Labor Demand for Workers with a Criminal Conviction

Last registered on March 20, 2020

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Pre-Trial

Trial Information

General Information

Title

Labor Demand for Workers with a Criminal Conviction

RCT ID

AEARCTR-0005200

Initial registration date

December 23, 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

January 03, 2020, 4:37 PM EST

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

Last updated

March 20, 2020, 5:38 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Will Dobbie

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Mitchell Hoffman

PI Affiliation

Rotman School of Management, University of Toronto

Contact Investigator

PI Name

Zoë Cullen

PI Affiliation

Harvard Business School

Contact Investigator

Additional Trial Information

Status

In development

Start date

2019-11-01

End date

2022-10-01

Keywords

Crime, Violence, & Conflict, Firms & Productivity, Labor

Additional Keywords

Labor Demand, Labor Discrimination, Criminal Law, Field Experiments

JEL code(s)

C93; J23; J71; K14

Secondary IDs

Abstract

We employ a randomized discrete choice experiment on a large on-demand staffing platform to estimate the labor demand for workers with a past criminal conviction (WCCs) under different policies and market conditions. Employers using the platform will have the option to have WCCs assigned to their vacancies at a randomly chosen wage subsidy, both with and without the option of crime and safety insurance and increased performance screening, and under different hypothetical labor market conditions. Employer choices at different wage subsidies will yield willingness to pay (WTP) estimates for this population of jobseekers under different counterfactual policies. These estimates will also allow us to estimate a labor demand function.

External Link(s)

Registration Citation

Citation

Cullen, Zoë, Will Dobbie and Mitchell Hoffman. 2020. "Labor Demand for Workers with a Criminal Conviction." AEA RCT Registry. March 20. https://doi.org/10.1257/rct.5200-1.1

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Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2020-01-02

Intervention End Date

2020-10-01

Primary Outcomes

Primary Outcomes (end points)

Employers’ choices over whether to have workers with a criminal record assigned to their vacancies at randomly chosen wage subsidies.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We will recruit participants through the online platform’s account managers.

In the survey experiment, we ask firms about their interest in hiring individuals with a criminal record. We cross randomize values of each of the following dimensions:

• Wage discount
• Level of commercial crime insurance
• Number of positive past performance scores on the platform
• Hypothetical local unemployment rate
• Years since the most recent criminal act

The survey is incentive-compatible because subjects are informed that choices will affect the workers assigned to their jobs.

Experimental Design Details

Randomization Method

The randomization will be conducted using a computer random number generator.

Randomization Unit

We will randomize at the level of the firm, where all managers from the same firm will be randomized into the same group.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

The survey will be administered to approximately 9,000 individuals at 7,600 firms. We will cluster at the firm level (where there are several managers from the same firm). Based on previous response rates to the online platform’s surveys, we expect between 760 and 2,000 firms to respond.

Sample size: planned number of observations

See above.

Sample size (or number of clusters) by treatment arms

We are cross randomizing all parameter values equally allocating firms to each value listed for each parameter.

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

Supporting Documents and Materials

IRB