Title：Emotion Contagion in School
Time：September 21, 2020，13:30-15:00
Venue：Zhonghui Building, 106Dr. Shun Wang is a professor at KDI School of Public Policy and Management, Korea. He obtained the Ph.D. in Economics at University of British Columbia, M.A. in Economics and B.A. in Business Administration at Peking University. His research lies mainly in subjective well-being, economic development, and Chinese economy. His papers were published in China Economic Review, Economic Development and Cultural Change, Journal of Comparative Economics, Journal of Happiness Studies, Social Indicators Research, and World Development etc. He is an associate editor and founding member of the UN World Happiness Report. He is a member of the Global Happiness Council and World Wellbeing Panel.
To identify the causal impact of emotional contagion, recent studies have been mainly focus on experimenting through online social networks or in laboratories, so that the identifications could be free (or partially free) from self-selection, common shocks, and simultaneous causality issue. However, the manner in which emotional contagion mainly occurs through face to face interaction with people we know could causes the contagion in our real life be very different to online interactions or interactions with strangers in a laboratory. This paper fills the gap in the literature by analyzing emotional contagion in middle schools using a nationally representative survey from China. We solve the self-selection issue by controlling for school fixed effects and focusing on schools randomly assign students to classrooms. We address common shocks and the reflection issue by isolating the variation in classmates' emotions that is determined outside the classroom, mainly, whether classmates got serious illness before primary schooling, and whether classmates have parental conflict at home. We find that a one standard deviation increase in a student's classmates’ unhappiness causes the student’s unhappiness level increase by about one-fifth of a standard deviation, which is much larger than the findings in previous studies using experimental data.