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Field
Trial Title
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Before
The Impact of Competitiveness on Employability
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After
Competitiveness and Employability
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Trial Status
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Before
in_development
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After
completed
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Trial End Date
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Before
June 21, 2019
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After
August 01, 2022
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JEL Code(s)
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Before
C9, D12, J01, J16
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After
C91, J70, J71, M51
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Last Published
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Before
April 08, 2019 04:09 PM
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After
December 06, 2022 08:13 PM
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Intervention (Public)
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Before
We will study our research question using three experiments: a laboratory experiment (Study 1), and two online experiments (Studies 2 and 3). In Study 1, student subjects will be in the role of a Firm and will decide to hire a Worker who performed in a task (math addition task) for several rounds. The hired worker's score in the first round will determine the firm's payoff. Workers will differ in their compensation scheme choices they made in the last round (choices being entering a tournament against another person, entering in a tournament against self, or not entering in a tournament). In Study 2, online participants will 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 will read six hypothetical cover letters which will 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 will be three treatments. We will randomly assign subjects to one of these treatments (randomized at the individual level).
1. Female treatment where all the workers will be females.
2. Male treatment, where all the workers will be males.
3. Neutral treatment, where the gender of the worker will not be known by the firm.
In Study 2, there will be four (2x2) conditions. The candidates will vary in gender and the work will either involve working independently or working as a team. Each subject will give advice to a male and a female candidate. We will randomly assign subjects in either of the work condition (independent vs. team).
In Study 3, there will be 12 (2x2x3) conditions. The candidates will vary in gender, the work will either involve working independently or working as a team and each candidate will have a different competitive preference (self-competitive, not competitive, other-competitive). Each subject will rate six cover letters varying in gender and the competitive preference. We will randomly assign subjects in either of the work condition (independent vs. team).
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After
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).
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Intervention End Date
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Before
June 21, 2019
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After
August 01, 2022
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Primary Outcomes (End Points)
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Before
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, c) getting a promotion.
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After
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.
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Primary Outcomes (Explanation)
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Before
Likelihood of being hired will be 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 a value assigned to each candidate using a Likert scale from 1 to 10.
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After
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.
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Experimental Design (Public)
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Before
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 will be paid based on a randomly picked round.
In a later experiment, we recruit another set of subjects – “firms”. Firms’ first task is to perform in 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 first-round score determines the Firm’s payoff. The lab study involves three treatments: male treatment, female treatment and neutral treatment.
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, they will 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 would then ask them who they would like to hire (whose round 1 performance will apply to their own payoff).
In neutral treatment, firms are matched to three individuals whose gender is not known. Rest of the procedures are identical to the gender treatment. In the end, we ask a question where we learn the believed gender of the worker.
At the end of the experiment, subjects are asked their belief about their own and the hired worker's performance in the task ( in round 1).
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 will implement four treatments: the work can involve independent decision making or can require teamwork and the gender of the advice seeker varies. We will incentivize the advice by paying subjects extra if their suggestions match with the majority of the participants.
Study 3 Design:
In Study 3, the participants rate different cover letters based on their employability. Keeping everything else constant, we will vary the gender of the applicant and their competitive tendencies (self-competitive, non-competitive, other-competitive). Additionally, we will vary the nature of the work condition: the work can involve independent decision making or can require teamwork. Each subject will rate six cover letters based on their: Likelihood of being invited for an interview; likelihood of being hired; likelihood of getting a promotion for an upper-level position. The way we will incentivize those ratings will involve a comparison of the subjects' ratings to an expert's ratings. The closer their ratings to the expert's, the higher the subjects will earn.
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After
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.
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Randomization Method
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Before
Treatment conditions will be randomized at the individual level. All randomizations will be done by the computer programs (zTree for Study 1 and Qualtrics for Studies 2 and 3).
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After
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).
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Randomization Unit
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Before
Treatments will be randomized at the individual level. In the online studies, the order which the subjects rate/give advice to different candidates will also be randomized at the individual level.
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After
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.
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Planned Number of Clusters
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Before
No clusters. The lab study will be run at GMU ICES Lab in Fairfax Campus and online studies will be run using Amazon Mechanical Turk.
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After
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.
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Sample size (or number of clusters) by treatment arms
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Before
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 will be implemented as within subject design (subjects will see both a male and a female candidate in random order).
Study 3: 500 observations for independent work condition, 500 for teamwork condition. Gender and competitive preference conditions will be implemented as within subject design (subjects will rate three male and three female cover letters based with varying competitive preferences).
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After
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).
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Keyword(s)
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Before
Gender, Labor, Other
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After
Gender, Labor, Other
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Building on Existing Work
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Before
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After
No
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