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Economic Game Study [Redistributive Behaviour and the Self-Serving Bias]
Last registered on June 24, 2018


Trial Information
General Information
Economic Game Study [Redistributive Behaviour and the Self-Serving Bias]
Initial registration date
February 20, 2018
Last updated
June 24, 2018 7:45 PM EDT
Primary Investigator
University of British Columbia
Other Primary Investigator(s)
PI Affiliation
University of British Columbia
Additional Trial Information
In development
Start date
End date
Secondary IDs
In a situation where individuals are uncertain about whether their level of earnings is due to chance or their own performance, are they more likely to attribute higher earnings levels to performance and lower earnings levels to chance? If so, does this “self-serving bias” decrease the amount of money individuals are willing to redistribute to others who earned less?

Using an experimental game setting, I examine differences in redistributive behaviour among individuals based on their earnings determinant (performance, chance, or uncertain). I will first examine whether people have different rates and levels of redistribution depending on their earnings determinant. I will then examine whether earnings level affects perceived earnings determinant. Finally, I will examine differences in redistributive behaviour among individuals who have different beliefs about payment type who are in uncertain payment attribution groups.
External Link(s)
Registration Citation
Francois, Patrick and Peter Ward-Griffin. 2018. "Economic Game Study [Redistributive Behaviour and the Self-Serving Bias]." AEA RCT Registry. June 24. https://doi.org/10.1257/rct.2654-3.0.
Former Citation
Francois, Patrick, Peter Ward-Griffin and Peter Ward-Griffin. 2018. "Economic Game Study [Redistributive Behaviour and the Self-Serving Bias]." AEA RCT Registry. June 24. https://www.socialscienceregistry.org/trials/2654/history/31133.
Experimental Details
Participants will be randomly chosen to have their earnings level determined by performance or chance and half of them will be told their earnings determinant while the other half will have their earnings determinant unknown. I examine the impact of this on redistributive behaviour among high payment participants and redistributive demand among low payment participants. I also examine whether individuals who have uncertain payment types are more likely to attribute payment to performance than chance when they have higher earnings.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Redistribution amount (in $), Redistribution demanded (in $), Redistribution(Yes ,No), Believed payment determinant (chance or performance),
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Perception of the fairness of the allocation (Is it is fair that, prior to redistribution, some individuals in this study receive $14 and others $1? Very Unfair-Very Fair) , political ideology (1-10 Left-Right individual scale, five item scale, individual statement: "Governments should redistribute income from the better off to those who are less well off.") Strongly Disagree-Strongly Agree 7 point scale)
Secondary Outcomes (explanation)
The five item scale for political ideology consists of five statements with participants responding on a 7 point scale from Strongly Disagree to Strongly Agree for each question. These questions are as follows: "Governments should redistribute income from the better off to those who are less well off." "Ordinary working people do not get their fair share of the nation's wealth." "Big Business benefits owners at the expense of workers." "There is one law for the rich and one for the poor." "Management will always try to get the better of employees if it gets the chance."
Experimental Design
Experimental Design
2 by 2 by 2 completely between subjects design with 50 subjects per group.
Experimental Design Details
Randomization Method
Randomization of individuals into groups was done by Qualtrics Survey software randomization tool, using the "equal randomization" option.
Randomization Unit
Individual randomization
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
400 Students
Sample size: planned number of observations
400 Students
Sample size (or number of clusters) by treatment arms
50 students in KRH, 50 students in KRL, 50 students in URH, 50 students in URL, 100 students in KP (of which approximately 50 will be in each of KPH and KPL), 100 students in UP (of which approximately 50 will be in each of UPH and UPL).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on a preliminary sample (which will not be included in the final results of the study), the expected difference in redistribution is $1 between individuals in performance and chance conditions while the standard deviation is $1.59. Given this standard deviation and difference in means, to have 80% power with .05 alpha should require 40 individuals per condition and the total sample should be at least 320 students. To further improve this power, I am going to be using a total of approximately 400 students, leading to an expected power of 88%.
IRB Name
University of British Columbia Behavioural Research Ethics Board
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers