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Home / Publications / Research / Economic Brief / 2021

Economic Brief
June 2021, No. 21-21

Why Is the U.S. Lagging in Adopting Mobile Payments?
Article by: Pengfei Han and Zhu Wang

The U.S. is considered a global leader in payment services. Yet, the U.S. appears
to lag some developing countries in adopting the latest mobile payment
innovations. We show that previous card payment leaders such as the U.S.
naturally tend to fall behind in mobile payment adoption. This can be explained
by optimal choices of card payment users in such countries because the
incremental improvement introduced by the current mobile payment technology
does not justify the costs for them to switch.
With the successful launch of credit cards in the late 1950s and debit cards in the mid1980s, the U.S. has been the world leader of the card payment revolution. However, the
U.S. appears to have fallen behind when it comes to mobile-phone-based payment
innovation (henceforth, "mobile payments").
The U.S. is often compared to two developing countries for mobile payment adoption:
Kenya and China. As shown in Figure 1, the U.S. boasts a much higher card payment
adoption rate, but it has been significantly surpassed by Kenya and China in mobile
payment adoption.

This raises concerns about the efficiency and competitiveness of the U.S. payments system.
Why did developing countries like Kenya and China lag in adopting card payments but
seemingly lead in adopting mobile payments?
Has the U.S. lost its global leadership in the payment area?
Should the U.S. government implement policies to boost mobile payment adoption?

What Is Mobile Payment?
A mobile payment refers to a money payment made for a product or service through a
mobile phone, regardless of whether the phone actually accesses the mobile network to
make the payment. Mobile payment technology can also be used to send money from
person to person.
While there are many mobile payment solutions, they fall into two basic categories:
Bypassing existing bank-related card payment systems, which we name cardsubstituting mobile payments

Complementing existing bank-related card payment systems, which we call cardcomplementing mobile payments
The card-substituting mobile payment (epitomized by Kenya's M-PESA) is often referred to
as Mobile Money in practice. It allows customers to use mobile payments without having
bank accounts. Most users of Mobile Money are concentrated in developing countries,
particularly in sub-Saharan Africa, South Asia and Southeast Asia.
In contrast, card-complementing mobile payments are popular in developed countries.
They piggyback on existing banking and card networks, with Apple Pay being a leading

The Relationship between Payment Methods and Country Income
In our study, we compile a novel dataset to uncover the general adoption patterns of card
and mobile payments across countries. The data show that the overtaking in mobile
payment adoption is a systematic pattern between developing countries and advanced
economies, beyond just Kenya, China and the U.S. Our data cover debit card and mobile
payment adoption in 94 countries.1
We first plot the adoption rates of debit card and mobile payments against log per capita
GDP in 2017. Once we run a regression line to the data, we find that debit card adoption
rate strictly increases with per capita GDP across countries, meaning that wealthier
countries tend to have higher card payment adoption rates. However, no clear relationship
is apparent between mobile payment adoption and per capita GDP.
As we delve further into the mobile payment adoption data, however, some patterns start
to emerge. First, we divide the sample into three groups:
Low-income countries, or those with per capita GDP less than $2,500
Middle-income countries, or those with per capita GDP between $2,500 and $30,000
High-income countries, or those with per capita GDP above $30,000
We then distinguish different payment technologies used in each country in the sample. We
find that most low- and middle-income countries choose card-substituting mobile
payments, while most high-income countries choose card-complementing mobile
Considering that mobile payments are a fairly recent technological innovation, it is possible
that some countries may not have fully introduced it due to information or coordination
frictions (e.g., unfamiliar with the technology or disagreeing on technical standards). Once
we remove countries that have very low adoption rates (less than 10 percent), we find that

mobile payment adoption displays a non-monotonic relationship with per capita GDP: It
increases in low-income countries, decreases in middle-income countries and increases
again in high-income countries.

Modeling Payment Method Adoption
We then construct a quantitative model to match the empirical pattern. Our model explains
the early success of advanced economies in adopting card payments and how their
advantage in card payments hinders their adoption of mobile payments.
In our model, three payment technologies — cash, card and mobile — arrive sequentially.
Newer payment technologies lower the ongoing costs of payment transactions, but there is
a significant upfront cost to adopt a new technology.
When card arrives after cash, high-income consumers who spend more on purchases find it
more attractive to adopt because they save more on the variable costs. That is, the savings
on ongoing transactions outweighs the upfront costs of adopting the new technology. This
explains the high adoption rate in rich countries.
However, when mobile arrives after card, the adoption incentives are different between
existing card users and cash users. Since the fixed cost for adopting card is already paid,
card users face a higher income threshold to adopt mobile payments than cash users. As a
result, the pre-existing composition of cash users and card users in each country leads to
the non-monotonic relationship between mobile payment adoption and per capita income
across countries.
Since both card and mobile payment adoption requires fixed costs, cash users would favor
mobile payments as a card-substituting solution, rather than paying the costs to establish
both card and mobile payments. This explains why most developing countries choose
Mobile Money, the mobile payment method bypassing card services. Card users, on the
other hand, would more likely consider mobile payments complementary, which is why
most advanced economies use card-complementing mobile solutions such as Apple Pay.
Using our model, we conduct quantitative analyses on several welfare and policy issues. We
find that lagging in mobile payment adoption does not necessarily mean that advanced
economies fall behind in overall payment efficiency,2 even though they benefit less from
mobile payment innovation than developing countries.
Moreover, in our model economy, lagging in adopting mobile payments is an optimal
choice for advanced economies, and greater technological advances in mobile payments
are needed for advanced economies to regain leading positions in the payment race.

Model Implications on Kenya, China and the U.S.

Kenya and China currently are front-runners in mobile payment adoption. In fact, their
adoption rates are much higher than their peer countries and our model prediction, which
suggests that their extraordinary performance may have idiosyncratic components beyond
the theory that we offer to explain the average cross-country pattern.
Some factors outside our model might help explain Kenya and China's adoption success.
For example, recent studies suggest that the unique urban-rural remittance pattern in
Kenya may have contributed to its exceptionally wide adoption of M-PESA.3 In China, two
giant tech firms have developed their mobile payment services — Tencent with WeChat Pay
and Alibaba with Alipay — strategically to extend their business models to cross-sell
consumer and business loan services based on payments data.4 It would be valuable for
future research to explore these additional factors.
Our model, however, does match the adoption pattern of most other countries well.
Particularly, the U.S. is in line with the cross-country average pattern explained by our
theory. Therefore, our model provides a useful framework for policy discussions in the U.S.
Our analysis shows that the previous card payment leaders (such as the U.S.) naturally tend
to fall behind in the mobile payment adoption race. Falling behind is an optimal choice for
such countries because the incremental improvement introduced by the current mobile
payment technology does not provide a sufficient incentive for most card users in those
countries to switch.
Given this finding, directly subsidizing mobile payment adoption would be socially
inefficient in those countries. Instead, policymakers may consider promoting mobile
payments in more productive ways. For example, they may encourage greater mobile
payment technology progress or reduce coordination frictions.
Pengfei Han is an assistant professor of finance at the Guanghua School of Management,
Peking University. Zhu Wang is vice president for research in financial and payments
systems in the Research Department at the Federal Reserve Bank of Richmond.


Our data sources include eMarketer and the Global Financial Inclusion (Global Findex)

Database of the World Bank.

"Payment efficiency" is measured as the fraction of the first-best welfare (i.e., in a frictionless
model without payment costs) that can be achieved by using particular payment methods.

For example, see William Jack and Tavneet Suri, "Risk Sharing and Transaction Costs: Evidence

from Kenya's Mobile Money Revolution," American Economic Review, January 2014, vol. 104, no.
1, pp. 183-223.


See Harald Hau, Yi Huang, Hongzhe Shan and Zixia Wang, "How Fintech Enters China's Credit

Market," AEA Papers and Proceedings, May 2019, vol. 109, pp. 60-64.

This article may be photocopied or reprinted in its entirety. Please credit the authors,
source, and the Federal Reserve Bank of Richmond and include the italicized statement
Views expressed in this article are those of the authors and not necessarily those of the Federal
Reserve Bank of Richmond or the Federal Reserve System.

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