Abstract
This study is one of the first to present causal evidence of the morbidity costs of fine particulates (PM2.5) for all age cohorts in a developing country, using individual-level health spending data from a basic medical insurance program in Wuhan, China. Our instrumental variable (IV) approach uses thermal inversion to address potential endogeneity in PM2.5 concentrations and shows that PM2.5 imposes a significant impact on healthcare expenditures. The two-stage least squares (2SLS) estimates suggest that a 10 μg/m3 (micrograms per cubic meter) reduction in monthly average PM2.5 leads to a 2.36% decrease in the value of health spending and a 0.79% decline in the number of transactions at pharmacies and healthcare facilities. Also, this effect, largely driven by spending at pharmacies, is more salient for males and children, as well as middle-aged and older adults. Moreover, our estimates may provide a lower bound on individuals’ willingness to pay, amounting to CNY 43.87 (or USD 7.09) per capita per year for a 10 μg/m3 reduction in PM2.5.
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Data availability
The data that support the findings of this study are available from the first author, Xin Zhang, upon reasonable request.
Notes
Medical expenses at pharmacies and healthcare facilities respectively account for 29.3% and 70.7% of the total spending in our data.
There are three main methods for valuing air quality. Each approach has its particular advantages and disadvantages. The hedonic approach infers the value of air quality from property values across regions with differing levels of air pollution exposure (Bayer, Keohane, and Timmins 2009; Chay and Greenstone 2005; Ito and Zhang 2020; Smith and Huang 1995). This approach generally suffers from omitted variable problems, which make the value of air quality endogenous. On the other hand, the contingent valuation method (CVM) directly asks about people’s WTP for better air quality (Sun, Yuan, and Yao 2016; Wang et al. 2015). However, this method is subject to the initial hypothetical monetary value adopted in the survey options and the manner in which the questions are framed. The happiness approach calculates the marginal rate of substitution between a reduction in air pollution and household per capita income by holding happiness constant to assess the monetary value of air pollution (Levinson 2012; Welsch 2006; Zhang et al. 2017b). This approach treats self-reported happiness as a proxy of utility and assumes that utility is comparable among respondents.
Refer to Appendix B for the theoretical model. The model illustrates that people’s WTP for clean air can be estimated by adding up different components of the impact of air pollution on the population’s health and behavior. The marginal effect of air pollution on health spending is just one of the components, other components include mortality impact, reduction in quality of life, and the sub-optimal level of consumption distortion by the exposure to pollution.
According to the 2018 Environmental Performance Index published by Yale University, the five countries with the most polluted air in the world are Nepal, Bangladesh, India, China, and Pakistan.
The UEBMI was launched in 1998 as an employment-based insurance program in urban areas, and its coverage reached 92% in 2010. The URBMI was launched in 2007 to target the unemployed, children, students, and the disabled in urban areas. It covered 93% of the target population as of 2010 (Yu 2015).
As medical expenses covered by the Chinese public health insurance programs are directly billed on medical payment cards, all the payments for people enrolled in public insurance programs—UEBMI and URBMI—are included in the official database by design. Any money saved in the insurance account can be conveyed to the next year. Using others’ insurance accounts to purchase any health services was not allowed during the sample period.
The medication expenses include Western medicine fees, Chinese patent medicine fees, and Chinese herb medicine fees. The examination expenses include laboratory examination fees and imaging examination (B ultrasound, CT and MRI) fees. The treatment expenses include non-surgical treatment fees, surgical treatment fees, and anesthesia fees.
The six air pollutant measures are particulate matter with a diameter smaller than 2.5 µm (PM2.5, fine particulates); particulate matter with a diameter smaller than 10 µm (PM10, coarse particulates); carbon monoxide (CO); nitrogen dioxide (NO2); ozone (O3); and sulfur dioxide (SO2).
Zhang et al. (2017b) suggest that people have a much greater WTP for a reduction in PM2.5 than they do for PM10.
For each individual, we calculate the number of visits to each pharmacy during the sample period and sort the number of visits in descending order. We pick the location of the pharmacy that a person visited most as their home address. If the person did not visit any pharmacy during the sample period, we choose the health facility that the person most often visited. The average number of pharmacies a person visited is 5.27.
The average matching distance between the residential address and the nearest monitoring station is 3.85 km.
We also conduct empirical analysis using data at the individual-daily level without assigning zeros, controlling for demographic variables, daily weather covariates, individual, pharmacy, county-by-year, month and day-of-week fixed effects. The results are displayed in Table A1. The pattern of the estimates is similar to those based on the individual-monthly level data. As we estimate the results only using the subsample for those whose healthcare expenses are positive, indicating that they may either tend to be less healthy or could afford more healthcare expenses, the estimated WTP becomes much larger.
58.7% of the value of health spending/number of transactions are zeros.
The arcsinh transformation is \(\mathrm{arcsinh}\left(\mathrm{y}\right)=\mathrm{ln}(\mathrm{y}+\sqrt{{y}^{2}+1})\).
In the estimable equation of the form \(\mathrm{arcsinh}\left(\mathrm{y}\right)=\mathrm{\alpha }+\mathrm{\beta x}+\upvarepsilon\), the semi-elasticity is \(\frac{\partial y}{\partial x}\cdot \frac{1}{y}=\widehat{\beta }\frac{\sqrt{{y}^{2}+1}}{y}\). As \(\underset{y\to \infty }{\mathrm{lim}}\frac{\sqrt{{y}^{2}+1}}{y}=1\), for large values of y, \(\frac{\partial y}{\partial x}\cdot \frac{1}{y}=\widehat{\beta }\). Therefore, \(\widehat{\beta }\) indicates a semi-elasticity in the arcsinh transformation of y of no less than 10, as suggested by Bellemare and Wichman (2020). Please refer to Bellemare and Wichman (2020) for details.
For example, the IV estimates are substantially (6–17 times) larger than the OLS estimates in (Deryugina et al. 2019).
Table A2 presents the correlations between air pollutants.
We tried to instrument for PM2.5 and another co-pollutant using the thermal inversion strength and the number of occurrences in columns (2)–(6) of Table 4. However, we could not pass the weak identification test.
See columns (2) through (3) of Table 2 for the first-stage estimates.
A similar practice can be found in Williams and Phaneuf (2019), who also study medical expenditure data with a large number of zeros.
The holiday month refers to the month that contains holidays.
Using the average 2013–15 exchange rate of USD 1 = CNY 6.1880 from the Wind Economic Database.
The average disposal income per capita per year of Wuhan urban residents during 2013–2015 was 33,175.87 yuan (Wuhan Statistical Yearbook 2016).
Considering that our current WTP measure only accounts for willingness to pay to mitigate pollution-related morbidity costs (excluding other health aspects of social costs, such as mortality costs and avoidance costs, like facemasks and air filters), and that our individual-monthly level sample has a large share of zero healthcare expenses (i.e., people in a healthy status or who could not afford medical treatment), our measured WTP as a share of disposal income should be a lower-bound estimate. When we instead measure using individual-daily data with positive healthcare expenses only, i.e., among those who were sick and who could afford medical treatment, our estimates suggest that these people are willing to pay much more (CNY 699.29; around 2.11% of average disposal income) per capita per year for the same 10 μg/m3 reduction in PM2.5.
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Acknowledgements
Xin Zhang acknowledges financial support from the National Natural Science Foundation of China (72003014). Xun Zhang thanks the National Natural Science Foundation of China (71973014) for financial support. Xi Chen is grateful for financial support from the James Tobin Research Fund at Yale Economics Department, Yale Macmillan Center Faculty Research Award (2017–2019), the U.S. PEPPER Center Scholar Award (P30AG021342, 2016–2018), NIH/NIA Career Development Award (K01AG053408, 2017–2022), and a NIH/NIA Research Award (R01AG077529, 2022-2027). The authors acknowledge helpful comments by participants and discussants at the various conferences, seminars and workshops, as well as from editor Kompal Sinha and three anonymous reviewers.
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Zhang, X., Zhang, X., Liu, Y. et al. The morbidity costs of air pollution through the Lens of Health Spending in China. J Popul Econ 36, 1269–1292 (2023). https://doi.org/10.1007/s00148-023-00948-y
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DOI: https://doi.org/10.1007/s00148-023-00948-y