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Trial Title
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Before
Sources of Discrimination, Inaccurate Beliefs, and the Role of Blind Hiring
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After
The Effects and Dynamics of Blind Hiring
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Abstract
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Before
We study labor market discrimination in the context of the US tech industry. Using a simple theoretical model, we show that blind hiring during the resume screening phase can result in employers recruiting more productive employees, regardless of their awareness of the applicants' group identity during interviews. We suggest a new two-step experimental approach that enables researchers to distinguish observed discrimination into either taste-based discrimination or statistical discrimination resulting from inaccurate beliefs.
In this study, we aim to test the following hypotheses:
1. Employers' subjective beliefs about the productivity of workers vary by race and gender.
2. Employers' subjective beliefs regarding the distribution of productivity by race and gender differ from the actual distribution of productivity by race and gender collected from UM students' coding test.
3. Job applicants from the discriminated group, who are hired by discriminatory employers, tend to have, on average, higher productivity compared to job applicants from the non-discriminated group, who are hired by discriminatory employers.
4. Blinding information about race and gender leads to changes in the average productivity as inferred by employers.
5. Blinding information about race and gender results in changes in the composition of hired workers.
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After
We examine how blinding workers’ observable group characteristics affects employers’ perceptions of productivity and hiring outcomes in a two-stage hiring process: an initial resume screening by HR and a subsequent technical interview by engineers.
We aim to answer the following primary questions under an experimental set-up:
1. Do agents in different stages of the hiring exhibit different hiring preferences (e.g., based on candidates’ demographic characteristics)?
2. How does blinding the observable group characteristics affect employers' subjective evaluation of job applicants?
(a) Does this, in turn, alter the composition of candidates who advance or are ultimately hired?
3. How does the effect of blind hiring interact with multiple stages of hiring?
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Trial Start Date
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Before
March 21, 2024
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October 30, 2025
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Trial End Date
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Before
December 31, 2025
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After
June 30, 2026
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Last Published
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Before
April 26, 2024 11:58 AM
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After
October 30, 2025 02:43 PM
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Intervention (Public)
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Before
Employers assigned to the first treatment group (i.e., Treatment 1) will receive a
set of 20 hypothetical resumes with the name of a job applicant revealed. However,
before they are told to rate each hypothetical resume, they will be informed about
the coding tasks that UM students took as well as about the distribution of students’
actual coding scores by gender and race.3 We hypothesize that such information
provision of the distribution will correct inaccurate beliefs that employers may hold
about the distribution of the coding ability by gender and race of the job applicants,
by providing accurate information on coding ability by each group. The estimates
obtained using this treatment group will be used to estimate the discrimination caused
by inaccurate beliefs as well as the discrimination caused by animus (see Section 3.2
for details about the decomposition).
Employers assigned to the second treatment group (i.e., Treatment 2) will receive the
same set of 20 hypothetical resumes and will initially rate the hypothetical resumes
with the name of a job applicant blinded. However, the names will be revealed when
employers reach to the “mock interview” stage of the experiment. This treatment
arm will be used to study how blind hiring affects employers’ evaluation of resumes
and to examine if the effect of blind hiring persists even after the name and gender
are revealed during the “mock interview’ stage. This is designed to mimic an actual
job application process where employers eventually observe the gender and race of
job applicants during the interview stage even if the name is initially blinded during
the resume-screening stage. More details on estimating the effect of Treatment 2 are
provided in Section 3.3.
Finally, employers assigned to the control group will receive the same 20 hypothetical
resumes but with the name of a job applicant revealed and without the information
on the distribution of students’ actual coding scores.
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After
Firm participants are randomized into control and treatment groups. Participants assigned to the treatment group receive resumes (and codes, for engineer participants) with applicants' names blinded, while participants in the control group receive the same set of resumes (and codes, for engineer participants), but the names of the applicants will be revealed.
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Intervention Start Date
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Before
March 21, 2024
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After
October 30, 2025
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Intervention End Date
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Before
May 30, 2025
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After
January 31, 2026
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Primary Outcomes (End Points)
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Before
We consider the following three primary outcomes:
** Employers' subjective evaluation of the hypothetical resumes in terms of coding skills.
-This outcome pertains to the question that reads: ``How good do you think this person will be in coding? [scale from 1-10]". Employers will be asked to answer this question for each of 20 hypothetical resumes during the ``resume-screening" phase.
- This outcome will be used to estimate and decompose into discrimination caused by animus versus discrimination caused by inaccurate beliefs as well as to estimate the effects of Treatment 2 (see Sections \ref{Estimating Discrimination} - \ref{Treatment Effect of Blind Hiring}).
**Coding scores estimated based on actual student resumes collated via the UM Student survey.
- This outcome will be used to decompose discrimination into discrimination caused by animus versus discrimination caused by inaccurate beliefs.
**Predicted coding scores associated with each hypothetical resume
- This outcome will be used to estimate the treatment effect of blinding names on the productivity of resumes that proceed to the ``mock interview" stage as well as on whether the resume is chosen for the final hiring (see Section \ref{Treatment Effect of Blind Hiring}).
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After
The primary outcome variables measured via the survey are:
1. How good do you think this person will be in coding? [scale of 0 - 10]
2. How good of a fit do you think this person will be in your workplace? [scale of 0 - 10]
3. How likely would this person stay in your company for the next 5 years? [scale of 0 - 10]
4. How would you rate this person overall? [value between 0 and 100]
5. (HR participants only) Would you select this person to advance to the next hiring stage for more in-depth technical evaluation? [Yes/No]
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Experimental Design (Public)
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Before
To test the predictions of our theoretical model, we designed a novel two-step experiment that allows us to: i) estimate the productivity of students via coding test; ii) estimate the preference of employers via Incentivized Resume Rating (IRR); iii) estimate the treatment effect of blind hiring on the average productivity of workers hired; and iv) decompose the discrimination into taste-based discrimination and statistical discrimination caused by inaccurate beliefs.
We run our experiment in the context of US tech industry, with the University of Michigan computer science junior and senior students as the job applicants. US tech industry is a good context to study discrimination because there is well-documented discrepancies in the representation of gender or race in the US tech industry. Census Bureau reports that women only comprises 21\% of the US tech industry, and White comprises 64\% of the US tech industry whereas Black only comprises of 4\% in 2021. In addition, employers in the tech industry typically give out `technical interview' that asks job applicants to perform some coding tasks to observe the applicants' productivity. This setting maps well to our model, with `technical interview' representing $\tilde{s}$ in our model.
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After
Please refer to the details of the experimental design in Section 4 in our registered pre-analysis plan.
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Planned Number of Observations
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120 employers
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200 employer participants.
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Sample size (or number of clusters) by treatment arms
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Before
33 employers for treatment 1, 33 employers for treatment 2, 34 employers for control
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After
100 employer participants assigned to the "blind" treatment arm, and 100 employer participants assigned to the control arm.
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Additional Keyword(s)
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Before
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After
blind hiring, discrimination, hiring bias
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Keyword(s)
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Before
Behavior, Gender, Labor
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After
Behavior, Gender, Lab, Labor
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Public analysis plan
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Before
No
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Yes
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