Efficiency vs. Fairness tradeoff in personnel selection: Study of human perceptions

Last registered on June 27, 2022


Trial Information

General Information

Efficiency vs. Fairness tradeoff in personnel selection: Study of human perceptions
Initial registration date
June 27, 2022

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 27, 2022, 4:49 PM EDT

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


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Primary Investigator

University of Zurich

Other Primary Investigator(s)

PI Affiliation
University of St.Gallen

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This study explores human perceptions of algorithms in HR context. This study follows our previous experiments on people's perception of efficiency vs. fairness tradeoff in personnel selection (AEARCTR-0008804). Here, we modify the scenario and focus on female dominated occupation to investigate how the degree of efficiency vs. fairness tradeoff affects people' choices of the algorithms and their fairness perceptions.
External Link(s)

Registration Citation

Kandul, Serhiy and Ulrich Leicht-Deobald. 2022. "Efficiency vs. Fairness tradeoff in personnel selection: Study of human perceptions." AEA RCT Registry. June 27. https://doi.org/10.1257/rct.9663
Experimental Details


We add a scenario with female dominated occupation and manipulate fairness metric (equality of opportunity vs. statistical partiy) and degree of efficiency vs. fairness tradeoff.
The intervention therefore closely follows the one described in prevoius trials AEARCTR-0008804
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The frequency of choices of a fair algorithm; fairness perceptions
Primary Outcomes (explanation)
fairness perceptions will be derived as average score from the respective scales

Secondary Outcomes

Secondary Outcomes (end points)
participants' reasoning behind their choices; participants' beliefs about existing inqualities on labor market.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We employ 2 (fairness metric) by 2 (female or male advantage) design. The degree of the tradeoff between efficiency and fairness of the selection algorithm is a within-subject manipulation.
Experimental Design Details
Not available
Randomization Method
Randomization by Qualtrics.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
We plan to recruit 280 participants (60 per each experimental condition)
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number