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dc.contributor.authorHanna, Rema N.
dc.contributor.authorAlatas, Vivi
dc.contributor.authorBanerjee, Abhijit
dc.contributor.authorChandrasekhar, Arun G.
dc.contributor.authorOlken, Benjamin A.
dc.date.accessioned2012-10-25T15:36:46Z
dc.date.issued2012
dc.identifier.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.en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9804490
dc.description.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.en_US
dc.language.isoen_USen_US
dc.publisherJohn F. Kennedy School of Government, Harvard Universityen_US
dc.relation.isversionofhttp://web.hks.harvard.edu/publications/workingpapers/citation.aspx?PubId=8585en_US
dash.licenseLAA
dc.subjectnetworksen_US
dc.subjectdiffusion of informationen_US
dc.subjecttargetingen_US
dc.subjectdevelopmenten_US
dc.titleNetwork Structure and the Aggregation of Information: Theory and Evidence from Indonesiaen_US
dc.typeResearch Paper or Reporten_US
dc.description.versionAuthor's Originalen_US
dc.relation.journalHKS Faculty Research Working Paper Seriesen_US
dash.depositing.authorHanna, Rema N.
dc.date.available2012-10-25T15:36:46Z
dc.identifier.doi10.3386/w18351
dash.contributor.affiliatedHanna, Rema


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