TESS VIGELAND: States across the country are beginning to loosen lockdown restrictions. That spells some relief for the economy, at least for those businesses that can start to reopen. But the big question for anyone venturing out is, which ones are OK to visit, and which ones should be avoided?
EDUARDO PORTER: New research can provide us with a pretty good idea of which are the safest businesses and which are more risky.
TESS VIGELAND: This is Pandemic Economics, a podcast about the global impact of COVID-19 from Stitcher and the Becker Friedman Institute for Economics. I’m Tess Vigeland.
EDUARDO PORTER: And I’m Eduardo Porter. We’ve been invited to have this series of conversations with University of Chicago economists.
TESS VIGELAND: In this episode, I speak with Kate Baker and Oeindrila Dube. They used cell phone data to look at which businesses could be riskier to visit based on how crowded they get–
EDUARDO PORTER: And how long people stay there.
TESS VIGELAND: So as you note in your op-ed in The New York Times, the issue of reopening the country to economic activity has been presented as this either/or proposition– an ugly tradeoff, as you say. So you keep the economy closed, continue to see the wreckage pile up from that, or you open it and risk more illness and death. But Kate, you argue that it’s not that binary.
KATE BAICKER: That’s right. Policymakers have a lot of different choices about what parts of the economy to reopen, how to weigh the costs and benefits. And so many public policy choices entail that kind of tradeoff. We really wanted to give policymakers more tools to work with so that it wasn’t such a stark either lives or livelihoods.
TESS VIGELAND: So the idea here is that you can take data and show where there is more risk and where there is less risk based on how crowded a business can get. And you used cell phone data for this, which I find just fascinating. Oeindrila, can you just walk us through what you were looking at and how you got the data in the first place?
OEINDRILA DUBE: Absolutely. So we have data that tells us pings from cell phones, which has been put together by two companies, SafeGraph and Veraset. And what these pings allow us to do is figure out how crowded different establishments get. So for example, we can see how many people turn up to an establishment, how long they stay, what time of day they turn up. And we can put all of this together to get a sense of what are the chances that two individuals will end up in close proximity with one another in a given establishment, what we call proximity risk.
TESS VIGELAND: Now, you have this cool animated chart in The New York Times. And on the vertical axis is the average time people spend in a certain place– say Chuck E. Cheese, CrossFit, Walmart, Petco. And on the horizontal axis are weekly visits to those places per square foot. And you can move your cursor around to all these bubbles and see how much time people are spending in these various places. And you found what you called super spreaders. Kate, what does that mean, and what kind of businesses are in that category?
KATE BAICKER: Some businesses, people are in and out in a hurry. They’re not likely to see somebody else. They don’t browse. They’re coming from a very local area. Other businesses, people are coming from all over the place. They stay for a long time looking at different things, touching different things, seeing lots of other customers during that time period. That’s much riskier for disease transmission.
So think about a sit-down restaurant where people stay for a long, leisurely dinner versus a fast food restaurant where people might zip in and out. You might at first think that the fast food restaurant was risky because of how many different people come in and out during the day. But if the establishment is big enough and people are coming in and out for short enough periods of time, they’re less likely to see each other and maybe less likely to spread disease. So you have to think about all of those different factors together.
OEINDRILA DUBE: And if I can jump in, there were some surprises along the way– for example, bookstores. You might think of bookstores as kind of calm places where people are sitting and reading books, but that’s exactly the point. People are sitting and reading books.
TESS VIGELAND: They’re hanging out.
OEINDRILA DUBE: Exactly. They’re hanging out. They’re staying for long periods of time. And so these kinds of establishments that invite you to peruse and hang around, like bookstores and used clothing stores, actually end up ranking quite high in terms of the proximity risk as well.
TESS VIGELAND: What are some companies that rank low?
OEINDRILA DUBE: Yeah, so I can start by telling you the sectors that rank low are lawn and garden stores, because it turns out no one really wants to peruse fertilizer for a long period of time. So they’re not there for very long, and they tend to have a pretty large footprint. So they’re kind of at the bottom of the list.
Auto repair shops are also pretty low on the list, which is great, because I need to get my car fixed. So that is an establishment that, again, people aren’t there for extended periods of time. They’re not terribly crowded either. So that’s pretty good. And then if you go one step up, dry cleaners are not too bad either.
KATE BAICKER: Yeah. One thing Oeindrila points out is that it matters how you interact in the store, in the enterprise. Are you touching a lot of things that other people are touching too? Do employees and customers touch each other? So our main data was cell phone data, but we also got data on how much physical contact there is between customers and employees and the goods that people are browsing. That can really add to the risk.
TESS VIGELAND: Yeah, because cell phones certainly can’t tell you that, right? I mean, they’re not monitoring what surfaces you’re touching.
KATE BAICKER: Not that we know of yet.
TESS VIGELAND: Right, not yet. Yeah, who knows? But you also can’t see, you know, who’s inside a store versus outside. So how do you overcome that limitation of the data?
OEINDRILA DUBE: Yeah, so cell phones definitely can’t tell us everything. So in order to get at the indoor-outdoor-ness, and also to get at the extent to which people are touching objects or touching each other in the store, we actually administered a survey. And we asked people to rank for different establishments, on a scale of 1 to 10, hey, to what degree is this activity indoors or outdoors, or to what degree do you end up touching surfaces when you’re in an establishment like this? And from that, we could glean some metrics of the degree to which people find themselves engaged in physical touch and also the extent to which they find themselves outdoors when they go to certain stores.
Highlighting very much what Kate was saying, florists and barbershops rank very similarly in terms of the crowding risk. If you look at the cell phone data, they’re about in the same place. But of course, if you look at the physical touch index that we gleaned from the survey data, obviously, barbershops are much higher on that metric.
TESS VIGELAND: Kate, was there anything that surprised you out of this data, particularly as far as where people spend time versus where they don’t? Take us through some of the comparisons that maybe you looked at and went, well, I wouldn’t have thought that.
KATE BAICKER: So, one thing that I hadn’t thought about was where people come from, that a mall might draw people from a much wider geographic area and lead to more mixing of people and germs, whereas a grocery store might actually draw people from a much smaller area, and they might have been likely to run into each other somewhere else as well. So maybe there is less of a spread risk there.
It was also interesting to see how much variation there was in the footprint of different fast food restaurants or bars, that some are much more spread out. So even though there are a lot of people there, they’re just not as jammed together, and they’re less likely to transmit germs because of that, whereas some fast food establishments have a really small footprint, and that means people are much more likely to brush shoulders and share air than they would be at a bigger footprint place.
TESS VIGELAND: Yeah. So that really surprises me that there’s so much variation within industry. Because I would think of a Wendy’s and McDonald’s and In-N-Out in California as pretty much the same. But what you’re saying is they’re very different, and that then has an effect on whether and how quickly they can open up.
OEINDRILA DUBE: Yeah, absolutely. We were also really surprised to see the degree of variation even within categories of cuisine. Now, Tess, let me play a guessing game with you.
TESS VIGELAND: OK.
OEINDRILA DUBE: So McDonald’s versus Ben and Jerry’s– which one do you think would have a higher crowding risk?
TESS VIGELAND: It’s a great question, and Oeindrila is going to give us her answer in a moment. But Eduardo, I’m going to guess McDonald’s. What do you think?
EDUARDO PORTER: Gosh. It’s hard to tell, you know? Those ice cream shops can get pretty crowded on sunny days.
TESS VIGELAND: They certainly can, because it’s delicious.
EDUARDO PORTER: There’s more Pandemic Economics, so hang tight.
TESS VIGELAND: We’re talking to Kate Baicker and Oeindrila Dube about super spreader businesses. Now, these are places that might be at higher risk of spreading COVID. Before the break, Oeindrila was quizzing us. Which is riskier, McDonald’s or Ben and Jerry’s? I said McDonald’s.
OEINDRILA DUBE: That is a very reasonable guess, but it turns out it’s actually the opposite.
TESS VIGELAND: Really?
OEINDRILA DUBE: And I’m not happy about this, because I’m a lover of ice cream.
TESS VIGELAND: You’re missing your Ben & Jerry’s these days.
OEINDRILA DUBE: Absolutely. So it turns out McDonald’s has a lot more visitors than Ben & Jerry’s, which is probably why you’re guessing what you did.
TESS VIGELAND: Right.
OEINDRILA DUBE: But it turns out people stay for very short periods of time. They’re in and out of there in 10 minutes. And they actually turn up throughout the day. You get your Egg McMuffin at 8:00 AM for breakfast, and then other people are turning up for lunch, and other people are turning up for dinner, whereas with Ben & Jerry’s, as with other ice cream shops, people stay for longer, but they also turn up at very particular times of day.
TESS VIGELAND: So it’s concentrated.
OEINDRILA DUBE: Absolutely. So I once tried to convince a gelato place to serve me ice cream at 8:00 in the morning, and they refused and thought I was very uncultured. But the point is we are– we are very much cultured to eat ice cream either on a hot Sunday afternoon or after dinner. And so they turn up in a much more concentrated manner. And you put all that together, and Ben & Jerry’s is actually higher on the crowding risk than McDonald’s.
TESS VIGELAND: So Kate, all of this seems like pretty grim news for the businesses that you, and of course the data, have shown a spotlight on as potential super spreaders. That would be a tag that I would not want on my business. What does it mean for them?
KATE BAICKER: Well, as economists, we’re used to delivering bad news. But we actually think this is very good news on multiple fronts.
TESS VIGELAND: Hmm. How so?
KATE BAICKER: First, some businesses are super spreaders, but lots of businesses are not. And so that means as policymakers start to reopen the economy, you can get a lot of activity up and running at very minimal health risk. So that’s one piece of good news.
The other piece of good news is that there is an opportunity for businesses to modify how they do business. Our data come from last year to use as a benchmark for what business as usual looks like, but we all know we’re not in the world of business as usual right now. So restaurants are doing takeout or delivery. Places are doing curbside pick-up. These are great examples of ways to minimize the risk.
There are lots of other creative things that businesses can do. You can meter people into stores so there are not too many people there at the same time. You could have people come on alternate days to limit people crossing paths. There are lots of opportunities to be creative and innovative in resuming business in a way that’s not going to elevate the risk as much as business as usual might suggest.
TESS VIGELAND: So Oeindrila, how can authorities use this data as they’re figuring out how, where, when to open up their communities and the businesses within those communities?
OEINDRILA DUBE: So of course, the risk entailed in proximity and these other factors are one component of what policymakers will want to take into account. There’s other factors that have to be taken into account, like the employment an industry produces or the value of a service to customers. So they’re going to be trading off these risks against those potential benefits.
But it does provide a key ingredient for understanding what are the sectors to– that can be opened up safely currently, and what are the sectors that we might need to wait to open up given the higher risks entailed there. With eateries and drinking establishments and bars being at the top of the list, we need to be more cautious, potentially, in opening up those places. And policymakers might want to consider some of the strategies, like curbside pickup for eateries, and recognize that those are going to be important to implement in terms of the sectoral opening.
There’s also interesting geographic targeting that can emerge from the analysis that we’re doing. So you can look at a city like Chicago, and you can find out that the most dense spots are going to be in the neighborhoods where people are working and having lunch during lunchtime. So that can signal that those might be areas that we need to take extra precautions in. So both the sectoral and geographic targeting can help policymakers sequence the opening in ways that prove beneficial.
TESS VIGELAND: So Kate, you’re not only an economist, you are a person in the world.
KATE BAICKER: Turns out.
TESS VIGELAND: Turns out, right? So how do you take this information that you now have, that you know, and use it as you start to go out in the world? What will you be looking for?
KATE BAICKER: Well, it gives you a way to gauge the risk of different activities in an empirical way, in a practical way. We’re probably going to be traveling less for the long term than we were doing in the past, because we’ve all gotten a little bit better at Zoom, and we’ve all learned the extra costs of the travel. So I think it’ll change my choices in a lot of aspects of my life.
TESS VIGELAND: How about you, Oeindrila?
OEINDRILA DUBE: Well, I might not go to a happy hour at a bar anytime soon. And on the flip side, actually, bars may use that as a strategy to not have a period of time like that where everybody sort of descends upon a place. And so this is the way in which it can be very helpful to think about, what are the types of things that end up concentrating people in a given place and a given time?
And I should actually mention that of course, right now, so far we have been looking at the historical data to recall and to set a benchmark for what the world looked like pre-COVID, but our hope is that we can extend the analysis going forward so that we look at time periods that are more current. Our hope is eventually to be able to say, what did different establishments look like last week, for example, so that people can actually use more current data to make some of these decisions that Kate was talking about in terms of what store to go at what point in time. For example, I would love to look at a list like that in figuring out which gym am I going to feel ready to go to at what point in time.
Kate, if I can share the example that made you very unhappy– so Kate, as my Dean, almost fired me when I showed her this list. So we looked at the list of gyms and how crowded they get. And both Kate and I are fans of Orangetheory Fitness.
TESS VIGELAND: They’re in Chicago?
OEINDRILA DUBE: They are in Chicago. They’re in Hyde Park, and we love the place. And we love that it’s in Hyde Park. But it has a pretty small footprint. And it is wildly popular, and so it does have pretty high crowding risk. So they might, of course, be able to alter their strategy so that people are more spread out in terms of the customer base. But that’s the kind of information that actually proves very useful as a starting point for trying to figure out where to navigate to when.
KATE BAICKER: So what I’ve learned from Oeindrila from all of this is ice cream for breakfast, daytime drinking, and never go to the gym.
TESS VIGELAND: And no gym.
KATE BAICKER: Thanks.
TESS VIGELAND: Actually I kind of like that. I like your findings. I appreciate them. And you know, it sounds like that would be very helpful information for people to have. Because I feel like, especially as states start opening up, all we’re really hearing about is, well, there are new cases of COVID, right? And that does not help you make decisions about where you’re going to be able to go.
But if you have access to this kind of information that shows you, well, here might be crowded at this time, here might be less crowded, here’s an industry that maybe you want to avoid for a while, that seems very practical and something that I can use in my daily decisions of how I’m going to come out of this.
KATE BAICKER: And it breaks this binary trap that you highlighted at the beginning where the choice isn’t lives or livelihood, or the economy is open versus the economy is closed. There are all these gradations of risk where some businesses really pose very little risk and other businesses can change modes of doing business so that they’re lower risk.
There’s also a continuum of economic value. Right now, we’ve kind of broken things into essential versus nonessential. But of course, there’s a whole range of economic value for different activities. And what we would love to do is provide policymakers with data on the health risks posed by different industries that they can juxtapose against the importance of those industries for employment, for customers, for local economic conditions, so that they can be really smart about targeting to reopen first the places that are providing the most important services and enterprises for their community at the lowest health risk.
EDUARDO PORTER: So Tess, do you think maybe a better way to organize containment strategies to mitigate this epidemic is to give businesses a letter grade? Like, restaurants get A, B, C, D’s, maybe get A, B, C, D’s for their risk of COVID infection?
TESS VIGELAND: Eduardo, I kind of like that. And of course, this is your idea. You should pitch it to your mayor. And I bet Kate and Oeindrila and their co-authors would love to hear that.
EDUARDO PORTER: You know, then the consumer can go and show up at the McDonald’s or the Ben & Jerry’s and figure out what their relative risk is.
TESS VIGELAND: Exactly. And you can use your phone to do that while all of the answers are being tallied by pings off of cellphone towers.
It really is amazing, Eduardo, how much our phones have changed our lives.
EDUARDO PORTER: Yeah, Tess, it’s amazing how much information we can get from our cell phones. And next week, we are going to hear Chang-Tai Hsieh tell us how Korea used cell phone data in a much more intrusive way than we have in the United States to actually manage their epidemic. And they got some pretty amazing results.
CHANG-TAI HSIEH: The government basically uploaded all the data on a publicly available website. But then what started to happen was that individuals started to put together apps. You could plan a route, and then the app would tell you the incidents of cases along the route that you were planning so that you know what the risks are so that you don’t have to absorb all that information.
EDUARDO PORTER: Pandemic Economics is produced by the University of Chicago’s Becker Friedman Institute for Economics. Our producers are Devin Robins and Dana Bialek. Our executive producer is Ellen Horne. Production and original music by Story Mechanics. Pandemic Economics is part of the University of Chicago Podcast Network. I’m Eduardo Porter.
TESS VIGELAND: And I’m Tess Vigeland. Thanks for listening.