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Data and Code for: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
- Alatas, Vivi (World Bank)
- Banerjee, Abhijit (MIT)
- Chandrasekhar, Arun (Stanford University)
- Hanna, Rema (Harvard University)
- Olken, Benjamin (MIT)
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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
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Indonesia
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Is supplement to
DOI: 10.1257/aer.20140705 (Text)
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Alatas, Vivi, Abhijit Banerjee, Arun G. Chandrasekhar, Rema Hanna, and Benjamin A. Olken. “Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia.” American Economic Review 106, no. 7 (July 1, 2016): 1663–1704. https://doi.org/10.1257/aer.20140705.
- ID: 10.1257/aer.20140705 (DOI)
Update Metadata: 2020-06-08 | Issue Number: 1 | Registration Date: 2020-06-08
Alatas, Vivi; Banerjee, Abhijit; Chandrasekhar, Arun; Hanna, Rema; Olken, Benjamin (2020): Data and Code for: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E119802V1
