What makes redistribution popular?

Last registered on July 27, 2026
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Pre-Trial

Trial Information

General Information

Title
What makes redistribution popular?
RCT ID
AEARCTR-0019220
Initial registration date
July 24, 2026

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
July 27, 2026, 7:01 AM EDT

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
Gregory Sun
Affiliation
Washington University in St Louis
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2026-07-22
End date
2026-07-23
Keywords
Electoral, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This experiment validates the predictions of a structural model of voter preferences over redistribution policy. In a previous study, I had participants make choices over who people would rather transfer money to, where I varied the characteristics of transfer recipients. I used the data from this prior study to estimate a model of how citizens form preferences over redistribution policy. In this study, I wish to test the predictions of the model in terms of which policies are popular versus unpopular.
External Link(s)

Registration Citation

Citation
Sun, Gregory. 2026. "What makes redistribution popular?." AEA RCT Registry. July 27. https://doi.org/10.1257/rct.19220-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants will be asked to choose yes or no about if they like the policy proposed in the experiment. The randomization will include three policies:

Policy 1: The policy described will be computed by solving the standard Mirrleesian social planner's problem. The policy will be motivated as helping medium and low-income Americans.
Policy 2: The policy described will be computed by solving the a modified Mirrleesian social planner's problem, with a direct penalty on labor supply effects of the policy. The motivation of the policy will be identical as Group 1.
Policy 3: The policy described will be identical to the one in Group 2, but it will be framed as also helping to mitigate concerns about job loss due to AI automation.

The randomization will simply involve randomizing the order of when these three policies are shwon.
Intervention (Hidden)
Intervention Start Date
2026-07-22
Intervention End Date
2026-07-23

Primary Outcomes

Primary Outcomes (end points)
Indicator for if the participant supports the policy or not.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The only outcome variable in the experiment is support for the policy described. As detailed above, the intervention is simply to change order in which the following 3 policy descriptions are shown.

Policy 1: The policy described will be computed by solving the standard Mirrleesian social planner's problem. The policy will be motivated as helping medium and low-income Americans.
Policy 2: The policy described will be computed by solving the a modified Mirrleesian social planner's problem, with a direct penalty on labor supply effects of the policy. The motivation of the policy will be identical as Group 1.
Policy 3: The policy described will be identical to the one in Group 2, but it will be framed as also helping to mitigate concerns about job loss due to AI automation.

Experimental Design Details
Randomization Method
Randomization will be implemented by a computer. More specifically, question order will follow a simple cyclic latin squares design, with row of the latin square chosen via the randomization engine of Qualtrics.
Randomization Unit
Randomization is at the survey-taker level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
750
Sample size: planned number of observations
750
Sample size (or number of clusters) by treatment arms
250/group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
10% MDE for pairwise comparison between any two treatments, at 5% significance level with 80% power.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Washington University in St Louis IRB
IRB Approval Date
2026-07-24
IRB Approval Number
202408067
Analysis Plan

Analysis Plan Documents

Redistribution PAP.pdf

MD5: 49f9139dd62cc094417863bccc8ce0ff

SHA1: 40c9311ade38b3028030f623a7e173dc9305d8a3

Uploaded At: July 24, 2026

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