The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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some probationers will be eligible for LIS but will not receive it because they abscond or for some other reason. However, the ITT approach involves an unavoidable trade-<strong>of</strong>f between this and a test <strong>of</strong> the actual effectiveness <strong>of</strong> the treatment. Because most experiments will include some degree <strong>of</strong> non-delivery and/or crossover, any treatment effect will be attenuated (Angrist, 2006). In this experiment it is very likely that nondelivery <strong>of</strong> treatment affected the outcome: 17.8 per cent <strong>of</strong> probationers assigned to LIS did not receive it (see above and Fig. 2.2). Crossover poses a smaller problem, but does exist: 3.2 per cent (N = 24) <strong>of</strong> the control group were assigned to LIS at some time. It is not known why the crossover occurred, but it is possible that <strong>of</strong>fender characteristics could be associated with the non-delivery. Factors like gender, race, age, and prior <strong>of</strong>fending history may well be linked to reasons for non-delivery such as absconding or transfer to a specialized caseload for more intensive supervision. A powerful technique for modeling heterogeneity in treatment effects created by treatment non-delivery and differential subgroup effects is the instrumental variables (IV) method (e.g., Angrist, 2006; Angrist & Pischke, 2009). IV methods can help to overcome the attenuation <strong>of</strong> the treatment effect involved in ITT analysis by using assigned treatment (ITT) as an instrument for predicting actual treatment take-up. Predicted take-up, rather than assigned treatment or treatment delivered, is then used as the experimental status variable in the crime outcome model. This is different from – and avoids the bias <strong>of</strong> – estimating the effect <strong>of</strong> treatment delivered. In more formal terms, it reflects the local average treatment effect (LATE), which is the treatment effect for “compliers” – those who receive the treatment to which they were randomly assigned – rather than the average effect on all treated individuals (ATET), who may be compliers or 79
control group members who received the treatment anyway (Angrist, 2006, pp. 30-31). Thus, we get as close as possible to estimating the actual effect <strong>of</strong> the treatment. <strong>The</strong> risk <strong>of</strong> bias in the basic treatment as delivered approach is minimized in the IV model because treatment as assigned (ITT) is incorporated in the prediction <strong>of</strong> treatment take-up. Due to the random assignment, treatment status is independent <strong>of</strong> observable features <strong>of</strong> the cases. Furthermore, by including interaction terms between assigned treatment and selected <strong>of</strong>fender characteristics (subgroups) as instruments in the IV model, we can also account for their potential indirect impact on treatment take-up and use the resulting estimates to examine differential effects <strong>of</strong> subgroup membership on recidivism for LIS compliers. <strong>The</strong> subgroups we explore include age, sex, race, socioeconomic status (based on neighborhood-level data for the <strong>of</strong>fender’s recorded address), prior <strong>of</strong>fending record, and probation region (West or Northeast). We include region as a subgroup because only two probation <strong>of</strong>ficers, one from each regional unit, handled low-risk cases, whereas a much broader range <strong>of</strong> <strong>of</strong>ficers was represented in the control group. With such a limited number <strong>of</strong> low-intensity supervision <strong>of</strong>ficers, it is likely that each <strong>of</strong>ficer’s personality and willingness or ability to follow the experimental protocol could have affected the operation <strong>of</strong> the low-intensity model. Unfortunately, the quality <strong>of</strong> additional data on important crime risk and protective factors like marital status, employment, and drug, alcohol, medical, and psychological issues is poor. Reporting by probation <strong>of</strong>ficers <strong>of</strong> these details is inconsistent. Thus, we are unable to explore these additional factors. IV methods are applied using two-stage least-squares regression (2SLS), which is related to ordinary least-squares regression (OLS) and incorporates simultaneous 80
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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 (
Page 43 and 44: direction of effec
Page 45 and 46: To what extent does program philoso
Page 47 and 48: 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: Differential treatment take-up and
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
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
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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 (
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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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