DP10120 Gossip: Identifying Central Individuals in a Social Network

Author(s): Abhijit Banerjee, Arun G Chandrasekhar, Esther Duflo, Matthew O. Jackson
Publication Date: August 2014
Keyword(s): centrality, diffusion, gossip, influence, networks, social learning
JEL(s): D13, D85, L14, O12, Z13
Programme Areas: Development Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=10120

Can we identify the members of a community who are best- placed to diffuse information simply by asking a random sample of in- dividuals? We show that boundedly-rational individuals can, simply by tracking sources of gossip, identify those who are most central in a network according to "diffusion centrality", which nests other standard centrality measures. Testing this prediction with data from 35 Indian villages, we find that respondents accurately nominate those who are diffusion central (not just those with many friends). Moreover, these nominees are more central in the network than traditional village leaders and geographically central individuals.