The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Table 2.11: Instrumental Variables Model: Prevalence <strong>of</strong> Recidivism (Violent Offenses, 2-Year Follow-Up) First Stage Treatment Take-Up Reduced Form (ITT) Post-RA Violent Off. Observations = 1,559 Observations = 1,559 R 2 = .661 R 2 = .117 Adjusted R 2 = .658 Adjusted R 2 = .107 Instruments b (S.E.) b (S.E.) Assigned LIS .808 (.088)*** -.073 (.062) Assigned LIS*West -.075 (.035)* .005 (.025) Assigned LIS*Male .013 (.032) .009 (.022) Assigned LIS*White -.021 (.037) .048 (.026) Assigned LIS*Age -.000 (.001) .000 (.001) Assigned LIS*Income20 .025 (.057) .078 (.040) Assigned LIS*Income30 .083 (.060) .060 (.043) Assigned LIS*Income40 .021 (.066) .053 (.047) Assigned LIS*Prior <strong>of</strong>fending -.004 (.099) -.104 (.070) Exogenous Months in jail post-RA -.013 (.002)*** .021 (.002)*** West probation region .056 (.025)* -.028 (.017) Male .010 (.023) .037 (.016)* White .009 (.027) -.028 (.019) Age at RA .001 (.001) -.001 (.001) Income $20,000-$29,999 -.001 (.039) -.066 (.028)* Income $30,000-$39,999 -.014 (.042) -.070 (.030)* Income $40,000 or more -.002 (.047) -.085 (.033)** Monthly <strong>of</strong>fending rate 1 year pre-RA (violent <strong>of</strong>fense) -.069 (.082) .133 (.058)* Constant -.029 (.063) .131 (.045)** * p ≤ .05; ** p ≤ .01; *** p ≤ .001. Second-Stage Instrumental Number <strong>of</strong> observations = 1,559 Variables Regression Wald χ 2 (10 d.f.) = 192.81 Pr > χ 2 = .000 New Charged Violent Off. R 2 = .110 Term b S. E z p Predicted treatment take-up -.007 .013 -.54 .586 Months in jail post-RA .020 .002 12.27 .000 Constant .010 .031 3.19 .001 Controlling for region, gender, race, age, SES and <strong>of</strong>fending history. 119
Table 2.12: Treatment Effect by Subgroups (Violent Offenses, 2-Year Follow Up) Region Gender Race Age at RA Income Offending rate per month at risk 1 year pre- RA Subgroup N Mean Pr. <strong>of</strong> Failure Northeast 339 .045 West 351 .030 Male 464 .053 Female 226 .006 White 283 .038 Nonwhite 407 .038 Under 25 34 .048 25-34 182 .050 35-44 176 .032 45-54 236 .034 55 + 62 .025 < $20,000 52 .059 $20,000-$29,999 261 .044 $30,000-$39,999 239 .042 $40,000 + 138 .011 No violent <strong>of</strong>fending Violent <strong>of</strong>fending † 2-sample t-test or one-way ANOVA (F-test). 667 .037 23 .061 t/F † p 3.09 .002 -9.76 .000 .01 .989 3.38 .009 12.13 .000 -1.79 .073 120
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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
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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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Page 85 and 86: Interventions and Follow-Up Probati
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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
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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: Table 2.9: Time to Failure (Violent
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
Page 167 and 168: We examine the potential interactio
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Page 173 and 174: of low-intensity s
Page 175 and 176: classified as low risk. The
Page 177 and 178: All the risk ratios in Table 3.9 in
Page 179 and 180: 0.55. Raising the threshold results
Page 181 and 182: are partly selected on the basis <s
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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
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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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