Precipitation is not significantly associated with the incidence in the baseline estimation, and thus, we are not able to confirm any containment potential emerging from rainfall. Although relative humidity is not significant in the baseline, we find statistically significant negative effects when using the notification rate and (log) cases as dependent variables. We find similar effects-in terms of size and significance-for wind speed where an increase from the first to the third quartile of its distribution lowers (log) new cases by 0.012. In addition, these results are robust across alternative epidemiological indicators used as dependent variables, which supports the validity of our findings (Table 1 columns II–III). The specification captures the bulk of variation in observed new cases (R 2 about 0.76)-suggesting that omitted variable bias is unlikely to be an important empirical issue. This effect is sizable: an increase in the average temperature between February and March in our sample (about 5.5 ☌) would lead to a decline of about 3.3% (5.5 × 0.6%) in the number of new cases per day. This baseline estimate implies that an increase in temperature by 1 ☌ decreases (log) new cases by 0.006-that is, it reduces cases by 0.6%. The effect is sizeable and highly significant. Table 1 shows that temperature has a significantly negative effect on different case indicators. Our main estimation considers the effect of weather conditions measured at 12:00 local time 7 days prior to the case. Temperature and wind speed have large containment effects This is essential since, as we show, weather affects contagion differently throughout the day depending on human activity (i.e. In addition, we extend the literature by exploring alternative time lags between weather and virus cases-to take the delay between infection and reporting into account-and the effect of temperature at different hours of the day. Such an exceptional regional granularity allows us to control for unobserved heterogeneity across counties-such as cultural factors-and regional-time-varying factors affecting the evolution of the pandemic-such as the imposition of lockdown measures, mask requirements and other factors affecting social distancing. To quantify the effect of these weather variables, we use state-of-the-art econometric techniques that enable us to exploit comprehensive cross-county and within-county variation and achieve very high statistical precision in the empirical estimates. As climatic indicators, we use hourly weather variables 24 capturing: (1) temperature (2) relative humidity (3) wind speed and (4) total precipitation in each county at a given date. Using over 1.2 million observations and coverage of all seasons of the year, we examine the effect of weather on three alternative indicators 22, 23 which aim to capture the pandemic situation within a county on a given date: (1) (log) new cases (2) number of new cases within the last 14 days per 100,000 habitants (notification rate) (3) (log) cases. To investigate the weather-pandemic nexus, we collect a unique dataset covering 3376 counties in 114 states/regions from nine countries (Austria, Denmark, Finland, Ireland, Italy, Norway, Portugal, Sweden, and the United States) between 1st of January 2020 and 31st of December 2020, at a daily frequency. Thus, while the epidemiological channel implies lower cases during higher temperatures, the direction of the effect of weather through the social channel is not clear a priori, which may explain the conflicting results of previous empirical studies. From a behavioral perspective, weather alters mobility levels, social distancing, and location of social gatherings, which in turn affects the spread of the virus across individuals 19, 20, 21. Since higher temperatures harm the lipid layer of the virus 10, 16, 17, the viability of the SARS Coronavirus is substantially impaired at higher temperature levels 18. From an epidemiological standpoint, the survival and spread of a virus depends on the temperature of its environment. Weather can influence virus contagion in two distinct ways. Although a range of studies has provided empirical evidence for the negative relationship between temperature and contagion 4, 5, 6, 7, 8, 9, 10, several scholars come to contrasting conclusions by showing that the containment potential of weather differs substantially with respect to effect sizes, significance levels, weather indicators, regions, and time periods 11, 12, 13, 14, 15. Like other epidemic diseases, the trajectories in many countries show strong seasonal patterns with fewer cases during summer and more during winter. The effect of weather on the spread of the coronavirus is one of the most investigated research questions since the onset of the pandemic 1, 2, 3.
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