The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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<strong>of</strong>fender’s monthly <strong>of</strong>fending rate in the year pre-random assignment. <strong>The</strong> monthly <strong>of</strong>fending rate was calculated by dividing the number <strong>of</strong> <strong>of</strong>fenses for which the <strong>of</strong>fender was charged that took place in the year prior to random assignment by the number <strong>of</strong> months during that year that the <strong>of</strong>fender was able to <strong>of</strong>fend (i.e., was not in jail). Our dataset contained dummy variables showing whether or not the <strong>of</strong>fender was in jail during each month in that time period. We checked for multicollinearity between the race and SES variables by obtaining the correlation coefficient for the two variables, which was 0.44. Although this is a fairly large coefficient, we also obtained the variance inflation factors, which were all between 1 and 1.5 – well within the conventional threshold for assessing multicollinearity. We account for time at risk post-random assignment slightly differently in each model. In count models, the logged number <strong>of</strong> months at risk post-random assignment is included as the exposure or <strong>of</strong>fset variable, allowing us to estimate the incidence rate ratios for person-months <strong>of</strong> follow-up time for the LIS versus SAU groups. In the binary and two-stage least squares models, we include the number <strong>of</strong> months at risk as a control variable. 13 We lacked detailed information about time at risk, which is an important limitation <strong>of</strong> our analysis. 14 <strong>The</strong> format <strong>of</strong> our jail time data causes the most problems for assessing time at risk in the survival analysis model. As previously explained, survival analysis techniques allow us to assess whether experimental participants were <strong>of</strong>fenders or non-<strong>of</strong>fenders on a daily basis. Because we are only interested in the time to first <strong>of</strong>fense, <strong>of</strong>fenders who fail are removed from the risk set because they are no longer at risk <strong>of</strong> that first failure. However, <strong>of</strong>fenders who are in jail cannot <strong>of</strong>fend, so the days on which they are 83
incarcerated must also be removed from the risk set – their risk <strong>of</strong> failure on those days is zero. This would be straightforward if we knew the exact dates <strong>of</strong> entry and exit from jail as well as the exact <strong>of</strong>fense dates, but our dataset only includes monthly jail status indicators. When the jail variable is measured at less frequent time intervals than the failure event, a possible solution is to treat jail as a time-varying covariate (TVC) and interpolate jail data for each day. For example, if the monthly jail indicator for January 2008 shows that the <strong>of</strong>fender was in jail during that month, we code each day from January 1 to January 31 as a day in jail. Cox regression allows for this approach using ‘episode splitting,’ which involves creating a separate observation for each person-day up to the day <strong>of</strong> failure or censoring. It is also possible to work around the potential problem <strong>of</strong> overlapping jail and failure dates created by the interpolation (e.g., when the original dataset indicates that the <strong>of</strong>fender was in jail in February 2008, but he <strong>of</strong>fends on February 23) by simply dropping the month <strong>of</strong> jail time in which the <strong>of</strong>fense took place. Thus, the <strong>of</strong>fender in this example is coded as being out <strong>of</strong> jail during February. An obstacle to using the episode-splitting approach in the present application is that the method is usually used in cases in which it is possible to observe either level <strong>of</strong> the TVC on the failure date. For example, if our TVC were employment status, the <strong>of</strong>fender could be either employed or unemployed on the <strong>of</strong>fense date. However, we forced the <strong>of</strong>fender to be out <strong>of</strong> jail on the failure day because it did not make sense theoretically to allow for the overlap. Thus, our jail indicator variable is always coded 0 when our failure variable is coded 1. This results in perfect collinearity between the covariate and the failure event, which prevents us from estimating parameters for the jail variable using the Cox model. We could still have dropped jail days out <strong>of</strong> the risk set 84
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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
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 and 94: Differential treatment take-up and
Page 95 and 96: control group members who received
Page 97: effect of LIS on r
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
Page 145 and 146: CHAPTER 3. Risk Prediction for Effe
Page 147 and 148: 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
Page 257 and 258:
Petersilia, J., Turner, S., & Desch
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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
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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
Page 271 and 272:
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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