The global spread of the COVID-19 virus is intimidating. In the very short run, the rise of the virus disrupts individual health, health care systems, economic systems and social systems. Governments, institutions and doctors have to act very rapidly, taking into account changes in confirmed cases, deaths, recoveries, capacity of infrastructure, policy decisions etc. At the same time, there is a very large amount of uncertainty: there are reporting lags, there is under-reporting of the number of cases as not everyone is tested, it’s not clear who is infected, we need to account for comorbidity, true mortality rates can only be calculated at the end of the pandemic, etc. However, there is no time to let the uncertainty resolve before taking actions that save lives; actions that in many cases only have an effect in the future, like steering a tanker blindfolded in unknown and unseen waters. It’s also not clear where we will end up after this devastating crisis. Do we revert back to business as usual, do output levels return to their previous levels or do we see systemic breaks with growth paths to new equilibria?
As explained in a previous post COVID-SIR, epidemiologists have very performant models to understand the evolution of an epidemic. Probably the best known is the so-called SIR that allows to easily model the evolution of an epidemic relying on the reproduction number R0 and the infectious period of a pathogen.
In a recent article regarding the impact on COVID-19 on our economies, I have used a more broader "economic welfare" approach than GDP alone to compute the size of the challenge we face. The insight is that fighting the COVID-19 by "all for one, one for all' containment or letting it go, will be generating a welfare loss with an impact for the total curse above 10 percent decline. This is a major hit - any way we look at and react to COVID-19.
The recent outbreak of Covid-19 has infected the world at an incredible speed. While there are many similarities across countries in terms of the characteristics of the epidemic spread, there are also large differences across regions. In this paper, we examine regional variation in the outbreak across continental France. We use information on the number of deaths and discharged patients from Covid-19 and socio-economic variables at the department level. Controlling for other factors, we corroborate existing evidence that, unfortunately, inequality kills: departments with more inequality face a higher incidence rate of the disease, expressed as the number of deaths and discharged (gravely ill) patients. Using covariance analysis combining both deaths and releases, we find no statistically differential relationship across factors that contribute to deaths or recoveries.
The COVID-19 crisis hit hard both healthcare and economic systems. The objective of this short column is to try and identify how badly the pandemic may damage our long-term productive capacity. While it is clear that it will bring about an extremely deep recession, we argue that the effects will mainly be determined by the demand-side of the economy. This calls for aggressive demand stabilization policies.
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