The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Table 2.15: Time to Failure (Drug Offenses, 2-Year Follow-Up) Cox Regression Number <strong>of</strong> subjects = 1,559 (Breslow Method for Ties) Number <strong>of</strong> failures = 148 Time at risk (person-days) = 1,071,848 Likelihood Ratio χ 2 (21 d.f.) = 142.68 Log likelihood = -1009.539 Pr > χ 2 = .000 Term Hazard Ratio S. E. z p Treatment group .93 .157 -.42 .673 West probation region .87 .178 -.70 .483 Male 1.96 .399 3.32 .001 White 1.14 .243 .63 .529 Age at RA .97 .009 -3.40 .001 Income $20,000-$29,999 .44 .127 -2.85 .004 Income $30,000-$39,999 .40 .122 -3.01 .003 Income $40,000 or more .26 .093 -3.77 .000 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged drug <strong>of</strong>f.) 1.46 .595 .93 .353 In jail Oct 2007 2.42 2.445 .88 .382 In jail Nov 2007 1.13 1.599 .08 .934 In jail Dec 2007 1.94 2.750 .47 .641 In jail Jan 2008 .22 .308 -1.08 .282 In jail Feb 2008 2.33 3.319 .60 .552 In jail Mar 2008 1.72 2.130 .44 .661 In jail Apr 2008 2.03 1.640 .88 .378 In jail May 2008 .99 .628 -.01 .991 In jail Jun 2008 .55 .428 -.76 .445 In jail Jul 2008 2.84 1.930 1.54 .124 In jail Aug 2008 .48 .257 -1.37 .171 In jail Sep 2008 .86 .395 -.33 .744 123
Table 2.16: Instrumental Variables Model: Prevalence <strong>of</strong> Recidivism (Drug Offenses, 2- Year Follow-Up) First Stage Treatment Take-Up Reduced Form (ITT) Post-RA Drug Off. Observations = 1,559 Observations = 1,559 R 2 = .662 R 2 = .103 Adjusted R 2 = .658 Adjusted R 2 = .093 Instruments b (S.E.) b (S.E.) Assigned LIS .803 (.088)*** -.035 (.085) Assigned LIS*West -.077 (.035)* .009 (.034) Assigned LIS*Male .011 (.032) .037 (.030) Assigned LIS*White -.021 (.037) .015 (.036) Assigned LIS*Age -.000 (.001) .000 (.001) Assigned LIS*Income20 .024 (.057) .018 (.055) Assigned LIS*Income30 .082 (.060) -.008 (.058) Assigned LIS*Income40 .021 (.066) .013 (.064) Assigned LIS*Prior <strong>of</strong>fending .193 (.092)* .022 (.089) Exogenous Months in jail post-RA -.013 (.002)*** .026 (.002)*** West probation region .056 (.025)* -.017 (.024) Male .009 (.023) .028 (.022) White .010 (.027) .004 (.026) Age at RA .001 (.001) -.002 (.001)* Income $20,000-$29,999 -.001 (.040) -.063 (.038) Income $30,000-$39,999 -.013 (.042) -.060 (.040) Income $40,000 or more -.002 (.047) -.104 (.045)* Monthly <strong>of</strong>fending rate 1 year pre-RA (drug <strong>of</strong>fense) -.025 (.054) .023 (.052) Constant -.028 (.063) .205 (.061)*** Second-Stage Instrumental Number <strong>of</strong> observations = 1,559 Variables Regression Wald χ 2 (10 d.f.) = 176.88 Pr > χ 2 = .000 New Charged Drug Offense R 2 = .102 Term b S. E z p Predicted treatment take-up -.010 .018 -.60 .550 Months in jail post-RA .026 .002 11.67 .000 Constant .191 .043 4.50 .000 Controlling for region, gender, race, age, SES and <strong>of</strong>fending history. 124
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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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the proportions of
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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 and 134: Table 2.9: Time to Failure (Violent
Page 135 and 136: Table 2.12: Treatment Effect by Sub
Page 137: Table 2.14: Frequency of</s
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
Page 169 and 170: age is based on this date regardles
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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
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Page 183 and 184: make up the majority of</st
Page 185 and 186: strategy allows probation o
Page 187 and 188: 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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