The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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to society. This is the ultimate purpose behind the Philadelphia APPD Low Risk Experiment, which aims to identify and divert the lowest-level <strong>of</strong>fenders in the agency into a large caseload with reduced (‘low-intensity’) supervision to allow probation <strong>of</strong>ficers to work more closely with serious <strong>of</strong>fenders. <strong>The</strong> implementation <strong>of</strong> a low-intensity model <strong>of</strong> probation supervision for lowrisk <strong>of</strong>fenders is dependent on the development <strong>of</strong> a reliable method <strong>of</strong> predicting the risk <strong>of</strong> serious recidivism. First and foremost, such a method must ensure that individuals at the highest risk <strong>of</strong> committing a serious crime do not receive less supervision than they need. However, if less severe <strong>of</strong>fenders are included, probation <strong>of</strong>ficers will continue to be overwhelmed. Furthermore, research has suggested that probation supervision that is too ‘intense’ (in terms <strong>of</strong> frequency <strong>of</strong> contact and time spent with probation <strong>of</strong>ficers) may increase recidivism among <strong>of</strong>fenders at the lowest risk level (e.g., Erwin, 1986; Hanley, 2006). Assigning low-risk <strong>of</strong>fenders to excessively intensive programs might provoke defiant reactions (Sherman, 1993), reinforce delinquent attitudes and behavior, and disrupt pro-social networks and opportunities such as family ties and employment by requiring too much intervention from the criminal justice system. All <strong>of</strong> these factors may have unfavorable effects on future <strong>of</strong>fending (Lowenkamp & Latessa, 2004). Following Sherman’s (2007) proposition, then, the definition <strong>of</strong> ‘serious’ must incorporate only those crimes that represent the greatest threat to public safety. <strong>The</strong> aim <strong>of</strong> this paper is to provide an in-depth analysis <strong>of</strong> the risk prediction and supervision strategies described above. We examine actual serious recidivism outcomes for a sample <strong>of</strong> probationers predicted by the statistical model used in the Philadelphia experiment to be at low risk <strong>of</strong> committing the most serious crimes. We examine the 131
sensitivity <strong>of</strong> the selected threshold for determining ‘low risk’ according to the model by contrasting the serious re<strong>of</strong>fending outcomes <strong>of</strong> <strong>of</strong>fenders predicted to be low risk to those <strong>of</strong> <strong>of</strong>fenders not predicted to be low risk. We also examine the model’s sensitivity to different definitions <strong>of</strong> <strong>of</strong>fending severity beyond the original substantive definitions it was designed to predict. Finally, we examine whether the intensity <strong>of</strong> supervision affects the relationship between past and future serious <strong>of</strong>fending by comparing low risk experimental participants randomly assigned to low intensity supervision with those subject to standard reporting requirements. Risk Prediction in Offender Management <strong>The</strong> prediction and assessment <strong>of</strong> risk is a long-standing concern <strong>of</strong> criminological theory and research. While it may not be possible (or indeed ethical) to precisely predict and act against those who will commit crimes in the future, the policy and practice implications <strong>of</strong> understanding the risk factors <strong>of</strong> crime and how they relate to <strong>of</strong>fender management and crime prevention are obvious. <strong>The</strong> history <strong>of</strong> risk prediction in criminal justice dates back at least to the first half <strong>of</strong> the twentieth century, when criminologists such as Burgess (1928) and the Gluecks (1950) developed simple predictive models based on checklists <strong>of</strong> risk and protective factors. Burgess, for example, combined unweighted predictors considered by expert opinion to be related to parole outcomes. More recently, risk prediction has been refined by extensive research on the factors that contribute favorably or unfavorably to <strong>of</strong>fending behavior, many <strong>of</strong> which are now strongly confirmed by numerous studies and meta- 132
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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
Page 95 and 96: control group members who received
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
Page 145: CHAPTER 3. Risk Prediction for Effe
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 and 158: assignment pool. Backfill probation
Page 159 and 160: Little research has been done on th
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 171 and 172: obberies. The only
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
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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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