|
Field
Last Published
|
Before
June 29, 2019 04:34 PM
|
After
July 24, 2019 02:48 PM
|
|
Field
Experimental Design (Public)
|
Before
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 of 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.
|
After
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.
|
|
Field
Planned Number of Observations
|
Before
We plan to collect 540 observations, i.e. 540 participants.
|
After
We plan to collect 1080 observations, i.e. 1080 participants.
|
|
Field
Sample size (or number of clusters) by treatment arms
|
Before
We plan to collect 180 observations per treatment, i.e. 180 participants in each treatment.
|
After
We plan to collect 360 observations per treatment, i.e. 360 participants in each treatment.
|
|
Field
Power calculation: Minimum Detectable Effect Size for Main Outcomes
|
Before
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 unconditional votes per treatment. As unconditional votes account for two thirds of observations per treatment, we need 180 observations per treatment and 540 observations in total.
|
After
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.
|