Author: Guojun He; Yuhang Pan; Takanao Tanaka
Title: COVID-19, City Lockdown, and Air Pollution: Evidence from China Document date: 2020_4_1
ID: if7av1x8_92
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is . https://doi. org/10.1101 org/10. /2020 where 1['()* ,-'./-01] ",#@ are a set of dummy variables indicating the treatment status at different periods. The dummy for H = −1 is omitted in Equation (A2) so that the postlockdown effects are relative to the period immediately before the launch of the policy. The parameter of interest 4 @ estimates the effect of city lockdown H .....
Document: The copyright holder for this preprint (which was not peer-reviewed) is . https://doi. org/10.1101 org/10. /2020 where 1['()* ,-'./-01] ",#@ are a set of dummy variables indicating the treatment status at different periods. The dummy for H = −1 is omitted in Equation (A2) so that the postlockdown effects are relative to the period immediately before the launch of the policy. The parameter of interest 4 @ estimates the effect of city lockdown H weeks after the implementation. We include leads of the treatment dummy in the equation, testing whether the treatment affects the air pollution levels before the launch of the policy. Intuitively, the coefficient 4 @ measures the difference in air quality between cities under lockdown and otherwise in period . relative to the difference one year before the lockdown. We expect lockdown would improve air quality with 4 @ being negative when . ≥ 0. If the parallel trend assumption holds, 4 @ would be close to zero when . ≤ −2.
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