The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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category and the remainder in Part II (Federal Bureau <strong>of</strong> Investigation, 2004). <strong>The</strong> classification is based on <strong>of</strong>fense seriousness, frequency <strong>of</strong> occurrence, nationwide pervasiveness, and likelihood <strong>of</strong> being reported. <strong>The</strong> eight Part I <strong>of</strong>fenses are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, motor vehicle theft, and arson. Thus, they are broader than the <strong>of</strong>fenses considered as ‘serious’ in the development <strong>of</strong> Philadelphia’s low-risk prediction model (although the Philadelphia model classifies a broader range <strong>of</strong> sexual <strong>of</strong>fenses as ‘serious’). An alternative to rating severity according to the substantive <strong>of</strong>fense is to assess the economic cost <strong>of</strong> crime. Interest in cost-benefit analysis as a part <strong>of</strong> criminal justice program evaluation is beginning to grow (e.g., Marsh, Chalfin, & Roman, 2008), and estimates <strong>of</strong> the cost <strong>of</strong> each type <strong>of</strong> crime to society is a key part <strong>of</strong> the methodology entailed by this approach. <strong>The</strong> severity <strong>of</strong> crime is ranked by the extent <strong>of</strong> its cost to society in terms <strong>of</strong> victimization costs (e.g., stolen property, loss <strong>of</strong> earnings, medical expenses, trauma, and suffering: Cohen, 1988), and criminal justice system costs (police investigation, court processing, cost <strong>of</strong> commensurate sentence: Marsh & Fox, 2008). Cohen (2000) and colleagues (2004) also propose that the cost <strong>of</strong> crime can be quantified in terms <strong>of</strong> the public’s theoretical willingness to pay for crime prevention programs that would reduce the prevalence <strong>of</strong> a particular <strong>of</strong>fense type by 10 per cent. More recently, some sophisticated statistical approaches to assessing <strong>of</strong>fending severity have emerged. One recent example is Ramchand et al.’s (2009) developmental model <strong>of</strong> crime severity. <strong>The</strong>y hypothesize that <strong>of</strong>fenders will progress to more serious crime after engaging in low-level <strong>of</strong>fending. This approach is appealing because, unlike previous classification models, it accounts for <strong>of</strong>fender preference and is culture-specific. 147
It assumes that if <strong>of</strong>fenders consider a particular crime type to be serious, they will only be drawn to it as their <strong>of</strong>fending career escalates. Thus, the sequencing <strong>of</strong> crime types over the life course provides insight into which <strong>of</strong>fenses <strong>of</strong>fenders perceive as more or less serious. We do not examine this approach in our paper because we lack full lifetime <strong>of</strong>fending data for our sample, so the utility <strong>of</strong> the developmental focus is limited. <strong>The</strong> quality <strong>of</strong> the data do not justify the complexity <strong>of</strong> the methods. Our operationalization <strong>of</strong> substantive severity is straightforward. We simply classify each charge as a serious or non-serious <strong>of</strong>fense according to the definition <strong>of</strong> severity used in the prediction model (see above). We also compare this definition <strong>of</strong> severity with the standard UCR distinction between serious and less serious <strong>of</strong>fending, which includes more <strong>of</strong>fenses than the Philadelphia model’s distinction. We propose a simpler method for analyzing economic severity. <strong>The</strong> more complex cost models may not be well suited to our data. Because our sample has already been deemed low-risk, in part because <strong>of</strong> their non-serious <strong>of</strong>fending backgrounds, serious <strong>of</strong>fenses will be rare events. Thus, it makes sense to simply dichotomize <strong>of</strong>fending history and outcomes into serious/non-serious rather than attempting to create more detailed categories. However, an economic severity rating is more difficult to dichotomize, beyond the rudimentary approach <strong>of</strong> assigning an arbitrary dollar value as a threshold between serious and non-serious <strong>of</strong>fending. <strong>The</strong> studies that do provide U.S. dollar estimates for crime types (e.g., Cohen, 1988; Miller, Cohen, & Wiersema, 1996) provide various formulas for assigning costs, and largely focus on the most serious crimes, so it is difficult to obtain estimates for the lower-level <strong>of</strong>fenses that are more prevalent in our sample. For example, cost estimates set out in Cohen (2000) contain no 148
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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
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probability of fai
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The count model ou
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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 and 158: assignment pool. Backfill probation
Page 159 and 160: Little research has been done on th
Page 161: already predicted to be at low risk
Page 165 and 166: involved. Crimes that obviously met
Page 167 and 168: We examine the potential interactio
Page 169 and 170: age is based on this date regardles
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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
Page 207 and 208: F14. Comparison group sample size f
Page 209 and 210: weight w is then recalculated (6),
Page 211 and 212: 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
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
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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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