How a Cash Crunch in India Led to the Widespread Adoption of E-Pay Technology

It’s “a policy intervention of unprecedented scale,” says Nicolas Crouzet, an associate professor of finance at Kellogg. And it also offered an intriguing laboratory to study how people adopt fintech, or new financial technology.

India’s demonetization was aimed at curbing production of counterfeit bills and at reducing tax evasion by forcing people to disclose how much cash they actually owned. But because of logistical hiccups, replacement bills weren’t distributed fast enough, and the country entered a huge cash crunch that lasted about three months.

This allowed a group of Kellogg researchers to study how people approached financial transactions while cash was in short supply. Crouzet, along with Kellogg assistant professor of finance Filippo Mezzanotti and PhD student Apoorv Gupta, looked specifically at one large Indian fintech app, which offered users the ability to make e-payments. They found that the cash crisis spurred people to adopt this app more quickly. And in areas that had been particularly hard-pressed for cash during the demonetization, e-payment adoption continued to grow long after the crisis ended and new cash had found its way back into the hands of the public.

This study suggests that even a temporary policy intervention can be sufficient to motivate people to switch to e-payment permanently. However, in order to design a policy intervention and predict how consumers will respond, it is also important to understand the importance of “network effects” for the technology—that is, how the technology might become more valuable to users as more people adopt it. And while the authors aren’t advocating for a massive monetary crisis, more moderate policies could also be effective in encouraging fintech use.

To spur adoption of new fintech, “you need to induce some sort of snowball effect,” Mezzanotti says.

Easier Access

“Fintech” encompasses products that use technology to change the way financial services are provided. Think Quicken Loans, which enables people to apply for loans online, and e-payment systems such as Venmo and Apple Pay.

The idea is to “lower the access cost,” Mezzanotti says. Traditional systems such as debit or credit cards may involve more fees and take longer to set up. In India, credit cards in particular tend to be difficult to obtain, Crouzet says.

And for vendors, accepting debit- or credit-card payments requires buying a machine to process transactions. “It’s maybe a trivial cost for a store in the United States, but it may not be a trivial cost for a small vendor in India,” Mezzanotti says.

E-payments also have other advantages over cash. Customers and stores don’t run the risk of getting robbed. And from the government’s point of view, e-payments reduce people’s ability to hide income and avoid taxation.

Building A Network

However, such technologies often rely on building a large network of users. If most vendors don’t accept e-payments, customers may not bother setting up the app. And if most customers don’t use the app, vendors may not see the point either.

“Oftentimes, that’s what slows down the adoption of network technology,” Crouzet says. “The network is pretty small to begin with, so nobody really wants to join.” Mezzanotti adds, “You wouldn’t use Uber if there are no Ubers outside.”

With the Indian demonetization, the team saw an opportunity to study whether an economic shock could help overcome this roadblock.

“This is a really cool case of a natural macro experiment, which doesn’t happen very often,” Crouzet says.

A Burst of Growth

The Kellogg researchers focused on a free e-payment system in India similar to Venmo. Vendors and customers could link bank accounts to the app, and the vendor was assigned a QR code that the customer could scan. If Internet access was unavailable, payments could be made via text messages.

When the researchers analyzed data provided by the company, they saw a huge increase in usage after the demonetization. Within a week, the number of transactions nation-wide jumped 150 percent, then roughly doubled each week over the next three weeks.

And usage didn’t drop off as soon as the cash problem was fixed a few months later. While growth slowed substantially, people kept using the app “long after things were over,” Crouzet says.

Debit-card usage also rose during the demonetization, but the increase was largely limited to people who already had debit cards. The number of new debit cards issued didn’t change much, perhaps because of the fees or time involved. In contrast, “the app takes five minutes” to set up, Mezzanotti says. (The number of credit cards and credit-card transactions stayed fairly steady.)

The researchers then developed a model to better understand the specific factors that contributed to the e-payment growth. Their analyses confirmed that network effects played a key role—that is, when more users signed up, others were more likely to jump on the bandwagon, too. In a hypothetical scenario where network effects didn’t affect usage, the researchers found that the rise in the adoption rate would have been about 60 percent lower.

The Snowball Effect

The team wondered what lessons the demonetization could offer to policymakers interested in pushing people toward fintech.

Even if they are committed to demonetization, or other policies that would have a similar impact, such as subsidizing the fintech product or taxing cash, policymakers might be unsure of how heavily to intervene or for how long.

So the researchers calculated the varying effects of shocks of different sizes. For a shock that was only half as severe as the intervention in India—say, 43 percent of cash taken out of circulation rather than 86 percent—they found that the average increase in terms of the share of stores that adopt the payment option would have risen by about half of the observed rate.

And what if the cash crunch had lasted only two weeks? The average adoption rate would have risen by about one-third of the observed rate, the team estimated.

However, a three-month cash crunch is still relatively short. So the fact that it prompted usage to spread quickly suggests that policy changes need not drag on forever. If the shock is dramatic enough, “it could be sufficient to have a relatively temporary intervention,” Mezzanotti says.

Risk of Usage Inequality

But there is a catch, the team cautions.

The model predicted that geographic areas would respond differently depending on how many people were already using the app there before the demonetization. Because of network effects, having a large existing base of users nearby should increase the chances that a customer or vendor would follow suit. The prediction was borne out in the data: if a place was close to a high-usage hub, e-payment growth tended to be higher than in a district farther away.

If a policy intervention is fairly short, it may widen differences in adoption rates, Crouzet says. Imagine you’re trying to get people in California (a high-tech hub) and Iowa to adopt Apple Pay. If a discount is only offered for a couple of weeks in both states, “you’re going to get a much larger long-run effect in California,” he says.

Whether this inequality is bad depends on the situation. If policymakers want citizens to start using a certain technology to pay taxes, they might prefer uniform use across the country. For other products, widespread adoption might not matter as much.

But variation in fintech use could lead to bigger disparities later. If the e-payment company in India expands its financial services, users might get easier access to other benefits such as assistance in learning how to save. Signing up is “probably a stepping-stone into participating in traditional financial services,” Crouzet says.

Fintech companies can also learn from the study. Considering network effects is “extremely important,” Mezzanotti says. When firms offer, say, a temporary discount to build their user base, they’re on the right track. But to figure out the best way to set up and scale the program, it is important to understand the network effects.

“At some point, the technology builds the critical mass to essentially be able to grow on its own,” Mezzanotti says. “They have to kick the can down the road, and then the can goes on by itself.”