When is Discrimination Unfair?

Last registered on September 19, 2021

Pre-Trial

Trial Information

General Information

Title
When is Discrimination Unfair?
RCT ID
AEARCTR-0006409
Initial registration date
September 21, 2020
Last updated
September 19, 2021, 12:06 PM EDT

Locations

Region

Primary Investigator

Affiliation
University of California, Santa Barbara

Other Primary Investigator(s)

PI Affiliation
UC Santa Barbara

Additional Trial Information

Status
In development
Start date
2020-09-22
End date
2023-09-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We conduct a vignette-based survey experiment to assess the perceived fairness of considering race in a hiring decision. Our vignettes illustrate two canonical forms of discrimination studied by economists: taste-based and statistical discrimination, plus two sub-types of each. In addition, we will randomly reverse the races of the discriminator and discriminatee. These interventions will allow us to estimate how the type of discriminatory action and race of the persons involved affect the perceived fairness of discriminatory actions. They will also allow us to assess the appropriateness of three broad models of perceived fairness in this context: utilitarian social preferences, in-group bias, and rules-based ethics. To our knowledge our study will be the first to assess the conditions under which a large sample of respondents perceive discrimination as more versus less unfair.
External Link(s)

Registration Citation

Citation
Kuhn, Peter and Trevor Osaki. 2021. "When is Discrimination Unfair?." AEA RCT Registry. September 19. https://doi.org/10.1257/rct.6409-1.3000000000000003
Experimental Details

Interventions

Intervention(s)
The interventions we administer are four fictitious vignettes describing incidents of discrimination. With equal probability, they describe either White-on-Black or Black-on-White discrimination.
Intervention Start Date
2020-09-22
Intervention End Date
2020-10-07

Primary Outcomes

Primary Outcomes (end points)
The outcome is the subject’s assessment of the fairness of the incident described in a vignette, on a seven-point scale.
Primary Outcomes (explanation)
In our main hypothesis tests, we plan to use standardized versions of the fairness ratings (with mean 0 and standard deviation 1).

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We randomly expose subjects to vignettes describing two broad types of discrimination (taste and statistical) and two sub-types of each. In addition we randomly vary the races of the discriminator and discriminatee. Treatments will vary both within and between subjects.
Experimental Design Details
Not available
Randomization Method
Randomization is performed by the Qualtrics survey platform.
Randomization Unit
The primary randomization unit is the individual survey respondent. Since each respondent will be exposed to multiple treatments, the order in which the treatments are received is randomly assigned as well.

In more detail, in stage one, respondents will be assigned with equal probability (0.25) to one of the four treatments: SB, TB, SW and TW. In stage two, each respondent is re-assigned with equal probability (0.33) to one of the three treatments they did not experience in stage one. Within each stage, the two scenarios are administered in random order.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
no clustering
Sample size: planned number of observations
We have funds to pay 714 subjects. Our power calculations assume 600 survey respondents to allow for unforeseen technical problems.
Sample size (or number of clusters) by treatment arms
150 respondents will be assigned to each of the SB, TB, SW and TW treatments in the first stage of the survey. In the second stage, each respondent will be assigned to one of the treatments they did not encounter in the first stage.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Depending on the amount of within-subject error correlation, we expect to be able to detect a perceived fairness differential of between 0.114 and 0.229 standard deviations between taste-based and statistical discrimination. For differences between sub-types of discrimination, and for the effects of discriminatee race with respondent racial groups, this range is from 0.162 to 0.323 standard deviations.
IRB

Institutional Review Boards (IRBs)

IRB Name
UCSB HUMAN SUBJECTS COMMITTEE
IRB Approval Date
2020-05-29
IRB Approval Number
16-20-0381
Analysis Plan

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