Impacts of social and economic factors on the transmission of coronavirus disease (COVID-19) in China

Yun Qiu; Xi Chen; Wei Shi

There are many potentially relevant weather characteristics, including daily average, maximum and minimum temperature, average and maximum wind speed, precipitation, air pressure, visibility, dew point, snow depth, and the presence of extreme weather. We use Lasso method to select environmental variables that have the highest predictive power for the average number of daily new confirmed cases during each of the past two weeks, with provincial and day fixed effects already included.The penalty parameters are given by the plugin method of Belloni et al. (2014) . There are four variables selected, including average maximum wind speed and average precipitation during the past third and fourth weeks.We also include average temperature during the past third and fourth weeks because temperature is known to affect the transmissions of flu virus and is conjectured to affect COVID-19 as well . We then regress the endogenous variables on the one to four week lags of the instrumental variables, city, and date fixed effects. Table 2 shows that F-tests on the coefficients of the three and four week lags of the instrumental variables all strongly reject joint insignificance, which confirms that the selected instrumental variables are not weak.