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In the Year 2030, How Will Applied Microeconomists Study the Consequences of the 2020 COVID Shock?

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An active research field in climate economics uses natural disasters as “natural experiments” to learn about the economic effects of such shocks.  Amine Ouazad and I study how major hurricanes impact bank lending and securitization patterns in this study.   Gary Lin, Rhiannon Jerch and I study local public finance dynamics in the aftermath of disasters in this study.    In both papers, our estimation strategy relies on the fact that we have a “treatment group” and a “control group”.   Intuitively, there are places that are roughly similar that have and have not been recently hit with a shock.  This allows us to use standard statistical methods to try to attribute the impact of the shock on those places that did get hit.  An assumption in such work is that “general equilibrium effects” can be ignored so that the set of places not hit by the shock are not affected by the responses in the places that were hit by shock. Intuitively, if all of the people in the places hit with a hurricane move to the areas not hit by the hurricane, then this is an enormous general equilibrium effect!   The U.S is a very big nation and even major storms do not trigger a mass migration.

The 2020 COVID shock is not a localized natural disaster.  All of have been hit and hit on multiple dimensions.  Think of yourself in the year 2030.  How will the 2020 shock’s effects persist?   I will turn 65 in 2030 so the 2020 shock (assuming I am still alive) will have little impact on my earnings prospects but my taxes are likely to be higher at that point.   
My son will turn 30 in 2030.  How will his life be affected by the shock of 2020?  How much less human capital will he have because of his schooling disruption?  How will the labor market be affected in the medium term because of the restructuring of firms that may occur? How will the new pattern of COVID induced transfers and taxes affect his career investments?  
How will structural labor economists model how the COVID shock influences human capital decisions, savings decisions and work decisions?  
To be specific, suppose that the marginal tax rate on high income earners rises over the next few years.  Will researchers who seek to study the labor supply implications of this incentive change pool data from 2010 through 2030 to study how this policy change affects labor supply?  What is the right control group here?   If a researcher has access to individual level IRS income data, she can include a person fixed effect in an earnings regression and study how the average person’s earnings varies with changes in the taxes (that rose in the post COVID economy) but that is assuming that the error term is uncorrelated with the change in taxes and that doesn’t sound kosher.  The dynamic error term in such a regression will feature many key contemporaneous time trends about the local macro economy.
COVID 2020 both changes us as people (in terms of health capital and maybe even aspirations and imagination), changes our portfolio’s worth and changes the tax rules we face going forward.  Given that economists are greatly interested in persistence, how will we model the persistence of the COVID 2020 shock when we don’t have a control group?  
We can follow Doug Almond’s 1918 flu methodology and estimate dummy variables such as “were you at a critical age in 2020″ and make relative comparisons for people of different ages in the year 2030 about their well being.  For example, if you have a 5 year old child in 2020 and this kid has spent more time at home with her WFH parents — does this kid at age 15 in 2030 now achieve more relative to siblings who were 10 in 2020 and lost schooling time?   


Source: http://greeneconomics.blogspot.com/2020/12/in-year-2030-how-will-applied.html



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