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Enabling an Innovation Ecosystem
and Participation at the Higher End
of Global Value Chains
MINSOO LEE, RAYMOND GASPAR,
AND
HUIYAN DU¤
Moving up the global value chain requires an enabling innovation ecosystem
alongside economy-specific endowments, a mix of supportive policies in broad
areas of infrastructure and institutions, and other enabling factors. Examining
sample economies globally and in developing Asia, the empirical results
suggest that during the transition from a low level of upgrading in a global
value chain to a medium-level one, efforts should focus on increasing the scale
of innovation inputs, allowing firms to improve in many areas of their capacity
to innovate. To move higher up a global value chain, the design of innovation
policies should gradually emphasize the production of technological,
knowledge, and creative outputs.
Keywords: developing Asia, global value chain upgrading, innovation
efficiency
JEL codes: F10, F14, F15
⁄Minsoo
Lee (corresponding author): Central and West Asia Department, Asian Development Bank.
E-mail: mlee@adb.org; Raymond Gaspar: Asian Development Bank. E-mail: rgaspar.consultant@adb.org;
Huiyan Du: Resident Mission in the People’s Republic of China, Asian Development Bank. E-mail:
hdu.consultant@adb.org. We would like to thank the managing editor and the anonymous referees for
helpful comments and suggestion. The Asian Development Bank recognizes “China” as the People’s
Republic of China.
This is an Open Access article published by World Scientific Publishing Company. It is distributed under
the terms of the Creative Commons Attribution 3.0 International (CC BY 3.0) License which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
Asian Development Review, Vol. 38, No. 2, pp. 123–157
DOI: 10.1142/S0116110521500013
© 2021 Asian Development Bank and
Asian Development Bank Institute.
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I.
Introduction
The rapid technological advances being made alongside stronger and broader
regional cooperation and integration have helped boost cross-border transactions
through trade and investment. Efficiency improvements for moving goods, delivering
services, and exchanging information have substantially reduced costs in globalized
transactions. This process has fostered the formation of global value chains (GVCs),
which have transformed international trade and production patterns. The World Bank
(2017) estimated that GVCs account for 60% of international trade and employ
17 million people.
In a GVC, the stages involved in producing a single product are dispersed across
countries, particularly those where the right skills and input requirements are available
at a competitive cost and quality. Despite the complexity of GVCs, evidence suggests
that participating in them is beneficial, although to varying degrees. The granular
division of production and task specialization enables countries to find their niches and
benefit from economies of scale and scope (Cheng et al. 2015). Baldwin and Yan
(2014) found that participating in GVCs results in significant productivity gains. They
found an 8%–9% productivity differential between GVC and non-GVC firms,
identified by their export and import transactions. GVCs help small and medium-sized
enterprises get into global markets, particularly by finding their niches (UNCTAD
2010). Industrialization in developing countries can make it possible for them to
participate in GVCs by obviating the need to build in-house production capacity.
Although emerging economies are fairly integrated in supply chains, there are
concerns that GVCs tend to favor advanced economies. This argument stems from the
limited ability of developing economies to upgrade to higher value-added activities.
Milberg (2004), in his theory of endogenous asymmetric market structure in GVCs,
posited that lead firms retain higher value-added activities (characterized as
oligopolistic with high entry barriers) in their home countries, while offshoring
lower value-added activities (highly competitive with low barriers to entry). This
pattern can be seen in Apple products, with lower value-adding activities largely
outsourced to East Asia, while higher value-adding activities in research and
development (R&D), product design, and marketing are done from their United States
headquarters.1 Relatedly, Heintz (2006) developed a model of the distributive
dynamics of GVCs that shows an unequal distribution of gains from global production
networks.
1This assessment is based on a study by Linden, Kraemer, and Dedrick (2009) that examined which
countries capture more value in the case of Apple’s iPod.
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Moving up the GVC—for example, by capturing more technologically
sophisticated functions—is a challenge for many developing economies. It is not,
however, an insurmountable one, as some developing economies in Asia have shown.
Firms in Taipei,China have made the transition from largely simple production tasks in
the 1980s to the more complex tasks of product design and logistics (Kishimoto 2004).
This upgrading can also be seen in the People’s Republic of China (PRC) (Kee and
Tang 2016, Organisation for Economic Co-operation and Development [OECD]
2007). Taglioni and Winkler (2016) observed that Southeast Asia is increasingly
becoming a hub for knowledge-intensive goods and services.
Much of the literature studying how GVC players from developing countries can
move up the value chain involves the crucial role of innovation. Upgrading the
technological sophistication of production or offering knowledge-intensive activities
within a value chain requires an enabling innovation ecosystem. For a GVC, this
should “shape the ability of actors to master and use existing technologies to carry out
routine tasks and to create new products and processes” (Sampath and Vallejo 2018,
486). A conducive innovation ecosystem for GVCs should involve improvements in
education and supportive policies that foster technological capabilities (e.g., sustained
public R&D and building domestic knowledge and technological absorptive capacity).
This paper aims to characterize an enabling innovation ecosystem that
corresponds to participation at the higher end of GVCs. It is important to understand
the intersection of different levels of GVC upgrading with several dimensions of the
innovation ecosystem. From a policy perspective, more value can be created from
GVCs through upgrading (Humphrey and Schmitz 2002, Kowalski et al. 2015).
Empirical exercises examine the key factors that affect how economies benefit
from GVCs, shedding light on which element of an innovation ecosystem enables
developing economies to catch up and thereby capture a higher portion of the value
chain. These exercises are conducted on both global and developing Asia samples.2
The extent of participation in the higher end of the value chain is derived from the
Asian Development Bank (ADB)’s multiregional input–output based GVC statistics.3
Among GVC components, we focus on the foreign value-added embedded in
intermediate exports, the increasing share of which suggests that industries are
2Developing Asia comprises Bangladesh; Bhutan; Brunei Darussalam; Cambodia; Fiji; Hong Kong,
China; India; Indonesia; Kazakhstan; the Kyrgyz Republic; the Lao People’s Democratic Republic;
Malaysia; Maldives; Mongolia; Nepal; Pakistan; the Philippines; the PRC; the Republic of Korea;
Singapore; Sri Lanka; Taipei,China; Thailand; and Viet Nam.
3These provide a detailed picture of an economy through which mutual interrelationships among
producers and consumers in the economy can be systematically quantified (the tables are discussed further
in a later section). Input–output tables have become a widely used tool for national accounting, economic
planning, and policy analysis.
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126 ASIAN DEVELOPMENT REVIEW
upgrading—that is, shifting more toward producing products required in other
economies’ final production or exports. For the innovation ecosystem, the paper uses
the Global Innovation Index indicators that assess the innovation performance in both
input and output dimensions, and the economy- and firm-level innovation capacity
indicators from the World Economic Forum’s Global Competitiveness Report.4
The paper generally finds that the transition to higher levels of GVC upgrading
requires an enabling innovation ecosystem alongside economy-specific endowments, a
mix of supportive policies in broad areas of infrastructure and institutions, and other
enabling factors. Policies that promote innovation are needed to create this ecosystem.
Efforts should focus on increasing the scale in innovation inputs during the transition
from a low level of upgrading to a medium one, allowing firms to improve in many
areas of their capacity to innovate. To move higher up a global value chain, the design
of innovation policies should gradually emphasize the production of technology,
knowledge, and creative outputs.
The rest of the paper is structured as follows. Section II discusses the intersection
of GVCs and innovation, focusing on the trends observed in developing Asia’s
participation in GVCs. Section III elaborates on the empirical analysis determining the
vital factors that affect how economies benefit from GVCs, with a particular focus on
which elements of the innovation ecosystem help economies capture more value from
GVCs, specifically in developing Asia. Section IV discusses the policy implications of
the paper’s findings.
II.
Upgrading Value Chain Participation through Innovation
GVCs are a prominent feature of international trade. Policies that try to leverage
economic growth and development from international production networks have thus
far been informed by efforts to better understand how these networks operate. This
takes its cue from the notion that this pattern of trade not only promotes the production
of exports of goods and services but also facilitates the movement of know-how,
technologies, and human capital.
With this in mind, governments in developing economies are trying to move up
the value chain by promoting the production of high value-added exports in GVCs.
4The Global Innovation Index measures innovation performance not only in human capital and
research but also in institutions, infrastructure, and market and business sophistication, among other areas.
It also measures the ability to produce knowledge, technology, and creative outputs. The index is compiled
by Cornell University, INSEAD, and the World Intellectual Property Organization.
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Upgrading a GVC can take on different forms, such as producing more sophisticated
products, improving efficiency and effectiveness, capturing higher value-adding
functions, and getting into new value chains specializing in higher value-added
activities (Humphrey and Schmitz 2002). The literature seems to have reached
consensus on the crucial role that knowledge, technology, and advanced skills play in
the upgrading process (Taglioni and Winkler 2016, UNESCAP 2015). Kaplinsky
(2015), however, emphasized that the ability to upgrade will necessarily vary
depending on domestic capabilities and the size of an economy and its resource
endowments, among other factors.
Establishing an enabling innovation ecosystem clearly merits policy attention,
especially for emerging economies trying to capture the better deals that participating
in a GVC can offer. To do this, it is essential to identify the elements of an innovation
ecosystem and how they interact with varying degrees of participation along the value
chain. Here, the coevolution of GVCs and innovation ecosystem is seen as invaluable,
particularly when this seeks to foster a synchronized system of innovation-building
activities in which participating in a value chain results in the acquisition of knowledge
and skills, and advanced technologies and processes.
Several empirical studies provide evidence that a strong innovation ecosystem
improves GVC participation in developing economies. Sampath and Vallejo (2018)
found that the ability of emerging economies to technologically diversify across export
categories—and hence to be able to participate in GVCs—is linked to a higher-level
innovation ecosystem in the form of public R&D investments, scientific publications,
intellectual property payments, and registered patents by residents. The study posited
that such a system forces firms in emerging economies to leverage knowledge flows
within and outside GVCs, and to build export capacity and diversify horizontally into
new GVCs. In a similar vein, Guan (2018) documented how innovation through
investment in R&D helped a chemical company in Singapore to offer new and
improved products amid stiff competition. The PRC’s rise in the global photovoltaic
industry is largely due to how public research institutions caught up with industry
leaders in critical technological areas (de la Tour, Glachant, and Ménière 2011).
Many developing Asian economies have made strides in improving their
innovation performance, as shown by their ranking on the Global Innovation Index
(Figure 1). The PRC experienced the largest increase in the index between 2008 and
2018, allowing it to nearly match the index values of the region’s innovation
powerhouses, especially the Republic of Korea and Singapore. Also performing well
on the index during the review period are India, Indonesia, Malaysia, the Philippines,
and Thailand. Among economies with lower levels of innovation on the index are
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Figure 1. Global Innovation Index, 2008 and 2018
PRC ¼ People’s Republic of China.
Note: The dashed lines are the calculated median of the Global Innovation Index in 2008 and 2018.
Source: Authors’ calculation using data from the Global Innovation Index compiled by Cornell University,
INSEAD, and the World Intellectual Property Organization.
Bangladesh and the Kyrgyz Republic. Economies in this group tend to have weak
human capital development and R&D. Per data from the United Nations Educational,
Scientific and Cultural Organization, Central Asian economies generally spend less
than 0.2% of gross domestic product (GDP) on R&D and have fewer than 1,000 R&D
personnel per 1 million people. In comparison, the PRC’s R&D expenditure is 2.2% of
GDP, and it has 3,000 R&D personnel per 1 million people.
III. Enabling Factors for Developing Asia to Capture More of the
Global Value Chain
Strengthening the participation of economies in GVCs by upgrading to higher
value-added activities is essential for maximizing the positive spillovers from value
chains—and as noted earlier, this can be achieved in different ways.
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This paper discusses GVC upgrading broadly, based on a multiregional input–
output framework that identifies which industries produce more goods (and more
intermediate goods) for other economies, including those exported to third economies
for final goods production. ADB documents, quantifies, and characterizes GVCs in its
Multiregional Input–Output (ADB MRIO) tables. This initiative builds on the latest
World Input–Output Database, following Timmer et al. (2015), to update coverage by
adding 19 ADB member economies. The ADB MRIO tables show from which
economy each industry sources inputs from around the world and to whom each
industry’s output is sold at home and abroad, whether as inputs to downstream
industries or to final end users. Other inter-economy input–output databases include
the Eora Multiregional Input–Output database, the OECD’s Inter-Country Input–
Output Tables, and the IDE-JETRO Asian Input–Output Tables.
Applying the GVC index system by Wang, Wei, and Zhu (2018) to the ADB
MRIO tables, ADB has built a panel dataset decomposing gross exports into several
value-added terms at the bilateral-sector level.5 The value-added structure of export
transactions, each with a distinct economic interpretation, is illustrated in Figure 2.
The first four parts are the domestic value added embodied in an exporter economy’s
gross exports (final or intermediates) to a foreign economy. The first three components
are directly and indirectly absorbed by the importer economy, while the fourth
component returns to the exporter economy through its imports and is consumed at
home.
The paper focuses on components 5–8 in Figure 2, which gives the
decomposition of the vertical specialization in trade and provides summary statistics
of international production sharing that are widely used in the GVC literature (e.g.,
Hummels, Ishii, and Yi 2001; Amador and Cabral 2009). The four elements forming
the vertical specialization is an extension of the VS1 measure proposed by Hummels,
Ishii, and Yi (2001). The decomposition by Wang, Wei, and Zhu (2018) enables
examining the components within vertical specialization that represent different types
of GVC participation. The term foreign value added in final exports (FVA_FIN)
reflects participation at the lower end of value chains because it involves largely finalassembly activities. By contrast, the foreign value added in intermediate exports
5The accounting methodology in Wang, Wei, and Zhu (2018) improved the gross exports
decomposition framework in Koopman, Wang, and Wei (2014) by identifying the different types of crosscountry production sharing arrangements at much greater disaggregated levels at either the sector, bilateral,
or bilateral-sector level. The distinction made between backward and forward industrial linkages, which is
also used in Borin and Mancini (2017), enables tracing the structure of international production sharing at a
disaggregated level. The online appendixes in Wang, Wei, and Zhu (2018) detail the decomposition
methodology in an input–output framework. The ADB panel dataset covers 63 economies (including the
rest of the world) with 35 sectors for the years 2000 and from 2007 to 2018.
130 ASIAN DEVELOPMENT REVIEW
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Figure 2.
Decomposition of Gross Exports
Source: Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu. (2013), 2018. “Quantifying International Production Sharing
at the Bilateral and Sector Levels.” NBER Working Paper Series No. 19677.
(FVA_INT) signals upgrading, which we use to examine the efforts of economies to
get a bigger share of the higher end of the value chain. The growing share of this
suggests that GVC transactions involve more intermediate inputs needed by other
economies’ production. The double-counted terms (PDC) in the vertical specialization
quantify the back-and-forth trade of intermediates, which serves as the measure of the
depth of participation. The vertical specialization structure of Taipei,China exemplifies
this trend as it occupies several different positions in the global production chain,
particularly in electronics by producing both memory chips and components that
embed the chips.
A.
Deepening Cross-Economy Production Sharing in Developing Asia
Developing Asia’s comparative advantage in factor inputs, such as labor and
natural resources, initially propelled the region’s crucial role in GVCs, with the PRC
becoming the “factory of the world.” Many countries in Southeast Asia have also
embraced cross-border production-sharing arrangements, largely at the lower end of
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Figure 3.
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Global Value Chain Participation in Developing Asia, 2000 and 2018
Notes: Dots represent bilateral economy-sector level information on the share of vertical specialization to gross
exports—the VS ratio. Pacific comprises only Fiji, and Central Asia comprises only Kazakhstan and the Kyrgyz
Republic, reflecting limitations on economy coverage in the Asian Development Bank’s Multiregional Input–
Output tables.
Source: Authors’ calculation using global value chain statistics from ADB’s Key Indicators Database. https://kidb.
adb.org/themes/global-value-chains (accessed March 15, 2020).
the value chain, such as assembly. Figure 3 shows the share of vertical specialization
to gross exports (VS ratio) at the bilateral economy-sector level in developing Asia as
well as by region in 2000 and 2018. Overall, developing Asia increased its
participation in value chains, with more than 60% of bilateral economy-sector
transactions showing a higher VS ratio in 2018 compared with 2000, driven primarily
by GVC transactions with partners from developing Asia. Only 59.1% of the GVC
transactions with economies outside the region showed a higher VS ratio in 2018 than
2000. This suggests that the region’s value chain participation involves more
intraregional transactions, which is consistent with observations that much of the
value-added distribution in GVCs tends to be within regional blocs, including outside
Asia (Baldwin 2013, De Backer and Yamano 2012).
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132 ASIAN DEVELOPMENT REVIEW
Regional integration lowers trade-related costs. The closer distance between
economies within developing Asia reduces transaction costs from transporting goods
and delivering services. This is notwithstanding sharing similarities in other aspects,
such as culture, language, and practices, as well as infrastructure connectivity,
movement of people and capital, and institutional integration. All these features make
cross-border production and trading more efficient. It seems the benefits of more
comprehensive regional integration come from the ability of these relationships to
deepen the involvement of economies in regional value chains, thereby expanding
their role in cross-border production sharing.
Figure 3 shows the striking variations in the extent of participation in crossborder production sharing across regions in developing Asia. The more mature GVC
players in East Asia and Southeast Asia have shown a stable expansion in GVC
activities over time. This pattern highlights the role of knowledge spillovers and
technology diffusion from investments from multinational firms. Stronger economic
ties among member states of the Association of Southeast Asian Nations could have
also played a role. By comparison, South Asia is less integrated; this is partly because
of high intraregional trade costs (Johns and Mclinden 2016) and the vertical
specialization content of exports in the Pacific and Central Asia remaining limited.
Central Asia’s GVC participation could be enhanced by strengthening the Central Asia
Regional Economic Cooperation Program and similar initiatives.
These observations coincide with the general pattern evidenced in the extent of
regional integration across different subregions in Figure 4. Southeast Asian and East
Asian economies are highly integrated, while Central Asian economies have a lower
level of integration. Integration among the member states of the Association of
Southeast Asian Nations is particularly strong in trade and investment, and the
movement of people. Institutional and social integration, alongside infrastructure and
connectivity cooperation, are boosting integration within East Asia. Regional
integration in Central Asia is being held back by weak integration in finance, trade
and investment, and the movement of people. Greater economic integration brings
substantial benefits through efficiency gains, increases in market size, and cost-sharing
in regional production and cross-border infrastructure (ADB 2019).
B.
Upgrading Concentrated in a Few Mature Global Value Chain
Players in Developing Asia
To determine whether GVC participation in developing Asia is at the lower or
higher end of value chains, we examine the pattern of the FVA_INT as a component of
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Figure 4. Asia-Pacific Regional Cooperation and Integration Index, 2007–2017
Notes: In this figure, Southeast Asia comprises Cambodia, Indonesia, the Lao People’s Democratic Republic,
Malaysia, the Philippines, Singapore, Thailand, and Viet Nam. Central Asia comprises Georgia, Kazakhstan, and
the Kyrgyz Republic. East Asia comprises the People’s Republic of China; Hong Kong, China; Japan; the
Republic of Korea; and Mongolia. South Asia comprises Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Asia
and the Pacific also includes Australia and New Zealand. The values refer to the index in 2017.
Sources: Asian Development Bank, Asia Regional Integration Center. Asia-Pacific Regional Cooperation and
Integration Index Database. https://aric.adb.org/database/arcii (accessed July 15, 2020).
vertical specialization. A growing share of this component suggests that industries are
shifting more toward producing products required in other economies’ final production
or exports. In this complex type of GVC, defined as those involving value added
crossing borders more than once, it can be assumed that industries are able to capture
more value by offering more sophisticated products or services.
Table 1’s all sectors column shows the level of value chain upgrading of
developing Asian economies classified by the share of foreign value added in
intermediate exports to vertical specialization, or FVA_INT share. From the
information at the bilateral economy-sector level, we calculated the average FVA_INT
share by economy and year (weighted by the size of the bilateral economy-sector VS
ratio). The levels of upgrading are then derived by classifying average FVA_INT share
in the first and second quintile as “low,” the third and fourth quintile as “medium,” and
the fifth quintile as “high.” We find that many economies in developing Asia have a
low level of GVC upgrading, including Bangladesh, Cambodia, and Nepal. Indonesia,
India, and Viet Nam have a medium level of upgrading. East Asia—particularly the
PRC; the Republic of Korea; and Taipei,China—shows higher levels of upgrading,
with the PRC moving from medium in 2000 to high in 2007.
134 ASIAN DEVELOPMENT REVIEW
Table 1.
Global Value Chain Upgrading Levels in Developing Asia, 2000, 2007,
and 2018
All Sectors
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Economy
Bangladesh
Brunei Darussalam
Bhutan
Fiji
Hong Kong, China
Kazakhstan
Kyrgyz Republic
Cambodia
Lao PDR
Sri Lanka
Maldives
Mongolia
Nepal
Pakistan
Philippines
Indonesia
India
Malaysia
Singapore
Thailand
Viet Nam
PRC
Republic of Korea
Taipei,China
Innovative Sectors
2000
2007
2018
2000
2007
2018
Low
Low
Low
Low
Medium
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
High
High
Medium
Low
Medium
High
High
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
Medium
Medium
Medium
Medium
Low
High
High
High
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
Medium
Medium
Medium
Medium
High
High
High
Low
Low
Low
Low
Medium
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
Medium
High
High
Medium
Low
Medium
High
High
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
Medium
Medium
Medium
Medium
Low
High
High
High
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Medium
Medium
Medium
Medium
Medium
Medium
Medium
High
High
High
Lao PDR ¼ Lao People’s Democratic Republic, PRC ¼ People’s Republic of China.
Source: Authors’ calculation using global value chain statistics from the Asian
Development Bank’s Key Indicators Database. https://kidb.adb.org/themes/global-valuechains (accessed March 15, 2020).
In East Asia, the Republic of Korea and Taipei,China have long been key players
in exports of sophisticated parts and components to international production chains.
The PRC is building its innovation capacity and—already at a high level of upgrading
—is steadily moving further up the GVC. Taglioni and Winkler (2016) observe that
Southeast Asia is increasingly becoming a hub for knowledge-intensive goods and
services. Interestingly, when looking only at upgrading in Table 1’s innovative sectors
column, the Philippines can be classified at the medium level of upgrading, a result of
its gradual shift toward advanced manufacturing and services from limited
manufacturing (World Bank 2020).6 Belderbos et al. (2016) documented the growing
6For
the definition of innovative sectors, see Figure 6.
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number of firms deciding to offshore R&D and innovative activities. Among the cities
in Asia benefiting from this trend are Bangalore (now Bengaluru), Beijing, Shanghai,
and Singapore. But many economies in the region, particularly low-income ones,
either have limited participation in GVCs or are at the low end of the value chain,
specializing in tasks that rely more on unskilled labor. If this persists, these economies
will have fewer opportunities to acquire the technological know-how and skills needed
to move up the GVC.
Later in the empirical section, the paper assesses how value chain upgrading also
varies depending on how economies are positioned in their production networks.
Using the ADB MRIO tables and following the production activity decomposition
framework in Wang et al. (2017), ADB has built a panel dataset of two GVC
participation indices in both simple and complex value chains, with the latter involving
factor content crossing a border at least twice.7 The first index relates to the domestic
value added generated from an economy-sector pair’s GVC activities through
downstream firms (backward-linkage participation index). The second index refers to
the value added that is involved in GVC activities through the upstream firms
(forward-linkage participation index). Intuitively, one can identify whether participation
in production networks involves more upstream or downstream activities.
Figure 5 shows the level of backward and forward GVC participation in
developing Asia. A higher degree of forward participation relative to backward
participation implies active involvement in upstream production activities in
production networks. GVC transactions in developing Asia are primarily in
downstream activities, particularly those involved in complex GVCs. In 2017, only
a third of economy-sector pairs had higher forward participation relative to backward
participation, largely from GVC activities from Malaysia, the PRC, the Republic of
Korea, and Thailand, and mainly from service sectors relating to finance, professional
activities, and trade.
Although the calculated ratio of forward to backward participation is relatively
lower in Hong Kong, China; Singapore; Taipei,China; and Viet Nam; their individual
forward and backward participation indices are above the median indices in the region.
This means that these economies have higher value-added content in global production
networks. Relatedly, there are many manufacturing activities that have a low ratio of
forward to backward participation, but still have above-median indices of both forward
and backward participation. This is particularly the case for R&D intensive
manufacturing activities, such as basic metals, chemicals, electrical equipment, and
7Detailed
mathematical derivations are in the appendix of Wang et al. (2017).
136 ASIAN DEVELOPMENT REVIEW
Figure 5.
Backward and Forward Global Value Chain Participation in Developing Asia
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(a)
(b)
(c)
GVC ¼ global value chain.
Note: Numbers refer to the percentage share of sector value added.
Source: Authors’ calculation using GVC statistics from the Asian Development Bank’s Key Indicators Database.
https://kidb.adb.org/themes/global-value-chains (accessed March 15, 2020).
machinery. It is also observed in transport services since these are critical for a wellfunctioning, cross-economy production chain.
IV. Empirical Analysis
This section details the empirical exercises to identify the essential factors that
influence how economies capture activities that create more value from GVCs. To
enable economies to deepen their participation at the higher end of GVCs, trade and
investment policies should foster openness and be complemented by policies
promoting well-functioning markets, such as fair competition (OECD 2007).
Van der Marel (2015) examines three major factors that affect the position of
economies in a GVC. The first is the importance of structural forces, such as domestic
market and economic size, and factor endowments. These include human, physical,
information, and communication-technology-related capital; knowledge capital;
internet penetration; and the rule of law. The second factor is the trade and regulatory
regimes that are vital for an economy’s participation in a GVC and, ultimately, its
capabilities to move up the chain. The third is the ease of conducting business
transactions and the extent to which regulatory barriers to trade-related services affect
the decisions of multinational companies on where to locate their innovation and
production facilities. Policies related to foreign direct investment, labor market
regulation, availability of credit, and competition also play important roles.
It is well-established in the GVC literature that moving up the value chain
requires a high level of sophistication, and technological and innovation capacity. The
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ENABLING
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PARTICIPATION 137
level of knowledge and technology in an economy’s production and exports are
determining factors for its position in a GVC (Taglioni and Winkler 2016). The
capacity to innovate will create opportunities to derive more value from production. It
is noteworthy that the PRC’s R&D is already higher than that of many advanced
economies and is on its way to surpassing R&D spending in the United States. To
sustain a culture of innovation and creativity, economies should equip their workers
with skills that can handle higher value-added activities. To stimulate innovation,
economies need to protect intellectual property rights and complement this with
practices, tools, and networks that increase access to knowledge. It requires a great
deal of innovation to be able to participate in a GVC focused on innovative activities
(World Bank 2020).
A.
Data and Estimating Equation
Using the calculated levels of upgrading based on the ratio of FVA_INT to
vertical specialization—low, medium, and high—we examine the various factors that
could help economies move toward a higher value-adding share of GVCs and, in
doing so, leverage these networks to advance economic development. The data are
from the ADB panel dataset on GVC-related statistics based on ADB MRIO tables
discussed in previous sections.
Given the three possible outcomes (low, medium, high) of upgrading, we use a
multinomial logit model to estimate the log-odds, log iJij , of economy i to move
upward (medium and high) relative to being in the lower end of the value chain, while
considering various factors, most importantly variations in several dimensions of
innovation. Assuming that the log-odds of each outcome follow a linear model, we
estimate the following:
ηij ¼ log
ij
¼ αj þ x 0it βj þ ijt ,
iJ
ð1Þ
where αj is a constant and βj is a vector of coefficients for j ¼ low, medium, or high,
with low or medium as the base category over the upgrading outcomes to consider
both leveling up from low to medium and from medium to high. The error term, ijt , is
assumed independent and identically distributed across all outcomes j.
The vector of independent variables, x 0it , based on the literature cited, are
essential factors for economies moving higher up the value chain that need to be
controlled to draw unbiased estimates on the influence of innovation variables. The log
of real GDP per capita tries to purge the effect of varying domestic market size and
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138 ASIAN DEVELOPMENT REVIEW
purchasing power. The variable also attempts to isolate expectations that higherincome economies tend to be at the higher end of value chains. The other indicator is
domestic market size index from the World Economic Forum’s Global Competitiveness Report.8
Several infrastructure variables with their corresponding quality also form part of
the equation; this is because good logistics and soft infrastructure facilitate the efficient
movement of goods and delivery of services. For trade logistics infrastructure, we use
the World Bank’s Logistics Performance Index.9 This examines the efficiency of
customs and border management clearance; the quality of trade and transport
infrastructure; the ease of arranging competitively priced shipments; the competence
and quality of logistics services (trucking, forwarding, and customs brokerage); the
ability to track and trace consignments; and the timeliness of shipments. The soft
infrastructure variable, which includes the intensive use of information and
communication technology, is captured in the innovation indicators discussed in the
rest of this section.
Selected policy-related variables also form part of the specification to investigate
how structural forces influence the participation of economies at the higher end of
GVCs. These include policies to promote openness to trade and investment,
institutional quality, and innovation. The ease of trading and investing overseas are
obviously critical elements for GVCs. We use information on the prevalence of trade
barriers from the Global Competitiveness Report and foreign direct investment (net
inflows, percentage of GDP) from the World Bank’s World Development Indicators
database.10 An equally important element for GVCs is the quality of domestic public
sector institutions. Businesses participating in GVCs tend to deal with quite a few
government procedures for trading and other activities. Here, governments should
ensure these transactions can be carried out smoothly to avoid backlogs. For this
aspect, we use the intellectual property protection and strength of investor protection
indicators in the Global Competitiveness Report alongside the number of procedures
involved in registering property from the World Bank’s Doing Business project.11
8The size of the domestic market is calculated as the natural log of the sum of the GDP valued at
purchasing power parity (PPP) plus the total value (PPP estimates) of imports of goods and services, minus
the total value (PPP estimates) of exports of goods and services. Data are then normalized on a 1–7 scale.
9World Bank. Logistics Performance Index. https://lpi.worldbank.org/ (accessed March 15, 2020).
10World Bank. World Development Indicators. https://databank.worldbank.org/source/worlddevelopment-indicators (accessed March 15, 2020).
11World Bank. Doing Business. https://www.doingbusiness.org/en/doingbusiness (accessed March
15, 2020).
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ENABLING
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To achieve the ultimate objective of this paper—characterizing the nexus of an
enabling innovation ecosystem and participation at the higher end of GVCs—several
dimensions forming part of the innovation ecosystem are added. The econometric
specification uses the Global Innovation Index. In separate exercises, we distinguish
the effect between innovation input (i.e., institutions, human capital and research,
infrastructure, market sophistication, and business sophistication) and innovation
output, which refers to the ability to produce knowledge, technology, and creative
outputs.
To provide more insights, several elements of innovation capacity from the
Global Competitiveness Report are also used, categorized here as national and firm
level. National innovation capacity includes the availability of scientists and engineers,
university–industry collaboration in R&D, the quality of scientific research
institutions, and the availability of latest technologies. Firm-level innovation capacity
includes the extent of marketing, company spending on R&D, the sophistication of
production processes, the quantity and quality of local suppliers, and the absorption of
technology by companies.
The baseline equation (1) is estimated using pooled multinomial logistic
regression with robust standard errors to account for possible correlation across
years. Fixed effects panel estimation is not carried out due to limited variation in the
outcome variable across the observations. The baseline cross-economy panel data have
an unbalanced structure because of the availability of information. The analysis covers
55 economies—18 from developing Asia—over the period 2009–2018.
B.
Summary Statistics
Table 2 summarizes the stylized facts of the varying characteristics of economies
at different levels of upgrading. It is clear from the table how upgrading in a GVC
differs with the level of development. Real GDP per capita is highest among
economies at the higher end of the value chain. It is therefore essential to include real
GDP per capita as one of the controlling factors. Economies with a better performance
in trade logistics—that is, those that can efficiently move goods and deliver services—
are classified under the economies with a high level of upgrading. This is particularly
true in all dimensions of logistics performance. Better telecommunication
infrastructure, especially fixed broadband, is apparent, on average, among economies
with a high level of upgrading. Trade barriers and the enforcement of investor
protection also vary with the level of GVC upgrading.
140 ASIAN DEVELOPMENT REVIEW
Table 2.
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Variable
Summary Statistics of Explanatory Variables
No. of
Observations
(a) Economies with a low level of GVC upgrading
Real GDP per capita (log)
181
Domestic market size index
181
Logistics Performance Index,
181
overall
Customs
181
Infrastructure
181
Ease of arranging shipments
181
Quality of logistics services
181
Tracking and tracing
181
Timeliness
181
Fixed-broadband subscriptions
181
Mobile-cellular telephone
181
subscriptions
Number of procedures to register
181
property
Strength of investor protection,
181
0–10 (best)
FDI, net inflows (% of GDP)
181
Intellectual property protection,
181
1–7 (best)
Prevalence of trade barriers, 1–7
181
(best)
Mean
Std Dev
Minimum
Maximum
8.7
3.4
2.8
1.3
0.8
0.5
6.3
1.9
0.0
10.6
5.0
3.9
2.6
2.6
2.8
2.7
2.8
3.2
15.0
115.6
0.5
0.5
0.5
0.5
0.5
0.6
12.6
30.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
20.8
3.8
4.0
3.8
3.9
3.9
4.1
43.7
270.0
6.1
2.5
1.0
11.0
5.8
1.0
3.0
8.0
10.3
3.6
32.3
0.8
–37.2
2.0
280.1
5.9
4.5
0.5
3.6
5.8
1.2
0.8
0.4
7.1
2.5
2.5
11.6
6.4
4.2
0.5
0.5
0.3
0.4
0.4
0.3
12.2
29.5
2.0
2.3
2.5
2.5
2.5
3.0
0.6
43.1
4.2
4.3
4.2
4.3
4.4
4.8
46.4
251.8
2.6
1.0
14.0
1.5
2.7
9.3
12.9
–41.5
80.8
(b) Economies with a medium level of GVC upgrading
Real GDP per capita (log)
250
9.9
Domestic market size index
250
4.6
Logistics Performance Index,
250
3.5
overall
Customs
250
3.3
Infrastructure
250
3.5
Ease of arranging shipments
250
3.4
Quality of logistics services
250
3.5
Tracking and tracing
250
3.6
Timeliness
250
3.9
Fixed-broadband subscriptions
250
22.7
Mobile-cellular telephone
250
126.6
subscriptions
Number of procedures to register
250
5.2
property
Strength of investor protection,
250
6.0
0–10 (best)
FDI, net inflows (% of GDP)
250
6.3
Continued.
ENABLING
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PARTICIPATION 141
Table 2. Continued.
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Variable
Intellectual property protection,
1–7 (best)
Prevalence of trade barriers, 1–7
(best)
No. of
Observations
Mean
Std Dev
Minimum
Maximum
250
4.7
1.2
2.5
6.6
250
4.8
0.7
3.4
6.7
10.4
5.6
3.8
0.7
0.8
0.3
8.3
3.9
3.1
11.3
7.0
4.2
3.6
3.9
3.6
3.9
3.9
4.1
31.3
109.3
0.3
0.3
0.2
0.3
0.3
0.2
8.8
20.6
2.7
2.9
3.0
3.0
3.0
3.4
7.6
54.9
4.1
4.4
4.1
4.3
4.3
4.5
44.8
162.3
6.0
1.4
4.0
9.0
6.3
1.2
4.3
8.7
4.2
5.2
9.2
0.8
26.2
3.4
41.9
6.5
4.7
0.5
3.8
5.9
(c) Economies with a high level of GVC upgrading
Real GDP per capita (log)
113
Domestic market size index
113
Logistics Performance Index,
113
overall
Customs
113
Infrastructure
113
Ease of arranging shipments
113
Quality of logistics services
113
Tracking and tracing
113
Timeliness
113
Fixed-broadband subscriptions
113
Mobile-cellular telephone
113
subscriptions
Number of procedures to register
113
property
Strength of investor protection,
113
0–10 (best)
FDI, net inflows (% of GDP)
113
Intellectual property protection,
113
1–7 (best)
Prevalence of trade barriers, 1–7
113
(best)
FDI ¼ foreign direct investment, GDP ¼ gross domestic product, GVC ¼ global value chain.
Source: Authors’ calculation.
Table 3 gives the summary statistics of the several elements of an innovation
ecosystem. Like the other explanatory variables, economies at different levels of
upgrading also vary in several dimensions of innovation, with those at the higher end
of value chains showing advances in many areas of the innovation ecosystem. The
Global Innovation Index, for example, is higher among economies classified as having
a high level of GVC upgrading. This observation holds for different innovation input
and output pillars. It also holds for innovative capacity, both at the national and firm
level.
142 ASIAN DEVELOPMENT REVIEW
Table 3.
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Variable
Summary Statistics of Innovation Ecosystem Indicators
No. of
Observations
(a) Economies with a low level of GVC upgrading
Global Innovation Index
181
Innovation input
181
Innovation output
181
Capacity for innovation, 1–7 (best)
181
National innovation capacity
Availability of scientists and
181
engineers
University–industry
181
collaboration in R&D
Quality of scientific research
181
institutions
Availability of latest
181
technologies
Firm-level innovation capacity
Extent of marketing
181
Production process
181
sophistication
Local supplier quantity
181
Local supplier quality
181
Company spending on R&D
181
Firm-level technology
181
absorption
Mean
Std Dev
Minimum
Maximum
36.2
42.8
29.7
3.5
8.7
9.2
9.4
0.7
21.1
23.7
12.1
2.0
55.5
66.7
53.3
5.0
4.0
0.5
2.8
5.4
3.4
0.6
2.0
4.9
3.7
0.8
2.0
5.4
4.8
0.7
3.3
6.3
4.1
3.6
0.5
0.6
2.6
2.3
5.3
5.4
4.5
4.4
3.1
4.6
0.4
0.5
0.5
0.5
3.5
3.3
1.8
3.4
5.5
5.4
4.4
5.6
10.4
11.5
10.4
0.9
27.5
30.6
18.5
2.7
68.4
74.9
68.6
6.2
0.6
3.3
6.3
0.8
2.9
6.0
0.8
3.2
6.6
0.8
3.6
6.9
0.6
0.9
3.2
3.0
6.2
6.5
0.5
0.6
0.9
3.4
3.6
2.7
5.9
6.4
6.1
(b) Economies with a medium level of GVC upgrading
Global Innovation Index
250
47.6
Innovation input
250
54.5
Innovation output
250
40.7
Capacity for innovation, 1–7 (best)
250
4.3
National innovation capacity
Availability of scientists and
250
4.6
engineers
University–industry
250
4.4
collaboration in R&D
Quality of scientific research
250
4.8
institutions
Availability of latest
250
5.6
technologies
Firm-level innovation capacity
Extent of marketing
250
4.9
Production process
250
4.9
sophistication
Local supplier quantity
250
5.0
Local supplier quality
250
5.1
Company spending on R&D
250
4.1
Continued.
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Table 3. Continued.
Variable
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Firm-level technology
absorption
No. of
Observations
Mean
Std Dev
Minimum
Maximum
250
5.3
0.6
3.4
6.5
53.1
59.8
46.4
4.9
6.4
6.9
7.3
0.6
34.6
42.2
26.4
3.1
63.4
70.8
60.9
6.0
4.9
0.5
4.0
5.9
4.9
0.7
3.1
5.9
5.4
0.7
3.4
6.4
5.9
0.7
4.2
6.6
5.3
5.5
0.6
0.7
4.3
3.7
6.5
6.6
5.4
5.5
4.7
5.5
0.4
0.5
0.7
0.5
4.7
4.3
3.1
4.2
6.4
6.4
6.0
6.4
(c) Economies with a high level of GVC upgrading
Global Innovation Index
113
Innovation input
113
Innovation output
113
Capacity for innovation, 1–7
113
(best)
National innovation capacity
Availability of scientists and
113
engineers
University–industry collaboration
113
in R&D
Quality of scientific research
113
institutions
Availability of latest technologies
113
Firm-level innovation capacity
Extent of marketing
113
Production process
113
sophistication
Local supplier quantity
113
Local supplier quality
113
Company spending on R&D
113
Firm-level technology absorption
113
GVC ¼ global value chain, R&D ¼ research and development.
Source: Authors’ calculation.
V.
Empirical Findings
Table 4 reports the coefficient estimates of the baseline multinomial logistic
regression given in equation (1). The estimates generally support the stylized facts in
earlier discussions. Column 1 shows how the factors of domestic market size, trade
logistics infrastructure, and regulatory quality influence the transition of economies
from the lower level of upgrading to the medium level. This is done by estimating the
baseline equation with the low level of upgrading as the reference category. The roles
of market size, quality of trade logistics, and a favorable environment for foreign
investment and property registration for moving up a GVC are apparent. It is
interesting to note that the strength of investor protection in an economy has a negative
coefficient, which poses a counterintuitive interpretation. This could partly be
explained by the fact that foreign investors do not have homogeneous motives when
144 ASIAN DEVELOPMENT REVIEW
Table 4.
Factors Influencing the Transition to a Higher Level of the Value Chain
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Low to Medium
Level of Upgrading
Variable
Real GDP per capita (log)
Domestic market size index
Logistics Performance Index
Number of procedures in property
registration
Strength of investor protection
FDI, net inflows (% of GDP)
Intellectual property protection
Prevalence of trade barriers
Global Innovation Index
Constant
Observations
Pseudo R 2
Log likelihood
All
Economies
(1)
Developing
Asia
(2)
Medium to High
Level of Upgrading
All
Economies
(3)
Developing
Asia
(4)
0.066
(0.259)
4.178***
(0.634)
4.881***
(1.232)
0.325**
(0.145)
0.563***
(0.119)
0.008*
(0.005)
0.246
(0.382)
0.305
(0.568)
0.223***
(0.055)
4.463**
(2.068)
11.077***
(3.516)
7.174***
(2.403)
1.912**
(0.754)
1.003**
(0.400)
0.219***
(0.074)
1.250
(0.802)
2.176***
(0.743)
0.797***
(0.289)
1.153***
(0.318)
3.013***
(0.360)
4.824***
(1.828)
0.282***
(0.085)
0.068
(0.116)
0.012
(0.015)
0.326
(0.314)
0.076
(0.331)
0.002
(0.038)
14.002***
(0.841)
19.380***
(0.732)
25.309***
(5.835)
1.708***
(0.537)
6.724***
(0.637)
0.953***
(0.062)
6.650***
(0.972)
5.730***
(1.167)
2.097***
(0.165)
34.556***
(6.334)
41.268***
(12.545)
44.971***
(5.852)
184.175***
(14.876)
544
0.705
168.3
181
0.951
8.506
544
0.705
168.3
181
0.951
8.506
FDI ¼ foreign direct investment, GDP ¼ gross domestic product.
Notes: Robust standard errors in parentheses. ***p < 0:01, **p < 0:05, and *p < 0:1.
Source: Authors’ calculation.
investing (e.g., priority may be given to other essential elements such as market
access). Similarities can be found in examining only economies in developing Asia,
with the exception of a significant positive influence for strengthening investor
protection (column 2). In both samples, economies at a low level of upgrading need to
improve their innovation ecosystems so they can move into the medium level.
Columns 3 and 4 are the empirical results of an exercise showing how
economies in the medium level of upgrading can potentially transition to a high
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ENABLING
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PARTICIPATION 145
level. This exercise necessitates using medium upgrading as the reference category
in estimating equation (1). The positive influence of market access and the quality of
logistics infrastructure is clear. But economies in developing Asia at the medium
level of upgrading seem to have reached gradual improvement in trade logistics,
causing a negative coefficient on the Logistics Performance Index in column 4. In
contrast to the transition from low to medium, the coefficient of property registration
becomes positive, while strength in investor protection is ambiguous. This can also
be explained by the differing motives of investors. Based on the results, marketseeking seems at play in GVC upgrading, which puts a priority on the size and
purchasing power of the domestic market and efficiency-seeking motives. In
developing Asia, the transition to a high level of upgrading from a medium one
requires the elimination of nontariff barriers that limit the entry of imported
intermediates. And, particularly in developing Asia, enhancing innovation
performance earns more weight in the transition from a medium to high level of
upgrading. Although, it should be noted that the innovation ecosystem shows an
ambiguous result in the global sample. As the results in Tables 5–8 show, this is
because of the differing needs or focus of each upgrading in terms of innovation. To
avoid potential collinearity resulting in biased estimates, note that each coefficient
from Tables 5–8 is derived by estimating equation (1) with all the baseline control
variables in separate regressions for each innovation variable, except for innovation
input and output, which are jointly added.
Table 5 shows that the ability of economies to produce knowledge and
technology, and creative outputs and content, is essential for capturing the higher end
of a value chain. From a policy perspective, while it is important to increase the scale
of investments in innovation inputs, policy makers should be aware of the need to
produce knowledge and creative outputs. It is the case, however, that the transition
from a low to medium level of upgrading requires a sizable effort to improve
innovation inputs. To move to the higher end of a value chain, especially for firms at
the medium level, innovation efficiency, as Table 5 shows, should be the focus.
Table 5 also shows the significant heterogeneity in the mix of innovation
components that could potentially facilitate a transition to higher levels of GVC
upgrading. It is also apparent that upgrading can be different in the developing Asian
context, with other areas of innovation capacity influencing upgrading in stark contrast
with the global sample. In general, as observed in the global and developing Asian
samples, GVC upgrading requires strengthening the role of local suppliers, particularly
for quality. A gradual shift to sophisticated production processes and increased R&D
spending are viable ways for economies to boost their GVC upgrading efforts.
146 ASIAN DEVELOPMENT REVIEW
Table 5.
Innovation Ecosystem and Value Chain Upgrading
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Low to Medium
Level of Upgrading
Variable
Global Innovation Index, overall
Innovation input
Innovation output
Areas of innovation capacity
Availability of scientists and engineers
University–Industry collaboration in R&D
Quality of scientific research institutions
Availability of latest technologies
Extent of marketing
Production process sophistication
Local supplier quantity
Local supplier quality
Firm-level technology absorption
Company spending on R&D
Medium to High
Level of Upgrading
All
Economies
(1)
Developing
Asia
(2)
All
Economies
(3)
Developing
Asia
(4)
0.223***
(0.055)
0.021
(0.066)
0.157***
(0.058)
0.797***
(0.289)
0.723***
(0.196)
0.348*
(0.179)
0.002
(0.038)
0.117**
(0.047)
0.065*
(0.038)
2.097***
(0.165)
0.429***
(0.166)
1.374***
(0.148)
0.626
(0.462)
0.647
(0.436)
0.088
(0.423)
1.674*
(0.862)
0.966*
(0.569)
2.562***
(0.573)
0.563
(0.515)
1.747***
(0.593)
0.073
(0.630)
2.846***
(0.533)
5.296***
(1.621)
14.856***
(3.296)
11.990***
(2.412)
16.217***
(3.378)
7.198**
(2.903)
3.485
(2.829)
3.996*
(2.048)
6.108***
(2.177)
26.820***
(7.875)
0.984**
(0.449)
0.869**
(0.418)
0.742*
(0.449)
0.142
(0.510)
0.844
(0.544)
2.377***
(0.632)
0.577
(0.479)
1.857***
(0.714)
0.594
(0.416)
0.831**
(0.369)
9.779***
(1.670)
1.009
(3.763)
21.563***
(2.127)
20.301***
(2.926)
16.193***
(1.195)
21.294***
(3.394)
12.290***
(1.109)
17.327***
(1.471)
11.650***
(3.817)
R&D ¼ research and development.
Notes: Innovation input and output are jointly added in equation (1). The maximum likelihood estimation
of the baseline multinomial logistic regression model fails to converge in the empty cells. Robust standard
errors in parentheses.
***p < 0:01, **p < 0:05, and *p < 0:1.
Source: Authors’ calculation.
It is interesting to note that a huge catch-up is expected by economies in
developing Asia at the low level of GVC upgrading to the medium level. This view is
supported by estimates that an improved performance in innovation input has more
weight relative to innovation output. Making progress on all components of capacity
ENABLING
Table 6.
AN INNOVATION
ECOSYSTEM
PARTICIPATION 147
Innovation Ecosystem and Value Chain Upgrading in Research and Development
Intensive Industries
Low to Medium
Level of Upgrading
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AND
Variable
Global Innovation Index, overall
Innovation efficiency
Areas of innovation capacity
Availability of scientists and engineers
University–Industry collaboration in R&D
Quality of scientific research institutions
Availability of latest technologies
Extent of marketing
Production process sophistication
Local supplier quantity
Local supplier quality
Firm-level technology absorption
Company spending on R&D
Medium to High
Level of Upgrading
All
Economies
(1)
Developing
Asia
(2)
0.005
(0.051)
5.517***
(1.866)
1.223***
(0.431)
1.877
(5.175)
0.023
(0.039)
1.732
(1.918)
1.980***
(0.255)
30.856***
(2.868)
0.060
(0.452)
1.701***
(0.471)
0.767
(0.469)
0.182
(0.547)
1.676***
(0.539)
4.479***
(0.696)
0.235
(0.543)
1.768***
(0.574)
0.814
(0.546)
2.966***
(0.694)
1.985
(1.682)
3.787
(3.460)
3.365
(2.551)
1.803
(2.596)
1.797
(1.980)
0.080
(2.530)
0.645
(3.006)
0.764
(2.928)
0.351
(1.896)
0.291
(0.484)
0.817*
(0.429)
0.707
(0.431)
0.082
(0.508)
0.482
(0.559)
1.700***
(0.605)
0.586
(0.455)
2.624***
(0.720)
0.485
(0.397)
0.608*
(0.341)
13.031***
(1.496)
1.176
(2.645)
11.647***
(4.201)
14.536***
(1.488)
16.396***
(1.232)
21.911***
(3.145)
19.056***
(4.414)
18.807***
(2.001)
9.670**
(3.955)
All
Economies
(3)
Developing
Asia
(4)
R&D ¼ research and development.
Notes: The innovation efficiency ratio substitutes joint addition of innovation input and output in equation
(1) to overcome convergence issues. The maximum likelihood estimation of the baseline multinomial
logistic regression model fails to converge in the empty cells. Robust standard errors in parentheses.
***p < 0:01, **p < 0:05, and *p < 0:1.
Source: Authors’ calculation.
to innovate is essential to make the transition from the low to the medium level of
upgrading. Capturing better deals in GVCs requires firm-level knowledge and
technology absorption largely from the use of the latest technologies, learning from
various sources such as university–industry collaboration and public research
institutions, and by developing human resources.
148 ASIAN DEVELOPMENT REVIEW
Table 7. Innovation Ecosystem and Upgrading at Different Global Value Chain Levels
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Low to Medium
Level of Upgrading
Variable
Global Innovation Index, overall
Innovation efficiency
Areas of innovation capacity
Availability of scientists and
engineers
University–Industry collaboration
in R&D
Quality of scientific research
institutions
Availability of latest technologies
Extent of marketing
Production process sophistication
Local supplier quantity
Local supplier quality
Firm-level technology absorption
Company spending on R&D
Medium to High
Level of Upgrading
Interaction with
Complex GVC
Participation
(4)
Baseline
(1)
Interaction with
Complex GVC
Participation
(2)
0.224**
(0.094)
9.379***
(3.496)
0.119
(0.096)
8.733**
(4.395)
0.067
(0.045)
2.406
(2.818)
0.073
(0.049)
0.884
(3.445)
0.998
(0.954)
1.039
(0.707)
0.007
(0.482)
0.168
(0.746)
1.259
(1.067)
3.019***
(0.794)
0.229
(1.435)
3.672**
(1.821)
2.000***
(0.734)
2.401***
(0.926)
1.311
(1.027)
1.533
(1.189)
1.282
(1.095)
1.827
(1.472)
1.965
(1.319)
1.118
(1.483)
3.870**
(1.752)
1.285
(1.680)
0.840
(1.828)
4.177***
(1.212)
0.528
(0.678)
0.634
(0.542)
0.606
(0.524)
0.900
(0.845)
1.572**
(0.797)
2.137***
(0.799)
0.186
(0.664)
1.268
(0.821)
1.217
(0.855)
0.279
(0.506)
1.031
(0.876)
0.197
(0.537)
0.283
(0.508)
1.031
(0.679)
1.269
(0.829)
1.297**
(0.629)
1.630
(1.031)
2.200***
(0.828)
0.448
(0.806)
0.790
(0.506)
Baseline
(3)
GVC ¼ global value chain, R&D ¼ research and development.
Notes: The innovation efficiency ratio substitutes joint addition of innovation input and output in
equation (1) to overcome convergence issue. Robust standard errors in parentheses. ***p < 0:01,
**p < 0:05, and *p < 0:1.
Source: Authors’ calculation.
A.
Global Value Chain Upgrading in Research and Development
Intensive Industries
The results discussed in the previous section offer insights that relate to
all sectors. In this section, we examine only the GVC participation of innovative
ENABLING
Table 8.
AN INNOVATION
ECOSYSTEM
PARTICIPATION 149
Innovation Ecosystem and Value Chain Breadth
Low to Medium
Level of Upgrading
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AND
Variable
Global Innovation Index
Innovation efficiency
Areas of innovation capacity
Availability of scientists and engineers
University–Industry collaboration in R&D
Quality of scientific research institutions
Availability of latest technologies
Extent of marketing
Production process sophistication
Local supplier quantity
Local supplier quality
Firm-level technology absorption
Company spending on R&D
All
Economies
(1)
Developing
Asia
(2)
Medium to High
Level of Upgrading
All
Economies
(3)
Developing
Asia
(4)
0.009
(0.027)
2.581**
(1.151)
0.028
(0.067)
3.755*
(2.229)
0.044
(0.048)
2.475
(1.947)
0.721
(0.473)
37.709**
(17.465)
1.685***
(0.359)
1.311***
(0.272)
0.656**
(0.270)
1.468***
(0.344)
2.894***
(0.457)
4.121***
(0.518)
2.752***
(0.380)
2.674***
(0.509)
1.628***
(0.326)
2.466***
(0.382)
4.789***
(1.435)
4.473***
(1.197)
6.135**
(2.386)
3.731**
(1.731)
9.108***
(1.940)
7.499***
(2.195)
3.158***
(1.160)
4.928***
(1.645)
4.697***
(1.568)
8.484***
(2.030)
0.038
(0.523)
1.243**
(0.485)
1.182**
(0.515)
0.295
(0.988)
1.657**
(0.799)
3.236***
(0.627)
2.362***
(0.453)
2.335***
(0.625)
0.153
(0.936)
1.635***
(0.482)
1.245
(3.545)
5.344
(4.669)
2.518
(4.370)
6.039*
(3.266)
10.325
(8.873)
6.752*
(4.019)
570.817***
(16.577)
17.426
(10.908)
14.369**
(5.647)
0.112
(2.881)
R&D ¼ research and development.
Notes: The innovation efficiency ratio substitutes joint addition of innovation input and output in equation
(1) to overcome convergence issue. Robust standard errors in parentheses. ***p < 0:01, **p < 0:05, and
*p < 0:1.
Source: Authors’ calculation.
sectors—that is, those identified as having above-average R&D intensity. It can be
assumed that the capacity of these sectors to undertake knowledge and marketknowledge activities, and the ability to be a critical source of sophisticated
intermediates, are higher than for other sectors. We use sector-level R&D intensity
information from the OECD’s Science, Technology and Industry Scoreboard 2017 for
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150 ASIAN DEVELOPMENT REVIEW
30 advanced economies and 35 sectors, which we map to the ADB MRIO sectors.12
Using the median R&D intensity of advanced economies in 2015, we classify sectors
in different levels: low, medium, medium high, and high. Less R&D-intensive sectors
are those with an R&D intensity below the median. Those with above-median R&D
intensity are classified as medium to high. With these distinctions, we recalibrate the
economy-specific GVC upgrading level covering only the nine sectors classified as
medium to high R&D intensive, as shown in Figure 6.
Table 6 tabulates the coefficient estimates in equation (1) with the upgrading
level derived from the information on R&D-intensive industries. In the global sample,
efforts to improve the efficiency of innovation policies are driving GVC upgrading
from the low to the medium level. This transition could be further facilitated by
making broad-based advances in many areas related to innovation capacity—for
example, the use of the latest technologies in production processes, increased R&D,
and collaboration with knowledge bodies. The transition from a medium to high level
of GVC upgrading requires making further progress in both areas.
For developing Asia, a gradual improvement in innovation performance is
needed to facilitate a shift toward the higher level of GVC upgrading. Our exercise
returned rather ambiguous results for the transition from the low to the medium level
of upgrading in examining the areas of innovation capacity. A transition to the high
level of upgrading requires considerable improvements in turning innovation
investments into relevant innovation outputs. Efforts to boost the capability of local
suppliers in value chains and intensify their technological absorption are needed. This
should be regarded as an issue of high importance.
B.
Concentration Effects in Upstream and Downstream Activities
Another potential source of the heterogeneity of the results is the position in a
global production network. Economies with higher forward than backward
participation is an indication of active engagement in the upstream production
activities of GVCs. These economies can be considered closer to the production
sources, while economies with dominant downstream activities are much closer to the
market. The difference can reflect varying innovation capability needs.
Table 7 summarizes the results of equation (1) adjusted to include the interaction
term with innovation indicators and a dummy variable classifying an economy on
whether it is largely upstream (value equals 1) or downstream (value equals 0). Using
12R&D
intensity is defined as the percentage of spending on R&D in a sector’s gross value added.
ENABLING
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PARTICIPATION 151
Research and Development Intensity by Industry
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Figure 6.
AN INNOVATION
nec ¼ not elsewhere classified, R&D ¼ research and development.
Source: Authors’ calculation using data from Organisation for Economic Co-operation and Development. 2017.
Science, Technology and Industry Scoreboard 2017: The Digital Transformation. Paris: OECD Publishing.
152 ASIAN DEVELOPMENT REVIEW
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Figure 7.
Value Chain Breadth Scores, 2008 and 2018
PRC ¼ People’s Republic of China.
Note: The dashed line refers to the median value chain breadth scores in the two periods.
Source: Authors’ calculation using data from World Economic Forum. Global Competitiveness Report.
https://www.weforum.org/reports/the-global-competitiveness-report-2017-2018 (accessed March 15, 2020).
the forward-linkage and backward-linkage complex GVC participation indices dataset,
following Wang et al. (2017), an economy is classified as largely upstream if its
forward-linkage complex GVC participation index is larger than its backward-linkage
complex GVC participation index; otherwise, the economy is largely downstream.
Columns 2 and 4 are the coefficients of the interaction term, which indicate whether
the baseline coefficients (columns 1 and 3) are statistically different depending on the
position to the global production network. For the transition from a low to a medium
level of upgrading, improved innovation is proved essential in both the extent of
participation and the efficiency of innovation investments, but the latter seems more
evident in economies with largely downstream activities. Intuitively, upgrading in
more upstream economies relies more on the quantity of local suppliers and,
interestingly, conducting more R&D.
The transition from a medium to a high level of upgrading largely occurred in
upstream activities, and this was achieved by further improvements in the
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sophistication of production processes, as well as the presence of numerous local
suppliers.
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C.
Robustness: Alternative Measure of Upgrading
Input–output-based measures of the degree and position of an economy’s
participation in global production networks have been well developed and widely
used, but analyses that could be drawn from this information have some limitations.
Considering that the main players in GVCs are multinational companies, a more
granular approach at the firm level remains indispensable in this type of research in
lieu of sector- or industry-based analysis. However, as compiling input–output tables
is becoming routine for many international organizations, and the data quality has been
improving, we can expect our theory being better tested in some years. Further, due to
data limitations, careful interpretation of the empirical results is needed. The analysis
covers a limited 10-year time frame observing little variation in the dependent
variable.13 Under the current empirical setup, the estimates might potentially be biased
downward given that a decade might be too short for innovation policies and efforts to
take full effect toward value chain upgrading.
For robustness purposes, we examine the survey-based measure of the economyspecific index of value chain breadth reported in the World Economic Forum’s Global
Competitiveness Report. This gives some micro perspectives on how economies
participate in value chains. The value chain breadth scores are derived by asking
survey respondents how broad their presence in the value chain is. Scores range from 1
(narrow or primarily involved in individual steps of the value chain, for example,
resource extraction or production) to 7 (broad or present across the entire value chain,
for example, production, marketing, distribution, and design). Figure 7 plots the
economy-level scores in 2008 and 2018 for developing Asian economies and the rest
of the world.
We generate a categorical variable that considers the value chain breadth index of
economies. Three levels of value chain breadth are derived by classifying the
economies with the index in the first and second quintile as “low,” the third and fourth
quintile as “medium,” and the fifth quintile as “high.” This variable replaces the level
of upgrading based on the FVA_INT share from ADB MRIO’s GVC statistics.
13Since 2000, only a few Asian economies have transitioned to participate in the higher end of GVC
activities, while most economies at the high end of GVCs upgraded long before the study period. It would
be interesting to empirically test if the specific historical factors that contributed to their success are
statistically significant.
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154 ASIAN DEVELOPMENT REVIEW
Table 8 summarizes the coefficients after estimating the adjusted baseline
multinomial logistic regression in equation (1). Intuitively, the results support the
baseline evidence. Broad participation in GVCs, including in the more sophisticated
functions of production and design, is made possible by the ability to improve the
efficiency of innovation policies and investments, which is observed in both the global
and developing Asian samples.
Similar to the baseline results, much of the catch-up should be undertaken early
in the transition process and moving higher up GVCs requires more focused
innovation policy agendas. A higher level of upgrading and participation in GVCs
requires improving the quality and sophistication of production processes, as well as
continuous learning from value chains (i.e., by strengthening technology absorption).
VI. Conclusions and Policy Implications
Developing Asia could be getting a lot more out of participating in GVCs if
economies take steps to move into a higher level of upgrading, both individually and
as part of deepening regional cooperation. Over the years, participation in GVCs by
many economies in the region, particularly the PRC and some Southeast Asian
economies, became instrumental for fast-tracking their growth processes. The
comparative advantage of these economies, particularly in labor inputs, made them
desirable destinations for foreign investment by multinational companies. But their
participation is still predominantly at the lower end of the value chain, characterized by
activities involving assembly and production of less sophisticated inputs. The
Philippines, for example, is a globally recognized offshoring center, but its offshoring
services are largely for low value-added functions, such as call centers. India, on the
other hand, has been successful in high value-added business process outsourcing.
Capturing the higher end of the value chain will remain a big challenge for many
economies in developing Asia, even as this paper reveals significant improvements in
some developing Asian economies. The PRC is moving up the value chain by
gradually undertaking high-tech manufacturing, although its share of low-tech
manufacturing remains high in comparison with other economies at its income level
and with advanced economies. Much of this shift toward higher value-added activities
can be attributed to the PRC’s strides in technology and innovation. The country’s
development plans have a strong focus on upgrading technological and innovation
capacity. It also spends more on R&D than advanced economies. This is creating
opportunities for other economies in the region, especially those with relatively low
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ENABLING
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PARTICIPATION 155
labor costs, such as Bangladesh and Central Asian countries, to integrate into regional
value chains and move up those chains.
The empirical results suggest that moving up a GVC heavily depends on
economy-specific endowments that can be strengthened by supportive policies in
infrastructure, institutions, and innovation. Governments should facilitate an enabling
innovation ecosystem by implementing effective innovation policies. The transition
from a low level of upgrading to a medium one necessitates an increase in the scale of
investments in innovation inputs as well as allowing firms to improve their innovative
capacity. But to get the biggest benefits from being in a GVC and move into the higher
end of the chain, the focus should be on innovation efficiency. The design of
innovation policies should focus more on how to produce technology, knowledge, and
creative outputs relative to actual investments in innovation inputs, such as
institutional quality, human capital, and infrastructure.
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