China’s cities suffer from extremely high levels of air pollution. Coal burning for winter heat and for electricity generation is a major source of such pollution. This pollution creates large social costs due to elevated levels of morbidity and mortality risk. Economists have sought to measure such effects using natural experiments. For example, a long lived Chinese Communist policy offered free winter heating to cities north of the Huai River. This extra demand for winter heating lead to more coal to be burned which elevated particulate levels north of the Huai River relative to southern areas. Chen et al (2013) use the Huai River winter heating policy as a natural experiment to examine the impact of air pollution on life expectancy in China. They conclude that an additional 100 μg/m3 of TSPs is associated with a reduction in life expectancy at birth of about three years.
Chinese urbanites are aware that air pollution affects their health and quality of life. Increased access to smartphone apps has allowed them to know in real time the level of outdoor pollution in their city on a given day. People who want to be healthy and productive have strong incentives to avoid such pollution exposure (Graff-Zivin and Neidell 2013, Zheng and Kahn 2013).
People have two strategies for reducing their air pollution exposure. First, they can move to a low pollution city such as Xiamen and Zhuhai but these cities do not have the economic and social opportunities offered by mega-cities such as Beijing. Given where a person lives, works and shops within a city, air pollution exposure is a function of the time one spends outside and this person’s investments in self-protection. All else equal, a person who wears a mask or has an air pollution filter will be exposed to less air pollution.
Many urban residents in major Chinese cities purchase products online. Alibaba Group is China’s largest e-commerce company and it provides the largest online shopping platform Taobao.com (with hundreds of million online consumers) in China similar to eBay and Amazon. According to Taobao’s statistics, Chinese consumers spent 870 million yuan (US$143 million) on 4.5 million online transactions purchasing anti-smog products in 2013. While concerns about the ‘digital divide’ raise the possibility that the poor are less likely to shop online, in China low income people prefer to use Taobao because its prices are lower than brick and mortar stores.
Using Taobao data, we study monthly sales of air filters and masks for 34 cities from April 2013 to April 2014. The data are broken down into three income categories. These categories correspond to consumers within the 75th-100th percentile (‘high-income’), 25th-75th percentile (‘middle-income’) and 0-25th percentile (‘low-income’) of the overall distribution of consumers’ purchase expenditures.
An air filter is much more expensive than a mask. Their average prices are 490 and 0.9 US dollars, respectively. Consumers have to change the air filter’s strainer once per year but a mask only last for about ten days. Thus, the daily user cost (including electricity expenditure) of an air filter is more than ten times that of a mask. For both the mask and air filter transactions on Taobao in 2013, the high-income group (the top 25% of total consumers) bought 31.9% of masks and 47.9% of air filters.
Air filters are more effective than masks in protecting people against air pollution. Research conducted by the Department of Building Science at Tsinghua University, and tests conducted by China Consumer Association show that the mean effectiveness of masks and air filters is 33% and 92% respectively. That is, people with masks or air filters are exposed to 67% or 8% of the original PM2.5 concentration, respectively.
In Figure 1, we present one of our main findings. This figure reports predictions based on an econometric model that we report in our NBER Working Paper (Zheng, Sun and Kahn 2015). In this figure, a city’s ambient air pollution level is graphed on the horizontal axis. The range for air pollution varies from the cleanest city (about 30μg/m3) to the most polluted one (about 120μg/m3). The vertical axis reports the percentage increase in the sales index as a function of outdoor pollution levels. Controlling for other city attributes, we predict mask and air filter purchases for the three different income groups as a function of ambient air pollution in a given city in a given month.
Figure 1. The relationship between a city’s PM2.5 concentration and self-protection product investment
This figure presents a prediction of how a city’s consumption of air pollution masks and air filters varies as a function of the city’s PM2.5 concentration level. Two key facts emerge. First, all urbanites, regardless of their income, sharply increase their mask purchases when it is more polluted outside. Given the low cost of masks, this absence of a differential income effect is not surprising. This fact is consistent with an independent work by Mu and Zhang (2014). For air filter purchases, we observe that all three income groups increase air filter purchases as a function of outdoor pollution but the rich and middle class are much more likely to increase their purchases. Given the effectiveness of air filters in reducing pollution exposure, this finding supports the claim that available products differentially protect Chinese urbanites from high levels of outdoor pollution. These results suggest that the poor are the least likely to use market goods to self-protect and thus are exposed to the most pollution.
Today there is great interest and concern about within-nation trends in income inequality. Research has documented that income inequality is rising in communist China (Fleisher et al 2010). Using recent Internet sales data on the sales of products that reduce exposure to urban air pollution, we have documented that the urban poor are less likely to engage in this health improvement strategy. This suggests that cross-sectional income comparisons understate lifetime inequality as the rich will be more likely to survive and gain a greater lifetime flow of wellbeing relative to poorer people (Hall and Jones 2004).
Chen, Y, A Ebenstein, M Greenstone and H Li (2013) “Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River Policy”, Proceedings of the National Academy of Sciences, 110(32): 12936-12941.
Fleisher, B, H Lin and M Q Zhao (2010) “Human capital, economic growth, and regional inequality in China”, Journal of Development Economics, 92(2): 215-231.
Graff Zivin, J M Neidell (2013) “Environment, health, and human capital”, Journal of Economic Literature, 51(3): 689-730.
Hall, E and C I Jones (2007) “The value of life and the rise in health spending”, Quarterly Journal of Economics, 122(1): 39-72.
Mu, Q and J Zhang (2014) “Air pollution and defensive expenditures: Evidence from particulate-filtering facemasks”, SSRN working paper, available at SSRN 2518032.
Zheng, S and M E Kahn (2013) “Understanding China's urban pollution dynamics”, Journal of Economic Literature, 51(3): 731-772.
Zheng, S, C Sun and M E Kahn (2015) “Self-protection investment exacerbates air pollution exposure inequality in urban China”, NBER Working paper, No w21301.