The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data
We build a publicly available database that tracks economic activity at a granular level in real time using anonymized data from private companies. We report daily statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by ZIP code, industry, income group, and business size. Using these data, we study how COVID-19 aﬀected the economy by analyzing heterogeneity in its impacts. 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 in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in aﬄuent ZIP codes. These businesses laid oﬀ many of their employees, leading to widespread job losses especially among low-wage workers in aﬄuent areas. High-wage workers experienced a “V-shaped” recession that lasted a few weeks, whereas low-wage workers experienced much larger job losses that persisted for several months. Building on this diagnostic analysis, we estimate the causal eﬀects of policies aimed at mitigating the adverse impacts of COVID-19. State-ordered reopenings of economies had small impacts on spending and employment. Stimulus payments to low-income households increased consumer spending sharply, but little of this increased spending ﬂowed to businesses most aﬀected by the COVID-19 shock, dampening its impacts on employment. Paycheck Protection Program loans increased employment at small businesses by only 2%, implying a cost of $377,000 per job saved. These results suggest that traditional macroeconomic tools – stimulating aggregate demand or providing liquidity to businesses – have diminished capacity to restore employment when consumer spending is constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic hardship through social insurance. More broadly, this analysis shows how public statistics constructed from private sector data can support many research and policy analyses without compromising privacy, providing a new tool for empirical macroeconomics.
A previous draft of this paper was circulated under the title “How Did COVID-19 and Stabilization Policies Aﬀect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data.” We thank Gabriel Chodorow-Reich, Emmanuel Farhi, Jason Furman, Steven Hamilton, Erik Hurst, Xavier Jaravel, Lawrence Katz, Emmanuel Saez, Ludwig Straub, Danny Yagan, and numerous seminar participants for helpful comments. We also thank the corporate partners who provided the underlying data used in the Economic Tracker: Aﬃnity Solutions (especially Atul Chadha and Arun Rajagopal), Burning Glass (Anton Libsch and Bledi Taska), CoinOut (Jeﬀ Witten), Earnin (Arun Natesan and Ram Palaniappan), Homebase (Ray Sandza and Andrew Vogeley), Intuit (Christina Foo and Krithika Swaminathan), Kronos (David Gilbertson), Paychex (Mike Nichols and Shadi Sifain), Womply (Derek Doel and Ryan Thorpe), and Zearn (Billy McRae and Shalinee Sharma). We are very grateful to Ryan Rippel of the Gates Foundation for his support in launching this project and to Gregory Bruich for early conversations that helped spark this work. The work was funded by the Chan-Zuckerberg Initiative, Bill & Melinda Gates Foundation, Overdeck Family Foundation, and Andrew and Melora Balson. The project was approved under Harvard University IRB 20-0586. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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