Does the Belt and Road Initiative Promote Bilateral Trade? An Empirical Analysis of China and the Belt and Road Countries

Abstract

The development of trade is an essential element of the Belt and Road Initiative (BRI) and a significant effort by China to promote inter-regional economic development. Using Chinese trade data from 190 countries and regions worldwide from 1993 to 2021, this article quantitatively analyzes (a) if and to what extent the BRI has promoted bilateral trade development, (b) how trade with China has contributed to the country’s economic growth and (c) what commodity and regional heterogeneity exist in these effects. The empirical results show that the BRI significantly increases China’s trade with the countries of the Belt and Road Initiative and countries along the Belt and Road route and significantly increases imports and exports in all five commodity categories—primary goods, resource-intensive manufactured goods, low-tech, medium-tech, and high-tech manufactured goods. Meanwhile, the trade volume with China substantially contributes to the economic development of the trading countries. However, Asia, Europe, and Africa show different characteristics in all three aspects mentioned above. These results show that the BRI not only benefits China’s international trade but also promotes the trade development of the involved countries. It clarifies the crucial role this open, inclusive inter-regional economic cooperation plays in global trade growth when facing the escalating anti-globalization sentiment.

Keywords

Belt and Road Initiative (BRI)international trade economic development gravity model

Introduction

Since the implementation of the Chinese reform policy and opening up in 1978, China’s economic development has been fast and is attributed to the rapid growth of international trade. Estimates show that between 1997 and 2001, China’s imports accounted for an average of 10.23% of its GDP, while its exports were 14.22% (Wu & Shen, 2004). International trade has become the driving force for sustaining China’s high economic growth. At the same time, stabilizing and expanding export markets is an essential guarantee for promoting China’s economic growth. Before 2008, the demand for Chinese goods in international markets was typically steady and robust, notwithstanding some fluctuations.

However, the financial crisis in 2008 resulted in a long-lasting negative impact on the global economy and trade development, and China’s economic growth also slowed down. Stabilizing exports became a high priority for the Chinese government. The Belt and Road Initiative (BRI) was a natural response to this situation. On the one hand, it could, to a certain extent, help China find larger overseas markets and trading partners for its exports, which is conducive to rebalancing China’s trade and creating a new model of regional cooperation. On the other hand, given the weak global economic growth, the BRI was also in the interest of most countries in the world. It will have a significant and far-reaching impact on improving China’s position in the global economic landscape. Economic and trade cooperation is an integral part of the BRI. By developing economic and trade relations with countries of the Belt and Road Initiative (BRCs) and countries along the Belt and Road route (BRACs) and gradually weakening trade barriers, China will be able to explore the trade potential of involved countries and increase the willingness of both sides to pursue further trade development.

The BRI has promoted trade and investment liberalization and facilitation in countries and regions that joined the BRI by signing a memorandum of understanding (MOU) and those located along the Belt and Road route.¹ BRI generally has reduced transaction and business costs, unlocked trade development potential, and enhanced the breadth and depth of their countries’ participation in economic globalization. The scope of trade is growing as the BRI makes progress. From 2013 to 2021, China’s total trade in goods with BRACs reached US$10.4 trillion, with an average annual growth rate higher than China’s foreign trade growth during the same period, accounting for 27.4% of China’s total trade goods. In 2021, China’s total import and export of goods with BRACs reached US$1.8 trillion, with a 23.6% year-on-year increase. As the scale of trade continues to grow, innovation in trade is becoming more apparent. 2019 saw RMB 186 billion of goods imported and exported at retail through China Customs’ cross-border e-commerce management platform. By 2021, this figure reached RMB 1.98 trillion, 10.65 times more than in 2019. China’s exports accounted for 73% of the total e-commerce trade. “Silk Road e-commerce” cooperation is flourishing where China has established bilateral e-commerce cooperation mechanisms with 22 countries on five continents. Cross-border e-commerce and other new business models are becoming increasingly important in promoting smooth trade flows, adding fresh impetus to trade cooperation between China and the BRACs.

This article focuses on the trading effect of BRI based on the assumption that BRI—as a regional cooperation policy—can increase the bilateral trade scale by introducing more infrastructure and reducing trade barriers. Apart from investigating the general trade scale, this article explores different commodities and countries in various areas to examine regional heterogeneity. The next section presents the literature review relevant to trade development in the context of BRI. This is followed by the section describing the development and the current status of the trade between China and BRACs. The following two sections empirically analyze BRI’s role in promoting trade and economic development, and finally, the conclusions are presented.

Literature Review

BRI is an opportunity for China to actively participate in global openness and cooperation, which has attracted much research attention. Current literature focuses on three perspectives. First, many studies are based on the classical gravity model, focusing on the trade impact factors of China and the Belt and Road countries. For instance, Chong et al. (2019) comprehensively analyzed the main economic and political factors affecting the Belt and Road trade. Liu et al. (2020) found that cultural distance can significantly promote the trade volume between China and countries on the Belt and Road route, while the negative effect of geographical distance is significant. Yu and Cao (2015) analyzed the role of RMB internationalization in the trade with BRCs. Second, studies examined the trade competitiveness and complementarities between China and relevant countries, focusing on the status quo analysis and the dynamic change trend of trade relations (Chen et al., 2020; Liu et al., 2018; Mohamad & Zainuddin, 2021). Third, several papers quantitatively analyzed the increment of regional trade brought by the BRI. For example, Herrero and Xu (2019) showed that with the infrastructure development relating to the BRI, the trade volume between EU landlocked countries and China had increased significantly, and the countries in Eastern Europe and Central Asia have also benefited from trade with China. Baniya et al. (2020) analyzed the trade impact of the BRI on 71 potential participating countries and anticipated an increase of 4.1% in the trade between participating countries.

One aspect of the research literature relevant to this study is the policy evaluation of the BRI from the perspective of trade development. Wu et al. (2020) studied the trade data between China and BRACs through PSM and DID methods and found a significant positive effect on imports and exports. A study by De Soyres et al. (2019) on trade costs found that BRI significantly reduced the shipment time and, therefore, the aggregate trade costs for BRCs and the world. Belt and Road economies located along the corridors (i.e., the BRACs in this article) where projects are built experience the most significant gains. Boffa (2018) found that BRI significantly boosted China’s exports to participating BRCs. Bastos (2020) asserts that China’s growing import demand boosted the exports of Belt and Road economies, but this effect was attenuated by increased competition in export markets. Rehman and Noman (2021) investigated the impact of BRI on export from the perspective of infrastructure during 1990–2017 by applying the system GMM approach. Their results confirmed a significant and positive impact of aggregate and sub-indices of infrastructure on export, providing evidence of the mechanism through which the BRI works on trade. However, there is a lack of research on the heterogeneity of how the BRI has contributed to trade growth and how to trade growth has contributed to regional economic development. The research literature in this regard is insufficient.

Trade Development Between China and the Belt and Road Countries

Overall Trade Scale

Figure 1 reports the history of China’s imports and exports with 65 countries along the Belt and Road Route (BRACs). In terms of aggregate changes, according to IMF data (IMF, 2022), China’s imports and exports from the 65 BRACs were on a year-on-year upward trend until 2008. Imports rose from US$10 billion in 1992 to US$262.3 billion in 2008, and exports from US$10.9 billion in 1992 to US$336.4 billion in 2008. Both import and export volumes declined in 2009 with the global financial crisis and then resumed a yearly upward trend from 2010 to 2014. Import and export volumes reached US$483.7 billion and US$641.9 billion, respectively, in 2014, before falling in 2015 and 2016 and resuming an upward trend after 2017. By 2021, China’s imports and exports to BRACs reached US$772.8 billion and US$1032.5 billion, respectively.

Figure 1.

Source: Author’s illustration using IMF Direction of Trade Statistics (2022).

Regarding trade share, China’s exports to BRACs account for an overall increase in the percentage of China’s total exports and maintain a large surplus. According to IMF data (IMF, 2022), in 2021, the total value of China’s imports and exports reached US$6.05 trillion, whereas trade with the BRACs amounted to US$1.81 trillion. It accounted for 29.86% of China’s total foreign trade, with an annual increase rate of 1.80%. From 2019 to 2021, despite the economic downturn caused by the pandemic and the severe disruption of trade in goods, China’s trade with BRACs rose rather than fell, with total trade with those countries rising by 1.04% in 2020 compared to 2019 and 32.15% in 2021 compared to 2020. In 2021, China’s imports amounted to US$772.97 billion from BRACs, accounting for 28.85% of total imports, and exports US$1.03 trillion to these countries, accounting for 30.65% of total exports. After 2013, there was a clear upward trend in the share of China’s exports to BRACs, with the highest in 2019 up to 30.87%.

Regarding the import and export trade ratio development, China’s exports to the BRACs have undergone three phases. First, between 1992 and 2000,² China’s exports and imports to these countries were unstable, and imports grew more rapidly than exports due to geopolitical advantages and China’s opening-up policy. Second, 2001–2012 witnessed rapid and sustained export growth, mainly stimulated by China’s accession to the WTO and rapid domestic economic growth. During this period, total export trade rose remarkably from US$266.7 billion in 2001 to US$2050.1 billion in 2012, with export trade to BRACs rising from US$39.4 billion in 2001 to US$505.8 billion in 2010 (IMF, 2022). After 2013, with the implementation of the BRI and the government-led export commodity diversification and market diversification strategy, China’s exports to the BRACs continued to grow, and the bilateral trade surplus continued to expand.

In addition, trends in China’s bilateral trade situation show that the general changes in China’s bilateral trade are consistent with random events. The emergence of the financial crisis in 2008 and the commodity price drop in 2015 led to a significant decline in China’s import and export trade. However, trade with BRACs was more resilient to these economic shocks. Although China’s total imports and exports to BRACs were downward in 2015, the downward trend in BRACs was much lower than the total volume. China was basically in a trade balance before and in the first few years after it acceded to the WTO in 2001 and has been in a trade surplus since then. In contrast, China’s bilateral trade with BRACs has mainly been in balance for a longer time, with a steady surplus since 2012 and a rapidly growing trade surplus afterward.

Looking into the country-specific trade data,³ the top five BRACs in terms of total trade volume with China in 2021 were Vietnam, Malaysia, Russia, Thailand, and India, accounting for 44.86% of the total trade volume. The proportion of trade volume of the top ten countries was as high as 70.29%.⁴ However, in terms of exports, the top five countries were Vietnam, India, Malaysia, Thailand, and Russia, with exports to these countries accounting for 43.73% of all exports to BRACs and 68.28% of exports to the top ten countries.⁵ It can be seen that the BRACs with whom China has close trade relations are mainly in Asia, which may be related to the geographical advantage as well as the fact that these countries are located in the main traffic routes of the Silk Road Economic Belt and the twenty-first Century Maritime Silk Road.

Commodity Structure of China’s Exports to BRACs

To further study the trade between China and the BRACs, this article classifies trade goods according to the International Convention on the Harmonized Commodity Description and Coding System (HS code). It is the identification code for goods in international trade and the reference classification standard for quantitative management of tariff rates for goods.

Figure 2 shows the proportion of China’s total exports to BRACs from 2011 to 2020.⁶ More than half of the Chinese exports of fruits and vegetables were to BRACs, reaching 61.74% in 2020 (United Nations, 2021). The share of oilseeds and oleaginous fruits exports to BRACs increased yearly, from 28.91% in 2011 to 56.09% in 2020. The share of cereal exports rose from 28.00% in 2011 to 40.22% in 2020. In contrast, for some commodities, exports to BRACs did not change significantly in the past 10 years. For example, the share of exports of the transport equipment manufacturing industry increased by only 4.7%, and the share of the machinery and equipment industry increased by 4.3%, which was much lower than the average increase. Generally, exports of resource-primary processing and labor-intensive products have grown the fastest. The main reason is that BRI improves the transport condition, facilitating trade from a logistical point of view. This is more favorable for exports of goods with low added value, low commodity heterogeneity, and greater dependence on transport conditions. Despite the significant progress in exports, data show that China’s trade in agricultural products with BRACs has been in a deficit for a long time, and the deficit is growing. According to data from the Chinese Ministry of Agriculture in 2019, China’s total agricultural trade with the BRACs⁷ was RMB 420.201 billion, of which China imported RMB 237.222 billion and exported RMB 182.979 billion. The deficit amounted to RMB 54.243 billion.

Figure 2.

Source: Author’s illustration using UN Comtrade Dataset (2021).

From the perspective of the trade volume of various commodities in China’s trade with BRACs, the commodity structure of China’s exports to BRACs has been optimized, but not significantly. As shown in Figure 3, from 2011 to 2020, China’s predominant exports were computers, electronics, and optical products. The proportion of these items in all exports to BRACs increased yearly, reaching nearly 30% in 2020. The share of machinery and equipment exports remained relatively stable, fluctuating between 15% and 18%. The percentage of the textiles and clothing industry declined from 15.22% in 2011 to 11.63% in 2020. Trade in steel and metal products remained stable at approximately 10% of total trade. From 2011 to 2020, the overall trade structure for exports to BRACs did not change much.⁸ The expansion of China’s trade in resource-based primary processing products to BRACs did not significantly change the trade structure. The reason is that the value added by the products of primary processing of resources is lower than that of the manufacturing industry, and the trade volume generated is smaller.

Figure 3.

Source: Author’s illustration using UN Comtrade Dataset (2021).

Commodity Structure of China’s Imports From BRACs

The structure of China’s imports is more complex than exports. Different trends exist in other commodity groups. This article selected the top ten commodity categories that account for the largest share of China’s imports. As shown in Figure 4, the key sectors of China’s import from the BRACs are mainly labor and resource intensive. China’s demand for industrial crop imports from BRACs has been strong, while the proportion of industrial crop imports showed a fluctuating upward trend in the last decade. Import dependence on fruits and vegetables decreased compared to previous years but remained at over 50%. The proportion of imports from the 65 BRACs in the cereal industry also rose in the last decade, reaching 45.89% by 2020. The proportion of fishery imports from the 65 BRACs has also been rising, up to 30%–40% of all Chinese fishery imports (United Nations, 2021).

Additionally, China imports nearly half of the goods in the extractive industry and half of the goods in the food processing and manufacturing industry from the BRACs. The proportion of steel imports from the 65 BRACs climbed significantly recently, from 14.51% in 2011 to 39.36% in 2020.

In summary, China’s dependence on industrial crops, vegetables, and fruits from the BRACs has been strong, while imports of light industries, such as cereals, textiles and clothing, and steel and metal products have been increasing yearly, with significant growth and increasing dependence. Dependence on imports of extractive industries, food processing, and manufacturing is decreasing.

Figure 4.

Source: Author’s illustration using UN Comtrade Dataset (2021).

Figure 5 shows the share of China’s imports from the 65 BRACs in different commodity categories. The extractive sector accounts for a large percentage of all imports from BRACs, close to 50% in 2014 and before, with a total import value of US$231.1 billion in 2014. However, there was a significant decline in 2015–2017 before imports rose again after 2018, up to US$239 billion in 2019. The second-largest share of trade was in computers, electronics, and optical products, with imports rising from US$71 billion in 2011 to US$141.9 billion in 2020. The share of trade in steel and metal products also expanded yearly, rising from US$17.2 billion in 2011 to US$40.2 billion in 2020. Imports of cereals, fruits and vegetables, cash crops, fisheries, and other light industries grew steadily over the last decade. Imports of food processing and manufacturing, textiles and clothing, and rubber and plastics remained relatively stable over the last decade.

Figure 5.

Source: Author’s illustration using UN Comtrade Dataset (2021).

Empirical Analysis: The Role of the BRI on China’s Exports and Imports

Model Setting

In empirical trade studies, the gravity model is most often used to investigate the factors that affect bilateral trade volumes. The gravity model was first applied to trade analysis by Tinbergen (1962) and Pöyhönen (1963). The two scholars considered the size of the economies of the trading countries and the distance between them as push-pull factors in bilateral trade. This model is now widely used in comparative static analysis for forecasting future trade and analyzing factors influencing trade patterns. The original gravity model is shown below.

T_{i j} = A \frac{Y_{i}^{β_{1}} Y_{j}^{β_{2}}}{C_{i j}^{β_{3}}}

where $T_{i j}$ denotes the volume of trade in goods between country i and country j, and $β_{1} β_{2} β_{3}$ are model parameters representing the degree of influence of the independent variables. $Y_{i}$ and $Y_{j}$ represent the size of the economies of countries i and j, respectively, usually measured in terms of gross domestic product (GDP). $C_{i j}$ represents the cost of trade, which was initially represented by the geographical distance (D) between the two countries. In later studies, researchers incorporated factors, such as transport costs, delivery time, market access, and trade frictions into the framework of trade costs as well. The gravity model is generally applied empirically by taking the logarithm of the gravity model, resulting in the following econometric model.

l n T_{i j t} = {l n A + β}_{1} {l n G D P}_{i t} + β_{2} {l n G D P}_{j t} + β_{3} {l n D}_{i j}

As international trade theory continues to develop in empirical studies on factors affecting bilateral trade volume, apart from the economic size, researchers have also started to consider geographical factors (e.g., whether there is a common border), political factors (e.g., the closeness of governments on both sides, level of governance), cultural factors (e.g., whether there is a common language), and social development factors (e.g., population size, labor market). The gravity model has become an extended gravity model, with the equation covering various variables and factors. This article uses the gravity model to explore the role of the Belt and Road policy on the trade between China and Belt and Road countries. Based on the literature, the three main types of control variables selected for this study are as follows.

First, this article controls for the macroeconomic factors and includes the GDP of the host and home countries, as this is a direct determinant of the size of the market. From an economic intuition, when a country’s market is more extensive, its domestic market is also larger. Also, this article considers the scale of Chinese direct investment in the trading country, expressed in terms of the stock of outward direct investment from China. The rationale is that more Chinese direct investment represents closer bilateral economic and trade communication. Second, this article controls for social development factors, including population size, density, and population dependency ratio. Population size is the most direct indicator of the size of the consumer goods market in a trading country. The larger the population size, the greater the regional GDP generated tends to be. Also, the higher the population density, the more likely it is that a positive agglomeration effect will occur, promoting industrialization and innovation activities, which will drive the increase in economic size. The higher the dependency ratio, which represents the number of children and elderly per 100 working-age people, the smaller the demographic dividend of the country and the less favorable it is for economic development. Also, the study considers the education level of the trading country, as population size and education level can also be combined to measure the richness and quality of the labor market in the trading country. This is an essential indicator of a country’s level of social development. A country’s social development level is often directly related to the type and value of trade between the trading country and its goods. Third, there are political and institutional factors. The BRI encompasses Eurasia, a part of the world’s most geopolitically complex regions like the Middle East and Ukraine, where political risks can impact trade. At the same time, a country’s legal norms and the level of corruption regulation can also impact the trade environment. In this regard, this study also incorporates the number of visits exchanged by top leaders from both sides to refer to the political closeness of the countries. In terms of government management factors, a percentile ranking indicator of government effectiveness was selected, considering the data available for each country in each year. This indicator reflects public perceptions of government capabilities, such as the quality of public services, the quality of policy formulation and implementation, and the credibility of government commitments, with a minimum score of 0 and a maximum of 100. The higher the effectiveness of the government, the more conducive it is to providing good production, investment, and business environment for economic activities, and the more conducive it is to increase the size of the economy.

The model uses an extended trade gravity model as follows.

For BRACs:

{l n t r a d e}_{i j t} = β_{0} + β_{1} {B R A C}_{j} * {P o s t}_{2013} + β_{2} {l n G D P}_{i t} + β_{3} {G D P}_{j t} + {W'}_{j t} γ + μ_{j} + μ_{t} + ε_{j t}

For BRCs:

{l n t r a d e}_{i j t} = β_{0} + β_{1} {B R C}_{j} + β_{2} {l n G D P}_{i t} + β_{3} {G D P}_{j t} + {W'}_{j t} γ + μ_{j} + μ_{t} + ε_{j t}

where the explanatory variable ${l n t r a d e}_{i j t}$ is the value of China’s trade in goods with the trading country in year t, that is, imports plus exports. ${B R A C}_{j}$ is a dummy variable for whether the country is located along the Belt and Road routes. ${B R A C}_{j}$ = 1 means that country j is along the Belt and Road route, and the opposite is 0. ${P o s t}_{2013}$ is a time dummy variable, as the BRI was formally launched in 2013, so ${P o s t}_{2013}$ =1 for 2013 and subsequent years, and 0 for previous years. ${B R C}_{j}$ is a dummy variable, denoted as 1 from the year of becoming a country of BRI with a signed MOU and 0 for the opposite case and all countries before 2013. ${W'}_{j t}$ are control variables for country j, including, as mentioned above, Chinese FDI stock, population size, education level, and the World Governance Index (WGI), which measures the political environment of trading countries. $μ_{j}$ are country fixed effects to control for the effect on the estimated results of all country-level factors that do not vary over time, such as geographical distance between the two countries. $μ_{t}$ are time fixed effects to control for time-level effects, such as changes in global economic fundamentals and the impact of exceptional events like financial crises, and $ε_{j t}$ is the residual term.

Information on BRCs is from the BRI official information platform (Belt and Road Portal, 2021). Data on Chinese FDI stock is from the Statistical Bulletin of China’s Foreign Direct Investment (Ministry of Commerce of the People’s Republic of China, 2021). Data on GDP, population size, education level, and WGI is from public data released by World Bank (World Bank, 2021). The WGI is a statistical measure of a country’s political environment, laws, and the strength of corruption regulation. Six indicators measure political stability and absence of violence, the rule of law, government efficiency, voice and accountability mechanisms, corruption control, and regulatory quality. The sum of the six indicators is taken for this study.

Primary Empirical Results

Table 1 presents the results of the trade promotion effects of the Belt and Road policy in the context of 65 BRACs with a fixed effect model. The explanatory variables are the total volume of trade, the volume of Chinese exports to these countries, and the volume of Chinese imports. Table 2 presents the results of the trade promotion effect of the Belt and Road policy on the BRCs. Unlike the BRACs, the BRCs are more widely distributed and geographically dispersed globally.

Table 1.

Trade promotion effects of the BRI: BRACs.

	Trade Volume		China’s Exports		China’s Imports
	(1)	(2)	(3)	(4)	(5)	(6)
BRACs	1.481***	0.286***	1.533***	0.464***	2.101***	0.232*
	(0.136)	(0.066)	(0.132)	(0.063)	(0.198)	(0.139)
National GDP		0.709***		0.412***		1.284***
		(0.041)		(0.039)		(0.086)
National population		0.005		0.325***		0.314***
		(0.044)		(0.042)		(0.092)
Chinese direct investment stock		0.244***		0.198***		0.347***
		(0.016)		(0.015)		(0.033)
Education level		0.084		0.254***		−0.159
		(0.100)		(0.095)		(0.209)
Senior leaders’ visits		0.114**		0.116**		0.005
		(0.048)		(0.045)		(0.100)
World governance index		0.001		0.058***		−0.057***
		(0.009)		(0.009)		(0.019)
Constant	6.544***	−10.962***	6.024***	−9.612***	4.558***	−21.985***
	(0.044)	(0.436)	(0.043)	(0.416)	(0.065)	(0.910)
Country variable control	No	Yes	No	Yes	No	Yes
Number of observations	4452	3012	4454	3012	4325	3012

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken due to the results from the Hausman test.

Table 2.

Trade promotion effects of the BRI: BRCs.

	Trade Volume		China’s Exports		China’s Imports
	(1)	(2)	(3)	(4)	(5)	(6)
BRCs	1.499***	0.247***	1.563***	0.455***	2.263***	0.202
	(0.140)	(0.066)	(0.136)	(0.063)	(0.203)	(0.138)
National GDP		0.710***		0.413***		1.285***
		(0.041)		(0.039)		(0.086)
National population		−0.002		0.330***		−0.311***
		(0.044)		(0.042)		(0.092)
Chinese direct investment stock		0.242***		0.195***		0.345***
		(0.016)		(0.015)		(0.033)
Education level		0.111		0.276***		−0.138
		(0.099)		(0.095)		(0.207)
Senior leaders’ visits		0.120**		0.120***		0.009
		(0.048)		(0.045)		(0.100)
World governance index		−0.002		0.056***		−0.059***
		(0.009)		(0.009)		(0.019)
Constant	6.560***	−11.050***	6.039***	−9.730***	4.565***	−22.056***
	(0.044)	(0.435)	(0.042)	(0.415)	(0.064)	(0.908)
Country variable control	No	Yes	No	Yes	No	Yes
Number of observations	4452	3012	4454	3012	4325	3012

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken due to the results from the Hausman test.

In both tables, Columns (1), (3), and (5) do not control for country variables, while Columns (2), (4), and (6) control for all the country variables mentioned above. The results show that, first, the dummy variable BRACs is positive and significant, which suggests that being a BRACs can promote trade with China as expected. This is because the Belt and Road construction targeted to encourage infrastructure development along the route provided opportunities for smooth trade flows. Therefore, countries geographically close to the Belt and Road could enjoy the trade dividends brought by the construction of railways, ports, and other infrastructure. Therefore, their trade with China is larger than those not along the route. Likewise, being BRCs can also facilitate trade with China. The BRI is based on the principle of ‘building together, discussing together and sharing,’ and cooperation with BRCs is based on an MOU signed by both sides, which usually covers the infrastructure construction and new rules and standards, providing the conditions for creating more significant trade volumes.

At the level of country control variables, the total amount of trade with China is positively influenced by the total GDP of the trading country, the stock of Chinese direct investment, and the number of bilateral exchange visits by senior leaders. The value of China’s exports is influenced by the total population, the level of education, and the World Governance Index, in addition to the above factors. On the other hand, China’s imports are not affected by the number of visits by senior leaders from both sides compared to the first two factors. The data shows that imports from countries with lower governance levels are higher. One possible explanation is that imports are more domestic demand-led than exports. The ability of a trading partner to supply China with the items it needs domestically is thus the first factor to be taken into account. Comparatively speaking, political factors are less significant.

This study further divides the countries into Asia, Europe, and Africa by geography for trade with BRCs. Table 3 shows the impact of being a country of the Belt and Road Initiative on the trade volume for those three regions. For countries in Asia and Europe, belonging to the Belt and Road Initiative increases the total trade volume with China. China’s exports to and imports from Asian BRCs have increased significantly. Meanwhile, China’s imports from European BRCs also increased significantly, while the effect of increased exports was insignificant. From the perspective of BRCs, European BRCs have significantly increased their exports to China under the BRI. In contrast, the size of imports from China has not changed significantly after controlling for time and country-specific fixed effects. This is also due to the small size of the European market as a whole and the difficulty of developing new market sizes due to the high level of economic development. However, the data shows that the BRI has no significant impact on China’s overall trade with the African region. Only regarding exports, China has increased its exports to African BRCs. A reasonable explanation is that Africa as a whole has a relatively small economic scale and large country heterogeneity.⁹ Thus, policy effects are less likely to be significant in the estimation. Also, China’s cooperation with Africa under the BRI focuses on infrastructure development, which is a traditional strength of China–Africa economic and trade cooperation. This study controls for the stock of Chinese direct investment in each country in the estimation model, making the role of the Belt and Road in a trade less significant.

Table 3.

Trade Relations Between BRCs and China.

	Trade Volume	China’s Exports	China’s imports
Asia	0.478*	0.497**	1.251***
	(0.271)	(0.253)	(0.418)
Europe	0.475*	0.080	0.695**
	(0.255)	(0.257)	(0.317)
Africa	0.321	0.668*	0.535
	(0.467)	(0.399)	(0.794)

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken due to the results from the Hausman test.

Heterogeneity in Commodities and Regions

This article closely examines how different trade types of goods have been affected by the BRI and how they have been affected by the BRI in different regions. According to the degree of processing of goods traded and based on the literature, we classified goods into primary products and manufactured products, which are further classified into high, medium, and low technology-intensive products and resource-intensive products according to their required technology intensity (Lall, 2000). Referring to Liu and Zhang (2016), this study uses the Standard International Trade Classification (SITC) three-digit codes to classify all products traded in goods into the following five categories: primary products, resource-intensive products, low-technology manufactured goods, medium-technology manufactured goods, and high-technology manufactured goods.¹⁰ The low-technology manufacturers include clothing, textiles, footwear, etc., the medium-technology includes automobiles, engineering, etc., and the high-technology includes electronics and electrical appliances.

Table 4 shows how the scale of trade in different product types has been affected by the Belt and Road policy, while Table 5 refines the analysis to three continents: Asia, Europe, and Africa. First, after controlling for country-level variables at the aggregate level, the estimates show that China’s trade with BRCs has increased in all five commodity segments. The geographical disparities are reflected in Table 5. Also, the BRI has boosted China’s exports to Asian countries in all five commodity groups. At the same time, for imports, there is a significant effect only with primary commodities and high-technology manufactured goods.

Table 4.

Trade Impact of the BRI on Different Types of Commodities.

	China’s Exports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	1.484***	1.449***	1.656***	1.650***	1.839***
	(0.029)	(0.042)	(0.028)	(0.028)	(0.038)
Constant	17.308***	18.056***	18.990***	18.999***	18.451***
	(0.007)	(0.010)	(0.006)	(0.007)	(0.009)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	1988	1981	1998	1998	1995
	China’s Imports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	1.381***	0.411***	2.473***	2.454***	4.067***
	(0.101)	(0.100)	(0.166)	(0.165)	(0.085)
Constant	16.438***	19.125***	14.458***	14.487***	13.242***
	(0.025)	(0.028)	(0.042)	(0.042)	(0.021)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	1839	1567	1816	1818	1910

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken due to the results from the Hausman test.

Table 5.

Trade Impact of BRI on Different Types of Commodities by Continent.

	China’s Exports
Asia	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	0.392***	0.349***	0.650***	0.653***	0.304***
	(0.040)	(0.027)	(0.043)	(0.044)	(0.063)
Constant	18.934***	19.698***	20.569***	20.568***	20.163***
	(0.025)	(0.017)	(0.027)	(0.027)	(0.039)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	450	450	450	450	450
	China’s Imports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	0.962**	0.028	−0.174	−0.186	0.432*
	(0.310)	(0.323)	(0.372)	(0.370)	(0.232)
Constant	17.409***	20.324***	16.679***	16.686***	16.548***
	(0.195)	(0.209)	(0.234)	(0.232)	(0.144)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	445	404	446	446	450
Europe	China’s Exports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	0.258**	−0.347***	−0.113*	−0.113*	0.189***
	(0.080)	(0.089)	(0.057)	(0.058)	(0.054)
Constant	17.666***	18.890***	19.851***	19.860***	19.721***
	(0.032)	(0.035)	(0.022)	(0.023)	(0.021)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	427	426	430	430	430
	China’s Imports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	−1.363***	−1.393***	−0.985***	0.921***	1.557***
	(0.239)	(0.122)	(0.169)	(0.166)	(0.120)
Constant	18.347***	19.548***	17.849***	17.829***	18.852***
	(0.096)	(0.051)	(0.068)	(0.067)	(0.048)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	415	402	418	418	425
Africa	China’s Exports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	0.353***	0.701***	0.531***	0.513***	1.065***
	(0.057)	(0.081)	(0.051)	(0.051)	(0.049)
Constant	17.322***	17.581***	18.914***	18.929***	18.293***
	(0.002)	(0.002)	(0.002)	(0.002)	(0.001)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	538	538	538	538	538
	China’s Imports
	Primary Products	Resource-Intensive Manufactured Goods	Low-Tech Manufactured Goods	Medium-Tech Manufactured Goods	High-Tech Manufactured Goods
BRCs	1.107***	−0.276	1.559***	0.552	0.480
	(0.449)	(0.219)	(0.113)	(0.420)	(0.398)
Constant	15.334***	18.687***	13.624***	13.710***	10.287***
	(0.015)	(0.008)	(0.004)	(0.004)	(0.013)
National control variables	Yes	Yes	Yes	Yes	Yes
Number of observations	492	391	471	472	490

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken due to the results from the Hausman test.

For the European region, the study finds that China’s exports of primary products and high-technology manufacturers to the European BRCs increased, while exports of resource-intensive, low-technology, and medium-technology manufacturers declined significantly. In terms of the import component, China imports more medium- and high-technology manufacturers from the European BRCs. The trade pattern is optimized with a shift towards higher value-added goods. African BRCs have increased their imports from China in all types of goods compared to their non-BRC counterparts. In contrast, African BRCs export more primary and low-technology products to China, as shown in Table 5. This indicates that Belt and Road construction has promoted trade interactions between China and the BRCs and that China’s trade with the BRCs is larger than with the non-BRCs. However, the impact of the Belt and Road policy varies by region and commodity type. This also informs international trade studies that regional and commodity-type distinctions should also be included in the analytical framework in addition to considering aggregate changes.

Trade Development with China and Economic Growth

The Chinese government asserts that the BRI has significantly promoted trade and investment liberalization and facilitation and stimulated consumption in the Belt and Road countries (Belt and Road Construction Leadership Group, 2019). From the demand side, investment, consumption, and exports are the three driving forces of economic growth. Since the introduction of the BRI, China’s imports from the BRACs have expanded rapidly in recent years. Clearly, the development of trade with China is an essential driver of economic growth in the BRACs.

The previous section empirically confirms the trade promotion effect of BRI. This section further discusses the trade promotion effect of BRI on the host country’s economic development. In terms of the economic impact of BRI, theoretical studies have found that international capital has a promoting effect on infrastructure, which can contribute to local economic growth. Construction in Central Asia can help local countries improve their infrastructure, distribute their products to new markets, and attract investment, all of which contribute to the regional economy’s growth (Imomnazar, 2018). Enderwick (2018) illustrates through qualitative analysis that the construction of BRI is expected to improve the efficiency of local resource flows and trade and promote regional economic integration.

Model Setting

This part of the empirical study uses two indicators to reflect the economic development of the BRACs and the BRCs. The first is the GDP per capita, which reflects the economic level of the involved countries. The second is the annual growth rate of GDP, which reflects the country’s economic growth. These two variables are included in the model as explained variables to reflect the economic development of the BRACs and the BRCs. The economic development of a country or region is influenced by external conditions but also by its domestic conditions. These internal factors are collectively referred to as country control variables. Referring to the previous section of the study, variables measuring country-specific factors like average education level and governance level were selected for inclusion in the model to control for differences in internal conditions across countries and regions in the study sample.

As an important external condition, the BRI is investigated as a dummy variable. For BRACs, the value is 1 for 2013–2021 and 0 for other years; for non-BRACs, the value is 0 for all years. For BRCs, the value is 1 from the year of signing the MOU and thereafter and 0 before signing; for non-BRCs, the value is 0 for all years. The trade volume is analyzed as an interaction term to determine whether being a Belt and Road country could promote economic development through trade. China’s BRI construction brings in a large amount of direct investment, and foreign investment is considered a critical factor contributing significantly to the host country’s economic development. Thus, the model also controls for the amount of direct investment from China, expressed in terms of the current year’s stock.

Based on the above framework and indicators, the research hypotheses to be tested in this section are:

Trade with China can significantly boost a country’s economic development when national control variables are considered.

The BRI can significantly enhance the role of trade as a driver of economic development, that is, China’s trade can significantly contribute to the economic development of BRACs and the BRCs.

The estimation models used in this part of the study are as follows:

{l n E C O N}_{i t} = β_{0} + β_{1} {l n t r a d e}_{i t} + β_{2} l n {t r a d e}_{i t} * {B R A C}_{i} + {W'}_{i t} γ + μ_{j} + μ_{t} + ε_{j t}

{l n E C O N}_{i t} = β_{0} + β_{1} {l n t r a d e}_{i t} + β_{2} l n {t r a d e}_{i t} * {B R C}_{i} + {W'}_{i t} γ + μ_{j} + μ_{t} + ε_{j t}

Equation (5) represents the case for BRACs while Equation (6) is for the BRCs, where ${E C O N}_{i t}$ represents the economic development of country i at time t. As mentioned earlier, the study uses two indicators, GDP per capita and GDP growth rate. ${l n t r a d e}_{i t}$ represents the trade volume of country i with China at time t. The study analyzes the trade volume from three perspectives: total trade volume, the trade volume of exports to China, and the trade volume of imports from China. $l n {t r a d e}_{i t} * {B R A C}_{i}$ and $l n {t r a d e}_{i t} * {B R C}_{i}$ are the interaction terms. $W_{i t}$ is a series of country control variables, including the stock of Chinese outward FDI in the trading country. $μ_{j}$ is country fixed effects. $μ_{t}$ is the time-fixed effects, controlling for time-level effects, like changes in global economic fundamentals and the impact of special events such as the financial crisis. $ε_{j t}$ is the residual term.

Estimation Results

Table 6 shows the impact of BRI on the GDP per capita of the BRACs and the BRCs in different regions.¹¹ All models are estimated with a fixed effect based on the results of the Hausman test. Columns (1), (2), (6), (7), (11), and (12) show the situation in Asian and European BRACs.¹² The remaining nine columns show the BRCs in Asia, Europe, and Africa. Columns (1)–(5) are for trade with China, (6)–(10) for exports to China, and (11)–(15) for imports from China. The results show that for the BRI-involved countries, be it in Asia, Europe, or Africa, trade with China significantly contributes to the per capita GDP growth, whether considered from the perspective of the BRACs or BRCs. This confirms Hypothesis (1). The interaction term with the Belt and Road countries is also significantly positive, indicating that being a BRI-involved country further enhances the impact of trade with China on the size of GDP per capita in the host country. This illustrates that BRI can promote the economic development effect of trade, which is exactly what Hypothesis (2) informs. When considering economic development in terms of GDP per capita, the economic boosting effect of trade with China does not differ significantly across continents. This suggests that BRI benefits all the regions involved, whether relatively developed European or underdeveloped African regions, demonstrating BRI’s effectiveness in international cooperation.

Table 6.

Impact of Trade Development Between the Belt and Road Countries and China on Their GDP per Capita.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
	Asia	Europe	Asia	Europe	Africa	Asia	Europe	Asia	Europe	Africa	Asia	Europe	Asia	Europe	Africa
Trade with China	0.731***	0.394***	0.725***	0.394***	0.327***
	(0.012)	(0.018)	(0.012)	(0.018)	(0.007)
Trade with China* BRACs	0.036***	0.105***
	(0.004)	(0.003)
Trade with China* BRCs			0.026***	0.105***	0.062***
			(0.003)	(0.003)	(0.002)
Exports to China						0.443***	0.211***	0.420***	0.211***	0.132***
						(0.010)	(0.012)	(0.009)	(0.012)	(0.007)
Exports to China* BRACs						0.077***	0.146***
						(0.005)	(0.006)
Exports to China* BRCs								0.050***	0.146***	0.118***
								(0.004)	(0.006)	(0.006)
Imports from China											0.769***	0.332***	0.761***	0.332***	0.291***
											(0.018)	(0.019)	(0.015)	(0.019)	(0.013)
Imports from China* BRACs											0.050***	0.129***
											(0.004)	(0.003)
Imports from China* BRCs													0.036***	0.129***	0.051***
													(0.002)	(0.003)	(0.002)
China’s outward FDI stock	−0.189***	0.037**	−0.181***	0.037**	0.034***	−0.158***	0.069***	−0.144***	0.069***	0.066***	−0.164***	0.040**	−0.155***	0.040**	0.089***
	(0.011)	(0.015)	(0.012)	(0.015)	(0.010)	(0.016)	(0.014)	(0.016)	(0.014)	(0.014)	(0.011)	(0.016)	(0.011)	(0.016)	(0.010)
National control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Number of observations	689	605	689	605	796	688	605	688	605	784	689	605	689	605	796

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken based on the results of the Hausman test.

Table 7 measures economic development in terms of GDP growth and the role of BRI in promoting economic development through trade. Similarly, Asian and European countries along the route and the Asian, European, and African BRCs were selected for the analysis. In contrast to Table 6, results indicate specific regional differences. For the BRACs, although trade with China significantly increases GDP growth for Asia and Europe, the effect is less significant for Europe, where it is statistically significant only at 10%. For the Asian region, imports, exports, and overall trade with China all contribute significantly to the GDP growth of the local country, and this effect is highly significant. When analyzed from the perspective of the BRCs, the size of trade with China can significantly contribute to the GDP growth rate of Asian countries positively but less significantly to the GDP growth rate of countries in the European region. For the African region, the study finds that the size of trade with China does not affect its growth rate. There are two possible explanations. First, the African region’s economic growth is unstable due to the region’s overall economic backwardness and varying political landscape. These unpredictable shocks may dilute the possible economic boost from trade with China. Second, the overall trade scale with China is small in the African region, with significant regional differences, so its effect is prone to be statistically insignificant.

Table 7.

Impact of Trade Development Between the Belt and Road Countries and China on Their GDP Growth Rates.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
	Asia	Europe	Asia	Europe	Africa	Asia	Europe	Asia	Europe	Africa	Asia	Europe	Asia	Europe	Africa
Trade with China	0.190***	0.117*	0.165***	0.117*	0.020
	(0.023)	(0.059)	(0.023)	(0.059)	(0.030)
Trade with China* BRACs	0.068***	0.052***
	(0.014)	(0.010)
Trade with China* BRCs			0.020***	0.052***	0.019*
			(0.006)	(0.010)	(0.011)
Exports to China						0.145***	0.087*	0.102***	−0.087**	0.016
						(0.015)	(0.044)	(0.013)	(0.034)	(0.021)
Exports to China* BRACs						0.082***	0.063***
						(0.015)	(0.013)
Exports to China* BRCs								0.028***	0.063***	0.023*
								(0.008)	(0.013)	(0.013)
Imports from China											0.197***	0.108*	0.172***	0.108*	0.022*
											(0.030)	(0.056)	(0.030)	(0.056)	(0.013)
Imports from China* BRACs											0.076***	0.060***
											(0.014)	(0.011)
Imports from China* BRCs													0.025***	0.060***	−0.011
													(0.007)	(0.011)	(0.018)
China’s outward FDI stock	0.098***	0.022	0.094***	0.022	−0.007	0.095***	0.015	0.087***	0.015	−0.011	0.091***	0.022	0.085***	0.022	0.000
	(0.021)	(0.015)	(0.023)	(0.015)	(0.013)	(0.023)	(0.016)	(0.024)	(0.016)	(0.017)	(0.022)	(0.015)	(0.023)	(0.015)	(0.014)
National control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Number of observations	656	512	656	512	718	655	512	655	512	707	656	512	656	512	718

Note: ***, **, * represent significance at the 1%, 5%, and 10% statistical levels, respectively. The fixed effect is taken based on the results of the Hausman test.

The interaction terms show that, despite the small trade scale with China, African BRCs can enjoy the economic dividends of trade. Specifically, the three interaction terms for Africa are all statistically significant at a 10% level, demonstrating that the scale of trade with China can contribute to the GDP growth of African BRCs compared to non-BRCs. This effect is also significant in Asian and European countries. Therefore, we add to the original Hypothesis (1) that trade with China significantly boosts the economic development of the trading countries, considering national control variables but with regional differences. The effect is not significant in the African region. And Hypothesis (2) is valid for all BRI-involved countries, regardless of whether GDP per capita or GDP growth is used as a measure of economic development.

The empirical evidence in this section shows that trade with China can significantly contribute to the economic development of the BRACs and BRCs. China is the world’s largest economy in terms of purchasing power parity. It makes sense that the scale of trade with China directly impacts the country’s economic development. What are the indirect paths through which trade contributes to economic growth? Based on the literature, the main factors are employment and upgrading industrial structures. In other words, expanding import and export trade between China and the BRI-involved countries will help them increase their employment rate and upgrade their industrial structure. An increase in import and export trade with China has created many tertiary service jobs in these countries. This has expanded the local job market in terms of scale. At the same time, industrial workers with specific skills or a higher level of education are more likely to accept these jobs and receive higher payments and benefits than primary agricultural workers. This leads to a change in the relative proportions of industrial and service workers in these countries and therefore contributes to upgrading the industrial structure of the BRACs and BRCs.

Conclusions

This article analyzes the scale of bilateral trade with the BRACs and BRCs. It examines whether the BRI has increased trade between China and BRI-involved countries and whether trade with China has boosted the economic growth of the trading countries. The study finds that implementing the BRI has significantly boosted China’s overall trade volume with the BRACs and BRCs, in terms of exports and imports. Still, there are some regional differences in this effect. For example, the BRI significantly boosted China’s imports from European BRCs but had no significant impact on China’s exports to Europe. The opposite is the case in Africa, where the BRI only affects Chinese exports to Africa. The initiative does not significantly affect imports from Africa and overall trade volumes. Considering the different product types, BRI has significantly impacted the volume of exports and imports of all types of goods, but regional differences are still evident. For example, China has significantly increased its imports of only primary products from Asian and African countries. The situation is more complex in Europe, where China increased its exports of primary goods and high-technology manufactures to the European BRCs, increased its imports of medium- and high-technology manufactures, and decreased its exports and imports in all other categories.

Through our analysis of trade for economic growth, we find that the scale of trade with China can significantly benefit economic development in countries in Asia, Europe, and Africa. These results remain significant after controlling for country variables. At the same time, we find that the BRI can boost the role of trade in economic growth, except in Africa. Compared to non-BRACs, BRACs can derive significant economic growth from trading with China.

It is important to note that this article examines only the short-term effect of the BRI on China’s imports and exports. The long-term effects will take a more extended time to appear. Future research could focus on this aspect. At the same time, this study finds that the BRI is a trade facilitator and that the BRI has indeed contributed to the “smooth flow of trade” between China and the concerned countries. Therefore, it is essential to continue promoting policy initiatives that facilitate trade. At a time when economic globalization is under threat, and the multilateral trading system is being challenged, China should take the opportunity provided by the BRI to develop inter-regional trade, improve the efficiency of bilateral and multilateral trade and contribute to the healthy development of economic globalization.

Footnotes

Acknowledgment

I appreciate the help extended by Hui Wang and Yingzi Qu for their detailed feedback and comments, which helped improve the article. All errors are the authors.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding support for this research study came from the ‘China Social Science Foundation Grant no. 19VDL012 and 19ZDA100’.

Note

References

Baniya

, Rocha

, & Ruta

(2020). Trade effects of the new Silk Road: A gravity analysis. Journal of Development Economics , 146, 102467. https://doi.org/10.1016/j.jdeveco.2020.102467

Bastos

(2020). Exposure of Belt and Road economies to China trade shocks. Journal of Development Economics , 145, 102474. https://doi.org/10.1016/j.jdeveco.2020.102474

Belt and Road Construction Leadership Group. (2019). Building the BRI: Progress, contributions and prospects . China Daily. Beijing (In Chinese).

Belt and Road Portal. (2021). International cooperation country profiles . Belt and Road Portal. https://eng.yidaiyilu.gov.cn/info/iList.jsp?cat_id=10076

Boffa

(2018). Trade linkages between the Belt and Road economies (World Bank Policy Research Working Paper, 8423). World Bank. https://openknowledge.worldbank.org/handle/10986/29768

Chen

, Chen

, & Yao

(2020). Trade development between China and countries along the Belt and Road: A spatial econometric analysis based on trade competitiveness and complementarity. Pacific Economic Review , 25(2), 205–227.

Chong

, Qin

, & Pan

(2019). The evolution of the Belt and Road trade network and its determinant factors. Emerging Markets Finance and Trade , 55(14), 3166–3177.

De Soyres

, Mulabdic

, Murray

, Rocha

, & Ruta

(2019). How much will the BRI reduce trade costs? International Economics , 159(C), 151–164.

Enderwick

(2018). The economic growth and development effects of China’s One Belt, One Road Initiative. Strategic Change , 27(5), 447–454.

10.

Herrero

A. G.

, & Xu

(2019). Countries’ perceptions of China’s Belt and Road Initiative: A big data analysis (Bruegel Working Paper, No. 2019/01). Bruegel. Vol. 1, pp. 1–9.

11.

IMF. (2022). International Monetary Fund direction of trade statistics . International Monetary Fund. https://data.world/imf/direction-of-trade-statistics-dots

12.

Imomnazar

(2018). Impact of the ‘One Belt, One Road’ project on the economy of Central Asian countries. The Business & Management Review , 9(3), 396–403.

13.

Lall

(2000). The technological structure and performance of developing country manufactured exports, 1985-98. Oxford Development Studies , 28(3), 337–369.

14.

Liu

, Lu

, & Wang

(2020). The roles of cultural and institutional distance in international trade: Evidence from China’s trade with the Belt and Road countries. China Economic Review , 61, 101234. https://doi.org/10.1016/j.chieco.201810.001

15.

Liu

Z. S.

& Zhang

(2016). Is China’s trade structure upgrading: Based on the classification, technology and quality indexes. South China Journal of Economics , 35 (8), 24–41 (In Chinese).

16.

Liu

, Wang

, Sonn

J. W

., & Chen

(2018). The structure and evolution of trade relations between countries along the Belt and Road. Journal of Geographical Sciences , 28(9), 1233–1248.

17.

Ministry of Commerce of the People’s Republic of China. (2021). Statistical Bulletin of Chinese Outward Foreign Direct Investment , 1993–2021. Ministry of Commerce of the People’s Republic of China. http://fec.mofcom.gov.cn/article/tjsj/tjgb/

18.

Mohamad

A. H. H.

, & Zainuddin

M. R. K.

(2021). Belt and Road Initiatives and the competitiveness of natural rubber exports: Evidence from the BRI region. The Journal of Asian Finance, Economics and Business , 8(11), 145–155.

19.

Pöyhönen

(1963). A tentative model for the volume of trade between countries. Weltwirtschaftliches Archiv , 93–100.

20.

Rehman

F. U

., & Noman

A.A.

(2021). Trade related sectorial infrastructure and exports of belt and road coun tries: Does Belt and Road initiatives make this relation structurally unstable? China Economic Journal , 14(3), 350–374.

21.

Tinbergen

(1962). Shaping the world economy: Suggestions for an international economic policy . The Twen tieth Century Fund.

22.

United Nations. (2021). UN United Nations Comtrade database . United Nations. https://comtrade.un.org/data/

23.

World Bank. (2021). World Bank Open Data . The World Bank. https://data.worldbank.org/

24.

Z. Y

, & Shen

L.S.

(2004). Empirical analysis on the contribution of China’s foreign trade to GDP. World Economy , 2, 13–20. (In Chinese)

25.

, Hou

, & Xin

(2020). Has the Belt and Road initiative brought new opportunities to countries along the routes to participate in global value chains? Sage Open , 10(1), 2158244020902088. https://doi.org/10.1177/2158244020902088

26.

, & Cao

(2015). The analysis and forecast of RMB internationalization on One Belt and One Road. International Business and Management , 10(3), 137–141.