metadata: authors: 0: author: Boffa,Mauro
count: World
docna: 0: docna: Trade linkages between the belt and road economies
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Transport Services,Trade Finance and Investment,Nonmetallic Mineral Product Manufacturing
teratopic: Industry,International Economics and Trade,Transport
abstracts: cdata!: This paper studies the production and
            trade linkages between a selected group of economies
            belonging to the Belt and Road Initiative (BRI). After
            defining a group of Belt and Road Economies, the paper uses
            three standard trade databases to analyze trade and
            production linkages among these economies. With the help of
            state of the art economic decompositions of input-output
            tables, coupled with standard international trade
            statistics, the analysis quantifies the amount of production
            sharing between the economies of the area. The main finding
            is that trade integration among Belt and Road Economies has
            largely increased: Intraregional exports went from 30.6
            percent in 1995 to 43.3 percent in 2015. Since the increase
            in gross exports was driven mostly by intermediate goods,
            the study investigates the evolution of regional production
            networks across Belt and Road Economies.
repnme: repnme: Trade linkages between the belt and road economies
display_title: Trade linkages between the belt and
            road economies
listing_relative_url: /research/2018/05/29854201/trade-linkages-between-belt-road-economies
topicv3: Transport m1326661 135,Nonmetallic Mineral Mining and Quarrying
            m1326877 1700,Trade m1326924 698,Trade in Value-Added m1504157 2613,Trade Linkages, Spillovers and
            Connectivity m1504235 2601
publishtoextweb_dt: 2018-05-01T00:00:00Z
historic_topic: Industry,International Economics and Trade,Transport
no_of_pages: 57
author_keys: 0: author_key: 1142608
model_extractions: WPS8423 Policy Research Working Paper 8423 Trade Linkages Between the Belt and Road Economies _Mauro Boffa_ Macroeconomics, Trade and Investment Global Practice May 2018
Policy Research Working Paper 8423

# **Abstract**

This paper studies the production and trade linkages between a selected group of economies belonging to the Belt and Road Initiative (BRI). After defining a group of Belt and Road Economies, the paper uses three standard trade databases to analyze trade and production linkages among these economies. With the help of state of the art economic decompositions of input-output tables, coupled with standard international trade statistics, the analysis quantifies the amount of production sharing between the economies of the area. The main finding is that trade integration among Belt and Road Economies has largely increased: Intraregional exports went from 30.6 percent in 1995 to 43.3 percent in 2015. Since the increase in gross exports was driven mostly by intermediate goods, the study investigates the evolution of regional production networks across Belt and Road Economies.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at mauro_boffa@outlook.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Trade Linkages Between the Belt and Road Economies<sup>*</sup>

MAURO BOFFA<sup>†</sup> Keywords: Belt and Road Initiative, Global Value Chains, Economic Integration. JEL: F01, F14, F15.

> * This study was fully funded by the China-World Bank Group Partnership Facility (CWPF).

> † This paper has been prepared by Mauro Boffa, consultant of the Macroeconomics, Trade and Investment Global Practice of the World Bank and Research Associate at the European University Institute, Florence, with guidance and inputs from Chunlin Zhang, Michele Ruta and Daria Taglioni, all staff of the World Bank. I thank, without implicating them personally, Antoine Coste, Michele Ruta and Paulo Bastos for their detail feedback and comments which helped strengthen the paper, for. All remaining errors are the author’s.
1. Background

# Contents

|1. Background ........................................................................................................................................... 3|
|---|
|2. Trade Linkages between B&R Economies: Gross Trade ...................................................................... 5|
|2.1 Gross Trade Linkages between B&R Economies Revealed by UN Comtrade Data ...................... 5|
|2.2 Gross Trade Linkages between B&R Economies Revealed by TiVA Data ................................. 10|
|3. Origin of Value-Added in Exports of B&R Economies ...................................................................... 13|
|3.1. Concept and Methodology ........................................................................................................... 13|
|3.2. Origins and Destinations of Foreign Value-Added in Exports of B&R Economies .................... 15|
|4. Quantifying Vertical Trade among the B&R Economies .................................................................... 18|
|4.1 Concept and Methodology ............................................................................................................ 18|
|4.2 Backward Linkages ....................................................................................................................... 20|
|4.3 Forward Linkages ......................................................................................................................... 23|
|5. Visualization of Value-Added Network of B&R Economies ............................................................. 27|
|6. Trade Linkages between China and other B&R Economies ............................................................... 31|
|6.1. Gross Trade between China and other B&R Economies ............................................................. 31|
|6.2. China’s Position in GVCs ............................................................................................................ 33|
|7. Potential Benefits from Further Integration ........................................................................................ 36|
|8. Conclusion ........................................................................................................................................... 37|
|References ............................................................................................................................................... 39|
|Appendix ................................................................................................................................................. 41|
|A1: Comparison between EORA and Tiva GVC Indicators .............................................................. 41|
|A2: Network Analysis of Origin and Destination of Value-added using EORA ................................ 45|
|A3: Indicators of Vertical Trade using EORA .................................................................................... 48|
|A4: B&R Economies in the Paper ...................................................................................................... 53|

2
1. Background

# **1. Background**

The Belt and Road Initiative (BRI), first proposed by President Xi Jinping of China in 2013, is an ambitious effort to improve regional cooperation and connectivity on a trans-continental scale. The initiative aims to strengthen infrastructure, trade, and investment links among countries along the Belt and Road. The “Belt” (Silk Road Economic Belt) links China to Central and South Asia and onward to Europe, while the “Road” (New Maritime Silk Road) links China to the nations of South East Asia, the Gulf Countries, North Africa, and on to Europe. Six economic corridors have been identified: The ChinaMongolia-Russia Economic Corridor; the New Eurasian Land Bridge; the China–Central Asia–West Asia Economic Corridor; the China–Indochina Peninsula Economic Corridor; the China-Pakistan Economic Corridor; and the Bangladesh-China-India-Myanmar Economic Corridor.

The overarching objective of the BRI as stated by the Government of China (GOC)<sup>1</sup> is “win-win cooperation that promotes shared development and prosperity”. It is a comprehensive package of five priorities, including but not limited to infrastructure connectivity. The other four are: coordination of development strategies and policies; trade facilitation to ensure “unimpeded trade”, financial integration, and people-to-people exchange.

Given the stated intention to promote trade flows among economies involved in the BRI, it is important to understand the existing trade linkages between economies along the Belt and Road (the “B&R Economies” from now on). The objective of this paper is to describe the existing trade linkages between B&R Economies in terms of gross trade as well as trade in value-added, which is a reflection of international shared production linkages. The intention is to lay a foundation for further study on potential impact of the BRI on trade and growth, as well as policies and institutions needed to maximize the benefit of BRI.

Since the BRI is presented as an open arrangement in which all countries are welcome to participate, there is not an official list of “BRI countries”. Different versions of unofficial lists of countries along the Belt and the Road exist, none of which received confirmation from the GOC. While some other longer lists include countries such as New Zealand and South Africa<sup>2</sup> , in this paper, we use one that has 66 countries and 68 economies as found in Table 1 of Appendix A1, where China; Taiwan, China; Hong Kong SAR, China are treated as three economies in order to be aligned with trade data. However, data are not available for every B&R Economy. The analysis in the paper revolves around two databases: the UN Comtrade and the OECD-WTO Trade in Value-Added (TiVA). The UN Comtrade database covers 60 of the 68 B&R Economies, while only 28 B&R Economies appear in the TiVA database (see Table 1 of Appendix A1 for details).

The first step of the analysis focuses on gross trade flows of intermediate and final goods. With the help of the UN Comtrade database we quantify the weight of the 60 B&R Economies in global trade. The trade data show that B&R Economies account in 2015 for 37 % of world trade. In addition, we notice that they are key players in the manufacturing of intermediate goods. However, there are strong regional discrepancies. Using the World Bank regional classification, we find that Europe and Central Asia (ECA) and East-Asia and Pacific (EAP) accounted, in 2015, for about 80 % of total exports from B&R economies. Thus, part of the spectacular growth in exports is driven by dynamic economies present in these two regions. Nonetheless, intra-regional trade has been on the rise and mostly because of high demand and supply of EAP economies for intermediate goods. The most important sector for B&R exports is the computer and electronics one.

We notice that there are strong sectoral disparities in the composition of B&R exports. The importance of the computer and electronics sector derives from the weight of EAP economies. Mining and quarrying

> 1 Vision and Actions on Jointly Building Silk Road Economic Belt and 21st-Century Maritime Silk Road. 2015/03/28.

> 2 For example, the list put together by Hong Kong Trade Development Council includes New Zealand and South Africa. See http://china-traderesearch.hktdc.com/business-news/article/The-Belt-and-Road-Initiative/The-Belt-and-Road-Initiative-CountryProfiles/obor/en/1/1X000000/1X0A36I0.htm.

3
1. Background

is important for both ECA and Middle-East North-Africa (MENA), while South Asia (SAR) excels in transport and storage services.

To move over gross trade figures, the second step consists in presenting the trade in value-added data to study production linkages. Value chains are the process of adding value to a product from its conception to its final consumption passing through design, production, transport, distribution and retail.<sup>3</sup> Nowadays, the life cycle of a product is no longer confined to a single firm. Firms specialize in business functions and take on different tasks that allow to serve the final consumer. Through the process of value addition, all factors that contributed to production, foreign and domestic, get remunerated .

In recent years, because of the decrease in transport and communication costs, value-chains have unbundled, and they are no longer tied to one specific geographical location. Firms use and supply both foreign and domestic contractors for a variety of business functions. In doing so, the firm at the top of the chain eventually exports a final good or service and is double counting all intermediate steps in the gross exports figures. National statistics do not cover these flows at the firm level, which poses a challenge for measuring Global Value Chains (GVCs).

Input-output tables allow for an accounting of sources of value-added by effectively slicing the valuechain. The sources involve revenues to labor, capital but also upstream sectors. In addition, they allow for a regional separation of value-added by geographical origin of imports. While input-output tables do not record specifically firms’ transactions, they measure intra- and inter-industry flows between countries. By slicing the value chain, input-output tables allow us to determine the amount of double counting derived from goods crossing the border several times.

In this study, we use the multi-regional input-output tables from the TiVA database to identify the existing value-added networks in the B&R Economies. The database records exports of intermediate and final goods across industries and countries through downstream and upstream industry channels. Therefore, it allows to track how countries contribute within GVCs into the production of final goods by specializing in segments where they exploit their comparative advantage. The TiVA database covers 62 economies, of which a group of 28 are members of the “B&R Economies” as defined above.<sup>4</sup> Economies that integrate into GVCs, and regardless of their position, are either sellers or buyers of valueadded. On the one hand, they supply intermediate goods to foreign industries. On the other hand, they import foreign goods for their own production purposes. Therefore, Koopman et al. (2014) and Wang et al. (2013) propose to look at the phenomenon of vertical integration from both the importing and the exporting side. We use their battery of indicators in the context of B&R Economies.

We determine the origin of value-added in exports in the B&R Economies using the Leontief decomposition. The Leontief decomposition of input-output tables that tracks the value-added by source country and industry. The Leontief insight lies in determining how much an economy has sourced from its partners. We exploit it to showcase the share of value-added that B&R Economies source form different country groups. We analyze the extent to which trade among B&R Economies is GVC driven, pointing out which countries are the most important. We find that China and the Russian Federation are the key partners for these economies.

As a third step, we look at how the value-added is used and the amount of vertical specialization embedded in exports. We use the decomposition developed by Wang et al. (2014) which traces value-added by final use. The decomposition complements the Leontief insight by showing which exports are going to serve final consumption and count the amount of value-added that is going to eventually return home. The decomposition allows for identifying the extent of vertical specialization embedded in trade flows, which in turn reveals if GVC trade is prevalent among these economies. Once again, the heterogeneity of the B&R Economies manifests itself. ECA and EPA have high shares of vertical trade while MENA and SAR are only mildly integrated.

> 3 Notice the crucial difference between Value Chains which are a succession of business functions (marketing, R&D, distribution, conception and production) and supply chains which are a succession of sales and acquisition of raw materials in view of manufacturing.

> 4 In addition, we provide for the sake of robustness an analysis of the EORA database in the Appendix (A1, A2 and A3) which has higher country coverage, with 171 economies, 63 of which B&R, at the expense of some quality issues.

4
2. Trade Linkages between B&R Economies: Gross Trade To help visualize the strength of the production sharing network, we provide a complete overview of sourcing patterns using network analysis tools. From the analysis, it turns out that China, and to a lesser degree Russia, are the main economic centers of the B&R network. Given the importance of China, we proceed to quantify its sourcing patterns as well as the extent of vertical trade.

We conclude the study by calculating the expected impact of GVC trade flows of a reduction of costs in the B&R. Because of the cascading effect of trade costs, which increase with each time crossing a border, GVC trade is more elastic to these changes. We estimate a regression model where GVC trade depends on these costs.

The rest of the paper is structured as follows. Section 2 presents stylized facts on the B&R Economies based on gross exports. Sections 3 and 4 introduce the economic methodology to decompose gross exports into several value-added components. Section 5 uses network analysis tools to visualize the strongest production linkages between the B&R Economies. Section 6 discusses the integration of China in GVCS. Section 7 discusses the implications of further integration within B&R and section 8 concludes.

# **2. Trade Linkages between B&R Economies: Gross Trade**

# **_2.1 Gross Trade Linkages between B&R Economies Revealed by UN Comtrade Data_**

The section presents stylized facts based on the evolution of trade patterns of B&R Economies using the UN Comtrade database, which covers bilateral trade of 182 economies in total. The database only contains product level information, so it can inform us only on gross trade flows of intermediate and final goods. The UN Comtrade database has the greatest country coverage of all the databases at disposal for scrutiny. For 182 countries, it covers all bilateral gross trade figures. These are exports and imports of final and intermediate goods that are recorded by customs agencies.

The first stylized fact is that the share of B&R Economies in world trade has been increasing through time. In Figure 1, we observe that in 2015 the share of the 60 B&R Economies in world exports (i.e., exports of the 182 economies covered in the database) is 37 %, and their importance in world exports has been growing since 1995 when their share was 21 %. Figure 2 further suggests that this has been mainly driven by exports of intermediate goods, of which the share of B&R Economies grew faster than that of final goods, reflecting an increase in GVC participation. B&R Economies also have a relatively larger share of intermediate goods exports, accounting for 42 % of total intermediate exports (Figure 2, panel A), compared with 24 % for the share of final goods (Figure 2, panel B).

5
<!-- Start of picture text --> 80<br>19 79<br>i)<br>60 66 63<br>% 40<br>34 37<br>27<br>20<br>21 21<br>)<br>1995 2000 2005 2010 2015<br>°<br>80 84 84 83<br>68<br>60 62 60<br>58<br>% %<br>40 42 40<br>38<br>32<br>20 25<br>23 20 yi 24<br>16 16 17<br>0 0<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 80 84 84 83<br>60<br>%<br>40<br>20 yi 24<br>16 16 17<br>0<br>1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> °<br>68<br>60 62<br>58<br>%<br>40 42<br>38<br>32<br>20 25<br>23<br>0<br>1995 2000 2005 2010 2015<br><!-- End of picture text -->
<!-- Start of picture text --> 21.4<br>20 19.3<br>15.3<br>15 14.2: 13.1<br>%<br>10 5<br>2<br>508 B.0 .3<br>wf 9 49|.9<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>GE Middle East & North Africa [9 South Asia<br><!-- End of picture text -->

<!-- Start of picture text --> 100<br>80<br>60<br>%<br>40<br>20<br>0<br>1995 2000 2005 2010 2015<br>HE East Asia & Pacific ME. Europe & Central Asia<br>GE “Middle East & North Africa [I South Asia<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>80<br>60<br>%<br>40<br>20<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>GQ «Middle East & North Africa ( South Asia<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>80<br>60<br>%<br>40<br>20<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>GQ «Middle East & North Africa ( South Asia<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>68.4 67.5<br>62.6<br>60 58.1 55.7<br>% 40<br>23.4 22.0 22.2 23.3 25.2<br>20<br>4 5 0.6 6<br>4 Al. 9. 4. J<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>MEE Middle East & North Africa [ME South Asia<br>MO non-B&R<br><!-- End of picture text -->
<!-- Start of picture text --> “te 62.5<br>60 55.9<br>51.2<br>40<br>%° 31.6<br>265 28.1<br>23.8 24.2 -<br>20<br>5 A 1.0 s 9<br>@. @. 8.<br>0<br>1995 2000 2005 2010 2015<br>HE East Asia & Pacific ME Europe & Central/<br>MEE Middle East & North Africa [ME South Asia<br>ME non-B&R<br><!-- End of picture text --> <!-- Start of picture text --> 80 75.0 76.8 77.4 746<br>60<br>%<br>* 40<br>21.1<br>20 16.93<br>N17, Giom2 B12<br>A, aay 4 . 1.<br>0<br>1995 2000 2005 2010 2015<br>HE East Asia & Pacific ME Europe & Central/<br>MEE Middle East & North Africa [ME South Asia<br>ME non-B&R<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>67.2<br>61.2<br>60 55.8 53.2 524<br>% 40<br>30.0<br>204 24.3 25.6 26.7<br>20<br>0.7 0<br>. 49 9 5 54<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific MERE Europe & Central Asia<br>HEE Middle East & North Africa [EE South Asia<br>MR non-B&R<br><!-- End of picture text -->
<!-- Start of picture text --> 80<br>67.2<br>61.8<br>60 55.0 53.2<br>% 40<br>27.7<br>212 23.6 24.1<br>20 18.6 -<br>9 %.0 1.92 1.86: 8.1<br>0<br>1995 2000 2005 2010 2015<br>HE East Asia & Pacific ME Europe & Central/<br>MEE Middle East & North Africa [ME South Asia<br>ME non-B&R<br><!-- End of picture text --> <!-- Start of picture text --> 50 45.0 48.7 47.1 49.7 49.7<br>41.8<br>40<br>33.7<br>31.1 296<br>30<br>%<br>20<br>1.0 2.3 1.4<br>10 ;6 5.<br>0<br>1995 2000 2005 2010 2015<br>HE East Asia & Pacific ME Europe & Central/<br>MEE Middle East & North Africa [ME South Asia<br>ME non-B&R<br><!-- End of picture text -->
<!-- Start of picture text --> 8%<br>31%<br>61%<br>MEE non -838R «=< aR<br><!-- End of picture text --> <!-- Start of picture text --> 34%<br>66%<br><!-- End of picture text -->
<!-- Start of picture text --> Agriculture, hunting, forestry and fishing<br>Hotels and restaurants<br>Other transport equipment<br>Rubber and plastics products<br>Fabricated metal products<br>R&D and other business activities<br>Manufacturing nec; recycling<br>Food products, beverages and tobacco<br>Motor vehicles, trailers and semi-trailers<br>Electrical machinery and apparatus, nec<br>Coke, refined petroleum products and nuclear fuel<br>Machinery and equipment, nec<br>Basic metals<br>Textiles, textile products, leather and footwear<br>Chemicals and chemical products<br>Transport and storage<br>Mining and quarrying<br>Wholesale and retail trade: repairs<br>Computer, Electronic and optical equipment<br>0 5 10 15<br>Share of Total B&R Exports, 2010<br><!-- End of picture text --> <!-- Start of picture text --> Other non-metallic mineral products<br>Other transport equipment<br>Agriculture, hunting, forestry and fishing<br>Manufacturing nec; recycling<br>Food products, beverages and tobacco<br>Motor vehicles, trailers and semi-trailers<br>Textiles, textile products, leather and footwear<br>Rubber and plastics products<br>Fabricated metal products<br>Electrical machinery and apparatus, nec<br>R&D and other business activities<br>Machinery and equipment, nec<br>Coke, refined petroleum products and nuclear fuel<br>Chemicals and chemical products<br>Basic metals<br>Transport and storage<br>Wholesale and retail trade; repairs<br>Mining and quarrying<br>Computer, Electronic and optical equipment<br>) 5 10 15 20<br>Share of B&R Intermediate Exports, 2010<br><!-- End of picture text --> <!-- Start of picture text --> Computer and related activities<br>Mining and quarrying<br>Financial intermediation<br>Construction<br>Agriculture, hunting, forestry and fishing<br>Other community, social and personal services<br>Other transport equipment<br>Electrical machinery and apparatus, nec<br>Coke, refined petroleum products and nuclear fuel<br>Chemicals and chemical products<br>Manufacturing nec; recycling<br>Hotels and restaurants<br>Motor vehicles, trailers and semi-trailers<br>Food products, beverages and tobacco<br>Machinery and equipment, nec<br>Transport and storage<br>Textiles, textile products, leather and footwear<br>Wholesale and retail trade; repairs<br>Computer, Electronic and optical equipment<br>0 5 10 15<br>Share of B&R Final Goods Exports, 2010<br><!-- End of picture text -->
3. Origin of Value-Added in Exports of B&R Economies A focus on intra-B&R trade reveals varying degree of importance of sectors from region to region (Figure 13). In particular, the dominance of Computer, Electronics and Optical Equipment is only an EAP phenomenon.

FIGURE 13: SHARE OF EXPORTS OF B&R SUB-REGIONS TO B&R ECONOMIES, TOP 5 SECTORS <!-- Start of picture text --> EAP ECA<br>Coke, refined petroleum Coke, refined petroleum<br>products and nuclear fuel products and nuclear fuel<br>Chemicals and chemical<br>Transport and storage<br>products<br>Transport and storage Basic metals<br>Wholesale and retail Wholesale and retail trade;<br>trade; repairs repairs<br>Computer, Electronic and<br>Mining and quarrying<br>optical equipment<br>0 5 10 15 20 25 0 5 10 15 20<br>MENA SAR<br>Wholesale and retail<br>Basic metals<br>trade; repairs<br>Transport and storage Mining and quarrying<br>Coke, refined petroleum Coke, refined petroleum<br>products and nuclear fuel products and nuclear fuel<br>Chemicals and chemical Wholesale and retail<br>products trade; repairs<br>Mining and quarrying Transport and storage<br>0 20 40 60 80 0 10 20 30<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the TiVA database and the Kummritz and Quast (2017) algorithm. The x-axis represents the share of a sector’s exports to B&R Economies.

# **3. Origin of Value-Added in Exports of B&R Economies**

# **_3.1. Concept and Methodology_**

Since gross trade data do not allow a full understanding of the importance of production linkages, this section turns to trade in value-added. We focus on the origin and destination of exports of value-added, effectively identifying the shares of foreign tasks needed to produce exports.

To assess the amount of value-added that B&R Economies source from each other, we separate the sample in three groups: B&R, Non-B&R and the Rest of the World (ROW). Once again, please notice that there are some countries in the ROW aggregate that are part of the B&R initiative but for whom it is not possible to separate the effect because of data issues. Although of high quality, the TiVA database only 13
3. Origin of Value-Added in Exports of B&R Economies covers 62 economies. Therefore, to support the conclusions in the paper, we turn to another database that has higher country coverage but exhibits some accuracy issues. For the sake of robustness, we replicate the analysis using another set of input-output tables in the appendix (A1).

Value-added is the remuneration of factors of production. Understanding the origin of value-added in gross exports figures allows us to establish the remuneration of tasks for value-chains in different industries. It should be noted that there is not a good or bad share of foreign value-added in a country’s gross exports. However, a larger share denotes a higher likelihood of being part of GVCs.

Value-added in exports refers to the use of foreign business functions that are necessary for the domestic industry to produce its own exports. The use of such activities results in linkages across industries and economies, where firms specialize in a given activity and thus provide value to a GVC. By both selling to and buying from GVCs, firms specialize in the activities for which they have a comparative advantage and associate with other firms around the globe, sourcing and contributing with value-added.

Identifying the origin of value-added in exports allows to quantify the linkages of economies and industries as “buyers of values added”. Thus, the origin of value-added is the origin of foreign content of activities/intermediate inputs/services that are needed for domestic exports. They are sometimes denoted as backward linkages in the literature.

Since for each buyer there is a seller, we can also identify the economies and industries that export their own value-added that third economies use in their domestic industries to produce exports. We refer to the third economies as the destinations of domestic value-added. This is the supply of domestic inputs/business functions/services that is exported and then used in the production of the partner’s exports. This type of linkages is also known as forward linkages.

In this section, we identify the share of value-added in exports by origin and destination using the Leontief decomposition of gross exports. The decomposition considers the flows of intermediate goods between countries and industries that are necessary to satisfy the global demand for consumption goods. By balancing out the unit requirements of each country-industry, the decomposition accounts for the remuneration of all industries’ and countries’ factors of production. To that effect, gross exports are deflated by their value-added, pinpointing the distribution of shared production between countries. A well-known example is the one of Apple’s iPhone. The full value of an iPhone that is assembled in China and shipped abroad is accounted in China’s gross exports, however, some intermediate tasks to produce it were done in other economies. The remuneration of those tasks is the foreign value-added in China’s iPhones’ exports.

# BOX 1: VALUE-ADDED IN EXPORTS BY ORIGIN AND DESTINATION

**As described by Wang et Al. (2013), if the question of interest is to find the origin of the value-added in exports, the simplest approach is to use the Leontief insight.** The approach has been extended to the multiregional case by Johnson and Noguera (2012). Wang et Al. (2013) discussed in depth the technical derivations that are necessary to perform the decomposition.

**The Leontief decomposition is based on the fact that for every final good that is consumed, a certain amount of intermediate goods (which themselves needed raw materials) has been produced.** These intermediate goods have either been traded between industries -in the same or different countries- or within an industry. Thus, in order to produce 1 unit of a final good, we need a certain amount of intermediate goods from a variety of industries. This ratio is referred to as the technical coefficient.

**Using the matrix of technical coefficients and all the inter-industry and intra-industry flows, Leontief accounted for the origin of all the goods consumed in an economy.** The difference between the value paid for the final goods and all the intermediates necessary for their production is the value-added. Value-added is used to remunerate all factors of production, labor, rent of land and capital.

**Extending the concept to many countries, for each 1 currency unit in gross exports some of it was used to pay intermediate goods and the rest is value-added to remunerate factors of production.** Whenever a good

14
<!-- Start of picture text --> non-B&R B&R<br>Dp<br>Faas Si<br>82% %<br>ED,<br>ROW<br>ME non-8&R 4M BER<br>ME Row<br><!-- End of picture text --> <!-- Start of picture text --> non-B&R B&R<br>11%<br>64% 5 ;<br>=)<br>ROW<br>ME non-8&R 4M BER<br>ME Row<br><!-- End of picture text -->
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<!-- Start of picture text --> 31%<br>50%<br>\<br>ME non-BR 46S «BR<br>ME ROW DE Domestic<br><!-- End of picture text -->
4. Quantifying Vertical Trade among the B&R Economies Formally, the first type of linkages is referred to as backward linkages. It is the amount of foreign valueadded in gross exports plus the double counted items from foreign sources.<sup>6</sup> The second type of linkages are denominated forward linkages. They are the amount of domestic value-added in intermediate goods that is used to generate exports to third countries.<sup>7</sup> Forward linkages are defined by the final use of the exports and categorized in that form. For example, there is a difference between exports that are going to be consumed in the partner country, exports that are going to be re-processed and shipped to third countries and exports that are going to be shipped back to the original exporter.

The two types of measures are crucial to understand how GVCs effectively redistribute a share of the export revenues to all participating countries. Therefore, we investigate the two types of linkages in depth considering the partners along the B&R and the non-B&R Economies.

We use the state of the art methodology developed by Wang et al. (2013) which decomposes bilateral gross exports in four major terms (Figure 20): domestic value-added absorbed abroad, foreign value-added, domestic value-added that returns home and purely double counted exports. Exploiting the algorithm of Quast and Kummritz (2016) the four terms can be combined to form statistics at the country level which explain vertical specialization from the importing side and from the exporting side. We discuss the findings for the aggregate economy in the next two sections.

# BOX 2: DECOMPOSING VALUE-ADDED IN DIFFERENT COMPONENTS

**We use the methodology pioneered by Johnson and Noguera (2012) and extended by Koopman et al. (2014) and Wang et al. (2013).** The methodology decomposes production on each country according to where it is finally consumed. The goal is to trace production stages and all possible intermediate steps. Through input-output linkages, exports are deflated by the amount of imported inputs, removing the double counting of trade statistics.  The composition relies in complex matrix algebra manipulation, the exact steps are described in Wang et al. (2013). We obtained the indicators with the help of the computational method developed by Quast and Kummritz (2016).

**The decomposition of Wang et al. (2014), which is the state of the art methodology, decomposes bilateral sectoral exports by destination country in 16 terms.** The 16 terms can be formalized in four major groups. Formally, for each exporting country s, exporting industry j and importing country m. Notice that all variables can be indexed by j, m and s. For the sake of simplicity, we omit the subscripts. Gross exports are divided in four major terms.

Exports= VAX+ RDV+ FVA+ PDC VAX is the domestic value-added in exports absorbed abroad. RDV is the domestic value-added first exported, then processed in third countries and then returned to the home country. FVA is the foreign value-added embedded in the exports of country s and industry i. PDC are pure double counted items.

**From these four major components, we further decompose at the bilateral level to get additional indicators of GVC integration between the partners.**

The domestic value-added absorbed abroad can be decomposed in three terms. First, the domestic value-added in exports of intermediate goods that is going to be absorbed by the direct partner, DVA_int. Second, the domestic value-added in final goods exports to the direct partner, DVA_int. Third the domestic value-added in intermediate goods that is going to be re-exported to third countries and finally be absorbed abroad.

RDV is the domestic value-added that returns home. Therefore, it can be decomposed in domestic value-added that returns home via final goods or via intermediate goods’ imports.

> 6 Double counted items occur because some goods cross borders several times and are therefore counted twice in trade statistics while they were only produced once. Input-output tables allow to estimate the percentage of this type of intermediate goods that cross a border several times. Notice that double counting in gross exports is large when GVC trade is prevalent.

> 7 Among backward linkages we might also separate between intermediate goods that are re-exported to third countries and intermediate goods that return to the home market for further processing.

19
4. Quantifying Vertical Trade among the B&R Economies <mark>FVA in the foreign value-added in bilateral sectoral exports. The foreign value-added can again be present in</mark> intermediate goods exports (FVA_int) or final goods exports (FVA_fin). Each of these sources of value-added can come from the direct partner or from third countries.

Finally, PDC are purely double counted items that can come from two sources. FDC and DDC.

The vertical specialization index from the importing side (VS, backward linkages) denotes the foreign content of exports. It is defined as the share of value-added in gross exports that comes from foreign sources plus the double counted terms.

## VS= FVA+ FDC

Each of the terms of VS indicates a country-sector position into global value chains (Wang et al. 2014).

From the exporting side, we define vertical specialization (VS1, forward linkages) as the domestic value-added that gets re-exported to third countries. In addition, we also separate the domestic value-added first exported and then returned home (VS1*=RDV).

**Source:** Author’s elaboration based on Koopman et Al. (2014) and Wang et Al. (2014)

# **_4.2 Backward Linkages_**

Backward linkages represent vertical specialization from the importing side. They refer to the use of foreign inputs (foreign value-added) embedded in a country’s gross exports. Using the decomposition of Wang et al. (2013), we can calculate backward linkages at the bilateral-sector level.<sup>8</sup> Therefore, we are able to map backward value-added linkages existing in exports of the selected group of B&R Economies.

We use the vertical specialization index (VS) discussed in Box 2 to map the foreign content of exports by the B&R Economies. The foreign content of exports refers to the remuneration of foreign activities embedded in a country’s gross exports. A large foreign content of exports implies stronger linkages from an inputs’ perspective to map the foreign content of exports by the B&R Economies. The foreign content of exports refers to the remuneration of foreign activities embedded in a country’s gross exports. A large foreign content of exports implies stronger linkages from an inputs’ perspective.

We notice in Figure 20 that the share of foreign content of exports of B&R Economies increased to 29 % in 2005 and started decreasing to reach 24 % in 2010. This is an indication that total exports are using more domestic inputs and rely a bit less in foreign intermediate goods for production. The pattern is similar for both exports to B&R and non-B&R Economies.

> 8 Trade in value-added at the bilateral-sectoral level is not properly defined using the pure Leontief decomposition.

20
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<!-- Start of picture text --> 1995 (a<br>East Asia & Pacific 359? —— <7<br>2010 |:<br>. 1995 aT<br>Europe & Central Asia 5995 eySS]<br>2010 |:| |:a<br>1995 Ty:7 TS 1578<br>Middle East & North Africa 3000 —=—<——$_——<br>5010 5SN<br>. 3000 —<br>South Asia 5995 5SE:<br>2010 1}\: Ss<br>0 20 40 60 80 100<br>Share of VS<br>ME FVA in Intermediate Goods<br>ME FVA in Final Goods<br>ME Double Counted<br><!-- End of picture text -->
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<!-- Start of picture text --> 1995 11<br>East . . 2000 13<br> Asia & Pacific 2005 -<br>2010 14<br>1995 14<br>Europe & Central Asia. 20002005 17 on<br>2010 18<br>1995 17<br>. . 2000 21<br>Middle East & North Africa 2005 on<br>2010 25<br>1995 11<br>South . 2000 14<br> Asia 2005 -<br>2010 14<br>0 5 10 15 20 25<br>%<br><!-- End of picture text -->
<!-- Start of picture text --> o<br>> 1.4<br>is]<br>a<br>=<br>fo}<br>Leo<br>o- 08<br>=<br>fo}<br>=<br>= 07;<br>Bo 07M.<br>5<br>3)<br>la<br>oy<br>aw<br>gz 04<br>o 03<br>3 03<br>San 0.2<br>2 ue 02<br>7)o 0.2<br>=<br>ae<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br><!-- End of picture text -->
5. Visualization of Value-Added Network of B&R Economies FIGURE 29: COMPARISON OF COUNTRIES WITH THE LARGEST VS1*, 2010 <!-- Start of picture text --> 5.3<br>2.0<br>1.6 1.6<br>1.2 1.3<br>1.1<br>0.8 0.8<br>0.5<br>IND RUS IDN CAN FRA GBR JPN CHN DEU USA<br>5<br>4<br>3<br>2<br>1<br>Domestic Value Added Returned Home (Forward, VS1*) 0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the TiVA database.

# **5. Visualization of Value-Added Network of B&R Economies**

In this section, we take the same value-added data by origin and destination used in the previous section to build network graphs.<sup>11</sup> Network analysis allows us to visualize the most important providers and users of value-added. The value-added that we are considering is the amount of foreign value added in exports and the domestic value added used by third countries for re-exports. Formally, the technique defines countries as “nodes” and the “trade flows” between them as edges. Network visualization algorithms map these graphs of nodes and edges to identify specific patterns.

We start by plotting the most important sources of value-added (Figure 30) for the world economy. The sources of value-added can be thought of as the origin of intermediate tasks that are necessary for countries to produce their own exports. For example, the automotive industry in Germany needs ignition mechanisms from Turkey. In our example, Turkey would be an important source of value-added for Germany. In Figure 30, we compare the top 3 most important sources of value-added in 1995 and 2010. The size of the arrows denotes the amount of value-added traded and the size of the node reflects the centrality (number of partners) of the economy in the network. We observe that in 1995 (Panel A) the two dominant sources of value-added were the United States and Germany. The links between Japan, the United States, Canada, Mexico and the Republic of Korea are obvious. Germany, together with some European countries (Italy, France, Great Britain, Belgium and the Netherlands) formed a strong network too. In 2010 (panel B), China emerged as the third important actor with much more activity at the global level. The Germany and United States networks are still present. To better grasp the role of China we plot, in Figure 31, only the nodes that are active in its network. We see that China is a truly global actor and we can distinguish a network with the Americas (United States, Brazil, Chile, Mexico, Canada and Colombia) and another one with mostly B&R Economies.<sup>12</sup> FIGURE 30: ORIGIN OF VALUE-ADDED IN GROSS EXPORTS, 1995 AND 2010, WORLD ECONOMY PANEL A: ORIGIN OF VALUE-ADDED IN EXPORTS, 1995 PANEL B: ORIGIN OF VALUE-ADDED IN EXPORTS, 2010

> 11 Taglioni and Winkler (2016) provide a detailed discussion on the use of network analysis for studying international shared production.

> 12 In the appendix (A4), we consider the same type of network using the EORA multi-regional input-output tables.

27
<!-- Start of picture text --> m MM<br>oRI<br>NOR SUK vam<br>cae AUS<br>EST<br>cAaN be<br>BRA apt LOX<br>MEX & ray BRN<br>TWN IPA NOD<br>PaL ovP swe (usa) A DaK maT<br>HAG KOR @ ~ @) a<br>sGP “< KR<br>HRV ZF eo kam = TON one<br>— AlRG CAE Ms ION HON<br>PRT .<br>coL TAA ia SAU<br>LeA CAL<br>POL TOR<br>IND<br>ZAF<br>CZE<br>a COL<br>POL<br>CAN RUs Kow IDN<br>KOR CHL<br>ink VNM<br>USA THA<br>HUN<br>MYS JPN{ TWN KHM<br>on MEX<br>PHL AUS HKG<br><!-- End of picture text --> <!-- Start of picture text --> IND SVK LVA TUR<br>SVN LYU AF mer<br>SAU<br>CZE BGR BEL<br>ISR NbD<br>PRT vey<br>ROM IRL ITA TUN<br>GRC FRA<br>POL oe<br>caVNM BRN RUS . GBR ESP IDN AUT<br>‘KORTM CHL DNK<br>BEA Pe i Shan CHE<br>mys HUN on SMe NOR<br>HRV nee TWN ay KHM<br>dS MEX ISL<br>ARG Ne<br>PHL aus<br>HKG<br><!-- End of picture text -->
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<!-- Start of picture text --> ARG MtT Tun CHE<br>ceL DAR<br>ISR LUX<br>BEL DNK<br>J CHL FRA<br>GBR<br>PRT ROU<br>ia, TH a CRI IRL AUT nTSWE L®¥U<br>Hie B“ en a<br>ae ; usa aL<br>KOR e. twa \ Neo<br>vem "ha| SGP cae POL<br>| a 4A / zap | SsSAU |<br>2 TuR = “FIN<br>SVK<br>KHM NZL GRC EST<br>CAN RUS<br>cryp IND HRV<br>MYS “% SGP<br>FN<br>svK iD HKG<br>|<br>co)<br>GRC INI<br>r<br>: yy ”<br>PHL<br>FOL BOR sau KE<br>yg<br>RUS LTU EST HRV<br>ROU vie<br>SVN HUN<br><!-- End of picture text -->
6. Trade Linkages between China and other B&R Economies

# **6. Trade Linkages between China and other B&R Economies**

# **_6.1. Gross Trade between China and other B&R Economies_**

B&R Economies are already important trading partners of China. In terms of the destination of total gross exports, B&R economies were the destination of 22 % of China’s gross exports, while non-B&R accounted for 66 % (Figure 36). Repeating the exercise in Figure 44 from the point of view of China’s gross imports, B&R economies are much more important to China as sources of imports, accounting for 30% of its total imports in 2010 ( Figure 37).

## FIGURE 36: EXPORTS OF CHINA BY DESTINATION, 1995-2010

<!-- Start of picture text --> 1995 2000<br>7%<br>22% 17% 7%<br>71% 75%<br>2005 2010<br>8% 12%<br>17%<br>22%<br>66%<br>74%<br>non-B&R B&R<br>ROW<br>Graphs by year<br><!-- End of picture text -->

**Source:** Author’s calculations using the Tiva database.

FIGURE 37: IMPORTS OF CHINA BY ORIGIN, 1995-2010 <!-- Start of picture text --> 1995 2000<br>5% 8%<br>30% 29%<br>65% 63%<br>2005 2010<br>7% 9%<br>31% 30%<br>62% 61%<br>non-B&R B&R<br>ROW<br>Graphs by year<br><!-- End of picture text --> 31
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations using the Tiva database.

LOOKING AT THE DIFFERENCE BETWEEN INTERMEDIATE AND FINAL GOODS, WE NOTICE THAT CHINA IMPORTS MORE INTERMEDIATE GOODS THAN IT EXPORTS. HOWEVER, EXPORTS TO OTHER B&R ECONOMIES HAVE BECOME MORE AND MORE INTENSIVE IN INTERMEDIATES (FIGURE 38), REACHING 59 % IN 2010. FROM THE IMPORTING SIDE, WE SEE THAT CHINA’S IMPORTS ARE MOSTLY INTERMEDIATE GOODS ( Figure 39). The results suggest that China has stronger GVC linkages as a buyer of value-added than as a seller.

FIGURE 38: SHARE OF INTERMEDIATE GOODS IN CHINESE EXPORTS TO B&R AND NON-B&R ECONOMIES, 1995-2010 <!-- Start of picture text --> 61<br>59<br>52<br>50 50 51 49 50<br>48<br>46<br>43 43<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>60<br>40<br>20<br>Share of Intermediate Goods in Gross Exports<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

FIGURE 39: SHARE OF INTERMEDIATE GOODS IN CHINESE IMPORTS FROM B&R AND NON-B&R ECONOMIES, 19952010 <!-- Start of picture text --> 87 89 91<br>80 80<br>77 79<br>74 73<br>70<br>67 67<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>100<br>80<br>60<br>40<br>20<br>Share of Intermediate Goods in Gross Exports<br>0<br><!-- End of picture text --> 32
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

# **_6.2. China’s Position in GVCs_**

We use the vertical specialization index discussed earlier (VS) to map the foreign content of China’s gross exports. We notice in Figure 40 that the share of foreign content of exports has increased up to 29 % in 2005 and started decreasing to reach 24 % in 2010. This is an indication that total exports are using more domestic inputs and rely a bit less in foreign intermediate goods for production. Figure 41 further shows that the pattern is similar for both exports to B&R and non-B&R economies.

FIGURE 40: SHARE OF FOREIGN CONTENT OF CHINESE EXPORTS, 1995-2010 <!-- Start of picture text --> 29<br>25<br>20<br>15<br>1995 2000 2005 2010<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

FIGURE 41: SHARE OF FOREIGN CONTENT OF CHINESE EXPORTS TO B&R AND OTHER ECONOMIES, 1995-2010 <!-- Start of picture text --> 30<br>28<br>26<br>25 25<br>23<br>20<br>17 17<br>16<br>14<br>13<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>30<br>20<br>10<br>Foreign Content of Exports (Backward, VS)<br>0<br><!-- End of picture text --> 33
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

From the point of view of backward linkages, B&R Economies have a trade structure with China that is increasingly reliant in GVCs. This is reflected in Figure 42, which report the indicator of GVC positioning in terms of the composition of the Vertical Specialization index. In 2010, double counting has accounted for 36 % of China’s vertical specialization index for exports to B&R Economies. In contrast, non-B&R economies have only a share of double counting of 21 %.

FIGURE 42: COMPOSITION OF THE VERTICAL SPECIALIZATION INDEX, CHINESE EXPORTS TO B&R AND OTHER ECONOMIES <!-- Start of picture text --> 13 16 19 21 21 31 41 36 10 14 11 10<br>49 48 56 58 56 55<br>47 44 51<br>45<br>38<br>34<br>38 36 34 35 35 33 36<br>28 24 25 26 28<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>FVA in Intermediate Goods FVA in Final Goods<br>Double Counted<br>100<br>80<br>60<br>Share of VS 40<br>20<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

Forward GVC linkages of China with B&R Economies are also stronger than with non-B&R economies. As shown in Figure 43, 20% of Chinese exports to other B&R Economies is domestic value-added from China that serves as intermediate goods to generate exports of the importing B&R Economies. China is therefore contributing to the development of GVCs in B&R Economies.

FIGURE 43: DOMESTIC VALUE-ADDED OF CHINA RE-EXPORTED, BY DESTINATION (% OF GROSS EXPORTS) <!-- Start of picture text --> 21<br>20<br>19<br>17<br>12<br>11<br>10 10<br>9 9<br>8<br>7<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>20<br>15<br>10<br>5<br>Domestic Value Added Re-Exported (Forward, VS1) 0<br><!-- End of picture text --> 34
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

In addition, the Chinese domestic market is more and more important to its B&R partners. The Chinese domestic value-added that returns to China (VS1*), of course, through Chinese imports, has been steadily increasing (Figure 44). The increase has been more important with B&R Economies, suggesting that GVC trade in view of serving Chinese domestic markets is becoming the new paradigm. About 3 % of China’s exports returns from B&R countries. As shown in Figure 29 in Section 4, in terms of the share of VS1*, China is comparable to some developed economies: in 2010 China is among the top with the United States, Japan and Germany.

FIGURE 44: CHINA, DOMESTIC VALUE-ADDED RETURNED HOME (% OF GROSS EXPORTS) <!-- Start of picture text -->
1.6<br>1.1<br>0.7<br>0.5<br>1995 2000 2005 2010<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

FIGURE 45: CHINA, DOMESTIC VALUE-ADDED RETURNED HOME, BY DESTINATION (% OF GROSS EXPORTS) <!-- Start of picture text --> 3.0<br>2.6<br>1.6 1.7<br>1.4<br>0.8<br>0.4<br>0.3 0.3 0.3 0.4<br>0.2<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>3<br>2<br>1<br>0<br>Domestic Value Added Returned Home (Forward, VS1*)<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

35
7. Potential Benefits from Further Integration

# **7. Potential Benefits from Further Integration**

To evaluate the potential benefits from further integration between B&R Economies and China, we estimate a regression model of value-added flows on trade costs (Figure 37). We match our value-added database to the trade costs database of Arvis et al. (2013). The trade costs explain the variation in our database well.

FIGURE 46: TRADE COSTS AND TRADE IN VALUE-ADDED INDICATORS, B&R ECONOMIES, 1995-2010 <!-- Start of picture text -->
-10 -5 0 5 10 -10 -5 0 5 10<br>Foreign Content of Exports, in Logs Domestic VA Re-Exported , in Logs<br>7<br>6<br>5<br>4<br>3<br>2<br>7<br>6<br>5<br>4<br>3<br>2<br><!-- End of picture text -->

**Source:** Author’s calculations based on Trade Costs Database (Arvis et Al. 2013) and GVC indicators extracted using the Wang et Al. (2013) methodology from TiVA.

We find that a reduction of trade costs of 1% is likely to increase bilateral trade between B&R Economies by 1.3 %. Concerning trade in value-added indicators, the order of magnitude stays the same except for the indicator of domestic value-added that returns home (VS1*). For this indicator, we find that trade cost reduction by 1% would yield an increase of more than 1.7 %. The result is especially appealing since this type of value chain is the most beneficial to both economies as it builds not only on their production capabilities but also on their domestic markets.

36
8. Conclusion

## BOX 3: CONSEQUENCES OF REDUCING TRADE COSTS

<mark>To estimate the impact of furthering economic integration between B&R Economies and China, we estimate a</mark> regression model where the dependent variable is the trade in value-added and the independent variables are the trade costs. The approach assumes that the B&R initiative will reduce trade costs between all B&R partners.

The estimating equation is log(TradeVAijt) = β1 log(TradeCostsijt) + αij + λt + εijt Where TradeVAijt is one of our trade in value-added indicators. β1 is the elasticity of trade with respect to trade costs and αij and λt are sets of fixed effects. The estimated coefficients are in Table 1.

## TABLE 1 REGRESSION RESULTS, IMPACT OF REDUCING TRADE COSTS

||(1)<br>Log<br>Gross<br>Exports|(2)<br>LogVS|(3)<br>LogVS1|(4)<br>LogVS1*|
|---|---|---|---|---|
|Log Trade Costs|-1.13***|-1.22***|-1.21***|-1.73***|
||(0.06)|(0.06)|(0.06)|(0.08)|
|Constant|10.32***|9.11***|8.67***|5.66***|
||(0.31)|(0.33)|(0.33)|(0.43)|
|Observations|12,408|12,408|12,408|12,408|
|R-squared|0.48|0.53|0.49|0.45|
|Number of pair_id|3,493|3,493|3,493|3,493|
|Pair FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

**Source:** Author’s elaboration

# **8. Conclusion**

Integration within the economies of the B&R has already begun. We find that our selected group of B&R Economies are well integrated with regard to both backward and forward linkages. Still, they source some of the value-added in exports from third partners.

There are two important interconnected production networks around China and Russia. They are central in the value-added as both providers and users of inputs. With their strong linkages, they are well connected to other B&R partners and they leverage on their domestic markets by building value-chains around them. In fact, the share of exports that returns to be consumed in their domestic markets is as high as that of some developed countries.

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8. Conclusion There is substantial scope for further integration in the key sector of Computer, Electronics and Optical Equipment sector which accounts for over 15 % of gross exports from the B&R Economies. Surprisingly, in this key sector only 15 % of the value added comes from B&R partners, while around 30 % comes from mostly developed economies. Therefore, there is substantial room for further integration and a more intensive use of B&R inputs.

China’s role in GVCs is evolving once again. From an exporting structure based on assembly and intensive in foreign inputs, China is moving up the value chain to become one of the most important GVC hubs. We observe that while overall the use of foreign value-added has diminished, the composition of the vertical specialization index with B&R Economies revolves around the double counted terms. This suggests that trade between China and B&R Economies is intensive in GVCs. China is nowadays at the center of the trade network under all angles. It occupies a dominant position together with Germany and the United States. B&R Economies are among its main partners and they are the most important partners for circular value chains that leverage on the domestic economy.

In the paper, we find that an eventual reduction of trade costs, generated by improved connectivity, is likely to increase not only trade but also the vertical specialization linkages in the region from the exporting and importing side. Given the increasingly large domestic market in China, it is not surprising that most of the gains from lower trade costs would come from domestic value-added that returns home (the circular value chain). The finding is encouraging as this is a win-win value chain were countries specialize in their comparative advantage and leverage their own domestic economies.

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