ABSTRACT
Uncertainty rises in recessions and falls in booms. But what is the causal relationship? We construct cross-country panel data on stock market levels and volatility and use natural disasters, terrorist attacks, and political shocks as instruments in regressions and VAR estimations. We find that increased volatility robustly lowers growth. We also structurally estimate a heterogeneous firms business cycle model with uncertainty and disasters and use this to analyze our empirical results. Finally, using our VAR results we estimate COVID-19 will reduce US GDP by 9% in 2020 based on the initial stock market returns and volatility response.
ABSTRACT
Assessing the economic impact of the COVID-19 pandemic is essential for policymakers, but challenging because the crisis has unfolded with extreme speed. We identify three indicators – stock market volatility, newspaper-based economic uncertainty, and subjective uncertainty in business expectation surveys – that provide real-time forward-looking uncertainty measures. We use these indicators to document and quantify the enormous increase in economic uncertainty in the past several weeks. We also illustrate how these forward-looking measures can be used to assess the macroeconomic impact of the COVID-19 crisis. Specifically, we feed COVID-induced first-moment and uncertainty shocks into an estimated model of disaster effects developed by Baker, Bloom and Terry (2020). Our illustrative exercise implies a year-on-year contraction in U.S. real GDP of nearly 11 percent as of 2020 Q4, with a 90 percent confidence interval extending to a nearly 20 percent contraction. The exercise says that about half of the forecasted output contraction reflects a negative effect of COVID-induced uncertainty.