Evaluating the Impact of Reduced Availability of Criminal History on Ex-Offenders' Labor Market Outcomes

Last registered on October 28, 2019

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Pre-Trial

Trial Information

General Information

Title

Evaluating the Impact of Reduced Availability of Criminal History on Ex-Offenders' Labor Market Outcomes

RCT ID

AEARCTR-0004414

Initial registration date

October 25, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

October 28, 2019, 1:32 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

United States of America

Region

California

Primary Investigator

Name

Bailey Palmer

Affiliation

Princeton

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Other Primary Investigator(s)

PI Name

Crystal Yang

PI Affiliation

Harvard University

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PI Name

Amanda Agan

PI Affiliation

Rutgers University

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PI Name

Alex Mas

PI Affiliation

Princeton University

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Additional Trial Information

Status

On going

Start date

2019-06-01

End date

2022-12-31

Keywords

Crime, Violence, & Conflict, Labor

Additional Keywords

JEL code(s)

J78, J31, K14

Secondary IDs

Abstract

California Proposition 47 allowed individuals convicted with felonies in several categories to petition for the felony to be reduced to a misdemeanor. In one California County, the Public Defender has worked together with the DA’s office to file these petitions on behalf of defendants without their needing to take any action or even know it is happening. Despite their desire to notify individuals of these reductions, the Public Defender’s Office does not have the resources to do so. We have partnered with the County Public Defender’s Office to randomly notify defendants who have received a reduction, petitioned on their behalf by the SJ public defender. The County Public Defender’s office will use 4 methods to contact these individuals: phone calls, text messages, email and mail to their most recent addresses. We also collect publicly available data on these individual’s criminal history, and match these data to employment and earnings outcomes and credit report data. The matched labor market/criminal record data and credit history/criminal record data will allow us to determine whether knowing that a felony has been reduced has any impact on these labor market and credit outcomes compared to those who are unaware of the reduction. Data collected from this study will also teach us how to best find and communicate with people who may be particularly transient and suspicious of authority.

External Link(s)

Registration Citation

Citation

Agan, Amanda et al. 2019. "Evaluating the Impact of Reduced Availability of Criminal History on Ex-Offenders' Labor Market Outcomes." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.4414-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Our intervention randomly notifies individuals that their California county felony was reduced to a misdemeanor due to Proposition 47.

Intervention Start Date

2019-08-15

Intervention End Date

2020-06-01

Primary Outcomes

Primary Outcomes (end points)

1. Does notification of felony reduction improve labor market and credit outcomes, compared to those who received a reduction but were not notified?
2. Do these effects vary by the “dosage” of treatment? In other words, are effects different for those who had their only felony reduced vs those who had one of several felonies reduced? Similarly, does having multiple felonies reduced have a different effect from having one felony reduced? 3. We will seek out other outcomes such as local social service use, housing, etc… if and when they become available.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

4. What is the best way to contact individuals who may be transient, difficult to find, and suspicious of government entities? What proportion of individuals were we able to contact via each method?

Secondary Outcomes (explanation)

Experimental Design

We randomly assign a portion of our sample [individuals who received a Prop 47 reduction in the California county] to receive a notification that a felony on their criminal record has been reduced. Those assigned to notification will receive notification as described in the Intervention section. We then match these data to employment and earnings data and a credit bureau’s credit history data to determine the effect of reduction and notification of reduction on labor market and credit outcomes.

Experimental Design Details

We partnered with the Public Defender’s office of a California county, who gave us access to a list of individuals who petitioned for a felony reduction under Prop 47. The public defender’s investigator then gave us these individuals’ contact information, including their last 3 phone numbers, email addresses and home addresses. We have hired a team of interns to collect publicly available information on the individual’s criminal record.

We randomly assigned a portion of our sample to receive a notification that a felony on their criminal record has been reduced. They will receive the notification from 4 sources: phone call, text messages, mail, and email. Those assigned to notification will receive notification as described in the Intervention section.

We will then send these data to a federal agency where they will be matched to earnings and employment data. We will also send these data to a credit bureau who will match our data to their records, which includes information reported by financial institutions and collections agencies, information from public records including bankruptcy, and information computed by the bureau including credit score. Both these matched datasets will be anonymized.

Randomization Method

Randomization was done in office using Stata.

Randomization Unit

Randomization is on the individual level.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

There are no clusters. Treatment is stratified on whether the subject had only one felony on their record or multiple felonies.

Sample size: planned number of observations

There are approximately 9000 individuals who received reductions from 2014-2017. An additional several thousand (TBA) separate individuals received reductions post-2017.

Sample size (or number of clusters) by treatment arms

Approximately 50% of the sample will be randomized into treatment. Randomization is stratified by whether the individual had only one felony on their record. In the first wave of data (from 2014-2017) approximately 800 people had only one felony on their record.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

One of our main outcomes is employment. Given a sample size of 9000 (which we expect to be larger when we collect the data on the 2nd wave of reductions) evenly split between treatment and control, assuming that the employment rate in this population is 50%, and focusing on an intention-to-treat effect, this sample size implies 80% power to detect a change in employment rates of around 3 percentage points at a 5% significance level comparing notification group to control group. For this sample size and effect size, power will increase if the true employment rate of the control is greater or less than 50%.

Supporting Documents and Materials

IRB