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

Author

Listed:
  • Alatas, Vivi

    (World Bank)

  • Banerjee, Abhijit

    (MIT)

  • Chandrasekhar, Arun G.

    (Microsoft Research New England)

  • Hanna, Rema

    (Harvard University)

  • Olken, Benjamin A.

    (MIT)

Abstract

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

  • Alatas, Vivi & Banerjee, Abhijit & Chandrasekhar, Arun G. & Hanna, Rema & Olken, Benjamin A., 2012. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," Working Paper Series rwp12-043, Harvard University, John F. Kennedy School of Government.
  • Handle: RePEc:ecl:harjfk:rwp12-043
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    References listed on IDEAS

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    Cited by:

    1. González, Felipe & Prem, Mounu, 2018. "Can television bring down a dictator? Evidence from Chile’s “No” campaign," Journal of Comparative Economics, Elsevier, vol. 46(1), pages 349-361.
    2. Clemens, Michael A. & Tiongson, Erwin R., 2012. "Split decisions : family finance when a policy discontinuity allocates overseas work," Policy Research Working Paper Series 6287, The World Bank.
    3. 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.
    4. Halim, Edward & Riyanto, Yohanes Eko & Roy, Nilanjan, 2017. "Costly Information Acquisition, Social Networks and Asset Prices: Experimental Evidence," MPRA Paper 80658, University Library of Munich, Germany.
    5. Diego A. Vera-Cossio, 2017. "Targeting credit through community members," Development Research Working Paper Series 07/2017, Institute for Advanced Development Studies.
    6. Giovanni Mastrobuoni, 2013. "The Value of Connections: Evidence from the Italian-American Mafia," Carlo Alberto Notebooks 335, Collegio Carlo Alberto.
    7. ITO Keiko & IKEUCHI Kenta & Chiara CRISCUOLO & Jonathan TIMMIS & Antonin BERGEAUD, 2019. "Global Value Chains and Domestic Innovation," Discussion papers 19028, Research Institute of Economy, Trade and Industry (RIETI).
    8. Christophe Muller, 2017. "Ethnic Horizontal Inequity in Indonesia," AMSE Working Papers 1715, Aix-Marseille School of Economics, France.
    9. 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.
    10. 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.
    11. Jessica Goldberg & Mario Macis & Pradeep Chintagunta, 2018. "Incentivized Peer Referrals for Tuberculosis Screening: Evidence from India," NBER Working Papers 25279, National Bureau of Economic Research, Inc.
    12. Buechel, Konstantin & Puga, Diego & Viladecans-Marsal, Elisabet & von Ehrlich, Maximilian, 2019. "Calling from the outside: The role of networks in residential mobility," CEPR Discussion Papers 13615, C.E.P.R. Discussion Papers.
    13. repec:bla:ecinqu:v:57:y:2019:i:1:p:141-161 is not listed on IDEAS
    14. repec:spr:sjecst:v:154:y:2018:i:1:d:10.1186_s41937-017-0011-x is not listed on IDEAS
    15. Drago, Francesco & Mengel, Friederike & Traxler, Christian, 2015. "Compliance Behavior in Networks: Evidence from a Field Experiment," IZA Discussion Papers 9443, Institute of Labor Economics (IZA).
    16. Elsner, Benjamin & Narciso, Gaia & Thijssen, Jacco J. J., 2013. "Migrant Networks and the Spread of Misinformation," IZA Discussion Papers 7863, Institute of Labor Economics (IZA).
    17. Christophe Muller, 2016. "Ethnic inequality and community activities in Indonesia," WIDER Working Paper Series 170, World Institute for Development Economic Research (UNU-WIDER).
    18. Marco Di Maggio & Francesco Franzoni & Amir Kermani & Carlo Sommavilla, 2017. "The Relevance of Broker Networks for Information Diffusion in the Stock Market," NBER Working Papers 23522, National Bureau of Economic Research, Inc.
    19. 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

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