Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/214870
Authors: 
Qiu, Yun
Chen, Xi
Shi, Wei
Year of Publication: 
2020
Series/Report no.: 
GLO Discussion Paper 494
Abstract: 
This paper examines the role of various socioeconomic factors in mediating the local and cross-city transmissions of the novel coronavirus 2019 (COVID-19) in China. We implement a machine learning approach to select instrumental variables that strongly predict virus transmission among the rich exogenous weather characteristics. Our 2SLS estimates show that the stringent quarantine, massive lockdown and other public health measures imposed in late January significantly reduced the transmission rate of COVID-19. By early February, the virus spread had been contained. While many socioeconomic factors mediate the virus spread, a robust government response since late January played a determinant role in the containment of the virus. We also demonstrate that the actual population ow from the outbreak source poses a higher risk to the destination than other factors such as geographic proximity and similarity in economic conditions. The results have rich implications for ongoing global efforts in containment of COVID-19.
Subjects: 
2019 novel coronavirus
transmission
JEL: 
I18
I12
C23
Document Type: 
Working Paper

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