The impact of voting rules on moral decisions: Free-riding or guilt sharing?

Last registered on March 31, 2020
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Pre-Trial

Trial Information

General Information

Title
The impact of voting rules on moral decisions: Free-riding or guilt sharing?
RCT ID
AEARCTR-0004317
Initial registration date
June 14, 2019

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
June 29, 2019, 4:34 PM EDT

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

Last updated
March 31, 2020, 8:04 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
Florian Kerzenmacher
Affiliation
University of Innsbruck
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Other Primary Investigator(s)

PI Name
Eberhard Feess
PI Affiliation
Victoria University of Wellington
Contact Investigator
PI Name
Gerd Mühlheußer
PI Affiliation
University of Hamburg
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Additional Trial Information

Status
On going
Start date
2019-06-24
End date
2020-06-30
Keywords
Other
Additional Keywords
group deicision making, moral decision making
JEL code(s)
C92, D02, D63, D70
Secondary IDs
Abstract
We experimentally analyze how and why the minimum number of votes required
for a moral transgression (the "voting threshold"') influences the
frequency of votes in favor of it. With simultaneous voting, this frequency
increases in the voting threshold and is thus largest for the unanimity
rule. To identify the underlying motives, we need to account for the fact
that different voting thresholds imply different incentives to free-ride on
the votes of other group members. We do so by considering only pivotal
voters in a sequential setting. We then develop a novel design which allows
us to disentangle several behavioral motives. Our data show that guilt
sharing and preferences for consensual decisions are important and
independent drivers of voting behavior.
External Link(s)

Registration Citation

Citation
Feess, Eberhard, Florian Kerzenmacher and Gerd Mühlheußer. 2020. "The impact of voting rules on moral decisions: Free-riding or guilt sharing?." AEA RCT Registry. March 31. https://doi.org/10.1257/rct.4317-4.1
Former Citation
Feess, Eberhard, Florian Kerzenmacher and Gerd Mühlheußer. 2020. "The impact of voting rules on moral decisions: Free-riding or guilt sharing?." AEA RCT Registry. March 31. https://www.socialscienceregistry.org/trials/4317/history/65191
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2019-06-24
Intervention End Date
2020-06-30

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 setting (voting threshold) and different treatments.
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 settings (one for each possible voting threshold) and assign subjects to exactly one of these settings (between-subject design):
Threshold 1: At least one “Yes” vote is required for transferring the money to the group. If all group members vote “No”, the money is donated.
Threshold 2: 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.
Threshold 3: All group members need to vote “Yes” for transferring the money to the group. If at least one group member votes “No”, the money is donated.
In the first treatment, we are going to ask participants to vote unconditionally, 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).
In the second treatment, we are going to split the group. Two randomly selected voters will decide unconditionally. The third group member will be asked for their vote conditional on the other two group members’ votes. The questions will be:
What would you vote for if none of your two group members voted “Yes”?
What would you vote for if exactly one of your two group members voted “Yes”?
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.
However, even when financial incentives are the same due to pivotality, there may still be different motivational factors including e.g. social conformity. To disentangle these factors, we will have two additional treatments. In these treatments, the actual votes of the group members are substituted by the votes of other participants.
In the third treatment, conditional voters are informed about the behavior of unconditional voters from the second treatment and the behavior of their group members. However, the votes of their group members are substituted with the votes of two random conditional voters from the second treatment.
In the fourth treatment, conditional voters are in the same situation as in the third treatment. However, they receive no information about the behavior of their actual group members.
Furthermore, we are also going to replicate the second treatment with a fifth treatment where we inform conditional voters about the behavior of unconditional voters from the second treatment.
Experimental Design Details
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 4680 observations, i.e. 4680 participants.
Sample size (or number of clusters) by treatment arms
We plan to collect 360 observations in the first treatment and 1080 observations in each of the other treatments.
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 “Threshold 1”, while 27% (28%) lied in “Threshold 3” (“Threshold 2”). 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 each setting per treatment. As conditional votes account for one third of observations in all but the first treatment, we need 1080 observations per treatment (in treatments 2 to 5) and 4680 observations in total.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

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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