Can the purchase of social insurance improve the subjective well-being of migrant workers: an empirical analysis based on life satisfaction of 2942 migrant workers in Shanghai

CHENG Mingwang1 HUA Hanyang1

(1.School of Economics & Management, Tongji University)

【Abstract】This study used 2942 sample data of migrant workers from the dynamic monitoring survey of migrant workers’ citizenization process of the survey office of the National Bureau of Statistics in Shanghai from 2016 to 2018, and used the ordered Probit model to empirically study the impact of social insurance on migrant workers’ subjective well-being. Studies have shown that purchasing social insurance can significantly improve the subjective well-being of migrant workers, and the more types of social insurance purchased, the higher the subjective well-being is. Migrant workers who purchase social insurance have a 21.70% higher subjective well-being than migrant workers who do not purchase social insurance. For each additional type of social insurance, their subjective well-being increases by 6.10%. The five types of social insurance can significantly improve the subjective well-being of migrant workers. Among them, the purchase of medical insurance has the highest marginal effect, followed by pension insurance, and the lowest is work-related injury insurance. Through the propensity score matching method and sub-sample analysis, it is found that the regression results of the original model have good robustness. Finally, based on the conclusions of the study, policy suggestions are proposed to further improve the subjective well-being of the migrant workers.

【Keywords】 social insurance; migrant worker; subjective well-being;


【Funds】 Key Project of Philosophy and Social Science Research of Ministry of Education (15JZD026) National Natural Science Foundation of China (71873095) National Natural Science Foundation of China (71673200)

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(Translated by Jessie Chen)


    [1]. ① National Bureau of Statistics. [^Back]

    [2]. (1) National Bureau of Statistics. [^Back]

    [3]. k = 1 means one-on-one matching. [^Back]

    [4]. k = 4 means one-on-four matching; due to space limitation, the balance test results (the results of other matching methods have all passed the balance test) and the propensity score matching results of other matching methods are not reported in this article. The results are available upon request. [^Back]

    [5]. ① In this study, the three methods of nearest neighbor matching (k = 1 and k = 4), radius matching and kernel matching were used to obtain the ATT value of the matched samples. Although the ATT values of the matched samples obtained by different matching methods are different, for each variable, the ATT value of the matched samples is reduced more, so this conclusion is obtained. Due to limited space, detailed results are not reported in the article, which are available upon request. [^Back]


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This Article


CN: 11-1262/F

Vol , No. 02, Pages 46-61

February 2020


Article Outline


  • 1 Introduction
  • 2 Theoretical analysis and literature review
  • 3 Data source and variable selection
  • 4 Model establishment and empirical analysis
  • 5 Further robustness analysis
  • 6 Conclusion and review
  • Footnote