We also investigate the mediating impacts of some socioeconomic and environmental characteristics on the transmission rates (3). To ease the comparison between different moderators, we consider the mediating impacts on the influence of the average number of new cases in the past 2 weeks. Regarding own-city transmissions, we examine the mediating effects of population density, GDP per capita, number of doctors, and average temperature, wind speed, precipitation, and a dummy variable of adverse weather conditions. Regarding between-city transmissions, we consider the mediating effects of distance, difference in population density, and difference in GDP per capita since cities that are similar in density or economic development level may be more closely linked. We also include a measure of population flows from Wuhan. Table 6 reports the estimation results of the IV regressions. To ease the comparison across various moderators, for the mediating variables of within-city transmissions that are significant at 10%, we compute the changes in the variables so that the effect of new confirmed infections in the past 14 days on current new confirmed cases is reduced by 1 (columns (2) and (4)).
Table 6 Social and economic factors mediating the transmission of COVID-19
(1) (2) (3) (4)
Jan 19–Feb 1 Feb 2–Feb 29
IV Coeff. IV Coeff.
Average # of new cases, previous 14 days
Own city − 0.251 0.672***
(0.977) (0.219)
× population density 0.000164 − 0.000202** + 495 per km2
(0.000171) (8.91e-05)
× per capita GDP 0.150*** − 66, 667 RMB 0.0102
(0.0422) (0.0196)
× # of doctors − 0.108* + 92, 593 0.0179
(0.0622) (0.0236)
× temperature 0.0849* − 11.78∘C − 0.00945
(0.0438) (0.0126)
× wind speed − 0.109 0.128
(0.131) (0.114)
× precipitation 0.965* − 1.04 mm 0.433* − 2.31 mm
(0.555) (0.229)
× adverse weather 0.0846 − 0.614*** + 163%
(0.801) (0.208)
Other cities 0.0356 − 0.00429
wt. = inv. distance (0.0375) (0.00343)
Other cities 0.00222 0.000192
wt. = inv. density ratio (0.00147) (0.000891)
Other cities 0.00232 0.00107
wt. = inv. per capita GDP ratio (0.00497) (0.00165)
Wuhan − 0.165 − 0.00377
wt. = inv. distance (0.150) (0.00981)
Wuhan − 0.00336 − 0.000849
wt. = inv. density ratio (0.00435) (0.00111)
Wuhan − 0.440 − 0.0696
wt. = inv. per capita GDP ratio (0.318) (0.0699)
Wuhan 0.00729*** 0.0125***
wt. = population flow (0.00202) (0.00187)
Observations 4032 8064
Number of cities 288 288
Weather controls Yes Yes
City FE Yes Yes
Date FE Yes Yes
The dependent variable is the number of daily new confirmed cases. The sample excludes cities in Hubei province. Columns (2) and (4) report the changes in the mediating variables that are needed to reduce the impact of new confirmed cases in the preceding 2 weeks by 1, using estimates with significance levels of at least 0.1 in columns (1) and (3), respectively. The endogenous variables include the average numbers of new cases in the own city and nearby cities in the preceding 14 days and their interactions with the mediating variables. Weekly averages of daily maximum temperature, precipitation, wind speed, the interaction between precipitation and wind speed, and the inverse log distance weighted sum of these variables in neighboring cities, during the preceding third and fourth weeks, are used as instrumental variables in the IV regressions. Additional instrumental variables are constructed by interacting them with the mediating variables. Weather controls include these variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces
*** p < 0.01, ** p < 0.05, * p < 0.1