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 - PowerPoint PPT Presentation

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

Factors Affecting Recidivism in Rhode Island

Determinants of Recidivism in Rhode Islands 2009 Prison Population

Vlad Konopelko, Lucian Drobot, Alex Gemma, David Rodin, Bill Garneau1TopicRI Recidivism study

Recidivist = Repeat offender28% returned with new sentence 34% were awaiting trial47% are for new crime rest for probation and parole violation

Important to everyone

Data availability

ObjectiveDetermine which factors impacts repeat offenders

Identify factors that can be influenced through policies Research HistoryThe Best Ones Come Out First! Early Release from Prison and Recidivism A Regression Discontinuity Approach Olivier Marie 2009Building Criminal Capital vs Specific Deterrence: The Effect of Incarceration Length on Recidivism. David S. Abrams 2010

Early paroleIncorceration length

4Data SetStarting Data Set450,000 data points150 variables3700 VariablesEnding Data Set47,000 data points28 Variables1670 SubjectsOur extensive data was received from the RI DOC (Rhode Island Department of Corrections). The data was based on the year of 2009 with over 7000 records and 170 different variables. Since this list of 7000+ records showed all events with a prisoner (i.e. admission, parole, probation, release, or readmission), we removed the duplicates for each inmate. After removing this data, 3700 records were left available.After slimming down our data to about half, we attempted to load our file into WinORS. Each time we loaded the data, WinORS would crash. Working with Professor Dash, we had discovered that the student version of WinORS has a limited cell count. In this case, we would need to eliminate variables to allow it to fit in the confines on the program. Using WinORS we eliminated the uncorrelated data using the factor analysis to reduce the data to orthogonal (uncorrelated) data dimensions. This helps meet the multicollinearity assumption. After this process, we were left with 50 variables which we then tried to load into WinORS. Although the WinORS did not crash, there were many problems with our data set that prevented us from being able to analyze the data in WinORS. After several more e-mails back and forth with Professor Dash, we realized that WinORS may not be the appropriate tool for our data set and what we were trying to find.With the help of Professor Dash and Professor Kajiji, we cleaned our data further and successfully loaded it into SPSS. We had 28 variable types and little fewer than 1700 records remaining. At this point, we were ready to begin binary regression.5Removed VariablesRedundant VariablesLength of stay, Total stay, % Time servedVariables Insignificant to Our StudyAddresses, birthdays, admittance dates, etcIncomplete records2000 Inmates did not have all the data points

Other than removing Variables through WinORS, we removed some data in our model for a few different reasons.One of the reasons being that some variables were redundant. For example, three of variables that we started with were Length of stay, total stay, and % time served.Some variables were removed due to the insignificance to our study. For example addresses, dates such as admittance, released, and birthdays, parole descriptors, and so forthThe last reason that we had to remove some of Inmates due to their data missing some data points.6Condensing the DataAge Bracket32 and Below33 and AboveEmployment Under/UnemployedEmployed / Outside of workforceHousing StatusHomeless/ Living in a shelterProgram Transitional/ Temporary/Permanently residentsEducationHigh school/GED +Below high school and no GED

We had condensed some of the variables to make the data more simplistic. For example, age had values at every age by numerical value. There were too many values to make any sort of correlation so to simplify this. For example for age we had found the mean was 33 years old and created two groups. One group consisted of inmates that were 32 and younger and the other 33 and older. We categorized this by 32 and younger being a value of 0 and 33 and older a value of 1.For Employment, we had categorized individuals that were under or unemployed a 1 and those that were employed or outside of the workforce a 0.For housing, we had categorized individuals that were homeless or living in shelter as 1 and those who had program/transitional housing, temporary, or a permanent residence as a 0For Education, those with a High School Education, GED, or greater were labeled as a 0 and those below a High School Diploma or without a GED and below were categorized as a 17Logistic Regression ModelThe use of simple or multiple least-squares regression for a categorical dependent variable violates the normality assumption

Odds Ratio- represents the probability of an event of interest compared with the probability of not having an event of interest

K is the number independent variables in the modelEps i- random error in observation i8Logistic Regression ModelThe logistic model is based on the natural logarithm (ln) of this odds ratio

K is the number independent variables in the modelEps i- random error in observation I

In logistic regression, a mathematical method called maximum likelihood estimation is usually used to develop a regression equation to predict the natural logarithm of this odds ratio9Logistic Regression ModelDetermine estimated odds ratioDetermine estimated probability of an event of interest10Model ResultsVariableBS.E.WalddfSig.Exp(B)Step 1Total Sentence-.001.00023.9501.000.999Constant-.622.07275.4341.000.537Step 2Age-.022.00616.0501.000.978Total Sentence-.001.00022.2061.000.999Constant.092.190.2331.6291.096Step 3Age-.025.00619.9421.000.975Total Sentence-.001.00022.1811.000.999Citizenship.806.20515.4581.0002.240Constant-.534.2534.4491.035.586Step 4Age-.028.00622.9491.000.973Total Sentence-.001.00022.9011.000.999Housing Status.336.1594.4701.0351.399Citizenship.811.20615.5661.0002.250Constant-.500.2543.8751.049.607Binary logistic regression is Similar to stepwise regressionB unstandardized beta weight based on logit values dont have a lSomewhat similar to probability in that on a scale you can correlate logit values and probability and get a positive correlationS.E. Standard errorSignificance is measured by wald statistic w/ 1 degree of freedom similar to t statistic as we see in a multiple regression analysis.Sig is signifance similar to P-value in ordinary least squares

ExpB/odds ratio expenentiated B using unstandardized beta weights and controlling for individual differences in the other variablesOnly interpetable in correlation to other factors

1132 and UnderVariableBS.E.WalddfSig.Exp(B)Age-.046.0205.3511.021.955Felony or Misdemeanor-.427.1567.4981.006.652Education.394.1615.9391.0151.482HousingStatus.505.2543.9411.0471.657Citizenship.804.24410.9071.0012.235Constant-.162.513.1001.752.851Key IndicatorsHomeless 91% vs 9% Unemployed 70.3% vs 29.7%, Less then GED 30.9% vs 69.1%, as an aside, 91.6% are single.13Policies 15 out of 28 variablesSingle vs married

For all: Age: The higher the age the less likelihood.Citizenship: US citizen are more likely to return

Policies 2For below 33:Felony vs misdemeanorEarly parole for misdemeanor convicts.Below GED or High school vs High school/GEDOffer education. Age admittedPrograms targeting young convicts.Housing vs HomelessInvest in programs around housing.