DATA SUBJECT TO MULTIPLE TREATMENT EFFECTS—DISENTANGLE THE IMPACTS OF GLOBAL PANDEMIC AND A SPECIFIC DISEASE CONTROL POLICY

Abstract

Most literature works on estimating treatment effects assume that the observed data are either under the specific “treatment” or not. However, in many cases, the observed data could be subject to multiple treatments. We propose to combine econometric methods developed for different purposes to disentangle the multiple treatment effects. We illustrate this strategy by considering the impact of global pandemic v.s. the strictest “lockdown” policy of Hubei, China implemented in January, 2020. We show that although the strictest “lockdown” policy quickly contained the spread of COVID-19, it also inflicted huge economic loss on Hubei economy. It lowered Hubei GDP by about 37% compared to the level had there been no “lockdown” under the pandemic. However, even though Hubei economy managed to recover from the “lockdown”, it could not escape the global impact of pandemic. Its economy is still about 90% of the level had there been no pandemic.