Discussion Paper series
SU-RCSDEA 2019-001

Domestic Value Creation in the Involvement in Global Value Chains in
Asian Economies: Role of Supporting Industries

Taguchi, Hiroyuki
Professor, Graduate School of Humanities and Social Sciences, Saitama University
255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan
E-mail: htaguchi@mail.saitama-u.ac.jp, Tel: +81-48-858-3324
Pham, Son Duong
Graduate School of Humanities and Social Sciences, Saitama University
August 20191

Domestic Value Creation in the Involvement in Global Value
Chains in Asian Economies: Role of Supporting Industries
Taguchi, Hiroyuki
Professor, Graduate School of Humanities and Social Sciences, Saitama University
255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan
E-mail: htaguchi@mail.saitama-u.ac.jp, Tel: +81-48-858-3324, Corresponding AuthorPham, Son Duong
Graduate School of Humanities and Social Sciences, Saitama University
255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan

Abstract
This article examines the structural changes in domestic value creation in exports in
the involvement process of global value chains with a focus on eight Asian economies,
through the quantitative analyses using the updated OECD value-added-trade data. The
major research questions are: what is an average turning point in terms of per capita GDP
in regaining domestic value added share to exports, and which industries, the exportindustry or supporting industries, have contributed to regaining domestic value added
share to exports. The empirical analyses could identify an accurate turning point at 2,032
US dollars as per capita GDP in regaining domestic value added share to exports, andcould also show that the supporting industries including service sector, rather than theexporting industry itself, have played an active role to push up the domestic value added
share to exports in the involvement process of global value chains. The two strategies,“enterprise clustering” and “linkages development”, facilitating technological transfersfrom international firms to local ones, could contribute to the domestic value creation in
the involvement process of global value chains.
Keyword: Domestic value creation, Global value chains, Asian economies, Value-addedtrade data, Supporting industries, Dynamic Panel Analysis
JEL Classification Codes: F14, L60, O53

2

1. Introduction
The global value chains (hereafter GVCs) have been one of the popular trends inglobal economic activities particularly in Asian area over the past two decades. According
to UNCTAD (2013) 1 , namely, the World Investment Report 2013, the GVCs arecharacterized by the fragmentation of production processes and the international
dispersion of tasks and activities among the economies with diversified development
stages, which have led to the emergence of borderless production networks. World Bank
(2016) expressed the GVC trade as “importing to export,” or I2E, by referring to theargument of Baldwin and Lopez-Gonzalez (2013).
The GVCs are a concept taken up by different schools of economic theory,development studies and international business disciplines. From the perspective of
economic analysis, Kimura (2006) described the GVCs in East Asia by using theterminology of “International Production and Distribution Networks”, and by extracting
18 stylized facts common to such networks based on a number of studies using
international trade data, micro-data of Japanese multinational-enterprises, and casual
observations. The theoretical message in Kimura (2006) is that the mechanics of suchnetworks in East Asia must basically follow “fragmentation theory”, which was initially
proposed by Jones and Kierzkowski (1990, 2005). It states that a firm’s decision onwhether to fragment production processes or not depends on the differences in location
advantages (e.g. the differences in factor prices like wages) and the levels of the “servicelink costs”, which are costs to link remotely-located production blocks (e.g. costs oftransportation, telecommunication and coordination). The larger differences in location
advantages and the lower service-link costs encourage firms to facilitate thefragmentation. In this context, Asian economies seem to have the greatest momentum andpotential for the GVCs to spread over its area, since they include a variety of economies
with different factor prices under different development stages and their public sectors
have made policy efforts to reduce the service-link costs through infrastructuredevelopment.
The question then arises as to whether developing and emerging market economies,
especially latecomer’s economies in Asia, can really achieve sustainable economic
growth by participating in and being involved in the GVCs, in other words, whether the
GVCs can accelerate the catch-up of latecomers’ economies and can lead to greater
1

The World Investment Report is published by the United Nations Conference on Trade and Development(UNCTAD). See the website below:
http://unctad.org/en/pages/DIAE/World%20Investment%20Report/WIR-Series.aspx.

3

convergence among the economies with diversified development stages in Asia.
UNCTAD (2013) argued that although the GVCs can make a contribution to economic
development through direct GDP and employment gains and by providing opportunities
for industrial upgrading, these benefits are not automatic and there are risks involved in
the GVC participation; and so public policies matter to optimize the economic
contributions of the GVC participation and involvement
The following point should be taken into account when the economic contributionsof the GVCs are discussed. At the initial stage of the GVC participation, an
underdeveloped economy usually accepts labor-intensive industries and labor-intensive
production processes such as assembling activities due to its lower labor costs. It is truethat the GVC participation itself creates job opportunities and domestic value added in
the host economy, but the dependence only on labor-intensive manufacturing activities
not necessarily guarantees sustainable development of the country-wide economy andindustrial upgrading for the following senses. First, the manufacturing and assemblingprocesses are identified as the low end of the value chain producing the lower value added
along with so-called “smile curve”. Shin (1996) and subsequent case studies of individual
firms have described a “smile” shaped curve with a vertical axis for value added and ahorizontal axis for value chain processes, suggesting that the middle part of the value
chain (manufacturing and assembling) creates lower value added than both ends of thevalue chain (concept/R&D, and sales/after service). Following this argument, accepting
only labor-intensive manufacturing activities in the GVCs would produce less value
added in an economy. Second, the continuous dependence on labor-intensive
manufacturing in the GVCs participation would lead to the “diminish returns” from theproduction and a slowdown in the economic growth. As Gill and Kharas (2007) argued
in the context of “middle income trap”, the growth based on factor accumulation is likely
to deliver steadily worse results, which is a natural occurrence as the marginal
productivity of factor inputs declines. To avoid the trap, an economy needs to transform
its growth pattern from factor-driven growth to productivity-driven one through industrial
upgrading. In the context of the involvement in GVCs, while an economy accepts foreign
investors in terms of manufacturing activities, it should upgrade its domestic productive
capacities including supporting industries by obtaining the technological transfers fromforeign investors.
This article aims to examine the structural changes in domestic value creation inexports in the GVC involvement process with a focus on Asian economies, through the4quantitative analyses using the OECD value-added-trade data 2 . The major researchquestions are: what is an average turning point in terms of per capita GDP in regaining
domestic value added share to exports, and which industries, the export industry or
supporting industries, have contributed to regaining domestic value added share toexports. The value-added-trade data have been developed recently by several
international organizations and the database enables us to identify the contributions ofdomestic and foreign value added embedded in gross exports, and also the contributionsof direct domestic value added by the export industry and indirect one by supporting
industries.
The rest of the paper is structured as follows. Section 2 reviews previous studies on
the economic impacts of GVCs in Asian countries, and clarifies this study’s contributions.
Section 3 represents the empirical evidence on the domestic value creation in exports in
the GVC involvement process with a focus on Asian economies. Section 4 summarizes
and concludes.
2. Literature Review and this Study’s Contribution
This section reviews previous studies on the economic impacts of GVCs in Asian
countries, and clarifies this study’s contribution.
There has been a plenty of literature on the “firm and industry level” analyses of
GVC impacts through case studies. In the field of traditional industries, some upgrading
effects have been identified in the context of GVCs. Picking up some examples, Nadvi etal. (2004) traced how Vietnamese garment and textile firms are inserted into global
garment and textile value chains, and examined how the nature of insertion into global
value chains leads to favorable gains for state owned and private enterprises, and fortextile and garment workers. Frederick and Gereffi (2011) argued that apparel exporters
in China and Asia have outperformed those in Mexico and Central America due to market
diversification by joining the GVCs. Rasiah (2011) picked up moving-up case of button
manufacturing in Qiaotou-city cluster in China in the context of joining GVCs. Zheng
and Sheng (2006) showed a case study of the Yunhe wood toy cluster in Zhejiang in China,
in which the GVCs has provided external channels of knowledge and learning
opportunities for local firms.
As for more sophisticate industries such as commonly-cited items of the Apple iPodand iPhone, most of previous studies have presented that the GVC participation has
2

See the website of OECD Stat.: https://stats.oecd.org/.

5

limited effects on industrial upgrading. Backer (2011), Linden et al. (2009), Xing andDetert (2010), for instance, argued that the products are designed and conceived indeveloped countries and manufactured in emerging countries like China with inputs
sourced from other third countries; thus manufacturing/ assembly constitutes only a smallpart of the value added, which is a direct result of the offshoring of these activities tolower-cost countries; and so being integrated in the GVCs is a necessary but not a
sufficient condition for capturing value within the GVCs.
Another category of the discussion on the GVC participation effect in the “firm andindustry level” analyses is the “smile curve” hypothesis with a focus on the value creationin production processes involved in GVCs. As was stated in the introduction, the conceptof the smile curve was initially proposed by Stan Shin, the founder of Acer. Shin (1996)
argued that in the case of personal computer industry, both ends of the value chain(concept/R&D, and sales/after service) create higher value added to the product than the
middle part of the value chain (manufacturing), by describing the shape of a smile with avertical axis for value added and a horizontal axis for value chain. This smile curve logic
has been widely used mainly in case studies of individual firms. Baldwin et al. (2014)
applied this smile-curve logic to an economy-wide analysis by using JETRO-IDE’s Asian
Input-Output Table with a focus on Asian economies, and verified the existence of thesmile curve with a horizontal axis for the stages of industrial processes and a vertical axis
for each stage’s product value added. Ye, et al. (2015) also applied this smile-curve
concept to an industry-level analysis by using the World Input-Output Tables. Their
analysis targeted exports of electrical and optical equipment from China and Mexico andexports of automobiles from Japan and Germany, and identified the existence of the smile
curve with a vertical axis for value added and a horizontal axis for a distance between
producers and consumers along GVCs.
The literature on “country” level analyses of GVC economic impacts has, on theother hand, been scarce probably because such analytical instruments as value-addedtrade have been just recently developed by several international organizations. It was
UNCTAD (2013) that addressed, for the first time, the country level analyses of GVC
impacts in comprehensive angles by utilizing the UNCTAD-Eora value-added-trade data.
Chapter IV of UNCTAD (2013) demonstrated the GVC economic impacts in terms of
local-value capture, job creation, technology dissemination as direct effects as well as ofupgrading and building long-term productive capabilities as indirect effects. We hereinpick up two major analytical outcomes related to the country-level contributions ofdomestic value added in GVC participation. First, a statistical analysis of GVC
participation and per capita GDP growth rates showed their significant and positive
6

relationship for both developed and developing economies, even while GVC participation
requires higher imported contents. Second, the combinations of GVC participation anddomestic value added creation, derived from value added trade patterns of 125 developing
countries over 20 years, suggested that there might be a set of distinct “GVC developmentpath” in host countries participating GVCs; some economies have managed to regain thedomestic value added share to exports, after its decline at the initial stage of GVC
participation, through upgrading productive capacities within GVCs and by expanding
them into higher-value chains, as in the Philippines, Malaysia and Thailand.
Taguchi (2014) applied the aforementioned county-level analyses of GVC effects in
UNCTAD (2013) to Asian developing economies for the reason that Asia has been thearea that has the greatest potential for GVCs to spread all over the area. In addition,
Taguchi (2014) modified the analysis of “GVC development path” into moresophisticated way such as estimating a non-linear, quadratic curve in the relationshipbetween domestic value added share to exports and development stage (per capita GDP)so that the regaining point of domestic value share to exports could be identified in the
dynamic GVC involvement process. The analysis covered the samples of the discrete four
years (1995, 2000, 2005 and 2008) for eight Asian economies on eight-categorized
manufacturing sectors as well as total manufactures, based on the data available in theOECD value-added-trade data (OECD TiVA May 2013). The findings of the study weresummarized as follows. First, an economy’s participation in GVCs in manufacturingsectors allowed an absolute domestic value added for exports to contribute to pushing up
GDP growth, which was consistent with the message above in UNCTAD (2013). Second,
the GVC development path in terms of the combination between domestic value added
share to exports and per capita GDP followed the non-linear “smile curve” (which will be
explained in later section). Third, the turning points of smile curves differed according to
manufacturing sectors: the sectors of machinery, electrical, and transport equipment
reached the turning point at the higher per capita GDP than those of food, textile, andwood products.
This study contributes to the reviewed literature as follows. First, this study obtains
a more accurate turning point in the smile curve between domestic value added share toexports and per capita GDP in Asian economies through the following ways. This study
uses the updated OECD value-added-trade data, i.e., OECD “Trade in Value Added(TiVA)” December 2016 and 2018. To be specific, this study samples the annual datafrom 1995 to 2015 instead of the discrete sample of 1995, 2000, 2005 and 2008 in Taguchi(2014). What is more important is that this study adopts a dynamic panel estimation
instead of a static panel one in terms of ordinary panel estimation. The data observation7

implies that there seems to be some inertia and hysteresis effect of domestic value added
share to exports against per capita GDP, thereby justifying the application of a dynamic
panel model. Another contribution is to provide a deep insight on the structural changes
in domestic value creation in exports, by decomposing the domestic value creation into a
direct effect by the export industry and an indirect effect by supporting industries. The
decomposition makes it possible to identify which industries, the export industry or
supporting industries, have contributed to regaining domestic value added share toexports.
3. Empirical EvidenceThis section first illustrates the concept of the non-linear “smile curve” as the
combination between domestic value added share to exports and per capita GDP
presented by Taguchi (2014), and then provides empirical evidence on the structuralchanges in domestic value creation in exports in the GVC involvement process with afocus on Asian economies. The empirical study decomposes the domestic value creation
into a direct effect by the export industry and an indirect effect by supporting industries,
and identifies which industries, the export industry or supporting industries, havecontributed to regaining domestic value added share to exports, through a dynamic panel
analysis of the smile curve, a vector auto-regression (VAR) estimation for causality tests
and a sectoral observation of the changes in decomposed domestic values in all sample
economies.
3.1 Concept of Smile Curve
This subsection describes the concept of the “smile curve” as the combination
between domestic value added share to exports and per capita GDP presented by Taguchi(2014). The smile curve represents a dynamic evolution process of domestic value
creation in exports for a host country who participates in GVCs along with its
development stage.
Figure 1 illustrates the curve with a vertical axis for domestic value added share toexports and a horizontal axis for per capita GDP. At the stage before GVC participation,an economy has a high domestic value added share to exports, in which most of export
goods are domestically produced by using its local resources. When an economy
participates in GVCs, it faces a decline in domestic value added share to exports at its
early stage, since an economy’s production for its exports has to depend highly on imports8

of parts, components and machineries from foreign countries due to the lack in theirproductive capacity. At the mature stage of GVC involvement, however, an economy
regain and restore the higher domestic value added share to exports, since the dependence
on imports of intermediate goods for exports declines due to the expansion of theirdomestic productive capacities through absorbing the technologies transferred fromforeign investors.
The domestic value added share to exports, therefore, follows not one-off moves but
such a sequence of moves as high, low and high ones along with the economy’s
development process, thereby creating the “smile curve” in the host country of GVCs.
The process of enhancing local productive capacities towards the mature stage of
GVC involvement is supposed to involve a number of scenarios as follows. The initial
step is that local firms and industries participate in the GVCs through local outsourcing
by foreign investors so that they can generate additional domestic value added. The
scenarios for regaining domestic value toward the mature stage would, however, differ
according to the contributors to the domestic value creation. One scenario is that theexporting firms and industries themselves would be a main contributor such that theyattain industrial upgrading through technology dissemination and skill building. The otheris that the supporting industries and firms could be a major contributor such that local
industries and firms extend their activities to producing and supplying parts andcomponents by obtaining technological transfers from the key exporting industries andforeign investors. In particular, the latter scenario could be a significant momentum totransform local economic structures from “thin” industrialization towards “thick”
industrialization. The subsequent analyses in the later sections will try to provide evidence
on the major contributor through the decomposition of domestic value creation. It should
also be noted, however, that the process of regaining domestic value is not necessarily
automatic and deterministic, and its achievements differ according to the characteristic of
the GVCs and the involved economies. In this context, government policies matter to
optimize the domestic value creation through the GVC participation and involvement.
3.2 Dynamic Panel Analysis of Smile Curve
This subsection conducts a dynamic panel analysis of the smile curve in Asian
economies. For estimating the smile curve, the following variables are targeted for theestimation as in Taguchi (2014). One is “domestic value added as a share of gross exports(DVA)” in manufacturing sectors, representing domestic productive capacities to produce
export goods. The DVA is further decomposed into a “direct” domestic value added9content as a share of gross exports (DDC) and an “indirect” domestic value added content
as a share of gross exports (IDC).3 The DDC represents the domestic value creation by
the export industry, while the IDC shows the one by the supporting industries, so that theorigin of domestic value creation could be identified. The other key variable is “real per
capita GDP (PCY)”, denoting the development stage of local economies. The data of DVA,
DDC and IDC are retrieved from OECD value-added-trade data (OECD TiVA December
2016 and 2018), and those of PCY are from UNCTAD STAT 4 by the series of “Grossdomestic product per capita, US Dollars at constant prices (2010)”.
Regarding the sample data for estimation, the OECD value-added-trade data confine
the sample period and countries as follows. The OECD TiVA December 2016 and 2018
sample the annual time-series from 1995 to 2013 and from 2005 to 2015, respectively.
This study combines these two time-series into the period from 1995 to 2015 by making
level adjustments by using the discrepancy between both of time-series data in 2005.5
The sample countries focus on eight Asian economies available in the OECD data:
Cambodia, China, India, Indonesia, Malaysia, the Philippines, Thailand and Vietnam. As
for the sample sector, this subsection focuses on total manufactures. Then the panel data
are constructed with eight Asian countries for 1995-2015 on total manufactures for a
dynamic panel estimation. All the data for DVA, DDC, IDC and PCY are converted into
natural logarithm form for the estimation to avoid the heteroskedastic in the error terms.
As for the specification of estimation model, the study first investigates theassociation between DVA and PCY, and also the associations between DDC and PCY andbetween IDC and PCY. The associations are examined by a quadratic equation as well asa linear one, for the purpose of identifying the “smile curve”. For the estimation
methodology, the study applies a dynamic panel model, since the observed data in Figure
2 implies that there seem to be a “state-dependent” effect of domestic value added shares
to exports (DVA, DDC and IDC) along with PCY. The model equation thus contains
lagged dependent variables as regressors for materializing a partial adjustment. 6 The
inclusion of lagged dependent variables as regressors requires the application of

The precise composition of the “domestic value added” is the “direct domestic value added content”,
the “indirect domestic value added content” and the “re-imported domestic value added content”.
Since the last one has a small share to gross exports, it is omitted in this study’s analysis.
4
See the website: http://unctadstat.unctad.org/EN/.
5
The OECD TiVA December 2018 revises that of 2016, for instance, by changing the benchmark
statistics from the 1993 System of National Accounts to the 2008 one. See the website:
https://www.oecd.org/industry/ind/tiva-2018-differences-tiva-2016.pdf
6This study includes two-year lagged dependent variables that show the best fit in estimation
performance after the trial estimations by including one-year to three-year lagged dependent
variables.
3

10

Generalized Method of Moments (GMM) to obtain a consistent estimator. The GMM
estimator eliminates country effects by first-differencing (as in Arellano and Bond, 1991)
as well as controls for possible endogeneity of explanatory variables. The firstdifferenced dependent variables with two lagged periods could be valid instruments
provided there is no second-order autocorrelation in the error terms. The explanatory
variable of PCY with one lagged period was also used as an instrumental variable, since
PCY could be correlated with the error term. The estimation adopts the white period as
the GMM weighting matrix, and conducts the test for autocorrelations and the SarganHansen test on over-identifying restrictions. The test for autocorrelations compute thefirst and second order serial correlation statistics and the first order statistic is expected
to be significant, while the second order one is expected to be insignificant. As for theSargan-Hansen test, the p-value of the J-statistic is expected to be more than five percent
to identify the validity of instrument variables.
Table 1 and Figure 2 represent the estimation outcome of the smile curve on total
domestic value creation (DVA) and on its direct (DDC) and indirect (IDC) value creations
for total manufactures. Focusing on the case of DVA in Table 1-1, it has a weakly (90percent level) significant coefficient of PCY in a linear equation, but a conventionallysignificant (95 percent level) coefficients of PCY (negative) and a square of PCY(positive) in a quadratic equation with the turning point being a reasonable level of PCY,
namely, 2,032 US dollars. The quadratic estimation of DVA also has expected values ofthe p-value of the J-statistic and of the first and second order serial correlation statistics.
As for the case of DDC in Table 1-2, there are no significant coefficients of PCY and asquare of PCY in linear and quadratic equations. Concerning the case of IDC in Table 13, however, similar to the case of DVA, the valid estimation is found only in in a quadratic
specification, where the coefficients of PCY and a square of PCY have conventionally(95 percent level) significant values with the turning point being 1,936 US dollars (the pvalue of the J-statistic is not fully satisfactory though the p-values of the first and second
order serial correlation statistics are expected ones).
In sum, the estimation outcome tells us that the non-linear smile curve could be
identified in the cases of total domestic value creation (DVA) and indirect domestic value
creation (IDC). Figure 2 also shows that the smile curve of IDC is synchronizing withthat of DVA, and reaches a turning point a bit earlier than that of DVA. These results thus
imply that the movement of total domestic value creation in exports originates from thatfrom the supporting industries.
Another observation from the perspective of individual country’s position in thesmile curves of DVA and IDC in Figure 2 is that such forerunners as Malaysia, China,
11Thailand, Indonesia and Philippines are already passing the turning point by regaining
domestic value creation, whereas such latecomers as Cambodia, Vietnam and India are
still staying at the declining phase of domestic value creation.
The serious question then arises on how to nurture local productive capacities inmanufacturing sectors in the context of the GVCs involvement in order to pass the turning
point. UNCTAD (2013) proposed the following key strategies as well as such generalpolicies as workforce skills development, for building up domestic productive capacitiesof developing economies: “enterprise clustering” and “linkages development”. The
enterprise clustering enables the local small and medium-sized enterprises (SMEs) toenjoy “collective efficiency” to enhance their productivity with clustered firms. The
linkage development provides the local SMEs with the necessary externalities for
successful participation in GVCs as first, second, or third-tier suppliers. These two
strategies in line with the GVCs involvement facilitate technological transfers frominternational firms to local ones, thereby contributing to enhancing the local productive
capacities even in sophisticated manufacturing sectors.
3.3 VAR Estimation for Causality Test
This subsection further investigates the statistical relationship among total domestic
value creation in exports (DVA), its direct value creation by the export industry (DDC)and its indirect value creation by the supporting industries (IDC), for the same sample as
the one of the previous analysis in 3.2, namely, total manufactures in the eight Asian
economies for 1995-2015. To be specific, the study conducts Granger-causality tests forthe combination between DVA and DDC and for the one between DVA and IDC under
VAR model estimations. The reason why the study adopts a VAR model is that it allows
for potential endogeneity among the interrelated variables of DVA, DDC and IDC, andlets the data determine their causalities in the Granger sense. The estimation takes oneyear lag length, following the Schwarz Information Criterion with maximum lag being
equal to two year lags under the limited number of observations.
Table 2 reports the estimation outcome of the VAR model (Table 2-1 and 2-2) and
the Granger causality tests (Table 2-3). Regarding the combination between DVA and
DDC, the causality from DDC to DVA is shown at a conventionally significant level of
99 %. The direction of causality from DDC to DVA is, however, negative as the estimatedVAR model in Table 2-1 indicates. As for the combination between DVA and IDC, theclear causality from IDC to DVA is identified at the conventionally significant level of
99 %, and its direction is positive judging from the estimated model in Table 2-2. The
12

causality from DVA to IDC is, on the other hand, negatively significant.
The estimation outcome thus suggests that it is the indirect domestic value creationby the supporting industries (IDC), but not the direct one (DDC) by the export industry,that positively affects the total domestic value creation in exports (DVA) in the Grangercausality sense. The negative causality from DDC to DVA could be interpreted such thatthe direct domestic value creation by the export industry should induce an increase inforeign value added in terms of the imports of necessary materials, parts and components
for exports, thereby reducing the total domestic value share to exports finally.
3.4 Sectoral Observation of Decomposed Domestic Value Creation
This subsection observes the changes in the decomposed domestic value creations inexports of DVA (total domestic value added share to exports), DDC (direct domestic value
added share to exports) and IDC (indirect domestic value added share to exports) in moredetails, by the eight manufacturing sectors in all sample economies, focusing on the
period between 2005 and 2015. The OECD value-added-trade data classifies the
manufactures into the following eight categories: “Food products, beverages and tobacco(hereafter food products)”, “Textiles, wearing apparel, leather and related products(textile products)”, “Wood and paper products, printing (wood products)”, “Chemicals
and non-metallic mineral products (chemical products)”, “Basic metals and fabricatedmetal products (metal products)”, “Computers, electronic and electrical equipment(electrical equipment)”, “Machinery and equipment, nec (machinery)” and “Transport
equipment”. The first year of 2005 and the last year of 2015 in the observation correspondto the first and last year of the newly-estimated OECD TiVA database (December 2018),
where the recent progress in domestic value creation in exports in Asian economies could
be covered.
Table 3 plots the cases of the increase in the domestic value added share to exports
from 2005 to 2015 by each sample economy and by each manufacturing sector in eachcategory of DVA, DDC and IDC. Table 3-3 in the category of IDC shows the increase invalue added share by diving the supporting industries into service sector and non-service
sector.7 The main observations are as follows. First, the cases of the increase in DVA areaccompanied with more of the cases of the increase in IDC than in DDC. Second, even
7

The service sector here is retrieved from “Total Services including Construction activities (code:
C45T95)” in the OECD value-added-trade data. The value added share of the non-service sector is
calculated by subtracting that of the service sector from that of IDC (total supporting industries), andthus the non-service sector contains the primary sectors such as agriculture and mining and the
manufacturing sectors other than the exporting sector.
13the cases without the increase in DVA have many of the cases of the increase particularly
in IDC. Third, the service sector as well as no-service sector contributes to the increasein IDC.
The observations above, in all, suggest that the supporting industries including
service sector, rather than the exporting industry itself, have played an active role to push
up the domestic value added in exports totally. The service sector defined in the OECDvalue-added-tradedata

containssuch

publicservicesastransportation,

telecommunication, real estate, research and development (R&D), education, health andthe other social work. Thus the greater contribution of the service sector to domestic value
creation in exports seems to be linked with the progress in infrastructure development in
Asian emerging market economies. The impact of service sector on domestic value
creation should, however, be further investigated by future researches such as casestudies, in order to identify the most influential public service provided by central andlocal governments.
4. Concluding Remarks
This article examined the structural changes in domestic value creation in exports in
the GVC involvement process with a focus on eight Asian economies, through thequantitative analyses using the updated OECD value-added-trade data. The major
research questions are: what is an average turning point in terms of per capita GDP inregaining domestic value added share to exports, and which industries, the export industry
or supporting industries, have contributed to regaining domestic value added share toexports.
The major findings from the empirical analyses are as follows. First, the dynamic
panel analysis identified the non-linear smile curves in the combination between total
domestic value added share to exports (DVA) and real per capita GDP with the turning
point being 2,032 US dollars, and in the combination between the “indirect” domestic
value added share to exports (IDC) and real per capita GDP with the turning point being
1,936 US dollars. There appeared to be a synchronization between the DVA smile curveand IDC smile curve, which implies that the domestic value movement in exports
originates from the one from the supporting industries. Second, the vector auto-regression(VAR) estimation verified the clear positive causality from IDC to DVA. Third, thesectoral observation of the decomposed domestic value creation in all the sample
economies for 2005-2015 showed that the DVA increases are accompanied with more

14

cases of the increase in IDC by the supporting industries including service sector than theincrease in DDC by the exporting industry.
To sum up, the empirical analyses using the dynamic panel analysis, the VAR
estimation for causality tests and the sectoral observation of the decomposed domestic
value creations in all the sample economies could identify an accurate turning point at
2,032 US dollars as per capita GDP in regaining domestic value added share to exports,
and could also show that the supporting industries including service sector, rather thanthe exporting industry itself, have played an active role to push up the domestic value
added share to exports in the GVC involvement process.
The strategic implication of this study is the significance in the supporting industries
to create domestic values for exports. The involvement of local firms and industries in the
supporting industries as first, second and third-tier suppliers would require thegovernment clear strategies for “enterprise clustering” and “linkages development tofacilitate technological transfers from foreign investors, as was mentioned in Section 3.2.
At the same time, in order to reinforce service industries to support export-oriented
manufacturing, the government is also expected to promote infrastructure developmentin the areas of transportation, telecommunication, education, and so on.
The remaining issue in this study could be to explore the theoretical underpinnings
for justifying the hypothesized “smile curve”. This study focuses mainly on empiricalanalyses for verifying the smile curve. However, to identify the theoretical mechanism
for an economy to regain the domestic value creation for exports would also be importantin order for the government to recognize the necessary conditions for reaching andfastening the turning point in the smile curve.

15

References
Arellano, M. and S. R. Bond, 1991. Some tests of specification of panel data: Monte Carloevidence and an application to employment equations. Review of Economic Studies
58: 277–297.
Baldwin, R., T. Ito and H. Sato, 2014. Portrait of Factory Asia: Production Network in
Asia and its Implication for Growth: The Smile Curve. Joint Research Program Series,
IDE-JETRO, No. 159.
Backer, K. D., 2011. Global Value Chains: Evidence, Impacts and Policy Issues. Review
of Business and Economic Literature 56(2): 110-128.
Baldwin, R. and J. Lopez-Gonzalez, 2013. Supply-Chain Trade: A Portrait of Global
Patterns and Several Testable Hypotheses. NBER Working Paper No. 18957.
Frederick, S. and G. Gereffi, 2011. Upgrading and restructuring in the global apparelvalue chain: why China and Asia are outperforming Mexico and Central America.
International Journal of Technological Learning, Innovation and Development 4: 6795.
Gill, I. and H. Kharas, 2007. An East Asian Renaissance. IBRD and World Bank.
Jones, R. W. and H. Kierzkowski, 1990. The Role of Services in Production andInternational Trade: A Theoretical Framework. In The Political Economy ofInternational Trade: Essays in Honor of Robert E. Baldwin, edited by R. Jones and
A. Krueger. Basil Blackwell.
Jones, R. W. and H. Kierzkowski, 2005. International Trade and Agglomeration: An
Alternative Framework. Journal of Economics 10(S1): 1-16.
Kimura, F., 2006. International Production and Distribution Network in East Asia:
Eighteen Facts, Mechanics, and Policy Implication. Asian Economic Policy Review
1(2): 326-344.
Linden, G., K. L. Kraemer and J. Dedrick, 2009. Who Captures Value in a Global
Innovation Network? The Case of Apple’s iPod. Communications of the ACM 52(3):140-144.
Nadvi, K., J. T. Thoburn, B. T. Thang, N. T. T. Ha, N. T. Hoa, D. H. Le and E. B. D. Armas,
2004. Vietnam in the global garment and textile value chain: impacts on firms andworkers. Journal of International Development 16(1): 111-123.
Shih, S., 1996. Me-Too is Not My Style: Challenge Difficulties, Break throughBottlenecks, Create Values. The Acer Foundation.
Rasiah, R., 2011. Moving up in the global value chain in button manufacturing in China.
Asia Pacific Business Review 17(2): 161-174.
Taguchi, H., 2014. Dynamic Impacts of Global Value Chains Participation on AsianDeveloping Economies. Foreign Trade Review 49(4): 313-326.
UNCTAD, 2013. World Investment Report - Global Value Chains: Investment and Tradefor Development. The United Nations Conference on Trade and Development.
World Bank, 2016. Making Global Value Chains Work for Development. World Bank.
Xing, Y. and N. Detert, 2010. How the iPhone Widens the United States Trade Deficit
with the People’s Republic of China. ADBI Working Paper Series No. 257.
Ye M., B. Meng and S. J. Wei, 2015. Measuring Smile Curves in Global Value Chains.
IDE Discussion Papers No. 530.
Zheng Y. and S. Sheng, 2006. Learning in a local cluster in the context of the global value
chain: A case study of the Yunhe wood toy cluster in Zhejiang, China. Innovation:
Management, Policy, & Practice 8: 120-127.
16

Figure 1 Concept of Smile Curve: A Country Example
Domestic Value Added Share to Gross Exports
Before GVC
Participation
Early Stage of
GVC Participation

Mature Stage of
GVC Participation

Turning Point

Source: Author’s description based on Taguchi (2014)17

Per Capita GDP

Table 1 Estimation on Domestic Value Creation in Exports in Total Manufactures[Table 1-1 Domestic Value Added Share to Gross Exports (DVA)]
Variales
PCY

DVA
0.026 *
(1.670)

PCY2
DVA-1
DVA-2
Turning Point USD
Prob (J-statistic)
AR(1) Prob
AR(2) Prob
Sample size

0.743 ***
(10.543)0.047(0.720)
0.189
0.000
0.203
152

DVA
-2.834 **(-2.381)0.186 **
(2.449)0.499 ***
(3.530)-0.057(-0.907)
2,032
0.335
0.003
0.803
152

[Table 1-2 Direct Domestic Value Added Share to Gross Exports (DDC)]
VarialesPCY

DDC-0.020(-0.658)
0.741 ***
(4.794)-0.068(-0.674)

DDC-2.062(-1.626)0.131(1.611)
0.491 **
(2.302)-0.106(-0.969)

0.284
0.000
0.082
144

0.412
0.006
0.053
144

PCY2
DDC-1
DDC-2
Turning Point USD
Prob (J-statistic)
AR(1) Prob
AR(2) Prob
Sample size

[Table 1-3 Indirect Domestic Value Added Share to Gross Exports (IDC)]
Variales
PCY

IDC
0.017
(0.388)

PCY2
IDC-1
IDC-2
Turning Point USD
Prob (J-statistic)
AR(1) Prob
AR(2) Prob
Sample size

0.612 ***
(6.676)0.025(0.226)
0.001
0.000
0.000
144

IDC
-6.293 ***(-7.062)
0.415 ***
(6.551)0.425 ***
(4.248)-0.176 *(-1.926)
1,936
0.023
0.000
0.571
144

Note: DVA, DDC and IDC denote domestic value added content, direct one and indirect one as a shareof gross exports respectively, and PCY denotes per capita real GDP. *, ** and *** denote therejection of null hypothesis at the 90, 95 and 99% level of significance. T-statistic is inparentheses attached in the coefficients. J-statistic and its probability represent the results of theSargan-Hansen test of over-identifying restrictions. AR (k) probability shows the p-value of atest that the average auto-covariance in residuals of order k is zero.
Source: Author’s estimation based on OECD value-added-trade data and UNCTAD STAT

18

Domestic Value Added Share of Gross
Exports (%)

Figure 2 Trends in Domestic Value Creation in Exports in Total Manufactures[Figure 2-1 Domestic Value Added Share to Gross Exports (DVA)]
Cambodia

China

India

Indonesia

Malaysia

Philippines

Thailand

Viet Nam[1995-2015]

100.0

90.0
80.0
70.0

60.0
50.0
40.0
30.0

20.0

2,032
1,000
Per Capita GDP (US dollar)

100

10,000

Direct Domestic Value Added Share of
Gross Exports (%)[Figure 2-2 Direct Domestic Value Added Share to Gross Exports (DDC)]
Cambodia

China

India

Indonesia

Malaysia

Philippines

Thailand

Viet Nam[1995-2015]

100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
100

1,000
Per Capita GDP (US dollar)

10,000Indirect Domestic Value Added Share of
Gross Exports (%)[Figure 2-3 Indirect Domestic Value Added Share to Gross Exports (IDC)]

100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
100

Cambodia

China

India

Indonesia

Malaysia

Philippines

Thailand

Viet Nam

1,936
1,000
Per Capita GDP (US dollar)

Source: OECD value-added-trade data and UNCTAD STAT

19

[1995-2015]

10,000Table 2 VAR Estimation on Domestic Value Creation in Exports in Manufactures[Table 2-1 Domestic Value Added (DVA) and its Direct Content (DDC)]
DDC

DVA

0.936 ***

-0.077 ***[49.150][-2.957]

0.001

0.971 ***

[0.110][43.607]1.860 *

4.271 ***

[1.809][3.014]

0.948

0.932

DDC-1

DVA-1

C
adj. R^2[Table 2-2 Domestic Value Added (DVA) and its Indirect Content (IDC)]
IDC

DVA

1.015 ***

0.077 ***

[50.187][2.887]-0.045 **0.893 ***[-2.267][33.609]2.428 **

4.305 ***

[2.258][3.025]

0.962

0.932

IDC-1

DVA-1

C
adj. R^2[Table 2-3 Granger Causality Tests]
Null Hypothesis

Lags

F-Statistic

DDC does not Granger Cause DVA

1

8.749 *** (negative)

DVA does not Granger Cause DDC

1

0.012

IDC does not Granger Cause DVA

1

8.337 ***

DVA does not Granger Cause IDC

15.140 ** (negative)

Note: DVA, DDC and IDC denote domestic value added content, direct one and indirect one as a shareof gross exports respectively, and PCY denotes per capita real GDP. ***, **, * denote therejection of null hypothesis at the 99%, 95% and 90% level of significance. T-statistic is inparentheses attached in the coefficients.
Source: Author’s estimation based on OECD value-added-trade data and UNCTAD STAT

20

Table 3 Increase in Domestic Value Added Share to Exports from 2005 to 2015
[Table 3-1 Domestic Value Added Share to Gross Exports (DVA)]
Cambodia

Vietnam

India

Philippines Indonesia

ThailandChina

Malaysia

+

+

+

+

Food

+

Textile

+

+

+

+

+

+

Wood

+

+

+

+

+

+

Chemical

+

+

+

+

+

+

+

Metal

+

+

+

+

+

+

Electrical

+

+

+

+

+

+

Machinery

+

+

+

+

+

+

Transport

+

+

+

+

+

[Table 3-2 Direct Domestic Value Added Share to Gross Exports (DDC)]
Cambodia
Food+

Textile+Vietnam

+

Metal

+

ElectricalThailand

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

Malaysia

+

+

+

+

China

+

MachineryTransport

Philippines Indonesia+

Wood
Chemical

India

+

+

+

+

+
+

+

[Table 3-3 Indirect Domestic Value Added Share to Gross Exports (IDC)]
Cambodia

Vietnam

India

Philippines Indonesia

ThailandChina

Malaysia

Food

+n

+s

+s

+s

+n +s

+n

+s

+n +s

Textile

+n +s

+n +s

+s

+s

+s

+s

+s

+s

Wood

+s

+n +s

+s

+s

+n +s

+n

+s

+n +s

Chemical

+n +s

+n +s

+s

+s

+n +s

+s

+s

+n +s

Metal

+n

+n +s

+s

+s

+n +s

+n +s

+s

+s

Electrical

+n +s

+s

+n +s

+n +s

+n +s

Machinery

+n +s

+s

+n +s

+n +s

+n +s

+n +s

Transport

+n +s

+s

+s

+n +s

+s

+n +s

Note: DVA, DDC and IDC denote domestic value added content, direct one and indirect one as a shareof gross exports respectively. The s and n mean service sector and non-service sector. “+”represents an increase in the respective value shares from 2005 to 2015.
Source: OECD value-added-trade data21

Appendix Changes in Domestic Value Added Share to Exports for 2005-2015
Total Manufactures
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Food products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Textile products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Wood products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Chemical products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Metal products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Electrical equipment
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
MachineryDomestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Transport equipment
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services

Cambodia
4.8
0.9
3.9
2.3
1.6
Cambodia
12.9
0.1
12.9
14.4
-1.6
Cambodia
6.3
3.6
2.7
0.3
2.4
Cambodia
0.0
-1.4
1.4
-0.4
1.8
Cambodia
6.2
0.3
5.9
4.1
1.8
Cambodia
1.2
0.8
0.4
0.9
-0.5
Cambodia
2.3
-4.0
6.3
3.4
2.9
Cambodia
3.4
-0.3
3.6
2.2
1.4
Cambodia
2.3
0.5
1.8
0.7
1.1

22

Vietnam
-6.5
-6.3
-0.1
-0.2
0.1
Vietnam
-3.5
-3.5
0.1
-0.2
0.3
Vietnam
-4.6
-8.5
3.8
3.0
0.8
Vietnam
-2.7
-5.9
3.3
2.6
0.6
Vietnam
1.0
-7.2
8.3
6.8
1.5
Vietnam
1.6
-6.1
7.7
6.4
1.3
Vietnam
-14.1
-7.9
-6.2
-4.8
-1.4
Vietnam
-7.3
-3.6
-3.7
-3.5
-0.2
Vietnam
-6.5
-1.5
-5.0
-4.2
-0.8

India
-2.0
2.0
-4.1
-4.8
0.7
India
-1.2
-3.2
2.0
-0.8
2.7
India
-1.1
1.1
-2.2
-3.7
1.5
India
-2.6
1.7
-4.4
-6.7
2.3
India
-0.1
6.3
-6.5
-6.8
0.3
India
-3.6
-2.0
-1.6
-3.0
1.4
India
-1.6
3.7
-5.4
-5.4
0.0
India
-3.4
-0.6
-2.8
-3.7
0.9
India
-1.0
3.3
-4.3
-4.9
0.6

Philippines
2.0
2.0
0.0
-0.9
0.9
Philippines
-0.9
0.5
-1.4
-2.9
1.5
Philippines
1.4
0.3
1.1
-0.5
1.6
Philippines
1.5
4.4
-2.8
-3.4
0.5
Philippines
2.2
2.0
0.1
-1.5
1.7
Philippines
3.0
5.8
-2.8
-3.8
0.9
Philippines
5.6
3.0
2.6
0.7
1.8
Philippines
2.5
0.0
2.5
1.3
1.2
Philippines
1.5
2.3
-0.8
-1.7
0.8

Total Manufactures
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Food products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Textile products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Wood products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Chemical products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Metal products
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Electrical equipment
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
MachineryDomestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services
Transport equipment
Domestic value added
Direct value added by exporting industry
Indirect value added by supporting industries
Non-services (primay & manufacturing)Services

Indonesia
7.0
0.8
6.1
4.9
1.3
Indonesia
3.9
-3.1
6.9
6.7
0.3
Indonesia
0.1
-0.1
0.1
-2.1
2.2
Indonesia
4.6
-5.9
10.4
7.8
2.6
Indonesia
5.4
0.8
4.5
1.7
2.8
Indonesia
5.6
-3.9
9.5
6.7
2.9
Indonesia
5.2
11.0
-5.8
-3.3
-2.4
Indonesia
16.8
-3.5
20.3
14.9
5.4
Indonesia
8.1
7.4
0.7
0.0
0.7

Source: OECD value-added-trade data

23

Thailand
3.9
3.0
1.0
1.6
-0.6
Thailand
4.1
-1.8
5.9
7.4
-1.5
Thailand
5.3
5.7
-0.4
-1.5
1.0
Thailand
2.0
0.3
1.7
2.7
-1.0
Thailand
1.0
1.4
-0.4
-0.9
0.5
Thailand
3.4
0.8
2.6
2.1
0.6
Thailand
6.4
8.6
-2.1
-0.8
-1.3
Thailand
-1.1
0.6
-1.7
-1.1
-0.5
Thailand
2.8
4.0
-1.2
-0.8
-0.4

China
9.7
0.3
9.2
-0.6
9.8
China
1.8
1.8
0.0
-7.0
7.0
China
7.3
3.7
3.5
-5.3
8.8
China
6.3
0.6
5.6
-4.3
9.9
China
8.2
-0.5
8.7
-1.6
10.2
China
7.4
-2.2
9.5
-1.8
11.3
China
13.1
0.1
12.5
2.5
10.0
China
8.9
-3.2
12.1
0.9
11.2
China
7.3
0.1
7.1
-1.9
9.0

Malaysia
9.6
3.2
6.5
4.8
1.7
Malaysia
1.5
-2.6
4.1
1.9
2.2
Malaysia
2.6
1.2
1.4
-0.2
1.7
Malaysia
0.8
-3.0
3.8
2.2
1.7
Malaysia
2.0
-1.9
3.9
3.4
0.6
Malaysia
-2.1
-1.7
-0.3
-0.6
0.3
Malaysia
9.9
6.6
3.4
1.5
1.9
Malaysia
6.8
6.6
0.3
0.1
0.1
Malaysia
4.0
-2.2
6.2
4.3
1.9