Consumption patterns
In this paper, Chetty and al. build a new data platform that tracks economic activity at a high-frequency, granular level using anonymized and aggregated data from private companies. They combine data from credit card processors, payroll firms, and financial services firms, to construct statistics on consumer spending, employment rates, business revenues, job postings, and other key indicators. They then present fine disaggregations of the data, reporting each statistic by county and by industry and, where feasible, by initial (pre-crisis) income level and business size. They find that:
- The vast majority of the reduction in consumer spending in the U.S. came from reduced spending by high-income households;
- As a result, small business revenues in the most affluent ZIP codes in large cities fell by more than 70% between March and late April, as compared with 30% in the least affuent ZIP codes;
- In the highest-rent ZIP codes, more than 65% of workers at small businesses were laid off within two weeks after the COVID crisis began;
- Stimulus payments provided through the CARES Act increased spending among low-income households sharply, nearly restoring their spending to pre-COVID levels;
- Purchases of durable goods surged, while consumption of in-person services (e.g., restaurants) increased very little;
- As a result, very little of the increased spending flowed to businesses most affected by the COVID-19 shock, such as small businesses in affluent areas – potentially limiting the capacity of the stimulus to increase economic activity and employment in the communities where job losses were largest.
How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data
Authors: Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner, The Opportunity Insights Team
From: Harvard University, Brown University
Savings patterns
In this paper Cox and al. use anonymized bank account information on millions of JPMorgan Chase customers to explore how spending and savings over the initial months of the pandemic vary with household-specific demographic characteristics, such as pre-pandemic income and industry of employment.
[W]hile increases in liquid balances are widespread during the pandemic and driven in large part by general declines in spending, we see that households at the bottom end of the income distribution –who see the largest stimulus relative to pre-pandemic income – have the largest growth in liquid savings during this period. As a result, liquid wealth inequality falls between February and May.
Initial Impacts of the Pandemic on Consumer Behavior: Evidence from Linked Income, Spending, and Savings Data
Authors: Natalie Cox, Peter Ganong, Pascal Noel, Joseph Vavra, Arlene Wong, Diana Farrell, Fiona Greig
From: University of Chicago, Princeton University, JPMorgan Chase Institute
