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The impact of voting rules on moral decision making in groups
Last registered on July 24, 2019

Pre-Trial

Trial Information
General Information
Title
The impact of voting rules on moral decision making in groups
RCT ID
AEARCTR-0004317
Initial registration date
June 14, 2019
Last updated
July 24, 2019 2:49 PM EDT
Location(s)

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Primary Investigator
Affiliation
University of Innsbruck
Other Primary Investigator(s)
PI Affiliation
Victoria University of Wellington
PI Affiliation
University of Hamburg
Additional Trial Information
Status
In development
Start date
2019-06-24
End date
2019-12-31
Secondary IDs
Abstract
We plan to experimentally test the effect of voting rules on the likelihood that individuals vote for moral transgression. Subjects vote independently from each other but monetary benefits are equally divided among group members. Our first hypothesis is based on a theoretical model by Rothenhäusler et al. (2018) which (in case of consequentialist moral costs) shows that the number of votes for moral transgression increases in the number of votes required (voting threshold). Our second hypothesis is that guilt sharing among group members is a main driver of this result.
External Link(s)
Registration Citation
Citation
Feess, Eberhard, Florian Kerzenmacher and Gerd Mühlheußer. 2019. "The impact of voting rules on moral decision making in groups." AEA RCT Registry. July 24. https://doi.org/10.1257/rct.4317-3.0.
Former Citation
Feess, Eberhard, Florian Kerzenmacher and Gerd Mühlheußer. 2019. "The impact of voting rules on moral decision making in groups." AEA RCT Registry. July 24. https://www.socialscienceregistry.org/trials/4317/history/50589.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-06-24
Intervention End Date
2019-12-31
Primary Outcomes
Primary Outcomes (end points)
Our primary outcome is whether subjects vote for or against taking money originally designated for donation to a charity depending on the treatment (voting threshold).
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The experiment will be conducted online and participants will be recruited using Amazon Mechanical Turk.
Subjects will be randomly assigned to groups of three. They vote on whether to take money designated for donation to a charity for their group (“Yes” or “No”). If, depending on the voting threshold, sufficiently many group members vote in favor of taking the money, the payoff is split equally among group members.
We test our hypotheses with different treatments (one for each possible voting threshold) and assign subjects to exactly one of these treatments (between-subject design):
T1: At least one “Yes” vote is required for transferring the money to the group. If all group members vote “No”, the money is donated.
T2: At least two “Yes” votes are required for transferring the money to the group. If at least two group members vote “No”, the money is donated.
T3: All group members need to vote “Yes” for transferring the money to the group. If at least one group members votes “No”, the money is donated.
In each of these three treatments, two out of the three group members make their vote unconditional, i.e. independent of the other group members' votes. These observations will be used to analyze the first hypothesis (on the impact of the voting threshold).
The third group member’s vote will be elicited conditional on the other group members’ votes. As this vote matters only for the group outcome in case the third vote is pivotal, we only ask for the following scenarios depending on the treatment:
T1: What would you vote for if none of your two group members voted “Yes”?
T2: What would you vote for if exactly one of your two group members voted “Yes”?
T3: What would you vote for if both of your two group members voted “Yes”?
Comparing the votes of the third group members who know that their vote is pivotal for the three thresholds allows us to test the second hypothesis (on the impact of guilt sharing). The reason is that the marginal (financial) benefit of voting “Yes” is the same in each treatment.
Experimental Design Details
Not available
Randomization Method
Participants will be randomly assigned to treatments (computerized).
Randomization Unit
Individual level randomization.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
We plan to collect 1080 observations, i.e. 1080 participants.
Sample size (or number of clusters) by treatment arms
We plan to collect 360 observations per treatment, i.e. 360 participants in each treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
In a previous experiment with a similar general setup, we found that in a lying task 14% of participants lied in T1, while 27% (28%) lied in T3 (T2). Assuming that we can replicate this effect in our donation experiment, we need about 120 observations (one-sided Chi-squared test with significance level of 5% and power of 80%) for conditional votes per treatment. As conditional votes account for one third of observations per treatment, we need 360 observations per treatment and 1080 observations in total.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number