Constantine Yannelis is assistant professor of finance at Chicago Booth. This transcript is taken from an interview conducted April 2, 2020.

How has COVID-19 shaped consumer behavior?

I have a new study—with coauthors at Columbia, Northwestern, and the University of Southern Denmark—demonstrating that this crisis has had profound effects on consumer behavior. First, we saw a massive increase in consumer spending, from early until mid-March, that is consistent with stockpiling. We saw increased spending on groceries as well as air travel. Presumably, people were leaving urban centers or trying to quickly get back to their families, anticipating shelter-in-place orders.

Now, this changed drastically toward the middle and the end of March, when we saw a corresponding sharp decrease in consumer spending. This was quite severe in many categories. For example, restaurant spending collapsed sharply, as did spending on public transportation and air travel, and in a number of other categories. We find that this decrease in spending was much more severe in states and cities that issued shelter-in-place orders. A couple of other things actually increased—for example, spending on food-delivery services, consistent with this being driven by COVID-19 and the corresponding massive changes to individuals’ lifestyles.

We explored heterogeneity across groups of consumers by demographics, income, and political affiliation. Just reading the news, [you see that] there are all kinds of stories about people behaving in different ways. We observed this with younger people: there was a delayed response, consistent with them not taking things as seriously early on. This could also be due to differential risk exposure, as COVID-19 doesn’t hit young people as badly as it does old people, on average.

We also find that families with children were likelier to stockpile. We find little effect by income, with high- and low-income households behaving quite similarly in response to this crisis.

We also explored heterogeneity by predicted political affiliation—using geography and demographics to predict whether someone was likelier to be a Republican or a Democrat. For example, consider an older rich man in West Texas or a young, lower-income woman in the Bay Area. According to surveys, these people are likely to have different political affiliations. We use that to explore differences among political partisanship.

The silver lining to this is that we can learn a lot about the world quickly in a way that is going to be useful for policy makers.

According to a Pew [Research Center] poll conducted in early March, twice as many Democrats as Republicans said that they were very concerned about the COVID-19 crisis. Yet, surprisingly, we find that Republicans were stockpiling more than Democrats in the early weeks of the crisis.

We do see some other small differences in behavior. For example, Republicans were likelier to spend at restaurants or to spend on retail, which could be consistent with them having differing beliefs [from Democrats] about the severity of the crisis, or the differential risk exposure for these groups. By and large, even though we observed differences in the magnitudes, we do find Republicans and Democrats behaving similarly, at least in terms of their household-spending portfolios.

In terms of whether this is a new normal, or whether this is just transitory behavior and people are substituting spending from one time period to another, it’s too early to tell. Of course, this crisis is new, so it’s too early to tell what the persistent effects will be.

How has real-time data helped us address the crisis?

There are tremendous opportunities for researchers given the new, high-frequency data that we didn’t have in the 2008-09 financial crisis. You can explore these questions in close to real time and inform the broader academic community and policy makers if things are changing.

One of the last things I did before self-isolation was to attend a play with my wife and one of my colleagues. We saw [former Chicago mayor] Rahm Emanuel there, and he said, “A crisis is a terrible thing to waste.” As terrible as this crisis is, we’re going to learn a lot about how the world works. Pandemics have been with mankind since the beginning of recorded history—think of the Spanish flu, the bubonic plague, or Justinian’s plague in the Eastern Roman Empire. This is the first time in history that we actually have high-frequency data on how household spending changes during a pandemic.

The silver lining to this is that we can learn a lot about the world quickly in a way that is going to be useful for policy makers. Hopefully we won’t end up Monday-morning quarterbacking, as was very much the response to the last financial crisis [in 2008–09], because we just have much more available data. Researchers can study the world and how it works, and then policy makers can make informed decisions about how to respond to this new threat.

Policymaking at a time of uncertainty

Have real-time data helped us address the crisis?

How has COVID-19 shaped consumer behavior?

The challenge for businesses in the age of COVID-19

In current research I’m conducting, my coauthors and I are using data from an online account aggregator. This is a not-for-profit financial-advice service where people link their various accounts. Something that’s ubiquitous today, which was quite uncommon 12 years ago, during the last crisis, is that most households use some form of financial technology and some form of online banking. This leads to high-frequency data that hopefully can be shared with researchers in real time to see what’s happening to household spending, and similar data for businesses, too, coming from credit bureaus and other companies that provide services to these businesses.

My understanding is that these data have tremendous potential in medicine too. For example, in China, new apps track the movement of and the potential exposure to somebody infected with the virus. At least from what I’ve heard, this has been effective in controlling COVID-19 there. Of course, that raises a host of other concerns with privacy and whether people want to share this information, but this technology offers quite a bit of potential to do both good and bad in the world.

What are the challenges for businesses and policy makers?

There are a lot of similarities between the problems facing businesses and consumers. Essentially, this is a crisis of liquidity. You have a lot of businesses that three or four weeks ago were profitable. They had no problems, and they were just hit with this exogenous shock from the sky. This virus came out of nowhere, and suddenly they had to close either by government order or because consumers didn’t want to show up because they were afraid of being infected.

Hopefully, there will be treatment or immunity to this virus over some period of time, and those businesses can go back to being successful. What they need at the moment is liquidity, which policy should be focused on providing through things such as loan guarantees, which would allow businesses that will be generating revenue in the future to access credit at a time when lenders are concerned about knowing which businesses will survive and which businesses won’t.

Also, the response of lenders depends on the policy response. Obviously, lenders don’t want to extend funds to businesses that may not exist in six months, so if there’s concern that these businesses will go under and there won’t be a policy response, that will affect their willingness to lend and provide liquidity to these businesses in a time of need. This is why, if there’s one thing that policy makers should be focusing on, it’s making sure that small businesses have access to liquidity.

The recommendation for policy makers in cases where there’s a lot of uncertainty is that we should be very cautious.

What characterizes this crisis is tremendous uncertainty. There’s just a lot of information that we don’t know. For example, we’re not sure what the case fatality rate is for this virus. If you look across countries, there are very different numbers, which has to do with differences in how countries record deaths, administer tests, and so on and so forth.

Beyond not knowing the true damage that this virus will cause from a health perspective in the short run, we don’t know much about the medium and the long runs. It’s possible that treatments will be developed, or vaccines. On the other hand, perhaps the virus will mutate and become even worse than it is, and perhaps there will be long-lasting effects.

One factor that has been missing is thinking, in a formal economic framework, about how the government should react in a situation with a great deal of uncertainty. There’s been a lot of work by my colleague [University of Chicago’s and Chicago Booth’s] Lars Peter Hansen and others about how policy should be made in cases where there’s a lot of uncertainty about future damage. This is called “robust control,” and an example of the application of this is in climate change. We know that temperatures are rising, and we know that this is caused by human activity, but there’s a lot of model uncertainty. We don’t know (1) by how much temperatures will rise and (2) what the impact on humans and the economy will be.

To summarize this literature, which is formal and mathematical, one of the big findings is that in cases of uncertainly, policy makers should adopt a cautious approach and try to minimize the maximum damage that can be done. The recommendation for policy makers in cases where there’s a lot of uncertainty is that we should be very cautious. One thing that we’ve seen in the media is the opposite conclusion from what economic theory would tell us. A lot of people have been saying, “Oh, we don’t know how bad this virus is; therefore, we should do nothing or proceed as normal.” No, that’s not what theory would tell us to do if we don’t know what’s going to happen. If we have a lot of uncertainty in a situation—it’s true, we don’t know how bad this virus is—we should be more, not less cautious.

There’s certainly a risk of people overreacting and taking too-severe responses, but the basic takeaway is that in a situation where we don’t know what’s happening, where we don’t know the true extent of potential damages, that’s a situation where we want to proceed cautiously, and a basic guiding principle should be to avoid severe damage—for example, a large portion of the population dying, if it turns out that this virus has a high fatality rate or that there’s a significant potential for it to mutate and turn into a more virulent and deadly form.

An overarching question is: How should the government respond in a situation where there’s uncertainty? Essentially, how should governments model the world? That’s one question I want to explore. Another question is: Looking at the longer-term effects of how households responded and the persistent effects of this crisis, what led households to behave in different ways? Not everybody stockpiled. People responded at different times, and why did one household respond differently than another? Was this due to differences in beliefs, or was this due to differences in potential risk exposure? I also want to explore how access to liquidity can save companies and jobs.

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