key: cord-0721156-oihqpbaz authors: Huang, Linyuan; Xie, Rui; Yang, Guohao title: The impact of lockdown on air pollution: Evidence from an instrument date: 2021-12-14 journal: China Econ Rev DOI: 10.1016/j.chieco.2021.101731 sha: 03bf64cf22d660879199792b1ea70d778996a73b doc_id: 721156 cord_uid: oihqpbaz This paper studies the impact of lockdown measures in response to the outbreak of COVID-19 on a prefecture's air pollution in China. To avoid potential endogenous problems, we exploit the bilateral population flow from the Baidu Migration Index to predict prefectures' probability to undertake lockdown measures. Our results using difference-in-differences with the instrumental variable show that a prefecture's lockdown measures significantly reduce its air quality index (AQI) by around 35%, and yet the result for difference-in-differences with OLS is only around 11%. We also find that a prefecture under lockdown reduces its PM 10 and PM 2.5 by around 25% and 35% respectively, and the results of diff-in-diff with OLS are only around 11% and 12%. The sharp difference between these two approaches seems to imply that there is a strong heterogeneity in lockdown stringency across prefectures. The outbreak of the highly infectious coronavirus COVID-19 has had a substantial impact on human health. In order to curb its spread, many local governments in China undertook lockdown measures which substantially limited the mobility of the population and the operation of factories and other businesses. This study contributes to our understanding of the impact of local Chinese prefectures" lockdown measures on their air quality, and explores the potential channel in air quality changes. To do so, we exploit the policy responses of the local governments in the face of the COVID-19 outbreak as a face a high risk of COVID-19 outbreak and should undertake lockdown measures (Hanming et al., 2020) . 2 To construct our instrument, we use a prefecture's population inflow from another prefecture p', to capture the relative importance of population flow from p' to p between January 1st and January 9th. 3 Before the outbreak of COVID-19, we believe the local governments had no incentives to manipulate this population flow out of the COVID-19 concerns. Therefore, this population flow could be treated as exogenous. To construct the time-varying aspect of a prefecture's COVID-19 risk, we use a time-varying dummy to indicate whether prefecture p' had more than a certain number of confirmed cases ̅ at time t. The combination of these two variables allows us to predict whether a prefecture received a high inflow of population from a COVID-19 epidemic area, and whether this prefecture should have undertaken a lockdown. We first use a diff-in-diff identification strategy to estimate the impact of lockdown on the local air pollution with our instrumental variable. What we identified with this instrumental variable is the effect of a prefecture's lockdown measures on air pollution if the lockdown measures were caused by the risk. Our estimates show that a prefecture's lockdown approach improves its air condition significantly. From the estimate of diff-in-diff with 2 stage-least-square (2SLS), a prefecture under lockdown reduces its air quality index by around 35%, and the estimate of diff-in-diff with OLS is only around 11%. We also find that a prefecture under lockdown reduces its PM 10 and PM 2.5 by around 25% and 35% respectively, and the results of diff-in-diff with OLS are only around 11% and 12%. The sharp difference between diff-in-diff with 2SLS and with OLS seems to suggest that there exists substantial heterogeneity in the stringency of lockdown implementation. This further suggests that the prefectures with a high risk of as predicted by our instrument did not undertake stringent lockdown measures, and prefectures with a low risk of COVID-19 did undertake stringent lockdown measures. This could also be confirmed by the fact that the accumulative confirmed cases of every prefecture located in Inner Mongolia and Liaoning province totaled less than 10 until the end of March. Both Inner Mongolia and Liaoning province declared a lockdown in every prefecture in their provinces on February 10. Given that a prefecture's lockdown significantly reduces the density of air pollutants, we still need to carefully explain the mechanism behind. A prefecture's lockdown measures limit both the mobility of a prefecture's population and the operation of its factories. Only a handful of companies in industries such as retail selling of medical supplies, medical services, and utilities were allowed to operate during the lockdown. We explore two major sources of a prefecture's air pollution: traffic and manufacturing production. To assess the effects of a traffic reduction on air pollution due to lockdown, we construct a prefecture's road intensity to capture its potential traffic volume in 2017. To capture the effects of production on pollution, we construct a prefecture's aggregate manufacturing firms' coal consumption intensity. A prefecture's aggregate manufacturing coal consumption intensity represents the dirtiness of this prefecture's production technology. If manufacturing production of a prefecture is at full capacity, more polluted technology implies worse air conditions. In other words, a prefecture is expected to experience a stronger improvement in air quality during its lockdown if it uses dirtier technology. Our estimates suggest that such prefectures experienced a stronger improvement in air conditions under lockdown, and that road intensity did not contribute significantly to the effects of lockdown on air quality. We also investigated the heterogeneity in the effects of the various lockdown measures. By collecting the official documents about lockdown measures, we obtained the rich information of the severity of lockdown in different prefectures. For example, some low risk regions such as Yinchuan and Wuzhong tended to adopt weaker regulations like setting up checkpoints, cleaning and disinfecting the community, canceling mass gatherings and so on. Therefore, we can conduct a series of regressions to check the heterogeneous effects of various lockdown measures. we find that there are significant differences among the effects of various lockdown measures on AQI. We provide a brief discussion herein on how our study is linked with the literature. Our paper employs daily population migration to predict a prefecture's lockdown decision, and is in close relation to the public events and migration Hanming et al. (2020) ; Qiu et al. (2020) ; Chinazzi et al. (2020) ; Gray and Mueller (2012) ; Lu et al. (2012) . 4 Our study 4 The public events in the literature referred to the epidemic, flooding, earthquake, etc. J o u r n a l P r e -p r o o f also relates to the literature about public environmental policy and its implications for the environment (Viard and Fu, 2015; Chen et al., 2013a; Davis, 2008) . In addition, some recent researches studied the impacts of COVID-19 on air pollution Dang and Trinh, 2021; Dai et al., 2021) , which are close to our research topic. We will discuss our differences and contributions in detail in the literature review. The remainder of the paper is organized as follows. Section 2 introduces the background of COVID-19. Section 3 reviews the related literature. Section 4 and Section 5 describe our empirical strategy and the data sources, respectively. Section 6 presents the estimation results. Section 7 offers concluding remarks. The World Health Organization (WHO) has declared a global pandemic over a new coronavirus which causes an illness officially known as COVID-19. This disease has quickly spread around the world in the past months. Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). A novel coronavirus (COVID-19) is a new strain that was discovered in 2019 and has not been previously identified in humans. Therefore, human body did not have immunity against this disease and vaccine had not been developed yet. Based on the studies of 41 Chinese patients Liu et al. (2020) suggest that the basic reproduction number (R0) for COVID-19 in 12 studies has a mean of 3.28 and a median of 2.79. This means that the COVID-19 is highly infectious, and one infected individual may lead three additional people to be infected on average. The findings in Huang (2020) suggest that 41 patients hospitalized show high rates of respiratory distress, intensive care admission, and abnormal findings on chest computer tomography (CT) from December 2, 2019 to January 2, 2020 in Wuhan, and the death rate is 15%. Therefore, it is necessary for the government to undertake progressive measures to reduce the contact between people during the outbreak of the disease. The epicenter in China, Wuhan, was the first prefecture to undertake a lockdown measure on January 23, 2020. The first infected case of COVID-19 was discovered in Wuhan, an inland port city with more than 11 million populations. Due to the highly J o u r n a l P r e -p r o o f infectious nature, the total number of infected cases increased tremendously. On January 23 of 2020, the central government announced to lock down the whole Wuhan city, when the documented accumulative confirmed cases in Wuhan was close to 500. This action included shutting down the cross-prefecture highway networks, ferries, high-speed railways and bus-line from and to Wuhan. Within Wuhan, restaurants, hotels, and the entertaining facilities were closed down. In the residence community in Wuhan, visitors were not allowed to go in. The residents living inside the community were only allowed to go outside three times a week for purchasing necessary groceries. These actions are believed to reduce people's contact, hence the contagion of the COVID-19. After Wuhan announced lockdown on January 23, other large prefectures outside Hubei province such as Wenzhou, Ningbo, Zhumadian, Hangzhou, Zhengzhou, Haerbin, and Fuzhou undertook similar measures, as they saw a rapidly rising number of infected cases. 5 We also observe prefectures undertaking lockdown with no more than five accumulative COVID-19 confirmed cases after the COVID-19 period. These prefectures include Yingkou, Benxi, Liaoyang, and Wuhai. The COVID-19 has caused substantially detrimental impact on both the health of the Chinese people and local economy. Up to October 1st, 2020, the total deaths of COVID-19 in China exceed 4,000 and the total number of infected cases exceed 90, 000. 6 In response to the outbreak of this disease, 129 prefectures including which of Hubei (the total number of prefectures in China is 326) received a lockdown measure. Fig. A1 which consist of one fifth of the total population in China. In the meantime, around 210 prefectures announced to shut down the cross-prefectures' bus-line, ferries, and its own public transport services within the prefecture. What determines the lockdown policy? In our opinion, whether to take lockdown measure mainly depends on the potential threat of COVID-19. Unlike the previous epidemic, the COVID-19 spread around the world quickly in a few days and has a high mortality. In addition, the COVID-19 broke out in the lunar new year holiday, which is the most active period of migrant population in China. After China's authorities confirmed that the virus can pass from person to person on January 20, the serious threat of epidemic increased the motivation of officers to lockdown the prefecture to contain the virus's spread. Although the prefecture's lockdown timing was affected by a lot of other factors including the development of local economy, the proportion of travelers choosing public transportation, its real-time resources and the local official's taste and ability to deal with the public health event. From the timing of lockdown and the characteristics of the prefectures with lockdown, we can conclude that the prefecture's exposure to the COVID-19 risk is the major factor to promote the lockdown decisions. In addition, under the conditions of the limited data, this factor is the most appropriate for constructing an exogenous instrumental variable. The lockdown measure substantially limited the mobility of the people within a prefecture. According to the Baidu migration data, a prefecture's mobility intensity which captures the flow of population within the prefecture experienced a stronger reduction for the prefectures undertaking lockdown measures during the Spring Festival which lasted from January 23 to February 9 in China. Fig. A3 shows us the dynamics of mobility intensity in prefecture groups with and without lockdown measures. We split the prefectures into two groups depending on their choice of undertaking lockdown measures before February 29. We also define a group of prefectures with lockdown measures as J o u r n a l P r e -p r o o f treatment group and all the other prefectures as control group. From our definition, the prefectures inside the treatment or control group does not vary over time. From this figure, the treatment group, which is in solid line, had a relatively high mobility intensity before January 23. After January 23, the mobility intensity dropped for prefectures in both the treatment and control groups, and the prefectures in the treatment group experienced a much stronger reduction, which means that the mobility of population inside the prefecture was more restrictive for the treatment than the control group. To estimate the effects of a lockdown on the COVID-19 related health outcomes, we employ our framework which is discussed fully in the previous section. The data of COVID-19 outcomes comes from the National Health Commission of China, which reports the accumulative confirmed cases, deaths and recovery. We are interested in the number of daily new cases, and hence we calculate a prefecture's daily new COVID-19 related outcomes by taking the difference of the accumulative outcomes between two days. This paper exploits the population migration between the epicenter and a prefecture to predict the optimal lockdown action of this prefecture. Our paper is in close relation to the public events and migration. Hanming et al. (2020) and Qiu et al. (2020) use the outmigration from the epicenter in China, Wuhan, to a prefecture, to predict this prefecture's infected cases. Chinazzi et al. (2020) propose to use a Global Epidemic and Mobility model to study the impact of travel limitation on the national and international spread of the COVID-19. Gray and Mueller (2012) investigate the consequences of climate-related natural disasters for long-term population mobility in rural Bangladesh, and find that flooding had modest effects on mobility that are most visible at moderate intensities and for women and the poor. Lu et al. (2012) analyze the movements of 1.9 million mobile phone users during the period from 42 days before, to 341 days after the devastating Haiti earthquake of January 12, 2010. Their findings suggest that population movements during disasters may be significantly more predictable than previously thought. Our study is different from the literature in the sense that we use the population migration from the epicenter to predict the optimal lockdown action outside the epicenter. Our study is also related to the literature about public policy and its implication for the environment. Viard and Fu (2015) evaluate the pollution and labor supply reductions from Beijing's driving restrictions. Beijing's driving restrictions reduced air pollution but at the cost of less work time by those with discretionary labor supply. Chen et al. (2013a) show that an arbitrary Chinese policy that greatly increases total suspended particulates (TSPs) air pollution caused the 500 million residents of Northern China to lose more than 2.5 billion life years of life expectancy. Chen et al. (2013b) show that the actions from the Chinese government to reduce traffic flow and production during and a little after the Beijing Olympic Games significantly improved the air quality. Almond et al. (2009) find that the heating policy led to dramatically higher pollution levels in the North China. Davis (2008) shows that the travel restrictions in Mexico have no effect on improving air quality. Our identification is different from the literature in the sense that we address the potential endogenous problem of a lockdown by the local government using the daily bilateral migration information in China. Our identification strategy substantially alleviates the potential political concern to the decision of a lockdown. The most relevant works to our paper are the researches of He et al. (2020), Dang and Trinh (2021) and Dai et al. (2021) . used the DID approach to J o u r n a l P r e -p r o o f examine the short-term impacts of COVID-19 on air pollution in China. Dang and Trinh (2021) offer an early assessment with cross-national evidence on the causal impacts of COVID-19 on air pollution by using a regression discontinuity design approach. And Dai et al. (2021) applied machine learning to quantify the impacts of the COVID-19 lockdown as well as the Chinese Spring Festival (CSF) holidays on the air quality changes in 31 major Chinese cities. Our paper joints the discussion of the environmental impact of COVID-19 by contributing in the followings: (1) Introducing a new instrumental variable to address the potential endogenous concerns. In order to address the endogenous concerns, we exploit the population flow from the epidemic area to construct an instrument variable, which could also capture the unobservable COVID-19 risk from the perspective of the local governments. Our results suggest massive difference in estimates with and without our IV, which seems to confirm our guess. (2) We also examine the longer-term effects of COVID-19 lockdown, which is not shown in the previous paper. In this section, we propose to use a diff-in-diff identification strategy to identify the impacts of a prefecture's lockdown. The challenge of identification is that a prefecture's lockdown decision may not be random, because local politicians are concerned with their promotion opportunities, which are closely linked with local economic performance (Li and Zhou, 2005) . If a politician undertakes radical isolation measures, he or she is aware that such measures may substantially slow the growth of the economy. Therefore, local politicians may have no incentive to impose lockdown measures. Additionally, we find that prefectures under lockdown were not the ones with the highest number of cases. For example, each prefecture in Liaoning province, with no more than 10 confirmed cases, undertook lockdown measures. Yet the average confirmed cases across different prefectures outside Hubei was around 40 as of the end of March, far higher than 10. Therefore, we expect to observe substantial heterogeneity with respect to the stringency of lockdown measures across prefectures. To address this issue, we predict a prefecture's lockdown decision outside Hubei according to inflow of population from prefectures with an epidemic. Hanming et al. (2020) suggests that a prefecture's inflow of population from Wuhan contributes J o u r n a l P r e -p r o o f significantly to its accumulative number of confirmed COVID-19 cases. We extended this measure by first identifying a prefecture p' to be an epidemic area at time t if its accumulative number of confirmed cases is higher than a certain threshold ̅ . A prefecture is subject to high risk of COVID-19 outbreak and should undertake lockdown measures if it receives a substantial inflow of population from epidemic areas. To capture the bilateral population flow from one prefecture p' to another one p and avoid the potential endogenous problem, we use their population flow two weeks before Wuhan's lockdown which marked the outbreak of COVID-19 in China (January 23rd). This bilateral population flow is meant to capture the daily commuting flow across different prefectures. We do not use a place's real-time population inflow from an epidemic area because the real-time population flow is likely to be affected by other COVID-19 related measures such as highway closures or the shutting down of cross-prefecture bus lines. Therefore, our instrumental variable (IV) is as follows: is a dummy variable equal to 1 if the number of confirmed cases of prefecture p' at time t is larger than ̅ , ′ , 0 captures the average bilateral flow of population from prefecture p' to prefecture p between January 1 and January 9, 2020, and ̂ captures the potential threat of COVID-19 via population inflow. Another problem here is that ̂ becomes larger as date index t increases. So we think of using the daily average ̂ as the final instrument. The potential COVID-19 threat to prefecture p depends on the past inflow of population from prefecture p' and its confirmed cases. ′ , 0 captures the relative importance for p of population flow from p'. If there was a large population flow from p' to p before the outbreak of COVID-19, prefecture p' is expected to have a non-negligible population flow to p after the outbreak of COVID-19. Here, we assume that a prefecture's lockdown decision does not affect its past population inflow, which is captured by ′ , 0 . Before January 9, a prefecture outside Hubei was less incentivized to manipulate population inflow. This is because January 9th is one day before the beginning of "Chunyun," the Spring Festival travel season, when there is a large population flow from one prefecture to another for the celebration and reunion. Politicians outside of Hubei may have been less aware of the detrimental effects of J o u r n a l P r e -p r o o f Journal Pre-proof COVID-19 on people's health and the economy before the Spring Festival. Also, prefecture p's lockdown decision might not have affected the accumulative number of confirmed cases in another prefecture p'. Our instrumental variable might also capture the relative stringency of lockdown measures, if more stringent measures were implemented in prefectures with higher risk of a COVID-19 outbreak. Using a prefecture's risk of a COVID-19 outbreak, we estimated the following equation to generate a prediction for a prefecture's lockdown decision, which is also the first stage of our 2SLS estimation: where ( = 1) is a dummy variable that takes a value of 1 if prefecture p undertook lockdown measures at date t. With this approach, we could predict the probability that prefecture p underwent a lockdown depending on the potential threat of COVID-19 via population inflow. Our instrumental variable allowed us to capture both the location and timing of a lockdown measure, depending on population inflow. If there was a past non-negligible flow of population from the potential epidemic area to prefecture p, prefecture p is expected to undertake lockdown measures. If the number of confirmed cases increases rapidly in the potential epidemic area, such that it quickly exceeds the threshold number C, this prefecture p is expected to implement earlier lockdown measures. We use the Baidu Migration Index to measure the population flow from one prefecture to another. The Baidu Migration Index provides daily information on the bilateral traveling population across different prefectures in China. With the predicted probability of a lockdown, we adopted a diff-in-diff approach to evaluate the impact of lockdown measures on the environment. We used a dummy variable to measure whether a prefecture announced lockdown measures at time t. If prefecture p undertook lockdown measures on date t, then it kept these measures till the end, implying March 31 of 2020 in our study. where represents the outcome of interest for prefecture p at date t, represents the prefecture fixed effects, and represents the time fixed effects. According to Imbens (2014), a valid instrumental variable has to satisfy two criteria: (1) The instrument is correlated with the endogenous variable, and (2) the instrument J o u r n a l P r e -p r o o f affects the dependent variable only indirectly, also known as the exclusion restriction. The correlation between the endogenous variable and the instrumental variable has been discussed in our construction process and will be tested statistically by conducting a first-stage F-test, which will been shown in Section 6. As for the exclusion restriction, it is hard to test whether or not the instrument is uncorrelated with the error term. We argue that potential threat of COVID-19 via population inflow between January 1 and January 9, 2020 is not directly related to the air quality of prefectures, but has the indirect effect by influencing the prefecture's lockdown decision. Given that the confirmed cases outside a prefecture can hardly be correlated with the air of this prefecture, the main concern for our exclusion restriction is that the transportation supplied for the population flow will affect a prefecture's environment. To deal with the problem, we adopted the past average inflow of population between January 1 and January 9, 2020 to capture the potential threat of COVID-19, which would not affect the transport activities later. Another evidence to support the exclusion restriction is that we find that the within-prefecture migration in prefectures with lockdown and which without lockdown had no significant difference. So our instrument do not affect our outcomes through channels other than the lockdown. Nevertheless, there are still some limitations in our construction. First, the accumulated flows between other prefecture with COVID-19 cases are not equivalent to COVID-19 risk. Second, we cannot control other factors which could affect the lockdown decision. Indeed, our instrument is not fully equal to the COVID-19 risk, and it is hard to measure other determinants due to the lack of daily data, but all we need for identification is a reliable exogenous variation. And our instrument could serve as a valid instrument to capture the potential risk of COVID-19, although it is not perfect. The first dataset we use comes from the official documents that report measures of the local government in response to the outbreak of the COVID-19. We manually collect all the related documents from the local offices. These documents report the date when they announce to undertake the lockdown measures. In addition, these documents also report specific measures, such as shutting down the public transport services and curfews. We have collected documents for around 130 different prefectures, which officially J o u r n a l P r e -p r o o f announced lockdown measures. The list of Locked-down prefectures is reported in Table A1 . Table A1 also describes the stringency of lockdown measures undertaken by prefectures, which can help to discuss the heterogeneity in the effect of lockdown measures on air pollution. The data on firms' pollution emissions comes from the Annual Environmental We obtain daily bilateral prefecture population flows from Baidu Migration, a Daily air quality data were collected from the records of 1,650 local monitoring stations and each monitor reports the air quality index and intensity of pollutants in the air. This dataset reports the concentration of coarse particulate matter (PM 10 ), fine particulate matter (PM 2.5 ), carbon monoxide (CO), sulfur dioxide (SO 2 ) and nitrogen oxides (NO X ). A general concern is that the local government in China has incentives to manipulate the local air quality data because the performance of the local air quality affects substantially his or her promotion possibility (Ghanem and Zhang, 2014) . However, this concern has been significantly alleviated as the China upgraded its air quality monitoring system to measure pollutants more precisely. This newly adopted monitoring system gathers pollutant sample automatically and at the same time reports the results. So the newly adopted system significantly improved the air quality as shown by Michael Greenstone (2019), because this monitoring system makes it very difficult for the local government to manipulate the air pollution data. Our air quality data covers all the monitoring stations in China. if a prefecture has multiple monitoring stations, we will take the arithmetical mean of them as the prefecture's outcome. Table 1 reports the summary statistics for the air pollution, cases and migration data. As seen from the Panel A, the lockdown prefectures were, on average, more polluted than the control prefectures before the lockdowns. And we can see a sharp decline in all air pollutants concentrations due to the lockdown. From the Panel B, we found that the new confirmed cases experienced a decline and the new cured cases gone up, on average, after the lockdown. Panel C shows that the lockdown prefectures experienced a strong reduction in both the within-prefecture migration and cross-prefecture migration. The threat of COVID-19 on a prefecture depends on how we define an epidemic area, which links with the value of ̅ . The value of ̅ determines prefecture p's risk of COVID-19 exposure. If we choose a relatively lower value for ̅ , a larger number of prefectures would be considered epidemic areas. This implies that a prefecture with population inflow from many different prefectures has a high risk of exposure to COVID-19. On the other hand, if we choose a relatively higher value for ̅ , a smaller number of prefectures will be considered epidemic areas. This implies that a prefecture with population inflow from many different prefectures could balance out the risk of COVID-19. As shown in the appendix, our estimates are robust to a reasonable variation of ̅ . We find that the average local accumulative number of confirmed cases on the day J o u r n a l P r e -p r o o f Journal Pre-proof when the lockdown started is around 48. Therefore, we tried several values, namely 5, 10, 30, 50, 80, and 100, for ̅ to run the first stage. We show our results for the second stage estimation with ̅ , and the results are robust if we choose ̅ from 5 to 100. Panel A of Table 2 reports the results for all the threshold values of ̅ , and they are all significant and positive. Column (1) of this table shows that a one-unit increase in prefecture p's threat of COVID-19 is associated with a 0.2% increase in this prefecture's probability of announcing a lockdown. As we increase the threshold value for ̅ , the significance does not change, and the correlation between a prefecture's threat and its lockdown behavior increases. Column (6) shows that a one-unit increase in a prefecture's threat of COVID-19 is 0.4% associated with a prefecture's lockdown decision, when we choose 100 for the value of ̅ . Column (2) shows that a prefecture's lockdown measures reduced its AQI by around 35% with diff-in-diff IV, and column (1) shows that a prefecture's lockdown measures reduced its AQI by around 11% with diff-in-diff with OLS. Column (4) suggests that a prefecture's lockdown reduced its density of PM 10 by around 25%. A prefecture's Lockdown reduced its density of PM 2.5 by around 35%. which is similar to the effects on AQI. Notes: Standard errors are in parentheses and clustered at the prefecture level. *** p<0.01, ** p<0.05, * p<0.1. Weather controls include daily temperature, its square, humidity, and precipitation. ( = 1) = 1 if a prefecture p undertook a lockdown at day t. Otherwise, ( = 1) = 0. ̂, ̅ refers to the threat of COVID-19 of prefecture p at day t with ̅ * , 10, 30, 0, 0, 100+. We choose 30 for the value of ̅ in the second stage. ( ) represents the log value of a prefecture's air quality index, ln( 10 ) represents the log value of a prefecture's coarse particulate matters (μg/m 3 ), ln( 2 5 ) represents the log value of a prefecture's fine particulate matters (μg/m 3 ). So, what can we learn from this exercise? The estimates of diff-in-diff with OLS treat every prefecture as the same as long as this prefecture announced a lockdown with an official document. In the OLS regression, what we estimated is the simple correlation between a prefecture's lockdown and air condition, with which we could not interpret as causal. But in the 2SLS regression, we adopted an instrument to capture the relative stringency of lockdown among the prefectures according to the COVID-19 risk, which can reflect the intensity of policy implementation. When a prefecture faced with higher risk, the local government had a stronger incentive to strengthen the enforcement of lockdown. Our results have confirmed this argument. The estimates of 2SLS were much larger than the estimates of OLS, suggesting that a prefecture with a higher risk of COVID-19 is associated with a more substantial reduction in air pollution after lockdown. In Section 6.3.2, we provide estimates with the same framework but taking into account the log value of real-time population inflow. The estimates are robust. Given that a prefecture's lockdown substantially reduces its air pollution, we examine the possible role of manufacturing production and traffic. Chen et al. (2013a) show that limiting the flow of daily traffic and the shut-down of manufacturing plants contributed substantially to Beijing's air quality during the 2008 Olympic Games. Following this idea, we use a prefecture's average coal consumption intensity, i.e., the J o u r n a l P r e -p r o o f ratio between coal consumption and sales, in 2010 from AESPF to measure the dirtiness of production at the prefecture level. 7 If a prefecture has a relatively higher coal consumption intensity, this means that the production of this prefecture relies heavily on coal as fuel in production. To capture the effect of reduced traffic due to lockdown measures, we use a prefecture's road intensity in 2017, i.e., the ratio between a prefecture's total road length and its population. To implement this idea, we estimate the following equation: where 0 ′ captures the road density of prefecture p at the initial period 0 ′ and 0 ′ captures the coal intensity of the manufacturing firms of prefecture p at time 0 ′ . We expect both the estimates for 1 and 2 to be negative and significant. A prefecture with dirtier production technology or a high volume of traffic is expected to experience a greater reduction in pollution once under lockdown. Table 3 reports the results of the estimates, controlling for the size of a prefecture in terms of its population. These estimates help us understand the extent to which traffic and manufacturing production contribute to the effects of a lockdown on the environment. Column (2) shows that a prefecture with a high initial level of road intensity does not have a stronger reduction in pollution. Column (3) shows that manufacturing firms' coal consumption intensity within a prefecture absorbs nearly all the effects of that prefecture's lockdown on the environment, indicating that the effects of a prefecture's lockdown are due to a reduction in manufacturing production. This argument can be confirmed if we put all the interaction terms into one regression as shown in column (4). In column (4), only the interaction term between coal consumption intensity and the lockdown dummy is significantly negative. Table 4 , where ( = 1) is a dummy variable that takes a value of 1 if prefecture p undertook the lockdown at date t. We found that there are significant differences among the effects of various lockdown measures on AQI. Among these measures, 12 had the biggest positive effect on air pollution, which reduced the AQI by around 112%. The results were correspondent with our expectation. Because the 12 stipulated that each family (exclude home quarantine families) could only J o u r n a l P r e -p r o o f assign one family member to purchase daily supplies every two/three days, and other citizens were not allowed to go out except for medical treatment, epidemic prevention and control, and working with the approval of local government. This regulation severely restricted the movement of people and production, and thus could reduce air pollution more significantly compared with other measures. Notes: Standard errors are in parentheses and clustered at the prefecture level. *** p<0.01, ** p<0.05, * p<0.1. ( = 1) = 1 if a prefecture p undertook a at day t. Otherwise ( = 1) = 0. The specific lockdown measures are as follows: 1) Contactless delivery; 2) Communities with confirmed cases may implement closed isolation; 3) All visitors and exotic vehicles are not allowed to enter the community; 4) Travelers from the Epidemic Area Need to be Quarantined for 14 Days upon Arrival to the Prefecture; 5) Close contacts need to be quarantined at assembly sites; 6) Public places not necessary for the lives of residents should be closed; 7) Suspension of non-essential project; 8) Communities should strengthen management of rental housing; 9) Persons Stranded in the Epidemic Area Could not Return; 10) Health QR code or pass shall be used to manage people; 11) Wedding suspended and funeral simplified; 12) Each family (exclude home quarantine families) could only assign one family member to purchase daily supplies every two/three days. When the COVID-19 epidemic started to slow down in China in late February, most prefectures started to cancel the lockdown measures. Although lockdown is a short-run Decision, whether it will cause a long-lasting effect excites our interest. In order to analyze this problem, we extended our sample period to March 31, 2021. Because all the prefectures cancelled the lockdown policy after March of 2020, what we can identify is just the coefficient of a dummy, which equal to 1 if a prefecture ever took the lockdown measure. Table 5 reports the results on the long-term effect of the lockdown measure. In the long run, the effect of lockdown on air quality has faded away. This suggests that the (1) (2) J o u r n a l P r e -p r o o f lockdown only improve the air quality in the short-term. We first evaluate the impacts of a prefecture's lockdown measure using an event study framework. A government is identified to undertake a lockdown approach if it declared to adopt a lockdown measure via an official document. To assess the impact, we compare the trends of the prefectures with lockdown measures to those without lockdown measures in the following specification: The effect of a prefecture's lockdown on the absolute value of the pollutants J o u r n a l P r e -p r o o f Table 6 reports the estimates of a prefecture's lockdown on the absolute value of air pollution. The results show that a prefecture's lockdown reduces its AQI, PM 10 and PM 2.5 by around 42 units, 45 units and 27 units using diff-in-diff with 2SLS, respectively. The results using diff-in-diff with OLS are less than one-third of which with 2SLS for all of these variables. These are consistent with what we find in the relative changes. Given that our estimates using the time period from January 1 to March 31 of 2020, which marks the end of COVID-19 in China. We use different time periods to compare our estimate to check whether our approaches are sensitive to timing. In order to do this, we first try January 1 of 2020 to March 31 of 2020. The reason why we use this time period is that the daily new confirmed cases of prefectures outside Hubei almost fell to 0 at the end of March. In another setting, we also include the same period in 2019. In this setting, we expand our control group. Since COVID-19 did not happen in the same period of 2019, we manually set a prefecture risk of COVID-19 to be 0. Table 7 reports these estimates. The columns in odd number include only the samples from 2020, and the columns in even number include samples both from 2020 and 2019. The estimates for the three dependent variables do not vary too much between two samples, as shown in the table. The sharp difference between the estimates using 2SLS and OLS indicates the heterogeneous effect of lockdown on the environment. Nevertheless, there is still concern that the exclusion assumption of our IV is not satisfied because that the inflow of population from epidemic areas might affect pollution other than lockdown. Since it is not possible to test this exclusion assumption directly, we control for the log value of actual inflow of population in our regression and check whether our estimate is robust. J o u r n a l P r e -p r o o f from 5 to 100, to implement our diff-in-diff with 2SLS framework. Table 9 reports the results. Our results suggest that our estimates do not vary too much for different values of ̅ . In the main results, we identify a prefecture p' to be an epidemic area at time t if its accumulative number of confirmed cases is higher than a certain threshold ̅ . In order to check the robustness of the empirical findings, we turn to use the newly-added confirmed cases to measure the risk of a prefecture. Since the average newly-added confirmed cases on the day the lockdown starts is around 3. We chose several values, namely 3, 6, 10, 15, 25, and 30, for new ̅ to run the first stage. And we also show the results for the second stage estimation with ̅ = 3 in Table 10 . The results are robust not only in the first stage but also in the second stage. In the main results, we eliminated the samples of Hubei province because it is the epicenter of COVID-19, and the preventive and control measures of Hubei might be fundamentally different from which of other regions. Therefore we're concerned about that the samples of Hubei may form the interference to our estimates. But the lockdown of Hubei province seems to be a more exogenous shock to identify the impact on air pollution. So we provide the related regression results which include the samples of Hubei province in Table 11 . These results are close to the main results when we excluded the samples of Hubei province. In the baseline regressions, we focus on the impacts of the lockdown on the main indicators of air quality. In order to test whether the results are robust to other air pollutant, we adopt the same method to investigate the effect of lockdown on sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO) and ozone (O 3 ). The results are shown in Table 12 . We find that lockdown measure reduces all the pollutants except for O3. This may be caused by the significant reduction of PM 2.5 and NO 2 , which could inhibit the chemical formation of ozone. Although we have discussed the exclusion restriction for the instrument variable, there is still spatial dimension that cannot be ignored. Suppose the pollutants can reach J o u r n a l P r e -p r o o f the nearby prefecture in a few hours, it may become a possible channel through which ̂ is correlated with air quality in p. So we hope to test the possible correlation. First, we control for a dummy which equal to 1 when a nearby prefecture adopts lockdown measures, the results of which are shown in columns (1)-(3) of Table 13 . Second, we eliminate the samples whose neighbor adopts the lockdown measures to run the related regression, which can be seen from columns (4)-(6) of Table 13 . Compare the results of Table 13 with the baseline results, we find that we might underestimated the effect of lockdown on air pollution without considering the spatial dependence. In our baseline model, for excluding the impact of the Spring Festival travel rush on the instrument, we only used the average bilateral flow of population between January 1 and January 9, 2020 to capture the potential threat of COVID-19. As days go by, this prefecture's exposure to the risk from prefecture p' keeps rising no matter prefecture p' has locked down or not. To solve this problem, we try to use the average population flow of longer time to predict the prefecture's exposure to the COVID-19 risk in consideration of decreasing flow after the breakout of COVID-19. The columns (1)-(3) of Table 14 shows that when we use the average bilateral flow of population between January 1 and February 12, 2020 to predict the potential threat of COVID-19, the estimates do not vary too much compared with the baseline results. However, there is still some concern that if a prefecture's connected area p' has confirmed cases over ̅ , whether prefecture p' has an earlier lockdown or not will affect the prefecture differently (Hanming et al. (2020); Qiu et al. (2020) ). Thus we adjust our construction of ̂ by turning ( ′ ′ ≥ ̅ ) into ( ′ ′ ≥ ̅ ) ( ′ = 0), which is a dummy variable equal to 1 if the number of confirmed cases of prefecture p' at date t is larger than ̅ and the prefecture p' did not undertake lockdown measures at date t. As shown in the columns (4)-(6) of Table 14 , the estimates are smaller than which of baseline results but they are still bigger than estimates of OLS results. Moreover, we also try the OLS regressions controlling for real population inflow from an epidemic area in lag days shown in the columns (7)-(9) of Table 14 . Adding this control variable does not change the estimates too much. The absolute values of estimates are still smaller than which of the 2SLS estimates. Notes: Standard errors are in parentheses and clustered at the prefecture level. *** p<0.01, ** p<0.05, * p<0.1. Weather controls include daily temperature, its square, humidity, and precipitation. ̂, ̅ refers to the threat of COVID-19 of prefecture p at day t with ̅ *3, , 10, 1 , 2 , 30+, which is measured by the newly-added confirmed cases. Observations 29484 29484 29484 29484 29484 29484 Notes: Standard errors are in parentheses and clustered at the prefecture level. *** p<0.01, ** p<0.05, * p<0.1. Weather controls include daily temperature, its square, humidity, and precipitation. ̂, ̅ refers to the threat of COVID-19 of prefecture p at day t with ̅ * , 10, 30, 0, 0, 100+. We choose 30 for the value of ̅ in the second stage. Notes: Standard errors are in parentheses and clustered at the prefecture level. *** p<0.01, ** p<0.05, * p<0.1. Weather controls include daily temperature, its square, humidity, and precipitation. ( , ) represents the log value of total population inflow of prefecture p at day t. This paper studies the effect of government response to COVID-19 on the environment. The high frequency (daily) data allows us to precisely compare changes in environmental quality across different prefectures before and after any prefecture undertook lockdown measures. In particular, we find that (1) a prefecture's lockdown had a substantial and significant clean-up effect on air quality, (2) the effect of the lockdown on the environment may be due to the reduction in manufacturing production, and (3) there are significant differences among the effects of various lockdown measures on air pollution. To estimate the effects of a lockdown on the environment, we exploit the high frequency of the data to develop a novel identification strategy which helps to predict the behavior of the government. As high frequency data become more available, our approach provides an example of how to make the best use of daily variations to identify the effects of the public events and the government decisions. Although our instrument is limited because it is hard to completely prove the validity of it, it is the most appropriate instrument we can find in the available data. None. guidance. We also want to express our thanks to Lorenzo Trimarchi, Eric Melander and Zhijie Xiao for their helpful comments and discussions. Notes: This figure shows the results of the event study for air pollution. The y axis represents the log of AQI, PM10 and PM2.5, and the x axis represents time. w1, w2, and w3 represents the first, second and the third week after the lockdown, respectively. w ≥ 4 represents four weeks after lockdown. w-1, w-2, and w-3 represents the first, second and the third week before the lockdown, respectively. w ≤ −4 represents four weeks before lockdown. Our estimates have already controlled for a prefecture"s daily precipitation and temperature, which are both time-variant. We also report 90% confidence bound for the daily estimates. We omit the estimates of a day before the lockdown. Winter heating or clean air? Unintended impacts of China's Huai river policy Two-stage least squares estimation of average causal effects in models with variable treatment intensity Evidence on the impact of sustained exposure to air pollution on life expectancy from China"s Huai river policy The promise of Beijing: Evaluating the impact of the 2008 Olympic Games on air quality The effect of travel restrictions on the spread of the 2019 novel coronavirus Spring festival and covid lockdown: Disentangling pm sources in major Chinese cities Does the covid-19 lockdown improve global air quality? new cross-national evidence on its unintended consequences The effect of driving restrictions on air quality in Mexico city effortless perfection:' do Chinese cities manipulate air pollution data? Natural disasters and population mobility in Bangladesh. Proceedings of the National Academy of Sciences The effect of air pollution on mortality in China: Evidence from the 2008 Beijing Olympic games Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet Political selection in China: The complementary roles of connections and performance Political turnover and economic performance: the incentive role of personnel control in China The reproductive number of COVID-19 is higher compared to SARS coronavirus Predictability of population displacement after the 2010 haiti earthquake Can Technology Solve the Principal-Agent Problem? Evidence from China"s War on Air Pollution (No. w27502) Impacts of social and economic factors on the transmission of coronavirus disease (covid-19) in China The effect of Beijing"s driving restrictions on pollution and economic activity This research was generously supported by the Science Fund for Distinguished Young Scholars of Hunan province (Grant No. 2021JJ10027). We would like to thank the participants at CRED workshops. We owe special thanks to Albrecht Glitz for his J o u r n a l P r e -p r o o f The specific lockdown measures are as follows: 1) Contactless delivery; 2) Communities with confirmed cases may implement closed isolation; 3)All visitors and exotic vehicles are not allowed to enter the community; 4) Travelers from the Epidemic Area Need to be Quarantined for 14 Days upon Arrival to the Prefecture; 5) Close contacts need to be quarantined at assembly sites; 6) Public places not necessary for the lives of residents should be closed; 7) Suspension of non-essential project; 8) Communities should strengthen management of rental housing; 9) Persons Stranded in the Epidemic Area Could not Return; 10) Health QR code or pass shall be used to manage people; 11) Wedding suspended and funeral simplified; 12)