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Medical cost of environmental pollution: evidence from the Chinese Social Survey

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Abstract

Environmental pollution impairs residents’ health, while the pursuit of health is highly correlated to medical costs. Understanding how environmental pollution affects medical costs is closely linked to the welfare of society. Based on theoretical analysis, this paper uses data from 5112 households of the Chinese Social Survey (CSS) in 2019, constructs a composite indicator to quantify environmental pollution using respondents’ evaluations, and empirically investigates the causal effect of environmental pollution on household medical cost and the mechanism. The conclusions are shown as follows. First, environmental pollution can increase household medical costs, and this estimation result still holds after dealing with the endogeneity problem and other robustness tests. Second, there is heterogeneity in the impact of environmental pollution on household medical costs, households in the upper socioeconomic class, with heavy pension burdens or with strong health insurance coverage are more sensitive to environmental pollution and incur relatively higher household medical costs. Third, environmental pollution reduces residents’ satisfaction with their spiritual life, which adversely affects their physical and mental health and can increase household medical costs. Residents’ satisfaction with their spiritual life is an important mechanism for environmental pollution to affect household health care expenditures. Therefore, governments should enhance the enforcement of environmental protection and governance, strengthen the awareness of green issues and health education, and increase the supply of facilities for leisure and sports, thus reducing medical costs due to environmental pollution and easing the medical burden of residents.

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Notes

  1. https://www.thelancet.com/commissions/pollution-and-health.

  2. When assigning values to the indicators, considering the differences in individual evaluation criteria for pollution, only when the respondents rated the pollution as “serious” or “quite serious” did they think the environment in which they lived was polluted. In addition, aiming to minimize the errors caused by individual judgments when measuring the degree of environmental pollution, the discrete variable with value of 0 and 1 was used for the evaluation of environmental problems.

  3. Provincial environmental pollution averages calculated by grouping samples based on the provinces where the respondents live, and the sample covers 30 provinces.

  4. The degree of environmental pollution is ignored here, and only when the core explanatory variable (pollution) is 0, it is considered that the environment where the respondents live is not polluted; otherwise, it is considered that there is environmental pollution.

  5. North China includes Beijing, Tianjin, Hebei Province, Shanxi Province, and Inner Mongolia Autonomous Region. Northeast China includes Liaoning Province, Jilin Province, and Heilongjiang Province. East China includes Shanghai, Shandong Province, Jiangsu Province, Zhejiang Province, Anhui Province, Fujian Province, and Jiangxi Province. South Central China includes Henan Province, Hubei Province, Hunan Province, Guangdong Province, Hainan Province, and Guangxi Zhuang Autonomous Region. Southeast China includes Chongqing, Sichuan Province, Yunnan Province, Guizhou Province, and Tibet Autonomous Region. Southwest China includes Shanxi Province, Gansu Province, Qinghai Province, and Ningxia Hui Autonomous Region.

  6. The data of the two instrumental variables are obtained from the “China Environmental Statistical Yearbook,” the unit of investment in industrial pollution governance is 10 billion yuan per year.

  7. Analyzing the sample data, the number of elderly people per household is approximately 0.8 on average. Therefore, we defined a family with no more than one elderly person as a family with low pension burden and defined a family with more than one elderly person as a family with high pension burden.

  8. The provincial medical insurance coverage ratios obtained from “China Social Statistical Yearbook” in 2019.

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Funding

This work was supported by The Youth Project of the National Social Science Fund of China (Grant numbers [20CJY017]) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant numbers [19YJC790056]).

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Authors and Affiliations

  1. School of Finance, Shandong University of Finance and Economics, Jinan, 250014, China

    Chengyou Li & Zhaojun Yan

  2. Clinical Nutrition Department, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China

    Jitian Zhang

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  1. Chengyou Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by C. L., Z. Y., and J. Z. The first draft of the manuscript was written by C. L., Z. Y., and J. Z., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jitian Zhang.

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Li, C., Yan, Z. & Zhang, J. Medical cost of environmental pollution: evidence from the Chinese Social Survey. Environ Sci Pollut Res 30, 120155–120173 (2023). https://doi.org/10.1007/s11356-023-30459-y

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