Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145703
Title: Social insurance, income and subjective well-being of rural migrants in China - an application of unconditional quantile regression
Authors: Fang, Zheng
Sakellariou, Christos
Keywords: Social sciences::Economic theory
Issue Date: 2015
Source: Fang, Z., & Sakellariou, C. (2016). Social insurance, income and subjective well-being of rural migrants in China - an application of unconditional quantile regression. Journal of Happiness Studies, 17(4), 1635-1657. doi:10.1007/s10902-015-9663-3
Journal: Journal of Happiness Studies
Abstract: This paper identifies determinants to positively influence the happiness level of rural-to-urban migrants at the bottom of the distribution of subjective well being (SWB) using an unconditional quantile regression rather than the conventional mean regression methodology. Using a basic regression specification, the positive effects of income and objective health status and the negative effect of work hours are found to be decreasing along the distribution of SWB, suggesting that standard factors are more relevant to the SWB of the subgroup of less happy migrants. Education seems to play a stabilizing role as it decreases the likelihood of extremes in well-being. From an examination of social insurance coverage and relative concerns, a positive relationship between pension and SWB is observed for the first time in happiness literature on Chinese migrants, suggesting interesting future research directions on the policy effects of the newly established New Rural Social Pension scheme on improving the SWB of people with rural hukou. Furthermore, the signal effect is found when migrants are compared with urban workers and the status effect is found when they are compared with other migrants. However, we find that only perceived, rather than objective income position matters.
URI: https://hdl.handle.net/10356/145703
ISSN: 1389-4978
DOI: 10.1007/s10902-015-9663-3
Schools: School of Social Sciences 
Rights: © 2015 Springer Science+Business Media Dordrecht. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SSS Journal Articles

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