Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

Vivi Alatas, Abhijit Banerjee, Arun G. Chandrasekhar, Rema Hanna and Benjamin Olken

American Economic Review, 2016, vol. 106, issue 7, 1663-1704

Abstract: We use unique data from over 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 network structure, which we show are borne out in the data. We apply our findings to a community-based targeting program, where citizens chose households to receive aid, and show that the networks that the model predicts to be more diffusive differentially benefit from community targeting.

JEL-codes: D14 D83 D85 I32 O12 Z13 (search for similar items in EconPapers)
Date: 2016
Note: DOI: 10.1257/aer.20140705
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Citations: View citations in EconPapers (55)

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Related works:
Working Paper: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2012)
Working Paper: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2012)
Working Paper: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2012)
Working Paper: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2012)
Working Paper: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2012)
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