The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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specific information for drug <strong>of</strong>fenses, which constitute 16 per cent <strong>of</strong> all the charges in our sample. Furthermore, the age <strong>of</strong> these studies and changes in prices and monetary value may limit the usefulness <strong>of</strong> their estimates (although procedures are available for converting them into current values). One proxy for dollar value that would also allow a basic distinction to be drawn between more or less ‘expensive’ crimes is victim status (crimes with victims, such as assault, versus ‘victimless’ crimes like drug and weapon possession). This approach assumes that crimes with victims cost society more because the victims themselves suffer both tangible (e.g., loss <strong>of</strong> earnings) and non-tangible (e.g. fear) costs, in addition to the criminal justice system costs <strong>of</strong> processing the <strong>of</strong>fender, while non-victim crimes (crudely) only involve <strong>of</strong>fender-related costs. Of course, the reality is less clear-cut, but victim status remains a useful proxy for cost, and does not fully overlap with substantive severity. 4 We define ‘victim’ crimes as those <strong>of</strong>fenses that were most likely to have involved injury or death, psychological distress, loss <strong>of</strong> earnings, or other costs such as loss <strong>of</strong> or damage to property <strong>of</strong> a personal (not corporate) victim. <strong>The</strong> process <strong>of</strong> applying the definition was somewhat crude, because only limited information about the <strong>of</strong>fense was available. Our criminal history database contained a free-text description <strong>of</strong> each <strong>of</strong>fense and a statute section and subsection reference. 5 <strong>The</strong> <strong>of</strong>fense type was initially determined by reference to the relevant statute, as we believed that variable would be more accurate than the free-text description. <strong>The</strong> description was used for confirmation and additional information about the <strong>of</strong>fense. However, without full crime reports for each <strong>of</strong>fense, it was not possible to know for certain whether a victim was 149
involved. Crimes that obviously met our definition included homicide, rape, assaults, and residential burglaries. Arson was also included due to the high probability <strong>of</strong> costly damage, although it was not always possible to determine whether the <strong>of</strong>fense was committed against personal or commercial property. Some other <strong>of</strong>fenses in Pennsylvania (e.g., criminal mischief) have specific subsections relating to different types <strong>of</strong> victims and these were classified as victim crimes where it was clear that personal victims were involved. Retail theft and some other acquisitive <strong>of</strong>fenses like theft <strong>of</strong> services, which were most likely to involve corporate victims, were excluded. We also excluded ‘non-permanent theft’ (unauthorized use <strong>of</strong>fenses) against any victim type. Analytic strategy We use straightforward tests to compare the prevalence (proportion <strong>of</strong> <strong>of</strong>fenders involved in serious <strong>of</strong>fending) and frequency <strong>of</strong> serious <strong>of</strong>fending across groups. Frequencies are compared using a two-sample t-test for the difference between means. To assess prevalence, we examine the relative risk (risk ratio) <strong>of</strong> serious <strong>of</strong>fending between groups. <strong>The</strong> risk ratio is not <strong>of</strong>ten used in criminological research, but it is common in epidemiological research for analyzing dichotomous outcomes in cohort studies (in which known exposure/non-exposure to a risk factor is cross-tabulated with disease/non-disease status). It is simply a ratio <strong>of</strong> the probability (risk) <strong>of</strong> disease given exposure status, calculated by dividing the proportion <strong>of</strong> subjects with the disease at one level <strong>of</strong> exposure by the proportion at the other level. As such, it is somewhat similar to an odds ratio, but has a considerably more intuitive interpretation. 6 Like the odds ratio, 150
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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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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 and 162: already predicted to be at low risk
Page 163: It assumes that if of</stro
Page 167 and 168: We examine the potential interactio
Page 169 and 170: age is based on this date regardles
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
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
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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
Page 261 and 262:
Wilson, D. B. (2010). Meta-analysis
Page 263 and 264:
Angrist, J. D. (2006). Instrumental
Page 265 and 266:
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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