Water quality monitoring and mortality: evidence from China

ABSTRACT

Understanding the implementation of environmental regulation is essential to the design of environmental policy. The impact of water pollution regulation on mortality was examined in this study using a spatial regression discontinuity (RD) design based on China’s water quality monitoring system. Because water quality readings are important to the political promotion of officials and water quality monitoring stations only capture water pollution from upstream, local governments have an incentive to combat water pollution upstream, not downstream. Exploring this discontinuity in the stringency of regulations by linking the 2010 census microdata with water quality monitoring station location data in China, we found that households immediately upstream of a monitoring station faced a more than 11% reduction in household mortality compared to households immediately downstream of a monitoring station. Strict water quality supervision had a greater effect on mortality in rural households. The difference in mortality between upstream and downstream households was even greater for monitoring stations with high implementation and automatic monitoring technology. The results of this study improve our understanding of the health effects of environmental regulation, as well as the design and implementation of environmental regulation policies under a centralized political system.

KEYWORDS:

• Water pollution
• water quality monitoring
• environmental regulation
• mortality

JEL CLASSIFICATION:

• I12
• I18
• Q53
• Q56

Acknowledgments

The authors would like to express their gratitude to the National Bureau of Statistics team for collecting the data. The authors would also like to thank the School of Economics, Shanghai University of Finance and Economics, for providing the datasets. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Author contributions

All authors have contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Tao Lin and Wenhao Qian. The first draft of the manuscript was written by Tao Lin and Wenhao Qian, and all authors have commented on previous versions of the manuscript. All authors have read and approved the final manuscript.

Consent to publish

All authors have read and agreed to the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the School of Economics, Shanghai University of Finance and Economics at http://www.stats.gov.cn/sj/pcsj/, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with the permission of the School of Economics, Shanghai University of Finance and Economics.

Notes

¹ Two studies are closely related to ours: He, Wang, and Zhang (2020) used the same identification strategy and found that immediate upstream firms faced a more than 24% reduction in total factor productivity (TFP). Lin, Li, and Sun (2022) used the difference-in-difference (DID) method and showed that the establishment of automatic water monitoring stations was detrimental to upstream water-polluting industries but was beneficial to human health.

² Most water quality monitoring stations have geographic coordinate information in the China Environment Yearbook. When the coordinates of a monitoring station were missing, we used the geocoding tool Amap to obtain its coordinates based on the detailed address of the monitoring station.

³ The fifth level of administrative division in China has different names; in the cities, they are called communities, while in the countryside, they are called villages. Here, we refer to them uniformly as villages. We used the geocoding tool Amap to obtain the coordinates of each village according to its detailed address.

⁴ Following Lee and Lemieux (2010) and He, Wang, and Zhang (2020), the station fixed effect was absorbed by running an OLS regression of mortality on a station-specific dummy and then applying the nonparametric estimations on the residualized mortality.

⁵ Using China’s 2005 Census data, Qin, Li, and Liu (2013) estimated that the VSL using the national sample is about 1.81 million CNY. Fan, He, and Zhou (2020) estimated that the average VSL of a typical Chinese would be around 7.46 million CNY or 1.15 million USD in 2015. We took the average of the above two numbers to provide a simplified calculation of the average VSL of Chinese people in 2010, which would be about 4.64.

Additional information

Funding

This work was supported by the Graduate Innovation Fund of Shanghai University of Finance and Economics (kycx-2020-05; CXJJ-2021-342).