The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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we also have crime outcomes for the non-randomized low-risk backfill cases and for a group <strong>of</strong> <strong>of</strong>fenders predicted to be non-low risk by the model. Data for the latter group <strong>of</strong> probationers were collected for comparison with low-risk cases in a regression discontinuity analysis, the outcomes <strong>of</strong> which were contrasted with the experimental results (Berk et al., forthcoming). <strong>The</strong> full dataset contains information on 93,540 charges for 3,207 <strong>of</strong>fenders (2,207 <strong>of</strong> whom were predicted to be low risk) covering a period <strong>of</strong> 42 years (1967-2009). One significant limitation <strong>of</strong> the charges database is that it only includes charges as an adult for <strong>of</strong>fenses in Philadelphia, as we only had access to local adult criminal justice system databases. While almost all <strong>of</strong> the participants reside in Philadelphia, the city’s proximity and ease <strong>of</strong> access to surrounding counties and state lines mean that the local data almost certainly underestimate the number <strong>of</strong> charges recorded for these <strong>of</strong>fenders. In addition, we do not have juvenile data available to give a full picture <strong>of</strong> <strong>of</strong>fenders’ lifetime criminal involvement. While most <strong>of</strong> the <strong>of</strong>fenders in our sample are older (see Table 3.1), we would expect to see the majority <strong>of</strong> their criminal <strong>of</strong>fending taking place during their teenage years, so the lack <strong>of</strong> data on charges filed under the age <strong>of</strong> eighteen is a substantial omission. However, data are available where the <strong>of</strong>fender was charged as an adult, even if the <strong>of</strong>fense was committed while s/he was under eighteen. Outcome measures As we noted above, the definition <strong>of</strong> <strong>of</strong>fending severity may extend beyond the substantive nature <strong>of</strong> the <strong>of</strong>fense itself. This is particularly true in a sample <strong>of</strong> <strong>of</strong>fenders 145
already predicted to be at low risk <strong>of</strong> serious re<strong>of</strong>fending. A successful model <strong>of</strong> low-risk prediction should minimize the possibility that these <strong>of</strong>fenders pose a serious threat to society by any measure. This section considers the various ways in which <strong>of</strong>fending severity may be conceptualized, and how we operationalize some <strong>of</strong> these ideas as outcome measures for the present study. <strong>The</strong> assessment <strong>of</strong> <strong>of</strong>fending severity has been a long-standing concern <strong>of</strong> criminological research. Blumstein et al. (1986), for example, describe <strong>of</strong>fending severity as a “key dimension <strong>of</strong> individual criminal careers” (p. 76) and note the crucial policy interest in focusing on understanding and identifying serious <strong>of</strong>fenders. Despite this interest, there remains little consensus on how best to measure severity. <strong>The</strong> conventional approach (Ramchand et al., 2009, p. 130) has been to weight different crime types based on the perceptual method established by Sellin and Wolfgang (1978). Sellin and Wolfgang asked panels <strong>of</strong> university students, juvenile court judges, and police <strong>of</strong>ficers to rate the severity <strong>of</strong> a range <strong>of</strong> crimes compared to a trivial baseline <strong>of</strong>fense <strong>of</strong> the theft <strong>of</strong> $1. Based on these ratings, they assigned a weight to each <strong>of</strong>fense relative to the baseline. This type <strong>of</strong> crime severity rating appears to hold across different samples and contexts (although some race-based differences have been noted), and remains popular despite criticisms that no context is provided to panel participants, leaving them free to speculate about unreported details <strong>of</strong> the <strong>of</strong>fenses (Ramchand et al., 2009). <strong>The</strong> U.S. Federal Bureau <strong>of</strong> Investigation (FBI) also provides an <strong>of</strong>fense severity classification that is used by police departments when they report crime data through the Uniform Crime Reporting (UCR) program. <strong>The</strong> classification <strong>of</strong> <strong>of</strong>fenses into Part I and Part II <strong>of</strong>fenses was introduced in 1929 and now contains eight <strong>of</strong>fenses in the Part I 146
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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
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 and 158: assignment pool. Backfill probation
Page 159: Little research has been done on th
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
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),
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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
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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