The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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CHAPTER 1. Is ‘More’ Really ‘Better’? <strong>The</strong> Impact <strong>of</strong> Intensive Probation Supervision on Recidivism. Introduction Probation is one <strong>of</strong> the most frequently-used criminal sanctions in the United States (American Correctional Association, 2006). At the end <strong>of</strong> 2008, nearly 5.1 million adults were on probation alone – 84 per cent <strong>of</strong> all adults under community supervision. In all, one in forty-five U.S. adults is on probation or parole. 1 Although growth slowed slightly in 2008, the population under community supervision has been steadily rising for some time, increasing by more than half a million between 2000 and 2008 (Glaze & Bonczar, 2009). Despite the extent <strong>of</strong> its use, probation has suffered from image problems, particularly a public perception that it is a ‘s<strong>of</strong>t’ approach to crime for <strong>of</strong>ten serious <strong>of</strong>fenders who are highly likely to recidivate. 2 Subsequently, many probation agencies have struggled to access sufficient funding (Petersilia, 1997). This highlights a clear need for probation agencies to identify supervision practices that are effective at reducing recidivism, and at the same time represent an efficient use <strong>of</strong> scarce resources. Taxman (2002) notes that considerable research has been dedicated to programming and services that are <strong>of</strong>ten provided in conjunction with or on referral from probation, such as cognitive-behavioral therapy, drug courts, and skill-building programs (see also MacKenzie, 2006a; 2006b). Yet comparatively little attention has been paid to the 1
impact <strong>of</strong> probation supervision itself on crime: the number <strong>of</strong> cases a probation <strong>of</strong>ficer handles, the frequency <strong>of</strong> contact between <strong>of</strong>ficer and client, and the nature <strong>of</strong> the interaction. Supervision is perhaps considered an uninteresting part <strong>of</strong> the probation process, “in the background <strong>of</strong> other programming” and therefore “inconsequential to effectiveness” (Taxman, 2002, p. 179). On the contrary, supervision is a crucial aspect <strong>of</strong> probation not only because it is the bedrock <strong>of</strong> programming, but also because in a chronically under-funded enterprise it may constitute the only interaction between client and agency. In this regard it may directly impact the client’s future criminal behavior. If a probation <strong>of</strong>ficer with a caseload <strong>of</strong> 150 clients has inadequate time to spend with each one, s/he may find it impossible to build an accurate picture <strong>of</strong> individuals’ needs in order to target programming most effectively. Supervision levels vary widely, from weekly or twiceweekly meetings for high-risk or delinquent probationers, to telephone reporting for those near the end <strong>of</strong> their sentences. In some busy agencies ‘supervision’ may constitute nothing more than a mail-in contact detail confirmation card (Petersilia & Turner, 1993, p. 285). It is not always clear whether supervision intensity is related to the client’s needs or risk, or whether it is simply determined by operational capabilities. In this paper, we conduct a systematic search for literature on probation supervision intensity and synthesize the results using meta-analytic techniques to present the most current knowledge about the effect <strong>of</strong> changing intensity on probationers’ subsequent criminal conduct. We find that the amount <strong>of</strong> supervision in itself does not appear to be associated with recidivism outcomes. More supervision may in fact increase 2
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: isk of committing
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
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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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