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

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  • 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.

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Bibliographic Info

Paper provided by Harvard University, John F. Kennedy School of Government in its series Working Paper Series with number rwp12-043.

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Date of creation: Oct 2012
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Handle: RePEc:ecl:harjfk:rwp12-043

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  1. Timothy G. Conley & Christopher R. Udry, 2005. "Learning about a new technology: pineapple in Ghana," Proceedings, Federal Reserve Bank of San Francisco.
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  3. Vivi Alatas & Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Julia Tobias, 2010. "Targeting the Poor: Evidence from a Field Experiment in Indonesia," NBER Working Papers 15980, National Bureau of Economic Research, Inc.
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  11. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," Review of Economic Studies, Oxford University Press, vol. 78(4), pages 1201-1236.
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  14. Galasso, Emanuela & Ravallion, Martin, 2005. "Decentralized targeting of an antipoverty program," Journal of Public Economics, Elsevier, vol. 89(4), pages 705-727, April.
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  16. Andrea Galeotti & Fernando Vega‚ÄźRedondo, 2011. "Complex networks and local externalities: A strategic approach," International Journal of Economic Theory, The International Society for Economic Theory, vol. 7(1), pages 77-92, 03.
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Cited by:
  1. 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).
  2. Clemens, Michael A. & Tiongson, Erwin R., 2012. "Split Decisions: Family Finance when a Policy Discontinuity Allocates Overseas Work," IZA Discussion Papers 7028, Institute for the Study of Labor (IZA).
  3. Mastrobuoni, Giovanni, 2014. "The Value of Connections: Evidence from the Italian-American Mafia," IZA Discussion Papers 7925, Institute for the Study of Labor (IZA).

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