The Effects of Noncompetes on Workers and Employers

Last registered on January 04, 2021

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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.

Locations

Country

United States of America

Region

Primary Investigator

Name

Bo Cowgill

Affiliation

Columbia Business School

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Other Primary Investigator(s)

PI Name

Evan Starr

PI Affiliation

University of Maryland

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Additional Trial Information

Status

In development

Start date

2021-03-01

End date

2022-06-01

Keywords

Firms & Productivity, Labor, Other

Additional Keywords

noncompete agreements

JEL code(s)

Secondary IDs

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 in which we occupy the role of the employer. As the employer we can randomly assign noncompetes (and various other treatment arms) and 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. 2021. "The Effects of Noncompetes on Workers and Employers." AEA RCT Registry. January 04. https://doi.org/10.1257/rct.6906-1.0

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Experimental Details

Interventions

Intervention(s)

We propose hiring 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 two dimensions of randomization. Our first randomization relates to the job offer. The second randomization occurs after the worker has completed their work and we post reviews of the worker and job. We describe these more in the hidden fields in order to keep the interventions private from potential subjects.

Intervention Start Date

2021-03-01

Intervention End Date

2021-06-01

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. For workers who reject the initial offer, we engage in a standard negotiation procedure, probing the worker's willingness to accept higher wages in return for signing the noncompete. This randomization permits us to infer the effects of noncompete agreements on the composition of hired workers, the job acceptance rate, the salary demands and bargaining patterns of workers, 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.

The outcomes we aim to measure from the first and second randomization 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

For HR workers who respond to our job ad, our experimental design involves two dimensions of randomization. Our first randomization relates to the job offer. The second randomization occurs after the worker has completed their work and we post reviews of the worker and job. 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?

Experiment Characteristics

Sample size: planned number of clusters

We expect to make about 2080 job offers. This is a high number in case they are declined.

Sample size: planned number of observations

2080 job offers to 2080 individuals.

Sample size (or number of clusters) by treatment arms

240 offers to 240 individuals in each of the 2 control conditions without noncompetes.
400 offers to 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 solicit 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.

Supporting Documents and Materials

IRB