CAPITALIZING ON NONRANDOM ASSIGNMENT TO TREATMENTS: A REGRESSION-DISCONTINUITY EVALUATION OF A CRIME-CONTROL PROGRAM

RICHARD A. BERK AND DAVID RAUMAMARCH, 1983

Program evaluation, spring 2010 Zuhdi Hashweh Sophio Bendiashvili

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.

GoalsEx-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.

THE PROGRAM (2) Eligibility

Obtained through working at prison jobsWork 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).

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.

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.

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.

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.

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.

RESULTS

FINDINGSLogit 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.

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.

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.