1. Escapes from Custody and Violence: A Critical Analysis 1 Bryce E. Peterson Adam G. Fera Jeff Mellow John Jay College/CUNY Graduate Center

2. Introduction  Armed Criminal Career Act (ACCA) of 1984  Firearm conviction + 3 prior violent felonies = 15 years  Crime is violent if it 1.has an element the use, attempted use, or threatened use of physical force against the person of another, or 2.is burglary, arson, or extortion, involves the use of explosives, or otherwise involves conduct that presents a serious potential risk of physical injury to another. 2

3. Are escapes violent?  U.S. Courts of Appeal and USSC say YES!  Chambers v. U.S., 555 U.S. 122 (2009)  Failure to report does not qualify as violent felony  Distinct from “escape” “[E]very escape scenario is a powder keg, which may or may not explode into violence…” (U.S. v. Gosling, 1994) 3

4. Escape Research  Limitations of previous research  Old research  Non-rigorous studies  Intra-state trends, prisons, and in-facility escapes  No central definition of “escape ”  Circuit Courts and USSC called for more research  Sentencing Commission (2008)  Violence: Overall, during escape, during recapture  Found: secure escapes have more violence  Limitations: federal prisons, few factors 4

5. The Current Study 5  Examine the role of violence in prison escapes  Major objectives 1.Identify facility, inmate, and suspect-level factors associated with violent escapes from custody 2.Examine violence overall, during the incident, post- incident, and during recapture. 3.Create and test a predictive model of violent escapes

6. Data  Correctional Incident Database (2009)  Rigorous open-source search protocol (Freilich & Chermak, ECDB)  Facility, incident, and suspect data  Escapes: a loss of correctional control over an inmate in custody 6 AdvantagesDisadvantages Detailed informationNon-representative sample National dataBias Broader scopeMissing data Consistent definition

7. Analytic Approach 7 Steps 1.Analyze descriptive statistics 2.Identify trends 3.Create a predictive model of violent escapes using bivariate analyses 4.Test model using logistic regression

8. Escapes from Custody: Correctional Incident Database  Total numbers (2009)  608 suspects, 501 incidents, 400 facilities  Current Study  270 suspects, 223 incidents, 198 facilities 8 Violence During Escapes (n=270) FrequencyPercent During incident3111.5% Post-incident207.4% During recapture83% Overall4918.1%

9. Facility Classification by Overall Violence 9 Minimum/ Low Medium/ Maximum Jail No Violence64 95.5% 32 71.1% 82 74.5% Violence3 4.5% 13 28.9% 28 25.5% Total67 100% 25 100% 110 100% Note: χ2 = 14.451, p <.01

10. Custody & Location by Overall Violence 10 Outside/ Unsecure Inside/ Unsecure Outside/ Secure Inside/ Secure No Violence46 93.9% 32 97% 39 63.9% 82 78.8% Violence3 6.1% 1 3% 22 36.1% 22 21.2% Total49 100% 33 100% 61 100% 104 100% Note: χ2 = 14.451, p <.01

11. Other Factors  Other significant associations  Assistance received, plan, violent record, male facility, staff to inmate ratio, private facility, start time  Insignificant associations  Season, day of week, facility age, ACA accreditation, suspect age, race, gender, property record, percent capacity, adult facility 11

12. 12 Variables in Predictive Models of Violent Escapes MeanStd. Dev.MinMaxn Dependent Variable Total Violence 0.19-01262 Independent Variables Age30.649.661659258 White0.46-01270 Violent Record0.39-01270 Escape Plan0.26-01270 Secure Custody0.67-01248 Inside Facility0.56-01261 No Assistance0.82-01249 ACA Accreditation0.22-01248 Percent Capacity99.283223.81212.86174 Minimum/Low0.26-01270 Medium/Maximum0.17-01270 Male Facility0.33-01257 Staff to Inmate Ratio5.142.74116.42167 Private Facility0.07-01235

13. 13 Models Predicting Violent Escapes Full ModelTruncated Model Odds Ratio Violent History7.14**2.72 * Escape Plan29.75**3.93* Secure Custody 4.96 † 4.49* Inside Facility.08**.22** ACA Accreditation.08.17 † Staff to Inmate Ratio.62**-- Private Facility28.58** 5.48 † Note. Only statistically significant variables are shown. **p <.01, *p <.05, † p <.10 two-tailed Analysis 1: n= 140, Wald χ 2 =39.82, p<.001, Pseudo R 2 =.347 Analysis 2: n = 187, Wald χ 2 =37.56.4, p<.001, Pseudo R 2 =.236

14. Discussion 14  Not much violence overall  Especially in non-secure settings  Data are biased TOWARD violence  Certain factors may lead to violence  Outside, secure custody, planning, violent records  Not all violence is equal  “shoving” ≠ murder

15. Conclusions 15  Implications  Longer sentences for inmates  Cost of incarceration  Overcrowding  Other sentencing enhancements  Future Research  More data collection  Violence at different points  Seriousness of violence

16. Contact info Bryce Peterson – bpeterson@jjay.cuny.edubpeterson@jjay.cuny.edu Adam G. Fera - afera@jjay.cuny.eduafera@jjay.cuny.edu Jeff Mellow – jmellow@jjay.cuny.edujmellow@jjay.cuny.edu 16