Data and Code for: DISCRIMINATION IN THE FORMATION OF ACADEMIC NETWORKS: A FIELD EXPERIMENT ON #ECONTWITTER

Principal Investigator(s): View help for Principal Investigator(s) Nicolás Azjenman, McGill University; Bruno Ferman, Sao Paulo School of Economics - FGV; Pedro C Sant'Anna, Massachusetts Institute of Technology

Version: View help for Version V1

Name	File Type	Size	Last Modified
Replication_Discrimination_Twitter			02/11/2025 06:36:PM

Project Citation:

Azjenman, Nicolás, Ferman, Bruno, and C Sant’Anna, Pedro. Data and Code for: DISCRIMINATION IN THE FORMATION OF ACADEMIC NETWORKS: A FIELD EXPERIMENT ON #ECONTWITTER. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-08-07. https://doi.org/10.3886/E210084V1

Project Description

Summary:

This paper documents discrimination in the formation of professional networks among academic economists. Specifically, we created 80 human-like bot accounts that claim to be PhD students, differing in three key characteristics: gender (male or female), race (Black or White), and university affiliation (top- or lower-ranked). The bots randomly followed 6,920 users in the #EconTwitter community. Follow-back rates were 12% higher for White students compared to Black students, 21% higher for students from top-ranked universities compared to those from lower-ranked institutions, and 25% higher for female compared to male students. Notably, the racial gap persists even among students from top-ranked institutions.

Scope of Project

Subject Terms: Online Experiment; Field Experiment; Discrimination; Economics Profession; Gender; Race; Social Media

JEL Classification:
      A11 Role of Economics; Role of Economists; Market for Economists
      C93 Field Experiments
      I23 Higher Education; Research Institutions
      J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
      J16 Economics of Gender; Non-labor Discrimination

Collection Date(s): 1/2022 – 8/2022

Universe: Twitter users who were part of the #EconTwitter community in 2022

Data Type(s): experimental data

Methodology

Response Rate: Treatment assignment was performed for each wave and block-randomized using gender, profession, and number of followers. Of the 10,173 accounts that comprise our subject pool, 6,920 ultimately formed part of the experiment

Sampling: To construct the sample, we began with the universe of accounts that tweeted or retweeted messages containing the term #EconTwitter in January and February 2022. We then restricted our sample to public accounts and excluded those with a follows/friends ratio above 15, fewer than ten followers, bots, and institutional profiles, ending up with 10,173 subjects.

Unit(s) of Observation: Individuals

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Published Versions

V1 [2025-08-07]

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