The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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‘typical’ survivor function for the model. Figure 2.4 shows the covariate-adjusted comparative survivor functions for the treatment and control groups. In this graph, the continuous covariates are held at their means and categorical covariates are set to the modal category. Thus, the ‘typical’ <strong>of</strong>fender is male, nonwhite, about 41 years old, supervised in the West region, lives in a zip code area with an average household income between $20,000 and $29,999, and was not incarcerated post-random assignment. <strong>The</strong> graph shows no difference in the comparative survivor functions for the average treatment group <strong>of</strong>fender compared to the average control group <strong>of</strong>fender, confirming earlier findings. <strong>The</strong> ‘typical’ <strong>of</strong>fender has a slightly lower risk <strong>of</strong> failure over time than the uncontrolled sample average. Only around 15 per cent had failed after two years. <strong>The</strong> results <strong>of</strong> the instrumental variables model used to explore whether differential treatment take-up impacts the effect <strong>of</strong> LIS are presented in Table 2.5. <strong>The</strong> first part <strong>of</strong> the table shows the results <strong>of</strong> the reduced form OLS regression, from which we obtain the probability <strong>of</strong> <strong>of</strong>fending by assigned treatment, and the first stage <strong>of</strong> the 2SLS regression, where assigned treatment and its interaction with <strong>of</strong>fender characteristics are used to predict treatment take-up. <strong>The</strong> second part <strong>of</strong> the table shows the outcomes from the second stage regression, which shows the actual effect <strong>of</strong> treatment on those who comply with random assignment. <strong>The</strong> first result to note is that the reduced form (ITT) model is largely consistent with our earlier findings about the impact <strong>of</strong> assigned treatment and other covariates. <strong>The</strong>re are slight differences because we include interaction terms in this model to adjust for heterogeneity in treatment assignment, but we still see little impact <strong>of</strong> LIS on the probability <strong>of</strong> recidivism. SES, region, and months in jail were again related to the 91
probability <strong>of</strong> failure regardless <strong>of</strong> group assignment. <strong>The</strong> second stage model shows no difference in recidivism for those who actually received the treatment, controlling for time at risk (b = .009, p ≤ .686). <strong>The</strong> results from the reduced form also show no significant interaction effects between observable case features and treatment assignment, suggesting that differences in the main null finding are not driven by differences in who was assigned treatment. We used the fitted values obtained from the IV model presented in Table 2.5 to assess subgroup differences in outcomes among those actually receiving the assigned LIS treatment (N = 690). Table 2.6 shows the mean probability <strong>of</strong> failure for each level <strong>of</strong> each subgroup. We observe significant differences in the probability <strong>of</strong> failure across all the subgroups except race. Consistent with the findings from the ITT models, the probability <strong>of</strong> <strong>of</strong>fending in the West was lower than that in the Northeast for LIS compliers (17.8% vs. 22.3%; p < .001). We included region as a subgroup because only one <strong>of</strong>ficer from each regional unit supervised all the low-risk <strong>of</strong>fenders. Thus, we hypothesized that the <strong>of</strong>ficers’ personalities and ability to follow the experimental protocol might affect outcomes. Although we cannot rule out the possibility that the difference is due to other unobserved factors, as discussed above, this finding provides some evidence in support <strong>of</strong> that hypothesis. <strong>The</strong> regional difference continues to appear when we examine only those <strong>of</strong>fenders who were actually exposed to the LIS <strong>of</strong>ficers, controlling for several other factors that may have affected treatment take-up and recidivism. Gender had a significant impact on the probability <strong>of</strong> failure, with males more likely to re<strong>of</strong>fend than females (20.7% vs. 18.3%, p ≤ .036). We see a significant decreasing probability <strong>of</strong> recidivism with increasing age (probability range: 16.2% - 92
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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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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 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: (IRR = 1.41, p ≤ .107). SES did n
Page 109 and 110: The count model ou
Page 111 and 112: estimate is also practically identi
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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
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
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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
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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
Page 275 and 276:
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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