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Social Insurance, Income and Subjective Well-Being of Rural Migrants in China—An Application of Unconditional Quantile Regression

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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.

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Notes

  1. Hereinafter, the terms happiness, life satisfaction and SWB are used interchangeably.

  2. The financial support for RUMiC was obtained from the Australian Research Council, the Australian Agency for International Development (AusAID), the Ford Foundation, IZA and the Chinese Foundation of Social Sciences.

  3. There are currently two waves (year 2008 and year 2009) publicly available. Since the panel is very short and much education information is missing when using the dataset with the same ID across years to construct the panel, we decided to employ the latest wave for analysis in this paper. While we are not able to account for individual fixed effects using cross-sectional data, we could still carry on analysis of the heterogeneous association between determinants and SWB, which is also our focus in this paper.

  4. Years since first migration is derived from the question “When did you first migrate out for work?” As the length of time migrants have been living in urban areas is likely to be associated with their SWB, years since first migration is used as a proxy in the regression. Depending on how long ago that happened, migrants’ SWB could be very different. City of residence includes: Bengbu, Chengdu, Chongqing, Dongguan, Guangzhou, Hefei, Hangzhou, Luoyang, Nanjing, Ningbo, Shanghai, Shenzen, Wuhan, Wuxi, and Zhengzhou.

  5. There are 55 unemployed out of 3594 observations in our sample.

  6. Due to space limitation, results are not presented.

  7. Binder and Coad (2011) also acknowledge the inability to assign a causal interpretation to the estimated effects.

  8. We also tried to use a categorical variable which takes the value of 1 (newbie migrants) if 0–4 years passed since first migration, 2 (medium duration migrants) if 5–9 years passed, and 3 (veteran migrants) if more than 10 years have passed. The frequency distribution of the three types of migrants is 39 % of newbie, 28 % of medium duration and 33 % of veteran migrants. The coefficient estimate of the categorical variable is still found to be negative and insignificant, suggesting that SWB is less likely to be affected by time since first migration. However, the possible influence of data limitations on the estimate of time since migration on SWB, remains unknown.

  9. The equalities of coefficient estimates for income, work hours and objective sickness between the 10th percentile and the 90th percentile are rejected at the conventional significant levels. Specifically, the p values of the test statistics for the three explanatory variables are 0.046, 0.069 and 0.000, respectively.

  10. Since we use the continuous variable “years of education” in the regression, the coefficient estimate is small in magnitude.

  11. The New Rural Social Pension Scheme was merged with Urban Resident Pension Scheme to establish a unified Urban and Rural Resident Basic Pension Scheme in 2014.

  12. The five insurance schemes refer to pension, medical, unemployment, and work injury insurances for both men and women and maternity insurance for women only.

  13. Only those who receive public health services (paid for by the government), medical care associated with employment and medical services from rural cooperatives are considered as “covered with support”. Other types of medical insurance such as commercial medical insurance, women and children health insurance etc. are deemed as self-financed.

  14. The law for the first national basic social insurance framework for employees across mainland China, aims to set up social insurance programs for non-employee urban and rural residents, eliminate discrimination in social insurance registration based on an employee’s household registration status, and facilitate the transfer of personal social insurance accounts across provincial jurisdictions.

  15. In April 2014, nearly 40,000 employees of Nike and Adidas supplier Yue Yuen went on strike in the southern province of Guangdong, demanding the company make full pension contributions.

  16. Besides the three age groups, Akay et al. (2012) also used a window of 5 years above and below a worker’s own age to estimate the income of a reference group. The size of such a reference group tends to become too small to remain meaningful. Therefore, in this paper we only employ the three broad age groups.

  17. The constructed reference groups may not tell the whole story, as it is likely that the reference group of someone is not exactly his peer in certain contexts. However, since it is almost impossible to find the exact reference group individuals use to compare, we followed a common practice in the literature to choose the reference groups based on certain clearly identified and important characteristics such as location and income, while balancing the window and frequency of the reference groups.

  18. When migrants are compared with their migrant peers, the relative income effect at the 50th percentile is found to be significantly different from the effects at the lower parts of the SWB distribution at the 10 % significant level.

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Acknowledgments

We would like to thank Professor Stephanie Rossouw, two anonymous referees and the Editor for their valuable suggestions and helpful comments which have greatly enhanced the quality of the paper.

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  1. Division of Economics, School of Humanities and Social Sciences, Nanyang Technological University, Singapore, 637332, Singapore

    Zheng Fang & Chris Sakellariou

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  1. Zheng Fang
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Correspondence to Zheng Fang.

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Fang, Z., Sakellariou, C. Social Insurance, Income and Subjective Well-Being of Rural Migrants in China—An Application of Unconditional Quantile Regression. J Happiness Stud 17, 1635–1657 (2016). https://doi.org/10.1007/s10902-015-9663-3

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