Skip to Content
Uncategorized

Mobile Money Helps Kenyans Weather Financial Storms

New study shows how electronic cash transfers help people cope with income problems.
January 23, 2014

Only about one-fourth of Kenyans have access to a traditional bank, and many people in the country farm for a living. Add those things together, and the result is that a large number of Kenyans are vulnerable to unpredictable income fluctuations.

But a new study co-authored by MIT economist Tavneet Suri shows that a growing form of electronic payments is helping Kenyans weather these financial problems by letting them informally borrow and lend money more easily. The electronic payments system, known as M-PESA, was introduced in 2007 and is now used by at least 70 percent of households in the country

In a new paper published in the American Economic Review, titled “Risk Sharing and Transaction Costs,” Suri and her co-author, William Jack of Georgetown University, show that income shocks force households without access to M-PESA to reduce their consumption by 7 percent more than households in the M-PESA network. That means the electronic money-transfers let people smooth out, as economists say, their spending — meaning they are less likely ever to have to cut back on paying for essential needs.

“The people who use M-PESA have a smaller drop in consumption when something bad happens,” says Suri, an associate professor of applied economics at the MIT Sloan School of Management. “They’re more likely to get money from their friends and family, and they receive from more different people.”

Informal insurance networks

As Suri and Jack emphasize, the agricultural nature of the Kenyan economy undergirds the sudden rise in M-PESA use. Droughts, storms, and other crop problems mean income can be quite irregular for millions of Kenyans; as a result, they don’t know how much money they will make, and save, from season to season or month to month. Many Kenyans also face financial crises due to health problems. In all, about 50 percent of households in the study reported serious negative income shocks in the six months preceding the survey.

“They face very high-risk environments and they don’t have the tools we have to deal with risk,” Suri says. “They also don’t have government programs like unemployment insurance or health insurance, and they don’t have private insurance either. So they end up making deals with each other.”

In Kenya — as in many developing countries — neighbors, friends, and relatives often rely on informal agreements to make loans with one another when times are hard. However, those networks can be strained by geography: People are most likely to be in contact with other people who live close to them, and use those contacts as part of their risk-sharing networks. But that proximity means that the same environmental or weather problems can diminish the wealth of an entire network.

“If I’m in the village next door and we both have a drought, then you can’t help me and I can’t help you,” Suri points out.

So use of M-PESA has flourished, up from 43 percent of households two years ago. Mobile phone usage is far more prevalent in Kenya than traditional banking is, and the system lets people transfer money by text message. Moreover, as Suri and Jack have found, the average distance over which an M-PESA operates is 150 to 200 kilometers, which means people are easily able to tap into money transfers from distant sources.

Connecting everywhere, not just the capital

Suri and Jack conducted their study over two years, evaluating 3,000 households in areas representing 92 percent of Kenya’s population. And they uncovered additional geographic patterns about the electronic money transfers: Not only is the average distance between parties significant, but many of the transfers take place entirely within rural areas. In short, money transfers are not just made from wealthier urban Kenyans to their poorer rural friends and relatives.

“Everybody assumes it’s just money going out from the capital, Nairobi, and that’s not true,” Suri says. “There are a lot of local transfers, this is not just [people in] the big city sending money.”

Other scholars say the results are interesting, and suggest follow-up questions about the larger impact, if it can be pinpointed, of mobile technologies.

“It’s intriguing to observe that this cost reduction allows families and friends to virtually fully insure themselves against negative events — from crop failure to health shocks — even though access to formal insurance is very limited,” says Francis Vella, an economist at Georgetown University who has read the paper.

However, Vella adds, “Moving forward, it will be important to ask if, as well as helping people share their resources more efficiently, mobile technology can increase the income levels of poor people, and indeed whether it can help them escape poverty. Identifying such an impact will be challenging, but it could help to validate opinions that until now have been aspirational at best.”

Suri has studied mobile money in Kenya extensively in recent years, but some of her new research will take her in different directions. Among other things, she is now studying the financing of small-scale distributed solar power in areas of Kenya without either a formal grid or established banking systems; she has also been examining housing prices in urban neighborhoods in Kenya, and the impact of new technologies on voter mobilization.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.