A Bayes Decision Rule to Assist Policymakers during a Pandemic.

A new decision rule based on net benefit per capita is proposed and exemplified with the aim of assisting policymakers in deciding whether to lockdown or reopen an economy-fully or partially-amidst a pandemic. Bayesian econometric models using Markov chain Monte Carlo algorithms are used to quantify this rule, which is illustrated via several sensitivity analyses. While we use COVID-19 data from the United States to demonstrate the ideas, our approach is invariant to the choice of pandemic and/or country. The actions suggested by our decision rule are consistent with the closing and reopening of the economies made by policymakers in Florida, Texas, and New York; these states were selected to exemplify the methodology since they capture the broad spectrum of COVID-19 outcomes in the U.S.

Covid-19’s adverse effects on a stock market index

We perform a panel data analysis of 14 daily stock market indices during 01/21/2020–06/30/2020 to document a stock market index’s negative responsiveness to Covid-19’s spread variations. We find that a stock market index’s elasticity estimate is −0.028 (p-value <0.01) for local cumulative confirmed cases. As a stock market index tends to move with Covid-19’s local and non-local spreads, international efforts of containment are expected to pare stock market losses.