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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

Determinants of Recidivism in Rhode Island’s 2009 Prison Population

VladKonopelko, Lucian Drobot, Alex Gemma, David Rodin, Bill Garneau


Topic
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


Objective
Objective

  • Determine which factors impacts repeat offenders

  • Identify factors that can be influenced through policies


Research history
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


Data set
Data Set

  • Starting Data Set

    • 450,000 data points

    • 150 variables

    • 3700 Variables

  • Ending Data Set

    • 47,000 data points

    • 28 Variables

    • 1670 Subjects


Removed variables
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


Condensing the data
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


Logistic regression model
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)


Logistic regression model1
Logistic Regression Model

  • Logistic Regression Model:

    ln (odds ratio)= …

  • Logistic Regression Equation:

    ln(estimated odds ratio)= …+


Logistic regression model2
Logistic Regression Model

Determine

Determine estimated odds ratio

Determine estimated probability of an event of interest





Policies 1
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


Policies 2
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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