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• W2164108909 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 D14, D83, D85, I32, O12, Z13)" @default.
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• W2164108909 date "2016-07-01" @default.
• W2164108909 modified "2023-10-16" @default.
• W2164108909 title "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia" @default.
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• W2164108909 doi "https://doi.org/10.1257/aer.20140705" @default.
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