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Identification of peer effects using group size variation

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  • Laurent Davezies
  • Xavier D'Haultfoeuille
  • Denis Fougère

Abstract

This paper studies the econometric properties of a linear-in-means model of social interactions. Under a slightly more restrictive framework than Lee (2007), we show that this model is generally identified when at least three different sizes of peer groups are observed in the sample at hand. While unnecessary in general, homoscedasticity may be required in special cases, for instance when endogenous and exogenous peer effects cancel each other. We extend this analysis to the case where only binary outcomes are observed. Once more, most parameters are semiparametrically identified under weak conditions. However, identifying all of them requires more stringent assumptions, including a homoscedasticity condition. We also develop a parametric estimator for the binary case, which relies on the Geweke-Hajivassiliou-Keane (GHK) simulator. Monte Carlo simulations illustrate the influence of group sizes on the accuracy of the estimation, in line with the results obtained by Lee (2007). Copyright The Author(s). Journal compilation Royal Economic Society 2009

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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 12 (2009)
Issue (Month): 3 (November)
Pages: 397-413

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Handle: RePEc:ect:emjrnl:v:12:y:2009:i:3:p:397-413

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  1. Angrist, Joshua & Lang, Kevin, 2004. "Does School Integration Generate Peer Effects? Evidence from Boston's Metco Program," IZA Discussion Papers 976, Institute for the Study of Labor (IZA).
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Citations

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Cited by:
  1. Boucher, Vincent & Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2010. "Do Peers Affect Student Achievement? Evidence from Canada Using Group Size Variation," IZA Discussion Papers 4723, Institute for the Study of Labor (IZA).
  2. Amara Mohamed, 2014. "Gibrat's Law and peer group effect: the case of Tunisian small manufacturing companies," Economics Bulletin, AccessEcon, vol. 34(1), pages 373-384.
  3. Chi, Feng & Yang, Nathan, 2010. "Twitter Adoption in Congress: Who Tweets First?," MPRA Paper 23225, University Library of Munich, Germany.
  4. Yaman, F., 2011. "Ethnic externalities and 2nd generation immigrants," Working Papers 11/08, Department of Economics, City University London.
  5. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
  6. Lawrence E. Blume & William A. Brock & Steven N. Durlauf & Rajshri Jayaraman, 2013. "Linear Social Interactions Models," NBER Working Papers 19212, National Bureau of Economic Research, Inc.
  7. Alberto Bisin & Andrea Moro & Giorgio Topa, 2011. "The Empirical Content of Models with Multiple Equilibria in Economies with Social Interactions," NBER Working Papers 17196, National Bureau of Economic Research, Inc.
  8. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.
  9. Lucifora, Claudio & Tonello, Marco, 2012. "Students' Cheating as a Social Interaction: Evidence from a Randomized Experiment in a National Evaluation Program," IZA Discussion Papers 6967, Institute for the Study of Labor (IZA).
  10. Giorgio Topa & Elizabeth Setren & Meta Brown, 2011. "Do Referrals Lead to Better Matches? Evidence from a Firm's Employee," 2011 Meeting Papers 711, Society for Economic Dynamics.
  11. Onur Ozgur & Alberto Bisin, 2011. "Dynamic linear economies with social interactions," Levine's Working Paper Archive 786969000000000036, David K. Levine.

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