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Estimation of social‐influence‐dependent peer pressure in a large network game

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  • Zhongjian Lin
  • Haiqing Xu

Abstract

Research on peer effects in sociology has long been focused on social interactions and the associated social influence process. In this paper, we extend a large‐network‐based game model to a model that allows for the dependence of social interactions on social‐influence status. In particular, we use the Katz–Bonacich centrality to measure individuals' social influences, which are obtained directly from the observation of a social network. To solve the computational burden when the data come from the equilibrium of a large network, we extend a nested pseudo‐likelihood estimation approach to our large‐network‐based game model. Using the National Longitudinal Study of Adolescent Health (Add Health) dataset, we investigate the peer effects of dangerous behaviour among high‐school students. Our results show that the peer effects are statistically significant and positive. Moreover, students benefit more (statistically significant at the 5% level) from conformity or, equivalently, pay more for disobedience, in terms of peer pressure, if their friends have a higher status of social influence.

Suggested Citation

  • Zhongjian Lin & Haiqing Xu, 2017. "Estimation of social‐influence‐dependent peer pressure in a large network game," Econometrics Journal, Royal Economic Society, vol. 20(3), pages 86-102, October.
    • Handle: RePEc:wly:emjrnl:v:20:y:2017:i:3:p:s86-s102
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    File URL: http://hdl.handle.net/10.1111/ectj.12102
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    Citations

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    Cited by:

    1. Yingyao Hu & Zhongjian Lin, 2018. "Misclassification and the hidden silent rivalry," CeMMAP working papers CWP12/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Aristide Houndetoungan, 2024. "Count Data Models with Heterogeneous Peer Effects under Rational Expectations," Papers 2405.17290, arXiv.org.
    3. Victor Aguirregabiria & Mathieu Marcoux, 2021. "Imposing equilibrium restrictions in the estimation of dynamic discrete games," Quantitative Economics, Econometric Society, vol. 12(4), pages 1223-1271, November.
    4. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," Papers 2109.01725, arXiv.org, revised Sep 2021.
    5. Chomsisengphet, Souphala & Kiefer, Hua & Liu, Xiaodong, 2018. "Spillover effects in home mortgage defaults: Identifying the power neighbor," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 68-82.
    6. Zhongjian Lin & Francis Vella, 2024. "Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem," Papers 2408.13971, arXiv.org.
    7. Chen, Denghui & Kiefer, Hua & Liu, Xiaodong, 2022. "Estimation of discrete choice network models with missing outcome data," Regional Science and Urban Economics, Elsevier, vol. 97(C).
    8. Lin, Zhongjian & Tang, Xun & Yu, Ning Neil, 2021. "Uncovering heterogeneous social effects in binary choices," Journal of Econometrics, Elsevier, vol. 222(2), pages 959-973.
    9. Lin, Zhongjian & Vella, Francis, 2021. "Selection and Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem," IZA Discussion Papers 14167, Institute of Labor Economics (IZA).
    10. Lin, Zhongjian & Hu, Yingyao, 2024. "Binary choice with misclassification and social interactions, with an application to peer effects in attitude," Journal of Econometrics, Elsevier, vol. 238(1).

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