In 2011 depositors of two banks in Latvia, Swedbank and SEB rushed to empty their bank accounts after a rumour spread on Twitter claiming that the banks were experiencing financial trouble. The banks quickly addressed the rumours as false and police investigations were launched to track down the source of the claims.

The episode showed how quickly bank runs can happen, even to entirely solvent institutions, and how, if enough people withdraw their cash, a bank run based only on rumour can become self-fulfilling. Such runs can turn into a systemic customer panic and affect other banks in the same lines of business, essentially made guilty by association. Similar panic in other industries is not uncommon. Even today Chinese consumers prefer to buy their baby milk formula overseas after the melamine scandal in 2008, even though not every milk producer was found to be tampering with their products.

In our recent paper, Ripples of Fear: The Diffusion of a Bank Panic, Jay Kim, Daphne Teh and I examined the largest customer-driven bank panic in U.S. history to understand why customer fears grow into serious bank runs. In our bank-specific sample, we found that customers do not actually lose trust in all banks. Instead, they target individual banks based on their assessment of which banks are similar to those that have already experienced a run. It only takes a run on a few banks to spark systemic breakdown, which has disproportionate economic knock-on effects. The 1893 bank panic triggered a devastating economic crisis that saw real earnings decline by 18 percent from 1892 to 1894. Despite the efforts made to control the panic and limit the damage for individual banks, 503 banks were suspended. Two thirds of them failed. But they weren’t all insolvent.

It was also surprising that paid-in capital made banks more vulnerable to bank runs. This perversely made them more prominent targets of bank runs despite being more financially stable.

Why a bank run turns into panic

In our study, we found three factors that influenced the likelihood of panic spreading.

First, bank runs had a higher likelihood of occurring in communities with similar compositions of race, national origin, religion or wealth, suggesting a role of prejudice even in such important economic decisions as withdrawing money. Essentially, people were more ready to believe members of their community who found reason to panic than they were members of different communities. Thus the more diverse the population the less vulnerable it is to a bank run, as weaker social ties reduce the likelihood of members agreeing on whether something, in this case a bank, was in crisis or not.

Secondly, we noted how customers drew associations between banks of a certain type, increasing the likelihood of panic at banks of a similar form. Customers tend to rely on heuristics rather than deeper reasoning when assessing a bank’s vulnerability. This was seen in the 1985 Ohio savings and loan crisis, which spread to savings and loans in other states but not to other forms of financial institutions in Ohio. Similarly, in the 1873 bank panic in New York City, the loss of depositor confidence was confined to savings banks, with nearly all savings banks experiencing a run, while only one national bank did.

Thirdly, customers also distinguished between banks that were structurally similar, in this case whether they had a shared position in the network of correspondent banks. The correspondent banking system did not directly transfer vulnerabilities between the banks during the 1893 panic, but customers viewed the links between their banks and the ones that experienced a run as a threat.

It is also interesting to note that customers made these decisions despite the efforts of the banks to avoid being stigmatised by the news of runs on other banks. An article in the Aspen Daily on July 21st, 1893 read, “there is no reason why Aspen people should get excited over the situation here. All of the Aspen banks are backed by conservative businessmen whose business careers have not been marked by wild speculations or daring ventures.” Despite this quote, typical of newspapers at the time, the panic continued.

Protecting your firm

Bank panics are an ideal setting to observe customer reactions to perceived threats because they are consequential but with weak economic rationale for each run. However, these findings have broad applicability to other organisations. Crucially, customer panics remove organisations from the narrative about their financial health. We find that customers make distinctions based on easily observable, even if superficial characteristics, which can disproportionately and adversely affect the firm, in this case, irrespective of a bank’s financial health.

Organisations can protect themselves against falling victim to a run of customer panic by differentiating themselves from their competitors as much as possible. It is common for businesses to create connections with their peers to gain acceptance as a legitimate player. But, as our research shows, if one organisation in the group breaks its customer’s trust, suspicion can fall on all. Apple’s recent stand-off with the FBI over creating backdoor access to customer devices is a good example of differentiating from the pack to maintain customers’ trust.

From a community perspective, smaller organisations, for example regional savings banks, should try to diversify their customer base. If banks approach niche markets, especially along community lines, they’re likely to build up a shared perception of their organisation among customers. With a greater diversity of customers, and therefore a greater diversity of opinion on the organisation,less customers are likely to follow if a small group panics.

For safety, firms should keep some distance from each other, and stay close to their customers. Their best bet is to have diverse customers, and to be seen as unique.

Henrich R. Greve is a Professor of Entrepreneurship at INSEAD and a co-author of Network Advantage: How to Unlock Value from Your Alliances and Partnerships

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