Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
Chandrasekhar, Arun G.
Olken, Benjamin A.
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CitationHanna, Rema, Vivi Alatas, Abhijit Banerjee, Arun G. Chandrasekhar, and Benjamin A. Olken. 2012. Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia. HKS Faculty Research Working Paper Series and CID Working Papers (RWP12-043 and 246), John F. Kennedy School of Government, Harvard University.
AbstractWe use a unique data-set from Indonesia on what individuals know about the income distribution in their village to test theories such as Jackson and Rogers (2007) that link information aggregation in networks to the structure of the network. The observed patterns are consistent with a basic diffusion model: more central individuals are better informed, and individuals are able to better evaluate the poverty status of those to whom they are more socially proximate. To understand what the theory predicts for cross-village patterns, we estimate a simple diffusion model using within-village variation, simulate network-level diffusion under this model for the over 600 different networks in our data, and use this simulated data to gauge what the simple diffusion model predicts for the cross-village relationship between information diffusion and network characteristics (e.g. clustering, density). The coefficients in these simulated regressions are generally consistent with relationships suggested in previous theoretical work, even though in our setting formal analytical predictions have not been derived. We then show that the qualitative predictions from the simulated model largely match the actual data in the sense that we obtain similar results both when the dependent variable is an empirical measure of the accuracy of a village’s aggregate information and when it is the simulation outcome. Finally, we consider a real-world application to community based targeting, where villagers chose which households should receive an anti-poverty program, and show that networks with better diffusive properties (as predicted by our model) differentially benefit from community based targeting policies.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9804490
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