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The role of prosociality and social capital in changes in subjective well-being during the COVID-19 pandemic

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  • Yuta Kuroda
  • Takaki Sato
  • Yasumasa Matsuda

Abstract

This study examines the role of local social capital, individual personality, and their interaction on changes in subjective well-being (SWB) during the COVID-19 pandemic. Our estimations use tracking panel data based on a unique survey of approximately 25,000 people in Japan from 2019 to 2022. The results show that before the pandemic, individuals with high prosociality had higher SWB, whereas individuals with low and moderate levels of prosociality had no significant difference in SWB. Additionally, the relationship between individual prosociality and local social capital did not affect SWB. However, after the pandemic, the SWB of non-prosocial individuals changed heterogeneously depending on the level of local social capital. Non-prosocial individuals in areas with high social capital showed little change in SWB, whereas non-prosocial individuals in areas with low social capital showed significantly decreased SWB. These results may be caused by the possibility of free-riding on the reduced risk of infection due to the preventive actions of others in areas with high social capital.

Suggested Citation

  • Yuta Kuroda & Takaki Sato & Yasumasa Matsuda, 2024. "The role of prosociality and social capital in changes in subjective well-being during the COVID-19 pandemic," DSSR Discussion Papers 142, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:dssraa:142
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    File URL: http://hdl.handle.net/10097/0002001327
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    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Carol Alexander, 2000. "Orthogonal Methods for Generating Large Positive Semi-Definite Covariance Matrices," ICMA Centre Discussion Papers in Finance icma-dp2000-06, Henley Business School, University of Reading.
    3. He, Changli & Teräsvirta, Timo, 2004. "An Extended Constant Conditional Correlation Garch Model And Its Fourth-Moment Structure," Econometric Theory, Cambridge University Press, vol. 20(5), pages 904-926, October.
    4. Fan, Jianqing & Fan, Yingying & Lv, Jinchi, 2008. "High dimensional covariance matrix estimation using a factor model," Journal of Econometrics, Elsevier, vol. 147(1), pages 186-197, November.
    5. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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