Determinants of Recidivism in Rhode Island’s 2009 Prison Population

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– 47% are for new crime rest for probation and parole violation

• Important to everyone

• Data availability

Objective

• Determine which factors impacts repeat offenders

• Identify factors that can be influenced through policies

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

• Starting Data Set– 450,000 data points– 150 variables– 3700 Variables

• Ending Data Set– 47,000 data points– 28 Variables– 1670 Subjects

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• 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 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 Model

Logistic Regression Model:

ln (odds ratio)= …

Logistic Regression Equation:

ln(estimated odds ratio)= …+

Logistic Regression Model

Determine

Determine estimated odds ratio

Determine estimated probability of an event of interest

Model Results Variable B S.E. Wald df Sig. Exp(B)

Step 1Total Sentence -.001 .000 23.950 1 .000 .999

Constant -.622 .072 75.434 1 .000 .537

Step 2

Age -.022 .006 16.050 1 .000 .978

Total Sentence -.001 .000 22.206 1 .000 .999

Constant .092 .190 .233 1 .629 1.096

Step 3

Age -.025 .006 19.942 1 .000 .975

Total Sentence -.001 .000 22.181 1 .000 .999

Citizenship .806 .205 15.458 1 .000 2.240

Constant -.534 .253 4.449 1 .035 .586

Step 4

Age -.028 .006 22.949 1 .000 .973

Total Sentence -.001 .000 22.901 1 .000 .999

Housing Status .336 .159 4.470 1 .035 1.399

Citizenship .811 .206 15.566 1 .000 2.250

Constant -.500 .254 3.875 1 .049 .607

32 and Under

Variable B S.E. Wald df Sig. Exp(B)

Age -.046 .020 5.351 1 .021 .955

Felony or Misdemeanor -.427 .156 7.498 1 .006 .652

Education .394 .161 5.939 1 .015 1.482

HousingStatus .505 .254 3.941 1 .047 1.657

Citizenship .804 .244 10.907 1 .001 2.235

Constant -.162 .513 .100 1 .752 .851

Less than GEDGED or Higher

Education

HomelessNot Homeless

Housing

UnemployedEmployed

Employment

Key Indicators

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

• For below 33:1. Felony vs misdemeanor

• Early parole for misdemeanor convicts.

2. Below GED or High school vs High school/GED• Offer education.

3. Age admitted• Programs targeting young convicts.

4. Housing vs Homeless• Invest in programs around housing.