Reducing perceptions of discrimination

Last registered on June 26, 2022

Pre-Trial

Trial Information

General Information

Title
Reducing perceptions of discrimination
RCT ID
AEARCTR-0009592
Initial registration date
June 22, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 26, 2022, 5:25 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
MIT

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2022-09-05
End date
2024-05-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This randomized experiment examines how individuals perceive discrimination under three job assignment mechanisms (with varying potential to discriminate) and the effects of the two mechanisms that reduce the scope for discrimination from the status quo on perceived discrimination, retention, effort, performance, cooperation with and reciprocity towards managers, and future labor supply. The study design and randomization ensure that the only differences between the three groups is what they believe about the job assignment process.
External Link(s)

Registration Citation

Citation
Ruebeck, Hannah. 2022. "Reducing perceptions of discrimination." AEA RCT Registry. June 26. https://doi.org/10.1257/rct.9592
Experimental Details

Interventions

Intervention(s)
The intervention varies what participants believe about how they were assigned to the easier, lower-paying of two tasks related to scientific communication. In the status quo arm, workers are told that managers who know worker demographics made the job assignment decisions. In two treatment arms, workers are told that the assignments were made using other mechanisms that are unable to discriminate.
Intervention Start Date
2022-10-03
Intervention End Date
2022-12-05

Primary Outcomes

Primary Outcomes (end points)
Perceived discrimination, cooperation with and reciprocity towards managers, effort, retention, and performance, and future labor supply
Primary Outcomes (explanation)
Several of the above variables can be combined into indices, which is described in more detail in the uploaded pre-analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)
Self-efficacy in the work task and task-related skills, job satisfaction, affective well-being
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Workers will be recruited with a screening survey and then evaluated by three job assignment mechanisms. Workers who are assigned to the harder task by any of the mechanisms will be assigned to the harder task and exit the sample of interest. The remaining workers will be randomly assigned which mechanism they are told was the one responsible for assigning them to the easier, lower-paying task. They will then answer questions about their interest in future work, do the easy task, answer survey questions, and finish the experiment.
Experimental Design Details
Not available
Randomization Method
Randomization is done in an office using Stata on a computer and treatment values are uploaded to Qualtrics for each participant when they return for the follow-up (experimental) survey. This allows stratification by race and gender, which is not possible when randomizing in Qualtrics directly.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3,500 workers will be initially recruited. 2,100 are expected to be assigned to the easier task and return for the experimental session.
Sample size: planned number of observations
3,500 workers will be initially recruited. 2,100 are expected to be assigned to the easier task and return for the experimental session.
Sample size (or number of clusters) by treatment arms
840 workers status quo, 840 workers manager treatment, 420 workers algorithm treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
All calculations assume a power of 80% and a significance level of 0.95. Regressions to test whether the manager treatment (algorithm treatment) reduces perceived discrimination relative to the status quo are powered to detect effects larger than 10pp (14pp). The percent of the population perceiving discrimination in the status quo group is assumed to match the rate of perceived discrimination in each race*gender cell in a pilot study describing the main experiment as a hypothetical scenario -- see the uploaded pre-analysis plan for details. Given the results from a pilot study, the effect sizes are expected to be larger than these MDEs. The study will also be powered to detect a difference in these effects for the two treatments of 14pp or more, assuming the algorithm treatment reduces perceived discrimination by up to 4pp and the manager treatment is more effective. The study is also powered to detect that the effect of the manager treatment (algorithm treatment) differs for whites and non-whites or men and women by 16pp (20pp). Regressions to test whether the manager treatment increases labor supply (whether a worker completes all eighteen paragraphs and whether they opt in to future work), or any continuous outcome that is standardized to be zero in the control group (e.g. reservation wages for working more closely with their manager, willingness to pay to choose one's own manager, job satisfaction, effort, proofreading quality) are powered to detect effects of at least 16pp and 0.06-0.12sd, respectively (where control group means and distributions are predicted from the pilot data where possible, including that 60 percent of workers in the status quo group are assumed to complete all 18 paragraphs).
IRB

Institutional Review Boards (IRBs)

IRB Name
MIT Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
2022-06-22
IRB Approval Number
2201000547
Analysis Plan

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