The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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supervision than they might need. When we repeated our analyses <strong>of</strong> the model using UCR Part I <strong>of</strong>fenses (those <strong>of</strong>fenses considered by the FBI to be <strong>of</strong> greater concern to the authorities, based on severity among other factors), and <strong>of</strong>fenses with victims that were likely to involve a greater economic cost, we saw similar patterns <strong>of</strong> <strong>of</strong>fending as for model-defined severity. <strong>The</strong> low risk group were still at lower risk <strong>of</strong> committing these <strong>of</strong>fenses than the non-low risk group, and our findings about the threshold held relatively constant. However, using these alternative definitions may defeat the key object <strong>of</strong> Philadelphia’s prediction model: to better distribute agency resources according to the risk <strong>of</strong> the type <strong>of</strong> serious <strong>of</strong>fending that poses the greatest threat to public safety and fear <strong>of</strong> crime. Only 31 per cent <strong>of</strong> low risk <strong>of</strong>fenders had ever been charged with such an <strong>of</strong>fense (3 per cent post-risk assessment), but nearly 70 per cent on average had been charged with a UCR Part I or victim/damage <strong>of</strong>fense (nearly 9 per cent post-risk assessment). While these are undoubtedly <strong>of</strong>fenses that contribute substantially to the crime problem, their high prevalence in a sample <strong>of</strong> <strong>of</strong>fenders known not to be causing the worst kinds <strong>of</strong> harm to society suggests that it is less crucial to focus on these crime types than homicide, robbery, serious assaults, and sexual crimes. Our choice <strong>of</strong> alternative definitions <strong>of</strong> severity was the main limitation <strong>of</strong> this part <strong>of</strong> the exploration because it was not possible with the available data to create a more detailed ranking <strong>of</strong> severity based on different factors. Our two proxy measures were necessarily too broad because <strong>of</strong> the difficulties (discussed above) in analyzing limited data on rare events. <strong>The</strong>y tended to overstate serious <strong>of</strong>fending by including <strong>of</strong>fenses that might not be considered serious at all when deciding which <strong>of</strong>fenders require more intensive criminal justice system intervention. UCR Part I <strong>of</strong>fenses, as discussed above, 165
are partly selected on the basis <strong>of</strong> substantive severity, but also on other factors such as frequency <strong>of</strong> commission and likelihood <strong>of</strong> detection. It is clear, given these additional qualities <strong>of</strong> the UCR Part I <strong>of</strong>fenses, that any <strong>of</strong>fender, regardless <strong>of</strong> risk, would be much more likely to commit some <strong>of</strong> these <strong>of</strong>fense types. Since there is some overlap <strong>of</strong> the most severe crimes in the UCR list with the model-defined serious <strong>of</strong>fenses, it is likely that the majority <strong>of</strong> additional UCR Part I <strong>of</strong>fenses committed by the low risk group were the less serious, more nationally prevalent crimes like motor vehicle theft. Similarly, our victim proxy for cost likely captured some less serious crimes (substantively or economically) simply because we selected them based on victim status only. <strong>The</strong> 1993 dollar estimates provided in Cohen (2000) show that the tangible and quality <strong>of</strong> life costs <strong>of</strong> victim crimes vary widely, not to mention the costs <strong>of</strong> some non-victim crimes. For example, the cost per victimization for homicide was thought to be nearly $3 million, compared to just $2,000 for an assault without serious injury, but our measure included both. A more refined analysis <strong>of</strong> economic severity in comparison to the model’s definition would require more data, updated cost estimates for a wide range <strong>of</strong> victim and non-victim crimes, and more detail about each charge in order to make more accurate judgments about injury and damage, beyond the simple victim/non-victim distinction. Additional data on lifetime <strong>of</strong>fending would also allow for some more sophisticated developmental analyses <strong>of</strong> severity and <strong>of</strong>fending escalation like the model proposed in Ramchand et al. (2009). In all, it would appear that the Philadelphia model has achieved its purpose with this sample <strong>of</strong> <strong>of</strong>fenders. Returning to the earlier discussion <strong>of</strong> the qualities that make the model a valuable contribution to the risk prediction literature, we can confirm that the 166
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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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estimate is also practically identi
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twice the risk of
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an end. On the other hand, research
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low-intensity supervision would imp
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Conclusion The aim
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We found that low-risk of</
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7 LIS participants who were transfe
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Tables Table 2.1: Sample Characteri
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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 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
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: 0.55. Raising the threshold results
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
Page 213 and 214: Study Dallas TX 1992 (Turner & Pete
Page 215 and 216: Study Minnesota ISR 1995 (Deschenes
Page 217 and 218: Study Clark WA 2003 (Barnoski, 2003
Page 219 and 220: Study Whatcom WA 2003 (Barnoski, 20
Page 221 and 222: Study Los Angeles CA 2005 (Zhang &
Page 223 and 224: Fed. Prob., 56, 12-17. Turner, Pete
Page 225 and 226: Study Reference Benekos & Sonnenber
Page 227 and 228: Study Reference Giblin (2002) Using
Page 229 and 230: 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
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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