Journal
AMERICAN ECONOMIC REVIEW
Volume 106, Issue 7, Pages 1663-1704Publisher
AMER ECONOMIC ASSOC
DOI: 10.1257/aer.20140705
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Funding
- World Bank Royal Netherlands Embassy trust fund
- AusAid
- National Science Foundation GRFP
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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.
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