Sectoral digital intensity and GDP growth after a large employment shock: A simple extrapolation exercise

We introduce a state-dependent algorithm with minimal data requirements for predicting output dynamics as a function of employment across industries and locations. The method generalizes insights of Okun (1963) by leveraging measures of industry heterogeneity. We use the algorithm to examine gross domestic product (GDP) dynamics following the COVID-19 pandemic of 2020, delivering informative projections of aggregate and sectoral output. Because the pandemic curtailed the ability to perform certain tasks at work, our application examines whether greater reliance on digital technologies can mediate employment and productivity losses. We use industry-level indices of digital task intensity and ability to work from home, together with publicly available data on employment and GDP for Canada, to document that: (i) employment responses after the shock's onset are milder in digitally intensive sectors and (ii) conditional on the size of employment changes, GDP responses are less extreme in digitally intensive sectors. Our projections indicate a return to pre-crisis aggregate output within eight quarters of the initial shock with significant heterogeneity in recovery patterns across sectors.