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

  • Alatas, Vivi

    (World Bank)

  • Banerjee, Abhijit

    (MIT)

  • Chandrasekhar, Arun G.

    (Microsoft Research New England)

  • Hanna, Rema

    (Harvard University)

  • Olken, Benjamin A.

    (MIT)

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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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. Banerjee, Abhijit & Chandrasekhar, Arun G & Duflo, Esther & Jackson, Matthew O., 2012. "The Diffusion of Microfinance," CEPR Discussion Papers 8770, C.E.P.R. Discussion Papers.
  2. Bandiera, Oriana & Burgess, Robin & Das, Narayan & Gulesci, Selim & Rasul, Imran & Sulaiman, Munshi, 2013. "Can Basic Entrepreneurship Transform the Economic Lives of the Poor?," IZA Discussion Papers 7386, Institute for the Study of Labor (IZA).
  3. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
  4. Oriana Bandiera & Imran Rasul, 2006. "Social Networks and Technology Adoption in Northern Mozambique," Economic Journal, Royal Economic Society, vol. 116(514), pages 869-902, October.
  5. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
  6. Banerjee, Abhijit V, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, MIT Press, vol. 107(3), pages 797-817, August.
  7. Matthew O. Jackson & Leeat Yariv, 2007. "Diffusion of Behavior and Equilibrium Properties in Network Games," American Economic Review, American Economic Association, vol. 97(2), pages 92-98, May.
  8. Oriana Bandiera & Iwan Barankay & Imran Rasul, 2009. "Social Connections and Incentives in the Workplace: Evidence From Personnel Data," Econometrica, Econometric Society, vol. 77(4), pages 1047-1094, 07.
  9. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
  10. Alderman, Harold & Haque, Trina, 2006. "Countercyclical safety nets for the poor and vulnerable," Food Policy, Elsevier, vol. 31(4), pages 372-383, August.
  11. Manski, C.F., 1991. "Identification of Endogenous Social Effects: the Reflection Problem," Working papers 9127, Wisconsin Madison - Social Systems.
  12. repec:cep:stieop:43 is not listed on IDEAS
  13. Galasso, Emanuela & Ravallion, Martin, 2005. "Decentralized targeting of an antipoverty program," Journal of Public Economics, Elsevier, vol. 89(4), pages 705-727, April.
  14. Vivi Alatas & Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Julia Tobias, 2012. "Targeting the Poor: Evidence from a Field Experiment in Indonesia," American Economic Review, American Economic Association, vol. 102(4), pages 1206-40, June.
  15. Kaivan Munshi, 2003. "Networks In The Modern Economy: Mexican Migrants In The U.S. Labor Market," The Quarterly Journal of Economics, MIT Press, vol. 118(2), pages 549-599, May.
  16. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2008. "Bayesian Learning in Social Networks," NBER Working Papers 14040, National Bureau of Economic Research, Inc.
  17. Jackson Matthew O. & Rogers Brian W., 2007. "Relating Network Structure to Diffusion Properties through Stochastic Dominance," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 7(1), pages 1-16, February.
  18. 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.
  19. Mueller-Frank, Manuel, 2013. "A general framework for rational learning in social networks," Theoretical Economics, Econometric Society, vol. 8(1), January.
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