| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T114 |
0-4 |
Sentence |
denotes |
Data |
| T115 |
6-15 |
Sentence |
denotes |
Variables |
| T116 |
16-205 |
Sentence |
denotes |
January 19, 2020, is the first day that COVID-19 cases were reported outside of Wuhan, so we collect the daily number of new cases of COVID-19 for 305 cities from January 19 to February 29. |
| T117 |
206-287 |
Sentence |
denotes |
All these data are reported by 32 provincial-level Health Commissions in China10. |
| T118 |
288-438 |
Sentence |
denotes |
Figure 2 shows the time patterns of daily confirmed new cases in Wuhan, in Hubei province outside Wuhan, and in non-Hubei provinces of mainland China. |
| T119 |
439-659 |
Sentence |
denotes |
Because Hubei province started to include clinically diagnosed cases into new confirmed cases on February 12, we notice a spike in the number of new cases in Wuhan and other cities in Hubei province on this day (Fig. 2). |
| T120 |
660-769 |
Sentence |
denotes |
The common effects of such changes in case definitions on other cities can be absorbed by time fixed effects. |
| T121 |
770-859 |
Sentence |
denotes |
As robustness checks, we re-estimate models A and B without the cities in Hubei province. |
| T122 |
860-1141 |
Sentence |
denotes |
In addition, since the number of clinically diagnosed cases at the city level was reported for the days of February 12, 13, and 14, we recalculated the daily number of new cases for the 3 days by removing the clinically diagnosed cases from our data and re-estimate models A and B. |
| T123 |
1142-1184 |
Sentence |
denotes |
Our main findings still hold (Appendix B). |
| T124 |
1185-1257 |
Sentence |
denotes |
Fig. 2 Number of daily new confirmed cases of COVID-19 in mainland China |
| T125 |
1258-1410 |
Sentence |
denotes |
Regarding the explanatory variables, we calculate the number of new cases of COVID-19 in the preceding first and second weeks for each city on each day. |
| T126 |
1411-1802 |
Sentence |
denotes |
To estimate the impacts of new COVID-19 cases in other cities, we first calculate the geographic distance between a city and all other cities using the latitudes and longitudes of the centroids of each city and then calculate the weighted sum of the number of COVID-19 new cases in all other cities using the inverse of log distance between a city and each of the other cities as the weight. |
| T127 |
1803-1968 |
Sentence |
denotes |
Since the COVID-19 outbreak started from Wuhan, we also calculate the weighted number of COVID-19 new cases in Wuhan using the inverse of log distance as the weight. |
| T128 |
1969-2178 |
Sentence |
denotes |
Furthermore, to explore the mediating impact of population flow from Wuhan, we collect the daily population flow index from Baidu that proxies for the total intensity of migration from Wuhan to other cities11. |
| T129 |
2179-2295 |
Sentence |
denotes |
Figure 3 plots the Baidu index of population flow out of Wuhan and compares its values this year with those in 2019. |
| T130 |
2296-2461 |
Sentence |
denotes |
We then interact the flow index with the share that a destination city takes (Fig. 4) to construct a measure on the population flow from Wuhan to a destination city. |
| T131 |
2462-2647 |
Sentence |
denotes |
Other mediating variables include population density, GDP per capita, and the number of doctors at the city level, which we collect from the most recent China city statistical yearbook. |
| T132 |
2648-2707 |
Sentence |
denotes |
Table 1 presents the summary statistics of these variables. |
| T133 |
2708-2822 |
Sentence |
denotes |
On average, GDP per capita and population density are larger in cities outside Hubei province than those in Hubei. |
| T134 |
2823-2902 |
Sentence |
denotes |
Compared with cities in Hubei province, cities outside Hubei have more doctors. |
| T135 |
2903-2951 |
Sentence |
denotes |
Fig. 3 Baidu index of population flow from Wuhan |
| T136 |
2952-3007 |
Sentence |
denotes |
Fig. 4 Destination shares in population flow from Wuhan |
| T137 |
3008-3034 |
Sentence |
denotes |
Table 1 Summary statistics |
| T138 |
3035-3059 |
Sentence |
denotes |
Variable N Mean Std dev. |
| T139 |
3060-3064 |
Sentence |
denotes |
Min. |
| T140 |
3065-3076 |
Sentence |
denotes |
Median Max. |
| T141 |
3077-3093 |
Sentence |
denotes |
Non Hubei cities |
| T142 |
3094-3114 |
Sentence |
denotes |
City characteristics |
| T143 |
3115-3175 |
Sentence |
denotes |
GDP per capita, 10,000RMB 288 5.225 3.025 1.141 4.327 21.549 |
| T144 |
3176-3246 |
Sentence |
denotes |
Population density, per km2 288 428.881 374.138 9.049 327.115 3444.092 |
| T145 |
3247-3302 |
Sentence |
denotes |
# of doctors, 10,000 288 1.086 1.138 0.030 0.805 10.938 |
| T146 |
3303-3339 |
Sentence |
denotes |
Time varying variables, Jan 19–Feb 1 |
| T147 |
3340-3406 |
Sentence |
denotes |
Daily # of new confirmed cases 4032 1.303 3.608 0.000 0.000 60.000 |
| T148 |
3407-3481 |
Sentence |
denotes |
Weekly average max. temperature, ∘C 4032 8.520 8.525 − 18.468 7.932 29.833 |
| T149 |
3482-3549 |
Sentence |
denotes |
Weekly average precipitation, mm 4032 0.238 0.558 0.000 0.033 5.570 |
| T150 |
3550-3615 |
Sentence |
denotes |
Weekly average wind speed, m/s 4032 2.209 0.842 0.816 2.014 6.386 |
| T151 |
3616-3652 |
Sentence |
denotes |
Time varying variables, Feb 1–Feb 29 |
| T152 |
3653-3720 |
Sentence |
denotes |
Daily # of new confirmed cases 8064 0.927 3.461 0.000 0.000 201.000 |
| T153 |
3721-3797 |
Sentence |
denotes |
Weekly average max. temperature, ∘C 8064 11.909 7.983 − 18.032 12.814 28.791 |
| T154 |
3798-3865 |
Sentence |
denotes |
Weekly average precipitation, mm 8064 0.193 0.491 0.000 0.027 5.432 |
| T155 |
3866-3931 |
Sentence |
denotes |
Weekly average wind speed, m/s 8064 2.461 0.913 0.654 2.352 7.129 |
| T156 |
3932-3973 |
Sentence |
denotes |
Cities in Hubei province, excluding Wuhan |
| T157 |
3974-3994 |
Sentence |
denotes |
City characteristics |
| T158 |
3995-4053 |
Sentence |
denotes |
GDP per capita, 10,000RMB 16 4.932 1.990 2.389 4.306 8.998 |
| T159 |
4054-4123 |
Sentence |
denotes |
Population density, per km2 16 416.501 220.834 24.409 438.820 846.263 |
| T160 |
4124-4177 |
Sentence |
denotes |
# of doctors, 10,000 16 0.698 0.436 0.017 0.702 1.393 |
| T161 |
4178-4214 |
Sentence |
denotes |
Time varying variables, Jan 19–Feb 1 |
| T162 |
4215-4283 |
Sentence |
denotes |
Daily # of new confirmed cases 224 22.165 35.555 0.000 7.000 276.000 |
| T163 |
4284-4354 |
Sentence |
denotes |
Weekly average max. temperature, ∘C 224 8.709 1.602 1.278 8.905 10.889 |
| T164 |
4355-4421 |
Sentence |
denotes |
Weekly average precipitation, mm 224 0.261 0.313 0.000 0.160 1.633 |
| T165 |
4422-4486 |
Sentence |
denotes |
Weekly average wind speed, m/s 224 1.970 0.600 0.893 1.975 3.439 |
| T166 |
4487-4523 |
Sentence |
denotes |
Time varying variables, Feb 1–Feb 29 |
| T167 |
4524-4592 |
Sentence |
denotes |
Daily # of new confirmed cases 448 28.871 51.793 0.000 8.000 424.000 |
| T168 |
4593-4665 |
Sentence |
denotes |
Weekly average max. temperature, ∘C 448 14.569 2.985 1.452 14.448 23.413 |
| T169 |
4666-4732 |
Sentence |
denotes |
Weekly average precipitation, mm 448 0.201 0.233 0.000 0.133 1.535 |
| T170 |
4733-4797 |
Sentence |
denotes |
Weekly average wind speed, m/s 448 2.063 0.648 0.705 2.070 4.174 |
| T171 |
4798-4881 |
Sentence |
denotes |
Variables of the city characteristics are obtained from City Statistical Yearbooks. |
| T172 |
4882-4938 |
Sentence |
denotes |
Time varying variables are observed daily for each city. |
| T173 |
4939-5008 |
Sentence |
denotes |
Weekly average weather variables are averages over the preceding week |
| T174 |
5009-5105 |
Sentence |
denotes |
We rely on meteorological data to construct instrumental variables for the endogenous variables. |
| T175 |
5106-5364 |
Sentence |
denotes |
The National Oceanic and Atmospheric Administration (NOAA) provides average, maximum, and minimum temperatures, air pressure, average and maximum wind speeds, precipitation, snowfall amount, and dew point for 362 weather stations at the daily level in China. |
| T176 |
5365-5633 |
Sentence |
denotes |
To merge the meteorological variables with the number of new cases of COVID-19, we first calculate daily weather variables for each city on each day from 2019 December to 2020 February from station-level weather records following the inverse distance weighting method. |
| T177 |
5634-5816 |
Sentence |
denotes |
Specifically, for each city, we draw a circle of 100 km from the city’s centroid and calculate the weighted average daily weather variables using stations within the 100-km circle12. |
| T178 |
5817-5911 |
Sentence |
denotes |
We use the inverse of the distance between the city’s centroid and each station as the weight. |
| T179 |
5912-6024 |
Sentence |
denotes |
Second, we match the daily weather variables to the number of new cases of COVID-19 based on city name and date. |
| T180 |
6026-6061 |
Sentence |
denotes |
Selection of instrumental variables |
| T181 |
6062-6142 |
Sentence |
denotes |
The transmission rate of COVID-19 may be affected by many environmental factors. |
| T182 |
6143-6268 |
Sentence |
denotes |
Human-to-human transmission of COVID-19 is mostly through droplets and contacts (National Health Commission of the PRC 2020). |
| T183 |
6269-6421 |
Sentence |
denotes |
Weather conditions such as rainfall, wind speed, and temperature may shape infections via their influences on social activities and virus transmissions. |
| T184 |
6422-6548 |
Sentence |
denotes |
For instance, increased precipitation results in higher humidity, which may weaken virus transmissions (Lowen and Steel 2014). |
| T185 |
6549-6613 |
Sentence |
denotes |
The virus may survive longer with lower temperature (Wang et al. |
| T186 |
6614-6634 |
Sentence |
denotes |
2020b; Puhani 2020). |
| T187 |
6635-6716 |
Sentence |
denotes |
Greater wind speed and therefore ventilated air may decrease virus transmissions. |
| T188 |
6717-6805 |
Sentence |
denotes |
In addition, increased rainfall and lower temperature may also reduce social activities. |
| T189 |
6806-6947 |
Sentence |
denotes |
Newly confirmed COVID-19 cases typically arise from contracting the virus within 2 weeks in the past (e.g., World Health Organization 2020b). |
| T190 |
6948-7123 |
Sentence |
denotes |
The extent of human-to-human transmission is determined by the number of people who have already contracted the virus and the environmental conditions within the next 2 weeks. |
| T191 |
7124-7414 |
Sentence |
denotes |
Conditional on the number of people who are infectious and environmental conditions in the previous first and second weeks, it is plausible that weather conditions further in the past, i.e., in the previous third and fourth weeks, should not directly affect the number of current new cases. |
| T192 |
7415-7639 |
Sentence |
denotes |
Based on the existing literature, we select weather characteristics as the instrumental variables, which include daily maximum temperature, precipitation, wind speed, and the interaction between precipitation and wind speed. |
| T193 |
7640-7789 |
Sentence |
denotes |
We then regress the endogenous variables on the instrumental variables, contemporaneous weather controls, city, date, and city by week fixed effects. |
| T194 |
7790-7977 |
Sentence |
denotes |
Table 2 shows that F-tests on the coefficients of the instrumental variables all reject joint insignificance, which confirms that overall the selected instrumental variables are not weak. |
| T195 |
7978-8066 |
Sentence |
denotes |
The coefficients of the first stage regressions are reported in Table 9 in the appendix. |
| T196 |
8067-8094 |
Sentence |
denotes |
Table 2 First stage results |
| T197 |
8095-8134 |
Sentence |
denotes |
Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29 |
| T198 |
8135-8143 |
Sentence |
denotes |
Own city |
| T199 |
8144-8199 |
Sentence |
denotes |
Average # new cases, 1-week lag F stat 11.41 4.02 17.28 |
| T200 |
8200-8228 |
Sentence |
denotes |
p value 0.0000 0.0000 0.0000 |
| T201 |
8229-8283 |
Sentence |
denotes |
Average # new cases, 2-week lag F stat 8.46 5.66 10.25 |
| T202 |
8284-8312 |
Sentence |
denotes |
p value 0.0000 0.0000 0.0000 |
| T203 |
8313-8374 |
Sentence |
denotes |
Average # new cases, previous 14 days F stat 18.37 7.72 21.69 |
| T204 |
8375-8403 |
Sentence |
denotes |
p value 0.0000 0.0000 0.0000 |
| T205 |
8404-8443 |
Sentence |
denotes |
Other cities, inverse distance weighted |
| T206 |
8444-8500 |
Sentence |
denotes |
Average # new cases, 1-week lag F stat 19.10 36.29 17.58 |
| T207 |
8501-8529 |
Sentence |
denotes |
p value 0.0000 0.0000 0.0000 |
| T208 |
8530-8586 |
Sentence |
denotes |
Average # new cases, 2-week lag F stat 36.32 19.94 37.31 |
| T209 |
8587-8615 |
Sentence |
denotes |
p value 0.0000 0.0000 0.0000 |
| T210 |
8616-8678 |
Sentence |
denotes |
Average # new cases, previous 14 days F stat 47.08 33.45 46.22 |
| T211 |
8679-8707 |
Sentence |
denotes |
p value 0.0000 0.0000 0.0000 |
| T212 |
8708-8868 |
Sentence |
denotes |
This table reports the F-tests on the joint significance of the coefficients on the instrumental variables (IV) that are excluded from the estimation equations. |
| T213 |
8869-9152 |
Sentence |
denotes |
Our IV include weekly averages of daily maximum temperature, precipitation, wind speed, and the interaction between precipitation and wind speed, during the preceding third and fourth weeks, and the averages of these variables in other cities weighted by the inverse of log distance. |
| T214 |
9153-9326 |
Sentence |
denotes |
For each F statistic, the variable in the corresponding row is the dependent variable, and the time window in the corresponding column indicates the time span of the sample. |
| T215 |
9327-9608 |
Sentence |
denotes |
Each regression also includes 1- and 2-week lags of these weather variables, weekly averages of new infections in the preceding first and second weeks in Wuhan which are interacted with the inverse log distance or the population flow, and city, date and city by week fixed effects. |
| T216 |
9609-9712 |
Sentence |
denotes |
Coefficients on the instrumental variables for the full sample are reported in Table 15 in the appendix |
| T217 |
9713-9826 |
Sentence |
denotes |
We also need additional weather variables to instrument the adoption of public health measures at the city level. |
| T218 |
9827-10050 |
Sentence |
denotes |
Since there is no theoretical guidance from the existing literature, we implement the Cluster-Lasso method of Belloni et al. (2016) and Ahrens et al. (2019) to select weather characteristics that have good predictive power. |
| T219 |
10051-10087 |
Sentence |
denotes |
Details are displayed in Appendix A. |
