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Attitudes toward Taxation: The Role of Luck and Merit
Last registered on August 06, 2019

Pre-Trial

Trial Information
General Information
Title
Attitudes toward Taxation: The Role of Luck and Merit
RCT ID
AEARCTR-0004455
Initial registration date
August 05, 2019
Last updated
August 06, 2019 11:52 AM EDT
Location(s)

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Primary Investigator
Affiliation
University of Heidelberg
Other Primary Investigator(s)
PI Affiliation
University of Heidelberg
Additional Trial Information
Status
In development
Start date
2019-08-06
End date
2021-09-01
Secondary IDs
C93, H23
Abstract
A growing literature suggests that people are willing to accept more inequality if it is the result of merit rather than the result of luck. However, it is often difficult, if not impossible, to relate economic success or inequality to the relative impact of the luck or merit. In this study, we investigate distributional situations in which the relative impact of luck and merit is uncertain.

External Link(s)
Registration Citation
Citation
Fehr, Dietmar and Martin Vollmann. 2019. "Attitudes toward Taxation: The Role of Luck and Merit." AEA RCT Registry. August 06. https://doi.org/10.1257/rct.4455-1.0.
Former Citation
Fehr, Dietmar and Martin Vollmann. 2019. "Attitudes toward Taxation: The Role of Luck and Merit." AEA RCT Registry. August 06. https://www.socialscienceregistry.org/trials/4455/history/51317.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-08-07
Intervention End Date
2019-08-31
Primary Outcomes
Primary Outcomes (end points)
Tax rate
Belief about Luck/Effort
Belief about Deservingness
Willingness to pay for Information
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will run a study on Amazon Mturk. Participants are randomly assigned to one of two versions of a real-effort task (RET). They are matched into pairs and paid according to their relative performance (high bonus and low bonus). The RET is designed such that one version always results in getting the high bonus. This allows us to causally identify the impact of the bonus payment on redistribution. Before and after they learn about the performance comparison (ie the bonus payment), they are asked several questions regarding the task and their (relative) performance. After learning about the bonus payment, they decide about a redistributive tax.
Experimental Design Details
Not available
Randomization Method
Randomization is pre-programmed in the survey program.
Randomization Unit
Randomization on individual level
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1800 participants recruited on Amazon Mturk
Sample size: planned number of observations
1800 participants recruited on Amazon Mturk
Sample size (or number of clusters) by treatment arms
900 per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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