Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
We use unique data from 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to different network structures, which we show are borne out in the data. We apply our findings to a community-based targeting program, where villagers chose which households should receive aid, and show that networks the model predicts to be more diffusive differentially benefit from community targeting.
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Copy CitationVivi Alatas, Abhijit Banerjee, Arun G. Chandrasekhar, Rema Hanna, and Benjamin A. Olken, "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," NBER Working Paper 18351 (2012), https://doi.org/10.3386/w18351.Download Citation
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Published Versions
Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2016. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," American Economic Review, vol 106(7), pages 1663-1704. citation courtesy of 
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