Fairness Across the World
Last registered on March 22, 2019


Trial Information
General Information
Fairness Across the World
Initial registration date
March 07, 2019
Last updated
March 22, 2019 12:30 PM EDT

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Primary Investigator
NHH Norwegian School of Economics
Other Primary Investigator(s)
PI Affiliation
Norwegian School of Economics NHH
PI Affiliation
Norwegian School of Economics NHH
PI Affiliation
Norwegian School of Economics NHH
Additional Trial Information
On going
Start date
End date
Secondary IDs
This is a pre-analysis plan for a unique study of the role of fairness and efficiency considerations in justifying inequalities across the world.1 The classical approach in economics has been to focus on the trade-off between equality and efficiency, where the basic idea is that there is a cost of redistribution and inequality acceptance therefore depends on the relative importance assigned to efficiency versus equality. A growing literature in political economy and behavioral economics, however, has shown that not only efficiency considerations seem to matter for inequality acceptance; fairness considerations may also be of great importance. For example, inequalities reflecting differences in effort appear to be more acceptable than inequalities reflecting differences in luck (Konow, 2000; Cappelen, Drange Hole, Sørensen, and Tungodden, 2007; Almaas, Cappelen, Sørensen, and Tungodden, 2010).

This project provides a novel data set on the nature of inequality acceptance. It provides unique descriptive data on inequality acceptance across the world; comprehensive data on people’s beliefs about the sources of inequality and cost of redistribution; novel data on how unfair people perceive the income distribution to be and their attitude to redistribution; causal evidence on the importance of the source of inequality and efficiency considerations for inequality acceptance; rich individual data that will support a number of studies on how inequality acceptance is associated with socioeconomic status, religion, gender, age, and other relevant background variables.
External Link(s)
Registration Citation
Almås, Ingvild et al. 2019. "Fairness Across the World." AEA RCT Registry. March 22. https://www.socialscienceregistry.org/trials/3980/history/43877
Experimental Details
International representative survey in which participants implemented an actual distribution of bonus payments for workers on an international labor market platform. The intervention randomized the context of pre-distribution earnings, which were caused by luck or merit, and whether redistribution was costless.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
fairness preferences, beliefs about the source of inequality, beliefs about the cost of redistribution, attitudes to income inequality and redistribution
Primary Outcomes (explanation)
Refer to enclosed PAP
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We use two types of participants in this study, spectators and workers, where our primary interest is in the distributive choices made by the spectators. The workers are recruited through an online international market platform, to conduct some work for the research team, where they are told that, except for a small participation fee, the payment for the work will be determined by a third party. The spectators are told that, in contrast to traditional survey questions that are about hypothetical situations, in this study they make a choice that has consequences in a real life situation. They are then presented with a real life situation involving two of the workers and are told that it is their task to determine the final payment for these two workers. The spectators are informed that different earnings have been assigned to the workers (not paid out) as well as the basis for the allocation. The decision that the spectators have to make is how much to redistribute between the two workers. Participants were randomized into different spectator treatments (between-individual design).

There are three main treatments, each informing us about an interesting outcome. We are interested in the treatment differences, as outlined in the enclosed PAP, but there is no treatment that is only meant as a "control", and hence no treatment is labeled as such.
Experimental Design Details
Not available
Randomization Method
Randomization by computer
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
60,000 individuals
Sample size: planned number of observations
60,000 individuals
Sample size (or number of clusters) by treatment arms
20,000 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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