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Why Do (Some) Ordinary Americans Support Tax Cuts For the Rich? Evidence From A Randomized Survey Experiment
Last registered on April 30, 2021

Pre-Trial

Trial Information
General Information
Title
Why Do (Some) Ordinary Americans Support Tax Cuts For the Rich? Evidence From A Randomized Survey Experiment
RCT ID
AEARCTR-0007620
Initial registration date
April 29, 2021
Last updated
April 30, 2021 11:45 AM EDT
Location(s)

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Primary Investigator
Affiliation
King's College London
Other Primary Investigator(s)
PI Affiliation
King's College London
PI Affiliation
King's College London
Additional Trial Information
Status
In development
Start date
2021-05-01
End date
2021-06-30
Secondary IDs
Abstract
In the US, the last 40 years have been a period of reducing tax rates on the richest members of society. These tax cuts have often had significant levels of support from the public. Why do (some) ordinary Americans support tax cuts for the rich? We test the impact of four predominant theories – unenlightened selfinterest, prospect of upward mobility, trickle-down beliefs, and fairness considerations using a survey experiment. In particular, we test these theories by randomly assigning a sample of US Americans to different information treatments. We then estimate the effects of these treatments on core beliefs, articulated preferences, and elicited preferences towards tax cuts for the rich.
External Link(s)
Registration Citation
Citation
Hope, David, Julian Limberg and Nina Weber. 2021. "Why Do (Some) Ordinary Americans Support Tax Cuts For the Rich? Evidence From A Randomized Survey Experiment." AEA RCT Registry. April 30. https://doi.org/10.1257/rct.7620-1.0.
Experimental Details
Interventions
Intervention(s)
While there are substantial theoretical and empirical literatures on the determinants of redistributive preferences (Alesina & Giuliano, 2011; Iversen & Goplerud, 2018) spanning all the way back to Meltzer and Richard (1981) seminal median-voter model of redistribution, we know much less about what drives ordinary Americans’ preferences for cutting taxes on the rich. Our study aims to shed new light on this important question through a randomized online information provision experiment. In particular, we randomly assign respondents into five groups which are presented a short statement and a bar chart. The four treatment groups receive factual information relating to potential drivers of preferences for tax cuts for the rich identified from the literatures on redistributive and tax policy preferences, namely 1) unenlightened self-interest (Bartels, 2005); 2) the prospect of upward mobility (Benabou & Ok, 2001; Piketty, 1995); 3) trickle-down effects (Stantcheva, 2020); and 4) fairness considerations (Almås, Cappelen, & Tungodden, 2019; Bastani & Waldenström, 2021). The control group receives factual information on the longest rivers in the USA. We then test whether the treatments have an impact on 1) core beliefs of individuals, 2) expressed preferences for or against tax cuts for the rich, and 3) elicited preferences.
Intervention Start Date
2021-05-01
Intervention End Date
2021-05-31
Primary Outcomes
Primary Outcomes (end points)
There are three main outcome variables.

First, we are interested in people's core beliefs about cutting taxes for the rich. These are measured via survey questions which ask respondents about there core beliefs (see survey instruments in the pre-analysis plan). Second, we are interested in people's expressed preferences. Do they support or oppose cutting taxes on the rich? Again, this will be measured via a survey question. Finally, we are interested in elicited preferences. We check whether the treatments have an effect on preference elicitation by presenting to them a non-profit organisation campaigning for lower taxes top personal income tax rates as to a non-profit organisation campaigning for higher taxes top personal income tax rates. We provide respondents with a link where they can join each organisation’s mailing list and trace whether respondents click on this link.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Respondents will be randomly assigned to one of five treatments. Each treatment consists of a short text and a bar chart, followed by a question of understanding in order to ensure participants have paid sufficient attention to the provided information. The first treatment uses the information about individuals’ self-declared household income to inform them whether they are currently paying the top federal income tax rate. The second treatment shows the chances of an individual to become part of the top 1% income earners over their lifetime. The third treatment shows average annual economic growth in the postwar period up until 1979 when top federal income tax rates were substantially higher and contrasts that with average annual economic growth since 1979. The fourth treatment compares the wealth of the richest US Americans who inherited their wealth to the wealth of the bottom 50%. The last (placebo) treatment presents individuals with information about the two longest rivers in the US. Afterwards, we measure the outcomes (expressed preferences, and elicited preferences).
Experimental Design Details
Not available
Randomization Method
Individuals will be randomised by a computer.
Randomization Unit
Individual randomization
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
4000 individuals and 5 treatment arms in the first experiment, and, depending on available funding, a second experiment with a further 2000 idividuals.
Sample size: planned number of observations
4000 individuals in the first experiment and, depending on the available funding, 2000 individuals in the second experiment.
Sample size (or number of clusters) by treatment arms
800 individuals per treatment arm in the first experiment and, depending on the available funding, 400 individuals per treatment arm in the second experiment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
King's College London Research Ethics Committee
IRB Approval Date
2021-04-20
IRB Approval Number
MRSP-20/21-22999
Analysis Plan

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