Misclassification and the Hidden Silent Rivalry

33 Pages Posted: 18 Nov 2018

See all articles by Yingyao Hu

Yingyao Hu

Johns Hopkins University - Department of Economics

Zhongjian Lin

Emory University - Department of Economics

Date Written: October 27, 2018

Abstract

The interaction of economic agents is one of the most important elements in economic analyses. While peer effects on subjective outcomes, behavior or decisions, are inherently difficult to identify and estimate because these variables are prone to misclassification errors. In this paper, we propose a binary choice model with misclassification and social interactions to rectify the misclassification problems in peer effects studies. We achieve identification of the model by the tool of repeated measurements and propose nested pseudo likelihood algorithm for estimation. We bring the model to estimate the peer effects among students on attitudes towards learning (silent rivalry). Peer effects on students’ attitudes towards learning are believed to have a significant impact on their achievements, while we find that these peer effects are distorted by the misclassification error. Our estimates suggest that peer effects are not only significant, but also much larger than estimates ignoring the misreporting errors and a significant proportion of students overreport their attitudes towards learning.

Keywords: Misclassification, Binary Choice, Peer Effects, Nested Pseudo Likelihood, Attitude Towards Learning, Social Desirability.

JEL Classification: C25, C57, C63, I20

Suggested Citation

Hu, Yingyao and Lin, Zhongjian, Misclassification and the Hidden Silent Rivalry (October 27, 2018). Available at SSRN: https://ssrn.com/abstract=3273697 or http://dx.doi.org/10.2139/ssrn.3273697

Yingyao Hu

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

Zhongjian Lin (Contact Author)

Emory University - Department of Economics ( email )

1602 Fishburne Drive
Atlanta, GA 30322
United States

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