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

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

No 18351, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: D83 D85 H23 O12 (search for similar items in EconPapers)
Date: 2012-08
New Economics Papers: this item is included in nep-net and nep-ure
Note: PE POL
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Citations: View citations in EconPapers (18)

Published as 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.

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Related works:
Journal Article: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2016)
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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