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Abstract
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Before
Our proposed research seeks to address the following questions:
1) Do workers from underrepresented groups take disproportionate actions to avoid failure on the job, such as not applying to jobs with a higher risk of failure?
2) If so, is this because they anticipate discriminatory penalties for failure on the job, either for themselves or for others in their demographic group?
The proposed design is an online two-sided labor experiment with two sets of participants: workers and employers. Our outcome of interest is whether an individual worker chooses to apply for a job. Treatment in our setting randomly varies the potential consequences of a worker’s poor performance on the job, as well as an employer’s ability to discriminate based on a worker’s URM status. Consequences for poor performance will range from simple negative feedback to losing out on a potential second round of employment. The employer’s ability to discriminate will stem from whether the employer has information on the worker’s gender and race. We aim to observe if ‘application gaps’ between URMs and other workers change in magnitude or sign based on the potential consequences of poor performance on the job and.or the scope for discrimination to affect those consequences for workers.
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After
When workers from underrepresented groups opt out of “higher risk, higher reward” jobs, income gaps arise. Workers may rationally avoid applying to jobs where they’d be disproportionately punished for failure, and employer bias informs how employers interpret negative productivity signals from workers of different identities. Socialized group differences in traits such as risk aversion or other-regarding preferences also influence job choice. Our proposed research aims to answer: Do workers from underrepresented groups take disproportionate action to avoid failure on the job? If so, what proportion of any resultant gaps in job choice are due to anticipated discrimination versus socialized differences in behavioral traits? We propose running an experiment in which we randomly vary the potential consequences for failure on the job, and observe how this affects job choices for workers from different demographic groups.
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Trial Start Date
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Before
October 01, 2025
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After
May 01, 2026
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Trial End Date
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Before
September 30, 2026
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After
May 01, 2027
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Last Published
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Before
February 20, 2025 05:13 AM
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After
October 19, 2025 11:49 AM
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Intervention Start Date
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Before
October 01, 2025
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After
May 01, 2026
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Intervention End Date
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Before
November 30, 2025
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After
June 01, 2026
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Primary Outcomes (End Points)
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Before
Our primary outcome of interest is whether an individual worker chooses to apply for a job (binary choice).
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After
Our primary outcome of interest is whether an individual worker chooses to remain in a 'simple' job or to transition to a 'harder' job (binary choice).
We will stratify our analyses by skill level, as our initial pilot and prior work suggests that treatment effects will be heterogeneous by skill.
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Experimental Design (Public)
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Before
The experiment will proceed as follows: 'Worker' subjects make application decisions. Then, 'employer' subjects make hiring decisions. Hired workers perform the job, and then employers evaluate workers.
Workers will be randomly assigned to one of eight treatment arms. We have three randomized dimensions of treatment:
1) Rounds. Workers will fall into one of three groups: In 'Second Round Only', workers can only be hired for one round, and no other workers will be hired by the same employer after. In 'First Round Only', workers can only be hired once, but a different worker can be hired by the same employer for the second round. In 'Two Rounds', workers are hired for one round and their employer can choose whether to retain them or hire someone new for the second round.
2) Initial selection. Workers who choose to apply might be initially hired by a computer using 'Random Selection', or be selected by an employer participant under 'Employer Selection'.
3) Demographic Reveal. Under 'Demographic Reveal', the worker’s demographic information (gender and race) is revealed to each of their employers and prospective employers along with their score and their Prolific work history. Under 'No Demographic Reveal', only the worker’s anonymized quantitative attributes are revealed.
Not all combinations of dimensions (3 X 2 X 2 = 12 possible treatment cells) are necessary for measuring our mechanisms of interest; we will use only 7 of 12 possible combinations. We will also have an eighth treatment arm in which workers are guaranteed to be retained for a second round of employment. The selected treatment cells are illustrated in the attached table.
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After
Our treatment changes the descriptions of the jobs workers are considering, thus changing the stakes and consequences of failure. We have two cross-randomized dimensions of treatment:
1) Demographic Reveal: Under Demographic Reveal, the worker’s demographic information (gender and race) is revealed to employer subjects along with their score and their Prolific work history. Under No Demographic Reveal, only the worker’s anonymized quantitative attributes are revealed.
2) Separation Risk: Under Separation Risk, the worker might get “fired” (i.e. not be invited back to complete the second round of puzzles). Under No Separation Risk, the worker will always be invited back to complete the second round and their performance will be used to train employers in a “practice round” of evaluation.
Our final design consists of four treatment cells.
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Was the treatment clustered?
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Before
Yes
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After
No
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Planned Number of Clusters
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Before
4000 'worker' participants
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After
4800 'worker' participants
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Planned Number of Observations
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Before
4000 application choices (one per 'worker' participants)
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After
4800 job choices (one per 'worker' participants)
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Sample size (or number of clusters) by treatment arms
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Before
500 participants in each treatment arm; eight total treatment arms.
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After
1200 participants in each treatment arm; four total treatment arms.
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Did you obtain IRB approval for this study?
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Before
No
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After
Yes
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