Determinants of recidivism in rhode island s 2009 p rison p opulation
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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

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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


Model results

Model Results


32 and under

32 and Under


Determinants of recidivism in rhode island s 2009 p rison p opulation

Key Indicators


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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