Fairness in Winner-Take-All Markets: A follow-up study

Last registered on June 20, 2025

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Pre-Trial

Trial Information

General Information

Title

Fairness in Winner-Take-All Markets: A follow-up study

RCT ID

AEARCTR-0016247

Initial registration date

June 18, 2025

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 20, 2025, 11:44 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Erik Ø. Sørensen

Affiliation

Norwegian School of Economics

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Other Primary Investigator(s)

PI Name

Alexander W. Cappelen

PI Affiliation

NHH Norwegian School of Economics

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PI Name

Mathias Ekström

PI Affiliation

NHH Norwegian School of Economics

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PI Name

Bertil Tungodden

PI Affiliation

NHH Norwegian School of Economics

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PI Name

Björn Bartling

PI Affiliation

University of Zurich

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Additional Trial Information

Status

In development

Start date

2025-06-18

End date

2025-06-30

Keywords

Behavior

Additional Keywords

fairness, inequality

JEL code(s)

C91, D31, D63

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This study is a follow-up on the previous trial #3155. A third party spectator makes a distribution decision for two workers whose earnings pre-redistribution were determined by a winner-take-all technology; outlined in the working paper:

Bartling, Björn and Cappelen, Alexander W. and Ekström, Mathias and Sorensen, Erik and Tungodden, Bertil, Fairness in Winner-Take-All Competitions (January 30, 2025). http://dx.doi.org/10.2139/ssrn.3175189.

External Link(s)

Registration Citation

Citation

Bartling, Björn et al. 2025. "Fairness in Winner-Take-All Markets: A follow-up study ." AEA RCT Registry. June 20. https://doi.org/10.1257/rct.16247-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We will implement one of the treatments from that registration as the baseline (the Winner-Take-All No Choice), together with two variants: One variant in which there are extra comprehension checks for the main information provided for the decisions, another in which the two workers in the distribution are put in the context of all workers that take part in the experiment. Each of the variants will be compared to the baseline, the variants are not intended to be compared to each other.

Intervention (Hidden)

Intervention Start Date

2025-06-19

Intervention End Date

2025-06-30

Primary Outcomes

Primary Outcomes (end points)

Same two outcomes as in the working paper: 1) Share given to the winner (0-1), 2) Dummy variable (0-1), equal to one if all earnings given to winner. These will be regressed on the winning margin (difference in real effort performance), and the regression coefficient is the statistic of interest.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

First, 1200 workers are recruited, and the workers take part in a winner-take-all real effort task (random pairs).

We then plan to have 3000 spectators in total, recruited on Prolific, randomized into 3 treatments. Each of the spectators make a suggestion for redistribution among a pair of workers. After the spectator 1 out of 5 spectators are chosen to have their suggestion implemented.

Experimental Design Details

Randomization Method

Randomization by computer.

Randomization Unit

Workers are randomly assigned into pairs (with the restriction that their performance on the real effort task (0-24) are not equal).
Individual spectators assigned to random pair of workers. Randomization by computer.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

3000 spectators.

Sample size: planned number of observations

3000 spectators.

Sample size (or number of clusters) by treatment arms

1000 spectators in each arm.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

For the two main outcomes in Table 3 of the working paper, "All to Winner" and "Share to Winner", we aim for being able to detect between-group differences of 0.01 in the regression coefficients in columns 1 and 4 (in which the outcomes are regressed on the "winning margin" which can take values between 1 and 24). Our calculations were based on 80% power and at 5% significance and the standard errors of the estimates in the working paper.

Supporting Documents and Materials

IRB