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Misclassification and the hidden silent rivalry

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  • Yingyao Hu

    (Institute for Fiscal Studies and Johns Hopkins University)

  • Zhongjian Lin

    (Institute for Fiscal Studies and Emory University)

Abstract

The interaction of economic agents is one of the most important elements in economic analyses. While most empirical studies investigate peer effects on objective final achievements, peer effects on subjective outcomes are inherently difficult to identify and estimate because these variables are prone to measurement errors. In particular, peer effects on students’ attitudes towards learning are believed to have a significant impact on their achievements, while we found the presence of misclassification errors in students’ self-reported attitudes. We develop a binary choice model with misclassification and social interactions and use a recently developed technique of measurement error models to correct misreporting errors for estimating the peer effects on attitude. Our estimates suggest that a significant proportion of students overreport their attitudes towards learning and that peer effects are not only significant, but also much larger than estimates ignoring the misreporting errors. Our method may be generalized to the identification and estimation of peer effects with imperfect data information.

Suggested Citation

  • 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.
    • Handle: RePEc:ifs:cemmap:12/18
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    3. Candelaria, Luis E. & Ura, Takuya, 2023. "Identification and inference of network formation games with misclassified links," Journal of Econometrics, Elsevier, vol. 235(2), pages 862-891.

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    Keywords

    Misclassification; Binary Choice; Peer Effects; Attitude Towards Learning; Silent Rivalry; Social Desirability;
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