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How does the Selection of FTA Partner(s) Matter in the Context of GVCs?

The Experience of China*

CHENG Dazhong, WANG Xinkui, XIAO Zhiguo, and Yao Weiquan

This preliminary version: February 29, 2016

Abstract

Drawing upon a large sample of matched trans-national input-output data and global FTA data,

this study examines whether and how the selection of partner in China’s FTA construction process

impact on the bilateral GVC linkage between China and the partner. We find that China tends to

have stronger GVC linkages with the economies with higher income levels, and the higher income

of FTA partner, the stronger mutual GVC dependence between China and the FTA partner. Such

FTA is of an upward vertical type for China. These findings are also robust to different model

specifications and product-/sector-level disaggregated analyses.

Keywords: Free Trade Area/Agreement, Global Value Chains, Input-output Analysis

JEL codes: F14, F15, F20

CHENG Dazhong

Department of World Economy

Fudan University

chengdz@fudan.edu.cn

WANG Xinkui

Shanghai WTO Center

wangxk@sccwto.org

XIAO Zhiguo

Department of Statistics

Fudan University

zhiguo_xiao@fudan.edu.cn

YAO Weiqun

Shanghai WTO Center

weiqunyao@sccwto.net

* The basic idea of this study was presented at the APEC Workshop of Measurement on Trade in Value-added

under Global Value Chains with Leading Economists’ Forum on GVCs in November 2015 in Shanghai. We are

grateful to the WTO chief economist Robert Koopman and the USITC leading economist Dr. Zhi Wang for their

kind encouragement.

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“Today’s global economy is characterized by global value chains (GVCs), in which

intermediate goods and services are traded in fragmented and internationally dispersed

production processes.”

--- UNCTAD (2013, p.X)

“Regional liberalization sweeps the globe like wildfire while multilateral trade talks proceed

at a glacial pace.”

---Richard Baldwin (1993)

“He that lies down with dogs must rise up with flea.” (近朱者赤，近墨者黑)

---FU Xuan (217-278) in Jin Dynasty of Ancient China

1. Introduction

The preferential trade and investment liberalization has acquired growing importance in the

past three decades. There are over 400 free (or regional) trade agreements (FTAs or RTAs)

(counting goods, services and accessions separately) currently in force, and almost every economy

on this planet is a member to at least one such agreement (see Figure A1-1). In about the same

period, flourishing global value chains (GVCs) have been revolutionizing world economic

relations①

, and all the economies are more or less involved in this new paradigm of division of

labor and specialization (Baldwin and Lopez-Gonzalez, 2013).

Driven largely by the vigorous development of both FTAs and GVCs, China is becoming

more and more active in the construction of FTAs and views this as a new channel of integration

into the world economy and GVCs since its WTO accession in 2001. Currently, there are 13

concluded FTAs (involving 21 individual economic partners), 7 under negotiation (concerning 25

individual partners), and four others under consideration (covering 4 individual partners) (see the

following Section 2 for details). Unlike the EU and the US, China’s FTAs follow no template, and

in particular, its FTA partners are quite heterogeneous in terms of development level, GVC

location and other aspects.

Given the current status of China in the GVCs, the selection of partner(s) is expected to be

crucial to the outcome of its FTAs construction. Therefore, it is worthwhile to conduct an ex post

GVCs-based evaluation of China’s FTA strategy. The results of such evaluation can be compared

with the prediction outcome calibrated on the pre-establishment values and can be used as a

benchmark for an ex ante policy simulation for revising and upgrading the current FTAs. More

generally, it can provide guidance for other economies, especially developing ones, in selecting

their FTAs partner in the context of global value chains.

For our theoretical analyses, we borrow the concept of trans-national smiling curve and

establish a simple framework to characterize the GVC-based FTA formation mechanism. We argue

① The emerging of GVCs was widely perceived by the experts in this field to have formally begun in the early

1990s, the motives behind which includes the development of MNCs, the widespread use of internet, and the

reduction of cross-border barriers because of the multilateral liberalization (e.g. at that time, the Uruguay Round

negotiation being about to close and welcoming the birth of WTO).

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that, for a low-end economy like China, the better choice is to select high-end partner(s) to form a

GVC vertical FTA, because the higher income of FTA partner, the stronger GVC linkage between

China and the partner in general and the higher value-added contribution of the partner to China in

particular, which no doubt dominates the positive correlation between participation in GVCs and

GDP per capita growth that has been proved by the existing literature, e.g. UNCTAD (2013).

For our empirical study, we first construct a large sample of matched trans-national

input-output data and global FTA data, and then follow Leontief (1936), Miller and Blair (2009),

Koopman et al. (2014), and Wang et al. (2014) to focus on the backward linkage based perspective

to quantify China’s GVC linkage with its FTA and non-FTA partners.

The final evidence supports our hypotheses. First, China tends to have stronger GVC

linkages with the economies with higher income levels in the context of GVCs. Second, the higher

income level of the FTA partner, the stronger mutual GVC dependence between China and the

FTA partner. This also means such an FTA is vertical (upward rather than downward) in terms of

the division of labor in the GVCs. Third, the mutual GVC linkage or dependence between China

(at the lower end) and a richer economy (at the higher end) is mostly asymmetric whether the

economy is China’s FTA partner or not.

The rest of the paper is organized as follows: Section 2 briefly examines the history of

China’s FTA development since its WTO accession. Section 3 provides an overview of the

literature and formulate our hypotheses. The data and econometric methods are described in

Section 4. Section 5 presents the detailed empirical results on whether and how the selection of

FTA partner(s) matters to China in the GVC context. Section 5 summarizes our findings and

concludes.

2. China’s FTA Development

China’s entry into WTO in 2001 has been widely viewed as a major milestone in China’s

economic development and integration into the world economy since the late 1970s (Lardy, 2002;

Branstetter and Lardy, 2006). But China has not halted at WTO accession. Since then China has

been increasingly active in the pursuit of regional and bilateral trade agreements and has made

considerable progress (Li, Wang and Whalley, 2014).

As shown in Figure 2-1 and Table 4-3, China has concluded and is implementing 13 FTAs

involving 21 individual economies (Australia, Chile, Costa Rica, Hong Kong, Iceland, Macao,

New Zealand, Pakistan, Peru, Singapore, South Korea, Switzerland, and the ten-member ASEAN

(Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and

Vietnam) group). There are another 11 bilateral and regional FTAs under negotiation or being

proposed, including those with Colombia, Fiji, Georgia, Japan, Korea, Maldives, Moldova,

Norway, Sri Lanka, the six-member GCC (Saudi Arabia, United Arab Emirates, Kuwait, Amen,

Qatar, and Bahrain) group, and the sixteen-member RCEP (ASEAN-10, Japan, South Korea,

Australia, New Zealand, and India, plus China) group. In the following, we make a sketch of the

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concluded FTAs chronologically and by continent. ①

Most of China’s FTA partners are in Asia. China’s first FTA was the CEPA (Closer Economic

Partnership Arrangement) between the Mainland and Hong Kong, which was signed on 29 June

2003 and came into force on Jan. 1, 2004. In October 2003, Macao signed the agreement which

took effect also on Jan. 1, 2004. The CEPA has been updated several times since implementation.

For commodity trade, Hong Kong continues to apply zero tariffs to all imported goods of

Mainland origin. For service trade, the Mainland issued various liberalization measures under

CEPA to provide Hong Kong service suppliers with preferential access to the Mainland market.

Both sides also agreed to enhance co-operation in various trade and investment facilitation areas to

improve the overall business environment. The CEPA with Macao is almost identical to that with

Hong Kong.

The ASEAN-China Free Trade Area (ACFTA) is a free trade area among the ten member

countries of the Association of Southeast Asian Nations (ASEAN) and China. The initial

Framework Agreement (FA) was signed in November 2002. The ASEAN–China Free Trade Area

is the largest free trade area in terms of population and third largest in terms of nominal GDP. The

agreement is less concrete than the CEPAs and only sets out a broad framework for more detailed

agreements that are to follow. As the documents state, the objectives of the ACFTA are economic,

trade and investment cooperation, progressive liberalization of trade in goods and services,

creation of a liberal and transparent investment regime, and closer economic integration within the

region. For goods trade, Parties reduced their tariffs for goods listed under the Normal Track 1

(NT1) from 2005-2010 and Normal Track 2 (NT2) from 2010-2012. For services trade, services

and services suppliers/providers in the region enjoy improved market access and national

treatment in sectors/subsectors where commitments have been made. In November 2015, China

and ASEAN countries concluded FTA-upgrading negotiation and signed the Protocol on Revising

the China-ASEAN Comprehensive Economic Cooperation Framework Agreement and Related

Agreements under this Agreement, ushering in a new era of bilateral economic cooperation.

China and Pakistan started negotiation on a free trade area in April 2005. The China-Pakistan

FTA was reached in November 2006 and took effect in July 2007. The Agreement on Trade in

Service under the FTA was signed on 21 February 2009 and entered into force on 10 October 2009.

The contents of the agreements include an early harvest program, free trade agreements, trade in

services, and supplementary agreements. On 18 September 2015, China and Pakistan agreed to

start the second phase of FTA negotiation. The two sides negotiated on the model of tax reduction

for trade in goods, further opening trade in service, Pakistan’s regulation tax, the direct transport

from the origin country, and the exchange cooperation of Customs data.

The China-Singapore Free Trade Agreement (CSFTA) is China’s first comprehensive

bilateral FTA with another Asian country, which was signed on 23 October 2008 and entered into

① We thoroughly update information on the recently signed or upgraded or proposed FTAs based on Antkiewicz

and Whalley (2005), Li, Wang and Whalley (2014), various media reports, as well as the relevant official websites

including http://rtais.wto.org/ and http://fta.mofcom.gov.cn/.

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force on 1 January 2009. Under this Agreement, the two countries agreed to accelerate the

liberalization of trade in goods on the basis of the Agreement on Trade in Goods of the

China-ASEAN FTA and further liberalize the trade in services.

The China-South Korea FTA negotiations started in May 2012. The Agreement was officially

signed on 1 June 2015 and entered into effect on 20 December 2015. More than 90% of the tariff

items and 85% of the trade volume are subject to the liberalization. Both sides agreed to continue

negotiations on service trade and investment involving negative listing and national treatment, and

enhance bilateral communication and cooperation in the “21th century trade and economic issues”

including E-commerce, government procurement, intellectual property rights and competition.

There are three FTA partners (Chile, Peru and Costa Rica) in Latin and South America. The

first one is China-Chile FTA, which was signed in November 2005 and entered into force in

October 2006. For the commodity trade, China and Chile extended zero duty treatment phase by

phase to cover 97 percent of products in a ten-year time frame. The Supplementary Agreement on

Trade in Services under the FTA was signed on 13 April 2008. The two countries also agreed to

further strengthen exchange and cooperation in such areas as economy, SMEs, culture, education,

science and technology, and environmental protection. An agreement on investment is under

negotiation. On 15 May 2015, China and Chile signed the Memorandum of Understanding for the

Upgrading of China-Chile Free Trade Agreement, agreeing to discuss the possibilities of

upgrading the FTA. The China-Peru Free Trade Agreement was signed on 28 April 2009 and

entered into force on 1 March 2010. This agreement lead to gradual removal of tariffs on over 90

percent of goods ranging from Chinese light industry, electronic products and machinery to

Peruvian fish products and minerals over 16 years. Both countries also pledged to further open

their service sectors and to offer favorable treatment to investors. The China-Costa Rica FTA

negotiation was launched in January 2009. The Agreement was signed in April 2010 and came

into force on 1 August 2011. Over 90 percent of goods trade between two countries will enjoy

zero tariff gradually. The two countries agreed to open services respectively involving Costa

Rica’s 45 service sectors including telecommunication, business services, construction, real estate,

distribution, education, environment services, IT services and tourism, and China’s 7 service

sectors including IT services, real estate, market research, translation and interpretation and sport.

China has signed two FTAs with partners in Oceania. The first is China-New Zealand FTA,

which is the first comprehensive FTA that China has ever signed, as well as the first FTA with a

developed economy. It was signed on 7 April 2008 and entered into force on 1 October 2008. The

China-New Zealand FTA covers areas of trade in goods, trade in services and investment. For

example, under the agreement all tariffs on Chinese exports to New Zealand will be eliminated by

2016, and 96% of New Zealand exports to China will be tariff free by January 2019. The second is

China-Australia FTA, the negotiations on which started in May 2005 and have lasted for one

decade. The Agreement was finally signed on 17 June 2015 and took effect on 20 December 2015.

The China-Australia FTA covers goods, service and investment. For trade in goods, both sides

have products of 85.4% of trade value to enjoy zero tariffs upon the enforcement of FTA. For

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trade in services, Australia opens its service sectors to China by negative listing, while China

opens its service sector to Australia by positive listing. For investment, two countries agree to give

the most-favored nation treatment to each other immediately. The FTA also sets rules to enhance

bilateral communication and cooperation in more than 10 areas of the “21th century trade and

economic issues” including E-commerce, government procurement, intellectual property rights

and competition.

In Europe, Iceland is the first developed country to recognize China as a full market economy

as well as the first country to negotiate a free trade agreement with China. The China-Iceland FTA

was signed on 15 April 2013 and went into effect on 1 July 2014. The Agreement covers trade in

goods and services, rules of origin, trade facilitation, intellectual property rights, competition and

investment. The second partner is Switzerland, which launched negotiations with China on the

FTA in January 2011. The Agreement was signed on 6 July 2013 and came into effect on 1 July

2014. As much as 99.7% of Chinese exports to Switzerland are immediately exempted from tariffs,

while 84.2% of Swiss exports to China will eventually receive zero tariffs. The FTA also

facilitates industrial cooperation between both countries and sets new rules in areas of

environment, labor, intellectual property and government procurement.

3. Prior Literature and Hypotheses

3.1 Literature

We aim to examine how the selection of FTA partner(s) matters in the context of global value

chains from the experience of China. Broadly speaking, our work builds on an active and growing

literature on the FTA impact and global value chains.

Plummer, Cheong and Hamanaka (2010) dichotomize the FTA impact into the “impacts of

what” and the “impacts on what” ①

. The FTA impact can be firstly understood through a purely

theoretical analysis, including the static and partial equilibrium model of Viner (1950), as well as

the general equilibrium models contributed by Meade (1955), Lipsey (1970), Kemp and Wan

(1976), Wonnacott and Wonnacott (1982), and Lloyd and Maclaren (2004). Nevertheless, much

more research is devoted to empirically evaluating the economic impact of the FTA. For the

proposed or negotiated FTA which has not yet entered into effect, researchers often turn to ex-ante

analysis by simply relying on trade indicators such as RCA index or using some form of a

multi-sector, multi-region CGE model (e.g. GTAP model). Koopman et al. (2013) was the first to

build a GVC-based GTAP model, and found that the GVC-based model could improve the quality

of the empirical analysis if comparing with the standard GTAP model. Following the idea of

Koopman et al. (2013), Cai et al. (2015) introduced a modified version of the static GTAP model

that incorporates an additional “nest” to model substitution across sources of intermediate inputs

to capture the effect of TTIP on value chains and captures spillover effects--a reduction of NTBs

in EU-US trade is assumed to reduce trade costs for third parties exporting to both markets, and

① For a thorough literature review on the FTA impact, see also Plummer, Cheong and Hamanaka (2010) and

Narayanan, Ciuriak, and Singh (2015).

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finally applied the dynamic GTAP model (2007) to measure the impact of TTIP on BRICS

economies.

After an FTA is established, its actual impact may be quite different from any prior projection

(i.e. the above ex-ante evaluation). The ex-post evaluation can be conducted by employing

different methods including various indicators or indices and gravity model. To account for the

GVC division of labor, the application of gravity model should be modified. Baldwin and Taglioni

(2013) present empirical evidence that the standard gravity equation performs poorly by some

measures when it is applied to bilateral flows where parts and components trade prevails. They

also provide a simple theoretical foundation for a modified gravity equation that is suited to

explaining trade where international supply chains are important.

The second strand of literature is on global value chains. The current focuses range from

modelling and theoretical analysis to empirics and measurement①

. Dixit and Grossman (1982),

Feenstra and Hanson (1999), Grossman and Rossi-Hansberg (2008, 2012), Costinot et al. (2013),

and Antras and Chor (2013) are among the theorists that attempt to characterize the trade in tasks

or trade in value-added under the GVC division of labor. The GVC measurement focuses on the

vertical specialization, the decomposition of gross trade into value-added trade, the forward and

backward linkage, and the GVC location. A budding literature has been devoted to such field

including Lau et al. (2007), Hummels, Ishii, and Yi (2001), Daudin et al. (2011), Johnson and

Noguera (2012), Stehrer, Foster, and de Vries (2012), Antras and Chor (2013), Antras et al. (2012),

Baldwain and Lopez-Gonzalez (2013), Baldwain and Nicoud (2014), Koopman, Wang, and Wei

(2014), and Wang, Wei, and Zhu (2014), among others.

This study intends to make four contributions. First, our research may enrich our

understanding of China’s FTA development and help to reexamine China’s FTA strategy including

the “One Belt and One Road” initiative. Several studies have recently analyzed the development

of China’s FTA since China’s accession to WTO, e.g. Antkiewicz and Whalley (2005), Li, Wang

and Whalley (2014). Our work differs from theirs in that we particularly focus on the perspective

of global value chains. More broadly, with the world moving towards more disintegrated

production process, the present study contributes the knowledge on the mechanism of FTA

formation under the GVCs from a developing country’s point of view. China’s experience in this

regard is expected to give some implications to other developing economies.

Second, we establish a large sample of matched trans-national IO data and global FTA data,

and use the current GVC linkage measurement method to quantitatively examine all Chinese FTA

and non-FTA partners and make a thorough comparison. This is different from Lopez-Gonzalez

(2012) and Kowalski et al (2015) in that their samples are much smaller.

Third, as much enlightened by Markusen (2013), we introduce real per capita GDP as a

criteria to determine an economy’s position along the GVCs. We also use this criteria to categorize

① A collection of papers in the volume edited by Mattoo, Wang, and Wei (2013) represents some of the latest

thinking on such subject from both scholars and international organizations such as the WTO, the OECD, the IMF

and the World Bank.

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the sample economies into four groups and to further decompose the FTAs into different types so

as to identify China’s FTA feature. A number of studies including Fally (2011), Antras and Chor

(2013), Antras, Chor and Fally (2012), Miller and Temurshoev (2013), and Hagemejer and Ghodsi

(2014) attempt to create indices to measure the downstreamness and upstreamness of GVCs.

However, these indices are designed to quantify the degree of integration of an economy/industry

into the GVCs rather than to specify the relative positions (i.e. the high-end or low-end) of GVC

in the sense of transnational smiling curve.

More importantly, our work deepens UNCTAD (2013) which shows that there is a positive

correlation between participation in GVCs and per capita GDP growth rates for an economy. The

unanswered question of UNCTAD (2013) is: which partner(s) may be more important to one

economy (e.g. China) in its participation in GVCs? Our study reveals that the higher the partner’s

income, the larger share it has in China’s foreign content of value added. So the extending logic is

that, for an economy like China, the positive correlation between the participation in GVCs and

per capita GDP growth is largely due to its closer GVC linkage with the higher income partners.

The implication is that selecting higher income (advanced) rather than lower income (developing

or less-developed) economy as an FTA partner may be China’s better choice when implementing

FTA strategy. This might also provide some hints to other low-end developing economies.

3.2 Hypotheses

The traditional textbook has grouped various forms of regional economic integration like

FTA into vertical and horizontal modes according to member countries’ economic development.

The former consists of economies with different development levels, while the latter is composed

of members with identical or similar economic development levels. In this study, we reclassify the

FTAs from the point of view of GVC.

Firstly, we borrow the concept of the common smiling curve and propose a transnational

smiling curve to characterize GVC division of labor (see Figure 3-1). In Figure 3-1, the horizontal

axis represents a continuum of tasks or stages of GVC ranging from upstream to downstream,

covering R&D, intermediates, assembling and processing, marketing and after-sale services, while

the vertical axis depicts the value-added generated from various tasks or stages, the relative

abundance of advanced factors supporting the corresponding tasks or stages, and the per capita

income for an economy as a whole.

The transnational smiling curve can be interpreted from both aggregate (national) and

sectoral aspects. If interpreted from national aggregate perspective, the transnational smiling curve

can also be regarded as a per capita income curve, which implies that the degree of high-end of

tasks conducted by the economies is roughly positively correlated with their per capita income

level①

.

① Markusen (2013) pointed out that a major role for per-capita income in international trade, as opposed to simply

country size, was persuasively advanced by many early economists, but this crucial element of their story was

abandon by most later trade economists in favor of the analytically-tractable but counter-empirical assumption that

all countries share identical and homothetic preferences. Markusen (2013) putted per-capita income back into trade

theory and obtained some new findings.

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Furthermore, we argue that the transnational smiling curve is not just a GVC curve, rather it

also reflects the relative abundance of advanced factors which lead to different tasks or stages

along the GVC. High-end (advanced) factors corresponds to higher income, and high-end factors

are qualified for high-end tasks or stages of GVC, so a higher level of per capita income means the

economy’s structure of factors tending to be higher-end. Taken together, the logic behind the

transnational smiling curve is that an economy’s relative abundance of advanced factors will

determine its relative position along the GVC, and will in turn determine the per capita income

level of the economy. Therefore, these three curve can be approximately integrated as a single

curve.

Based on the transnational smiling-curve-like GVC division of labor, we can regroup FTAs

into vertical and horizontal ones. In this case, the vertical FTA is formed as the result of GVC

vertical division of labor, with member economies locating at different GVC positions, while the

horizontal FTA is established as the result of GVC horizontal division of labor, with member

economies standing at same or similar GVC locations. Furthermore, for the horizontal FTA, we

can also identify two extremes: GVC high-end horizontal FTA and GVC low-end horizontal FTA①

,

the former consisting of economies all being at the high-end of GVC, while the latter including

economies all being at the low-end of GVC.

To highlight the GVC development, much literature proposes such new concepts as “trade in

tasks” and “trade in value-added” (Hummels et al, 2001; Grossman and Rossi-Hansberg, 2008;

Mattoo et al, 2013; Baldwin and Lopez-Gonzalez, 2013). Actually, for “trade in tasks”, we can

further identify “inter-task trade” and “intra-task trade”. The mode of trade and investment within

a GVC vertical FTA is an inter-task mode, with high-end partner(s) conducting high-end

tasks/activities (such as R&D) and low-end partner(s) undertaking low-end tasks/activities (such

as assembling and processing). The mode of trade and investment within a GVC high-end

horizontal FTA is a high-end intra-task mode, with all partners conducting high-end

tasks/activities (such as R&D), while the mode of trade and investment within a GVC low-end

horizontal FTA is a low-end intra-task mode, with all partners undertaking low-end tasks/activities

(such as assembling and processing).

In our sample’s starting year 1990 and ending year 2011, China’s real per capita GDP (in

2005 USD) was respectively 457 USD, ranking the 160th among 185 economies and falling

between the minimum and the 25th percentile, and 3108 USD, ranking the 106

th among 185

economies and falling between the 25th percentile and the 50

th percentile (see the data of Section

4). This indicates that China’s position has moved up during the sample period, but is still at a low

level in terms of per capita income as shown in Figure 3-1.

Then, for a low-end country like China in the open economy, the question on how to choose a

suitable FTA partner is as important as for a closed economy to decide whether to open or not. We

argue that, for a low-end economy like China, a better choice is to form a GVC vertical FTA with

① Theoretically speaking, we can identify a continuum of GVC horizontal FTA based on a continuum of tasks and

stages along the GVC.

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high-end partner(s) rather than to form a GVC low-end horizontal FTA with low-end partner(s).

The reason is that, within GVC low-end horizontal FTA, all member countries are at the low

levels of economic development, so the magnitude of domestic demand is very limited. The

degrees of marketization of these countries are also very low, so the FTA construction is

frequently dominated by government intervention, ignoring the market basis or microeconomic

basis. Moreover, the member countries within such FTA share similar and even identical industrial

and product structure, and the differentiation degrees of the low-end products produced by these

members are very low. Therefore, far from being complementary to each other, these low-end

economies are mutually substitutable. This poses a deadly challenge to the development of such

kind of FTA. Nowadays, most of the regional economic integration arrangements (e.g. FTAs) in

the vast developing area of Asia, Africa and Latin America are actually of low-end horizontal type

(see Table 4-1).

Based on the above discussion, we formulate two hypotheses:

Hypothesis 1: China tends to have stronger GVC linkages with the economies (including FTA

and non-FTA partners) with higher income levels in the context of GVCs.

Hypothesis 2: The higher income level of the FTA partner, the stronger the mutual GVC

dependence between China and the FTA partner, and such FTA is a vertical (upward rather than

downward) type in terms of GVC division of labor.

4. Data and Method

4.1 Basic model

Our intention is to examine how the selection of FTA partner(s) matters in the context of

global value chains from the experience of China. More specifically, we estimate econometrically

whether and how the selection of partner in China’s FTA construction process impact on the

bilateral GVC linkage between China and the partner. Our econometric model builds on the

following equation:

iCHNiiCHNiiCHN FTAYPartnerFTAYPartnerlinkageGVC *___ 321 （4-1）

where GVC_linkageCHN-i is the bilateral GVC linkage between China and its FTA or non-FTA

partner i; Partner_Y is the log of the partner country’s real per capita GDP in US Dollars at

constant prices (2005) and constant exchange rates (2005); FTACHN-i is a binary dummy with one

for being the FTA partner of China and zero otherwise; Partner_Y * FTACHN-i is an interaction

term of partner’s income level (roughly representing GVC location) and FTA dummy, which is

used to identify the type of FTA or the selection of FTA partner(s) of China. capitures the fixed

effects of time (1990-2011) or sector (25) or product usage (4)①

; and is a stochastic error.

4.2 Quantifying China’s linkage with the FTA and non-FTA partners

① As Fuchs and Klann (2013) discussed, the effect of bilateral distance and other time-invariant factors such as

being landlocked or contiguous can be captured by the partner country fixed effects, but the inclusion of a full set

of country-by-year effect is not feasible in our model as we estimate bilateral GVC linkage between a specified

country (China) and its partners.

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The dependent variable in Eq. (4-1) is about China’s GVC linkage with its partner. In order to

quantify such inter-country value (or supply) chains, we follow Miller and Blair (2009), and Wang

et al. (2014) to focus on the backward linkage based perspective (or the user-side perspective)

which aligns well with case studies of supply chains of specific sectors and products, as the iPod

or iPhone examples frequently cited in the literature. The backward linkage traces all upstream

sectors/countries’ contributions to value added in a specific sector/country’s production,

consumption and trade.

Based on the computational procedure of Leontief (1936), Miller and Blair (2009), and Wang

et al. (2014), the total value added induced by one unit of output can be calculated as the sum of

direct and all rounds of indirect value added generated from the one unit of output production

process (as shown in Figure 4-1). Expressing this process mathematically, we have

... VAAAVAAVAV VLAIVAAAIV 132 )(...)( (4-2)

where V is the direct value-added coefficient (i.e. the ratio of value added to total output) vector; A

is the intermediate input coefficient (i.e. the ratio of intermediate input to total input) matrix; L=

(I−A)-1

, which is known as the Leontief inverse or the total requirements matrix. The power series

of matrix A is convergent and the inverse matrix L= (I−A)-1

exists as long as A is in full rank

(Miller and Blair, 2009). VL is also called the total value added coefficient matrix or the total

value added multiplier in the input-output literature.

In the light of the Leontief’s insight, we can carry out the decomposition of the country/sector

level value-added. For a case of C countries and N sectors, we have

CCCCC

C

C

C Y

Y

Y

LLL

LLL

LLL

V

V

V

YLV

000

000

000

000

000

000

000

000

ˆˆ2

1

21

22221

11211

2

1

CCCCCCCC

CC

CC

YLVYLVYLV

YLVYLVYLV

YLVYLVYLV

2211

2222221212

1121211111

(4-3)

where V is the “(CN) (CN)” diagonal matrix of direct value-added coefficients of all

countries/sectors; Y is the “(CN) (CN)” matrix of each country/sector’s production (for final

use or intermediate use, for domestic use or export) sub-matrix arranging along the diagonal (but

Y is not a diagonal matrix). The matrix of final equation of Eq. (4-3) details the sector and country

sources of value added in each country’s production. As Wang et al (2014) defined, the sum of the

YLV ˆˆ matrix across columns along a row accounts for how each country’s domestic value-added

originated in a particular sector is used by the sector itself and all its downstream countries/sectors,

while the sum of the YLV ˆˆ matrix across the rows along a column accounts for all upstream

countries/sectors’ value-added contributions to a specific country/sector’s production. The former

traces forward linkages across all downstream countries/sectors from a supply-side perspective

12

which is consistent with the literature on factor content of trade, while the latter traces backward

linkages across upstream countries/sectors from a user’s perspective which is primarily focused on

in our paper. Hence, in a multi-country setting, the total value added in a specific sector/country is

expected to originate either from itself or from abroad, and the sum of both sources’ shares should

be equal to 100%.

Therefore, if pivoting on China, we can specify both the dependence of China (CHN) on an

upstream partner (l) (denoted by CHN_Dependence) and the dependence of a partner on China at

the upstream of GVC (denoted by Partner_Dependence). For the former dependence, we calculate

the share of an upstream partner’ value added in China’s total value added, i.e.

CHN_Dependence=CHNCHN

C

l

CHNl

CHNl

VV

V

__

_

, and a larger share means a higher dependence of

China on its partner. And for the latter, we obtain the share of (upstream) China’s value added in a

partner’s total value added, i.e. Partner_Dependence=ll

C

m

lm

lCHN

VV

V

__

_

, and a larger share implies

a stronger dependence of a partner on China. The sum of these two indicators leads to the third

indicator--mutual dependence (denoted by Mutual_Dependence). Thus, the dependent variable

GVC_linkageCHN-i in Eq. (4-1) actually includes these three indicators which will be regressed on

respectively.

To construct the two indicators of GVC_linkageCHN-i, we use the Eora MRIO database which

covers 188 economies, 26 sectors/items①

and 22 years (Lenzen et al., 2013) (see Appendix Tables

A4-1 and A4-2). This database has broader coverage of economies than all other ICIC data

sources such as the OECD-WTO data, the IDE-JETRO Asian ICIO data, the GTAP and the WIOD.

And more importantly, it includes all the partner economies of China’s actual and potential FTAs.

Table A4-3 provides descriptive statistics on the GVC_linkageCHN-i. A close look shows that there

exists a pronounced asymmetry of the bilateral GVC linkage between China and its partners. For

example, in 2011, all the ratios of CHN_Dependence to Partner_Dependence are larger than 1

except for four countries including the United States, Japan, Myanmar and Angola. The contours

of Figure A4-1 and Figure A4-2 describe the mutual GVC linkages between China and other

economies with different income levels.

4.3 Income levels of sample economies

We use the real per capita GDP as the indicator of the income levels of China’s FTA and

non-FTA partners. The data are mainly from UNCTADstat, with two missing economies--Monaco

and Liechtenstein--from the World Bank database. The sample consists of 214 economies.

In the regressions, we use the GDP variable in two versions. In the first version, we simply

① The last item is “Re-export & Re-import” which is not the usual sector or industry, but to keep the data intact we

don’t drop it. Actually this item only appears as a minimal figure for most sample economies.

13

use the logrithm of real per capita GDP to represent the income level, i.e., we use it as a

continuous variable. In the second version, we classify economies into four groups by the quartiles

of the real per capital GDP: Group 1, the low-end group, which is below the 25th percentile (Group

1<=p25); Group 2, the low-mid-end group, which is between the 25th percentile and 50

th percentile

(p25< Group 2<=p50); Group 3, the mid-high-end group, which is between 50th

percentile and

75th percentile (p50< Group 3<=p75), covering two subgroups (one is between p50 and mean, and

the other between mean and p75); and Group 4, the high-end group, which is above 75th percentile

(p75< Group 4) ①

. We use Group 1 as the benchmark group and construct three dummies for the

other three groups: Y_H (1 for high-end group, 0 for others), Y_MH (1 for mid-high-end group, 0

for others), Y_LM (1 for low-mid-end group, 0 for others). Figure A4-3 presents the geographic

distribution of four groups of sample economies in 2011.

4.4 China’s FTA partners

To construct the binary dummy FTACHN-i (1 for being the FTA partner of China, 0 for

otherwise), we combine the datasets both from WTO RTA database

(http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx) and from China’s Ministry of Commerce

(http://fta.mofcom.gov.cn/) to obtain a complete dataset of FTAs partnership (in force and under

negotiation or consideration). The dataset involves 211 economies in total.

To decide the FTA types, we match the per capita GDP dataset and the FTA dataset by the

abbreviation of the economy names as well as the country id. The final results show that 200

economies are perfectly matched, accounting for 88.5% of the aggregate sample, and 26 are not

matched with 11 economies only found in the FTA dataset and 14 economies only found in the per

capita GDP dataset (see Table A4-5 for the unmatched).

The combinations of the above economies of four groups generate two broad types of

FTAs--vertical and horizontal FTAs. The former includes 5 kinds: low-end vertical FTA (L_V),

low-mid-end vertical FTA (LM_V), low-high-end vertical FTA (LH_V), mid-high-end vertical

FTA (MH_V), and full-range vertical FTA (F_V), while the latter includes low-end horizontal FTA

(L_H), mid-end horizontal FTA (M_H), and high-end horizontal FTA (H_H). The definitions of

different FTAs are presented in Table A4-4.

Next, we match the above three datasets (the Eora MRIO database, the per capita GDP

dataset and the FTA dataset) all together also by the country id. In the end, 180 economies are

perfectly matched between all these three datasets, and 8 are in both Eora MRIO database and the

per capita GDP dataset but not in the FTA dataset (see Table 4-2 and Table A4-5). The group of

180 matched economies include all practical and potential FTA partners of China. All types of

FTAs all over the world are listed in Table 4-1②

. For China’s FTAs, see Table 4-3.

① The main reason for not using World Bank’s Atlas method is that more economies cannot be matched with those

in ICIO and FTA datasets if using the World Bank data. However, our classification can be comparable with that of

the World Bank. ② The more detailed data are available upon request.

14

5 Empirical analysis

5.1 Main results

Table 5-1 reports the empirical results for Fixed Effects regression analysis on the aggregate

(i.e., national) level. The first three models tell the story about the mutual dependence between

China and its partner in terms of GVC linkage. The coefficients on the income variables of the

equations (1), (2), and (3) are all positive and statistically significant at the 5 percent level, which

means China tends to have closer GVC linkage with partners with higher income levels. The

effect of FTA partnership on GVC linkage is more subtle. Although overall FTA partnership helps

to enhance mutual GVC linkage, such positive effect only happens when the partner economy’s

income surpasses certain threshold. That is, when the partner economy’s income is too low, the

FTA partnership won’t improve the mutual GVC linkage.

The decomposition of the mutual dependence leads to results on both sides. From China side,

we can see a positive correlation between partner’s income level and the dependence of China on

the partner (equation (4)). To be more specific, if the income of an upstream partner increases by

1%, the share of this partner in China’s total value added (that is, the dependence of China on this

partner) will rise by 0.014%①

. On the other hand, if the income of a downstream partner increases

by 1%, the share of China in this partner’s total value added (i.e. the dependence of this partner on

China) will go up by 0.348% (equation (7)). The coefficients on the FTA dummy of equations (5)

and (8) and those on the FTA interaction term of equation (9) are positive and statistically

significant, which means that overall forming an FTA between China and partner(s) will not only

strengthen China’s dependence on the partner but also strengthen the partner’s dependence on

China, and the latter effect is much larger in magnitude. The effect of FTA on China’s dependence

on the partner is always positive and increases as the partner’s income increases; while the effect

of FTA on partner’s dependence on China is positive only when the partner’s income is high

enough. Comparing the coefficients of equations (4)-(6) and those of equations (7)-(9), we find

that the impacts on partner’s dependence actually dominate the mutual dependence. Even though

the bilateral GVC linkage is strengthened, such GVC linkage between China and its partners is

asymmetric. Therefore, the results of these regressions support our hypotheses.

Next, we introduce dummy variable to replace the previous level variable for partner’s

income to test our hypotheses (see Table 5-2). The definitions of dummies are given in Section 4.

Columns (1)-(3) of Table 5-2 consistently show that, comparing with the partner in the low-end

group, if the partner belongs to the high-end group, the income effects on the bilateral GVC

dependence are always positive and statistically significant, and are dominated by those on the

partner’s dependence.

Forming an FTA between China and partner(s) will increase the overall bilateral dependence

as well as China’s dependence on the partner and the partner’s dependence on China respectively.

Once again, the partner’s dependence on China dominates the mutual dependence. The inclusion

①

It is shown that the coefficients are not as large as imagined, but the impacts are still considerable since the

dependent variable is defined as a percentage rather than a level value. This is true for all other regressions.

15

of FTA interaction terms in equations (3), (6) and (9) reveal that the FTA promoting effect on the

GVC linkage hinges on which partner will be chosen by China as an FTA partner. If the FTA

partner belongs to high-end group, such impact will be positive and statistically significant for the

cases of mutual dependence and the partner’s dependence. If China does not choose to form an

FTA with low-end economies, the FTA interaction effects on the China’s GVC dependence on

partners are always positive and statistically significant. These results imply that choosing to form

FTA with the higher income economy especially with Group 4 will not only raise the share of an

upstream partner’ value added in China’s total value added (i.e. China’s dependence on partner),

but also increase the share of (upstream) China’s value added in a partner’s total value added (i.e.

partner’s dependence on China). This confirms the findings in Table 5-1.

5.2 Disaggregated Analysis

In the real world, some FTAs actual function as or evolve from a form of partial economic

integration on a sectoral/product basis. For instance, European Union (EU) traces its origins partly

from the European Coal and Steel Community (ECSC), which was formed by the Inner Six

countries in 1951 to create a common market particularly for coal and steel among its member

states, and served to neutralize competition between European nations over natural resources.

Therefore, whether for the ex-ante negotiation and arrangement or for the ex-post performance

evaluation, it is necessary to investigate what the practical or potential FTA would look like and

how it would develop if the heterogeneity of products or sectors is considered. In this study, our

data allow us to decompose the products into 4 categories, and to divide the sectors into 25

groups.

We first present a simple Pearson correlation analysis (see Table 5-3) as a preliminary test of

our first hypothesis. The first four rows of Table 5-3 list results for 4 product categories and usages.

We can see that, for any kind of product (whether the product is used as final or intermediate, or

the product is for export or for domestic uses), China always tends to be more dependent on the

partner with higher per capita GDP on the one hand, and the higher income partner also tends to

be more dependent on China on the other hand. The difference between the two sides is that the

correlation coefficients on the latter are much smaller than the former especially for domestic use

products which altogether account for nearly 92% of Chinese total output.

The rest rows of the Table 5-3 demonstrate the sectoral Pearson correlation results. For all the

25 sectors and whether good-producing or service sectors, China tends to be more closely

dependent on the partner with higher per capita GDP. For the sectors except “Transport

Equipment”, “Electricity, Gas and Water”, “Maintenance and Repair”, “Financial Intermediation

and Business Activities”, “Public Administration”, “Education, Health and Other Services”,

“Private Households”, and “Others”, the higher income the partner has, the stronger dependence it

has on China. Comparatively, the higher income economies have less dependence on China

especially in services, which is partly due to the less openness of this area to the outside world if

China has been a WTO member for more than one decade. “Textiles and Wearing Apparel” is

China’s traditional comparative advantage sector, which accounts for 26.46% of China’s final

16

goods export, 8.75% of China’s intermediate goods export. Just in this sector, we could find a

strong mutual GVC dependence between China and the higher income partners. Such close mutual

dependence can also be observed in another two important manufacturing sectors-- “Electrical and

Machinery” and “Petroleum, Chemical and Non-Metallic Mineral Products”. In 2011, “Electrical

and Machinery” accounted for over 13% of China’s total output and over 30% of China’s final and

intermediate goods export respectively, while “Petroleum, Chemical and Non-Metallic Mineral

Products” took up nearly 13% of China’s total output, second only to the former sector. These two

sectors altogether accounted for more than 15% of China’s value added and over 30% of China’s

intermediate goods for domestic use.

In Table 5-4 to Table 5-8, we report the subgroup regression results for four kinds of products

and usages. The basic conclusions from Table 5-1 still apply to Table 5-4. For any kind of product

or usage, the higher income the economy has, the closer GVC linkage between China and the

economy, and in particular the stronger dependence of the economy on China (i.e. in terms of

value-added contribution by China). The FTA promoting effects on the GVC linkage are also

positive and statistically significant, but such effects rely on the income level of the partner.

Comparing all the corresponding significant coefficients across the models for four

products/usages, we find the values following a decreasing trend from the final products for export

down to the intermediates for domestic use (e.g. in column (1), the coefficients are 0.466, 0.366,

0.319, and 0.294 respectively). This suggests that the income and FTA effects on China’s bilateral

GVC linkages are sensitive to the product category and usage. These effects are much stronger for

exported products than for domestic ones, and are more pronounced for partner’s dependence on

China than for China’s dependence on the partner.

Differing from Table 5-4, Table 5-5 to Table 5-8 use income dummies to proxy the income

levels of partners. The regression results once again confirm the findings obtained from Table 5-2

and Table 5-4.

Table 5-9 reports the results for 25 sectors①

. For both mutual dependence and partner’s

dependence, positive and statistically significant coefficients are found on the income term for all

sectors except for “Textiles and Wearing Apparel”, “Transport Equipment”, “Recycling”,

“Maintenance and Repair”, and “Private Households”, and on the FTA interaction term for all

sectors. For China’s dependence, we find positive and statistically significant coefficients on the

income term for all the good-producing sectors except for “Mining and Quarrying” and “Wood

and Paper” and three service sectors (“Construction”, “Public Administration”, and “others”), and

on the FTA interaction term only for three sectors (“Recycling”, “Post and Telecommunications”,

“Financial Intermediation and Business Activities”). Comparing China’s dependence and partner’s

dependence, we can see that the latter is much stronger than the former and dominates the bilateral

GVC linkage. This is perhaps the scenario that China is most willing to expect in the process of

developing foreign trade and investment.

① To save space, we omitted some information. The detailed results are available upon request.

17

Tables 5-10, 5-11, and 5-12 introduce income dummies to represent the income levels of

partners. A clear message conveyed by Table 5-10 is that, for almost all sectors, if the partner

belongs to the high-end group, a closer GVC linkage is found between China and the partner. The

FTA promoting effects are found to rely on whether the partner is a high-end economy, and

positive and statistically significant for all sectors. These findings also apply to Table 5-12 which

describes the partner’s dependence on China. Table 5-11 presents a different results, in which we

can find a positive and significantly strong dependence of China on its partner for almost all

sectors as long as there exists an FTA between them and the partner does not belong to the

low-end group.

5.3 Robust check

This study uses the partner’s income and its interaction term with FTA to explain the GVC

linkages.①

We now address the endogeneity issue of the regression analyses. We have reason to

believe that the endogeneity problem doesn’t exist in the relationship between the partner’s

income and the bilateral linkage, but it is not obvious whether there is endogeneity in the link

between the FTA formation and the bilateral linkage. To shed light on the latter, we need to

understand the nature of the causal link between FTA formation and bilateral GVC linkage. It

seems natural that forming an FTA with a partner (specifically the higher-income one) leads to

stronger bilateral GVC linkages between China and the partner. However, whether stronger GVC

linkage might cause FTA partnership is not clear, since in the real world, the road to FTA is a very

complicated process which involves many factors besides GVC linkage. For example, the US,

Japan and Germany all have very strong GVC linkage with China, but none of them have signed

FTA with China. To account for the potential endogeneity of the FTA formation, we introduce the

lag of FTA dummy and the interaction term of the lag of FTA dummy and income as the

instrumental variables for the FTA dummy and the interaction term of the FTA dummy and

income②

, respectively, and employ 2SLS method to estimate the effects of FTA and income on

GVC linkage. The preliminary results show that the previous Fixed Effects estimates are still

robust③

.

Next, to address the potential problem regarding the grouping of the sample economies, we

drop the high-income oil-producing countries (especially in Middle East), still no changes are

found for the previous findings.

And finally, the FTAs in the real world are actually heterogeneous in terms of the coverage

and liberalization level. Some only focus on trade in goods, some include both trade in goods and

trade in services, while others cover not only trade but also other areas including investment and

intellectual property rights protection. It can be imagined that the GVC linkages might be sensitive

①

The literature argues that the prevalence of zero trade flows in gravity models may cause biased estimates. Our

index of GVC linkage is similar to that of trade flows. In our sample, however, this issue seems to be negligible

since the number of zero GVC linkages (only for China’s dependence) is very small (for China’s dependence, 20

zeros of 16456 observations in the aggregate analysis, and 520 zeros of 106964 observations in the sectoral

analysis). ② The literature (e.g. Cameron and Trivedi, 2005) proposes the lagged regressors as instruments. ③ We are still working on the robust check.

18

to the heterogeneity of FTAs. But for China (especially in the period of 1990-2011), the FTA

heterogeneity is not obvious, so we do not consider this issue in the analysis. ①

6 Conclusion

This paper contributes to our understanding of whether and how the selection of FTA

partner(s) matters in the context of global value chains from the experience of China. Specifically,

we examine the effect of partner’s income and the FTA type on the bilateral GVC linkage between

China and the partner. We use real per capita GDP to proxy the GVC position of an economy, and

use the interaction term of real per capita GDP and FTA dummy to specify the FTA types. For the

bilateral GVC linkage, we construct three measures from the backward linkage perspective. Our

results based on fixed effects and instrumental variable estimation methods show that China tends

to be more closely linked with partner economies of higher income levels. If the income of an

upstream partner increases by 1%, the share of this partner in China’s total value added will rise

by about 0.01%; if the income of a downstream partner increases by 1%, the share of China in this

partner’s total value added will go up by about 0.3%. Moreover, the higher income of the FTA

partner, the closer linkage between China and the partner, and such effect is particularly stronger

for the dependence of the economy on China if the economy is of higher income and is chosen as

China’s FTA partner. These results are basically robust for different model specifications and

product-/sector-level disaggregated analyses.

Our findings have clear policy implications. For China, a country still at the lower end of

GVCs, the right choice is to select higher income (advanced) partner(s) to form (upward) vertical

FTA(s), rather than to choose lower income (developing or less-developed) partner(s) to establish

low-end horizontal or low-end (downward) vertical FTA. Such choice matter much to the positive

correlation between a country’s participation in GVCs and its GDP per capita growth that has been

proved by the existing literature. At the same time, the stronger dependence of partner(s) on China

is also what China is most willing to expect. We do believe that this can also give some

enlightenment to other low-end developing economies including those in African continent.

However, the vertical FTA is more likely to exert asymmetric impacts on member countries.

The partner(s) at the low-end of GVC will be probably locked in without successful

“learning-by-doing”. The major task facing the low-end partner(s) in GVC vertical FTA is to

absorb the development benefits on the one hand, and to effectively hedge “low-end lock-in” risks

on the other hand, so as to successfully climb up along the GVC and finally develop into a GVC

high-end economy. But for a low-end country or region in the context of GVCs, the first important

thing is to seize any opportunity to take part in GVC division of labor, and then to try to do best at

the low-end. At the same time, the low-end economy must be cautious of “low-end loss” if unable

to successfully climb up the GVC. Otherwise, far from solving the risks of “low-end lock-in”, the

① We are examining the FTAs of all other economies based upon our unique matched database in an attempt to

provide an international comparison with China, and especially introduce the heterogeneity of FTAs. And

moreover, we are going to conduct a microeconomic-based FTA analysis for China by employing the matched

Chinese trade data and industrial firm data.

19

low-end economy will suffer “low-end loss”.

Over the past thirty years, unlike most African countries, China has successfully broken away

from the club of poorest countries and entered into a group of relatively higher income (low-mid

end). Even though the pace of improvement is slow, the achievement is still worth praising. This is

no doubt the outcome of China’s implementation of reform and opening strategy, which helps

China successfully integrate into the vertical GVC division of labor (being equivalent to joining

the GVC vertical FTA).

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Cross-Border Trade, The World Bank, Washington, DC, USA.

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Industries/Countries in World Production”, GGDC Research Memorandum No. 133.

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22

Tables and Figures

Table 4-1 Development of FTA of Different Types All over the World

Continent Sub-region L_H L_V LM_V LH_V M_H MH_V H_H F_V Total

Africa Eastern Africa 2

5

1

6 14

Middle Africa

3

2 5

Northern Africa

4 4

7 15

Southern Africa

4

1

2 7

Western Africa 1 1

1

3 6

Asia Central Asia 2 4 18

3 1

28

Eastern Asia 5 4 5 27

11 14 10 76

South-Eastern Asia

1 4 15 1 9 9 13 52

Southern Asia 3 6 12 6

6 33

Western Asia 6 2 25 5 3 35 4 32 112

Caribbean & Central A Caribbean

3

2

5 10

Central America

18 5 7 13

9 52

Europe Eastern Europe 6 6 22 3 4 26

28 95

Northern Europe

17

33 11 29 90

Southern Europe 2

7 4 1 25 1 29 69

Western Europe

16

33 10 29 88

North America Northern America

8

13 10 4 35

Oceania Australia and New Zealand

6

6 8 5 25

Melanesia 1 1 1 1

1

2 7

Micronesia

1

1 2

Polynesia

1

1

1 3

Seven seas (open ocean) Seven seas (open ocean)

1

1

South America South America 2

18 6 9 13

8 56

Total 30 25 151 123 29 225 67 231 881

Notes: The data on real per capita GDP are from UNCTADstat. All the economies on this map are ranked in

terms of real per capita GDP (in 2005 USD), and are categorized into four groups based on the specific statistical

values ranging from minimum (193), 25th percentile (1109), median (4066), 75th percentile (16282) and

maximum (82170) of real per capita GDP in 2011. See Table A4-1 in the Appendix for individual economies in

each region or continent, and Figure A1-1 for the geographic concentration of FTAs.

Table 4-2 Results of Economy Matching between All Three Datasets

Matching Eora MRIO dataset

(188 economies)

unmatched matched Total

Matching FTA dataset

(211 economies) and

GDP dataset (214 economies)

only in FTA dataset 11 0 11

only in GDP dataset 6 8 14

matched 20 180 200

Total 37 188 225

23

Table 4-3 China’s FTA Construction

FTA Group 1 Group 2 Group 3 Group 4 Group FTA Date in force or to start negotiation

<=p25 (p25, p50] (p50, mean] (mean, p75] >p75 Combinations Type

In Force China-ASEAN Y Y Y Y 1,2,3,5 F_V Jul. 20, 2005

China-Australia Y Y 2,5 LH_V Dec. 20, 2015 China-Chile Y Y 2,3 LM_V Oct. 1, 2006

China-Costa Rica Y Y 2,3 LM_V Aug. 1, 2011 China-Hong Kong Y Y 2,5 LH_V Jan. 1, 2004

China-Iceland Y Y 2,5 LH_V Jul. 1, 2014 China-Macao Y Y 2,5 LH_V Jan. 1, 2004

China-New Zealand Y Y 2,5 LH_V Oct. 1, 2008 China-Pakistan Y Y 1,2 L_V Jul. 1, 2007

China-Peru Y 2 L_H Mar. 1, 2010 China-Singapore Y Y 2,5 LH_V Jan. 1, 2009

China-South Korea Y Y 2,5 LH_V Dec. 20, 2015 China-Switzerland Y Y 2,5 LH_V Jul. 1, 2014

Under negotiation

China-GCC Y Y Y 2,4,5 F_V Apr. 23-24, 2005

China-Georgia Y 2 L_H Dec. 10, 2015 China-Japan-Korea Y Y 2,5 LH_V Mar. 26-28,

2013 China-Maldives Y Y 2,3 LM_V Dec. 21-22,

2015 China-Norway Y Y 2,5 LH_V Sep. 18, 2008

China-RCEP Y Y Y Y 1,2,3,5 F_V 9-May-13 China-Sri Lanka Y 2 L_H Sep. 17-19,

2014

Under consideration China-Colombia Y Y 2,3 LM_V

China-Fiji Y 2 L_H China-India Y Y 1,2 L_V

China-Moldova Y Y 1,2 L_V

Notes: 1. GCC refers to Gulf Cooperation Council which comprises Saudi Arabia, United Arab Emirates,

Kuwait, Amen, Qatar, and Bahrain. ASEAN refers to the Association of South East Asian Nations which includes

Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand and

Viet Nam. RCEP refers to Regional Comprehensive Economic Partnership which includes ASEAN-10, Japan,

South Korea, Australia, New Zealand, and India, plus China.

2. China-ASEAN concluded FTA-upgrading negotiation in November 2015, ushering in a new era of bilateral

economic cooperation.

3. On September 18, 2015, China and Pakistan agreed to start the second phase of FTA negotiation.

4. See Table A4-4 for the definition of FTA types.

Source: Based on authors’ own dataset and http://fta.mofcom.gov.cn/.

24

Table 5-1 China’s FTA and GVC Dependence

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Y 0.361*** 0.137** 0.297*** 0.014** 0.009 0.01 0.348*** 0.128** 0.287***

[0.070] [0.060] [0.057] [0.006] [0.007] [0.007] [0.068] [0.058] [0.055]

FTA

1.820*** -6.149***

0.034*** 0.011 1.786*** -6.160***

[0.258] [1.255]

[0.006] [0.025] [0.259] [1.259]

Y*FTA

0.966***

0.003 0.963***

[0.177]

[0.003] [0.177]

R2 0.807 0.833 0.854 0.944 0.944 0.944 0.796 0.823 0.847

Obs. 4004 4004 4004 4004 4004 4004 4004 4004 4004

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0). Y

is for partner’s real per capita GDP in logarithm. * significant at 10%, ** significant at 5%, *** significant at 1%.

Table 5-2 China’s FTA and GVC Dependence: China’s Partners Divided into Four Groups

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Y_H 0.415*** 0.441*** 0.443*** 0.035 0.035 0.035 0.380*** 0.406*** 0.408***

[0.093] [0.092] [0.092] [0.024] [0.024] [0.024] [0.077] [0.075] [0.074]

Y_MH 0.002 0.029 0.03 -0.014** -0.013** -0.013** 0.015 0.042 0.043

[0.045] [0.042] [0.042] [0.006] [0.006] [0.006] [0.043] [0.041] [0.040]

Y_LM -0.002 0.009 0.01 -0.007* -0.007* -0.007* 0.005 0.016 0.016

[0.041] [0.040] [0.039] [0.004] [0.004] [0.004] [0.040] [0.039] [0.039]

FTA 1.855*** 0.650*** 0.036*** -0.011*** 1.819*** 0.661***

[0.258] [0.194] [0.006] [0.003] [0.258] [0.195]

Y_H*FTA 3.617*** 0.021*** 3.596***

[0.738] [0.008] [0.739]

Y_MH*FTA 0.18 0.108*** 0.072

[0.288] [0.023] [0.273]

Y_LM*FTA 0.11 0.114*** -0.004

[0.237] [0.014] [0.236]

R2 0.804 0.831 0.854 0.944 0.944 0.945 0.793 0.821 0.847

Obs. 4114 4114 4114 4114 4114 4114 4114 4114 4114

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0).

Y_H =1 for high-end group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for

low-mid-end group, 0 for others. * significant at 10%, ** significant at 5%, *** significant at 1%.

25

Table 5-3 Pearson Correlation to Test H 1

CHN_Dependence

and Partner_Y

Partner_Dependence

and Partner_Y

Relative Importance in Chinese Economy in

2011

id Product/sector Coef. Obs. Coef. Obs. %TO %VA %FE %IE %FH %IH

Final (for export) 0.2585* 4004 0.1503* 4004 3.53

Intermediate (for export) 0.2624* 4004 0.1436* 4004 5.00

Final (for home) 0.2686* 4004 0.0560* 4004 29.06

Intermediate (for home) 0.2705* 4004 0.0601* 4004 62.37

1 Agriculture 0.2808* 4004 0.1215* 3976 5.80 11.53 1.06 1.77 8.09 5.31

2 Fishing 0.2647* 4004 0.1239* 3965 0.65 1.24 0.16 0.13 1.05 0.53

3 Mining and Quarrying 0.2761* 4004 0.1030* 3388 3.71 5.44 0.06 2.62 0.49 5.50

4 Food & Beverages 0.2828* 4004 0.1279* 4004 4.93 3.49 4.04 2.12 7.29 4.10

5 Textiles and Wearing Apparel 0.2497* 4004 0.1646* 3983 4.83 3.18 26.46 8.75 2.46 4.43

6 Wood and Paper 0.2808* 4004 0.0815* 3959 2.34 1.99 1.68 2.47 0.45 3.25

7 Petroleum, Chemical and

Non-Metallic Mineral Products

0.2742* 4004 0.0436* 3937 12.84 8.10 4.14 15.84 1.98 18.15

8 Metal Products 0.2695* 4004 0.0149 3812 9.39 4.48 2.27 11.14 0.62 13.73

9 Electrical and Machinery 0.2483* 4004 0.0788* 3953 13.44 7.17 33.75 30.76 10.39 12.32

10 Transport Equipment 0.2650* 4004 -0.0196 3923 4.03 2.08 3.35 2.06 3.68 4.40

11 Other Manufacturing 0.2707* 4004 0.1088* 3968 1.27 0.87 10.15 2.71 0.96 0.79

12 Recycling 0.1647* 4004 0.0464* 3808 0.38 0.85 0.06 0.13 0.00 0.59

13 Electricity, Gas and Water 0.2709* 4004 -0.0041 4004 4.20 3.85 0.02 0.03 1.23 6.16

14 Construction 0.2710* 4004 0.0791* 4004 8.19 5.54 0.22 0.50 26.45 0.75

15 Maintenance and Repair 0.2678* 4004 0.0098 4004 0.09 0.17 0.09 0.13 0.07 0.09

16 Wholesale Trade 0.2678* 4004 0.0410* 4004 1.14 2.26 1.11 1.69 0.88 1.21

17 Retail Trade 0.2678* 4004 0.0514* 4004 2.53 5.01 2.46 3.76 1.95 2.70

18 Hotels and Restaurants 0.2891* 4004 0.0940* 4004 1.77 2.08 2.25 0.58 2.53 1.49

19 Transport 0.2735* 4004 0.0517* 4004 4.09 5.63 2.93 4.10 2.11 5.09

20 Post and Telecommunications 0.2627* 4004 0.1185* 4004 1.11 2.37 0.22 0.87 0.82 1.32

21 Financial Intermediation and

Business Activities

0.2646* 4004 0.0225 4004 6.77 12.34 1.86 6.32 7.87 6.55

22 Public Administration 0.2660* 4004 0.0085 4004 0.22 0.39 0.00 0.00 0.75 0.00

23 Education, Health and Other

Services

0.2792* 4004 0.0135 4004 4.32 6.36 1.63 1.51 11.15 1.51

24 Private Households 0.2604* 4004 -0.0860* 4004 0.02 0.03 0.02 0.03 0.03 0.01

25 Others 0.2660* 4004 -0.017 3982 1.95 3.53 0.00 0.00 6.70 0.00

Notes: * significant at 5%. The sample period is 1990-2011. Partner_Y represents partner’s real per capita

GDP. %TO , %VA, %FE, %IE, %FH, and %IH are respectively for the percentages of Chinese total output, value

added, final for export, intermediate for export, final for home and intermediate for home.

26

Table 5-4 China’s FTA and GVC Dependence by Product

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Final for

export

Y 0.466*** 0.201*** 0.389*** 0.015* 0.01 0.01 0.452*** 0.192*** 0.380***

[0.087] [0.076] [0.071] [0.008] [0.008] [0.008] [0.085] [0.074] [0.069]

FTA

2.148*** -7.205***

0.042*** 0.024 2.106*** -7.229***

[0.293] [1.400]

[0.008] [0.031] [0.293] [1.405]

Y*FTA

1.134***

0.002 1.132***

[0.198]

[0.004] [0.198]

R2 0.814 0.839 0.859 0.939 0.939 0.939 0.804 0.83 0.852

Obs. 4004 4004 4004 4004 4004 4004 4004 4004 4004

No. of partners 182 182 182 182 182 182 182 182 182

Intermediate

for export

Y 0.366*** 0.138** 0.315*** 0.016** 0.011 0.012 0.350*** 0.127** 0.303***

[0.076] [0.067] [0.063] [0.008] [0.008] [0.008] [0.074] [0.064] [0.060]

FTA

1.846*** -6.925***

0.038*** 0.005 1.807*** -6.930***

[0.259] [1.225]

[0.007] [0.028] [0.259] [1.229]

Y*FTA

1.063***

0.004 1.059***

[0.174]

[0.003] [0.174]

R2 0.802 0.827 0.852 0.935 0.935 0.935 0.79 0.816 0.844

Obs. 4004 4004 4004 4004 4004 4004 4004 4004 4004

No. of partners 182 182 182 182 182 182 182 182 182

Final for

home

Y 0.319*** 0.108** 0.257*** 0.011** 0.008 0.008 0.308*** 0.100** 0.249***

[0.061] [0.052] [0.050] [0.005] [0.005] [0.005] [0.060] [0.051] [0.049]

FTA

1.715*** -5.703***

0.027*** 0.009 1.688*** -5.711***

[0.259] [1.286]

[0.005] [0.020] [0.259] [1.290]

Y*FTA

0.899***

0.002 0.897***

[0.181]

[0.002] [0.182]

R2 0.794 0.821 0.843 0.949 0.95 0.95 0.785 0.813 0.836

Obs. 4004 4004 4004 4004 4004 4004 4004 4004 4004

No. of partners 182 182 182 182 182 182 182 182 182

Intermediate

for home

Y 0.294*** 0.100* 0.228*** 0.013** 0.009 0.009 0.281*** 0.091* 0.218***

[0.060] [0.051] [0.050] [0.006] [0.006] [0.006] [0.059] [0.050] [0.048]

FTA

1.569*** -4.762***

0.029*** 0.009 1.540*** -4.771***

[0.229] [1.142]

[0.006] [0.022] [0.229] [1.146]

Y*FTA

0.768***

0.002 0.765***

[0.159]

[0.003] [0.159]

R2 0.809 0.833 0.851 0.95 0.951 0.951 0.798 0.824 0.842

Obs. 4004 4004 4004 4004 4004 4004 4004 4004 4004

No. of partners 182 182 182 182 182 182 182 182 182

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0). Y

is for partner’s real per capita GDP in logarithm. * significant at 10%, ** significant at 5%, *** significant at 1%.

27

Table 5-5 China’s FTA and GVC Dependence: Final (for Export)

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Y_H 0.560*** 0.592*** 0.593*** 0.044 0.045 0.045 0.516*** 0.547*** 0.549***

[0.111] [0.110] [0.109] [0.028] [0.028] [0.028] [0.093] [0.090] [0.090]

Y_MH 0.039 0.071 0.073 -0.018** -0.018** -0.018** 0.057 0.089 0.09

[0.062] [0.059] [0.058] [0.007] [0.007] [0.007] [0.060] [0.057] [0.057]

Y_LM 0.026 0.039 0.039 -0.009** -0.009** -0.009** 0.035 0.048 0.048

[0.050] [0.047] [0.047] [0.004] [0.004] [0.004] [0.049] [0.046] [0.046]

FTA 2.195*** 0.647*** 0.044*** -0.008* 2.151*** 0.655***

[0.293] [0.220] [0.008] [0.005] [0.293] [0.220]

Y_H*FTA 4.311*** 0.020* 4.291***

[0.830] [0.012] [0.831]

Y_MH*FTA 0.357 0.123*** 0.234

[0.344] [0.027] [0.325]

Y_LM*FTA 0.544* 0.128*** 0.416

[0.291] [0.016] [0.289]

R2 0.811 0.837 0.858 0.939 0.939 0.94 0.8 0.828 0.852

Obs. 4114 4114 4114 4114 4114 4114 4114 4114 4114

No. of partners 187 187 187 187 187 187 187 187 187

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0).

Y_H =1 for high-end group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for

low-mid-end group, 0 for others. * significant at 10%, ** significant at 5%, *** significant at 1%.

Table 5-6 China’s FTA and GVC Dependence: Intermediate (for Export)

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Y_H 0.554*** 0.581*** 0.582*** 0.044 0.045 0.044 0.510*** 0.536*** 0.538***

[0.112] [0.112] [0.112] [0.028] [0.028] [0.028] [0.092] [0.091] [0.091]

Y_MH 0.035 0.063 0.064 -0.017** -0.017** -0.017** 0.052 0.079* 0.081*

[0.051] [0.049] [0.048] [0.008] [0.008] [0.008] [0.049] [0.047] [0.046]

Y_LM 0.014 0.025 0.025 -0.009** -0.008** -0.008** 0.022 0.033 0.034

[0.045] [0.043] [0.043] [0.004] [0.004] [0.004] [0.044] [0.042] [0.042]

FTA 1.881*** 0.374** 0.041*** -0.013*** 1.840*** 0.388***

[0.259] [0.150] [0.007] [0.004] [0.259] [0.150]

Y_H*FTA 4.089*** 0.028*** 4.061***

[0.724] [0.009] [0.724]

Y_MH*FTA 0.485* 0.123*** 0.362

[0.272] [0.026] [0.252]

Y_LM*FTA 0.596*** 0.126*** 0.470**

[0.226] [0.015] [0.224]

R2 0.799 0.825 0.851 0.935 0.935 0.936 0.786 0.814 0.843

Obs. 4114 4114 4114 4114 4114 4114 4114 4114 4114

No. of partners 187 187 187 187 187 187 187 187 187

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0).

Y_H =1 for high-end group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for

low-mid-end group, 0 for others. * significant at 10%, ** significant at 5%, *** significant at 1%.

28

Table 5-7 China’s FTA and GVC Dependence: Final (for Home)

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Y_H 0.219*** 0.244*** 0.245*** 0.025 0.026 0.026 0.194*** 0.218*** 0.220***

[0.076] [0.072] [0.072] [0.019] [0.019] [0.019] [0.063] [0.059] [0.059]

Y_MH -0.009 0.017 0.018 -0.009* -0.009* -0.009* 0.000 0.026 0.027

[0.038] [0.035] [0.035] [0.005] [0.005] [0.005] [0.037] [0.034] [0.034]

Y_LM -0.001 0.01 0.01 -0.005 -0.005 -0.005 0.004 0.014 0.015

[0.038] [0.037] [0.037] [0.003] [0.003] [0.003] [0.038] [0.037] [0.037]

FTA 1.746*** 0.743*** 0.029*** -0.011*** 1.716*** 0.754***

[0.258] [0.193] [0.005] [0.003] [0.259] [0.193]

Y_H*FTA 3.312*** 0.018*** 3.294***

[0.749] [0.006] [0.750]

Y_MH*FTA -0.008 0.090*** -0.098

[0.265] [0.018] [0.254]

Y_LM*FTA -0.241 0.098*** -0.339

[0.221] [0.012] [0.219]

R2 0.791 0.819 0.844 0.949 0.95 0.95 0.782 0.81 0.837

Obs. 4114 4114 4114 4114 4114 4114 4114 4114 4114

No. of partners 187 187 187 187 187 187 187 187 187

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0).

Y_H =1 for high-end group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for

low-mid-end group, 0 for others. * significant at 10%, ** significant at 5%, *** significant at 1%.

Table 5-8 China’s FTA and GVC Dependence: Intermediate (for Home)

Mutual_Dependence CHN_Dependence Partner_Dependence

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Y_H 0.326*** 0.349*** 0.350*** 0.025 0.025 0.025 0.301*** 0.323*** 0.325***

[0.083] [0.081] [0.081] [0.020] [0.020] [0.020] [0.071] [0.068] [0.068]

Y_MH -0.058 -0.035 -0.034 -0.010* -0.009 -0.009* -0.049 -0.026 -0.025

[0.038] [0.036] [0.036] [0.006] [0.006] [0.006] [0.037] [0.035] [0.034]

Y_LM -0.046 -0.037 -0.036 -0.005 -0.005 -0.005 -0.041 -0.032 -0.032

[0.038] [0.037] [0.037] [0.003] [0.003] [0.003] [0.037] [0.036] [0.036]

FTA 1.599*** 0.837*** 0.031*** -0.012*** 1.568*** 0.849***

[0.229] [0.220] [0.006] [0.003] [0.229] [0.220]

Y_H*FTA 2.757*** 0.020*** 2.737***

[0.664] [0.007] [0.665]

Y_MH*FTA -0.114 0.097*** -0.211

[0.284] [0.019] [0.273]

Y_LM*FTA -0.460* 0.105*** -0.565**

[0.237] [0.013] [0.237]

R2 0.806 0.831 0.852 0.95 0.951 0.951 0.795 0.821 0.844

Obs. 4114 4114 4114 4114 4114 4114 4114 4114 4114

No. of partners 187 187 187 187 187 187 187 187 187

Notes: All regressions with economy and year fixed effects. Constants are omitted to save space. Standard

errors are adjusted for clustering across partner economies. Robust standard errors are in brackets. FTA is a

dummy for China’s FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0).

Y_H =1 for high-end group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for

low-mid-end group, 0 for others. * significant at 10%, ** significant at 5%, *** significant at 1%.

29

Table 5-9 China’s FTA and GVC Dependence: Sectoral Evidence

Mutual_Dependence CHN_Dependence Partner_Dependence

id sector Y Y*FTA Obs. Y Y*FTA Obs. Y Y*FTA Obs.

1 Agriculture 0.216*** 0.877*** 3976 0.006*** 0.001 4004 0.210*** 0.876*** 3976

2 Fishing 0.263*** 1.059*** 3965 0.005* 0.000 4004 0.258*** 1.058*** 3965

3 Mining and Quarrying 0.477*** 1.377*** 3388 0.006 0.002 4004 0.479*** 1.375*** 3388

4 Food & Beverages 0.333*** 1.203*** 4004 0.005* 0.001 4004 0.327*** 1.202*** 4004

5 Textiles and Wearing Apparel 0.069 1.567*** 3983 0.009* -0.009* 4004 0.06 1.576*** 3983

6 Wood and Paper 0.297*** 1.037*** 3959 0.01 -0.001 4004 0.294*** 1.040*** 3959

7 Petroleum, Chemical and

Non-Metallic Mineral Products 0.353*** 1.146*** 3937 0.018*** 0.000 4004 0.334*** 1.148*** 3937

8 Metal Products 0.309*** 1.028*** 3812 0.018** 0.003 4004 0.306*** 1.025*** 3812

9 Electrical and Machinery 0.320*** 0.856*** 3953 0.025* 0.006 4004 0.294*** 0.849*** 3953

10 Transport Equipment 0.082 0.518*** 3923 0.016* 0.002 4004 0.064 0.516*** 3923

11 Other Manufacturing 0.251*** 1.218*** 3968 0.012* -0.001 4004 0.238*** 1.220*** 3968

12 Recycling -0.123** 1.108*** 3808 -0.001*** 0.001*** 4004 -0.122** 1.108*** 3808

13 Electricity, Gas and Water 0.127*** 0.233*** 4004 0.004 0.003 4004 0.123*** 0.230*** 4004

14 Construction 0.231*** 0.969*** 4004 0.014* 0.002 4004 0.217*** 0.966*** 4004

15 Maintenance and Repair 0.069 0.847*** 4004 0.004 0.002 4004 0.065 0.845*** 4004

16 Wholesale Trade 0.168*** 0.880*** 4004 0.004 0.002 4004 0.163*** 0.878*** 4004

17 Retail Trade 0.185*** 0.805*** 4004 0.004 0.002 4004 0.180*** 0.803*** 4004

18 Hotels and Restaurants 0.219*** 0.963*** 4004 0.004 0.002 4004 0.215*** 0.961*** 4004

19 Transport 0.219*** 0.890*** 4004 0.003 0.003 4004 0.216*** 0.887*** 4004

20 Post and Telecommunications 0.176*** 0.735*** 4004 0.004 0.004*** 4004 0.172*** 0.731*** 4004

21 Financial Intermediation

and Business Activities 0.160*** 0.546*** 4004 0.004 0.003* 4004 0.156*** 0.543*** 4004

22 Public Administration 0.186*** 0.781*** 4004 0.006** -0.001 4004 0.180*** 0.781*** 4004

23 Education, Health

and Other Services 0.162*** 0.637*** 4004 0.003 0.001 4004 0.159*** 0.636*** 4004

24 Private Households 0.051 0.484*** 4004 0.006 -0.001 4004 0.045 0.485*** 4004

25 Others 0.137*** 0.587*** 3982 0.006** -0.001 4004 0.131*** 0.588*** 3982

Notes: Model specifications are the same as those in Table 5-1. All regressions with economy and year fixed

effects. Standard errors are adjusted for clustering across partner economies. Constants, Robust standard errors and

coefficients on FTA dummy are omitted to save space. FTA is a dummy for China’s FTA in force at present (1

represents the economy being China’s FTA partner, otherwise 0). Y is for partner’s real per capita GDP in

logarithm. * significant at 10%, ** significant at 5%, *** significant at 1%.

30

Table 5-10 China’s FTA and GVC Dependence by Sector: Mutual Dependence

id sector Y_H Y_MH Y_LM FTA Y_H*FTA Y_MH*FTA Y_LM*FTA Obs.

1 Agriculture 0.064* -0.002 -0.041* 0.633*** 3.187*** -0.174 -0.551** 4085

2 Fishing 0.172** 0.068 0.112** 0.368** 3.945*** -0.164 -0.484*** 4073

3 Mining and Quarrying 0.084 0.022 0.001 0.322** 5.161*** -0.299** -0.410*** 3473

4 Food & Beverages 0.311*** 0.053 0.017 0.706*** 4.362*** 0.28 -0.582*** 4114

5 Textiles and Wearing Apparel 0.584*** -0.054 -0.028 0.422 6.188*** 1.038** 0.807** 4091

6 Wood and Paper 0.193*** 0.064 0.068 0.688*** 3.822*** -0.128 -0.286 4068

7 Petroleum, Chemical and

Non-Metallic Mineral Products 0.296*** 0.06 0.088 0.868*** 4.343*** -0.297 -0.660** 4046

8 Metal Products 0.088 -0.021 0.044 1.476*** 3.664*** -0.305 -0.854** 3907

9 Electrical and Machinery 0.785*** -0.049 -0.028 1.253*** 3.157*** -0.14 0.993** 4057

10 Transport Equipment 0.092 -0.111 -0.005 1.562*** 1.433*** 0.275 -0.849** 4029

11 Other Manufacturing 0.467*** 0.13 0.102 0.552** 4.534*** 0.282 0.124 4078

12 Recycling -0.022 0.029 0.04 0.072 4.650*** 0.556** 0.713*** 3916

13 Electricity, Gas and Water 0.208*** 0.009 0.003 0.474*** 0.748*** -0.044 -0.278* 4114

14 Construction 0.215*** 0.014 0.03 0.912*** 3.534*** 0.576 -0.246 4114

15 Maintenance and Repair 0.055 0.074 0.028 0.376*** 3.208*** -0.265* -0.589*** 4114

16 Wholesale Trade 0.025 -0.005 0.012 0.493*** 3.303*** -0.283* -0.608*** 4114

17 Retail Trade 0.039 0.017 -0.003 0.601*** 2.988*** -0.297* -0.621*** 4114

18 Hotels and Restaurants 0.178*** 0.078** 0.023 0.451*** 3.638*** 0.218 -0.293** 4114

19 Transport 0.240*** -0.012 -0.016 0.863*** 3.248*** -0.053 -0.517** 4114

20 Post and Telecommunications 0.214*** 0.031 -0.032 0.383*** 2.687*** 0.387* -0.390*** 4114

21 Financial Intermediation

and Business Activities 0.112*** 0.026 0.003 0.382*** 2.031*** -0.042 -0.066 4114

22 Public Administration 0.175** -0.001 0.013 0.620*** 3.001*** -0.19 -0.386** 4114

23 Education, Health

and Other Services 0.129*** 0.037 0.005 0.512*** 2.384*** -0.059 -0.233* 4114

24 Private Households 0.009 0.085 0.041 0.442*** 1.750*** -0.045 -0.270* 4114

25 Others 0.202*** 0.101** 0.046 0.461*** 2.093*** 0.273 0.192 4092

Notes: All regressions with economy and year fixed effects. Standard errors are adjusted for clustering across

partner economies. Constants and Robust standard errors are omitted to save space. FTA is a dummy for China’s

FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0). Y_H =1 for high-end

group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for low-mid-end group, 0 for others.

* significant at 10%, ** significant at 5%, *** significant at 1%.

31

Table 5-11 China’s FTA and GVC Dependence by Sector: China’s Dependence

id sector Y_H Y_MH Y_LM FTA Y_H*FTA Y_MH*FTA Y_LM*FTA Obs.

1 Agriculture 0.007 -0.001 -0.001 -0.004*** 0.006** 0.045*** 0.040*** 4114

2 Fishing 0.014 -0.006* -0.003* -0.005*** 0.007** 0.046*** 0.057*** 4114

3 Mining and Quarrying 0.014 -0.005 -0.002 -0.009*** 0.015*** 0.060*** 0.067*** 4114

4 Food & Beverages 0.014 -0.005 -0.003 -0.008*** 0.012*** 0.068*** 0.081*** 4114

5 Textiles and Wearing Apparel 0.016 -0.008* -0.003 0.030** -0.031 0.032* 0.071*** 4114

6 Wood and Paper 0.037* -0.012 -0.006* -0.012*** 0.018* 0.149*** 0.191*** 4114

7 Petroleum, Chemical and

Non-Metallic Mineral Products 0.017 -0.008 -0.004 -0.009 0.014* 0.090*** 0.123*** 4114

8 Metal Products 0.041 -0.01 -0.005 -0.020*** 0.018*** 0.096*** 0.097*** 4114

9 Electrical and Machinery 0.039 -0.021* -0.011* -0.011** 0.037*** 0.199*** 0.178*** 4114

10 Transport Equipment 0.041 -0.014* -0.007* -0.018*** 0.014* 0.105*** 0.109*** 4114

11 Other Manufacturing 0.037 -0.012* -0.006 -0.007* 0.011 0.118*** 0.147*** 4114

12 Recycling 0.001*** 0 0 -0.001*** 0.004*** 0.007*** 0.008*** 4114

13 Electricity, Gas and Water 0.019 -0.007* -0.004 -0.008*** 0.021*** 0.073*** 0.083*** 4114

14 Construction 0.038 -0.011 -0.006 -0.018*** 0.020*** 0.105*** 0.122*** 4114

15 Maintenance and Repair 0.012 -0.005* -0.003* -0.005*** 0.011*** 0.051*** 0.054*** 4114

16 Wholesale Trade 0.012 -0.005* -0.003* -0.005*** 0.011*** 0.051*** 0.054*** 4114

17 Retail Trade 0.012 -0.005* -0.003* -0.005*** 0.011*** 0.051*** 0.054*** 4114

18 Hotels and Restaurants 0.011 -0.003 -0.002 -0.006*** 0.013*** 0.056*** 0.067*** 4114

19 Transport 0.025* -0.007* -0.004 -0.011*** 0.022*** 0.073*** 0.095*** 4114

20 Post and Telecommunications 0.015 -0.008** -0.004** -0.009*** 0.023*** 0.069*** 0.063*** 4114

21 Financial Intermediation

and Business Activities 0.017 -0.006** -0.003* -0.006*** 0.020*** 0.072*** 0.073*** 4114

22 Public Administration 0.009 -0.005* -0.003* 0.001 0.002 0.041*** 0.050*** 4114

23 Education, Health

and Other Services 0.021 -0.007* -0.003 -0.007*** 0.015*** 0.066*** 0.081*** 4114

24 Private Households 0.021 -0.010** -0.005* 0.003 0.006 0.074*** 0.092*** 4114

25 Others 0.009 -0.005* -0.003* 0.001 0.002 0.041*** 0.050*** 4114

Notes: All regressions with economy and year fixed effects. Standard errors are adjusted for clustering across

partner economies. Constants and Robust standard errors are omitted to save space. FTA is a dummy for China’s

FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0). Y_H =1 for high-end

group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for low-mid-end group, 0 for others.

* significant at 10%, ** significant at 5%, *** significant at 1%.

32

Table 5-12 China’s FTA and GVC Dependence by Sector: Partner’s Dependence

id sector Y_H Y_MH Y_LM FTA Y_H*FTA Y_MH*FTA Y_LM*FTA Obs.

1 Agriculture 0.057 -0.001 -0.040* 0.637*** 3.181*** -0.219 -0.591** 4085

2 Fishing 0.158* 0.074 0.115** 0.374** 3.937*** -0.21 -0.542*** 4073

3 Mining and Quarrying 0.091 0.021 0 0.330*** 5.145*** -0.359*** -0.491*** 3473

4 Food & Beverages 0.297*** 0.058 0.02 0.714*** 4.351*** 0.212 -0.663*** 4114

5 Textiles and Wearing Apparel 0.568*** -0.046 -0.025 0.392 6.219*** 1.005** 0.736** 4091

6 Wood and Paper 0.182*** 0.072 0.073 0.697*** 3.805*** -0.277 -0.477** 4068

7 Petroleum, Chemical and

Non-Metallic Mineral Products 0.278*** 0.067 0.091 0.877*** 4.346*** -0.387 -0.787*** 4046

8 Metal Products 0.116 -0.03 0.038 1.492*** 3.646*** -0.401 -0.955*** 3907

9 Electrical and Machinery 0.746*** -0.029 -0.017 1.265*** 3.120*** -0.339 0.815* 4057

10 Transport Equipment 0.049 -0.099 0.002 1.580*** 1.418** 0.121 -0.958** 4029

11 Other Manufacturing 0.430*** 0.141* 0.108 0.562** 4.520*** 0.161 -0.028 4078

12 Recycling -0.022 0.029 0.039 0.074 4.647*** 0.548** 0.705*** 3916

13 Electricity, Gas and Water 0.188*** 0.016 0.007 0.482*** 0.727*** -0.117 -0.362** 4114

14 Construction 0.177*** 0.025 0.036 0.930*** 3.514*** 0.47 -0.367 4114

15 Maintenance and Repair 0.043 0.079 0.031 0.381*** 3.197*** -0.316** -0.644*** 4114

16 Wholesale Trade 0.013 0 0.015 0.498*** 3.291*** -0.334** -0.662*** 4114

17 Retail Trade 0.028 0.022 0 0.606*** 2.977*** -0.347** -0.675*** 4114

18 Hotels and Restaurants 0.167*** 0.081** 0.024 0.457*** 3.624*** 0.162 -0.360*** 4114

19 Transport 0.216*** -0.005 -0.012 0.874*** 3.226*** -0.126 -0.613*** 4114

20 Post and Telecommunications 0.199*** 0.039 -0.028 0.392*** 2.664*** 0.318 -0.453*** 4114

21 Financial Intermediation

and Business Activities 0.096*** 0.032 0.006 0.389*** 2.011*** -0.114 -0.139 4114

22 Public Administration 0.166** 0.004 0.015 0.619*** 2.999*** -0.23 -0.436** 4114

23 Education, Health

and Other Services 0.108*** 0.045* 0.008 0.519*** 2.369*** -0.126 -0.314** 4114

24 Private Households -0.012 0.095* 0.046 0.438*** 1.744*** -0.119 -0.362** 4114

25 Others 0.193*** 0.106** 0.048 0.460*** 2.092*** 0.232 0.142 4092

Notes: All regressions with economy and year fixed effects. Standard errors are adjusted for clustering across

partner economies. Constants and Robust standard errors are omitted to save space. FTA is a dummy for China’s

FTA in force at present (1 represents the economy being China’s FTA partner, otherwise 0). Y_H =1 for high-end

group, 0 for others; Y_MH =1 for mid-high-end group, 0 for others; Y_LM =1 for low-mid-end group, 0 for others.

* significant at 10%, ** significant at 5%, *** significant at 1%.

33

China's FTA partner (under negotiation or consideration)China's FTA partner (in force)Other economy

Figure 2-1 The Geographic Distribution of China’s

Practical and Potential FTA Partners up to Feb. 2016

Source: Authors’ plot.

Figure 3-1 GVC-based FTA Construction:

An Analysis Based on Transnational Smiling Curve

Notes: advanced factors not only refer to the usual high-skilled human capital, but also include factors

regarding management, institution, system and mechanism which are conducive to climbing up and maintaining

the higher ends of GVCs. GVC-based FTA can be constructed from both national and sectoral perspectives.

Source: Authors’ plot.

34

Figure 4-1 Tracing both Direct and Indirect Value-added Embodied in One Unit of Output

1 unit of output

Value-added (V): k, l Intermediate

Value-added (V): k, l Intermediate

Value-added (V): k, l Intermediate

35

Appendix

Table A4-1 188 Sample Economies in Eora MIRO Database

Economy Abr. id Sub-region Continent Economy Abr. id Sub-region Continent

Burundi BDI 205 Eastern Africa Africa Bulgaria BGR 316 Eastern Europe Europe

Djibouti DJI 214 Eastern Africa Africa Belarus BLR 340 Eastern Europe Europe

Eritrea ERI 258 Eastern Africa Africa Czech Rep. CZE 352 Eastern Europe Europe

Ethiopia ETH 217 Eastern Africa Africa Hungary HUN 321 Eastern Europe Europe

Kenya KEN 224 Eastern Africa Africa Moldova MDA 343 Eastern Europe Europe

Madagascar MDG 227 Eastern Africa Africa Poland POL 327 Eastern Europe Europe

Mozambique MOZ 233 Eastern Africa Africa Romania ROU 328 Eastern Europe Europe

Mauritius MUS 231 Eastern Africa Africa Russian Federation RUS 344 Eastern Europe Europe

Malawi MWI 228 Eastern Africa Africa Slovakia SVK 353 Eastern Europe Europe

Rwanda RWA 238 Eastern Africa Africa Ukraine UKR 347 Eastern Europe Europe

South Sudan SDS 261 Eastern Africa Africa Denmark DNK 302 Northern Europe Europe

Somalia SOM 243 Eastern Africa Africa Estonia EST 334 Northern Europe Europe

Seychelles SYC 241 Eastern Africa Africa Finland FIN 318 Northern Europe Europe

Tanzania TZA 247 Eastern Africa Africa United Kingdom GBR 303 Northern Europe Europe

Uganda UGA 250 Eastern Africa Africa Ireland IRL 306 Northern Europe Europe

Zambia ZMB 253 Eastern Africa Africa Iceland ISL 322 Northern Europe Europe

Zimbabwe ZWE 254 Eastern Africa Africa Lithuania LTU 336 Northern Europe Europe

Angola AGO 202 Middle Africa Africa Latvia LVA 335 Northern Europe Europe

Central African Rep. CAF 209 Middle Africa Africa Norway NOR 326 Northern Europe Europe

Cameroon CMR 206 Middle Africa Africa Sweden SWE 330 Northern Europe Europe

Congo, Dem. Rep. COD 252 Middle Africa Africa Albania ALB 313 Southern Europe Europe

Congo COG 213 Middle Africa Africa Andorra AND 314 Southern Europe Europe

Gabon GAB 218 Middle Africa Africa Bosnia and Herzegovina BIH 355 Southern Europe Europe

Sao Tome and Principe STP 239 Middle Africa Africa Spain ESP 312 Southern Europe Europe

Chad TCD 211 Middle Africa Africa Greece GRC 310 Southern Europe Europe

Algeria DZA 201 Northern Africa Africa Croatia HRV 351 Southern Europe Europe

Egypt EGY 215 Northern Africa Africa Italy ITA 307 Southern Europe Europe

Libya LBY 226 Northern Africa Africa Macedonia MKD 354 Southern Europe Europe

Morocco MAR 232 Northern Africa Africa Malta MLT 324 Southern Europe Europe

Sudan SUD 246 Northern Africa Africa Montenegro MNE 362 Southern Europe Europe

Tunisia TUN 249 Northern Africa Africa Portugal PRT 311 Southern Europe Europe

Botswana BWA 204 Southern Africa Africa San Marino SMR 329 Southern Europe Europe

Lesotho LSO 255 Southern Africa Africa Serbia SRB 363 Southern Europe Europe

Namibia NAM 234 Southern Africa Africa Slovenia SVN 350 Southern Europe Europe

Swaziland SWZ 257 Southern Africa Africa Austria AUT 315 Western Europe Europe

South Africa ZAF 244 Southern Africa Africa Belgium BEL 301 Western Europe Europe

Benin BEN 203 Western Africa Africa Switzerland CHE 331 Western Europe Europe

Burkina Faso BFA 251 Western Africa Africa Germany DEU 304 Western Europe Europe

Côte d'Ivoire CIV 223 Western Africa Africa France FRA 305 Western Europe Europe

Cape Verde CPV 208 Western Africa Africa Liechtenstein LIE 323 Western Europe Europe

Ghana GHA 220 Western Africa Africa Luxembourg LUX 308 Western Europe Europe

Guinea GIN 221 Western Africa Africa Monaco MCO 325 Western Europe Europe

Gambia GMB 219 Western Africa Africa Netherlands NLD 309 Western Europe Europe

Liberia LBR 225 Western Africa Africa Aruba ABW 403 Caribbean North America

Mali MLI 229 Western Africa Africa Netherlands Antilles ANT 449 Caribbean North America

Mauritania MRT 230 Western Africa Africa Antigua and Barbuda ATG 401 Caribbean North America

Niger NER 235 Western Africa Africa Bahamas BHS 404 Caribbean North America

Nigeria NGA 236 Western Africa Africa Barbados BRB 405 Caribbean North America

Senegal SEN 240 Western Africa Africa Cuba CUB 416 Caribbean North America

Sierra Leone SLE 242 Western Africa Africa Cayman Islands CYM 411 Caribbean North America

Togo TGO 248 Western Africa Africa Dominican Republic DOM 418 Caribbean North America

Kazakhstan KAZ 145 Central Asia Asia Haiti HTI 425 Caribbean North America

Kyrgyz Republic KGZ 146 Central Asia Asia Jamaica JAM 427 Caribbean North America

Tajikistan TJK 147 Central Asia Asia Trinidad and Tobago TTO 442 Caribbean North America

Turkmenistan TKM 148 Central Asia Asia Virgin Islands, British VGB 446 Caribbean North America

Uzbekistan UZB 149 Central Asia Asia Belize BLZ 406 Central America North America

China CHN 142 Eastern Asia Asia Costa Rica CRI 415 Central America North America

Hong Kong, China HKG 110 Eastern Asia Asia Guatemala GTM 423 Central America North America

Japan JPN 116 Eastern Asia Asia Honduras HND 426 Central America North America

36

South Korea KOR 133 Eastern Asia Asia Mexico MEX 429 Central America North America

Macao, China MAC 121 Eastern Asia Asia Nicaragua NIC 431 Central America North America

Mongolia MNG 124 Eastern Asia Asia Panama PAN 432 Central America North America

North Korea PRK 109 Eastern Asia Asia El Salvador SLV 440 Central America North America

Taiwan, China TWN 143 Eastern Asia Asia Bermuda BMU 504 Northern America North America

Brunei Darussalam BRN 105 South-Eastern Asia Asia Canada CAN 501 Northern America North America

Indonesia IDN 112 South-Eastern Asia Asia Greenland GRL 503 Northern America North America

Cambodia KHM 107 South-Eastern Asia Asia United States USA 502 Northern America North America

Laos LAO 119 South-Eastern Asia Asia Australia AUS 601 Oceania Oceania

Myanmar MMR 106 South-Eastern Asia Asia Fiji FJI 603 Oceania Oceania

Malaysia MYS 122 South-Eastern Asia Asia New Caledonia NCL 607 Oceania Oceania

Philippines PHL 129 South-Eastern Asia Asia New Zealand NZL 609 Oceania Oceania

Singapore SGP 132 South-Eastern Asia Asia Papua New Guinea PNG 611 Oceania Oceania

Thailand THA 136 South-Eastern Asia Asia French Polynesia PYF 623 Oceania Oceania

Viet Nam VNM 141 South-Eastern Asia Asia Vanuatu VUT 608 Oceania Oceania

Afghanistan AFG 101 Southern Asia Asia Samoa WSM 617 Oceania Oceania

Bangladesh BGD 103 Southern Asia Asia Argentina ARG 402 South America South America

Bhutan BTN 104 Southern Asia Asia Bolivia BOL 408 South America South America

India IND 111 Southern Asia Asia Brazil BRA 410 South America South America

Iran IRN 113 Southern Asia Asia Chile CHL 412 South America South America

Sri Lanka LKA 134 Southern Asia Asia Colombia COL 413 South America South America

Maldives MDV 123 Southern Asia Asia Ecuador ECU 419 South America South America

Nepal NPL 125 Southern Asia Asia Guyana GUY 424 South America South America

Pakistan PAK 127 Southern Asia Asia Peru PER 434 South America South America

United Arab Emirates ARE 138 Western Asia Asia Paraguay PRY 433 South America South America

Armenia ARM 338 Western Asia Asia Suriname SUR 441 South America South America

Azerbaijan AZE 339 Western Asia Asia Uruguay URY 444 South America South America

Bahrain BHR 102 Western Asia Asia Venezuela VEN 445 South America South America

Cyprus CYP 108 Western Asia Asia

Georgia GEO 337 Western Asia Asia

Iraq IRQ 114 Western Asia Asia

Israel ISR 115 Western Asia Asia

Jordan JOR 117 Western Asia Asia

Kuwait KWT 118 Western Asia Asia

Lebanon LBN 120 Western Asia Asia

Oman OMN 126 Western Asia Asia

Palestinian Authority PSE 128 Western Asia Asia

Qatar QAT 130 Western Asia Asia

Saudi Arabia SAU 131 Western Asia Asia

Syria SYR 135 Western Asia Asia

Turkey TUR 137 Western Asia Asia

Yemen YEM 139 Western Asia Asia

Notes: When necessary, we will only use the abbreviation of economy name to save space.

Table A4-2 26 Sectors/Items in Earo MIRO Database

Sector id

Agriculture 1

Fishing 2

Mining and Quarrying 3

Food & Beverages 4

Textiles and Wearing Apparel 5

Wood and Paper 6

Petroleum, Chemical and Non-Metallic Mineral Products 7

Metal Products 8

Electrical and Machinery 9

Transport Equipment 10

Other Manufacturing 11

Recycling 12

Electricity, Gas and Water 13

Construction 14

Maintenance and Repair 15

37

Wholesale Trade 16

Retail Trade 17

Hotels and Restaurants 18

Transport 19

Post and Telecommunications 20

Financial Intermediation and Business Activities 21

Public Administration 22

Education, Health and Other Services 23

Private Households 24

Others 25

Re-export & Re-import 26

Notes: When necessary, we will only use the abbreviation of sector/item name to save space.

Table A4-3 Descriptive Statistics on the GVC Linkage between China and its Partners

CHN_Dependence Partner_Dependence

id sector N mean sd min max N mean sd min max

Final (for export) 4114 0.072 0.284 0 4.049 4114 0.986 1.646 0.007 31.542

Intermediate (for export) 4114 0.070 0.275 0 3.925 4114 0.858 1.394 0.005 27.313 Final (for home) 4114 0.053 0.206 0 2.688 4114 0.604 1.290 0.003 28.139

Intermediate (for home) 4114 0.059 0.220 0 2.799 4114 0.658 1.223 0.004 25.319

1 Agriculture 4114 0.022 0.076 0 0.872 4085 0.481 1.101 0.003 21.759 2 Fishing 4114 0.026 0.102 0 1.301 4073 0.814 1.329 0.018 22.584

3 Mining and Quarrying 4114 0.040 0.150 0 1.924 3473 0.697 1.715 0.004 27.882

4 Food & Beverages 4114 0.033 0.114 0 1.264 4114 0.738 1.455 0.005 25.219 5 Textiles and Wearing Apparel 4114 0.061 0.230 0 2.977 4091 1.614 2.339 0.009 43.501

6 Wood and Paper 4114 0.066 0.226 0 2.538 4068 0.819 1.409 0.005 25.560

7 Petroleum, Chemical and Non-Metallic Mineral Products

4114 0.070 0.241 0 2.921 4046 0.962 1.626 0.005 26.869

8 Metal Products 4114 0.074 0.263 0 3.047 3907 1.101 1.698 0.006 27.180

9 Electrical and Machinery 4114 0.115 0.509 0 7.433 4057 1.278 1.668 0.004 24.327

10 Transport Equipment 4114 0.085 0.354 0 4.582 4029 1.341 1.436 0.007 16.600 11 Other Manufacturing 4114 0.066 0.242 0 3.099 4078 1.227 1.653 0.009 28.307

12 Recycling 4114 0.002 0.005 0 0.089 3916 1.067 1.448 0.018 28.307

13 Electricity, Gas and Water 4114 0.044 0.174 0 2.190 4114 0.473 0.622 0.007 7.335 14 Construction 4114 0.072 0.274 0 3.542 4114 0.744 1.438 0.003 30.512

15 Maintenance and Repair 4114 0.032 0.129 0 1.767 4114 0.648 1.235 0.018 27.507

16 Wholesale Trade 4114 0.032 0.129 0 1.767 4114 0.556 1.302 0.002 27.507 17 Retail Trade 4114 0.032 0.129 0 1.766 4114 0.443 1.210 0.002 27.508

18 Hotels and Restaurants 4114 0.029 0.098 0 1.088 4114 0.560 1.293 0.003 27.507

19 Transport 4114 0.042 0.161 0 1.972 4114 0.683 1.362 0.003 27.508 20 Post and Telecommunications 4114 0.041 0.178 0 2.431 4114 0.474 0.866 0.002 17.495

21 Financial Intermediation and Business Activities

4114 0.035 0.141 0 1.856 4114 0.342 0.790 0.001 17.211

22 Public Administration 4114 0.031 0.117 0 1.543 4114 0.543 1.040 0.000 20.615 23 Education, Health

and Other Services 4114 0.039 0.145 0 1.837 4114 0.423 0.886 0.002 19.588

24 Private Households 4114 0.046 0.177 0 2.451 4114 0.704 0.957 0.000 17.495

25 Others 4114 0.031 0.117 0 1.543 4092 0.659 0.955 0.000 17.495

38

Table A4-4 FTA Types Defined by Real Per capita GDP

FTA Type Id for FTA

Type

Group 1 Group 2 Group 3 Group 4 Group

<=p25 (p25, p50] (p50, mean] (mean, p75] p75< Combinations

Low-end horizontal FTA

L_H Y

1

Y

2

Low-end vertical FTA

L_V Y Y

1,2

Low-mid-end

vertical FTA

LM_V Y Y Y

1,2,3

Y Y Y Y

1,2,3

Y

Y

1,3

Y Y

2,3

Low-high-end

vertical FTA

LH_V Y Y Y 1,2,4

Y

Y 1,4

Y

Y 2,4

Mid-end

horizontal FTA M_H

Y

3

Mid-high-end

vertical FTA

MH_V

Y Y Y 3

Y

Y 3

Y Y 3

High-end

horizontal FTA H_H

Y 4

Full-range vertical

FTA

F_V Y Y Y Y Y 1,2,3,4

Y Y Y

Y 1,2,3,4

Y

Y Y Y 1,3,4

Y Y Y Y 2,3,4

Y Y

Y 2,3,4

Y

Y Y 2,3,4

Table A4-5 Unmatched Economies between All Three Datasets

partner id Abr. Unmatched

French Southern Territories 810 ATF Only in the FTA

dataset (11

economies)

Falkland Islands (Islas Malvinas) 451 FLK

Faroe Islands 361 FRO

British Indian Ocean Territory 150 IOT

Mayotte 259 MYT

Niue 630 NIU

Pitcairn 631 PCN

South Georgia and the South Sandwich Islands 812 SGS

Saint Helena 811 SHN

Saint Pierre and Miquelon 448 SPM

Wallis and Futuna Islands 625 WLF

Cura 鏰 o 417 CUW Only in the real

per capita GDP

dataset (6

economies)

Czechoslovakia 365 CSVK

Palau 622 PLW

Serbia and Montenegro 349 YUG

Sint Maarten (Dutch part) 438 MAF

Timor-Leste 144 TLS

Bermuda 504 BMU In both Eora

MRIO database

and the per capita

GDP dataset but

not in the FTA

dataset (8

economies)

Congo, Dem. Rep. 252 ZAR

Djibouti 214 DJI

Monaco 325 MCO

Mongolia 124 MNG

Sao Tome and Principe 239 STP

Somalia 243 SOM

South Sudan 261 SDS

39

(40,53](30,40](20,30](10,20][1,10]No data

Figure A1-1 Number of FTAs by Economy in the World (up to Jan. 2016)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Secto

r

CH

NE

RI

BD

IE

TH

CO

DN

ER

MD

GS

OM

LB

RG

INM

WI

RW

AC

AF

SL

EA

FG

NP

LT

GO

MM

RT

JK

HT

IG

MB

MO

ZB

FA

ML

IP

RK

UG

AB

EN

TC

DT

ZA

KG

ZB

GD

KH

ML

AO

KE

NZ

MB

MR

TS

EN

ZW

EP

AK

UZ

BV

NM

YE

ML

SO

CIV

CM

RS

TP

SD

SS

UD

PN

GM

DA

GH

AIN

DU

SA

DE

UJP

NK

OR

Economy

0

1

2

3

4

5

6

7

VA

sha

re

(1) Group 1+ USA, DEU, JPN and KOR

40

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Secto

r

CH

NE

RI

BD

IE

TH

CO

DN

ER

MD

GS

OM

LB

RG

INM

WI

RW

AC

AF

SL

EA

FG

NP

LT

GO

MM

RT

JK

HT

IG

MB

MO

ZB

FA

ML

IP

RK

UG

AB

EN

TC

DT

ZA

KG

ZB

GD

KH

ML

AO

KE

NZ

MB

MR

TS

EN

ZW

EP

AK

UZ

BV

NM

YE

ML

SO

CIV

CM

RS

TP

SD

SS

UD

PN

GM

DA

GH

AIN

DU

SA

DE

UJP

NK

OR

Economy

0

1

2

3

4

5

6

7

VA

sha

re

(2) Group 2+USA, DEU, JPN and KOR

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Secto

r

CH

NB

LZ

CO

LN

AM

MN

EB

GR

BL

RD

OM

CU

BK

AZ

TK

MR

OU

SU

RM

DV

CR

IB

RA

ZA

FV

EN

BW

AM

YS

RU

SG

AB

PA

NM

US

LB

NU

RY

AR

GM

EX

TU

RL

VA

CH

LL

TU

PO

LH

RV

AT

GH

UN

ES

TS

VK

SY

CO

MN

TT

OB

RB

CZ

EU

SA

DE

UJP

NK

OR

Economy

0

1

2

3

4

5

6

7

VA

sha

re

(3) Group 3+ USA, DEU, JPN and KOR

41

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Secto

r

CH

NB

HR

ML

TS

AU

PR

TS

VN

GR

CA

BW

TW

NB

HS

PY

FA

RE

ISR

CY

PB

RN

ES

PN

ZL

KW

TIT

AN

CL

HK

GV

GB

GR

LA

ND

SG

PF

RA

CA

NB

EL

AU

SG

BR

FIN

AU

TN

LD

SW

EM

AC

IRL

DN

KC

YM

SM

RIS

LC

HE

QA

TN

OR

BM

UL

UX

LIE

MC

OA

NT

US

AD

EU

JP

NK

OR

Economy

0

1

2

3

4

5

6

7

VA

sha

re

(4) Group 4

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Secto

r

CH

NM

MR

KH

ML

AO

PA

KV

NM

MD

AIN

DP

HL

IDN

LK

AG

EO

TH

AF

JI

PE

RC

OL

MD

VC

RI

MY

SC

HL

OM

NB

HR

SA

UK

OR

AR

EB

RN

NZ

LK

WT

HK

GS

GP

JP

NA

US

MA

CIS

LC

HE

QA

TN

OR

US

AD

EU

Economy

0

1

2

3

4

5

6

7

VA

sha

re

(5) China’s FTA partners+USA, DEU

Figure A4-1 China’s (Sectors) GVC Dependence on Other Economies

Notes: The classification of Groups 1, 2, 3, 4 is based on Section 4. The data of China is specified as 0 as we

only consider the foreign content of value added. Each figure is added USA, DEU, JPN and KOR to guarantee the

same scale for comparison. The ids for economies are listed in the Appendix.

42

CHNERIBDIETH

CODNERMDGSOMLBRGIN

MWIRWACAFSLEAFGNPLTGOMMRTJKHTI

GMBMOZBFAMLI

PRKUGABENTCDTZAKGZBGDKHMLAOKENZMBMRTSENZWEPAKUZBVNMYEMLSOCIV

CMRSTPSDSSUDPNGMDAGHAIND

USADEUJPNKOR

Econ

om

y

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Sector

0

5

10

15

20

25

30

35

40

45

VA

sha

re

(1) Group 1+USA, DEU, JPN and KOR

CHNDJI

BOL

NICPHL

MNG

HND

NGASYR

EGY

IDN

IRQPSE

LKA

PRYBTN

COG

GEO

ARMVUT

UKR

GUY

GTMWSM

SWZ

MARJOR

AGO

CPV

SLVAZE

DZA

BIHALB

THA

ECU

FJIIRN

TUN

SRB

MKDPER

LBY

JAMUSA

DEU

JPN

KOR

Econ

om

y

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Sector

0

5

10

15

20

25

30

35

40

45

VA

sha

re

(2) Group 2+USA, DEU, JPN and KOR

43

CHN

BLZ

COL

NAM

MNE

BGR

BLR

DOM

CUB

KAZ

TKM

ROU

SUR

MDV

CRI

BRA

ZAF

VEN

BWA

MYS

RUS

GAB

PAN

MUS

LBN

URY

ARG

MEX

TUR

LVA

CHL

LTU

POL

HRV

ATG

HUN

EST

SVK

SYC

OMN

TTO

BRB

CZE

USA

DEU

JPN

KOR

Econ

om

y

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Sector

0

5

10

15

20

25

30

35

40

45

VA

sha

re

(3) Group 3+USA, DEU, JPN and KOR

CHNBHRMLTSAUPRTSVNGRCABWTWNBHSPYFAREISR

CYPBRNESPNZL

KWTITA

NCLHKGVGBGRLANDSGPFRACANBELAUSGBRFIN

AUTNLD

SWEMAC

IRLDNKCYMSMR

ISLCHEQATNORBMULUXLIE

MCOANTUSADEUJPNKOR

Econ

om

y

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Sector

0

5

10

15

20

25

30

35

40

45

VA

sha

re

(4) Group 4

44

CHN

MMR

KHM

LAO

PAK

VNM

MDA

IND

PHL

IDN

LKA

GEO

THA

FJI

PER

COL

MDV

CRI

MYS

CHL

OMN

BHR

SAU

KOR

ARE

BRN

NZL

KWT

HKG

SGP

JPN

AUS

MAC

ISL

CHE

QAT

NOR

USA

DEU

Econ

om

y

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Sector

0

5

10

15

20

25

30

35

40

45

VA

sha

re

(5) China’s FTA partners+USA, DEU

Figure A4-2 Other Economies’ (Sectors) GVC Dependence on China

Notes: The classification of Groups 1, 2, 3, 4 is based on Section 4. The data of China is specified as 0 as we

only consider the foreign content of value added. Each figure has USA, DEU, JPN and KOR to guarantee the same

scale for comparison. The ids for economies are listed in the Appendix.

(16282,82170(max)](11542,16282(p75)](4066,11542(mean)](1109,4066(p50)][193(min),1109(p25)]No data

Figure A4-3 Classification of Sample Economies by Per capita Income in 2011

Notes: The data on real per capita GDP are from UNCTADstat. All the economies on this map are ranked in

terms of real per capita GDP (in 2005 USD), and are categorized into 4 groups based on the specific statistical

values ranging from minimum (193), 25th percentile (1109), median (4066), 75th percentile (16282) and

maximum (82170) of real per capita GDP. The highest income group (above 75th percentile or 16282 USD) is

depicted in the most darkly shaded areas, which are the three core regions of the GVC (without considering the

few oil-producing high-income economies in Middle East).

Source: Authors’ plot.