The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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equations. <strong>The</strong> first stage equation uses the instrument variables to predict treatment take-up (the endogenous variable): T^ = " 0 + " TA + " REGION + " GENDER + " RACE + " AGE + " SES + " PRIORS + " TAR ! ! ! where +" TA * " REGION + " TA * " GENDER + " TA * " RACE + " TA * " AGE + " TA * " SES + " TA * " PRIORS T^ is the endogenous variable (predicted treatment take-up), " 0 is the intercept, " TA is the instrument for assigned treatment, the interactions between " TA and the ! subgroup variables are the additional instruments, and " TAR is a control (exogenous) ! variable for post-RA time at risk. Note that only the interactions and not the main effects variables for our subgroups are used as instruments. ! We hypothesize that race, gender, etc. predict treatment take-up through their association with treatment assignment. In the second stage equation, we replace the instruments with the predicted treatment take-up from the first stage ( "T^ ) to predict the crime outcome Y: Y = " 0 + "T^ + " TAR + " REGION + " GENDER + " RACE + " AGE + " SES + " PRIORS ! <strong>The</strong> main effects for the subgroups remain in the second stage model as controls for any direct ! variation in outcomes by subgroup. Another important part <strong>of</strong> the 2SLS approach is the estimation <strong>of</strong> the ‘reduced form,’ which is simply the OLS estimate <strong>of</strong> the ITT effect <strong>of</strong> the instrument and exogenous covariates on crime. Angrist (2006) notes that it is acceptable to use OLS even if the outcome variable is dichotomous. <strong>The</strong> coefficient for "T^ tells us the actual effect <strong>of</strong> treatment received, or local average treatment effect (LATE) on the probability <strong>of</strong> re<strong>of</strong>fending. It can be compared with the coefficient ! for assigned treatment in the reduced form model (or the outcomes from our logistic participation model, as described above) to assess whether the estimated 81
effect <strong>of</strong> LIS on recidivism is different when treatment actually received is predicted rather than treatment assigned. We can then use the predicted values from the 2SLS model to assign an individual probability <strong>of</strong> re<strong>of</strong>fending for each subject, and compare the mean probability <strong>of</strong> re<strong>of</strong>fending at different levels <strong>of</strong> each subgroup for those who received LIS. Although 17.8 per cent (N = 142) <strong>of</strong> the 800 <strong>of</strong>fenders assigned to LIS are known to have been excluded from the treatment, only 16.8 per cent (N = 134) are recorded in our data as such. Since we cannot tell which <strong>of</strong>fenders constitute the remaining eight non-treated cases, we simply analyze them as if they received the treatment. We do not expect this to be a substantial limitation <strong>of</strong> this analysis, since cases with missing data represent just 1 per cent <strong>of</strong> the treatment group and 0.5 per cent <strong>of</strong> the entire study sample. Another limitation <strong>of</strong> our treatment take-up prediction is that it does not account for the actual dosage <strong>of</strong> contacts received, which varied from the experimental protocol in some cases. However, we are able to estimate outcomes for those <strong>of</strong>fenders who were assigned to the LIS caseload and were likely to have seen the LIS <strong>of</strong>ficer at least once during the course <strong>of</strong> the experiment. Model construction All the models we estimate include the same covariates and controls for time at risk, both pre- and post-random assignment. <strong>The</strong> covariates we include are gender (male = 1), race (white vs. non-white), 11 the <strong>of</strong>fender’s age on the date <strong>of</strong> random assignment, a basic indicator <strong>of</strong> socioeconomic status (SES), 12 probation region (West = 1), and the 82
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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
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 and 94: Differential treatment take-up and
Page 95: control group members who received
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
Page 145 and 146: 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
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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