Is It Safer To Visit A Coffee Shop Or A Gym And Will That Change Behavior?

As more analysis is done about how Covid-19 spreads, researchers are increasingly able to model risk. Nail salons are riskier than self-storage facilities; restaurants more dangerous than gas stations.

But behavioral economics may be 'immune' to this data at first. In areas which have reopened, consumers have been slow to embrace new shopping opportunities. Uncertainty about the quality of information based on recognition that there is still a great deal scientists admit they don't know means dissemination of knowledge from trusted sources could be an even more important driver of behavior than ever. JL

Katharine Baicker and colleagues report in the New York Times:

The(ere is) variation in risk between different types of businesses. People spend twice as much time at electronics stores as they do at lawn and garden stores; three times as much time at a Salvation Army as they do at a Dollar General. Another reason for differences is how concentrated people are: The same number of customers spaced over the day poses less risk than if they arrive in short windows of time. The disease-spreading risk generated by the economy is concentrated in a portion of it.

As states begin to reopen, Americans are looking at any trip outside through the lens of contagion. Is it safe to go back to Starbucks? What about the gym? Nail salons are out of the question, right?

The country faces an ugly trade-off. Keep the economy closed and prolong the economic misery. Or open up the economy and risk a resurgence of Covid-19, undoing the gains earned through weeks of social isolation.

We believe there’s another option.

Some businesses, like some people, are “super-spreaders.” Through the lens of contagion, a yoga class, a busy corner store or a crowded neighborhood bar may look a lot like a wet market in China.

Cellphone data can’t tell us everything. For example, businesses in low-income neighborhoods with fewer smartphones may appear to have less foot traffic. We looked into this, and to date, we have not found any appreciable bias in the measures we are using.

The anonymized location pings also don’t give us any insight into how customers interacted or how many surfaces they touched. And it’s tricky to determine whether people were inside a building or moving around outdoors, where air can move freely, and infection risk may be lower.

To overcome some of these limitations, we asked people to rate, on a scale of 1 to 10, how often they interacted with people or touched shared surfaces at various businesses, as well as how much activity in different sectors occurs indoors.

These numbers help us flag risky industries, like beauty and nail salons, that our other metrics didn’t. These businesses should be particularly attentive to maintaining social-distancing measures.

The variation in risk between different types of businesses was surprising. People spend twice as much time at electronics stores as they do at lawn and garden stores. A display of new phones and gadgets is an invitation to mill around; you don’t linger over fertilizer. Similarly, we found that people spend nearly three times as much time searching through the racks at a Salvation Army as they do scanning the shelves at a Dollar General.

Another reason for differences is how concentrated people are: The same number of customers spaced out evenly over the day poses less risk than if they all arrive in a few short windows of time.

Even within a sector, there is tremendous variation. Consider two similar restaurants: Denny’s and the Original Pancake House. Both serve a similar number of customers every week, who stay for a similar length of time. But customers at the Original Pancake House are far more concentrated (at breakfast, of course), producing a far higher risk of customers getting crowded into the same space at the same time.

The existence of super-spreader businesses might seem like bad news. In fact, it means that most of the disease-spreading risk generated by the economy is concentrated in a small portion of it – which means that we can resume a lot of economic activity with minimal risk.

Many governors are considering contagion risk as a factor in determining which businesses to reopen first. Gov. Gavin Newsom of California, for example, has called for reopening “low risk” stores such as those selling toys, books, sporting goods and flowers. Indeed, in our data, florists are among the lowest risk. But toy stores, bookstores and sporting goods stores are in the top quartile of risk. Curbside pickup, as Governor Newsom suggested, could mitigate these risks, but that would be true for many other sectors, too.

But these data alone cannot tell us which businesses to open first, and we can’t simplify all these different metrics into a “yes” or “no” decision on any single business. Common sense and local knowledge are just as important. And we should ensure that policies based on these data do not have a disparate impact on people who are already more heavily hit by Covid-19.

Researchers have already begun using these data. But to make policy, we must work with models from epidemiology. The ultimate health consequences of any contagion-risk measure depend on health system capacity, available treatments and disease prevalence – all of which will change over time and across areas.

Second, we must account for economic factors. Reopening certain businesses will create or diminish demand for others, and post-Covid consumer behavior with a partially open economy may look quite different from before.

Finally, this data comes from standard operations, but American companies are already modifying “business as usual.” They can continue to limit the number of people in stores, modify how employees work and change how customers shop.

Our research provides a baseline to spur further ingenuity and adaptation. With the right mix of numbers and on-the-ground knowledge, we can develop policies to minimize both the spread of the virus and the economic hardship of the pandemic.