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


  • Hanna, Rema N.
  • Alatas, Vivi
  • Banerjee, Abhijit
  • Chandrasekhar, Arun G.
  • Olken, Benjamin A.


We 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.

Suggested Citation

  • Hanna, Rema N. & Alatas, Vivi & Banerjee, Abhijit & Chandrasekhar, Arun G. & Olken, Benjamin A., 2012. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," Scholarly Articles 9804490, Harvard Kennedy School of Government.
  • Handle: RePEc:hrv:hksfac:9804490

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    Cited by:

    1. Giovanni Mastrobuoni, 2013. "The Value of Connections: Evidence from the Italian-American Mafia," Carlo Alberto Notebooks 335, Collegio Carlo Alberto.
    2. Christophe Muller, 2017. "Ethnic Horizontal Inequity in Indonesia," Working Papers halshs-01508026, HAL.
    3. Michael Clemens & Erwin Tiongson, 2012. "Split Decisions: Family finance when a policy discontinuity allocates overseas work," CReAM Discussion Paper Series 1234, Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London.
    4. Matthew O. Jackson, 2014. "Networks in the Understanding of Economic Behaviors," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 3-22, Fall.
    5. Elsner, Benjamin & Narciso, Gaia & Thijssen, Jacco J. J., 2013. "Migrant Networks and the Spread of Misinformation," IZA Discussion Papers 7863, Institute for the Study of Labor (IZA).
    6. Perkins, Jessica M. & Subramanian, S.V. & Christakis, Nicholas A., 2015. "Social networks and health: A systematic review of sociocentric network studies in low- and middle-income countries," Social Science & Medicine, Elsevier, vol. 125(C), pages 60-78.
    7. Drago, Francesco & Mengel, Friederike & Traxler, Christian, 2015. "Compliance Behavior in Networks: Evidence from a Field Experiment," IZA Discussion Papers 9443, Institute for the Study of Labor (IZA).
    8. Arun Advani & Bansi Malde, 2014. "Empirical methods for networks data: social effects, network formation and measurement error," IFS Working Papers W14/34, Institute for Fiscal Studies.
    9. Emily Breza & Arun G. Chandrasekhar, 2015. "Social Networks, Reputation and Commitment: Evidence from a Savings Monitors Experiment," NBER Working Papers 21169, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development


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