1
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
WANG Xinkui
Shanghai WTO Center
XIAO Zhiguo
Department of Statistics
Fudan University
YAO Weiqun
Shanghai WTO Center
* 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.
2
“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).
3
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
4
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/.
5
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
6
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).
7
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.
8
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.
9
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.
10
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.
11
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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Industries/Countries in World Production”, GGDC Research Memorandum No. 133.
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Comprehensive Approach”, WIOD Working Paper No. 7.
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Curious Case of the Missing Foreign Tariffs”, American Economic Review, 71 (4), pp. 704–714.
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.