How do race, gender, and initial signals of productivity affect the willingness to learn more about others?

Last registered on March 30, 2023

Pre-Trial

Trial Information

General Information

Title
How do race, gender, and initial signals of productivity affect the willingness to learn more about others?
RCT ID
AEARCTR-0010639
Initial registration date
December 14, 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
January 03, 2023, 4:18 PM EST

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

Last updated
March 30, 2023, 11:23 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
University of Oregon

Other Primary Investigator(s)

PI Affiliation
University of Oregon

Additional Trial Information

Status
In development
Start date
2023-04-10
End date
2023-07-10
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We consider how people respond to race, gender, and initial signals of productivity when deciding whether to invest in acquiring more information about others. To this end, we have designed a survey task in which we allow subjects to purchase additional information when making incentivized decisions about a series of individuals.
External Link(s)

Registration Citation

Citation
Raze, Kyle and Glen Waddell. 2023. "How do race, gender, and initial signals of productivity affect the willingness to learn more about others?." AEA RCT Registry. March 30. https://doi.org/10.1257/rct.10639-1.2
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2023-04-10
Intervention End Date
2023-07-10

Primary Outcomes

Primary Outcomes (end points)
We will consider 1) whether a signal was purchased and 2) the number of signals purchased.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We will also consider 3) whether the individual was entered into a random comparison and 4) time to decision.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We employ a within-subjects design to measure how an individual’s race, gender, and initial signal of productivity affect the willingness of others to learn more about the individual.
Experimental Design Details
Specifically, we will present each subject with a sequence of 20 individual profiles and ask them to make decisions about each profile. Of the 20 profiles, 2 will represent Asian individuals, 8 will represent Black individuals, 2 will represent Latino/a individuals, and the remaining 8 will represent white individuals. Within each race category, half of the profiles will represent females and the other half will represent males. To manipulate subjects’ perception of the race and gender of each individual, we use portraits from the Chicago Face Database (Ma et al., 2015) and names from a list of common names by race and gender maintained by the Georgia Department of Public Health (https://oasis.state.ga.us/oasis/babynames/).

In each profile, we will reveal to subjects an initial signal—either a C- (lowest), C, C+, B-, B, B+, A-, A, or an A+ (highest)—from 32 potential signals associated with each individual. The mix of signals in each set of potential signals depends on an individual’s type—A, B, or C—which is unobservable to subjects. An individual of any type can receive any grade, but A types are more likely to have A signals in their set of potential signals, B types are more likely to have B signals, and C types are more likely to have C signals. The distribution of signals is roughly triangular for each type, and, with positive weight on all possible signals, all three distributions overlap.

To ensure that initial signals overlap across race and gender within each subject, we use the following assignment mechanism to reveal initial signals:
- Among the 4 Black female profiles, we assign one B-, one B, one B+, and one randomly selected non-B signal.
- Among the 4 Black male profiles, we assign one B-, one B, one B+, and one randomly selected non-B signal.
- Among the 4 white female profiles, we assign one B-, one B, one B+, and one randomly selected non-B signal.
- Among the 4 white male profiles, we assign one B-, one B, one B+, and one randomly selected non-B signal.
- Among the 4 Asian and Latino/a profiles, we assign signals at random.
Profiles will then appear sequentially in random order.

For each profile, we will ask subjects to decide whether to 1) enter the individual into a comparison with a B type or 2) pass on the individual. In the comparison, a signal will be randomly drawn from the individual and from the B type and whoever draws a higher signal will win the comparison. If the individual wins, then the subject will earn up to $1.00. If the individual loses, then the subject will earn up to $0.15. Alternatively, if the subject decides to pass on the individual, then they will earn up to $0.50. At the end of the experiment, we will randomly select an individual and pay each subject based on the outcomes associated with their decisions about that individual.

Before making a decision, subjects will have the option to purchase up to 15 additional signals for $0.01 per signal. The cost of the total purchase for an individual will be deducted from the payoffs described above.
Randomization Method
Randomization will occur on a shinyapps.io server using R.
Randomization Unit
Profile (within subject).
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Up to 1,000 subjects.
Sample size: planned number of observations
Up to 20,000 subject-profile observations (up to 1,000 subjects times 20 profiles per subject).
Sample size (or number of clusters) by treatment arms
Up to 4,000 observations with a Black female profile (even split between B-, B, B+, and non-B signals).
Up to 4,000 observations with a Black male profile (even split between B-, B, B+, and non-B signals).
Up to 4,000 observations with a white female profile (even split between B-, B, B+, and non-B signals).
Up to 4,000 observations with a white male profile (even split between B-, B, B+, and non-B signals).
Up to 1,000 observations with an Asian female profile (any signal).
Up to 1,000 observations with an Asian male profile (any signal).
Up to 1,000 observations with a Latina female profile (any signal).
Up to 1,000 observations with a Latino male profile (any signal).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Oregon Research Compliance Services
IRB Approval Date
2022-09-02
IRB Approval Number
STUDY00000500 (MOD00000913)

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials