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American Economic Review Vol. 106 No. 7 2016

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

Vivi Alatas1; Abhijit Banerjee2; Arun G. Chandrasekhar3; Rema Hanna4; Benjamin A. Olken5

1 World Bank, World Bank Office Jakarta Indonesia Stock Exchange (IDX) Tower 2 JL. Jendral Sudirman, Kebayoran Baru 12190 Jakarta Selatan Indonesia (e-mail: ) · 2 MIT, 50 Memorial Drive, Building E52, Room 540 Cambridge, MA 02139 (e-mail: ) · 3 Stanford University, 579 Serra Mall, Room 234, Stanford, CA 94305 (e-mail: ) · 4 Harvard University, 79 JFK Street, Mailbox 26, Cambridge, MA 02138 (e-mail: ) · 5 MIT, 50 Memorial Drive, Building E52, Room 542, Cambridge, MA 02139 (e-mail: )

open access

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.

DOI
10.1257/aer.20140705
Volume
106
Issue
7
Pages
1663-1704
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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