An experiment about discrimination

Last registered on September 21, 2023
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Pre-Trial

Trial Information

General Information

Title
An experiment about discrimination
RCT ID
AEARCTR-0009507
Initial registration date
May 27, 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
May 30, 2022, 8:13 AM EDT

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

Last updated
September 21, 2023, 8:39 AM EDT

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

Locations

Country
Brazil
Region

Primary Investigator

Name
Pedro Sant'Anna
Affiliation
São Paulo School of Economics Fundação Getulio Vargas
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Bruno Ferman
PI Affiliation
São Paulo School of Economics Fundação Getulio Vargas
Contact Investigator
PI Name
Nicolás Ajzenman
PI Affiliation
São Paulo School of Economics Fundação Getulio Vargas
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2022-05-26
End date
2023-03-01
Keywords
Behavior
Additional Keywords
JEL code(s)
A11, I23, J15, J16
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We design an experiment to test for discrimination.
External Link(s)

Registration Citation

Citation
Ajzenman, Nicolás, Bruno Ferman and Pedro Sant'Anna. 2023. "An experiment about discrimination." AEA RCT Registry. September 21. https://doi.org/10.1257/rct.9507-2.1
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-05-26
Intervention End Date
2022-12-20

Primary Outcomes

Primary Outcomes (end points)
See pre-analysis plan
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See pre-analysis plan
Experimental Design Details
We create fictitious accounts on Twitter that resemble humans and that claim to be PhD students with interests in topics related to economics. The accounts differ on three observable characteristics: gender (male or female), race (black or white) and university affiliation (highly ranked or not). The accounts follow approximately 100 subjects who are part of the "EconTwitter" community. After being active for twelve days, we measure how many follow backs each bot account obtained.

Our subject pool is composed of all Twitter accounts that either tweeted or re-tweeted a status containing the term "#EconTwitter" between January 1st and February 28th, 2022 (approximately 15,000 accounts). Then, we will create 240 fictitious accounts claiming to be PhD students and who differ in their gender, race and university affiliation (overall, we have 30 accounts of each group). Each account will be active for a period of twelve days and follow approximately 100 subjects from the ‘EconTwitter’ sample. Our main outcome is whether or not
each subject followed back the bot accounts. Given that the accounts are identical in all respects but in the three dimensions we varied, differences in follow-back associated with the groups will be due to discrimination.

The experiment will be conducted in 30 waves of twelve days. In each wave, 8 accounts will be active. They will follow their respective subjects on the first day of activation, and we will measure follow-backs twice a day.

The subjects followed by each bot account will be defined randomly. Specifically, we perform block randomization as a way to improve balance. For the block randomization, we will use the following variables: gender (male, female, missing); profession (professor; graduate student; other; missing); the number of followers (above or below median). This gives us 24 strata. We will sample randomly from within each stratum, assigning the same proportion of users in each stratum to each bot account. Specifically, each bot account will be assigned to approximately 100 accounts to follow.
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
240 accounts
Sample size: planned number of observations
24000 subjects (individuals)
Sample size (or number of clusters) by treatment arms
3000 subjects
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Compliance Committee on Research Involving Human Beings of Fundaçao Getulio Vargas
IRB Approval Date
2022-04-07
IRB Approval Number
Opinion n. 034/2022
Analysis Plan

Analysis Plan Documents

PAP_DiscriminationTwitter_Aug11.pdf

MD5: 76e58f0326c1659cd89d0963651d442b

SHA1: 79ef79a9028cbd970fbcf91f47cb248a8954cee6

Uploaded At: August 12, 2022

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
August 12, 2022, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
August 12, 2022, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
80 bot-wave pairs
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
6920 econtwitter accounts
Final Sample Size (or Number of Clusters) by Treatment Arms
approximately 900 accounts per treatment arm (bot type)
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials