Evidence on the impact of persistent exposure to air pollution on patent-based innovation from China's Huai River polity. We examine the effect of air pollution on innovation activity by studying city-level patenting activities and the Air Pollution Index (API) in China. We use a novel quasi-natural experiment of China's Huai River policy, which provides winter heating subsidy to cities north of the Huai River but not to cities south, to assess the impact of air pollution on innovation performance. .
The Air Pollution Index (API) in China exceeds the recommended levels of the World Health Organization (WHO). Therefore, the purpose of this paper is to investigate the actual effect of air pollution on innovation activity in China. 3 Other articles also suggest that ambient air pollution is harmful to human health, including Brunekreef and Holgate (2002), Pope et al.
We follow Chen et al. 2020) to address the endogeneity problem and adopt a quasi-natural experiment using China's Huai River policy, which provides winter heating subsidies to cities north of the Huai River, but not to cities south of the Huai River, to estimate the impact of air pollution on innovation performance. We then use a spatial regression discontinuity design (RDD) to identify the causal effect of air pollution on innovation, because there is discontinuous variation in air pollution across the Huai River and the corresponding line. We find that the API is 8.565 higher in the north and that this difference explains more than half of the API standard deviation, a result consistent with Chen et al (2013), who suggest that air pollution is more severe in northern China because of the Huai River. policy.
Overall, our results show that severe air pollution in northern China significantly hinders patent-based innovation activity.
Methodology
In Figure 1, color corresponds to interpolated API levels at the 12 nearest monitoring stations, where green, yellow, and red indicate areas with relatively low, moderate, and high API levels, respectively. As shown in Figure 1, the areas shaded in red are well separated by the Huai River/Qinling Mountains compared to the areas shaded in yellow and green, indicating a significant difference in air pollution between the two sides of the central heating boundary. This means that the 𝛽1 estimate cannot be interpreted as a causal effect of air pollution.
To address this endogeneity concern, we exploit the spatial regression discontinuity (RDD) design implicit in China's Huai River policy. First, we conduct an innovation RDD estimation to determine the extent to which innovation changes in the Huai River frontier. 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 = 𝛿0+ 𝛿1𝑁𝑗+ 𝑓(𝐿𝑗) + 𝑁𝑗𝑓(𝐿𝑗) + 𝑋𝑗 ,𝑡+ 𝜁𝑡+ 𝜀𝑗,𝑡, (2) where 𝑁𝑗 is an indicator variable that has a value of one if city j is located north. from Linija river Huai and nothing else; 𝑓(𝐿𝑗) is a polynomial of order k in degrees of north latitude of city j relative to the Huai River line; and 𝑁𝑗𝑓(𝐿𝑗) is included so that latitude may affect the results north and south of the Huai River differently.
Next, we use the following two-stage least squares (2SLS) RDD model to estimate the impact of air pollution on innovation. We treat the location north of the Huai River as an instrument for air pollution, as measured by the API, in the first stage, and we regress innovation on the instrumented API in the second stage. This 2SLS approach addresses the problem of confounding or omitted variables associated with estimating the impact of air pollution on innovation.
We expect 𝛼1 to be significantly positive, indicating that cities located north of the Huai River/Qinling Mountain Range have a higher air pollution level. The estimate of 𝛽1 in equation (4) can be used to interpret the causal effect of air pollution on innovation activity.
Empirical Results
The x-axis indicates the degree of latitude of the city relative to the Huai River, with 0 indicating the latitude of the Huai River and positive (negative) degrees indicating degrees north (south) relative to the Huai River. The discontinuity in innovation is illustrated in a very intuitive way in Figure 2, which clearly shows that innovation jumps as we move from north to south across the Huai River. Specifically, we find a strikingly discrete decline in innovation at the border, suggesting that innovation is lower in the north than in the south, as defined by the Huai River border.
To examine whether the Huai River policy causes a drastic change in air pollution, we plot the RDD of the API against the degrees of latitude north of the Huai River in Figure 3. We find that there is a sharp discrete jump in the API around 15 at the border, implying that the API is about one standard deviation higher in the north than in the south around the Huai River border. More clearly, cities located north of the Huai River exhibit significantly higher pollution and lower innovation.
Second, we examine whether city characteristics (i.e., GDP growth, GDP per capita, and temperature) associated with innovation change smoothly as they cross the Huai River. Next, we test whether the expected innovation is significantly different between south and north one degree of latitude on both sides of the Huai River. Overall, our test results show that innovation factors are independent of the Huai River policy.
To summarize, the diagnostic test results presented in this subsection support the validity of implementing the Huai River RDD, which removes potential sources of bias and enables us to obtain reliable causal inference. In Model (1), we find that the coefficient estimate on the indicator variable North (𝑁𝑗) is negative and significant at the 1% level, which means that innovation declines as we cross the border of the Huai River/Qinling mountain range. Overall, consistent with the literature, our first-stage estimation results provide evidence that the Huai River policy has created a discontinuity in air pollution and made air pollution more severe in northern China.
In the first set of additional analyses, we restrict our sample to a narrower bandwidth by including only cities located within 10 degrees of latitude, approximately 1000 km, on either side of the Huai River line. In unreported results of the OLS estimation, we find results similar to those in Table 3, showing that air pollution has a significantly positive impact on innovation. In accordance with Table 5, the coefficient estimate for the North dummy variable (𝑁𝑗) obtained from the first-stage estimation is positive and significant at the 1% level, indicating that air pollution in North China is getting worse .
Conclusion
Therefore, we conclude that air pollution hinders innovation in China, even when we scale innovation performance by city population. Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River Policy. New evidence on the impact of chronic exposure to air pollution on life expectancy from China's Huai River Policy.
Environmental Regulations on Air Pollution in China and Their Impact on Infant Mortality.” Journal of Health Economics. Notes: This line in the middle of the map is the Huai River/Qinling Mountains. Notes: This figure shows innovation by degrees of latitude north of the Huai River/Qinling Mountains, where innovation is measured as the natural logarithm of one plus the number of patents filed with the USPTO by individuals and organizations in cities.
The x-axis represents latitude, where 0 indicates the latitude of the Huai River and positive (negative) degrees indicate the degrees north (south) relative to the Huai River. Notes: In this figure, the API is plotted against latitudes north of the Huai River. Notes: This table reports the averages and differences in the innovation and air pollution index (API) between the south and north of the Huai River.
Differences in city characteristics and expected innovation between South and North around the Huai River by a small margin. Notes: This table reports the differences in city characteristics and expected innovation between the south and north of the Huai River, within a small range (one degree of latitude). The Air Pollution Index (API) is the air quality index, which measures the concentration and duration of several major air pollutants (e.g. sulfur dioxide (SO2), nitrogen oxides (NO2) and inhalable particulate matter (PM10) or total particulate matter) near the ground .
Notes: This table presents the regression discontinuity estimate of the effects of the Huai River policy on innovation. North is an indicator variable that equals one if a city is north of the Huai River/Qinling Mountain Range, and zero otherwise. The year fixed effect and a quadratic polynomial in latitudes north of the Huai River are included, while N indicates the number of city-year observations.
Notes: This table presents the two-stage least squares estimate of innovation based on the Huai River policy. Notes: This table presents the two-stage least squares estimate of innovation based on the Huai River policy using a subsample including cities located within 10 degrees of latitude on either side of the Huai River/Qinling mountain range.
