The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Instrumental Variables Model: Prevalence <strong>of</strong> Recidivism (Violent Offenses, 2-Year Follow-Up, Original Table 2.11) First Stage Treatment Take-Up Reduced Form (ITT) Post-RA Violent Off. Observations = 1,559 Observations = 1,559 R 2 = .654 R 2 = .028 Adjusted R 2 = .650 Adjusted R 2 = .017 Instruments b (S.E.) b (S.E.) Assigned LIS .818 (.089)*** -.089 (.065) Assigned LIS*West -.083 (.035)* .018 (.026) Assigned LIS*Male .014 (.032) .007 (.023) Assigned LIS*White -.022 (.038) .051 (.028) Assigned LIS*Age -.001 (.001) -.000 (.001) Assigned LIS*Income20 .034 (.058) .065 (.042) Assigned LIS*Income30 .085 (.061) .058 (.045) Assigned LIS*Income40 .036 (.067) .031 (.049) Assigned LIS*Prior <strong>of</strong>fending -.011 (.100) -.093 (.073) Exogenous West probation region .055 (.025)* -.027 (.018) Male .004 (.023) .045 (.017)** White .007 (.027) -.025 (.020) Age at RA .001 (.001) -.002 (.001)* Income $20,000-$29,999 -.007 (.040) -.053 (.029) Income $30,000-$39,999 -.014 (.042) -.070 (.031)* Income $40,000 or more -.006 (.047) -.079 (.034)* Monthly <strong>of</strong>fending rate 1 year pre-RA (violent <strong>of</strong>fense) -.055 (.083) .111 (.061) Constant -.053 (.064) .167 (.047)*** * p ≤ .05; ** p ≤ .01; *** p ≤ .001. Second-Stage Instrumental Number <strong>of</strong> observations = 1,559 Variables Regression Wald χ 2 (9 d.f.) = 35.44 Pr > χ 2 = .000 New Charged Violent Off. R 2 = .023 Term b S. E z p Predicted treatment take-up -.008 .014 -.61 .541 Constant .128 .033 3.91 .000 Controlling for region, gender, race, age, SES and <strong>of</strong>fending history. 227
Instrumental Variables Model: Prevalence <strong>of</strong> Recidivism (Drug Offenses, 2-Year Follow- Up, Original Table 2.16) First Stage Treatment Take-Up Reduced Form (ITT) Post-RA Drug Off. Observations = 1,559 Observations = 1,559 R 2 = .665 R 2 = .023 Adjusted R 2 = .651 Adjusted R 2 = .012 Instruments b (S.E.) b (S.E.) Assigned LIS .812 (.089)*** -.054 (.088) Assigned LIS*West -.086 (.035)* .027 (.035) Assigned LIS*Male .012 (.032) .034 (.032) Assigned LIS*White -.023 (.038) .018 (.038) Assigned LIS*Age -.000 (.001) .000 (.001) Assigned LIS*Income20 .032 (.058) .004 (.057) Assigned LIS*Income30 .083 (.061) -.010 (.060) Assigned LIS*Income40 .033 (.067) -.013 (.066) Assigned LIS*Prior <strong>of</strong>fending .200 (.093)* .006 (.092) Exogenous West probation region .055 (.025)* -.016 (.025) Male .004 (.023) .039 (.023) White .007 (.027) .009 (.027) Age at RA .001 (.001) -.003 (.001)** Income $20,000-$29,999 -.007 (.040) -.046 (.040) Income $30,000-$39,999 -.013 (.042) -.060 (.042) Income $40,000 or more -.005 (.047) -.098 (.047)* Monthly <strong>of</strong>fending rate 1 year pre-RA (drug <strong>of</strong>fense) -.010 (.055) .006 (.055) Constant -.052 (.064) .254 (.064)*** Second-Stage Instrumental Number <strong>of</strong> observations = 1,559 Variables Regression Wald χ 2 (9 d.f.) = 34.39 Pr > χ 2 = .000 New Charged Drug Offense R 2 = .102 Term b S. E z p Predicted treatment take-up -.013 .018 -.71 .475 Constant .230 .044 5.21 .000 Controlling for region, gender, race, age, SES and <strong>of</strong>fending history. 228
Page 1 and 2:
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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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 (
Page 191 and 192: Table 3.6: Predictive Ability at Al
Page 193 and 194: Table 3.9: Post-Random Assignment O
Page 195 and 196: APPENDICES Appendix A: Systematic R
Page 197 and 198: combination. The s
Page 199 and 200: B. ELIGIBILITY CHECKLIST B1. First
Page 201 and 202: Other: txcxloth C6d. Additional pro
Page 203 and 204: D. SAMPLE LEVEL CODING SHEET Instru
Page 205 and 206: E. DEPENDENT VARIABLE CODING SHEET
Page 207 and 208: F14. Comparison group sample size f
Page 209 and 210: weight w is then recalculated (6),
Page 211 and 212: Study Sheffield (Males) 1976 (Folka
Page 213 and 214: Study Dallas TX 1992 (Turner & Pete
Page 215 and 216: Study Minnesota ISR 1995 (Deschenes
Page 217 and 218: Study Clark WA 2003 (Barnoski, 2003
Page 219 and 220: Study Whatcom WA 2003 (Barnoski, 20
Page 221 and 222: Study Los Angeles CA 2005 (Zhang &
Page 223 and 224: Fed. Prob., 56, 12-17. Turner, Pete
Page 225 and 226: Study Reference Benekos & Sonnenber
Page 227 and 228: Study Reference Giblin (2002) Using
Page 229 and 230: Study Reference Lasater et al. (n.d
Page 231 and 232: Study Reference Robertson (2000) Co
Page 233 and 234: Study Reference Turner & Greene (19
Page 235 and 236: Appendix E: Philadelphia APPD Low-<
Page 237 and 238: Prevalence of Reci
Page 239 and 240: Frequency of Recid
Page 241: Instrumental Variables Model: Preva
Page 245 and 246: Post-RA Months in Jail and Probabil
Page 247 and 248: Scaled Schoenfeld Residuals Time =
Page 249 and 250: BIBLIOGRAPHY Chapter 1 ** Denotes t
Page 251 and 252: Cook, T. D., & Campbell, D. T. (197
Page 253 and 254: Glaze, L. E, & Bonczar, T. P. (2009
Page 255 and 256: MacKenzie, D. L. (2006a). What work
Page 257 and 258: Petersilia, J., Turner, S., & Desch
Page 259 and 260: safety first: 13 strategies for suc
Page 261 and 262: Wilson, D. B. (2010). Meta-analysis
Page 263 and 264: Angrist, J. D. (2006). Instrumental
Page 265 and 266: Clear, T. R., & Hardyman, P. L. (19
Page 267 and 268: period266.pdf Lowenkamp, C. T., Lat
Page 269 and 270: Rosch, J. (2006). Deviant peer cont
Page 271 and 272: Taxman, F. S., & Thanner, M. (2006)
Page 273 and 274: 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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