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“Network Position and Collective Action in Unions”
Initial registration date
March 10, 2020
March 12, 2020 7:02 PM EDT
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Other Primary Investigator(s)
Additional Trial Information
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.
Hertel-Fernandez, Alexander, Suresh Naidu and Adam Reich. 2020. "“Network Position and Collective Action in Unions”." AEA RCT Registry. March 12.
Intervention Start Date
Intervention End Date
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 (end points)
Secondary Outcomes (explanation)
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
Done in stata using splitsample command.
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
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)
INSTITUTIONAL REVIEW BOARDS (IRBs)
Columbia University IRB
IRB Approval Date
IRB Approval Number