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Volunteering and Self-Rated Health in Urban China: New Evidence from Analyses of Treatment-Effects Models

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A Correction to this article was published on 30 December 2020

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Abstract

While most previous studies in the Western context have found a positive correlation between volunteering and health, this positive relation is not conclusive since the self-selection bias inherent in this question has not been addressed well. Meanwhile, this relation has been rarely explored in non-Western countries, especially mainland China, where institutionalized volunteer practices are more emergent. Using a nationally representative sample (N = 4967) from the 2013 Survey on Philanthropic Behaviors of Urban Citizens in China, this study followed the counterfactual framework under quasi-experimental design and adopted two treatment effects models - propensity score matching and nearest neighbor matching - to detect the net effect of volunteering on individuals’ self-rated health. Analyses from both matching models consistently indicate that after conditioning on the covariates, volunteers on average, have a higher self-rated health score than non-volunteers. The results also provide evidence of upward bias about the positive effect of volunteering on health in models that use standard multiple regression approach. In sum, the findings demonstrate that volunteering is a real benefit for health, but the positive effect is likely to be overestimated when self-selection bias is not accounted for. Finally, this study presents new evidence that the positive effect of volunteering is consistent across national boundaries to the Chinese context.

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Acknowledgments

The authors would like to thank Dr. Peter Frumkin and other fellows in the Upenn Summer Doctoral Program, as well as attendees in 2018 ARNOVA annual conference for their helpful and constructive comments and suggestions on previous versions of this paper.

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Correspondence to Zhongsheng Wu.

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Wu, Z., Bies, A. Volunteering and Self-Rated Health in Urban China: New Evidence from Analyses of Treatment-Effects Models. Applied Research Quality Life 16, 2185–2201 (2021). https://doi.org/10.1007/s11482-020-09868-5

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