The Effects of Noncompetes on Workers and Employers

Last registered on June 21, 2023

Pre-Trial

Trial Information

General Information

Title
The Effects of Noncompetes on Workers and Employers
RCT ID
AEARCTR-0006906
Initial registration date
December 30, 2020

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 04, 2021, 9:10 AM EST

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

Last updated
June 21, 2023, 12:14 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
Columbia Business School

Other Primary Investigator(s)

PI Affiliation
University of Maryland

Additional Trial Information

Status
In development
Start date
2023-05-23
End date
2023-11-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The goal of our study is to causally estimate of the effects of noncompetes themselves on the employment outcomes of workers. To do this, we propose to run a large field experiment where we work alongside an employer to randomly assign noncompetes and their salience, as well as wage offers, to examine how the assignment of noncompetes affects individual willingness to accept a job offer. We can also examine subsequent employment outcomes for the workers, and even directly test the worker’s willingness to violate the noncompete by working with a second employer seeking to hire the workers.
External Link(s)

Registration Citation

Citation
Cowgill, Bo and Evan Starr. 2023. "The Effects of Noncompetes on Workers and Employers." AEA RCT Registry. June 21. https://doi.org/10.1257/rct.6906-1.2
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Experimental Details

Interventions

Intervention(s)
We propose to work with a firm to hire professional HR workers, who will be tasked with reviewing and screening resumes. For HR workers who respond to our job ad, our experimental design involves three dimensions of randomization. In the job offer we will randomly assign the use of a noncompete, the salience of the noncompete, and wages. We describe these more in the hidden fields in order to keep the interventions private from potential subjects.
Intervention Start Date
2023-05-23
Intervention End Date
2023-11-01

Primary Outcomes

Primary Outcomes (end points)
The outcomes we aim to measure from the first randomization are below:
--Job Acceptance and Wages: We then measure who accepts the job offer. The randomization permits us to infer the effects of noncompete agreements on the composition of hired workers, the job acceptance rate, compensating differentials for noncompetes, and the length of employer search.
-- Performance. For workers who accept the job, we will send recruiting work and instructions to review sixteen resumes or job applications. We will measure who complies with these instructions for all workers as a measure of their quality. Exploiting the salience intervention allows us to address selection into second-stage performance.

Other outcomes we aim to measure are below:
-Other Employment Experiences and Earnings: During the prohibited noncompete period (6 months), we will also gather information from the platform on other work that the worker has completed (i.e., number of other employers) and their cumulative earnings. We will supplement this information with any new information on their LinkedIn profile related to employment.
-Testing Willingness to Violate the Noncompete: During the prohibited noncompete period (6 months), we will assume the role of a poaching firm and reach out to the workers we hired previously. We will make them a job offer to measure if (a) those bound by noncompetes are less willing to accept our offer, and (b) whether they require higher wages to violate the provision.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
For HR workers who respond to the job ad, our experimental design involves three dimensions of randomization related to the job offer. We describe these more in the hidden fields in order to keep the interventions private from potential subjects.
Experimental Design Details
Randomization Method
We will randomize using a computer script and a random number generator.
Randomization Unit
The randomization will occur at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We expect to hire approximately 2080 workers. Anticipating a 20-25% acceptance rate, this is approximately 8-10k workers in the initial outreach.
Sample size: planned number of observations
It depends on the stage of the survey. At the outset, we hope to hire 2080 individuals, out of approximately 10k job offers.
Sample size (or number of clusters) by treatment arms
In the second stage, we expect to have:
240 individuals in each of the 2 control conditions without noncompetes.
400 individuals in each of the 4 treatment conditions with noncompetes.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The power calculations are complicated by the fact that the first stage outcomes and second stage outcomes are linked. In the first stage we examine who accepts the job offer, and in the second stage we examine the effect of noncompetes among those who have accepted the job offer. If noncompetes make it exceedingly unlikely to accept a job offer, our treatment alone will affect our power in the second stage. Accordingly, our power calculations were done via backward induction from the second stage to the first stage. We do not have precise information on several key variables to perform a power analysis for all of them. But based on prior experience and research we expect a 25% job acceptance rate in the first stage in the control group and a 15% job acceptance rate in the treatment groups. For the second stage, we expect a 70% job acceptance rate for the control groups and a 35% acceptance rate for the treatment groups. The upper envelope of our power calculations imply that we need to hire approximately 400 individuals in each of our four treatment cells, and 240 individuals in the control cells to have power of at least 80% in both stages.
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Maryland
IRB Approval Date
2020-04-20
IRB Approval Number
1368691-8
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials