Mobile Payment in China: Practice - Specialized Research AI at MIT

Mobile Payment in China: Practice
and Its Effects*

Yiping Huang
National School of Development / Institute of Digital Finance
Peking University
yhuang@nsd.pku.edu.cn

Xue Wang
National School of Development / Institute of Digital Finance
Peking University
xwang2017@nsd.pku.edu.cn

Xun Wang
National School of Development / Institute of Digital Finance
Peking University
xunwang@nsd.pku.edu.cn

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Abstract
This paper offers a comprehensive review and careful assessment of China’s mobile payment busi-
ness. With broad access, low costs, and reliable transactions, mobile payments are creating a revolu-
tion of financial inclusion, changing people’s daily lives and commercial business models. This study also
confirms that mobile payment improves risk sharing among individuals and increases entrepreneurial
opportunities. These mobile payment successes can be traced to three key factors: supply shortages
of alternative payment services, a friendly regulatory environment, and recent technological develop-
ments. A number of outstanding issues remain, however, including data ownership, data inequality, and
regulatory shortcomings.

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1. Introduction

Mobile payment services, dominated by two leading players, Alibaba’s (now owned
by Ant Financial Services) Alipay and Tencent’s WeChat Pay, have become a ﬁxture in
China’s daily lives and businesses. Both players have built eco-systems around their mo-
bile payment tools. People use Alipay or WeChat Pay to purchase goods, order coffee and

* Xun Wang is the corresponding author. The authors are grateful for the ﬁnancial support by Na-

tional Social Science Foundation of China (project no. 18ZDA091) titled “Innovation, Risk and Reg-
ulation of China’s Digital Inclusive Finance.” The authors also would like to thank the anonymous
referees, Wing Thye Woo, Ninghua Zhong, and Iikka Korhonen as well as other participants at the
AEP Meeting on 18 September 2019 at Keio University for their insightful discussions and com-
ments. The authors declare that they do not have relevant or material ﬁnancial interests related to
the research reported in this paper.

Asian Economic Papers 19:3

Technology

https://doi.org/10.1162/asep_a_00779

Mobile Payment in China: Practice and Its Effects

food delivery, pay electricity bills, book taxis, buy air tickets, make donations, transfer
money, and even invest in ﬁnancial products. Almost all commercial outlets in China, in-
cluding streetside stores, widely utilize mobile payment services’ Quick Response (QR)
codes to conduct business. Chinese tourists also use mobile payment services to purchase
souvenirs and luxury goods at international airports and major department stores around
the world. In 2018, the mobile payment service made a total of 60.5 billion transactions,
an increase of 61.2 percent from a year before, according to report by the People’s Bank of
China (PBOC).1

China was not the original inventor of mobile payment business—M-Pesa in Kenya and
PayPal in the United States are both older and well known. But Alipay and WeChat Pay
have turned mobile payment into a global phenomenon, attracting massive interest from
business practitioners, academics, and policymakers around the world. Why did it grow?
Can it be replicated in other countries? What are the major economic and ﬁnancial con-
sequences? How should it be regulated? There is a growing literature addressing these
questions and more, looking at the effects of mobile money adoption on household welfare
(Aker et al. 2016; Munyegera and Matsumoto 2016; Beck et al. 2018), saving behavior (Aker
et al. 2016; Mbiti and Weil 2013), informal insurance network and risk sharing (Jack and
Suri 2014; Klapper and Singer 2014; Riley 2018), ﬁnancial inclusion (Demirguc-Kunt et al.
2018), and monetary frameworks (Mbiti and Weil 2013).

The success of China’s mobile payment business could be attributed to three factors: a sup-
ply shortage of payment services, a friendly regulatory environment, and recent techno-
logical developments. With broad access, low cost, and reliable transactions, people’s daily
lives and commercial business models have been changed signiﬁcantly. The empirical in-
vestigation also provides convincing evidence that mobile payment not only promotes
household entrepreneurship in China, but also enhance the ability of households to smooth
risks when experiencing negative idiosyncratic income growth shocks. Nevertheless, al-
though revolutionary ﬁnancial inclusion has been created, China’s new mobile payment
business still needs to evolve further to address outstanding issues, including consumer
protection, data inequality, and regulatory arbitrage.

The rest of this paper is structured as follows. In the next section we introduce the inno-
vation and development of mobile payment in China. Section 3 reviews the literature re-
garding the development and effects of mobile money in different countries. In Section 4
we provide evidence that mobile payment system has been complementary to the tradi-
tional bank-based ﬁnancial sector by improving individual and household risk sharing and
promoting entrepreneurship in China. Section 5 concludes.

1 Retrieved from: http://www.pbc.gov.cn/goutongjiaoliu/113456/113469/3787878/index.html;

accessed on 10 January 2020.

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Mobile Payment in China: Practice and Its Effects

2. Background

In retrospect, 2005 might be identiﬁed as the ﬁrst year of China’s mobile payment indus-
try, with Alipay coming online at the very end of 2004. Since then, mobile payments have
grown at a truly astonishing pace. A number of events played important roles in this devel-
opment. First, the release of Apple’s ﬁrst iPhone in January 2007 marked the beginning of
the new era of smartphones, making it possible to use mobile payment services anywhere,
any time. Second, the success of Ant Financial’s money market fund Yu’E Bao signiﬁcantly
boosted awareness of, and enthusiasm for, the ﬁnancial technology (ﬁntech) industry, in-
cluding mobile payment. Third, the distribution of red (cash) envelopes on WeChat Pay
during the Chinese New Year holiday in 2014 became almost viral and attracted hundreds
of millions of new users. And ﬁnally, the adoption of the QR code for mobile payment
made it possible for any businesses, formal and informal, to use mobile payment service
by simply printing out the code on a piece of paper.

In recent years there has been a rapid expansion of China’s mobile payment business, in
terms of active users, number of transactions, and transaction values. The number of active
users of Alipay increased from a little over 100 million in 2013 to 900 million in 2018, and
that of WeChat Pay grew from about 350 million to 1.1 billion. The total transaction value
jumped from RMB 14.5 trillion in 2013 to RMB 277.4 trillion in 2018, recording an annual
growth rate of 80 percent (Figure 1). The number of mobile payment transactions reached
60.5 billion in 2018, rising by 61 percent from the previous year. The share of mobile pay-
ment in total non-cash payment value rose from less than 1 percent in 2013 to 7.4 percent
in 2018, and the share of mobile payment in the total number of non-cash payment trans-
actions increased from 3.3 percent in 2013 to 27.3 percent in 2018. Some mobile payment
platforms also evolved into large ecosystems, covering a wide range of businesses such as
funding, ﬁnancial management, credit reference and big data.

China has consistently ranked number one in mobile payment markets (whether in trans-
action volume or penetration rate) in the world. The proportion of adults using mobile
payment in China was as high as 76.9 percent in 2017, and even in rural areas the propor-
tion was 66.5 percent. According to Ipsos statistics, the penetration rate of mobile payment
in China was 77 percent in 2016, compared with rates in the United States, UK, Germany,
and France of 48 percent, 47 percent, 48 percent, and 38 percent, respectively. The penetra-
tion rate in Japan was only 27 percent. The leapfrogging of China’s mobile payment reﬂects
the underdeveloped social credit system in China. In advanced economies like the United
States, the primary method of payment is the credit card. Although China has the largest
credit card system in the world with nearly 7.6 billion cards, according to PBOC, 91 per-
cent of these are debit cards. The percentage of the population aged above 15 years with
a credit card was only 20.8 percent in China at 2017, signiﬁcantly lower than that in Japan

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Mobile Payment in China: Practice and Its Effects

Figure 1. Transaction value of mobile payment in China, 2013–18 (RMB trillion)

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Source: CEIC; PBOC.

(68.4 percent), the United States (65.6 percent), the United Kingdom (65.4 percent), and
South Korea (63.7 percent).

The development of mobile payment services has contributed signiﬁcantly to ﬁnancial
inclusion. Delivering responsible and sustainable ﬁnancial services, including payment
services, to small and medium-sized enterprises (SMEs) and low-income households is
a global challenge (Nanda and Kaur 2016; Demirguc-Kunt et al. 2018). Mobile payment
has overcome many difﬁculties, reaching massive numbers of SMEs and individuals. The
Mobile Payment Coverage Sub-Index of the Peking University Index of Digital Financial
Inclusion showed signiﬁcant convergence among Chinese prefectures between 2011 and
2018 (Figure 2). In 1935, economic geographer Hu Huanyong drew a line on China’s map
from Heihe in Heilongjiang to Tengchong in Yunnan, which was later known as the Hu
Huanyong Line (The line in the right panel of Figure 2).2 On the right side of this line, about
46 percent of total land area supported 96 percent of the population, and on the left side
of the line, about 54 percent of the land area supported only 4 percent of the population.
During the past decades, these numbers have changed somewhat, but the basic point of
extreme population concentration in one-half of the country’s geography remains largely
true. This was also true for the picture of mobile payment coverage in 2011 (the left panel

2 Hu Huanyong elaborated ﬁrst presented the idea of this Hu Huanyong Line in the journal article

“Distribution of China’s Population” in Journal of Geographical Sciences (in Chinese) in 1935.

Asian Economic Papers

Mobile Payment in China: Practice and Its Effects

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Mobile Payment in China: Practice and Its Effects

of Figure 2), in which we see that the mobile payment business was more developed on the
right side of Hu Huanyong Line. In the following seven years, however, mobile payment
coverage moved rapidly to western China—the gap between the regions on the right- and
left-hand side of the Hu Huanyong Line has been narrowed greatly (Figure 2).

The promotion of ﬁnancial inclusion is at least partly due to the better risk assessment of
ﬁntech companies having access to real-payments data—for example, social media activ-
ity and digital footprint. Fintech companies that offer payment services to consumers can
acquire more accurate information about their consumption behavior and income streams
to assess creditworthiness, and may therefore provide ﬁnancing to borrowers who are un-
able to take personal loans from banks because of the lack of an established credit registry.
Using real-time payments data, big data, machine learning technology, and other complex
artiﬁcial intelligence algorithms, ﬁntech credit platforms can also develop a more accurate
picture about people’s ﬁnancial lives and creditworthiness both from the extensive (ex-
clusion) and intensive (default and price) margins (Gambacorta et al. 2019; Jagtiani and
Lemieux 2019). MYbank, an Internet-based commercial bank and one of the brands under
Ant Financial Services Group, has lent RMB 2 trillion to over 15.74 million small compa-
nies. Borrowers apply with a few taps on a smartphone and receive loans within three min-
utes if approved. The default rate so far is about 1 percent.3 The size of each loan is around
RMB 10,000, reﬂecting the inclusive nature of MYbank business loans. The world’s biggest
market for digital payments is changing the way in which banks interact with smaller com-
panies that were previously underserved by state-owned banking giants.

Three key factors contributed to the successful ascendancy of mobile payment in China:
lack of supply, regulatory policy, and technological development.

First, while the initial motivation of developing Alipay was to solve the problem of a lack
of trust among counterparties, there was generally a signiﬁcant shortage of supply for pay-
ment services. Compared with ﬁnancial systems in other countries, the Chinese system
exhibits two unique features: the high proportion of banks in the ﬁnancial sector (Zhong
et al. 2019) and the high degree of ﬁnancial repression (Figure 3) (Herrala and Jia 2015). Im-
proving ﬁnancial inclusion is an especially challenging task in China. For example, in the
past the average penetration rate of credit cards was relatively low, and traditional card
payment services, such as the point of sale machine, were often slow, inefﬁcient and expen-
sive. Most SMEs and low-income individuals had to rely on cash for ﬁnancial transactions.
When mobile payment services came online, they were immediately embraced by the mar-
ket. Nowadays, withdrawals up to RMB 20,000 are free, with amounts above that charged
with a 0.1 percent cost. Over 90 percent of residents in large cities use mobile payment as

3 Retrieved from: https://www.bloomberg.com/news/articles/2019-07-28/jack-ma-s-290-billion

-loan-machine-is-changing-chinese-banking; accessed on 31 December 2019.

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Mobile Payment in China: Practice and Its Effects

Figure 3. International comparison of ﬁnancial systems, 2015

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Source: Huang and Ge (2019).

Note: The horizontal axis indicates share of banks in total ﬁnancial assets and the vertical axis represents degree of ﬁnancial repression.

their primary payment method, followed by cash and debit/credit card. For these reasons,
some experts argue that countries like the United States will not be able to replicate the
Chinese experience of mobile payment service (Klein 2019).

Second, a tolerant regulatory environment has also offered room for China’s mobile
payment system to experiment and grow. There were no strict regulatory restrictions on
mobile payment until PBOC released its “Measures for the Administration of Payment
Services for Non-Financial Institutions” report in June 2010. Since then, PBOC has issued
nearly 270 third-party payment licenses. Although it now looks that the number of licenses
is excessive, granting third-party payment licenses was an innovative policy step. In other
countries, this kind of experimental approach might not be possible.

And, ﬁnally, technological development has provided the necessary conditions for both
spread of payment coverage and improvement in service quality. According to a survey by
Nielson, the smartphone penetration rate in China was 66 percent in 2017, which was sim-
ilar to most developed countries but signiﬁcantly higher than Brazil (36 percent), Turkey
(19 percent), and India (10 percent). The number of transactions that Alipay can handle
per second increased from about 200 in 2011 to 210,000 in 2017 and the fund-loss rate was
reduced to below one in a million. The adoption of QR codes also brought about revolu-
tionary changes to the extension of mobile payment services.

Asian Economic Papers

Mobile Payment in China: Practice and Its Effects

In sum, lack of supply, tolerant regulatory attitudes, and a relatively mature ﬁntech in-
dustry have underpinned China’s domestic experience with mobile payment services.
Chinese payment technology has also started to support mobile payment businesses in
Asia. Alipay has cooperated with local partners to establish nine local e-wallets, includ-
ing Thailand (TrueMoney), the Philippines (GCash), Indonesia (DANA), India (Paytm),
Malaysia (TnGD), Pakistan (easypaisa), Bangladesh (bKash), Korea (Kakao pay), and Hong
Kong (Alipay HK). Cross-border payment of local wallets is also available between Hong
Kong and mainland China, and between Hong Kong and Japan, as are cross–border remit-
tances supported by block chain from Hong Kong to the Philippines, and from Malaysia
to Pakistan.

3. Relevant literature on mobile payments

Mobile payment in China is predominantly linked to pre-existing bank accounts but can
be more easily accessed through mobile devices, enabling mobile phone owners to trans-
fer money instantly, securely, and cheaply, leading to enormous changes in the organi-
zation of economic activity. The reduction in transaction frictions, including visible costs
and invisible attrition such as theft, makes remittances and payments easier and safer than
traditional payment methods, especially in developing economies. Suri (2017) notes that
mobile money provides a dramatic reduction in transaction costs, as well as improvements
in convenience, security, and time taken for the transaction.

The reduction in transaction frictions could facilitate trade by improving the existing trade
efﬁciencies and by enabling new transactions that would not have happened without
mobile money. For example, Plyler et al. (2010) ﬁnd that M-Pesa in Kenya has promoted
the growth rates of small-scale ﬁrms at the community-level, largely driven by the in-
creased circulation of money. Beck et al. (2018) analyze the effects of payment technology
innovation on entrepreneurship and economic development, and conclude that broader
approaches to improving access to ﬁnancial services, including payment services, are
important for alleviating ﬁnancial constraints and stimulating business performance.

Convenience and lower transaction frictions could also play an important role in informal
insurance networks (Yang and Choi 2007; Aycinena et al. 2010); speciﬁcally, by connecting
individuals to the broader economy beyond those physically proximate, and strengthening
informal insurance networks (Klapper and Singer 2014). In developing and low-income
countries, informal risk sharing through mechanisms such as inter-household transfers,
marriage, precautionary saving, and state-contingent loan repayments provide impor-
tant means by which households mitigate risk (Rosenzweig and Start 1989; Udry 1994;
Townsend 1994, 1995). Despite intense academic and policy debates over the best way to
improve household risk bearing, few studies have incorporated the effect of digital ﬁnan-
cial inclusion into the analysis. A recent exception is Jack and Suri (2014), who estimate

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Mobile Payment in China: Practice and Its Effects

the impact of using M-Pesa on risk sharing, and ﬁnd that compared with non-users, the
consumption of M-Pesa users is unaffected when facing negative shocks. In addition, such
informal savings and insurance mechanisms could lead to more efﬁcient business decisions
by relaxing the tradeoff between risk and return that households would otherwise face
(Jakiela and Ozier 2016; Di Falco and Bulte 2013).

We try to provide some preliminary evidence on the impact of mobile payment in China
by examining the household’s entrepreneurial decision and ability to smooth consump-
tion risks. Previous studies gauging the impact of mobile money have almost exclusively
focused on the adoption decision and welfare of the household, and few of them have ad-
dressed the importance of household entrepreneurial activity and risk sharing. Beck et al.
(2018) show signiﬁcant quantitative implications of mobile money for entrepreneurial
growth and macroeconomic development. Using provincial-level data, Xie et al. (2018)
provide evidence that the development of digital ﬁnance promotes the registration of new
businesses. Yin et al. (2019) conduct cross-section analysis to study the impact of mobile
payment on household entrepreneurial decisions and business performance. We extend
further by using the panel data to better control for potential selection bias. And we exam-
ine how the transition between agricultural and business families changed due to mobile
payment. Moreover, as far as we know, there is a lack of statistical evidence on the impact
of digital ﬁnancial instruments on household risk sharing.

4. Empirical analysis

It is not difﬁcult to observe the positive changes that mobile payment has brought to the
Chinese economy, particularly to ordinary households and SMEs. In this section, we pro-
vide some empirical evidence, focusing on household entrepreneurship and risk sharing
among households. We use two sets of data: the China Household Finance Survey con-
ducted in 2013, 2015, and 2017; and the Peking University Index of Digital Financial Inclu-
sion for China (PKU-DFI) for the years between 2011 and 2018 (Guo et al. 2020).

The China Household Finance Survey is a nationally comprehensive household-level sur-
vey conducted via face-to-face interviews. The questionnaire is composed of four parts:
demographics, ﬁnancial assets and liabilities, social security and insurance, and income
and expenditure. The survey tracks households every two years and adds new households
to the sample in each wave. The tracked sample allows us to construct a balanced panel us-
ing the 2011, 2013 and 2015 surveys (34,430 household-year observations) for the analysis
of household entrepreneurial activities. After controlling for the necessary non-missing
variables, 7,963 households were left in the 2013, 2015, and 2017 surveys. Because we fo-
cus only on the change of consumption and income, we then take the ﬁrst difference of the
variables between three rounds, the ﬁnal sample consists of 15,926 household-year obser-
vations (7,963 × 2) for the analysis of household risk sharing.

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Mobile Payment in China: Practice and Its Effects

PKU-DFI is developed by Peking University’s Institute of Digital Finance, in collabora-
tion with Ant Financial Services Group of Alibaba. Using the data from Ant Financial, a
large representative mobile payment platform, PKU-DFI measures digital inclusive ﬁ-
nance in China during 2011–18 at three levels (national, provincial, and municipal) and
from three dimensions (breadth of coverage, depth of usage, and level of digitization).4
The three dimensions include 33 dimensionless bottom indicators. More speciﬁcally, the
PKU-DFI is constructed from bottom to top using the bottom indicators. First, the coefﬁ-
cient of variation method is used when calculating the weights of each speciﬁc indicator.
Then, the analytic hierarchy process method is applied to calculate the weights for each
sub-dimension that are used to calculate the breadth of coverage, the depth of usage, and
the level of digitalization. Finally, the PKU-DFI Index is aggregated using these three di-
mensions whose weights are again determined by the analytic hierarchy process method.
The PKU-DFI is comparable both horizontally (in a regional dimension) and vertically (in a
time dimension).

4.1 Promoting entrepreneurship and informal business
With mobile payment, the rate of processing fees varies from 0 to 0.3 percent—the lower
transaction fees matter considerably for informal small-scale business. With the help of mo-
bile payments, there is no need to fumble for cash, write out checks, or wait for invoices,
which greatly improves settlement efﬁciency (Jack and Suri 2011). The reduction in trans-
action frictions could facilitate trade from two aspects: on the one hand, by improving the
efﬁciency on existing trade, and on the other, by enabling new transactions that would
not have happened without mobile money. These reductions in transaction costs, along
with the strengthening of informal insurance networks and increasing ﬁnancial inclusion,
could help households make more efﬁcient decisions by relaxing the trade-off between
risk and return that households would otherwise face (Di Falco and Bulte 2013; Jakiela
and Ozier 2016).

We examine the association between the adoption of mobile payment and business activity
from a micro perspective. We disaggregate business into formal and informal businesses.
Formal business includes four types: company limited by shares, limited liability company,
partnership enterprise, solely owned enterprise. Informal business includes two types:
registered self-employed business and unregistered business, such as market stalls or street
musicians. Compared with formal businesses, informal businesses are often small in scale
and without standardized management systems.

Although the mobile payment tool came out in 2004, mobile payment business exploded in
2013, after the advent of Yu’E Bao, and 2013 is always regarded as the start year of digital

4 See more in Guo et al. (2020).

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Mobile Payment in China: Practice and Its Effects

Table 1. Impact on entrepreneurial activity by pre-MP activity

Pre-MP activity

Agricultured

Informald

Formald

Informald
(2)

Dependent variables
Agricultured
(1)
−0.1275***
(−6.5412)
0.0016
(0.0882)
0.0533
(1.0125)

0.0852***
(4.4491)
0.0648***
(2.9864)
−0.2035*
(−1.7579)

Formald
(3)

Income
(4)

0.0008
(0.1277)
0.0091
(1.5550)
0.0515
(0.4777)

2998.1578***
(2.8294)
4175.3053**
(2.3724)

10100.00

(0.6050)

Note: Reported are coefﬁcients on the interaction term MPd × Post-2013d. t-statistics are in

parentheses. Each row is a regression in sub-samples that split by households’ pre-MP period
activities. Regressions include time and household ﬁxed effects, municipality × year dummies,

as well as household control variables. As the space is limited here, coefﬁcients on control

variables are available upon request from the authors. The superscript “d” indicates a dummy
variable. MP = mobile payment.
***Statistically signiﬁcant at the 1 percent level; **statistically signiﬁcant at the 5 percent
level; *statistically signiﬁcant at the 10 percent level.

ﬁnance in China. This allows us to use the following difference-in-difference estimation,
which compares households with and without mobile payment before and after the explo-
sive growth of mobile payment.

yict

= α + β

+ γ
t

+ δMPict

+ π Xict

+ η
ct

+ ε

ict

(1)

where yict is outcome variables for household i in municipality c in period t, β
hold ﬁxed effect, γ
t is a year ﬁxed effect, Xict is a matrix of household control variables, and
η
ct are a set of municipality-by-time dummies. The main variable of interest is the dummy
MPict, which is an interaction between a post-2013 dummy and a dummy that equals one
for households using mobile payment after 2013 and zero for all other households in the
sample. The coefﬁcient δ estimates the effect of using mobile payment on outcomes. We
cluster the standard errors at the household level.

i is a house-

Table 1 shows how transactions between agricultural, informal business, and formal busi-
ness households changed through mobile payment adoption. To save space, only the co-
efﬁcients on MPd×Post-2013d are reported. More speciﬁcally, each cell is the coefﬁcient on
MPd×Post-2013d from the estimation of equation (1) in the subsamples split by households’
pre-mobile payment (pre-MP) period business participation.

The ﬁrst row shows that households that engaged in agricultural production in the pre-MP
period are less likely to remain operating agricultural activities, but more likely to be in-
formal business owners after the adoption of mobile payment. Results in the second row
indicate that households who owned an informal business in the pre-MP period are more
likely to continue operating after adopting mobile payment than to transition into formal

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Mobile Payment in China: Practice and Its Effects

business owners. This suggests that mobile payment mainly tends to help agricultural fam-
ilies start self-employed informal business and existing informal business to continue their
operations, rather than transition into formal business owners.5 In developing countries,
informal ﬁrms account for up to half of economic activity, and they provide livelihood for
billions of people (Porta and Shleifer 2014), thus the positive impact of mobile payments
on informal business that we found is both economically and statistically signiﬁcant. Col-
umn (4) shows that those previously in the agricultural and informal business categories
experienced a signiﬁcant increase in income following the use of mobile payments, which
conﬁrms the ﬁnding of the positive impact on the probability of starting business. To better
understand how the support on the continuation of informal business could be associated
with an increase in income, there are some intuitive explanations. It is plausible that a more
convenient and safe payment tool improves the transaction efﬁciency and lowers the risk
of these informal business, among which most are businesses without a brick-and-mortar
storefront. In addition, the accumulated payment data enables the ﬁntech companies to
proﬁle the business owners who have no credit history, and makes providing further ﬁnan-
cial services for these business owners possible.

There are two possible explanations for the weak evidence on the previously formal busi-
ness owners. First, the payment efﬁciency gains from the lower transaction costs, shorter
settlement time, and lower possibility of losing cash are relatively subtle for those who
have owned a business with larger scale and more standardized management. Second, we
admit that there is some noise in our estimates because the fraction of households owning a
formal business is relatively small in our sample.

One of the channels through which mobile payment may affect household business ac-
tivity is the enhanced informal networks through transfers among friends and families
due to lower transaction costs. We examine whether there is any difference in the remit-
tance behavior across mobile payment users and non-users. The results in Table 2 show
that being a mobile payment user is associated with a signiﬁcantly higher probability of
both transferring funds out and receiving remittances compared with non-users. The pro-
portion of households who transferred funds out is 3.51 percent higher among the mobile
payment adopters, and the difference in the proportion of households who received remit-
tances is even greater, at 6.82 percent higher for mobile payment users. Two-way ﬂows of
remittances among friends and relatives are also more likely to be observed for households
using mobile payment. It is plausible that the increase in the proportion of households hav-
ing remittances among mobile payment users is more likely to reinforce and expand their
informal insurance network, which could relax the trade-off between risk and return that
they would otherwise face (Jack and Suri 2011).

5 More detailed analysis is conducted in Wang (2019).

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Mobile Payment in China: Practice and Its Effects

Table 2. Impact on remittance: Full sample

Dependent variables

Transfer outd
(1)

Remittance receivedd
(2)

Two-way ﬂow remittanced
(3)

MPd×Post-2013d

Adj. R2
Observations
HH.Controls
HH.FE
Municipality × year

0.0351***
(3.9651)
0.19
34,098
Yes
Yes
Yes

0.0682***
(5.5972)
0.17
34,074
Yes
Yes
Yes

0.0619***
(5.0866)
0.18
33,992
Yes
Yes
Yes

Note: Reported are coefﬁcients on the interaction term MPd×Post-2013d, and t-statistics are in parentheses. The

average proportion of households transferred out, received, and had two-way remittances in the pre-MP period are 81

percent, 48 percent, and 43 percent, respectively. Regressions include time and household ﬁxed effects, municipality
× year dummies, as well as household control variables. As the space is limited here, coefﬁcients on control variables

are available upon request from the authors. The superscript “d” indicates a dummy variable.
***Statistically signiﬁcant at the 1 percent level.

4.2 Improving household risk-sharing
Enhanced informal social networks through more convenient remittances could provide
an important means by which households and individuals share risk, especially for those
previously underserved by the formal ﬁnancial system. Having access to a transaction
account is the ﬁrst step toward broader ﬁnancial inclusion because it allows people to store
money and send and receive payments (Demirguc-Kunt et al. 2018). Rapid development
and acceptance of mobile payment could help individuals rapidly transfer purchasing
power within the ecosystems of Alipay or WeChat Pay, improving their ﬁnancial inclusion,
and hence their ability to smooth risk.

We seek to identify the effects of digital ﬁnancial inclusion on household risk-sharing. We
use a ﬁxed-effect strategy to examine the impacts of digital ﬁnance development in China
and also of traditional banking credit on risk-sharing by investigating their role in affecting
the sensitivity of a household’s idiosyncratic consumption growth to idiosyncratic income
growth in the following speciﬁcation (similar to that used by Kose et al. [2009], Jack and
Suri [2014], and Li and Liu [2018]):

(cid:9)log ˜Ci jt

= α

+ β(cid:9)log ˜Yi jt

+ γ (cid:2)

log(FI jt ) × (cid:9)log ˜Yi jt

+ ψZi jt

+ φZi jt

× (cid:9)log ˜Yi jt

+ α
t

+ ε

i jt

, (2)

(cid:9) log ˜Yi jt

− (cid:9) log ¯Ct

= (cid:9) log Yi jt

= (cid:9) log Ci jt

− (cid:9) log ¯Yt Ci jt and Yi jt repre-

where (cid:9) log ˜Ci jt
sent annual per capita consumption and annual per capita income (income) for household
i in city j at period t, respectively; ¯Ct and ¯Yt are the sample average of per capita household
consumption and income, respectively; and the subtraction of the common component of
each variable from household growth captures idiosyncratic household-speciﬁc ﬂuctuation
in that variable (Sørensen et al. 2007). α
effect, FI jt is a vector of ﬁnancial indices that measure the development of digital inclusive
ﬁnance (DFjt ) and traditional bank lending (BC jt), proxied by the ratio of bank credits to

i is the household ﬁxed effect, α

t is the time ﬁxed

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Mobile Payment in China: Practice and Its Effects

Table 3. The effect of digital ﬁnance on household risk sharing

Full sample:HH idiosyncratic consumption growth
(1)

(2)

(3)

(cid:9)log ˜Yi jt

logDFjt × (cid:9)log ˜Yi jt

logBCjt × (cid:9)log ˜Yi jt

Controls
Controls interactions
HH. FE, time FE
Observations
R2

0.849***
(0.285)
−0.147***
(0.055)
0.027
(0.017)
N
N
Y
15,926
0.30

0.678**
(0.285)
−0.114**
(0.055)
0.019
(0.017)
Y
N
Y
15,926
0.31

0.632**
(0.291)
−0.150***
(0.057)
0.019
(0.018)
Y
Y
Y
15,926
0.31

Note: Dependent variable: household idiosyncratic consumption growth. DFjt is the measure of digital

inclusive ﬁnance, and BCjt is the ratio of bank credits to GDP in city region. Controls: household

demographics, employment dummies, gender and age of the household head, the education level of

family members, smartphone ownership, the use of ﬁnancial instruments (bank card, participation

in social insurance), and city level GDP growth. Control interactions are the interactions terms of
controls with (cid:9)log ˜Yi jt . Robust standard errors reported in brackets are clustered at household level.
***Statistically signiﬁcant at the 1 percent level; **statistically signiﬁcant at the 5 percent level.

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GDP in city region j in period t; and Zi jt is a vector of controls containing both household-
and city-level variables. Household demographics, employment dummies, gender and
age of the household head, education level of family members, smartphone ownership,
and the use of ﬁnancial instruments (bank card, participation in social insurance) are at the
household level. Besides FI jt, the city-level variable GDP growth is also included to control
for local economic conditions. The interaction term captures other household factors that
might affect the ability of the household to smooth risks.

Therefore, β + γ (cid:2)log(FI jt ) captures the average level of co-movement between the id-
iosyncratic consumption growth and income growth across households; it can be used to
measure the degree of risk sharing/smoothing, as a smaller magnitude indicates a
greater degree of risk sharing across households. Table 3 presents the results of our base-
×(cid:9)log ˜Yi jt in-
line estimation. The negative coefﬁcient on the interaction term logDFjt
dicates/suggests that the improvement of digital ﬁnance helps households reduce the
sensitivity of household idiosyncratic consumption growth to income growth (i.e., delink-
ing the ﬂuctuations in household-speciﬁc consumption and income growth), which implies
that digital inclusive ﬁnance plays a signiﬁcantly positive role in household risk-sharing.
The coefﬁcient on the interaction of idiosyncratic income and digital ﬁnance is negatively
signiﬁcant and robust through column (1) to column (3). In column (3), we include house-
hold ﬁxed effects, time ﬁxed effects, covariates, and the interactions between covariates
and idiosyncratic income growth. Households are estimated to suffer a 6.32 percent re-
duction in consumption when facing a 10 percent decrease in income growth, and a 1 per-
cent increase in digital ﬁnancial inclusion index is able to help households smooth this
shock and leads only to a 4.82 percent reduction in consumption. This difference is both

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Mobile Payment in China: Practice and Its Effects

Table 4. Mechanism: Where do remittances come from?

From parents
Full
sample

Low
income

0.381**
(0.166)
−0.074**
(0.033)
0.003
(0.010)
Y
Y

0.460**
(0.186)
−0.087**
(0.036)
0.014
(0.009)
Y
Y

High
income

0.078
(0.251)
−0.017
(0.050)
−0.028
(0.018)
Y
Y

2,560
0.73

1,118
0.70

1,442
0.75

(cid:9)log ˜Yi jt

logDFjt × (cid:9)log ˜Yi jt

logBCjt × (cid:9)log ˜Yi jt

Controls
Controls interactions
HH FE, time FE

Observations
R2

From relatives & friends
Low
Full
income
sample

0.136
(0.239)
−0.027
(0.047)
0.012
(0.014)
Y
Y

2,560
0.65

0.438*
(0.260)
−0.086*
(0.051)
0.033*
(0.017)
Y
Y

1,118
0.65

High
income
−0.249
(0.505)
0.044
(0.098)
−0.014
(0.025)
Y
Y

1.442
0.66

Note: Dependent variable: whether household received transfer from parents and parents in law (columns

1–3), received transfer from friends and relatives (column 4–6). DFjt is the measure of digital inclusive

ﬁnance, and BCjt is the ratio of bank credits to GDP in city region. Controls: household demographics, em-

ployment dummies, gender and age of the household head, the education level of family members, smartphone

ownership, the use of ﬁnancial instruments (bank card, participation in social insurance), and city level GDP
growth. Control interactions are the interactions terms of controls with (cid:9)log ˜Yi jt . Robust standard errors
reported in brackets are clustered at household level.
**statistically signiﬁcant at the 5 percent level; *statistically signiﬁcant at the 10 percent level.

statistically and economically signiﬁcant, which indicates that the improvement of digital
ﬁnance services signiﬁcantly enhances the ability of the household to insure against shocks
of income growth. Households that suffer from negative income shocks exhibit a lower re-
duction in consumption growth in regions with better developed digital ﬁnance. For more
complete and detailed analysis, please refer to Wang et al. (2019).

Table 4 reports the results of the mechanism. Column (1) examines the response of trans-
fer from parents to income shocks in the full sample, then we divide the full sample into
poor households (with income below the median level) and rich households (with income
above the median level) in column (2) and (3), respectively. The interaction term of inter-
est is negative and signiﬁcant in columns (1) and (2), implying that when suffering from
negative income shocks, households in areas with better-developed digital ﬁnance receive
more remittance from parents in terms of the probability of receipt, and this effect is more
signiﬁcant for poor households. Columns (4) through (6) show similar evidence: It is more
likely for low-income households in areas with better-developed digital ﬁnance to receive
transfer from friends and relatives when facing income risks.

5. Conclusions

The mobile payment business in China is a revolution of ﬁnancial inclusion. It connects
hundreds of millions of individuals and tens of thousands of SMEs that previously had
no access to payment services other than cash. The mobile payment business is also a rev-
olution in the broader ﬁnancial industry. Building on mobile payment records and other

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digital footprints, mobile payment service providers have developed comprehensive
ecosystems, covering all sorts of services. The ﬁnancial industry itself has also started to
change with a new credit assessment system, online lending, digital insurance, robot in-
vestment advice, and more.

We believe this success can be attributed to three key factors: a signiﬁcant supply shortage
of payment services in the traditional ﬁnancial industry; a friendly regulatory environ-
ment that encourages and even supports payment innovation; and the rapid development
of internet technology, including smartphones, big data analyses, and cloud computing.
The empirical analysis in this study show that mobile payments signiﬁcantly improve risk-
sharing among individuals, which is particularly important in a society where there are
massive numbers of migrant workers. Our study also provides preliminary evidence that
the adoption of mobile payment increases one’s chance of starting a new business and rais-
ing one’s income.

Second, although mobile payment strongly supports ﬁnancial inclusion, it also creates
new problems because of digital inequality. Ono (2005) ﬁnds that pre-existing inequality
in other socioeconomic areas leads to inequality in information and communication tech-
nologies access, use, and skills. In fact, if one does not own a smartphone and have a mobile
payment account, life could become extremely inconvenient and even difﬁcult. It is now
common in some Chinese cities for shops to refuse to accept not only credit cards but also
cash. Therefore, policymakers and business practitioners need to ﬁnd ways to reduce the
negative consequences of digital inequality.

And, ﬁnally, ﬁnancial regulation needs to adapt to the new characteristics of mobile pay-
ment speciﬁcally, and ﬁntech more generally. Unlike in the United States and Europe,
China’s ﬁntech industry is dominated by a number of unicorn Internet companies. Al-
though this has been helpful for promoting ﬁnancial inclusion, it also complicates the task
of ﬁnancial regulation. One problem relates to the speed and scope of risk transmission
of ﬁnancial risks because of the new technology. Another problem is the possible/likely
abuse of market power by the unicorn ﬁrms’ duopoly. Although potential entry threats
motivate these ﬁrms to continue providing a more convenient and efﬁcient market in-
frastructure and higher quality services, consumer protection will remain a large concern,
given the highly concentrated market structure. Additionally, the general universal bank-
ing business model pursued by these Internet companies can be a problem. The current
regulatory system in China, which is industry-segregated and institution-focused, is not
suitable for managing ﬁnancial risks for this new business—as it tends to result in regu-
latory gaps and arbitrage. Regulatory authorities should improve the efﬁciency by trans-
forming the institution-based framework to functional-based regulation and adopting
more advanced supervision technology (so-called suptech). Finally, protection of ﬁnancial
consumers should be emphasized and strengthened in China by regulating conducts of
ﬁnancial institutions and improving consumers’ ﬁnancial literacy.

Asian Economic Papers

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