Characterizing Firm-Level Discrimination
Last registered on January 12, 2020

Pre-Trial

Trial Information
General Information
Title
Characterizing Firm-Level Discrimination
RCT ID
AEARCTR-0004739
Initial registration date
September 20, 2019
Last updated
January 12, 2020 5:14 PM EST
Location(s)

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Primary Investigator
Affiliation
UC Berkeley
Other Primary Investigator(s)
PI Affiliation
UC Berkeley
PI Affiliation
UC Berkeley
Additional Trial Information
Status
In development
Start date
2019-10-01
End date
2021-10-01
Secondary IDs
Abstract
We propose a stratified correspondence study of large American employers aimed at detecting firm-level discrimination on the basis of race, sex, and age. To construct accurate firm-level estimates, 125 (geographically distinct) job vacancies will be sampled from each employer and 8 fictitious applications will be sent to each job. Employer responses to the experiment will be used to measure the degree to which discriminatory jobs are clustered in particular firms. We will then characterize how discriminatory behavior covaries with firm and establishment level characteristics.
External Link(s)
Registration Citation
Citation
Kline, Patrick, Evan Rose and Christopher Walters. 2020. "Characterizing Firm-Level Discrimination." AEA RCT Registry. January 12. https://doi.org/10.1257/rct.4739-1.2000000000000002.
Experimental Details
Interventions
Intervention(s)
We will conduct a resume correspondence study that randomly assigns race, gender, and age to job applications submitted to a large number of job vacancies. We will study variation in gaps in callback rates across these groups to determine the extent to which discriminatory jobs are clustered in particular firms.
Intervention Start Date
2019-10-01
Intervention End Date
2021-10-01
Primary Outcomes
Primary Outcomes (end points)
The key outcomes are callbacks from employers to applications. We are interested in mean differences in callback rates across legally protected categories as well as between-firm and within-firm variation in these differences.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our experiment will send 8 resumes with randomly assigned characteristics to 125 job vacancies at each of a set of large firms. Race will be indicated using racially distinctive names. Each job will receive 4 white and 4 black applicants. Gender (male or female) and age (above or below age 40) will be randomly assigned to each resume.
Experimental Design Details
Not available
Randomization Method
All resume characteristics will be randomly assigned by computer as part of our resume generation software.
Randomization Unit
Race, gender and age will be randomly assigned to resumes. Race assignments will be stratified so that half of applicants are white and half are black at each job. Other resume characteristics will be unconditionally randomly assigned.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
100,000 total resumes sent to 12,500 jobs.
Sample size: planned number of observations
100,000 resumes
Sample size (or number of clusters) by treatment arms
We will have 50,000 resumes with white names and 50,000 resumes with black names. We will also have approximately 50,000 male and 50,000 female, as well as 50,000 above age 40 and 50,000 below, though the randomization does not ensure exact balance by gender and age.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimal detectable effect size for the mean difference between black and white callback rates is 0.004. We are also interested in the share of variation in this difference across jobs due to between-firm differences. The minimum detectable effect size for the between-firm variance share is approximately 5 percent.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
UC Berkeley Committee for the Protection of Human Subjects
IRB Approval Date
2019-04-19
IRB Approval Number
2019-04-12080
Analysis Plan

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