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How pandemic affects trust among people from different backgrounds?
Last registered on June 19, 2020

Pre-Trial

Trial Information
General Information
Title
How pandemic affects trust among people from different backgrounds?
RCT ID
AEARCTR-0006017
Initial registration date
June 18, 2020
Last updated
June 19, 2020 11:24 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Monash University
Other Primary Investigator(s)
PI Affiliation
Monash University
Additional Trial Information
Status
In development
Start date
2020-06-19
End date
2020-07-22
Secondary IDs
Abstract
We study how COVID_19 affects trust and trustworthiness between people from different backgrounds in a trust game experiment. In the first treatment dimension, we vary whether to prime the participants with information about the COVID_19 pandemic; in the second treatment dimension, we vary whether to prime the participants with the possibility that some participants come from different backgrounds.
External Link(s)
Registration Citation
Citation
Bao, Zhengyang and Andreas Leibbrandt. 2020. "How pandemic affects trust among people from different backgrounds? ." AEA RCT Registry. June 19. https://doi.org/10.1257/rct.6017-1.0.
Experimental Details
Interventions
Intervention(s)
Treatment dimension 1: whether to prime the participants with information about COVID_19.
Treatment dimension 2: whether to prime the participants with the possibility that some participants come from a different background.
Intervention Start Date
2020-06-19
Intervention End Date
2020-07-22
Primary Outcomes
Primary Outcomes (end points)
The amount of money that the trustors send to the trustees.
The amount of money that the trustees send back to the trustors.
Primary Outcomes (explanation)
The main outcomes are trust and trustworthiness. Trust is measured as the average amount of money that the trustors send to the trustees. Trustworthiness is the average amount of money that the trustees return to the trustors.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Treatment dimension 1: whether to prime the participants with information about COVID_19.
Treatment dimension 2: whether to prime the participants with the possibility that some participants come from a different background.
Experimental Design Details
We have a 2x2 experimental design. The baseline is a trust game. Each participant is randomly matched with another one and randomly assigned the role as a trust or trustee. The trustor is given $2 and decides how much of the $2 to give to the trustee. Any given amount is doubled and passed to the trustee. The trustee needs to decide how much of the doubled money to be sent back to the trustor. The trustor receives whatever he or she kept from the original $2, plus anything return to them by the trustee. The trustee receives whatever was given to them by the trustor and then doubled, minus whatever they returned to the trustor. Treatment dimension 1: whether to prime the participants with information about COVID_19. In treatments with the prime, we inform participants to keep social distance to prevent the disease before the trust game starts. In treatments without COVID_19 prime, this information is hidden. Treatment dimension 2: whether to prime the participants with the possibility that some participants may come from different backgrounds. In treatments with the prime, below the English instructions, we provide the instructions translated in Chinese. In treatments without the Chinese prime, we hide the instructions in Chinese.
Randomization Method
Randomization done in by a computer.
Randomization Unit
We perform randomization on the individual level.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
There is no cluster. We will hire 800 MTurkers (participants) from the US depending on the participantion rate and dropout rate.
Sample size: planned number of observations
We will hire 800 MTurkers (participants) from the US depending on the participantion rate and dropout rate.
Sample size (or number of clusters) by treatment arms
200 MTurkers in the control group, 200 MTurkers in COVID_19 priming, 200 MTurkers in Chinese participant priming, 200 MTurkers in both priming.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We need 106 groups (212 participants) per treatment for a 0.5 standard deviation difference at the 99% level.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
The Monash University Human Research Ethics Committee
IRB Approval Date
2020-05-08
IRB Approval Number
24201
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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