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Program evaluation, spring 2010 Zuhdi Hashweh Sophio Bendiashvili

Capitalizing on Nonrandom Assignment to Treatments: A Regression-Discontinuity Evaluation of a Crime-Control Program Richard A. Berk and David Rauma March, 1983. Program evaluation, spring 2010 Zuhdi Hashweh Sophio Bendiashvili. The Program. Former Inmate Insurance Program

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Program evaluation, spring 2010 Zuhdi Hashweh Sophio Bendiashvili

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  1. Capitalizing on Nonrandom Assignment to Treatments: A Regression-Discontinuity Evaluation of a Crime-Control ProgramRichard A. Berk and David RaumaMarch, 1983 Program evaluation, spring 2010 Zuhdi Hashweh SophioBendiashvili

  2. The Program • Former Inmate Insurance Program • Mandated by California Senate Bill 224. • Began in 1978. • Unemployment benefits in California extended to ex-offenders incarcerated in state prisons. • Goals • Ex-offenders face many difficulties in making a transition from prison life to normal life. • Because of the Stigma, many return to crime and get re-incarcerated. • Program aims to induce fewer returns to prison.

  3. The Program (2) • Eligibility • Obtained through working at prison jobs • Work for at least 652 hours at minimum wage ($2.30/hour) over a 12 months period. • After release ex-prisoners can apply at the unemployment office locally. • Amount of support depended on hours worked in prison ($30-$70 per week, up to 26 weeks).

  4. Data • Source: • California Employment Development Department. • California Department of Corrections. • Ex-offenders were followed for 12 months immediately after release. • Follow-up period redefined as 10 months from the time of application for the benefits. • Sample contained a total of 920 experimentals and 255 controls all of whom applied for the program.

  5. Data (2) • Reported hours were the sole factor determining eligibility. • All Individuals at or above the threshold received the benefits. • By knowing the accumulated reported hours, ex-prisoners can be assigned into experimental and control groups. • Experimental Group: Those who applied for the program and received the benefits. • Control Group: Those who applied for the program but did not receive the benefits.

  6. Selected Characteristics of the California Parole Population, By Year, 1977-1979, Compared With the Final Sample • External Validity • Sample looks similar to the population from which it was drawn.

  7. Model Specification • How is the outcome (failure) defined? • A felony resulting in parole revocation/return to prison. • A parolee at large. • Misdemeanors. • In short, a failure was basically a return to prison. • Nature of the outcome (return to prison or not) lead the authors to use a logistic regression.

  8. Method • Regression-Discontinuity Design • Sharp design: Cutoff value at 652 hours. • Logit Model. • Failure = f(Benefits, Eligibility, Control Variables) • Nonlinear relationship was suspected • Included hours and the square of hours in the model • Lost 106 subjects due to redefinition of follow-up period. • May cause selection bias. • Used Heckman procedure for correction.

  9. Results

  10. Findings Logit coefficient of -0.51 • Implies that the treatment group are 13% less likely to return to prison. • Program saves about $2,000 per participant. • Control variables replicated other common findings in the literature. • Ex: Older, and better educated ex-offenders are less likely to return to prison • Selectivity bias correction left the story unchanged. • Causal effect increased to 14% • To be expected, since only 9% of the cases were dropped.

  11. Evaluation of the Study (1) • Can we assume External Validity. • Yes. Program sample is very similar to population sample. • Is the regression model correctly specified? • Authors tried different specifications (ex: including length of sentence). • Among the varying specifications, treatment effects ranged from 5-15% • Is the assignment variable (hours) properly controlling for the treatment and control groups? • Yes. Correlation values are low for the control variables.

  12. Evaluation of the Study (2) • Issues with the Regression-Discontinuity Method • Distribution of hours • How smooth is the distribution before and after the threshold level? • How wide is the window • Bandwidth? • Do we believe the results? • There is something there (the effect is relatively large), but more can be done in present time. • Due to the time the study was conducted, it was hard to more.

  13. Any Questions ?

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