Social Marginal Welfare Weights

Last registered on December 07, 2021

Pre-Trial

Trial Information

General Information

Title
Social Marginal Welfare Weights
RCT ID
AEARCTR-0008372
Initial registration date
November 29, 2021
Last updated
December 07, 2021, 2:41 PM EST

Locations

Region

Primary Investigator

Affiliation
University of Zurich

Other Primary Investigator(s)

PI Affiliation
Erasmus University Rotterdam

Additional Trial Information

Status
In development
Start date
2021-12-01
End date
2023-11-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this project, we estimate ``Generalized Social Marginal Welfare Weights" (henceforth welfare weights), introduced by Saez and Stantcheva (2016), using an experimental approach. Welfare weights measure the value of providing an additional dollar of consumption to any individual in the society. These weights are general enough to capture many different fairness ideals. In a variety of policy settings, such as taxation, cash transfers, and in-kind transfers, welfare weights can be used to compare alternative policies. To understand how people assign welfare weights, we administer a large-scale incentivized online experiment in a general population sample of the U.S. population. In our experiment, participants in the role of ``Social Architects," assign welfare weights to seven real ``Recipients" with different real-world after-tax incomes. We provide the first estimate of welfare weights using a general population sample of the U.S. population. The weights obtained from our project can be directly used to evaluate several policies such as taxation, cash transfers, and in-kind transfers. We shed light on the factors (both demographics and political affiliations) that affect people's welfare preferences. Finally, we compare the welfare weights obtained from our experiment to the weights implied by existing policies in the U.S.
External Link(s)

Registration Citation

Citation
Capozza, Francesco and Krishna Srinivasan. 2021. "Social Marginal Welfare Weights." AEA RCT Registry. December 07. https://doi.org/10.1257/rct.8372-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-12-01
Intervention End Date
2023-11-01

Primary Outcomes

Primary Outcomes (end points)
Slope of the weights
Weight assigned by an Architect to each Recipient
Primary Outcomes (explanation)
Slope of the weights: this is the slope coefficient obtained by regressing an Architect's weights on the vector (-1,-2,-3,-4,-5,-6,-7)

Secondary Outcomes

Secondary Outcomes (end points)
Government should do something to reduce inequality (1-7)
Government should increase top-taxes (1-7)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We recruit the participants in the role of Social Architects or Recipients. In the main task, Social Architects face 6 decision screens. In each decision screen, they face a different pair of Recipients (Recipient i and Recipient j) and have to decide how to allocate some money between them. A Social Architect faces a ``staircase" with 4 questions within each Decision Screen. In each question, the Social Architect has to indicate whether she prefers a Constant reform or a Variable reform. The Constant reform option takes $500 away from Recipient j and gives $500 to Recipient i. The variable Reform option takes away $t from Recipient j and gives $pt to Recipient i.

The four questions that Social Architects are selected from a set of 15 questions based on the staircase procedure. The first question that Social Architects face is common for all Social Architects. The second, third, and fourth questions that Social Architects face depend on the choices that the Social Architects made in the first, second, and third questions respectively. In each decision screen, we take the Social Architects' choices in the fourth question to identify the relative weight (the value of p) they assign to the two Recipients. The selected value of p is the value that makes the Social Architect just indifferent between a Constant reform (500,-500) and Variable Reform (pt,-t).

After the task of assigning weights, Social Architects face a second task where we elicit their policy views. The first question asks them whether they would like to increase the tax on millionaires and the second question asks them if they would like the government to increase redistribution.

We inform the Social Architects that there is a chance that they will be randomly selected in this study. At the end of the study, we will randomly select one Social Architect. For the randomly selected Social Architect, one of the six decision screens will be randomly selected, and one of the four questions within the selected decision screen will be randomly selected and implemented. At the end of the study, the randomly selected question will involve two Recipients. We will recruit these two Recipients from a survey panel. The bonus payments of the two Recipients will depend on the choices of the randomly selected Social Architect.

The experimental flow described above is for the Loss x Moderate treatment in the study. We implement other treatments to assess how robust the welfare weights estimation is. In treatment Gain x Moderate, we change the framing of the reforms such that the reform amounts are all positive. In treatments Loss x Moderate and Gain x Moderate, every Recipient is compared to the Recipient earning $70000. Treatments Loss x High and Gain x High are similar to treatments Loss x Moderate and Gain x Moderate, with the exception that all the Recipients are compared to the Recipient earning $500,000.
Experimental Design Details
Not available
Randomization Method
Qualtrics 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
2000 participants in Wave 1 of data collection
Sample size (or number of clusters) by treatment arms
500 participants per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
1. Slope of the weights - Mean = 0.02, sd = 0.08, n per group = 500, power = 80%, minimum detectable effect size = 0.014 (70% increase over mean). 2. Weights assigned to a Recipient - N/A
IRB

Institutional Review Boards (IRBs)

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
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
Analysis Plan

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