The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Our final analysis, which examined subgroup effects for those actually receiving the treatment, did show some substantial differences between LIS and SAU. Age, gender, SES, and to a lesser extent prior <strong>of</strong>fending history were significantly associated with outcomes for general and specific crime types. Overall, it appeared that low-risk <strong>of</strong>fenders, despite considerable heterogeneity in characteristics compared to the traditional image <strong>of</strong> the young, male <strong>of</strong>fender that appears in the criminological literature, were homogeneous in their propensity to re<strong>of</strong>fend. However, when we predicted outcomes according to <strong>of</strong>fender characteristics, we saw that the <strong>of</strong>fenders with the highest probability <strong>of</strong> recidivism were those who look more similar to that ‘traditional’ <strong>of</strong>fender. Young males from low-income neighborhoods were close to or above the sample average in their likelihood <strong>of</strong> re<strong>of</strong>fending, while the less traditional <strong>of</strong>fenders were generally well below it. Our sample, on average, was ‘non-traditional,’ being older and containing a broad mix <strong>of</strong> gender, SES and other characteristics. Thus, we may conclude that there is such a thing as a typical low-risk <strong>of</strong>fender, who looks different from the norm. <strong>The</strong> propensity to re<strong>of</strong>fend is homogeneous within this <strong>of</strong>fender subpopulation. <strong>The</strong> low-risk prediction model may also identify <strong>of</strong>fenders who have had little contact with the criminal justice system, or younger <strong>of</strong>fenders who have so far only engaged in low-level <strong>of</strong>fending, but exhibit some <strong>of</strong> the risk factors usually associated with more extensive criminal careers. Procedures could be built into the low-intensity supervision model to subject these <strong>of</strong>fenders to somewhat more monitoring than ‘typical’ low-risk cases (perhaps more frequent check-ins by telephone than the experimental protocol requires). Focusing some more attention on the more traditional <strong>of</strong>fenders in 101
low-intensity supervision would improve the likelihood <strong>of</strong> picking up the false positives, but resources would still have been conserved if the risk prediction proved accurate. Two other notable findings from this analysis were the differential effects by probation region, and the finding that <strong>of</strong>fenders without any <strong>of</strong>fending history in the previous year were more likely to fail than those without. <strong>The</strong> latter can be easily explained. This variable was a rate calculated by dividing the number <strong>of</strong> <strong>of</strong>fenses by the number <strong>of</strong> months in the year in which the <strong>of</strong>fender was out <strong>of</strong> jail and able to re<strong>of</strong>fend. Thus, those who spent the entire year in prison have a rate <strong>of</strong> zero, as do <strong>of</strong>fenders who were at liberty the whole time but did not engage in criminal behavior. Offenders who spent more time in jail are more likely to be serious <strong>of</strong>fenders with a higher risk <strong>of</strong> failure once free. It is difficult to tell whether the difference in supervision styles between the two probation <strong>of</strong>ficers really drove the difference in recidivism outcomes between the two regions, but reduced recidivism in the West was consistently observed. Since the <strong>of</strong>ficers were required to have minimal contact with their clients, it seems unlikely that their personalities would have made a great deal <strong>of</strong> difference to <strong>of</strong>fenders’ behavior. Thus, unobserved factors most likely operated here. We discussed above some <strong>of</strong> the differences in <strong>of</strong>fender characteristics between the two regions. It is also possible that more <strong>of</strong>fenders from the West region were incarcerated (or incarcerated for longer periods) during the follow-up period than those in the Northeast. A major limitation <strong>of</strong> our subgroup analysis was a lack <strong>of</strong> available information about <strong>of</strong>fenders. <strong>The</strong> subgroups we studied – gender, race, SES, age, and <strong>of</strong>fending history – are theoretically some <strong>of</strong> the most important covariates with <strong>of</strong>fending, but there is no reason why they would specifically impact the performance <strong>of</strong> low-risk <strong>of</strong>fenders 102
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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
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treatment group, and those to the r
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Discussion of Resu
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were evaluated in the same report (
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direction of effec
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To what extent does program philoso
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Which other components of</
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.450; technical violations: Q B < .
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it should be noted that those progr
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up in unpublished reports than thos
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violations first went before a judg
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2 One U.S. estimate indicates that
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Tables Table 1.1: Characteristics <
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Table 1.3: Moderator Variable <stro
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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 and 88: officers more <str
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: an end. On the other hand, research
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
Page 139 and 140: Table 2.16: Instrumental Variables
Page 141 and 142: Figures Figure 2.1: Risk-Based Allo
Page 143 and 144: Figure 2.5: Survival Time for LIS E
Page 145 and 146: CHAPTER 3. Risk Prediction for Effe
Page 147 and 148: sensitivity of the
Page 149 and 150: Programs should be designed to adhe
Page 151 and 152: weight information in an appropriat
Page 153 and 154: statistical model that would predic
Page 155 and 156: The Philadelphia m
Page 157 and 158: assignment pool. Backfill probation
Page 159 and 160: Little research has been done on th
Page 161 and 162: already predicted to be at low risk
Page 163 and 164: It assumes that if of</stro
Page 165 and 166: 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
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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
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Federal Bureau of
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Marsh, K., & Fox, C., (2008). <stro
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Sherman, L. W. (1993). Defiance, de
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