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The Influence of Digital Economy Development
Level on China’s Export Trade
Yuexian Tang1and Liang Liang2(B)
1School of Economics and Trade, Guangzhou Xinhua College, Guangdong, China
2Department of Economics and Management, Maoming Polytechnic, Guangdong, China
liangliang-gd@163.com
Abstract. At present, the global digital economy is developing rapidly. How to
develop digital economy and enhance the digital competitivenessof Chinese export
enterprises is of strategic significance to promoting China’s economic develop-
ment. General Secretary Jinping Xi pointed out that it is necessary to speed up the
transition from a big trade country to a strong trade country, consolidate the tradi-
tional advantages of foreign trade, actively cultivate new competitive advantages,
and expand the space for foreign trade development. Therefore, this paper con-
ducts an empirical study on the influencing factors in the process of export trade,
analyzes the relationship between the development of digital economy develop-
ment and export trade, analyzes the direction and degree of the influence of digital
economy development level on China’s export trade, and puts forward policy sug-
gestions to promote the development of digital economy and promote China’s
export trade.
Keywords: Digital Economy ·Export Trade ·Digital Competitiveness
1 Introduction
With the continuous development of digital technology, the digital economy has devel-
oped into a new economic form. In the digital wave, the digital economy also affects
the development of China’s foreign trade. The extensive use and integration of digital
technology is conducive to increasing my country’s export trade volume and optimizing
the export trade structure. China’s digital economy will continue to maintain a strong
momentum of development in the future, accurately enabling the steady development
of foreign trade.
2 Status of China’s Export Trade
2.1 China’s Total Export Trade
Exports play a positive role in promoting China’s economic growth. China’s trade in
goods and services exports have developed steadily. As can be seen from Fig. 1, China’s
export of goods declined slightly in 2015 and 2016, down by 2.9% and 7.7%, respectively,
© The Author(s) 2023
N. Radojevi´c et al. (Eds.): ICAID 2022, AHIS 7, pp. 117–126, 2023.
https://doi.org/10.2991/978-94-6463-010-7_14
118 Y. Tang and L. Liang
Fig. 1. Changes in China’s exports of goods and service from 2013–2020 (unit: US $100 million).
Data source: China Statistical Yearbook 2021, Ministry of Commerce data
mainly due to the impact of the more complex and severe international situation. In 2017,
it reversed two consecutive years of decline. China’s services exports maintained a steady
growth, reaching $394.27 billion in 2021, which was a 40.5% increase compared to last
year. According to the Ministry of Commerce, knowledge-intensive services account
for more than half, including personal cultural and entertainment services, intellectual
property royalties, and telecommunications computers and information services [5].
Although the share of services trade in China’s export trade has been increasing year
after year, the proportion is still low, with only 11.72% in 2021. This shows that China’s
competitiveness in service trade needs to be improved, including further optimizing its
export trade structure and transforming its economic growth mode. The development of
digital economy can effectively promote the growth of service trade exports. The wide
application of digital technology can break the restrictions of traditional service trade,
reduce transaction costs, improve transaction efficiency and service trade-ability [1].
2.2 Structure of Export Trade Mode
Processing trade is the important part of China’s open economy. With the continuous
development of digital economy and the deep integration of digital technology and
traditional industries, many foreign trade processing enterprises have carried out digital
network upgrading, and some enterprises have adopted the technical route of promoting
digital manufacturing and Internet+manufacturing, spanning the manufacturing stage
of Internet+manufacturing.
According to Fig. 2, general trade exports were $2049.848 billion, accounting for
60.9% of the total export in 2021, 33.4% higher than last year; processing trade exports
were $826.299 billion, accounting for 24.6%, 17.6% higher than last year. The proportion
of processing trade exports decreased, while the proportion of general trade exports
increased. In 2021, China’s general trade exports accounted for 60.9%, while processing
trade accounted for 24.6%, as shown in Fig. 3.
The development of digital economy has accelerated the development of industrial
automation. Many enterprises update intelligent equipment and improve the level of
The Influence of Digital Economy Development Level 119
Fig. 2. Changes in China’s export trade mode in 2015–2021 (unit: $100 million). Data source:
According to China statistical monthly report
Fig. 3. The proportion of China’s export trade mode from 2015 to 2021. Data source: According
to China statistical monthly report
automation. “Machine replacement” can effectively promote the technical transforma-
tion of enterprise production capacity, thus forming new advantages in the development
of processing trade.
2.3 Export Commodity Structure
The proportion of manufactured goods greatly exceeded that of primary products. In
2011, China’s export of primary products reached $100.55 billion, accounting for 5.3%,
and industrial products reached $1,797.84 billion, accounting for 94.7%, as shown in
Fig. 4. The proportion of China’s primary products in total exports has continued to
decline, and the proportion of manufactured goods in total exports has increased steadily.
Since 2012, China’s primary products have accounted for about 5% of its total exports,
while industrial goods have accounted for more than 95%. In 2021, China’s exports
of primary products reached $139.44 billion, down to 4.15% and exports of industrial
products reached $3228.79 billion, which increased to 95.98%. This shows that China’s
120 Y. Tang and L. Liang
Fig. 4. China’s total exports, primary products and industrial goods in 2011–2021 (US $100
million). Data source: China Statistical Yearbook 2021, China Statistical Monthly Report 2021
Fig. 5. Scale and Growth Rate of Cross-border E-commerce Transactions in China in 2015–2021
(Unit: 100 million RMB). Data source: 2020 China Cross-border E-commerce Market Data Report
export trade structure has shifted from the export of primary products to the export
of manufactured goods, and from labor-intensive manufactured goods to capital and
technology-intensive manufactured goods.
With the development of digital economy, digital technology will be widely used
in all aspects of socialized mass production, which will help to further optimize the
structure of China’s export commodities.
2.4 Export Cross-Border E-Commerce
Due to national policy support and improved market environment, China’s cross-border
e-commerce exports have maintained a trend of rapid expansion. According to online
economic statistics, the transaction scale of China’s export cross-border e-commerce
market increased from 4.5 trillion RMB in 2015 to 11.5 trillion RMB in 2021, with an
average annual growth rate of 18%, as shown in Fig. 5.
From 2015 to 2021, the scale of cross-border e-commerce transactions increased
rapidly. In recent years, the world economy has been continuously sluggish, coupled
with the impact of COVID-19 in 2020, and the escalating trade frictions, and China’s
The Influence of Digital Economy Development Level 121
foreign trade exports are facing severe challenges. With the support of the national digital
economy development strategy, cross-border e-commerce has become an important force
to enhance the development of foreign trade, and will effectively promote the further
optimization of China’s export trade structure [2].
3 Empirical Analysis of the Influence of Digital Economy
Development Level on China’s Export Trade
3.1 Sample Selection
This paper will establish a multiple regression model to explore the influence of digital
economy development level on China’s export trade, because the export trade has many
factors, so the digital economy development level index (digital economy index) as the
core variables of the model, join trade openness, general trade, processing trade, primary
products, industrial goods, explanatory variables, using multiple regression model empir-
ical analysis of digital economy development level on China’s export trade direction and
degree.
In the empirical analysis, the monthly data of seven variables from January 2016 to
December 2021 were selected as research data, totalling 504 data. Except for the data
of digital economy indicators from Caixin insight, all other data came from the General
Administration of Customs of China. Meanwhile, the 5% significance level is chosen
as the rejection probability of the multiple regression model. Accordingly, when the
decision to accept the null hypothesis is made, the probability of a correct conclusion is
95%.
3.2 Model Construction
The multiple regression model established in this paper is designed as follows:
ˆ
Y=c+β1∗X1+β2∗X2+β3∗X3+β4∗X4+β5∗X5+β6∗X6+μ
In the formula, ˆ
Yis the goods export variable, X1is the digital economic index
variable, X2is trade openness variable, X3is general trade variable, X4is processing
trade variable, X5is primary product variable, X6is manufactured product variable, c is
a constant term, β1is the parameter of the goods export volume variable to the digital
economic index, β2is the parameter of the variable of export to trade, β3is the parameter
of the goods export amount variable to the general trade variable, β4is the parameter of
the goods export amount variable to the processing trade variable, β5is the parameter
of the goods export amount variable to the primary product variable, β6is the parameter
of the goods export volume variable and μis the residual item.
3.3 Model Test Analysis
Since this paper collects time series data, it is necessary to test the stationarity of the
data. This paper uses EViews software to perform unit root test, and the results are as in
Table 1.
122 Y. Tang and L. Liang
Tabl e 1. Test of unit root for all variables
Symbol ADF value Conclusion
Y−4.406983 steady
X1 0.754695 non-steady
X2 −4.564 steady
X3 3.8507 non-steady
X4 −6.016714 steady
X5 1.3477 non-steady
X6 −5.899510 steady
DX1 −9.0324 steady
DX3 −7.2506 steady
DX5 −2.2571 steady
Note: DX1, DX3, and DX5 are the first-order differential
sequences of X1, X3, and X5.
Date: 04/15/22 Time: 23:26
Sample (adjusted): 2016M04 2021M12
Included observations: 69 after adjustments
Trend assumption: Linear deterministic trend
Series: Y 1 X1 X2 X3 X4 X5 X6
Lags interv al (in first diff erenc es): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.753097 213.7516 125.6154 0.0000
At most 1 * 0.501918 117.2371 95.75366 0.0008
At most 2 0.322738 69.14477 69.81889 0.0565
At most 3 0.284791 42.25566 47.85613 0.1517
At most 4 0.145225 19.12820 29.79707 0.4837
At most 5 0.103654 8.300948 15.49471 0.4338
At most 6 0.010815 0.750325 3.841466 0.3864
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothes is at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Fig. 6. Results of the cointegration test for the model
The Influence of Digital Economy Development Level 123
Dependent Variable: Y
Method: Stepw ise Regression
Date: 04/15/22 Time: 23:34
Sample: 2016M01 2021M12
Included observations: 72
No alw ays included regr essors
Number of s earch regr essors: 7
Selection method: Stepw ise forw ards
Stopping criterion: p-v alue forw ards/backw ards = 0.05/0.051
Variable Coeff icien... Std. Error t-Statistic Prob.*
X3 0.000752 7.56E-05 9.943000 0.0000
X2 4137636. 475221.9 8.706745 0.0000
X6 0.000118 4.08E-05 2.885532 0.0053
X1 27.68717 7.100293 3.899440 0.0002
C -29048.16 8219.594 -3.534013 0.0007
R-squared 0.976249 Mean dependent var 212514.8
Adjusted R-squared 0.974831 S.D. dependent var 45517.84
S.E. of r egression 7221.349 Akaike info criterion 20.67439
Sum squared r esid 3.49E+09 Schw arz c riterion 20.83249
Log likelihood -739.2779 Hannan-Quinn criter. 20.73733
F-statistic 688.4706 Durbin-Watson stat 2.137243
Prob(F-s tatistic) 0.000000
Selection Summary
Added X3
Added X2
Added X6
Added X1
Added C
*Note: p-values and subsequent tests do not account f or stepw ise
selection.
Fig. 7. Parameter estimation results for the model
According to the unit root test, Y, X2,X4,X6original sequence is smooth, and X1,
X3,X5are the first order single whole. And then do the cointegration test (Fig. 6).
The results of the cointegration test show that the above variables can establish a
regression model. Parameter estimation using Eviews yields the results in Fig. 7.
From the above parameter estimation results, obtain the following regression
equation:
ˆ
Y=−29048.16 +27.69X1+4137635.89X2+0.00075X3+0.00012X6
Best of fit test: Model adjustment determination coefficient is 0.9748, with high
goodness of fit.
Equation significance test: the F-value is 688.47, corresponding to the p-value is less
than 0.05, and the overall linear relationship of the equation is significantly established.
124 Y. Tang and L. Liang
Significant test of variables: the linear effects of X1, X2, X3 and X6 on Y were
significant. Digital economy has a positive impact on export trade. For every additional
unit of digital economy, export trade has increased by an average of $27.69 million.
3.4 Empirical Results
In the multiple regression model, the variables are stable and cooperative; the goodness
of fit of the model is high, the overall linear relationship of the equation is significant, and
the linear influence of digital economy, trade openness, general trade and manufactured
goods on goods exports is significant.
According to the parameter estimation of the model, it can be concluded that digital
economy, trade openness, general trade, manufactured goods have a positive impact on
export trade, it also shows that the digital economy development level has a significant
impact on China’s export trade, and present a positive impact, namely the digital economy
growth, export trade will grow.
Trade openness is the proportion of a country’s total import and export trade in total
GDP. The higher the value, the higher the country’s trade openness, the more significant
the impact on export trade. The regression analysis results of this model also verify the
conclusion that the higher the value of trade openness, the more significant the impact
on export trade [3].
4 Policy Recommendations
4.1 Increase the Financial Input in the Development of Digital Technology
Help enterprises to accelerate the deep integration with the new generation of infor-
mation technology. The development of digital technology requires not only the inter-
nal scientific and technological research and development activities of enterprises, but
also the support and assistance of government departments. The government needs to
increase its support for the development of digital technology and help enterprises to
accelerate the process of deep integration with the digital economy. Only by enhancing
the digital competitiveness of enterprises can they improve the labour productivity of
enterprises, reduce the transaction costs of enterprises’ exports, and thus promote export
trade. Continue to grow and create new advantages in export competition.
4.2 Pay Attention to the Further Opening of Import Trade
It is very important to change the import trade from “big in and big out” to “excellent
in and excellent out”. While using the digital economy for industrial transformation
and upgrading, we will shift from extensive development mode to intensive develop-
ment mode, from processing and manufacturing at the low end of the value chain to
independent innovation at the middle and high end, so as to create new trade growth
points.
The Influence of Digital Economy Development Level 125
4.3 Cultivate Professional Talents for Digital Innovation
It is recommended to add digital economy-related disciplines to the category of higher
education and set up relevant majors. At the same time, focusing on the training goals
of digital professional talents, carry out industry-university-research cooperation in the
field of digital economy, so as to realize the symbiosis and integration of universities
and digital economy industrial parks, so as to create digital competition new advantage,
inject new development impetus into China’s export trade [4].
5 Conclusions
This paper selects the monthly data of goods export volume, general trade, processing
trade, primary products, manufactured products, and trade openness from January 2016
to December 2021 as samples, and establishes a model based on trade openness, general
trade export volume, the export value of processing trade, the export value of primary
products, and the export value of manufactured products are the explanatory variables, the
export trade value is the explained variable, and the digital economy development level
index (digital economic index) is the regression model of the core explanatory variable
to discuss the digital economy. The direction and extent of the impact of development
level on China’s export trade. The empirical results show that the development level of
the digital economy plays a significant role in promoting general trade exports, process-
ing trade exports, and primary product exports. Aside from the variable of the digital
economy, which has a significant impact on export trade, it is worth paying attention to
the impact of the variable trade openness on export trade. The higher the degree of trade
openness of a country, the more it can promote the development of the country’s export
trade, and there is a more significant relationship between the two. Therefore, this paper
further puts forward relevant policy suggestions based on the empirical results: increase
financial investment in the development of digital technology to help enterprises accel-
erate the in-depth integration with the new generation of information technology; attach
importance to the further opening of import trade, and shift from “big in and big out” to
“Excellent in and excellent out”; cultivate professional talents in digital innovation and
create new advantages in digital competition.
Acknowledgements. The research of this paper has funded by the 2016 “Public Management”
construction project of Guangdong Province and Guangzhou Xinhua University Teaching Reform
Project “Research on Problem-Oriented Mixed Teaching Mode Based on Network Platform -
Taking “E-commerce” Course as an Example” (2021J019).
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