Competitiveness and Employability

Last registered on December 06, 2022


Trial Information

General Information

Competitiveness and Employability
Initial registration date
March 20, 2019

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
March 23, 2019, 8:27 PM EDT

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

Last updated
December 06, 2022, 8:13 PM EST

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Austin Peay State University

Other Primary Investigator(s)

PI Affiliation
George Mason University

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Hiring an employee is an essential task for decision makers and can significantly affect the future performance of an organization. Even though some research addresses the effect of hiring competitive employees on the organization’s performance, little is known about the impact of different competitive preferences on employee hiring decisions. Would decision makers like to hire agents who compete against other people or the ones who continuously challenge against their own previous performance or the ones who work efficiently without competing against anyone? Using one laboratory and two online experiments this project aims to investigate whether and to what extent competitiveness as a trait/preference impact one's likelihood of being hired. The laboratory study focuses on the performance aspect of competitiveness; i.e., are competitive individuals perceived as better performers than non-competitive individuals? Online studies focus on the social aspect; i.e., would competitive individuals be liked/disliked at different extents than less competitive individuals in the workplace? Would hypothetical cover letters be evaluated differently based on the competitive preferences of the candidate? The study additionally involves a particular focus on gender. We will answer whether being a competitive woman is more likely to be penalized/disliked in a hypothetical job search situation and whether being a self-competitive individual (i.e., being committed to constant self-improvement as opposed to being rivalrous against other people) would increase women's likelihood of being hired.
External Link(s)

Registration Citation

Demiral, Elif E and Johanna Mollerstrom. 2022. "Competitiveness and Employability." AEA RCT Registry. December 06.
Former Citation
Demiral, Elif E and Johanna Mollerstrom. 2022. "Competitiveness and Employability." AEA RCT Registry. December 06.
Experimental Details


We study our research question using three experiments: a laboratory experiment (Study 1) and two online experiments (Studies 2 and 3). In Study 1, the student subjects are in the role of a Firm and decide to hire a Worker who performed a task (math addition task) for several rounds. The hired worker's score in the fourth round determines the firm's payoff. Workers differ in the compensation scheme choices they made in the last round (choices being entering a tournament against another person, entering a tournament against themselves, or not entering a tournament). In Study 2, online participants give advice to a hypothetical job seeker on what to include in his/her cover letter regarding his/her competitive preferences (options being self-competitive, non-competitive, or other-competitive). In Study 3, online participants read two hypothetical cover letters, which vary in gender (as signaled with names) and the competitive preferences (possibilities being self-competitive, non-competitive, and other-competitive) of the candidates and rate them based on their employability prospects.

In Study 1, there are three treatments. We randomly assign subjects to one of these treatments (randomized at the individual level).
1. Female treatment where all the workers are females.
2. Male treatment, where all the workers are males.
3. Neutral treatment, where the gender of the workers is not known by the firm.

In Study 2, there are four (2x2) conditions. The candidates vary in gender, and the work either involves working independently or working as a team. Each subject gives advice to a male and a female candidate. We randomly assign subjects in either of the work condition (independent vs. team).

In Study 3, there are 12 (2x2x3) conditions. The candidates vary in gender, the work either involves working independently or working as a team, and each candidate has a different competitive preference (self-competitive, not competitive, other-competitive).​ Each subject rates two cover letters varying in gender and competitive preference. ​We randomly assign subjects in either of the work condition (independent vs. team).
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Likelihood of being hired based on competition choice (Study 1).
The content of the advice given regarding competitive preferences (Study 2).
The rating (on a Likert scale) that each cover letter receives regarding its employability (Study 3). The ratings include the likelihood of the candidate a) getting an interview, b) being hired, and c) getting a promotion.
Primary Outcomes (explanation)
The likelihood of being hired is a binary variable denoting whether a certain worker is hired.
The content of the advice is the advice (among three alternatives) that the subjects give to the hypothetical candidates to mention in a cover letter.
The rating is assigned to each candidate using a Likert scale from 1 to 10.

Secondary Outcomes

Secondary Outcomes (end points)
Firms' belief about the worker's performance. (Study 1)
Firms' belief about their own performance. (Study 1)
Self-reported competitiveness of the firms. (Study 1)
Self-reported causal attributions that the firms report regarding worker performance. (Study 1)
Self-reported risk preferences. (Study 1)
Self-reported competitiveness. (Study 2)
Self-reported risk preferences. (Study 2)
Rating of how enjoyable would it be to work with the candidate. (Study 3)
Rating of easiness to work with the candidate. (Study 3)
Rating of how confident the candidate would be. (Study 3)
Rating of how productive the candidate would be. (Study 3)
Self-reported competitiveness. (Study 3)
Self-reported risk preferences. (Study 3)

Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Study 1 Design:

The experiment involves two groups of subjects. The first group is the “workers” who perform in a math addition task for four rounds. In the last round, they choose among three different payment schemes: self-tournament, other tournament and piece rate. The workers are paid based on a randomly picked round.

In a later experiment, we recruit another set of subjects – “firms.” Firms’ first task is to perform the math task for five minutes for one round – so that they would be familiarized with the nature of the task that the workers finished. We pay them 20 cents per correctly answered problem. Their second task is to choose a person who performed under the three different payment scheme rounds and in a final choice round. The Firms decide who to hire based on their choice in the final round and the worker’s fourth-round score determines the Firm’s payoff. The lab study involves three treatments: male treatment, female treatment, and neutral treatment.

In a version of this Study 1, firms decide who to hire based on their choice in the final round and the worker’s first-round score determines the Firm’s payoff, keeping all other aspects the same.

In gender (male and female) treatments, the Firm first learns the gender of their potential workers. That is, we tell them that among three only male/only female workers, and they hire one. And that each of those males/females has chosen different compensation schemes in their final rounds: one of them competed against herself, one of them competed against another individual, and one of them chose not to compete. We then ask them who they would like to hire (whose round 1 or round 4 performance determines their own payoff).

In neutral treatment, firms are matched to three individuals whose gender is not known. The rest of the procedures are identical to the gender treatment. In the end, we ask the question of where we learn the believed gender of the worker.

At the end of the experiment, subjects are asked about their belief about their own and the hired worker's performance in the task.

Study 2 Design:
In Study 2, online subjects (MTurk workers) are told that a friend of theirs is seeking for advice about what to include his/her cover letter for a job s/he is interested in applying. The advice seeker can have three competitive traits (s/he is sometimes self-competitive, is sometimes other-competitive, and sometimes refrains from competitions). The advice giver reads all these traits and what they mean in detail. We implement four treatments: the work can involve independent decision-making or can require teamwork, and the gender of the advice seeker varies. We incentivize the advice by paying subjects extra if their suggestions match with the majority of the participants.

In an extension to Study 2 (Study 2-B), we conduct the same experiment with some modifications. First, we change the word “challenge” with the word “compare” to soften the language of the competition paragraphs. In order to endorse higher ambition in the non-competitor paragraph and to make the three types of (non)-competitors more equal in that regard, we add a phrase where the candidate communicates that they are a good performer also in the non-competition paragraph. Finally, we run Study 2-B on Prolific.

Study 3 Design:
In Study 3, the participants rate different cover letters based on their employability. Keeping everything else constant, we vary the gender of the applicant and their competitive tendencies (self-competitive, non-competitive, other-competitive). Additionally, we vary the nature of the work condition: the work can involve independent decision-making or require teamwork. Each subject rate two cover letters based on their likelihood of being invited for an interview, likelihood of being hired, and the likelihood of getting a promotion to an upper-level position. The way we incentivize those ratings involves a comparison of the subjects' ratings to an expert's ratings. The closer their ratings are to the expert's, the higher the subjects earn.

Experimental Design Details
Randomization Method
Treatment conditions are randomized at the individual level. All randomizations is done by the computer programs (zTree for Study 1 and Qualtrics for Studies 2 and 3).
Randomization Unit
Treatments are randomized at the individual level. In online studies, the order in which the subjects rate/give advice to different candidates is also randomized at the individual level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clusters. The lab study is run at GMU ICES Lab in Fairfax Campus and online studies are run using Amazon Mechanical Turk and Prolific.
Sample size: planned number of observations
Around 120 subjects. (Study 1) Around 800 subjects. (Study 2) Around 1000 subjects. (Study 3)
Sample size (or number of clusters) by treatment arms
Study 1: 40 observations for female treatment, 40 observations for male treatment, 40 observations for neutral treatment.
Study 2: 400 observations for independent work condition, 400 for teamwork condition. Gender condition are implemented as within subject design (subjects see both a male and a female candidate in random order).
Study 2-B: 400 observations for independent work condition, 400 for teamwork condition.
Study 3: 600 observations for independent work condition, 600 for teamwork condition. Gender and competitive preference conditions are implemented as within-subject design (subjects rate three male and three female cover letters based with varying competitive preferences).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
GMU Office of Research Development, Integrity, and Assurance
IRB Approval Date
IRB Approval Number


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