Harvard Economist Finds Surprising Pandemic Impacts at Both Ends of Wealth Spectrum

Harvard Economist Finds Surprising Pandemic Impacts at Both Ends of Wealth Spectrum

At the outset of the coronavirus pandemic, Harvard economist Raj Chetty and his team “dropped everything—including work on inequality in housing, higher education, and longevity—to document the pandemic’s lopsided impact.” This according to an article in Bloomberg.

Chetty’s team of 40 researchers and policy specialists normally spends its time mining data to “paint a vividly detailed picture of inequality in the U.S.” But when the pandemic struck, their shift in focus resulted in the creation of a data tracker that “gives a day-by-day, state-by-state, and even neighborhood-by-neighborhood view of the coronavirus economy.”

The tracker, which was first uploaded in May and has been updated frequently since, reportedly “relies on nonpublic, proprietary data supplied by some of America’s largest corporations to give a level of detail, in real time, that traditional economic indicators can’t match.”

The picture the tracker painted in April—a month or so into the pandemic—was not pretty for the lower-income segment of the population, and only worsened as time went on. By June, it showed that the highest-paid Americans had recovered nearly all jobs lost since the pandemic began, while the bottom half of American workers represented nearly 80% of the jobs still missing.”

Once the tracker had five weeks under its belt, other academics started “poring through the rich data sets,” the article reports, to study “everything from inflation to partisanship.” Then state and local governments began to use it to identify those industries in need of help. The article quotes Rob Dixon, director of the Missouri Department of Economic Development: “We literally use it every single day.”

Chetty’s team didn’t realize the breadth of the project when it first started what the article describes as a “hodgepodge of data” to reflect the pandemic’s effects on inequality, like food pantry access. The work quickly morphed into something more involved, leading to the development of rigorous metrics. Chetty told Bloomberg, “Normally I wouldn’t have thought of approaching big companies like Intuit or Mastercard, but the virus made them much more willing to help.” The article reports there are now 11 companies signed up to contribute data to the tracker.

The tracker revealed the following trends:

  • In March, “almost every household in America was reeling, with overall consumer spending plunging 33%.”
  • In March and April, small businesses in affluent neighborhoods saw revenue drop 70%, more than double the decline in the least affluent areas. “Saddled with high rents, many of these shops, restaurants and bars shut their doors for good.”
  • By about April 15, spending rose, “with bigger jumps in low-income neighborhoods: That’s the first round of stimulus payments landing in people’s wallets.” Once jobless benefits were implemented—that paid many more than they were earning before—spending continued to rise.
  • By late June, residents of low-income neighborhoods were spending more than before the crisis.
  • In early August, when unemployed Americans stopped getting an extra $600 a week, spending and small business revenue stalled rather than plummeted. But payroll data showed that high-income workers had regained almost all the jobs lost in March and April. According to Chetty, this “all the more heightens the need for targeted unemployment benefits.”

The article underscores the usefulness of the tracker for helping lawmakers “target stimulus with precision to the industries and sectors of the population that need it most and get instantaneous feedback on whether it’s working.” It also notes potential applications that extend beyond a pandemic situation, including how “a state hit by a natural disaster could pinpoint which communities were lagging in the recovery. A city trying to revive its downtown could get a rapid read on retail spending.”

Chetty continues to seek out new data inputs for his tracker and better ways to measure cash transactions, healthcare spending, housing costs and household finances. The more data he can collect, he says, the more “it brings to light the interconnected nature of the economy.”