 82:
fatal shootings in Philadelphia are
Page 83 and 84:
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
Page 97 and 98:
effect of LIS on r
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incarcerated must also be removed f
Page 101 and 102:
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
Page 131 and 132:
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 &
Page 223 and 224:
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
Page 251 and 252:
Cook, T. D., & Campbell, D. T. (197
Page 253 and 254:
Glaze, L. E, & Bonczar, T. P. (2009
Page 255 and 256:
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
Page 265 and 266:
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)
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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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