Skip to content Skip to navigation

Gossip: Identifying Central Individuals in a Social Network

Feb 2016
Working Paper
Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, Matthew O. Jackson
Is it possible, simply by asking a few members of a community, to identify individuals who are best placed to diffuse information? A simple model of diffusion shows how boundedly rational individuals can, just by tracking gossip about people, identify those who are most central in a network according to “diffusion centrality” (a measure of network centrality which nests existing ones, and predicts the extent to which piece of information seeded to a network member diffuses in finite time). Using rich network data from 35 Indian villages, we find that respondents accurately nominate those who are diffusion central – not just traditional leaders or those with many friends. In a subsequent randomized field experiment in 213 villages, we track the diffusion of a piece of information initially given to a small number of “seeds” in each community. Seeds who are nominated by others lead to a near tripling of the spread of information relative to randomly chosen seeds. Diffusion centrality accounts for some, but not all, of the extra diffusion from these nominated seeds compared to other seeds (including those with high social status) in our experiment.
Publication Keywords: 
Social Learning
Geographic Regions: