“Network Position and Collective Action in Unions”

Last registered on March 12, 2020

Pre-Trial

Trial Information

General Information

Title
“Network Position and Collective Action in Unions”
RCT ID
AEARCTR-0005539
Initial registration date
March 10, 2020
Last updated
March 12, 2020, 7:02 PM EDT

Locations

Region

Primary Investigator

Affiliation
Columbia University

Other Primary Investigator(s)

PI Affiliation
Columbia University
PI Affiliation
Columbia University

Additional Trial Information

Status
On going
Start date
2019-10-01
End date
2021-03-01
Secondary IDs
Abstract
Union workers make between 10 and 20% more in wages, and receive better non-wage benefits, than comparable non-union workers, a pattern that is remarkably stable across labor market contexts. This “union premium” is what unions can negotiate in collective bargaining agreements, yet the terms they negotiate depend heavily on their ability to mobilize their memberships to participate in “work actions” around contract negotiation time, in order to signal to employers a willingness and capacity to strike. We will conduct an experiment within the context of an approaching April 2020 contract negotiation covering a large population of union workers in the United States. Our project will (1) build and validate a predictive model for identifying shops and employees exhibiting untapped mobilizing potential for the union, and (2) randomize the distribution of this information about each building to union representatives in order to learn more about whether and how they are able to make use of it, including measuring contract mobilization outcomes.
External Link(s)

Registration Citation

Citation
Hertel-Fernandez, Alexander, Suresh Naidu and Adam Reich. 2020. "“Network Position and Collective Action in Unions”." AEA RCT Registry. March 12. https://doi.org/10.1257/rct.5539-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2020-03-09
Intervention End Date
2020-05-01

Primary Outcomes

Primary Outcomes (end points)
Participation in union contract rally or social media, political engagement with the union over the course of the 2020 election (e.g. phonebank/canvas participation), political opinion data.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Within a union, we randomize information about which union members are likely to be leaders in their shop across field representative staff, to see how they use it and whether it helps mobilization efforts.
Experimental Design Details
Randomizing information about which union members are likely to be leaders in their shop across buildings. Leaders are important for mobilizing for a strike threat during a contract negotiation, and we have built a predictive model using union data for which members are likely to be leaders, using a variety of data from the union (including a survey we designed). We then randomize which buildings field representatives are given the information on which members are likely leaders, and will measure outcomes at both the building and individual level.
Randomization Method
Done in stata using splitsample command.
Randomization Unit
Account
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
412 buildings.
Sample size: planned number of observations
9000
Sample size (or number of clusters) by treatment arms
213 buildings are treated, 199 in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Columbia University IRB
IRB Approval Date
2019-11-18
IRB Approval Number
AAAS7757
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