Measuring the Impact of the COVID-19 Pandemic on Employment Using Time Series Generated Synthetic Controls

This paper measures the change in US unemployment and participation rates by gender due to the onslaught of the COVID-19 pandemic. It uses a triple difference estimation, DiDiD, and time series generated synthetic controls to compare these rates. The results suggest, thought not strongly, that the effects of the pandemic on male and female unemployment and participation rates do not differ statistically. This paper encourages the use of time series methods to complement the traditional impact evaluation approaches, which to date have leaned mostly on cross-section and panel observations.