Algorithmic Bias and the Effects of Gender-Blind Wage Suggestions: A Field Experiment on a Job Platform

Last registered on July 25, 2025
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Pre-Trial

Trial Information

General Information

Title
Algorithmic Bias and the Effects of Gender-Blind Wage Suggestions: A Field Experiment on a Job Platform
RCT ID
AEARCTR-0016347
Initial registration date
July 19, 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
July 25, 2025, 11:36 AM EDT

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

Locations

Country
India
Region

Primary Investigator

Name
Borui Sun
Affiliation
University of Maryland College Park
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Other Primary Investigator(s)

PI Name
Erin Kelley
PI Affiliation
University of Chicago
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PI Name
Gregory Lane
PI Affiliation
University of Chicago
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PI Name
Gaurav Chiplunkar
PI Affiliation
University of Virginia
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Additional Trial Information

Status
Completed
Start date
2020-11-12
End date
2022-09-06
Keywords
Behavior, Gender, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates how employers and job seekers respond to wage suggestions generated by algorithms on a job posting platform. Motivated by concerns that algorithms trained on historical data may reflect social biases—such as gender-based wage disparities—we experimentally test the effects of removing demographic information from the algorithm’s input. Vacancy postings were randomly assigned to one of three groups: receiving wage suggestions from the platform’s status quo algorithm, receiving gender-blind wage suggestions, or receiving no wage suggestion at all. We examine how these design choices affect posted wages, employer behavior, and job-seeker engagement.

This trial was launched as a one-month pilot in partnership with a job platform but was discontinued before full rollout due to shifts in the partner company's priorities. As a result, the experimental data is limited to the pilot period, and no additional data can be collected.
External Link(s)

Registration Citation

Citation
Chiplunkar, Gaurav et al. 2025. "Algorithmic Bias and the Effects of Gender-Blind Wage Suggestions: A Field Experiment on a Job Platform." AEA RCT Registry. July 25. https://doi.org/10.1257/rct.16347-1.0
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Experimental Details

Interventions

Intervention(s)
To study the effects of gender-blind algorithmic design, we partnered with a job platform that provides employers with suggested wage ranges when creating vacancy postings. These suggestions are generated by a model trained on historical job data with similar requirements, including the employer’s stated gender preference.

We restrict the sample to female-only postings. In the control arm, employers receive wage suggestions from the platform’s standard algorithm, which includes inputs such as education requirements, work experience, occupation, location, city, and gender. In the gender-blind arm, gender is removed as an input, and suggestions are based on male-only postings—effectively equalizing algorithmic wage recommendations across gender lines. In the no-suggestion arm, employers receive no wage recommendation.
Intervention (Hidden)
Intervention Start Date
2022-02-24
Intervention End Date
2022-03-31

Primary Outcomes

Primary Outcomes (end points)
Primary outcomes include:
(1) the posted wage on the job listing;
(2) the stated job requirements, including education, experience, and language proficiency; and
(3) hiring outcomes, proxied by the number of applications received and the characteristics of applicants, such as education level, English proficiency, and prior work experience
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment was implemented on a job platform over a one-month pilot period. The sample was restricted to job postings that specified a female-only hiring preference. The unit of randomization was the individual job posting. Random assignment was implemented via a deterministic rule, and all interface elements were held constant across conditions to ensure a consistent user experience.

The partner company discontinued communication before the full rollout, and no additional data can be collected beyond the completed pilot.
Experimental Design Details
Randomization Method
Randomization was done in office by a computer
Randomization Unit
Individual job posting
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable (treatment was assigned at the individual job-posting level)
Sample size: planned number of observations
14,199
Sample size (or number of clusters) by treatment arms
14,199
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Yes
Data Collection Completion Date
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials