Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
利用印度尼西亚600多个社区关于贫困认知的独特数据，研究网络结构如何影响信息聚合，并应用于社区瞄准项目，发现更具扩散性的网络能从社区瞄准中获益更多。
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.