Hiring Discrimination Based on Gender in Startup Companies

Last registered on July 02, 2020


Trial Information

General Information

Hiring Discrimination Based on Gender in Startup Companies
Initial registration date
July 01, 2020

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 02, 2020, 1:43 PM EDT

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


Primary Investigator

Yale University

Other Primary Investigator(s)

Additional Trial Information

On going
Start date
End date
Secondary IDs
The study is derived from the initial idea that companies tend to hire employees with similar demographics to their current employees and/or leadership, which may lead to discrimination against underrepresented groups, such as women. This will be explored specifically in startups, where companies tend to be smaller and gender dynamics are a major topic of public discussion. The study will investigate whether or not gender discrimination can be observed at the initial applicant screening portion of the hiring process, and how any detected discrimination relates to the gender demographic and other characteristics of the company’s leadership, as can be determined through online research. Furthermore, if a large enough sample size can be obtained, the study will examine how gender discrimination might differ across types of firms and job titles within the startup world.
External Link(s)

Registration Citation

Finley, Marley. 2020. "Hiring Discrimination Based on Gender in Startup Companies." AEA RCT Registry. July 02. https://doi.org/10.1257/rct.5588
Experimental Details


The intervention is the gender of the applicant, as signalled by the name on the resume.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The response to a job application: no response, rejection, or request for follow-up.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
A correspondence study involves sending a collection of mock resumes to different companies. The mock resumes are all near-identical, the only differences being minor changes that signal the gender of the applicant (e.g. the name, sorority vs. fraternity, etc.). Some studies send both male/female-signaled resumes to each company, but I will be sending one or the other to each company in a randomized way to reduce the chance of the study being detected. For a period after the applications are all submitted, the responses from the companies are monitored and recorded. Protecting the integrity of this type of study is key; the companies should never know that the mock resumes sent were not real. There will be no follow-up correspondence unless necessary (e.g. to deny offers to continue with the interview process), in order to minimize interruption of the companies’ normal functions and prevent detection.
Experimental Design Details
Companies included in the experiment will be selected from an online startup platform with job listings. They will only be contacted through the submission of a fake resume. The applications will be sent by the principal investigator and research assistants who have satisfied IRB training requirements. Criteria includes if the startup has an active job listing for a role in sales and/or software development, and less than ~200 employees. A company’s characterization as a startup can be determined by its online presence, where its involvement with certain websites self-identifies it as a startup. Generally, these companies are young (<10 years) and operate through a technology platform, many of them offering SAAS (Software as a Service) products to clients. The plan is to not restrict inclusion by any further specific industry segments in order to increase sample size. Industry and geographical characteristics will be recorded in data for cross-sectional analysis.
Randomization Method
Randomization is done through R software.
Randomization Unit
The gender of the applicant for each firm is randomized.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
~1,000 male, 1,000 female
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Yale University Institutional Review Boards
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials