We assess the impact of discrimination on Black individuals’ job networks in the U.S. using a two-stage field experiment with 400+ fictitious LinkedIn profiles. Varying race via A.I.-generated images only, we find that Black profiles’ connection requests are accepted at significantly lower rates (Stage I) and their networks provide less information (Stage II). Leveraging our experimental design to eliminate first-stage endogeneity, we identify gatekeeping as the key driver of Black-White disparities. Examining users’ CVs reveals widespread discrimination across different social groups and – contrary to expert predictions – lower discrimination among men and older users.

Politicians have discovered Twitter as a tool for political communication. If information provided by politicians is circulated in ideologically segregated user networks, political polarization may be fostered. Using network information on all 1.78 million unique followers of German Members of Parliament by October 2018, follower homogeneity across politicians and parties is measured. While the overall homogeneity is low, politicians of the AfD —a right-wing populist party —stand out with very homogeneous follower networks. These are largely driven by a small group of strongly committed partisans that make up around 7 percent of the party’s but around 55-75 percent of the average AfD politician’s followers. The findings add to the literature by showing potentially unequal distributions of network segregation on Twitter. Further, they suggest that small groups of active users can multiply their influence online, which has important implications for future research on echo chambers and other online phenomena.