Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
- (pp. 1663-1704)
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.Citation
Alatas, Vivi, Abhijit Banerjee, Arun G. Chandrasekhar, Rema Hanna, and Benjamin A. Olken. 2016. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia." American Economic Review, 106 (7): 1663-1704. DOI: 10.1257/aer.20140705Additional Materials
JEL Classification
- D14 Household Saving; Personal Finance
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- D85 Network Formation and Analysis: Theory
- I32 Measurement and Analysis of Poverty
- O12 Microeconomic Analyses of Economic Development
- Z13 Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification