Understanding Drivers of Worker Organizing and Unionization in the Low-Wage Sector

Last registered on December 03, 2024
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Pre-Trial

Trial Information

General Information

Title
Understanding Drivers of Worker Organizing and Unionization in the Low-Wage Sector
RCT ID
AEARCTR-0014948
Initial registration date
December 02, 2024

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
December 03, 2024, 1:41 PM 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
Heather Sarsons
Affiliation
University of British Columbia
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Other Primary Investigator(s)

PI Name
Ellora Derenoncourt
PI Affiliation
Princeton
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PI Name
Niharika Singh
PI Affiliation
University of Notre Dame
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PI Name
Arin Dube
PI Affiliation
University of Massachusetts Amherst
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PI Name
Suresh Naidu
PI Affiliation
Columbia
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Additional Trial Information

Status
In development
Start date
2024-12-09
End date
2025-02-28
Keywords
Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project aims to understand the drivers of the recent increase in worker collective action in the United States. Since 2020, there has been a surge in union organizing activity, particularly among low-wage retail and service sector workers, that has corresponded with two other shifts in the economy. First, labor market tightness increased dramatically, making it easier for workers to find new jobs. Second, organizing attempts received a great deal of media attention, particularly on social media. Each of these factors could independently facilitate organizing efforts, by lowering the costs of organizing and by improving beliefs about the likelihood that organizing will succeed. These factors could also reinforce each other, further driving increases in willingness to engage in collective action. In a survey experiment, we attempt to disentangle these mechanisms.
External Link(s)

Registration Citation

Citation
Derenoncourt, Ellora et al. 2024. "Understanding Drivers of Worker Organizing and Unionization in the Low-Wage Sector." AEA RCT Registry. December 03. https://doi.org/10.1257/rct.14948-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants will be randomly assigned to one of four groups listed below. In each group, participants will watch a short informational video that varies in its content.

Control: Participants receive information on the employment rate in the United States. The video states that “There are currently roughly 158.5 million employed individuals in the U.S.” and notes that the estimate is from the Bureau of Labor Statistics.

Tightness treatment: Participants receive information on their local labor market conditions.

"Winning" treatment: Participants receive information on the number of union elections and their success rates from 2017-2023.

Cross treatment: Participants receive both videos regarding their local labor market conditions as well as recent union activity in the retail and services sectors.
Intervention Start Date
2024-12-09
Intervention End Date
2025-02-28

Primary Outcomes

Primary Outcomes (end points)
Labour market beliefs and behavior (including reservation wage and job search intentions)
Union support and beliefs about effectiveness
Interest in more information about unions
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants are randomized into one of four treatments. Each treatment involves watching an informational video.
Experimental Design Details
Not available
Randomization Method
Randomization by computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
NA
Sample size: planned number of observations
10,000-12,500
Sample size (or number of clusters) by treatment arms
2,500-3,125
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Princeton University IRB
IRB Approval Date
2024-08-28
IRB Approval Number
15185
Analysis Plan

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