Research Article |
Corresponding author: Sofya Karlovskaya ( karlovskayasofi@mail.ru ) Academic editor: Marina Sheresheva
© 2024 Sofya Karlovskaya, Alexander Chelombitko.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Karlovskaya S, Chelombitko A (2024) Prospects for the development of cross-border e-commerce between Russia and China. BRICS Journal of Economics 5(2): 45-63. https://doi.org/10.3897/brics-econ.5.e119490
|
The paper examines the prospects and peculiarities of the development of cross-border e-commerce between Russia and China. The degree of e-commerce impact on trade turnover between Russia and China is assessed using an econometric model based on the analysis of trade flows between BRICS countries in the period from 2000 to 2022. The results obtained indicate that with an increase of 1% in the number of Internet users in Russia and its trading partners from the BRICS countries, the trade turnover increases by 0.19%. The paper also identifies the key challenges faced by entrepreneurs engaged in cross-border e-commerce between Russia and China and offers guidelines for these countries’ cooperation in the field of e-commerce. Based on the analysis of the relevant data the paper concludes that integration of digital technologies and increased access to the Internet contribute to the strengthening of trade ties between Russia and the BRICS countries, thus opening up new prospects for the development of cross-border e-commerce.
Данная статья посвящена исследованию перспектив и особенностей развития трансграничной электронной коммерции между Россией и Китаем. Степень влияния электронной коммерции на товарооборот между Россией и Китаем оценивалась с помощью эконометрической модели основанной на анализе торговых потоков между странами БРИКС в период с 2000 по 2022 годы. Полученные результаты свидетельствуют о том что при увеличении на 1% количества пользователей Интернета России и ее торговых партнеров среди стран БРИКС товарооборот между ними увеличивается на 0 19%. В статье также обозначены ключевые вызовы и проблемы с которыми сталкиваются предприниматели осуществляющие трансграничную электронную коммерцию между Россией и Китаем и предложены основные направления сотрудничества между странами в сфере электронной коммерции. Исходя из анализа данных представленного в данной статье можно сделать вывод что интеграция цифровых технологий и расширение доступа к Интернету способствуют укреплению торговых связей между Россией и странами БРИКС что открывает новые перспективы для развития трансграничной электронной коммерции.
BRICS, cross-border e-commerce, digital economy, e-commerce, gravity model, Sino-Russian trade relationships
БРИКС, трансграничная электронная торговля, цифровая экономика, электронная коммерция, гравитационная модель, российско-китайские торговые отношения
Today’s global online commerce market is one of the fastest growing sectors of the global economy. UNCTAD estimates that the global online commerce in 2019 amounted to approximately $26.7 trillion and 30% of global GDP. In recent years, China’s e-commerce market has become one of the largest in the world, having grown from $4.56 trillion to $6.66 trillion between 2018 and 2022. The Russian e-commerce market is also showing high growth rates. In the period from 2018 to 2022, it has tripled in size amounting to $59.64 billion. The potential for cross-border e-commerce between Russia and China is rather high, given the size of the economies of these countries and the growing demand for Chinese goods from Russian consumers caused by the economic sanctions against Russia in 2022. However, there are challenges that may hinder the development of e-commerce between the countries, including differences in the regulatory framework, logistics infrastructure and cultural factors.
The purpose of this study is to examine the features of China and Russia’s cooperation in cross-border e-commerce, identify its prospects and assess its impact on the trade turnover between the countries. The results of this study may be useful both for entrepreneurs engaged in cross-border e-commerce and for government authorities that regulate this industry.
There are different approaches to defining e-commerce, as well as different ways of measuring it. Statista defines e-commerce as the sale of physical goods through a digital channel to a private end user (B2C). This definition covers purchases via desktop computers (including laptops and laptops) as well as purchases via mobile devices. In our view, this definition is too narrow as it misses an important aspect of B2B trade, which accounts for more than 80% of total global e-commerce. UNCTAD defines e-commerce as the sale or purchase of a good or service conducted over computer networks by methods specifically designed to receive or place orders. This definition includes trade in goods and services with both individuals and organizations.
We base our study on the analysis of prior academic research on the topic of e-commerce. Chen Y. points out the increasing trend in cross-border e-commerce research from 2019. In 2021, due to the effects of the coronavirus pandemic, the topic of cross-border e-commerce has attracted considerable attention from researchers (
Hazarika B. and Mousavi R. provide an analytical review of academic publications on e-commerce and highlight the key factors contributing to the development of cross-border e-commerce (
He X. and co-authors analyze the dynamics of cross-border e-commerce (CBEC) research development over different time periods and categorize the field into five different research clusters (
Zou M. notes in his study that the performance of cross-border e-commerce in the eastern region of China is superior to that of the central and western regions. This is due to the higher internet penetration rate, the intensity of foreign direct investment and the significant number of government programs to support e-commerce (
Zhao J. emphasizes that the “One Belt, One Road” initiative is seen as a key point in the study of cross-border e-commerce development between China and Russia (
Liu A. and co-authors point out that the main obstacles to China’s cross-border e-commerce development are low customs clearance efficiency, high logistics costs and associated risks (
Kashin V. and Yankova A. identify key obstacles to the development of cross-border e-commerce between Russia and China, including the limited market capacity of Russian border areas, insufficiently developed legal and regulatory framework for Russian-Chinese cross-border cooperation, and low implementation rates of joint projects (
Lei Y. and Qiu X. apply machine learning algorithms to analyze China’s cross-border e-commerce and evaluate China’s trade relations with 62 countries (
He Y. and Wang J. carry out the panel analysis of cross-border e-commerce using ASEAN countries as a case study. The researchers find that GDP and real exchange rate have a significant impact on cross-border e-commerce. However, population size and terms of trade exclusively affect cross-border e-commerce imports (
Wang C. and co-authors analyze the impact of cross-border e-commerce on China’s exports using the gravity trade model. The study shows that cross-border e-commerce has a significant impact on China’s export performance. The results indicate that for every 1% increase in the number of Internet users among China and its trading partners, China’s export volume increases by 0.28% (
Yin Z. and Choi C. examine the impact of China’s cross-border e-commerce (CBEC) on exports of goods and services to Belt and Road Initiative (B&R) countries from 2000 to 2018 using a gravity model. Their analysis shows that CBEC has a stronger positive impact on trade in services than on trade in goods (
Also, there is a number of companies, such as Data Insight, Asia Pacific, AdIndex, NetEconomy, that provide detailed reports on the Russian and Chinese e-commerce markets.
The study employed both quantitative and qualitative research methods. The global e-commerce market was analyzed using UNCTAD statistical data. To analyze the Russian and Chinese markets, data from the Online Retail Association and the Electronic Commerce Research Center were used (
The e-commerce market has been growing steadily in recent years, driven by the epidemic situation in the world and the development of digital technologies. According to a UNCTAD report, the global e-commerce market was $26.7 trillion in 2019 (
The major players in the global e-commerce market in 2022 were Walmart, Amazon, Apple, The Home Depot, JD.com, and Alibaba (Figure
Cross-border e-commerce was also showing high growth rates. In 2019, cross-border online commerce totaled $440 billion, up 9% year-on-year.
One of the key drivers of cross-border e-commerce growth is the emergence of online marketplaces such as Amazon, Alibaba and eBay, which have enabled sellers and buyers to connect across borders and facilitate cross-border transactions. These marketplaces have also allowed small businesses to enter global markets, levelling the playing field and creating new opportunities for growth.
Today, the increasing internet penetration and availability of smartphones are expected to drive further growth as the majority of millennials use smartphones and tablets to order goods. The market is also driven by the growing preference for online shopping due to the influence of social media on the purchasing habits of customers. Besides, the growing ease and affordability of international shipping, availability of information about overseas products and prices, and development of online payment systems that enable cross-border transactions are also contributing to the expansion of the cross-border e-commerce.
Yet, the development of the global e-commerce market faces a number of obstacles, including regulatory challenges, logistical complexities, cultural and language differences, and data privacy and security concerns. Regulatory barriers often arise from differences in national trade policies, taxes and e-commerce standards, making it difficult for businesses to operate seamlessly across borders. Logistical challenges, such as high cost and delivery times, reduce the efficiency of cross-border e-commerce. Cultural and language differences require businesses to tailor marketing strategies and localize websites to effectively communicate and resonate with consumers in different regions. Concerns about protecting personal information and the fear of data breaches may deter consumers from engaging in online transactions.
China’s e-commerce market has grown significantly in recent years. According to a report published on the website of China E-Commerce Research Center, the market size in 2022 reached $6.65 trillion (
Chinese e-commerce market in 2018-2022. Source: China E-Commerce Research Center. Retrieved from http://www.100ec.cn/zt/2022dzswscbg/
China’s cross-border e-commerce market has increased by 74% over the past 5 years to reach a value of $2.198 trillion (Figure
Chinese cross-border e-commerce market in 2018-2022. Source: China E-Commerce Research Center. Retrieved from http://www.100ec.cn/zt/2022dzswscbg/
According to CNNIC, the number of online shoppers in China crossed the 900 million mark in 2022, accounting for 86.0% of the country’s total Internet users (China Internet Network Information Center, 2022).
An important target group of the booming e-commerce sector in China is the mobile user segment. As of 2021, about 69 percent of e-commerce transactions were conducted via mobile devices, and this share is expected to rise to 75 percent by 2025 (
The domestic platforms dominating the Chinese e-commerce market are Taobao and Tmall (Alibaba companies), accounting for 50.8% of the market share. They are followed by JD.com and Pinduoduo with 15.9% and 13.2% of the market respectively (
A distinctive feature of the e-commerce market in China is the use of domestic social media for marketing strategy. WeChat is China’s most popular mobile social network, followed by Twitter-like Weibo, messaging app QQ, and short video app Douyin (Tiktok). Other platforms, such as Xiaohongshu, are specifically designed to optimize the intersection of e-commerce and social media.
Another factor of e-commerce expansion is China’s own payment systems, such as Alipay, WeChat Pay and China UnionPay. Alipay and WeChat Pay allow users to make online and offline payments using QR codes, facial recognition or phone numbers. China UnionPay unites all banks and payment service providers in China and supports cross-border transactions and international cooperation with other types of cards.
Russia’s e-commerce market is the 14th largest in the world, with total sales expected to reach $42.46 billion in 2023 (
E-commerce market value in Russia in 2018-2022. Source: Association of Internet Trade Companies. Retrieved from https://www.akit.ru/
The Russian market of cross-border e-commerce has decreased over the last 5 years from $6 billion to $2 billion (Figure
Cross-border e-commerce market value in Russia in 2018-2022. Source: Association of Internet Trade Companies. Retrieved from https://www.akit.ru/
A distinctive feature of Russia’s online commerce market is the predominance of domestic commerce - about 96% of the market in 2022. Cross-border e-commerce held 3.55% of the market in the same year (Figure
Share of cross-border trade in e-commerce in Russia in 2018-2022. Source: Association of Internet Trade Companies. Retrieved from https://www.akit.ru/
The share of online commerce in Russia’s GDP has also grown: in 2019 it was 2.5% of GDP, while in 2022 it increased to 3.9% (
In recent years, Russia has seen significant growth in online shopping: in 2013, 10% of urban residents between 16 and 55 years old made online purchases more than once a year; in 2021, this figure increased to 52% (Figure
E-commerce penetration in Russia from 2013 to 2021. Source: Yandex. Retrieved from https://yandex.ru/company/researches/2021/ecomdash#whatToBuy
The Wildberries and Ozon marketplaces have a significant share of the online shopping market in Russia. In November 2022, the share of online orders made through these marketplaces amounted to 75% of all online orders. Data Insight estimated that Wildberries and Ozon would have a 53% share of revenue and 77% of online orders in 2023 (Data Insight, 2023).
After the departure of Apple Pay, Google Pay, Visa and Mastercard from Russia, the use of proprietary payment systems has increased. Due to the transition of public sector employees to Mir cards, the share of this payment system in the Russian market increased to 25% (Bank VTB, 2023). However, Visa and Mastercard cards can still be used when making online orders within the country.
Although Russia and China have increased cooperation in e-commerce in recent years, there are several challenges faced by entrepreneurs in this field:
Despite all these difficulties, there are good prospects for cooperation between China and Russia in cross-border online commerce, especially in the following areas:
Although this study was initially focused on analyzing e-commerce between Russia and China, the sample was expanded to include the other BRICS countries in order to increase the number of observations and improve the quality of data. This expansion broadened the base for the analysis and made it more representative, facilitating the construction of a more accurate model.
In order to find out whether the development of e-commerce contributes to the growth of Russia’s trade turnover with the BRICS countries, a gravity model of trade was chosen:
ln(TRADEijt) = β0 + β1ln(GDPitGDPjt) + β2ln(ECitECjt) + β3ln(POPitPOPjt) + + β4ln(REX)ijt + β5CBijt + β6lnDISTANCEijt + μi + εit, (1)
where j denotes Russia and the dependent variable TRADEijt denotes trade turnover between Russia and one of the BRICS countries (Brazil, China, India, South Africa) in year t. β0 is a constant, β1, β2, β3, β4, β5, β6 are estimated coefficients under explanatory variables.
The variables GDPit and GDPjt denote the GDP of country i and Russia in period t and POPit represent the population of country i and Russia in period t. REXijt denotes the ruble exchange rate, CBijt is a dummy variable that equals one if Russia shares a border with country i and zero if it does not. DISTANCEijt represents the distance between the capital city of Russia and country i. ECit and ECjt denote the level of e-commerce use by Russia and country i.
In this model, the variables ECit and ECjt play a key role, reflecting the level of e-commerce development in Russia and the BRICS countries, respectively. According to the transaction cost theory, the integration of e-commerce into trade processes can reduce trade barriers and simplify search and transaction procedures, thereby increasing the total volume of trade. The variables ECit and ECjt are expected to have a positive effect on TRADEijt.
Other explanatory variables in the model are also important. The DISTANCEijt variable reflects the geographical distance between Russia and the BRICS countries, which is traditionally considered a significant factor in trade. However, given digitalization and the development of e-commerce, its influence may be weakened.
The CBijt variable as a binary indicator of common border can affect trade by reducing logistics and cross-border costs. In the case of Russia and BRICS countries, this variable will show differences in trade flows based on geographical proximity.
REXijt, reflecting the exchange rate, also plays an important role. Fluctuations in exchange rates can influence the competitiveness of goods, which in turn affects the volume of trade.
To analyze the impact of e-commerce development on trade turnover between Russia and the BRICS countries, data for the period from 2000 to 2022 are used. The main source of trade data is the UN COMTRADE database (
The key variable of the study is EC, representing the level of e-commerce development. Given that e-commerce is based on Internet technologies, the level of Internet penetration is considered a proxy variable for EC. Internet penetration data were collected from the World Bank database (
The distance between Russia and the BRICS countries, important for analysis in the context of the gravity trade model, was obtained from geographic databases. The main variables are described in Table
Variable | Description | |
EC | Number of Internet users per 100 people | |
GDP | GDP of the country, USD | |
POP | Population of the country, people | |
REX | Ruble exchange rate | |
CB | Presence of a common border between Russia and one of the BRICS countries | |
DISTANCE | Distance between Moscow and one and BRICS countries |
To determine whether multicollinearity exists between the variables in model (1), the correlation matrix shown in Table
lnGDP | lnPOP | lnEC | lnREX | CB | lnDISTANCE | |
lnGDP | 1 | 0,5741 | 0,6062 | 0,1697 | 0,5070 | -0,3290 |
lnPOP | 0,5741 | 1 | -0,0880 | -0,4048 | 0,5575 | -0,6663 |
lnEC | 0,6062 | -0,0880 | 1 | 0,3852 | 0,0538 | 0,2133 |
lnREX | 0,1697 | -0,4048 | 0,3852 | 1 | 0,1796 | 0,6806 |
CB | 0,5070 | 0,5575 | 0,0538 | 0,1796 | 1 | -0,3425 |
lnDISTANCE | -0,3290 | -0,6663 | 0,2133 | 0,6806 | -0,3425 | 1 |
The table shows that all the correlation coefficients between different variables are less than 0.7, so there is no obvious multicollinearity between the variables.
We conducted a unit root test for the variables used in the model, which includes LLC-test and IPS-test. The null hypothesis is that the series is non-stationary and the alternative hypothesis is that the series is stationary. The lag order was determined using the Schwartz information criterion. The results of the test are shown in Table
Variable | LLC-test | IPS-test | ||
Levels | 1st differences | Levels | 1st differences | |
lnTRADE | -1,82** | -5,30*** | 0,08 | -5,10*** |
lnGDP | -3,60*** | -5,14*** | -1,53 | -3,51*** |
lnEC | -7,12*** | -2,51*** | -5,63*** | -2,38*** |
lnPOP | -2,01** | -2,38*** | 0,63 | -2,00** |
lnREX | -0,69 | -3,75*** | -0,24 | -4,52*** |
The null hypothesis is rejected for the lnEC series at the 1% significance level. The lnTRADE, lnGDP, lnPOP, lnREX series are 1st order integrated, while the lnEC series is zero order integrated.
In our study, we used Johansen test to detect cointegration among variables, which includes two approaches: trace test and max-eigen test. We tested whether the number of cointegrating equations is less than a given number ‘r’ without considering the constant and trend. The alternative hypotheses were that the number is not less than ‘r’ (trace test) or equal to ‘r+1’ (max-eigen test). The results of our test led to rejection of the null hypothesis for all values of ‘r’ from zero to four as indicated in Table
Number of cointegration equations | Trace test | Max-eigen test | ||||
F-statistics | p-value | F- statistics | p-value | |||
None | 135,6 | 0,00 | 66,09 | 0,00 | ||
At most 1 | 87,44 | 0,00 | 40,01 | 0,00 | ||
At most 2 | 56,63 | 0,00 | 24,25 | 0,00 | ||
At most 3 | 40,67 | 0,00 | 25,71 | 0,00 | ||
At most 4 | 26,12 | 0,00 | 26,12 | 0,00 |
To account for the impact of the crisis on trade between Russia and the BRICS countries, model (1) was augmented with a dummy variable D, which is equal to one in 2008, 2009, 2015, 2020 and 2022 and zero otherwise. Dummy interaction variables were also added to the new regression: D * ln(GDPitGDPjt), D * ln(ECitECjt), D * ln(POPitPOPjt) и D * ln(REX)ijt. The obtained model (2) looks as follows:
ln(TRADEijt) = β0 + β1ln(GDPitGDPjt) + β2ln(ECitECjt) + β3ln(POPitPOPjt) + + β4ln(REX)ijt + β5CBijt + β6lnDISTANCEijt + β7D + β8D * ln(GDPitGDPjt) + β9D * ln(ECitECjt) + β10D * ln(POPitPOPjt) + β11D * ln(REX)ijt + μi + εit, (2)
To choose between Pooled least squares and fixed effects regression for models (1) and (2), the Breusch-Pagan test was performed, and the results in both cases favor the first regression estimation method. The results of model estimation are shown in Table
Regressors | (1) Pooled least squares | (2) Pooled least squares |
CONST | -80, 3423*** (14,2939) |
-72,6382*** (13,3786) |
ln GDP | 0,4409*** (0,0878) |
0,3075*** (0,0957) |
ln EC | 0,1391* (0,0712) |
0,1919*** (0,0697) |
ln POP | 1,3059*** (0,1969) |
1,3684*** (0,1809) |
ln REX | -0,3746* (0,1916) |
-0,2235 (0,1847) |
CB | 1,0809*** (0,2706) |
1,0160*** (0,2434) |
ln DISTANCE | 3,0498*** (0,9082) |
2,6868*** (0,8514) |
AR (1) | 0,6907*** (0,0887) |
0,6701*** (0,0903) |
D | -6,6624** (2,7894) |
|
D * ln GDP | 0,401*** (0,1311) |
|
D * ln EC | -0,1025** (0,0503) |
|
D * ln POP | -0,3841*** (0,1266) |
|
D * ln REX | -0,2642*** (0,0802) |
|
R2 | 0,9869 | 0,9885 |
F-statistics | 936,7994 | 624,2867 |
In both models the EC variable is significant, F-statistics show that the equations as a whole are also significant. The extended regression gives more accurate results because the dummy variables related to the crisis were found to be significant.
Model (2) shows that a 1% increase in the number of Internet users among Russia and its trading partners among the BRICS countries increases trade turnover by 0.19%.
The results of this study provide evidence of the significant impact of digitalization on international trade, specifically highlighting the role of e-commerce in enhancing trade turnover between Russia and its BRICS trading partners. A noteworthy finding is that a 1% increase in the number of Internet users among these countries corresponds to a 0.19% increase in trade turnover. This underscores the importance of digital connectivity in facilitating cross-border trade and supports the notion that e-commerce is an important driver of international trade dynamics.
Comparing the results of this study with prior research reveals some interesting insights. For instance, He Y. and Wang J.’s analysis of ASEAN countries, utilizing data from 1996 to 2016, highlights the significance of GDP and real exchange rate on cross-border e-commerce (
Wang C. and co-authors’ study (
On the other hand, Yin Z. and Choi C.’s research (
Additionally, this study incorporates dummy variables indicating the presence of a common border and crisis dummy variables, which adds a layer of complexity to the analysis. These variables help to account for geopolitical and economic events that could influence trade patterns, providing a more nuanced understanding of the factors driving international trade turnover. This distinction underscores the importance of considering a variety of factors, both economic and non-economic, in understanding the dynamics of cross-border e-commerce and international trade.
However, the database used to build the econometric model in my research includes indicators for the BRICS countries but does not sufficiently reveal the specifics of the impact of e-commerce on trade turnover between Russia and China. In further research, we plan to refine the model using modern quarterly data to obtain more relevant results. This data can be obtained from national statistical authorities, taking into account the limitations of open access, to analyze the dynamics of trade turnover of the countries under consideration more accurately, as international statistics often publish data with a significant time lag.
The study confirmed the significant impact of e-commerce on international trade turnover between Russia and China. E-commerce is demonstrating dynamic growth in both B2C and B2B segments, significantly expanding the boundaries of international trade and providing new opportunities for businesses and consumers.
Contemporary challenges such as the COVID-19 pandemic and rapid development of information technology have become catalysts for accelerating the digitalization of trade processes and the increasing role of e-commerce in international economic relations. These factors have contributed to rethinking and adapting trade strategies, and have spurred innovation in digital platforms and logistics.
The dynamics of e-commerce and cross-border commerce development in Russia and China have shown differences caused, among other things, by the degree of rigidity of covid restrictions and different scales of national markets. These differences point to utmost importance of taking into account national peculiarities and flexibility of the regulatory environment for the development of e-commerce.