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Determinants of Recidivism in Rhode Island’s 2009 P rison P opulation

Determinants of Recidivism in Rhode Island’s 2009 P rison P opulation. Vlad Konopelko , Lucian Drobot , Alex Gemma, David Rodin, Bill Garneau. Topic. RI Recidivism study Recidivist = Repeat offender 28% returned with new sentence 34% were awaiting trial

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Determinants of Recidivism in Rhode Island’s 2009 P rison P opulation

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  1. Determinants of Recidivism in Rhode Island’s 2009 Prison Population VladKonopelko, Lucian Drobot, Alex Gemma, David Rodin, Bill Garneau

  2. Topic • RI Recidivism study • Recidivist = Repeat offender • 28% returned with new sentence • 34% were awaiting trial • 47% are for new crime rest for probation and parole violation • Important to everyone • Data availability

  3. Objective • Determine which factors impacts repeat offenders • Identify factors that can be influenced through policies

  4. Research History • “The Best Ones Come Out First! Early Release from Prison and Recidivism A Regression Discontinuity Approach” Olivier Marie 2009 • Building Criminal Capital vs Specific Deterrence: The Effect of Incarceration Length on Recidivism. David S. Abrams 2010

  5. Data Set • Starting Data Set • 450,000 data points • 150 variables • 3700 Variables • Ending Data Set • 47,000 data points • 28 Variables • 1670 Subjects

  6. Removed Variables • Redundant Variables • Length of stay, Total stay, % Time served • Variables Insignificant to Our Study • Addresses, birthdays, admittance dates, etc… • Incomplete records • 2000 Inmates did not have all the data points

  7. Condensing the Data • Age Bracket • 32 and Below • 33 and Above • Employment • Under/Unemployed • Employed / Outside of workforce • Housing Status • Homeless/ Living in a shelter • Program Transitional/ Temporary/Permanently residents • Education • High school/GED + • Below high school and no GED

  8. Logistic Regression Model • Depending variable 0 – 1 • The dependent variable is categorical with two possible values • It is based on the odds ratio: odds ratio = Example: odds ratio (for a 0.75 probability of interest)=0.75/(1-0.75)=3 (or 3 to 1)

  9. Logistic Regression Model • Logistic Regression Model: ln (odds ratio)= … • Logistic Regression Equation: ln(estimated odds ratio)= …+

  10. Logistic Regression Model Determine Determine estimated odds ratio Determine estimated probability of an event of interest

  11. Model Results

  12. 32 and Under

  13. Key Indicators

  14. Policies 1 • 5 out of 28 variables • Single vs married • For all: • Age: The higher the age the less likelihood. • Citizenship: US citizen are more likely to return

  15. Policies 2 • For below 33: • Felony vs misdemeanor • Early parole for misdemeanor convicts. • Below GED or High school vs High school/GED • Offer education. • Age admitted • Programs targeting young convicts. • Housing vs Homeless • Invest in programs around housing.

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