The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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(albeit based on our interpolated dates rather than accurate ones), but to maintain consistency this would require us to ignore failures that overlapped with jail time. Since this would have meant ignoring one-third <strong>of</strong> our failure outcomes (111 <strong>of</strong> 335 failures overlapped with interpolated jail days), we compromised by including the twelve original monthly indicator variables in our model to control for each post-RA month in jail. This is another important caveat in interpreting the results. Outcome data and measures Our main outcomes <strong>of</strong> interest in the present analyses are the prevalence and frequency <strong>of</strong> any <strong>of</strong>fense committed within two years after the date <strong>of</strong> random assignment and resulting in a formal charge. Our pre-RA measures <strong>of</strong> crime are also based on charged <strong>of</strong>fenses occurring in the year before the RA date. To answer our final research question, we examine these outcomes using specific data on drug and violent <strong>of</strong>fenses. We select violent and drug <strong>of</strong>fending as secondary outcomes <strong>of</strong> interest because they may be most interesting to policymakers 15 . One considerable limitation <strong>of</strong> our crime outcomes is that they only reflect charges as an adult for <strong>of</strong>fenses in Philadelphia, as we only had access to local adult criminal justice system databases. While almost all <strong>of</strong> the participants reside in Philadelphia, the city’s proximity and ease <strong>of</strong> access to surrounding counties and state lines mean that the local data almost certainly underestimate the number <strong>of</strong> charged <strong>of</strong>fenses recorded for these <strong>of</strong>fenders. In addition, we do not have juvenile data available to give a full picture <strong>of</strong> <strong>of</strong>fenders’ lifetime criminal involvement. While most <strong>of</strong> the 85
<strong>of</strong>fenders in our sample are older (see Table 2.1), we would expect to see the majority <strong>of</strong> their criminal <strong>of</strong>fending taking place during their teenage years, so the lack <strong>of</strong> data on charges filed under the age <strong>of</strong> eighteen is a substantial omission. However, data are available where the <strong>of</strong>fender was charged as an adult, even if the <strong>of</strong>fense was committed while s/he was under eighteen. Some <strong>of</strong> the covariates and control variables we include in our model are likely to be confounded with crime outcomes. For example, it is well-established that younger <strong>of</strong>fenders commit more crime than older <strong>of</strong>fenders. Months in jail post-random assignment may also be associated with crime outcomes, as those who spend more time in jail are likely to be more serious <strong>of</strong>fenders, so will be at greater risk <strong>of</strong> re<strong>of</strong>fending while at liberty. For reference we present the conditional distributions <strong>of</strong> these two variables with the likelihood <strong>of</strong> committing a new charged <strong>of</strong>fense in Appendix G. Neither <strong>of</strong> these issues poses a substantial threat to the validity <strong>of</strong> the data because the covariates are not additionally confounded with the treatment instrument. Sample characteristics We assess our four research questions using the full experimental sample <strong>of</strong> 1,559 <strong>of</strong>fenders (800 LIS treatment and 759 SAU control). <strong>The</strong>y were followed up from the date <strong>of</strong> random assignment, October 1, 2007, for two years to September 30, 2009. Of the 800 <strong>of</strong>fenders randomly assigned to LIS, 94.5 per cent (N = 756) actually received the treatment and 5.5 per cent (N = 44) did not. Of the 759 SAU <strong>of</strong>fenders, 3.2 per cent (N = 24) were inadvertently placed on low-intensity supervision. Offenders are analyzed according to assigned treatment except in our instrumental variables model, as discussed 86
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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</
Page 49 and 50: .450; technical violations: Q B < .
Page 51 and 52: it should be noted that those progr
Page 53 and 54: up in unpublished reports than thos
Page 55 and 56: violations first went before a judg
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: incarcerated must also be removed f
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 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
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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 (
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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
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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