BREAD Working Paper No. 425, August 2014

Gossip: Identifying Central Individuals in a Social Network

Esther Duflo, Abhijit Banerjee, Arun G. Chandrasekhar, Matthew O. Jackson


Can we identify the members of a community who are best-placed to diffuse information simply by asking a random sample of individuals? 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.

Keywords: Centrality, Gossip, Networks, Diffusion, Influence, Social Learning

JEL classification codes: D85, D13, L14, O12, Z13

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