The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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therefore a safe strategy for restructuring probation supervision according to APPD’s plans (Barnes et al., forthcoming). <strong>The</strong> Present Study While the results <strong>of</strong> the Low Risk Experiment are promising, this summary does not provide a full picture <strong>of</strong> the predictive power <strong>of</strong> the model or the severity <strong>of</strong> <strong>of</strong>fending in our sample. Despite the compelling evidence in favor <strong>of</strong> statistical risk prediction, it can never be an error-free endeavor. As Grove and Meehl (1996) note: “[T]he statistics furnish us with the probabilities so far as anything can” (p. 306). Actuarial prediction for correctional policy provides neither individualized predictions nor actual outcomes for a given person. <strong>The</strong> Philadelphia model identifies low-risk <strong>of</strong>fenders in part based on their prior history, but it is entirely possible that <strong>of</strong>fenders with a criminal history considered to be ‘serious’ on some basis could have been assigned a low-risk prediction, based either on the balance <strong>of</strong> other factors or varying definitions <strong>of</strong> <strong>of</strong>fending severity. It is also possible that low-intensity supervision could lead to an escalation in <strong>of</strong>fending severity as it becomes less likely that probation <strong>of</strong>ficers will pick up and act on violations and transgressions (and as <strong>of</strong>fenders begin to realize this). <strong>The</strong>se are the cases that get picked up by the public, politicians, and the media, and serve to undermine an otherwise rational policy. To this end, it is important to ensure that the low-risk prediction model and lowintensity supervision do not have hidden harmful effects. <strong>The</strong> APPD experiment also raises a broader theoretical question – what is the nature <strong>of</strong> ‘non-serious’ <strong>of</strong>fending, and how does it differ from higher-level <strong>of</strong>fending? 143
Little research has been done on the characteristics <strong>of</strong> low-level <strong>of</strong>fenders. While focusing on the more serious <strong>of</strong>fenders is logical from a public safety perspective, the premise <strong>of</strong> the Philadelphia model is that the majority <strong>of</strong> <strong>of</strong>fenders are likely to be lowrisk. Thus, the population is worth considering for its size alone. <strong>The</strong> objectives <strong>of</strong> this paper are to investigate the sensitivity <strong>of</strong> Philadelphia’s prediction model to serious <strong>of</strong>fending at various risk levels, and to examine whether lowintensity supervision could have the unintended consequence <strong>of</strong> increasing <strong>of</strong>fending severity in a sample that should indicate little history <strong>of</strong> serious criminal behavior. In doing so, we learn more about the nature and degree <strong>of</strong> ‘serious’ crime among the majority <strong>of</strong> lower-level <strong>of</strong>fenders. Our specific research questions are: 1. How successfully does the model categorize <strong>of</strong>fenders as low or non-low risk? 2. Does the sensitivity <strong>of</strong> the model change under different definitions <strong>of</strong> <strong>of</strong>fending severity? 3. How does the selected threshold for determining low/non-low risk (reliability score > 0.5) compare in its predictive ability to alternative cut-points? 4. Does low-intensity probation supervision affect <strong>of</strong>fenders’ propensity for serious <strong>of</strong>fending? Methodology Outcome data A wide range <strong>of</strong> data collected as part <strong>of</strong> the experiment and model development are available for the present analysis. In addition to data on charges for <strong>of</strong>fenses pre- and post-random assignment for the low-risk <strong>of</strong>fenders who participated in the experiment, 144
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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</
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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
Page 107 and 108: probability of fai
Page 109 and 110: The count model ou
Page 111 and 112: estimate is also practically identi
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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
Page 151 and 152: weight information in an appropriat
Page 153 and 154: statistical model that would predic
Page 155 and 156: The Philadelphia m
Page 157: assignment pool. Backfill probation
Page 161 and 162: already predicted to be at low risk
Page 163 and 164: It assumes that if of</stro
Page 165 and 166: involved. Crimes that obviously met
Page 167 and 168: We examine the potential interactio
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Page 173 and 174: of low-intensity s
Page 175 and 176: classified as low risk. The
Page 177 and 178: All the risk ratios in Table 3.9 in
Page 179 and 180: 0.55. Raising the threshold results
Page 181 and 182: are partly selected on the basis <s
Page 183 and 184: make up the majority of</st
Page 185 and 186: strategy allows probation o
Page 187 and 188: Notes 1 In this study, murder, atte
Page 189 and 190: 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
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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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