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

• 成果类型: Article
• 署名作者: Alatas, Vivi; Banerjee, Abhijit; Chandrasekhar, Arun G.; Hanna, Rema; Olken, Benjamin A.
• 署名单位: The World Bank; Massachusetts Institute of Technology (MIT); Stanford University; Harvard University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20140705
• 发表日期: 2016
• 页码: 1663-1704
• 关键词: field experiment; POOR

摘要

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.