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Identification of Peer Using Group Size Variation

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  • Laurent Davezies

    (Crest)

  • Xavier d'Haultfoeuille

    (Crest)

  • Denis Fougère

    (Crest)

Abstract

This paper considers the semiparametric identification of endogenousand exogenous peer effects in the linear-in-means model. We showthat this model is generically identified when at least three differentsizes of peer groups are observed in the sample at hand. While unnecessaryin general, homoskedasticity may be required in special cases torecover all parameters. Extensions to asymmetric responses to peersand binary outcomes are also considered. Once more, most parametersare semiparametrically identified under rather weak conditions.However, recovering all of them requires more stringent assumptions.Finally, we bring theoretical evidence that the model is more adaptedto small groups.

Suggested Citation

  • Laurent Davezies & Xavier d'Haultfoeuille & Denis Fougère, 2007. "Identification of Peer Using Group Size Variation," Working Papers 2007-34, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2007-34
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    References listed on IDEAS

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    8. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
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    12. Brock, William A. & Durlauf, Steven N., 2007. "Identification of binary choice models with social interactions," Journal of Econometrics, Elsevier, vol. 140(1), pages 52-75, September.
    13. Ammermüller, Andreas & Pischke, Jörn-Steffen, 2006. "Peer Effects in European Primary Schools: Evidence from PIRLS," ZEW Discussion Papers 06-027, ZEW - Leibniz Centre for European Economic Research.
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    Cited by:

    1. Gonzalo Vazquez-Bare, 2020. "Causal Spillover Effects Using Instrumental Variables," Papers 2003.06023, arXiv.org, revised Dec 2021.
    2. Luisa Corrado & Roberta Distante & Majlinda Joxhe, 2019. "Body mass index and social interactions from adolescence to adulthood," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(4), pages 425-445, October.
    3. 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.
    4. 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.
    5. Marisa Miraldo & Carol Propper & Christiern Rose, 2020. "Identification of Peer Effects using Panel Data," Discussion Papers Series 639, School of Economics, University of Queensland, Australia.
    6. Arthur Lewbel & Samuel Norris & Krishna Pendakur & Xi Qu, 2022. "Consumption peer effects and utility needs in India," Quantitative Economics, Econometric Society, vol. 13(3), pages 1257-1295, July.
    7. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    8. Chi, Feng & Yang, Nathan, 2010. "Twitter Adoption in Congress: Who Tweets First?," MPRA Paper 23225, University Library of Munich, Germany.

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    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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