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Identification of peer effects using group size variation

Author

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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

Suggested Citation

  • Laurent Davezies & Xavier D'Haultfoeuille & Denis Fougère, 2009. "Identification of peer effects using group size variation," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 397-413, November.
  • Handle: RePEc:ect:emjrnl:v:12:y:2009:i:3:p:397-413
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    Citations

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    Cited by:

    1. 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.
    2. Vincent Boucher & Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2014. "Do Peers Affect Student Achievement? Evidence From Canada Using Group Size Variation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 91-109, January.
    3. Leonard, Tammy & McKillop, Caitlin & Carson, Jo Ann & Shuval, Kerem, 2014. "Neighborhood effects on food consumption," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 51(C), pages 99-113.
    4. ÖZGÜR, Onur & BISIN, Alberto, 2011. "Dynamic Linear Economies with Social Interactions," Cahiers de recherche 04-2011, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Gonzalo Vazquez-Bare, 2017. "Identification and Estimation of Spillover Effects in Randomized Experiments," Papers 1711.02745, arXiv.org.
    6. 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).
    7. Yaman, F., 2011. "Ethnic externalities and 2nd generation immigrants," Working Papers 11/08, Department of Economics, City University London.
    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. 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.
    10. Mohamed Amara & AbdelRahmen El Lahga, 2015. "A note on MAR and Jacobs externalities in the Tunisian manufacturing industries," Letters in Spatial and Resource Sciences, Springer, vol. 8(2), pages 151-167, July.
    11. Horrace, William C. & Liu, Xiaodong & Patacchini, Eleonora, 2016. "Endogenous network production functions with selectivity," Journal of Econometrics, Elsevier, vol. 190(2), pages 222-232.
    12. Vincent Boucher & Bernard Fortin, 2015. "Some Challenges in the Empirics of the Effects of Networks," Cahiers de recherche 1504, CIRPEE.
    13. Alberto Bisin & Andrea Moro & Giorgio Topa, 2011. "The empirical content of models with multiple equilibria in economies with social interactions," Staff Reports 504, Federal Reserve Bank of New York.
    14. 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.
    15. Lawrence E. Blume & William A. Brock & Steven N. Durlauf & Rajshri Jayaraman, 2015. "Linear Social Interactions Models," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 444-496.
    16. Chandra Bhat, 2015. "A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation," Transportation, Springer, vol. 42(5), pages 879-914, September.
    17. repec:bla:presci:v:96:y:2017:i:3:p:627-645 is not listed on IDEAS
    18. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Alexis Le Chapelain, 2014. "Market for Education and Student Achievement," Sciences Po publications info:hdl:2441/1jgbspo1909, Sciences Po.
    20. Chi, Feng & Yang, Nathan, 2010. "Twitter Adoption in Congress: Who Tweets First?," MPRA Paper 23225, University Library of Munich, Germany.
    21. Tobias Stöhr, 2015. "Siblings’ interaction in migration decisions: who provides for the elderly left behind?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(3), pages 593-629, July.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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