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

26 Pages Posted: 28 Jul 2021

Date Written: March 2021

Abstract

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.

Keywords: COVID, employment, DiDiD, time series, synthetic controls

JEL Classification: C31, C32, J21

Suggested Citation

Montenegro, Alvaro, Measuring the Impact of the COVID-19 Pandemic on Employment Using Time Series Generated Synthetic Controls (March 2021). Available at SSRN: https://ssrn.com/abstract=3880094 or http://dx.doi.org/10.2139/ssrn.3880094

Alvaro Montenegro (Contact Author)

Universidad Javeriana ( email )

Bogota
Colombia

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
35
Abstract Views
430
PlumX Metrics