From the earliest days of the coronavirus pandemic, local shops, restaurants, and other small businesses have struggled with how best to respond to the ever-changing crisis.
A new Berkeley Haas study found that when it came to closures, the big chains set the tone: In the first few weeks of the pandemic, local businesses not affiliated with a chain were more likely to close their doors if competing chain outlets in the same ZIP code shut theirs.
The study, based on cell-phone location data and published in the journal Management Science, sheds light on how businesses influence each other through social learning.
And while the focus was on business closures, the key lessons are applicable to some of the questions businesses are grappling with now, such as whether to impose mask or vaccine mandates or let employees work from home, said co-author Mathijs de Vaan, an assistant professor of management at Berkeley Haas.
The researchers, who included Berkeley Haas professors Sameer Srivastava and Abhishek Nagaraj along with Ph.D. student Saqib Mumtaz, used anonymized cell-phone tracking data to determine whether local and chain establishments were open or closed each day between March 1, 2020— just before local governments began issuing stay-at-home orders—and April 15, 2020.
Many of these directives were ambiguous or not enforced, leaving business owners with latitude to interpret the guidance as they see fit, the authors noted.
Business owners had to make unprecedented closure decisions not knowing how their customers and employees would react. The situation was so uncertain that going into the experiment, the team couldn’t predict whether the closure of a chain store would cause an independent business nearby to do the same or remain open for competitive reasons.
If I’m a small business owner, it’s not so straightforward what I should do, de Vaan said. If the big guy stays closed, maybe I can make more money. Conversely, maybe the big guy is better equipped to know the right response.
The researchers analyzed daily visits to 230,403 local businesses that were in the same industries and ZIP codes as chain outlets affiliated with 319 large national brands. They focused on service-oriented outfits such as retail shops, restaurants, movie theaters, and gyms, and excluded industries deemed essential, such as grocery stores and gas stations.
The team tried to control for other local variables that could cause establishments to close, such as shelter-in-place orders, local infection rates, or demographics. Interestingly, we found the decisions of these branded chains were uncorrelated with the local Covid conditions, de Vaan said.
In a typical example, the researchers looked at fitness centers in two neighboring ZIP codes in Collin County, Texas, on March 25, 2020. One ZIP code had an Orangetheory chain gym that was closed, while the other had an Anytime Fitness chain location that was open. They found that all six local gyms in the same ZIP code as the closed Orangetheory were closed, while three out of five local establishments in the same ZIP code as the Anytime Fitness were also open.
Looking at all industries and locations nationwide, they estimated that if a chain store closed one day, a competing community business in the same ZIP code was, on average, 3.5% more likely to close the next day. That may not sound like a lot, but that’s just the daily level. If you accumulate 3.5% across days and establishments and places, it adds up to be a fairly consequential effect in a town that may have hundreds of businesses, Srivastava said.
The researchers concluded that, if you don’t have clear-cut information, you are going to look at people around you, de Vaan said.
Given that local governments are unlikely to mandate vaccines, it creates an arena for social influence to pop up, de Vaan said. If a large company required vaccinations, small competitors have to decide whether following suit would cause them to gain or lose customers and employees. Based on their findings, de Vaan predicts that small businesses would be more inclined to follow suit, but cautions that they didn’t study that question.
Perhaps most importantly, the authors concluded, this paper shows that when government directives and health guidelines are ambiguous, firms will look for other information to guide their decision making. Obviously, such ambiguity may have been intentional if local governments believe that firms are well positioned to make these important decisions. But if one assumes that this is not the case, policy makers and local governments should consider the consequences of a lack of clarity and precision in their directives.
