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

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.