Social Marginal Welfare Weights 2

Last registered on August 12, 2025
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Pre-Trial

Trial Information

General Information

Title
Social Marginal Welfare Weights 2
RCT ID
AEARCTR-0016001
Initial registration date
May 12, 2025

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
May 19, 2025, 10:08 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
August 12, 2025, 8:54 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
United States of America
Region

Primary Investigator

Name
Krishna Srinivasan
Affiliation
UniDistance Suisse
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Other Primary Investigator(s)

PI Name
Francesco Capozza
PI Affiliation
WZB
Contact Investigator

Additional Trial Information

Status
In development
Start date
2025-05-12
End date
2026-05-12
Keywords
Behavior, Governance, Welfare
Additional Keywords
Welfare Weights, Policy Views, Income Taxation
JEL code(s)
D60, D63, H21, H23, I38
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Evaluating the desirability of a reform typically involves weighing the gains of the winners against the losses of the losers using welfare weights. Welfare weights measure the value that citizens assign to a $1 gain in consumption to individuals. They can capture various normative ideals like utilitarianism and equality of opportunity. What are the welfare weights that citizens assign to individuals in society? We conduct an experiment using the general population of the U.S. to elicit their welfare weights. We validate the welfare weights from our approach and compare them to other estimates of welfare weights.
External Link(s)

Registration Citation

Citation
Capozza, Francesco and Krishna Srinivasan. 2025. "Social Marginal Welfare Weights 2." AEA RCT Registry. August 12. https://doi.org/10.1257/rct.16001-1.1
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2025-05-12
Intervention End Date
2026-05-12

Primary Outcomes

Primary Outcomes (end points)
We estimate the progressivity of a Social Architect's welfare weights using a commonly used parametric function, in which welfare weights are proportional to c_r^nu, where c_r represents the Recipients' disposable income and nu is a parameter that governs the progressivity of the weights. The estimated parameter nu is our key outcome variable. We estimate nu using a Poisson Pseudo Maximum Likelihood (PPML) estimation.

See PAP for more details.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Preferences for redistribution at the margin (ranging from -2 to +2)
Preferences for general redistribution (1-7)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants in our experiment assume the role of either a Social Architect or a Recipient. Social Architects face pairs of Recipients with different real-world disposable incomes. They are presented with various decisions that involve allocating monetary amounts to the Recipients. Their choices are used to identify the welfare weights they assign to the Recipients. The Recipients are passive subjects who receive money based on the Social Architects' choices. See PAP for more details
Experimental Design Details
To assess the robustness of the welfare weights to the features of the experimental design, we include several treatments in the experiment that vary the features across Social Architects. The first three treatment dimensions vary the features of the decision environment. They involve randomization of the (i) order of the Recipients on the screen (left or right), (ii) the order of the decision screens (small income differences presented first or last), and (iii) the Recipient common across the decision screens ($60,000 or $120,000).

The last two treatment dimensions vary the features of the elicitation method. To test whether Social Architects' decisions depend on the first decision in the staircase, we randomize the first decision to be either ($300, -$700) or ($700, -$300). To test whether Social Architects' decisions are based on mistakes, we vary whether Social Architects are prompted to consider the consequences of their choices before they can proceed. Social Architects in the treatment with this prompt are asked to choose between two final income distributions, which reflect the consequences of the two options they were presented with. If their choice on whether to implement the change aligns with their preferred final income distribution, they can proceed with the survey. If there is an inconsistency, they will see an error message. Social Architects can proceed with the survey only when there is no inconsistency.
Randomization Method
oTree random number generator
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1
Sample size: planned number of observations
We plan to collect 2,000 U.S. respondents in the first wave of data collection. All participants who complete the survey are invited to a follow-up survey (Wave 2).
Sample size (or number of clusters) by treatment arms
Participants are randomly assigned to treatments based on 5 treatment dimensions. In each treatment dimension, we will randomly assign the sample of participants to one of two treatments.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Internal Review Board, section experimental research, of the Erasmus School of Economics, Erasmus University Rotterdam
IRB Approval Date
2021-05-25
IRB Approval Number
2021-07
IRB Name
The Human Subjects Committee of the Faculty of Economics, Business Administration and Information Technology at the University of Zurich
IRB Approval Date
2021-06-28
IRB Approval Number
2021-049
Analysis Plan

Analysis Plan Documents

PAP_2025_05_12.pdf

MD5: 866c59d4f89d6ca6686648899d7d608d

SHA1: 757294766bf09f0191e4d18b0cdb80295631f07f

Uploaded At: May 12, 2025

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