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A Real-Time Portrait of the COVID-19 Recession

Eakinomics: A Real-Time Portrait of the COVID-19 Recession

If you are going to read one 85-page empirical research paper on the mechanics of the current recession, it should be “How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data,” if only for the very pithy title.

But don’t read it. Read this, which is much shorter and will give you the bottom line. The key fact is that the authors – Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner, and the Opportunity Insights Team – have built a platform that collects, anonymizes, and displays real-time, private-sector data on economic activity in granular (e.g., ZIP code) geographic regions. The drawback to this approach is that there is no guarantee that it will be representative of the economy as a whole (although they’ve done their best to make sure the data tracks the aggregate history). The good news is that the data is not over a month old and can be isolated to specific areas. The key variables are weekly statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by county, industry, and income group.

What do they find? The results are fascinating and in contradiction with some conventional wisdom.

The first key finding is that the driving force for the downturn was a sharp pullback in high-income consumer spending: “We first show that high-income individuals reduced spending sharply in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors that require physical interaction.” Indeed, they estimate that by mid-April the top quartile accounted for 40 percent of the spending decline, while the bottom quartile contributed 13 percent.

The second finding is the pattern of layoffs and unemployment. “This reduction in spending greatly reduced the revenues of businesses that cater to high-income households in person, notably small businesses in affluent ZIP codes. These businesses laid off most of their low-income employees, leading to a surge in unemployment claims in affluent areas.”

The final implications are for the impact of policies when consumer spending is limited by health concerns. Government-ordered re-openings seem to have little impact on local employment, seemingly because people are still avoiding those same businesses. Similarly, stimulus payments to low-income households increased consumer spending sharply, but had modest impacts on employment. Again, presumably the spending was not at the most-affected businesses. Finally, the authors argue that “Paycheck Protection Program loans have also had little impact on employment at small businesses.” This final finding is in sharp contrast with the apparent success of the program in aggregate data.

The platform and analysis in the paper are a really valuable contribution to our understanding of the dynamics of the downturn. They reinforce my conviction that people – not officials – will dictate the pace of re-opening parts of the economy and that a central focus of policymakers must be efforts to make people feel safe to work and engage in commerce in the presence of the coronavirus.


Fact of the Day

If all DACA recipients were removed, U.S. GDP would decrease by nearly $42 billion.

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