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Do Gender-Neutral Job Ads Promote Diversity?
Last registered on November 27, 2020


Trial Information
General Information
Do Gender-Neutral Job Ads Promote Diversity?
Initial registration date
February 27, 2020
Last updated
November 27, 2020 9:45 AM EST

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Primary Investigator
Princeton University
Other Primary Investigator(s)
PI Affiliation
Additional Trial Information
In development
Start date
End date
Secondary IDs
The project seeks to study the effect of using gender-neutral language in job advertisements on the quantity, quality (level of education and skill), and gender breakdown of the applicant pool in a widely used platform for the hiring of talent in the Tech Sector in Latin America (where Spanish is the main language). We implement a randomized controlled trial in which Job ads are randomly selected, at pre-approval, to be compliant with gender-neutral language.
External Link(s)
Registration Citation
Del Carpio, Lucia and Thomas Fujiwara. 2020. "Do Gender-Neutral Job Ads Promote Diversity?." AEA RCT Registry. November 27. https://doi.org/10.1257/rct.5509-1.1.
Experimental Details

Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Number of Job applicants, Gender breakdown, Applicant average quality scores (based on prior performance on platform, company assessments and other)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
There are two possible status for a job ad. A job ad under “control” status is treated in the same way the platform usually treats its ads. A job ad under “treatment” status must comply with a gender-neutral language protocol. The randomization is done at the Job Ad level in the following manner. Each time a company submits a job ad for pre-approval, a random draw occurs and with 50% chance the ad is assigned to control status, and with 50% it is assigned to treatment status. We follow the application processes of all the job ads that constitute our sample until the closing of the ad and application process.

The estimated effect of the treatment(s) (“intent-to-treat”) can be obtained by comparing average outcomes between treatment and control groups. We also estimate the effect of treatment on whether or not the ad has gendered language and use this to re-scale the “intent-to-treat” estimates appropriately to obtain the estimated effect of having gendered language. The analysis will also include a series of robustness checks, such as using different measures of outcomes, controlling for additional variables (such as the date that the ad is posted, the location of the job, and testing for effect heterogeneity (e.g., are effects larger for ads from a particular sector or region). We are also interested in measuring whether treatment has an impact on the gender neutrality of subsequent ads posted by the company. To study this, we will compare the gender-neutrality of the language in the second, third, fourth, and so forth, ad that the company posts in the study period.

Experimental Design Details
Not available
Randomization Method
Coin flip (C/T) generating algorithm on Job Ad arrival for publication
Randomization Unit
Job Ad level
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
500 to 1000 job ads
Sample size: planned number of observations
500 to 1000 job ads
Sample size (or number of clusters) by treatment arms
Sample equally split between control and treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Princeton University IRB
IRB Approval Date
IRB Approval Number