Analysis of the Effect of Digital Financial Inclusion in Promoting Inclusive Growth: Mechanism and Statistical Verification

Abstract

As the main goal of economic development, inclusive growth (IG) is an important strategic measure to achieve common prosperity. Whether digital inclusive finance can make use of the advantages of scientific and technological innovation to promote IG is of great significance to promote the fairness, effectiveness, and inclusiveness of global development. Based on the panel data of 30 provinces in China from 2011 to 2021 (excluding Tibet, Hong Kong, Macao and Taiwan), this article first measures the IG index of China from three dimensions: sustainable economic growth, income distribution, and social equity. Subsequently, the article uses a series of mathematical statistical models to verify the transmission path and mechanism of the influence of digital inclusive finance on IG. The findings are as follows: (1) The level of IG in China shows a decreasing trend from east to middle to west, while the average annual growth rate of IG in the eastern region is obviously lower than that in the central and western regions; (2) digital inclusive finance has a significant promotion effect on IG, and digital inclusive finance in the central and western regions has a more obvious promotion effect on IG; (3) digital inclusive finance can achieve IG by increasing innovation activity and improving the level of human capital. Finally, based on the research conclusions, the article puts forward relevant policy suggestions, which provide reference value for formulating high-quality national development strategies and promoting high-quality economic development.

Full text

Research Article
Jingyi Yang, Xiaoyang Guo, and Xiuwu Zhang*
Analysis of the Effect of Digital Financial Inclusion
in Promoting Inclusive Growth: Mechanism and
Statistical Verification
https://doi.org/10.1515/econ-2022-0078
received November 02, 2023; accepted February 16, 2024
Abstract: As the main goal of economic development,
inclusive growth (IG) is an important strategic measure
to achieve common prosperity. Whether digital inclusive
finance can make use of the advantages of scientific and
technological innovation to promote IG is of great signifi-
cance to promote the fairness, effectiveness, and inclusive-
ness of global development. Based on the panel data of 30
provinces in China from 2011 to 2021 (excluding Tibet, Hong
Kong, Macao and Taiwan), this article first measures the IG
index of China from three dimensions: sustainable economic
growth, income distribution, and social equity. Subsequently,
the article uses a series of mathematical statistical models to
verify the transmission path and mechanism of the influence
of digital inclusive finance on IG. The findings are as follows:
(1) The level of IG in China shows a decreasing trend from
east to middle to west, while the average annual growth rate
of IG in the eastern region is obviously lower than that in the
central and western regions; (2) digital inclusive finance has
asignificant promotion effect on IG, and digital inclusive
finance in the central and western regions has a more
obvious promotion effect on IG; (3) digital inclusive finance
can achieve IG by increasing innovation activity and
improving the level of human capital. Finally, based on
the research conclusions, the article puts forward relevant
policy suggestions, which provide reference value for for-
mulating high-quality national development strategies and
promoting high-quality economic development.
Keywords: digital financial inclusion, inclusive growth,
sustainable economic growth, income distribution, fair
opportunity
1 Introduction
Governing the country regularly and benefiting the people:
In the first year of the “14th Five-Year Plan,”China won a
comprehensive victory in the fight against poverty, and the
problem of absolute poverty was solved historically. The
Sixth Plenary Session of the 19th Central Committee clearly
pointed out that it is necessary to “unswervingly follow the
road of common prosperity for all people and make sub-
stantial progress in promoting common prosperity in high-
quality development.”The 20th National Congress of the
Communist Party of China further emphasized that the
promotion of common prosperity should focus on enhan-
cing balance and accessibility. However, poverty-stricken
households and marginal households still face a high risk
of returning to poverty due to the fragility of poverty alle-
viation foundation, limited coverage of assistance policies,
and multidimensional poverty-causing factors. At the same
time, income inequality, opportunity inequality, and social
inequality between urban and rural areas, regions, and
industries will make the problem of relative poverty per-
sist for a long time (Hong et al., 2022). In addition, in the
process of rapid evolution of China’s economic and social
development, agricultural and rural development lags
behind, urban and rural factors mismatch, infrastructure
construction gap is obvious, and ecological pollution caused
by agricultural land overload has become a stumbling block
on the road to achieving the goal of Common prosperity
(Hao et al., 2023). To address this series of issues, inclusive
growth (IG) has emerged. IG implies equal opportunities
for growth, with a core emphasis on eliminating serious
environmental inequalities to reduce inequality in out-
comes, with a focus on creating productive employment
opportunities and enabling equal access to opportunities
for all (Jiang et al., 2022; Klasen, 2010; Zhou, 2022). Therefore,
IG is not only a high-quality sustainable development path
with healthy development as its core, but also an inherent
requirement for enhancing people’s sense of happiness and
gain. It is of great significance for promoting regional
Jingyi Yang, Xiaoyang Guo: School of Statistics, Institute of Quantitative
Economics, Huaqiao University, Xiamen, China

* Corresponding author: Xiuwu Zhang, School of Statistics, Institute of
Quantitative Economics, Huaqiao University, Xiamen, China,
e-mail: zxwxz717@hqu.edu.cn
Economics 2024; 18: 20220078
Open Access. © 2024 the author(s), published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License.

coordinated development and maintaining social harmony
and stability.
With the acceleration of the process of building a
digital society, digital financial inclusion (DFI) with big
data, cloud computing, and blockchain technology as the
core is regarded as one of the important keys to achieve IG
(Kun et al., 2022; Ren & Li, 2019; Ren et al., 2023; Wang et al.,
2022). In 2006, China introduced the concept of financial
inclusion, and constantly carried out Chinese transforma-
tion. The Development Plan for Promoting Financial inclu-
sion (2016–2020) issued by the State Council in 2016 has
carefully defined the connotation and principles of financial
inclusion, which has greatly promoted the prosperity and
development of financial inclusion in China. Restricted by
barriers such as scattered Human settlement, opaque infor-
mation, and imperfect infrastructure in rural areas, tradi-
tional financial inclusion is bound by the dual shackles of
physical outlets and extremely high promotion costs, so the
“long tail group”is difficult to cover, and the “last mile”of
financial supply is still a long way off. Based on the concept
of “internal hematopoiesis”and sustainable development,
DFI, which was born from the deep integration of modern
digital technology and traditional Financial inclusion, has
made up for the shortcomings of traditional Financial inclusion,
such as high operating costs, difficult to promote financial cov-
erage, and work efficiency cannot be guaranteed, giving full play
to its “multiplier effect”of resource allocation, and achieving
intelligent analysis and accurate delivery of financial services
required by the poor and weak groups. We have expanded the
funding supply channels for various industries such as agricul-
ture, improved production efficiency, improved farmers’living
conditions, continuously stimulated entrepreneurial potential,
and developed characteristic industries on the spot, achieving
truly “universal preferential”financial convenience (Liu, 2022;
Mou et al., 2021; Tang et al., 2023). This is of great significance in
alleviating the economic imbalance between urban and rural
areas and fully demonstrates the core connotation of IG.
Based on this, the article will focus on the development
and relationship between DFI and IG, explore the transmission
path and mechanism of DFI’s impact on IG from the theoretical
and empirical levels, and verify whether DFI affects IG by
increasing innovation activity and improving the level of
human capital (HC), with a view to providing a useful refer-
ence for relevant theoretical research and policy practice.
2 Literature Review
Under the dual drive of financial resources and digital
technology, how to fully release the DFI to boost IG, so
that finance can better serve the people, has become a
hot issue concerned by the government and scholars in
recent years. By organizing existing literature, research
related to the topic of this article can be roughly divided
into the following aspects.
One is the interpretation of the connotation, measure-
ment methods, and indicator system construction of IG. At
present, the academic community has not yet formed a
unified conclusion on the connotation of IG, all of which
are summarized around dimensions such as sustainable
economic growth, equal opportunities, and achievement
sharing. The only difference lies in the different focus.
Fan and Wu (2011) and Rauniyar and Kanbur (2010) define
IG as “equal opportunity growth,”with the core meaning of
reducing or even eliminating the exclusion of the poor in
terms of power and social experience, achieving the pro-
cess of everyone having equal access to opportunities and
being able to contribute to economic growth fairly, and being
able to share the fruits of economic growth reasonably. Ge
et al. (2022a,b) and Ghouse et al. (2022) believe that IG has
three meanings, namely, sharing the development achieve-
ments of the Sharing economy, giving basic development
opportunities and cultivating basic development capabilities.
That is to say, IG no longer only focuses on the poor, but
expands the investigation of inequality to all income classes,
constantly removes obstacles for people to participate in eco-
nomic development and share the national development divi-
dend, and gradually establishes a social justice system with
fairness in power, opportunity, rules and distribution as the
main contents, so as to provide equal development opportu-
nities for all. This is also similar to the implication of the EU
2020 strategy, that is, economic participants should be helped
to predict and manage changes through high-level employ-
ment, investment skills, poverty reduction, and a modern
labor market, coupled with a more complete training and
social protection system, so as to build a cohesive society
and enable the public to Sharing economy throughout the
life cycle, including the outermost regions of society. For
the scientific measurement of IG in China, most scholars
use entropy method (Grigory & Simen, 2022; Ma et al., 2022),
fuzzy comprehensive evaluation method (Abimbola & Ade-
kunle, 2021; Zhu & Jiang, 2017), dynamic factor analysis
method(Pengetal.,2018;Zheng&Su,2022),andfunction
derivation to calculate. Fu et al. (2021) and Silber and Son
(2010) calculated China’sIGlevelbasedonthegeneralized
Bonferroni curve and characterized its spatiotemporal evolu-
tion and convergence, indicating that the IG level is showing
an increasing trend year by year, and there are significant
regional differences. Li and Bian (2021) constructed an iden-
tification and decomposition method for IG through NIGIC
curves and FFL-OB decomposition techniques, thereby
2Jingyi Yang et al.

constructing a multidimensional analysis index system for IG
in China. Apart from differences in measurement methods,
there are also slight differences in the indicator systems con-
structed for IG indices in existing literature. Most research
still revolves around the two major aspects of growth
process and growth outcomes, involving three specific
dimensions: equal opportunities, income distribution, and
economic growth. A few scholars define the extension of IG
as a sustainable development approach that pursues eco-
nomic growth, social equity, achievement sharing, resource
conservation, and a good ecological environment, in order
to construct an indicator system for IG. Based on the per-
spectives of social equity and environmental sustainability,
Gu and Sun (2022) calculated China’s IG level from four
dimensions: economic development, social opportunity
equity, green production and consumption, and ecolo-
gical environment protection.
Second, research is on the mechanism and path inno-
vation of DFI affecting IG. Because the concept of DFI and
IG is proposed in a relatively short period, there are few
literatures that have in-depth explored the relationship
between DFI and IG, most of which focus on DFI to alleviate
factor mismatch, promote economic growth, narrow the
income gap between urban and rural areas, and promote
the improvement of social security mechanisms (Dollar
et al., 2016; Sarma & Pais, 2011). For example, Kapoor
(2014), Li et al. (2020), Park (2015), and Zhang et al. (2019)
all showed that DFI is conducive to enterprise innovation
and regional entrepreneurship in backward and remote
areas, and financial resources sink into the “tail”area,
prompting financial service institutions to compete in the
market and user groups to reduce their own transaction
costs of financial services and meet capital needs. This
further enhances the “hematopoietic”effect of financial
resources to alleviate poverty and increase income, narrow
the wealth gap, and achieve sustainable economic develop-
ment, promoting high-quality development of the Chinese
economy. Lambert et al. (2021) found that digital inclusive
finance and its sub-dimensions will have different degrees
of impact on IG. Specifically, the coverage and depth of use
of DFI can promote the equality of access to opportunities
and the sharing of development dividends, and thus pro-
mote the level of IG. But in this process, the degree of
digitization is not significant. Khémiri et al. (2023) pointed
out that DFI can promote IG in areas with high govern-
ment efficiency, well-developed urban markets, and high
innovation levels. At the same time, digital inclusive
finance can achieve inclusive economic growth by easing
the financing constraints of small- and medium-sized
enterprises and promoting the entrepreneurship of low-
income families.
To sum up, the existing literature has done a lot of
useful research on DFI and IG, which provides ideas and
experience enlightenment for this article. However, the
existing literature lacks attention to the nonlinear relation-
ship between DFI and IG. At the same time, there are few
literature studies discussing the role of DFI in promoting IG
from the perspectives of innovation activity and HC.
The contribution of this article is as follows: (1)
According to the connotation and purpose of IG, this article
constructs an IG index system from three dimensions: sus-
tainable economic growth, income distribution, and fair
opportunity, and quantitatively evaluates the level of IG in
China from multiple dimensions, so as to reflect the influ-
ence of DFI on IG more comprehensively (2). Combining the
channel mechanism of digital inclusive finance’sinfluence
on IG innovatively brings innovation activity and HC into
the analytical framework, so as to identify the effective
channels of DFI in China to promote IG at this stage and
further enrich the relevant research content (3). This article
clearly compares the differences in the influence of DFI on
IG in different regions of China and expands the research
depth in this field. This is also more conducive to finding the
breakthrough point of DFI and speeding up the realization
of more just resource allocation (4). Most of the existing
studies focus on the static effect or spatial spillover effect
of the development of DFI on IG, and there is little research
on whether there is a dynamic effect between them. In this
article, the quadratic term of DFI is included in the model
analysis, and the relationship between the development of
DFI and IG is discussed.
3 Mechanism Analysis and
Research Hypotheses
3.1 Direct Impact of DFI on IG
3.1.1 Economic Growth Effect
The academic community has basically formed a consensus
that financial development is conducive to economic
growth, and the emergence of DFI will further strengthen
the economic growth effect of traditional finance (Beck
et al., 2000; Li & Jiang, 2006). The DFI developed by relying
on the Internet and mobile communication technology can
expand the coverage of rural financial services, identify
potential and explicit soft information of lending farmers,
effectively alleviate financial exclusion in rural areas, espe-
cially in remote and poor areas, and reduce the cost of poor
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 3

groups and marginal groups to access financial services
(Xiong et al., 2023). Specifically, due to the lack of credit
characteristics and risk preference of rural residents, the
accuracy of financial poverty alleviation is seriously hin-
dered, and the resource dividends that urban and rural
residents can obtain are biased. With the Matthew effect,
capital, labor, and other production factors will quickly con-
verge in cities, exacerbating the long-term “dual structure”
of China’sfinance (Fan & Chen, 2022; Náñez Alonso et al.,
2022). DFI, by virtue of big data technology, collects the con-
sumption and lending information of a large number of
users, establishes a corresponding customer credit informa-
tion database, reduces the impact of information asym-
metry, alleviates the problem of “notdaringtolend,not
willing to lend”at the supply side, guides financial resources
to serve the real economy more efficiently, and strengthens
the effectiveness of finance in promoting economic growth.
At the same time, due to the high cost of laying physical
network points for traditional financial institutions, coupled
with the “patchwork”distribution of “large scattered and
small settlements”in rural areas, it is difficult to provide
financial services to rural areas and there is insufficient
effective supply (Ezzahid & Elouaourti, 2021). The “time-
space penetration”of DFI itself can reduce the dependence
of traditional finance on physical outlets, which can not only
improve the accessibility of financial services, but also
enable customers to easily access online financial services
by virtue of intelligent terminals, save transaction costs,
help promote the sinking of financial resources, meet the
capital requirements for industrial development in the “tail”
region, and enhance the “hematopoietic”effect of poverty
alleviation and income increase (Mei et al., 2022; Wang et al.,
2023; Xiong et al., 2022).
3.1.2 Income Distribution Effect
As the “best choice”for traditional financial institutions, a
high interest rate level undoubtedly weakens the supply of
microcredit, insurance, wealth management, and savings to
low-income groups. In addition, the profit-seeking nature of
financial capital itself makes most advanced production fac-
tors gather in various industrial sectors in cities and towns,
exacerbating the phenomenon of resource scarcity in rural
areas. The borderless feature of DFI has changed the “threshold
effect”of marginal groups’access to financial services, corrected
the discriminatory distribution of funds, guaranteed the eco-
nomic security of low-income groups, and reduced the problems
caused by the traditional urban-rural dual structure (Liu et al.,
2023; Yu & Wang, 2021). Specifically, DFI, by establishing a big
data sharing platform between developed and remote areas,
enables different consumer groups to share decision-making
information, solves the problemsoflow-incomegroupsbeing
excluded from financial coverage due to lack of collateral and
imperfect credit information, and respects and protects the
rights and interests of vulnerable groups in digital dividends,
alleviating the current financial resource allocation problem of
“heavy urban and light rural areas”inChina(Liuetal.,2021).At
the same time, the integration of digital technology into the
financial sector has improved the speed of capital circulation,
transfer, and effective distribution, improved the livelihood
transformation ability of the poor and the sustainability of indus-
trial development in underdevelopment, accumulated capital
for regional development to promote rural economic develop-
ment, and increased the disposable income of marginal groups,
which is conducive to achieving IG between urban and rural
areas.
3.1.3 Digital Divide Effect
With the continuous development of DFI, there will inevi-
tably be “digital divide”and “knowledge gap hypothesis,”
which is not conducive to inclusive economic growth to a
certain extent. Specifically, in the face of similar financial
exclusion, different groups of users of financial services
have significant differences in their ability to accept digital
inclusive finance. The groups with higher financial literacy
and digital literacy have a strong ability to apply digital
technology, can understand the economic benefits that can
be generated by using digital inclusive finance, take the
initiative to accept emerging financial services, and allo-
cate financial assets through peer-to-peer lending, invest-
ment, and insurance purchase, which is conducive to
increasing their disposable income (Falak et al., 2022;
Ling et al., 2023; Orkun et al., 2022). However, people
with low digital literacy can’t get in touch with and under-
stand digital technology in time and lack the awareness of
using digital inclusive finance to prevent risks and smooth
their survival and consumption, so it is difficult to actively
respond to digital inclusive finance services, resulting in
“self-exclusion”and enjoying the dividend brought by
digital inclusive finance (He et al., 2020; Liu et al., 2023). It
can be seen that digital inclusive finance virtually reduces
the rights of some groups who lack the ability to obtain and
use digital information services to participate in social activ-
ities, resulting in relative deprivation and digital exclusion,
andthusproducesthe“Matthew effect,”which widens the
gap between urban and rural residents and within rural
society (Ozili, 2018).
Based on the above analysis, this article puts forward
research hypothesis 1:
4Jingyi Yang et al.

Hypothesis 1. Digital inclusive finance has an effect on IG,
but the positive and negative effects are not clear.
3.2 Indirect Impact of Digital inclusive
finance on IG
3.2.1 Innovation Activity
The application of financial technology has become the
development trend of the financial industry, which has
brought great influence on consumer identification, con-
sumer participation, enterprise value delivery, and realiza-
tion, and is an important driving force to realize the change of
business model and promote the innovation of financial
market (Sadok, 2021; Sun & You, 2023). For example, the intro-
duction and application of virtualrealitytechnologyfurther
promoted the innovation of inclusive finance’sonlinebusi-
ness model and transferred the transaction negotiation and
other links online, greatly reducing the transaction cost,
transportation, and verification cost of financial services
(Han et al., 2022). At the same time, more innovative subjects
(universities, research institutes, and scientificandtechnolo-
gical enterprises) tend to gather in areas with higher levels of
scientific and technological innovation ability, and the busi-
ness activities of enterprises are also more active. DFI
improves the matching degree between financial support
and the demand for enterprise funds through emerging
technologies such as big data, especially easing the financing
constraints of vulnerable groups such as small- and medium-
sized enterprises. Specifically, the solutions that DFI can pro-
vide for SMEs include digital credit and equity products, such
as supply chain finance, loans, guaranteed revolving credit
lines, corporate accounts receivable financing market lending,
and equity crowdfunding. In addition, digitalization of internal
business processes and business-to-business (B2B) processes,
such as electronic invoices and token assets using blockchain
(distributed ledger) technology, will help solve the difficulties
faced by small- and medium-sized enterprises in obtaining
financing and significantly reduce financing costs (Chinoda
& Kapingura, 2024; Suhrab et al., 2024). This will not only
play a good “catalyst”role in existing innovation and entre-
preneurship activities but also increase the base of potential
innovation and entrepreneurship groups due to financial
constraints and provide a large number of employment
opportunities, thus contributing to the realization of IG
(Deng et al., 2019; Li et al., 2020). According to the data of
the fourth economic census, the number of employees in
small- and medium-sized enterprises accounts for 80% of
the total number of employees in enterprises. It can be
seen that the improvement of innovation activity is helpful
in promoting sustainable economic development, enhancing
wealth, and maintaining social stability.
3.2.2 HC
The foundation of digital inclusive finance lies in inclusive
finance, and the development of inclusive finance is devoted
to solving the problem of financial exclusion for disadvan-
taged groups in modern financial services, including the
financing constraints of family and individual education
investment funds. On the one hand, the in-depth develop-
ment of DFI can reduce the risks and costs of financial
institutions in issuing education credit and lower the
threshold for obtaining education credit, so that people
who could not afford instant education consumption can
invest in HC beyond their own consumption level through
borrowing (Zhou et al., 2018). At the same time, the enhance-
ment of financial availability and diversified financial pro-
ducts and services can not only promote the disadvantaged
groups in traditional employment, such as low education
level, to increase their educational opportunities, but also
support high-tech talents to rebuild their skills and increase
their educational opportunities at a higher level, thus pro-
moting the upgrading of HC (Song et al., 2022). On the other
hand, adult groups are facing pressure from the elderly and
their children’s life investment while meeting their own HC
investment in later education, skills training, and health,
which directly affects the mental health of HC investors and
affects the quality of HC to a certain extent (John et al., 2022).
However, the low credit constraint in DFI relieves the pres-
sure of family capital shortage, especially the family residents
in underdeveloped areas, and weakens the pressure of caring
for children and supporting the elderly, thus improving the
quality and quantity of HC (Li et al., 2022). With the spillover
effect of HC flow and the increase of skills upgrading ways
such as “learning by doing”and “re-education”of labor force,
the structure of HC will continue to be optimized. In addition,
the accumulation of HC contributes to the matching of supply
and demand in the labor market, and the optimization of HC
structure is conducive to stimulating the effect of technolo-
gical progress, which will improve not only the labor produc-
tivity of society but also the efficiency of labor distribution in
the market, thus effectively achieving IG (Abdulla, 2023).
Therefore, the development of DFI has made it possible for
residents in remote areas to obtain rich educational resources
and improve the level of HC, so that they can apply for more
jobs in the future, improve production methods, narrow the
inherent gap with the rich, and thus promote inclusive eco-
nomic and social growth.
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 5

Based on the above analysis, this article puts forward
research hypothesis 2:
Hypothesis 2. The development of digital inclusive finance
can achieve IG by increasing innovation activity and improving
the level of HC.
4 Research Design and Data
Sources
4.1 Variable Selection
4.1.1 Explained Variable
4.1.1.1 IG
IG takes social opportunity equity as the core, which
enables individuals to participate in economic develop-
ment opportunities and share development achievements
equally, and enables the society to widely mobilize produc-
tion factors and distribute the achievements fairly. The
macro-level performance is sustainable economic growth,
increased social welfare and fairness, and the income gap
tends to narrow. To measure the level of IG, we need to
consider the multiple characteristics of its connotation.
According to the definition of IG by academic circles and
international organizations such as the World Bank, refer-
ring to the practices of Abor et al. (2018), Fowowe and
Folarin (2019), Ge and Li (2020), and Hu et al. (2022, 2023),
combining the current situation of regional development
in China, this article constructs an IG index system from
three dimensions: sustainable economic growth, income
distribution, and fair opportunity. In the process of con-
structing the index system, it is considered that equality of
opportunity belongs to a multidimensional comprehensive
concept, including economic participation, employment,
education, medical care, and other aspects. In order to
avoid concept generalization, core indicators in all aspects
are selected (Table 1 shows specific indicators).
Sustainable economic growth is the basis of IG. Only
reasonable, high-speed, and sustainable economic growth
can provide more opportunities for economic participation
and enhance social welfare. Sustainable economic growth
includes economic growth, income growth, and green pro-
duction. The economic growth level reflects the efficiency,
structure, and speed of growth. The level of income growth
reflects the extent to which economic growth benefits resi-
dents’income. The increase in residents’income is the
starting point of the next round of consumption increase,
which is conducive to enhancing the potential for economic
growth. The requirement of green production reflects that
energy consumption and carbon emission are the constraints
of economic growth, which is in line with the concept of green
development. Among them, the total factor productivity
refers to the practices of Guan et al. (2022), Ma and Zhang
(2022), Xu and Zhao (2023), and Ying et al. (2023), using a
mixed distance function with both radial and non-radial dis-
tance functions to measure production efficiency. In order to
better describe the dynamic evolution of production effi-
ciency, the Global Malmquist–Luenberger (GML) index is
introduced to measure the total factor productivity. Input
indicators include labor input, capital input, and energy
input, which are characterized by the number of employees
at the end of the year, capital stock, and total energy con-
sumption, respectively. Output indicators include expected
output and unexpected output, in which the expected output
is expressed by the actual GDP of each province in the current
year, and the unexpected output includes sulfur dioxide emis-
sions and chemical oxygen demand (COD).
Social opportunity equity is a direct embodiment of
the core connotation of IG, aiming at eliminating the
inequality caused by the environment and ensuring the
fairness of the growth process. It is beneficial to technolo-
gical innovation, industrial upgrading, and social stability
to improve the fairness of social opportunities and enable
more individuals to have high quality, skills, and security.
Social opportunity equity includes economic participation
opportunities, employment opportunities, education opportu-
nities, medical opportunities, social security level, and resources
and environment level. Among them, the market potential
has an important impact on industrial agglomeration and
the evolution of spatial economic structure, which can reflect
the differences in economic participation opportunities
between regions. Therefore, this article refers to the practice
of Wang and Xu (2018), and uses the formulas listed in the
table to measure the market potential level. Here,
Yjt
repre-
sents the actual GDP of jprovince in the period of t,
D
i
j
represents the distance between the capital cities of iand j
provinces, and
D
i
i
represents the internal distance of ipro-
vince.
=
D
sπ/
ii i
2
3
,
s
i
is the land area of i province.
Income equality is a positive result feedback to oppor-
tunity fairness and a measure of the equality of growth
results. Based on the concept of sharing development
achievements and the vision of common prosperity, the
income distribution gap is reflected from multiple levels,
including urban-rural income gap, regional income gap,
and industry income gap. Among them, the urban-rural
income gap is characterized by the urban-rural income
ratio. The regional income gap is based on the ratio of
per capita disposable income (PCDI) in cities and towns
6Jingyi Yang et al.

Table 1: Index system of IG
Dimension layer Domain layer Index layer Specific explanation Attribute
Sustainable economic
growth
Economic growth level Total factor productivity Calculation of EBM–GML index based on DEA +
Proportion of secondary and tertiary
industries
The sum of the proportion of secondary industry and tertiary industry +
Per capita GDP growth rate Per capita GDP growth rate +
Income growth level PCDI of urban residents PCDI of urban residents at constant prices in 2011 +
PCDI of rural residents PCDI of rural residents at constant prices in 2011 +
Green production level Carbon emission intensity Ratio of total carbon dioxide emissions to GDP −
Energy consumption per unit GDP Ratio of total energy consumption to GDP −
Fair opportunity Opportunities for economic
participation
Market potential
∑
+
≠YD YD//
ij jt ij it ii +
Total highway mileage per 10,000 people The ratio of total highway mileage to permanent population +
Employment opportunities Employment rate of secondary and tertiary
industries
Proportion of employed persons in secondary and tertiary industries +
Registered urban urbanization rate Registered urban urbanization rate −
Educational opportunities Intensity of investment in education funds The ratio of education expenditure to GDP +
The number of full-time teachers per 10,000
people
The sum of the number of full-time teachers in ordinary colleges and high
schools
+
Medical opportunity The number of doctors per 10,000 people The ratio of practicing (assistant) doctors to total resident population +
The number of beds per ten thousand
people
The ratio of practicing (assistant) doctors to total resident population +
Social security opportunities Number of basic old-age insurance per
10,000 people
The ratio of the number of participants in basic old-age insurance to the
total resident population
+
Number of basic old-age insurance per
10,000 people
The ratio of the number of participants in basic medical insurance to the
total resident population
+
Resource and environmental level Forest coverage rate Forest coverage rate +
Per capita output of general industrial solid
waste
The ratio of general industrial solid waste output to permanent
population
−
Per capita sulfur dioxide emissions The ratio of sulfur dioxide emission in waste gas to permanent population −
Per-capita discharge of COD The ratio of COD discharge in wastewater to resident population −
Income equality Degree of income equality Urban–rural income ratio The ratio of PCDI of urban to rural residents −
Regional income gap (town) The ratio of disposable income of urban households to benchmark area in
each province in that year
+
Regional income gap (rural) The ratio of disposable income of rural households to benchmark areas in
each province in that year
+
Industry income gap Gini coefficient of industry −
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 7

or rural areas in each province to the benchmark area in
that year, and the benchmark area is selected as Shanghai.
The industry income gap is expressed by the Gini coeffi-
cient calculated by Chen and Gao (2012), and the calculation
formula is =∑∑ |−|Gppyy
uij
ij ij
1
2.Amongthem,
u
repre-
sents the average wage of the whole industry,
y
i
and
y
j
repre-
sent the average wages of the
i
and
j
industries, and
p
i
and
pj
represent the employment share of the
i
and
j
industries.
4.1.2 Core Explanatory Variable
4.1.2.1 DFI
The Digital Finance Research Center of Peking University,
together with Ant Financial Group, compiled the “Digital
inclusive finance Index”by using the massive data of Ant
Financial on digital inclusive finance. Starting from the
three first-level dimensions of coverage, usage depth, and
digital support service level, the index uses 24 specific
indicators, such as the number of Fubao accounts per
10,000 people, the proportion of Alipay users and the
average number of bank cards bound to each Alipay
account, to reflect the development of digital inclusive
finance. At present, the index has been widely used in
academic circles (Cao et al., 2022; Chen et al., 2023; Ge
et al., 2022a,b). In view of this, this article chooses this
index to measure the development level of digital inclusive
finance in China.
4.1.3 Mediator Variable
4.1.3.1 Innovation Activity Degree (IAD)
According to Solow’s economic growth model, scientific
and technological innovation plays an obvious role in
boosting high-quality economic growth. Due to the long
period of patent approval in China, which is generally
6–18 months, the number of patents granted is lagging
behind and cannot reflect the current patent application.
Therefore, this article refers to the idea of Feng et al. (2022)
and selects the number of patent applications in each pro-
vince (autonomous region and municipality) to represent
the active situation of technological innovation.
4.1.3.2 HC
Compared with material capital, the improvement of HC
level not only means the increase of labor quantity but also
means the improvement of labor quality. According to
Romer’s economic growth model, the development of knowledge
and technology is the source of economic growth, and the
improvement of labor quality has a more positive effect on
inclusive economic and social growth than the simple increase
in labor population. Therefore, this article refers to the ideas
of Duc et al. (2021) and selects the average wage level of
employeesinvariousprovinces(autonomousregionsand
municipalities) to measure the development level of HC.
4.1.4 Control Variable
Because there are many macro and micro factors affecting
IG, in order to minimize the error caused by the omission
of important variables in causal inference of the model,
this article selects the following control variables
according to the research perspective of existing literature
(Afolabi et al., 2023; Borice et al., 2023; Katuka et al., 2024):
Specifically, (1) PCDI: The reform of the income distribution
mechanism is an important channel to achieve IG, and how
to correctly handle the problems of efficiency and fairness
in primary distribution and how to play the role of the
adjustment mechanism of secondary distribution is an
important issue to achieve economic and social inclusion
at present. Therefore, this article uses the logarithm of
regional PCDI to measure PCDI; (2) Urbanization rate
(UR): On the one hand, urbanization stimulates regional
economic growth and promotes the formation of inclusive
ideas by expanding market scope; on the other hand, in the
process of urban expansion, there may be problems such
as a widening income gap among residents and insufficient
living security for landless farmers, which hinder IG.
Therefore, this article uses the ratio of urban population
to total population to measure the urbanization rate; (3)
Financial marketization (FM): Because in the process of
institutional change to achieve IG, it often means that the
original pattern of interest distribution has been broken.
However, FM can improve productivity by strengthening
budget constraints and improving management level and
then affect the degree of economic and social inclusion.
Therefore, this article selects the regional FM index to char-
acterize FM; (4) UR: In the traditional Cobb Douglas produc-
tion function, labor is input as an important factor of
economic growth. Similarly, the labor force is also one of
the important factors affecting IG, so this article selects the
ratio of the unemployed population to the working popula-
tion to measure the UR; (5) Foreign direct investment (FDI):
With the continuous improvement of China’s opening to the
outside world, FDI has become an important source of funds
for China, which has played an important role in promoting
economic growth and inclusive development. Therefore,
this article selects the proportion of FDI in GDP to measure.
8Jingyi Yang et al.

4.2 Model Setting
4.2.1 Fixed Base Range Entropy Weight Method
This article takes 30 provinces in China from 2011 to 2021
(excluding Tibet, Hong Kong, Macao, and Taiwan) as the
research object, and uses the entropy weight method of
fixed-base range to measure and analyze their IG level.
This method is a combination of entropy weight method
and fixed-base range method, which can not only avoid the
subjectivity of weight setting but also make the global uni-
versal reference system through fixed-base, thus reflecting
the changing trend in both space and time dimensions. In
addition, this method can also deal with positive and nega-
tive indicators and form three sub-dimension indexes. The
specific calculation steps are as follows:
Step 1: Dimensionless processing of index data.
()
() ()
()
() ()
=
⎧
⎨
⎪
⎪
⎩
⎪
⎪
−
−
−
−
Y
XX
XX
X
XX
XX
X
min
max min , is a positive indicator
max
max min , is a negative indicator.
ij
t
ij
tij
t
ij
tij
tij
t
ij
tij
t
ij
tij
tij
t
where
X
i
j
t
represents the original data of the
j
index of the
i
province in the
t
year, and
Y
ij
t
is the data after dimension-
less processing by using the range method.
Step 2: Calculate the specific gravity of indicators.
[] []=∑∈∈
=
P
Y
Yinjm,1,,1,
.
ij
tij
t
i
nij
t
1
If the specific gravity value
=
P
0
ij
t
,()×=
→
PPlim ln
0
Yij
tij
t
0
ij
t
is defined.
Step 3: Calculate the index information entropy.
[ ( )] [ ( )]
∑
=− × ×
−
=
E
nPPln ln
.
j
t
i
n
ij
tij
t
1
1
Among them,
E
j
t
represents the information entropy of
the jth index in the tth year.
[]∈
E
0, 1
.
j
tThe smaller the
index information entropy, the greater the degree of data
dispersion, the greater the amount of information it pro-
vides, and the greater the index weight; on the contrary,
the smaller the index weight.
Step 4: Calculate the index weight.
()
()
=−
∑−
=
W
E
E
1
1
,
j
tj
t
j
mj
t
1
where
W
j
is the weight of the j-th index.
Step 5: Use the fixed base range method to process the
original data.
()
() ()
=−
−
Z
XX
XX
min
max min
.
j
tj
tj
jj
,min
2011
,max
2011 ,min
2011
Among them,
Z
j
t
represents the dimensionless index
value of the jth index after being processed by the fixed-
base range method in the tyear, and
X
j
t
is the original data.
X
j
t,min and
X,
j
t,max
respectively, represent the minimum and
maximum values of the jth index in the original data of all
cities in the base year. The article takes 2011, the initial year
of the sample, as the base year.
Step 6: Calculate the comprehensive index. Weighting
the index weight determined by the entropy weight method
and the dimensionless index value processed by the fixed
base range method to obtain the comprehensive index:
()
∑
=×
=
SWZ
.
j
t
j
m
j
tj
t
1
4.2.2 Econometric Model
In order to verify the comprehensive effect of digital inclu-
sive finance on IG, combined with research hypothesis 1,
this article constructs the following panel econometric
model:
∑
=+ + +++
=
αα αγ νλεIG DFI Control
.
it it ijijt t i it01 2
1
5
Among them, subscripts
i
and
t
represent the city and
year respectively;
α
0
represents a constant term,
α
1
and
α
2
represent the regression coefficients of the core explana-
tory variables and control variables respectively;
ν
t
and
λ
i
represent year fixed effect (FE) and city FE respectively;
ε
i
t
represents the random disturbance term that obeys the
white noise process. In the actual fitting calculation pro-
cess, in order to slow down the influence of heteroscedas-
ticity and reduce the data level of variables, all variables
are logarithmically transformed in this article.
Considering that there may be a nonlinear relation-
ship between digital inclusive finance and green poverty
reduction, this article puts the square term of digital inclu-
sive finance (
D
FI
2
) into the model framework, and in order
to avoid collinearity, the
D
FI
is decentralized and then
multiplied by square, and the following econometric model
is established:
∑
=+ + + +++
=
αα α αγ νλεIG DFI DFI Control
.
it it it ijijt t i it01 2
231
5
In addition, in order to test the channel of digital inclu-
sive finance’sinfluence on IG, that is, to verify the channel
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 9

effect played by innovation activity and HC in its process,
combined with research hypothesis 2, this article uses the
form of recursive equations to test and then constructs the
following equations:
=+ + +++βθ γ νλεIAD DFI Control ,
it it it t i it
01
=+
′++ +++ββ σ γ νλεIG DFI IAD Control
,
it it it it t i it
011
=+ + +++βθ γ νλε
H
CDFIControl
,
it it it t i it
02
=+
″++ +++ββ σ γ νλεIG DFI HC Control
.
it it it it t i it
012
4.3 Data Source
According to the principle of data availability, this article
selects the panel data of 30 provinces in China from 2011 to
2021 (except Tibet, Hong Kong, Macao, and Taiwan) as the
research sample. The original data of all variables mainly
come from the China Economic Net database, EPS data
platform, website of National Bureau of Statistics, China
Statistical Yearbook, China Energy Statistical Yearbook,
China Labor Statistical Yearbook, China Population and
Employment Statistical Yearbook, China Education Expenditure
Statistical Yearbook, China Education Statistical Yearbook,
China Environment Statistical Yearbook, Peking University
Digital inclusive finance Index (Phase III), and Chinese people.
For a few missing values, LaGrange interpolation polynomial
is used to complete them.
5 Analysis of empirical results
5.1 Measurement Results of IG Level
According to the above-mentioned IG index system, the
total index and sub-index of IG of 30 provinces in China
(excluding Tibet, Hong Kong, Macao, and Taiwan) from
2011 to 2021 are calculated by using the fixed-base range
entropy weight method, and the specific results are shown
in Tables 2 and 3.
It is not difficult to see from Table 2 that from 2011 to
2021, the total index of IG in China showed an obvious upward
trend, with an average of 0.291. Taking 2021 as an example,
Shanghai and Beijing are the leading cities in China, with IG
levels of 0.546 and 0.539, respectively. In terms of increment
and growth rate, Shanghai, Beijing, and Tianjin have higher
starting point, slower growth rate and smaller increment,
while Jiangsu, Zhejiang, Guangdong, and Shandong have lower
starting point, faster growth rate, and larger increment. The
reason behind this phenomenon may be the difference in
regional economic development levels (Zeng et al., 2022). The
core indicators such as per-capita GDP and PCDI in the region
where the former is located are relatively high, which leads to
a slower growth rate. From this point of view, although there
are regional differences in IG among provinces in China, this
difference is narrowing with the passage of time. This conclu-
sioncanalsobeseenfromTable3thattherearegreatdiffer-
ences in the total index and fractal index of IG in different
regions of China, among which the eastern regions are in the
leading position, showing a decreasing trend of “East-Middle-
West”in turn. However, the average annual growth rate of the
total IG index and fractal dimension index in the eastern
region is obviously lower than that in the central and western
regions.
5.2 Benchmark Regression Result
Common statistical models used in panel data include pool
least square method (POLS), random effect model, and FE
model. Which method is most suitable for the sample data
of this article needs further testing. The test results show
that the P values of houseman test and likelihood ratio test
both reject the original hypothesis at a 1% level. Based on
this, this article uses the FE model as the benchmark
regression model for subsequent empirical tests. At the
same time, in order to eliminate the interference of hetero-
scedasticity, sequence correlation, and cross-section corre-
lation on regression results, Driscoll–Kraay standard error
is mainly used to deal with it. The estimation results of
specific parameters are shown in Table 4.
The results show that digital inclusive finance can
obviously promote the total index of IG, the sub-index of
sustainable economic growth, the sub-index of opportunity
fairness, and the sub-index of income equality, and all of
them have passed the significance level test of at least 10%.
At the same time, digital inclusive finance has the highest
contribution to income distribution, with a regression coef-
ficient of 0.1671. The possible reason behind it is that digital
inclusive finance, because of its natural ubiquity, uses
information technologies such as remote account opening
and capital exchange to realize the zero marginal cost
effect of the network, break through the barriers of tradi-
tional financial physical outlets, effectively improve the
efficiency of information exchange, greatly improve the
availability of financial services for marginal groups, and
guide the inclined allocation of capital to remote rural
areas, thus promoting the “grassroots”people to innovate
and start businesses, broadening their income channels,
10 Jingyi Yang et al.

Table 2: Total index of IG of provinces in China from 2011 to 2020
Province 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Average
Shanghai 0.453 0.460 0.478 0.486 0.509 0.511 0.530 0.540 0.540 0.523 0.546 0.507
Beijing 0.491 0.478 0.510 0.487 0.492 0.485 0.507 0.501 0.504 0.482 0.539 0.498
Jiangsu 0.277 0.329 0.332 0.366 0.391 0.402 0.443 0.472 0.490 0.494 0.511 0.410
Zhejiang 0.276 0.316 0.331 0.352 0.382 0.395 0.427 0.452 0.472 0.468 0.514 0.399
Guangdong 0.268 0.312 0.301 0.346 0.369 0.379 0.413 0.444 0.467 0.512 0.543 0.396
Shandong 0.227 0.274 0.288 0.319 0.344 0.406 0.448 0.457 0.489 0.501 0.512 0.388
Tianjin 0.314 0.335 0.322 0.341 0.357 0.349 0.336 0.329 0.364 0.365 0.378 0.345
Sichuan 0.165 0.204 0.263 0.280 0.307 0.357 0.370 0.385 0.407 0.429 0.435 0.327
Shanxi 0.180 0.216 0.244 0.253 0.298 0.294 0.353 0.362 0.383 0.384 0.387 0.305
Chongqing 0.190 0.244 0.242 0.262 0.276 0.271 0.298 0.303 0.305 0.294 0.343 0.275
Fujian 0.193 0.231 0.226 0.246 0.261 0.262 0.289 0.315 0.329 0.328 0.337 0.274
Hubei 0.169 0.213 0.243 0.260 0.277 0.286 0.314 0.321 0.347 0.329 0.240 0.273
Liaoning 0.225 0.244 0.258 0.256 0.248 0.267 0.278 0.288 0.309 0.305 0.313 0.272
Shanxi 0.165 0.197 0.238 0.233 0.236 0.253 0.268 0.289 0.310 0.326 0.354 0.261
Xinjiang 0.203 0.218 0.250 0.244 0.259 0.284 0.273 0.272 0.281 0.275 0.299 0.260
Anhui 0.140 0.192 0.210 0.220 0.240 0.245 0.273 0.305 0.303 0.327 0.349 0.255
Ningxia 0.157 0.200 0.237 0.241 0.252 0.261 0.274 0.281 0.285 0.281 0.321 0.254
Henan 0.133 0.178 0.197 0.211 0.228 0.235 0.277 0.299 0.318 0.328 0.337 0.249
Gansu 0.141 0.182 0.235 0.225 0.243 0.253 0.271 0.268 0.276 0.284 0.301 0.244
Jiangxi 0.147 0.195 0.204 0.208 0.224 0.228 0.265 0.279 0.293 0.312 0.330 0.244
Hainan 0.205 0.247 0.231 0.232 0.240 0.231 0.237 0.247 0.256 0.266 0.270 0.242
Neimenggu 0.159 0.184 0.221 0.216 0.225 0.240 0.254 0.268 0.290 0.300 0.304 0.242
Hunan 0.141 0.164 0.198 0.197 0.215 0.224 0.253 0.278 0.303 0.323 0.338 0.239
Heilongjiang 0.171 0.200 0.217 0.206 0.214 0.235 0.240 0.238 0.292 0.296 0.301 0.237
Qinghai 0.170 0.202 0.224 0.205 0.220 0.234 0.229 0.233 0.270 0.287 0.330 0.237
Yunnan 0.144 0.170 0.209 0.210 0.221 0.239 0.274 0.277 0.277 0.278 0.283 0.235
Hebei 0.145 0.177 0.184 0.191 0.210 0.233 0.248 0.270 0.296 0.305 0.313 0.234
Jilin 0.167 0.190 0.195 0.201 0.213 0.219 0.228 0.239 0.284 0.284 0.294 0.229
Guizhou 0.118 0.163 0.195 0.192 0.206 0.216 0.229 0.256 0.253 0.251 0.269 0.213
Guangxi 0.120 0.156 0.185 0.177 0.191 0.199 0.226 0.234 0.246 0.256 0.267 0.205
Average 0.202 0.236 0.256 0.262 0.278 0.290 0.311 0.323 0.341 0.346 0.361 0.291
Table 3: IG and Its Sub-dimension changes in different regions of China
Dimension Region 2011 2021 Average Average annual growth rate (%)
Total index of IG Whole country 0.202 0.361 0.291 6.090
Eastern 0.285 0.446 0.369 4.620
Middle 0.149 0.325 0.254 8.320
Western 0.159 0.322 0.254 7.510
Economic sustainable growth index Whole country 0.264 0.384 0.334 3.540
Eastern 0.388 0.529 0.470 2.905
Middle 0.203 0.345 0.300 5.110
Western 0.188 0.289 0.242 3.932
Income equality index Whole country 0.172 0.386 0.314 8.956
Eastern 0.276 0.493 0.440 6.302
Middle 0.151 0.339 0.293 8.669
Western 0.084 0.333 0.222 16.566
Opportunity equity index Whole country 0.176 0.339 0.259 6.943
Eastern 0.231 0.371 0.295 4.915
Middle 0.120 0.307 0.218 10.504
Western 0.164 0.346 0.265 8.007
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 11

and sharing financial digital dividends (Tan et al., 2022). At
the same time, the strengthening of the supply of financial
resources in rural areas is conducive to further improving
infrastructure construction, optimizing the business envir-
onment, enhancing the siphon effect to attract high-quality
talents and production enterprises, thereby improving the
soft power of production in rural areas and narrowing
the huge gap between urban and rural areas. Judging
from the square term of digital inclusive finance, its fitting
coefficient to the total index of IG is 0.0832, which is
significant at the level of 1%. This conclusion shows that
with the continuous improvement of the level of digital
inclusive finance, its marginal utility in promoting the IG
of the economy and society is slightly weakened. With
China entering a new stage of relative poverty control
characterized by transformational secondary poverty, the
production and living conditions of residents have been
significantly improved, and the dependence of industries
on resources and environment has been reduced, which
has promoted the upgrading and transformation adjust-
ment of industrial structure. However, for remote areas
in the west or a few deep poverty-stricken areas, the pos-
sibility of poverty and the income gap between urban and
rural areas are more obvious, which makes the promotion
effect of digital inclusive finance on IG show an invisible
slowdown.
5.3 Robustness Test
In order to verify the robustness and reliability of the
benchmark regression estimation results, this article uses
the following three methods to demonstrate:
First, replace the model: When there is an intra-group
correlation, inter-group correlation, and same-period cor-
relation in the random disturbance term, the estimation
results of the two-way FE model may produce certain bias,
and Driscoll–Kraay standard error is used to solve the
possible heteroscedasticity, autocorrelation and cross-sec-
tion correlation problems in the above benchmark regres-
sion analysis. However, in addition to using this standard
error, the feasible generalized least squares (FGLS) can
also deal with the three threats faced by short panel
data. Due to the small number of sections, this article
allows each individual to have the same autoregressive
coefficient in the estimation process and uses the unique
AR (1) autocorrelation structure of panel data.
Second, tail-shrinking treatment: In order to prevent
extreme value interference, such as economic shocks or
major natural disasters in various industries in China
during the epidemic period, this article makes a 1% trun-
cated treatment for all continuous variables and then uses
the two-way FE model to re-estimate.
Third, change the core explanatory variables: The
development of digital inclusive finance has improved
the coverage, availability, and convenience of financial
services, which is an important way to achieve business
transformation and upgrading in the financial industry
and gain competitive advantages. For example, compared
with the traditional financial model, the convenient and
fast characteristics of online payment overcome the geo-
graphical restrictions and the dual division between urban
and rural areas, so that the majority of long-tail users
can enjoy basic financial services. Therefore, this article
replaces the digital inclusive finance total index with the
coverage index. The index is mainly reflected by the
number of electronic accounts (such as Internet payment
accounts and the number of bank accounts bound to
them), which mainly reflects the coverage of digital inclu-
sive finance services.
As can be seen from the robustness test results in Table 5,
the fitting results of the three methods all show that digital
inclusive finance has a significant promotion effect on IG, and
Table 4: Benchmark regression results
Variable IG IGe IGi IGo IG IGe IGi IGo
DFI
0.1593*** 0.0501** 0.1671*** 0.0259* 0.1588*** 0.0438* 0.1592*** 0.0277*
(4.33) (3.95) (5.89) (2.17) (4.33) (3.05) (4.50) (2.21)
DFI
2
0.0832*** 0.0217*** 0.1184** 0.0164**
(1.18) (0.97) (2.23) (0.75)
Control variabl
e
Yes Yes Yes Yes Yes Yes Yes Yes
Individual effect
Yes Yes Yes Yes Yes Yes Yes Yes
Time effect Yes Yes Yes Yes Yes Yes Yes Yes
R
Squar
e
0.7328 0.7012 0.8305 0.8214 0.8317 0.7023 0.6724 0.8072
Note: ***, **, and * indicate significant at the level of 1%, 5%, and 10%, respectively, and t statistics are reported in brackets.
12 Jingyi Yang et al.

the significance level has not changed significantly, which
fully shows that the benchmark regression results are reliable
and robust.
5.4 Endogenous Treatment
Although this article selects a series of control variables to
avoid the endogenous problems caused by omitting impor-
tant variables as much as possible, the model still faces
the endogenous threat of omitting important variables
because some confusing factors are difficult to quantify,
such as residents’perception of financial risks, lending
preferences, and other factors. At the same time, there
may be a two-way causal relationship between digital
inclusive finance and IG. On the one hand, digital inclusive
finance can effectively improve the efficiency of informa-
tion exchange, reduce the cost of information transaction,
improve the efficiency of regional industrial production
organization, and guide the balanced allocation of capital,
labor, and other factors, which is conducive to improving
the existing economic structure, promoting high-quality
industrial development, narrowing the income gap between
urban and rural areas, and completely getting rid of the
“resource curse”poverty trap. On the other hand, only
when the inclusive level of economy and society is steadily
improved, the relationship between financial lenders and bor-
rowers can develop steadily and continuously, thus further
promoting the development of digital inclusive finance. In
view of this, in order to eliminate the deep endogenous rela-
tionship between them, this article adopts two-stage least
square method (2SLS) and double difference method (DID)
to deal with the potential endogenous problems of the model.
Specifically:
Tool variable method: The precondition of the 2SLS
model is to select one or more tool variables with strict
exclusiveness. Considering that the development level of
digital inclusive finance is closely related to the Internet
carrier, and digital inclusive finance aims to provide finan-
cial products and services to the public by taking advan-
tage of scientific and technological innovation, it is difficult
to influence the IG level through the penetration rate of
mobile phones. Therefore, this article chooses the mobile
phone penetration rate (PRMT) as a tool variable for two-
stage least square estimation. In addition, in order to avoid
the influence of weak instrumental variables, this article
refers to the practices of Nunn and Nancy (2014), Qian et al.
(2020) and selects the per-capita postal business volume as
the second instrumental variable. The development and
application of digital technology began with the post and
telecommunications business, and digital finance often
develops rapidly in areas with high post and telecommu-
nications business, which shows that there is a high corre-
lation between the per capita post and telecommunications
business and the level of digital inclusive finance. More-
over, compared with the development speed of digital tech-
nology and the change in information technology, the
influence of post and telecommunications services on IG
can be ignored, so this tool variable also meets the assump-
tions of relevance and exclusivity.
Double difference method: The underlying logic of
digital inclusive finance is still the traditional inclusive
finance,butitbreaksthroughthetimeandspacerestrictions
with the help of modern information technology, which is
more conducive to giving play to the “general”and “benefit”
attributes of finance. “Promoting the Development Plan of
inclusive finance (2016–2020)”and “Advanced Principles of
G20 Digital inclusive finance”provide a good “quasi-natural
experiment”for this article. Compared with the eastern
coastal areas, the development of the western region is still
relatively backward, and the degree of financial development
is generally low. Digital inclusive finance, which is based on
modern information technology, inherits the inclusive and
Table 5: Results of robustness test and endogenous treatment
Variable Robustness test Endogenous treatment
FGLS Tail shrinking treatment Replace the core explanatory variable 2SLS DID
DFI 0.0868*** 0.1075*** 0.0972*** 0.1158*** 0.1047***
(2.65) (3.10) (2.91) (3.77) (3.38)
Control variable Control Control Control Control Control
Unidentifiable test 83.447***
Weak instrumental variable test 251.733
Over-identification test 0.209
Individual effect Control Control Control Control Control
Time effect Control Control Control Control Control
Note: *** indicates significant at the level of 1%.
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 13

inclusive characteristics of traditional inclusive finance. With
the help of the natural “ubiquitous”characteristics of the
Internet, its marginal promotion cost opportunity is zero,
and the promulgation of two major policies provides a rea-
listic opportunity for backward areas to develop digital inclu-
sive finance and improve the financial system. Therefore,
using the practices of Guo and Ma (2023) and Shen et al.
(2021), this article sets 2016 as the exogenous policy impact
node, the western region as the experimental group, and the
eastern and central regions as the control group, and uses the
DID model to verify the policy effectofthedevelopmentof
digital inclusive finance.
It can be seen from the results of endogenous treatment
in Table 5 that the 2SLS method has passed the unidentifi-
able test, the weak tool variable test, and the over-identifica-
tion test, and it is considered that the tool variable is
exogenous and has nothing to do with the disturbance
term. From the specific numerical point of view, the effec-
tiveness of digital inclusive finance in promoting IG is 0.1158
and 0.1047, respectively, and both of them have passed the
significance test of 1%. The result is similar to that of bench-
mark regression, and the sign direction and significance
have not changed significantly. Therefore, the conclusion
that digital inclusive finance can promote IG is still valid
after eliminating possible endogenous problems.
6 Analysis of Path Mechanism
6.1 Heterogeneity Analysis
According to the above, the overall development of the
eastern, central, and western regions of China is extremely
uneven due to the influence of resource endowment,
human ecology, economic development, financial develop-
ment level, and HC, which will inevitably lead to regional
differences in the influence of digital inclusive finance on
IG. Therefore, this article divides the sample into three
regions: the east, the middle, and the west, and analyzes
the heterogeneity of the relationship between them by
using the FE model. The specific results are shown in
Table 6.
As can be seen from the table, digital inclusive finance
has a significant role in promoting IG nationwide and
passed the significance level test of 1%. The only difference
is that the size of the promotion effect is slightly different
between regions; that is, the marginal effect of digital inclu-
sive finance on IG in the western region is 0.1002, which is
higher than that in the central and eastern regions. The
reason is that the infrastructure construction in the wes-
tern region is relatively slow, the level of economic devel-
opment is relatively backward, and the degree of FM is
relatively low. In recent years, with the gradual strength-
ening of China’s policy inclination for the development of
the western region, the idle funds of online investors are
loaned to vulnerable groups, small- and micro enterprises,
and other long-tail users in remote western regions by
using scientific and technological innovation technologies
such as big data and cloud computing, thus expanding the
financial coverage and lowering the financial transaction
cost and financial service threshold, thus effectively alle-
viating the phenomenon of financial service exclusion in
the western region, narrowing the income gap between
regions, and promoting.
6.2 Mediation Effect Analysis
In order to reveal whether digital inclusive finance can
affect IG through the intermediary channels of innovation
and HC, according to the above-mentioned intermediary
effect test procedure and related principles, this article
uses the Sobel intermediary factor test model to fit and
calculate the panel data, and the specific results are shown
in Table 6.
It is not difficult to see that the Zstatistics of the Sobel
test are all greater than the critical value of 0.96, which
indicates that Digital inclusive finance can promote IG
through the intermediary channels of improving innova-
tion activity and HC level, in which the average estimation
coefficients of innovation activity and HC are 0.325 and
0.0491, respectively, and pass the significance test of 5%
Table 6: Path Mechanism Analysis Results
Variable Heterogeneity analysis Mediation effect
analysis
Eastern Middle Western IAD HC
DFI 0.0767*** 0.0850*** 0.1002*** 0.0325** 0.0491*
(2.32) (2.47) (2.98) (0.94) (1.03)
Control
variable
Control Control Control Control Control
Individual
effect
Control Control Control Control Control
Time
effect
Control Control Control Control Control
Sobel (Z) 6.58 4.46
Sobel
(boot Z)
5.79 4.95
Note: ***, **, and * indicate significant at the level of 1%, 5%, and 10%.
14 Jingyi Yang et al.

and 10%. This result shows that Digital inclusive finance
uses big data, blockchain, artificial intelligence, and other
digital technologies to accurately support small and micro
enterprises and residents in remote areas to solve finan-
cing constraints in a low-cost, convenient, and fast way,
realize the redistribution of funds, and stimulate regional
innovation vitality, thus promoting sustainable economic
development and IG. At the same time, by improving
the efficiency of the use of funds, using idle funds to
develop education and strengthen vocational skills training,
and cultivating and introducing high-level and compound
talents, China will develop in a more balanced and sufficient
direction.
7 Research Conclusions and Policy
Recommendations
7.1 Research Conclusions
On the basis of combining the relevant theoretical support
and intermediary transmission mechanism of the influ-
ence of digital inclusive finance on IG, this article combines
the panel data of 30 provinces in China (excluding Tibet,
Hong Kong, Macao, and Taiwan) from 2011 to 2021, first
measures the total index and fractal index of IG in China
by using the fixed-base range entropy weight method, and
then comprehensively uses the fixed-effect model, instru-
mental variable method, DID and Sobel intermediary
factor method to verify the influence of digital inclusive
finance on IG and whether digital inclusive finance can
improve innovation. The results show that (1) the level of
IG in China is gradually decreasing, while the average
annual growth rate of IG in the eastern region is
obviously lower than that in the central and western
regions; (2) Digital inclusive finance can significantly
improve the level of IG, and its contribution to income
distribution is the highest. This conclusion still holds
after a series of robustness tests and endogenous treat-
ment. At the same time, with the continuous improvement
of the level of digital inclusive finance, its promotion
effectonthelevelofIGshowsahiddenslowdown;(3)
heterogeneity analysis shows that digital inclusive
finance plays a more significant role in promoting the
levelofIGinthewesternregion.Theanalysisofthe
mediating effect shows that innovation activity and HC
level play a positive mediating role in the process of
digital inclusive finance promoting IG, but there are dif-
ferences in the effect.
7.2 Policy Recommendations
In order to give full play to the positive role of digital
inclusive finance in IG, combined with the conclusion of
the article, the following policy suggestions are put forward:
First, attach great importance to the construction of a
digital inclusive financial system and improve the inclu-
siveness, coverage, and accuracy of financial services.
Considering that the marginal effect of digital inclusive
finance in the western region on IG is 0.1002, the govern-
ment should increase the degree of digital support in the
western region and remote areas, constantly improve the
digital inclusive financial system and infrastructure con-
struction, and fully release the promotion effect of digital
inclusive finance on IG. Specifically, by optimizing digital
functions such as personal payment, micro-credit and
internet insurance, Digital inclusive finance realizes the
accurate delivery of financial products, such as guiding
Internet companies such as “JD Finance,”“Ant Financial
Service,”and “Du-xiaoman”to sink the market, developing
and designing digital financial products and services that
benefit the people and facilitate the people according to local
conditions, and ensuring the rights of economic entities such
as deeply poor individuals, farmers, small- and medium-sized
enterprises to obtain financial services, which will help alle-
viate financial exclusion in rural areas, reduce the incidence
of regional multi-dimensional poverty, and improve the
quality of life of residents.
Second, while developing digital inclusive finance, we
should dredge innovation channels, deepen the “stream-
line administration, delegate power, strengthen regulation
and improve services”reform, strengthen fair supervision,
create a fair, convenient and efficient business environ-
ment, coordinate the designation of digital inclusive finance
and innovation and entrepreneurship support policies,
guide the optimal allocation of technology, capital and
talents, and cultivate more dynamic, sustainable and stable
innovation subjects, so as to enhance the regional innova-
tion activity and enable low-income people to better enjoy
the inclusive and inclusive economic growth brought by
digital inclusive finance. At the same time, strengthen the
“cluster effect”of developed economic regions in the eastern
region, build relevant policy demonstration areas and pilot
areas, fully stimulate the development momentum, and
create a strong regional synergy effect.
Third, when developing digital financial products or
services, financial institutions or financial technology com-
panies should fully consider the HC level of residents in
rural areas and remote areas, and try their best to increase
the convenience of using digital finance in remote areas by
Effect of Digital Financial Inclusion in Promoting Inclusive Growth 15

increasing voice assistants and reduce the constraints of
low HC level in developing digital financial services in such
areas. At the same time, we should attach importance to
consumer education, continuously increase residents’knowl-
edge reserves, and further improve the financial literacy level
of residents in low-income groups and underdeveloped areas
through flexible online and offline training, so as to better
play the role of digital inclusive finance in promoting IG.
Funding information: This work was financially supported
by the Natural Science Foundation of Fujian Province (grant
number: 2022J01320) and supported by the Fundamental
Research Funds for the Central Universities in Huaqiao
University.
Author contributions: Conceptualization, X.Z.; methodology,
J. Y., and X.G.; software, J.Y., and X.G.; formal analysis, J.Y.,
and X.G.; investigation, X.Z; resources, X.G.; writing –ori-
ginal draft, J.Y.; writing –review and editing, J.Y.; supervi-
sion, J.Y.; project administration, X.Z. All authors have read
and agreed to the published version of the manuscript.
Conflict of interest: The authors declare that the research
was conducted in the absence of any commercial or finan-
cial relationships that could be construed as a potential
conflict of interest.
Article note: As part of the open assessment, reviews and
the original submission are available as supplementary
files on our website.
Publisher’s Note: All claims expressed in this article are
solely those of the authors and do not necessarily represent
those of their affiliated organizations, or those of the pub-
lisher, the editors, and the reviewers. Any product that
may be evaluated in this article, or claim that may be
made by its manufacturer, is not guaranteed or endorsed
by the publisher.
Data availability statement: The datasets used during the
current study are available from the corresponding author
on reasonable request.
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