Tsukuba Economics Working Papers
No. 2023-001

The Value Added-Exports Puzzle and Global Value Chainsby
Zhe Chen and Yoshinori Kurokawa
February 2023

UNIVERSITY OF TSUKUBA
Faculty of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Value Added-Exports Puzzle and Global Value Chains
Zhe Chen∗

Yoshinori Kurokawa†February 15, 2023

Abstract
While most OECD countries experienced declines in manufacturing value added relative to GDP over 1970–2001, they have experienced increases in manufacturing exports
relative to GDP during the same period. Bergoeing et al. (2004) documented this“value added-exports puzzle” and predicted that vertical specialization can explain it.
Using the 1995-2018 data for 22 OECD countries and 17 manufacturing industries, we
empirically investigate whether vertical specialization, or global value chain (GVC) participation, is a factor significantly affecting the puzzle. Our regressions show that the
puzzle is stronger for countries and industries with the greater GVC backward linkage,
while it is weaker for countries and industries with the greater GVC forward linkage.
We also find that the puzzle is weaker for countries and industries that focus more on
the upstream stage. Thus Bergoeing et al. (2004) were right, but we must be carefulthat the two measures of vertical specialization, the backward and forward GVC linkages, have the opposite effects.

Keywords: manufacturing value added, manufacturing exports, vertical specialization,
GVC participation, GVC position

JEL classification: F1, F4∗

School of International Trade and Economics, University of International Business and Economics. 1302
Boxue Building, 10 Huixin Dongjie, Chaoyang District, Beijing 100029, China. E-mail: zhechen@uibe.edu.cn.
†
Faculty of Humanities and Social Sciences, University of Tsukuba. 1-1-1 Tennodai, Tsukuba, Ibaraki
305-8571, Japan. E-mail: kurokawa.yoshi.fw@u.tsukuba.ac.jp.

11Introduction

Bergoeing et al. (2004) documented that while most OECD countries have experienced declines in manufacturing value added as a share of GDP during the period 1970–2001, they
have experienced increases in manufacturing exports as a share of GDP during the same
period (see their Figure 1; we also show it in Panel (a) of Figure 1). They argued that thisis puzzling, because these observations imply that most of the world’s major economies areexporting more and more of goods that they are producing less and less of.
We now extend Bergoeing et al.’s data analysis in two ways. First, we update their data
to check if this “value added-exports puzzle” still holds in most OECD countries after 2001.
This is because after 2001 China became a member of the WTO and some Eastern European
countries joined the EU, which might have changed the patterns of manufacturing tradeand production in related OECD countries. Panel (b) of Figure 1 shows the average annualchanges of manufacturing exports and value added for 22 OECD countries1 from 1996-2018.2
While it held in most OECD countries for the period 1970-2001 as shown in Panel (a), we
can see in Panel (b) that the puzzle seems to hold only in 12 OECD countries but seems
not to hold in the other 10 OECD countries from 1996-2018. As can also be seen, somecountries, such as Greece, show the relatively strong puzzle; some, such as Denmark, show
the relatively weak puzzle; and some, such as United States, do not show the puzzle. Thus,
the heterogeneity in the puzzle across countries has become more significant from 1996-2018.
Second, we investigate the puzzle at the industry level besides the country level. This isbecause, as Kehoe et al. (2015) argued, when policy makers debate trade liberalization, theworry could be not only over the aggregate increase in exports but over the unequal impact
on exports across industries. Panel (c) of Figure 1 shows the average annual changes ofexports and value added for 17 individual manufacturing industries from 1996-2018. As canbe seen, over 1996-2018 this value added-exports puzzle seems to hold for 12 manufacturingindustries but seems not to hold for 5 ones. Some industries, such as rubber and plastics
products, show the relatively strong puzzle; some, such as electronic products, show the
relatively weak puzzle; and some, such as textiles and clothing, do not show the puzzle.
1

These 22 countries are the same as those in Figure 1 of Bergoeing et al. (2004): Australia, Austria,
Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Japan, Italy, Mexico, Netherlands,
Norway, New Zealand, Portugal, South Korea, Spain, Switzerland, United Kingdom, and United States.
2Here, the annual change for 1996, for example, is calculated as a percent change from 1995.

2

6

GRC

4

DEU

MEX

2

KOR
AUT
ESP
PRTITA
FRA

NLD
IRL

BEL
SWE

FIN DNK

0

Manufacturing Gross Exports/Total GDP

JPN

NZL
NOR

-2

AUS

USA
CAN

GBR

-4

-2

0

2

4

Manufacturing Value Added/Total GDP(a) Figure from Bergoeing et al. (2004)(b) Country level

6

Petroleum products

4

Other transport equipment
Motor vehicles

2

Rubber Basicand plastics
metalsproducts

Metal products
Machinery and equipment
nec
ChemicalManufacturing
products
Electrical equipment

Food and beverages

Wood products
non-metallic productsElectronic products

0

Industry Gross Exports/Total GDP

medicinal products

Paper products and printing-2

Textiles and clothing

-4

-2

0

2

4

Industry Value Added/Total GDP(c) Industry level

Figure 1: Average annual changes of exports and value added
Note: This figure shows the average annual changes of exports and value added for 22 OECD countries and 17 manufacturingindustries. Figure (a) is at the country level from 1970 to 2001 in Figure 1 in Bergoeing et al. (2004). Figure (b) is our calculation
at the country level from 1996 to 2018. Figure (c) is our calculation at the industry level from 1996 to 2018. When we calculate theaverage annual change, we remove annual changes in the three years 2008, 2009, 2010, which can be affected by the Great Recession.
The Great Recession started in December 2007 and ended in June 2009.
Source: OECD Trade in Value Added (TiVA) indicators

Thus, there is also significant heterogeneity in the puzzle across manufacturing industries
from 1996-2018.
Based on the above motivational evidence, we now raise our empirical question: Whatcan explain the heterogeneity in the value added-exports puzzle among countries/industries
from 1996-2018? To answer this question, as Bergoeing et al. (2004) suggested, we consider
vertical specialization, or global value chain (GVC) participation, as one of the possible factors that can explain the above observations.3 As an example, consider the increased vertical3

Bergoeing et al. (2004) suggested vertical specialization as a factor that can explain the value addedexports puzzle, but they did not empirically test it.

3specialization after the U.S.-Canada Auto Pact of 1965. The United States now primarily
exports motor vehicle parts to Canada and imports and re-exports assembled vehicles embodying those U.S. parts. This can increase exports and decline outputs in the U.S. auto
industry. It, however, should be emphasized that we consider not only the vertical specialization measure (VS) in Hummels et al. (2001), that is, GVC backward linkage, but also anothermeasure (VS1), that is, GVC forward linkage.4 The former is defined as foreign value added
content of gross exports (from the import perspective); the latter is defined as the domestic
value added in gross exports that are further re-exported to third countries (from the export
perspective). In this paper, we define the GVC backward linkage plus forward linkage as
total GVC participation. We thus consider the three measurements of GVC participation:
backward, forward, and total. Moreover, in addition to this GVC participation, we also consider GVC position—upstreamness—as another possible factor that can explain the above
observations. It is defined as the ratio of the GVC forward linkage to backward linkage.
In fact, data suggest that these GVC backward linkage, forward linkage, and position
might affect the differences in the value added-exports puzzle across countries and industries.5
As shown in Panels (a) and (b) of Figure 2, the GVC backward linkage is positively relatedwith the average annual change differences between exports and value added, both at thecountry and industry levels. It implies that if a country or industry has a higher degree of
GVC backward linkage, the growth rate of exports is faster than that of value added. On theother hand, as shown in Panels (c) and (d) of Figure 2, the GVC forward linkage is negatively
related both at the country and industry levels, thus implying that if a country or industry
has a higher degree of GVC forward linkage, the puzzle is weaker. Moreover, as shown inPanels (e) and (f), the GVC position—upstreamness—is negatively related, thus indicatingthat if a country or industry more focuses on the upstream stage, the puzzle is weaker.
Therefore, to investigate whether the GVC participation and position are factors significantly affecting the value added-exports puzzle, this paper sets and econometrically tests
the following hypotheses: (1) The value added-exports puzzle is stronger for countries andindustries with the greater GVC backward linkage, while it is weaker for countries and in4

Hummels et al. (2001) mentioned both measures of vertical specialization (VS and VS1); however, they
could not calculate the forward type (VS1) because of data constraints.
5
We explain the variables and data in more detail in Section 3. We note that though her interest is in
the impacts on Chinese manufacturing productivity, Hua (2021) also considers the GVC backward linkage,
forward linkage, and position.

4

5

FRA

ESP
PRT
ITA
NLD

2

KOR

AUT

AUS

BEL
NZL

SWE NOR

FIN

USA

CAN

DNK

4
Petroleum products
Chemical products

Textiles and clothing

Manufacturing nec

Metal products

Food and beverages
Machinery and equipment
Motor vehicles

Paper products and printing
non-metallic products

Wood products
Other transport equipment

Electronic products

1

0

GBR

Basic metals
Electrical equipment

Rubber and plastics products

3

Industry Gross Exports/Total GDP - Value Added/Total GDP

DEU
MEX

medicinal products

2

6
4

JPN

-2

Country Gross Exports/Total GDP - Value Added/Total GDP

GRC

IRL

-1

-.5

0

.5

1

0

.5

GVC Backward Linkage

5

DEU
ESP
PRT
ITA

2

NLD
NOR

NZL

KOR
AUT

BEL

SWE
FIN
USA
CAN
DNK

IRL

-1

0

1

2

4

Basic andmetalsRubberplastics products

Petroleum products
Chemical products

Food and beverages
Machinery and equipment

0

5
IRL

2

4

4

Petroleum products
Chemical products

Textiles and clothing

Manufacturing nec

Metal products

Paper products and printing
non-metallic products

Motor vehicles

Food and beverages
Machinery and equipment

Wood products

Other transport equipment

Electronic products

1

0

GBR

Basic metals
Rubber and plastics productsElectrical equipment

3

Industry Gross Exports/Total GDP - Value Added/Total GDP

FIN
USA
DNK

CAN

medicinal products

2

6
4
2

KOR

SWE

-2

Country Gross Exports/Total GDP - Value Added/Total GDP

PRT
AUT

BEL

0

2

(d) Industry level

MEX
ITA

-2

1
GVC Forward Linkage

DEU

NOR
NZL

Other transport equipment

Electronic products

-1

JPN

NLD

Motor vehicles

Wood products

3

GRC

AUS

Manufacturing nec

Metal products(c) Country level

ESP

Textiles and clothing

Paper products and printing
non-metallic products

GVC Forward Linkage

FRA

Electrical equipment

1

0

GBR

medicinal products

3

4

JPN

2

Industry Gross Exports/Total GDP - Value Added/Total GDP

GRC

AUS

1.5

(b) Industry level-2

Country Gross Exports/Total GDP - Value Added/Total GDP

6(a) Country level

FRAMEX

1

GVC Backward Linkage

6-2

GVC Position

-1

0

1

2

GVC Position(e) Country level(f) Industry level

Figure 2: Value Added-Exports Puzzle and GVC
Note: This figure shows the relationship between the value added-exports puzzle and the GVC for 22 OECD countries and 17
manufacturing industries. Figures (a)-(c) show the relationship at the country level, and figures (d)-(f) show that at the industry
level. When we calculate the average annual change, we remove annual changes in the three years 2008, 2009, 2010, which can beaffected by the Great Recession. The Great Recession started in December 2007 and ended in June 2009.
Source: OECD Trade in Value Added (TiVA) indicators5

dustries with the greater GVC forward linkage. (2) The puzzle is weaker for countries andindustries that focus more on the upstream stage. The intuition is as follows: The greater
GVC backward linkage indicates that the share of foreign value added in gross exports ishigher and the share of domestic value added is lower. Thus, the greater is GVC backward
linkage, the stronger is the value added-exports puzzle. The greater GVC forward linkage
indicates that the domestic value added of gross exports that is re-exported to third countries
is higher. Thus, it is possible that the total domestic value added that is exported to trade
partners (part of which is further re-exported to third countries) is higher and therefore thevalue added-exports puzzle is weaker. When a country or an industry is on the upstream
stage, the backward GVC linkage is smaller and the forward GVC linkage is greater. Thus,
the puzzle is weaker.
The rest of this paper is organized as follows. Section 2 reviews the related literature.
Section 3 defines our key variables, explains our regression specifications and data, and shows
our main results and robustness checks. Section 4 concludes.

2

Literature Review

2.1

Literature on Manufacturing Trade and/or Value Added

First, our paper contributes to the literature on the increased manufacturing trade and/or
the decreased manufacturing value added. One set of studies focused on the increased manufacturing trade/exports. Bridgman (2012) analyzed manufacturing trade by using a threestage Ricardian trade model with raw materials, manufactured parts, and final goods. The
model shows that a reduction in trade costs in manufactured parts can cause vertical specialization and manufacturing trade to grow faster than overall trade. Dalton (2013) provided analternative explanation for the increased manufacturing trade on the basis of Just-in-Timeinventories. Flückiger and Ludwig (2015) looked at EU’s industry level and showed thatthe value of exports relative to GDP for the product groups facing strong Chinese export
competition increased marginally between 1995 and 2000 and has subsequently decreased,
despite a continuous decline in transport costs. On the other hand, the export volume of
manufactured products facing only weak competition grew considerably relative to GDP.

6

Another set of studies focused on the decreased manufacturing value added.6 Pilat et al.
(2006) argued that the declined share of manufacturing value added in GDP in OECD countries is due to price effects and to relatively slow growth in demand for manufacturing products, as demand for services is growing more rapidly. Using data gathered at 5-year intervals
during the period 1970-1990, Saeger (1997) estimated equations for a 23-country panel andfound that imports of manufactures from developing countries had a negative impact on the
manufacturing sector’s value added to GDP. Cáceres (2018) empirically explained the reason
why manufacturing value added/GDP declined in El Salvador. He found that the extreme
form of trade liberalization that was implemented in El Salvador is the main reason for thecontraction of tradable goods sectors. Cruz (2015) empirically explained why manufacturing
value added/GDP declined in Mexico. His results suggest that the evolution of income, capital accumulation, labor manufacturing productivity, trade openness, and the exchange rate
provide an explanation for this process.
In this line of studies, Bergoeing et al. (2004) linked both the increased manufacturingexports and the decreased manufacturing value added and argued that this is puzzling. Then
their model shows that a reduction in trade costs can qualitatively generate the puzzle facts.
This, however, is particularly true when the elasticity of substitution between manufacturing
and non-manufacturing goods is low, indicating that the elasticity of substitution between
manufactured goods is counterfactually high. Thus they suggested vertical specialization as
an additional factor that can explain this value added-exports puzzle, although they did notprovide an empirical test. Our paper now provides an empirical test for the puzzle on thebasis of vertical specialization and upstreamness, or GVC participation and position.

2.2

Literature on Vertical Specialization

Second, our paper also contributes to the literature on vertical specialization, or GVC participation. Pioneer studies such as Hummels et al. (1998) and Hummels et al. (2001) empiricallymeasured vertical specialization as the use of imported inputs in producing goods that areexported (VS), that is, GVC backward linkage.7 They showed evidence that vertical special6

Baily and Bosworth (2014) mentioned that manufacturing value added/GDP remained constant in the
U.S. over 1960-2011 (in 2005 prices).
7
Note again that Hummels et al. (2001) proposed both backward and forward measures of vertical specialization (VS and VS1); however, they could not calculate the forward type (VS1) because of data constraints.

7

ization played a significant role in the increase in trade.8 After these pioneer studies, there
has been vast literature on vertical specialization, or GVC participation (see, for example,
Amador and Cabral (2016) for a survey). In the literature, besides the pioneer studies’ vertical specialization measure (VS), that is, GVC backward linkage, another measure (VS1),
that is, GVC forward linkage has also been used. It is measured as the domestic value addedin gross exports that are further re-exported to third countries. Yi (2003), for example, developed, calibrated, and simulated a dynamic Ricardian trade model and quantitatively showedthat tariff reductions propagate trade via increased vertical specialization. He compared theincrease in vertical specialization in the model with that in the data measured as the increasein the total GVC participation (VS plus VS1), and found that the model accounts for muchof the data. Moreover, in the literature, a measurement for GVC position—upstreamness—has also been developed by taking the ratio of GVC forward to backward linkage. See, forexample, Koopman et al. (2014) for all of the aforementioned measures: GVC backward
linkage, GVC forward linkage, total GVC participation, and GVC position.
In this line of studies, our paper now relates the GVC backward linkage (VS), the GVC
forward linkage (VS1), and the GVC position (upstreamness) to the value added-exports
puzzle. We find that all of the three significantly affect the puzzle and, in particular, theGVC backward and forward linkages have the opposite effects.

3

Empirical Analysis

3.1

GVC Measurement Definitions

We use three indicators to measure vertical specialization, or GVC participation: F V Ash,
DV AF Xsh, and their sum. First, we define the GVC backward linkage (F V Ash) as follows:
F V Ashij = 1 −Exgrddcij
Exgrij(1)where Exgrddcij is the direct domestic industry value added content of gross exports ofindustry j from country i and Exgrij is the gross exports of industry j from country i.
8Klasing et al. (2013) argued that vertical specialization cannot justify why the volume of world trade
was so much larger in the 19th century than basic theory would suggest.

8

Notice that since Exgrddcij focuses on industry j of country i, F V Ashij includes not only
foreign value added but also other domestic industries’ value added of gross exports. Our
F V Ashij is thus the adjusted GVC backward linkage. The indicator is larger when the GVC
backward linkage is higher.
Second, we define the GVC forward linkage (DV AF Xsh) as follows:
DV AF Xshij =Dvaf xij
Exgrij(2)where Dvaf xij is the domestic value added in gross exports of industry j from country i thatare further re-exported to third countries. The indicator is larger when the GVC forward
linkage is higher.
Third, we define the total GVC participation (GV CP A) as the sum of F V Ash and
DV AF Xsh.
GV CP Aij = F V Ashij + DV AF Xshij(3)

In addition, we use an indicator to measure GVC position—upstreamness—which is defined as the ratio between the GVC forward linkage and the GVC backward linkage.
GV CP Oij =DV AF Xshij
F V Ashij(4)

The indicator is larger when the GVC position is more upstream.

3.2

Regression SpecificationsFirst, we test our hypothesis (1), that is, we investigate whether the GVC participation—the
GVC backward and forward linkages—can explain the heterogeneity in the value addedexports puzzle across country-industry pairs. The regression is:∆Eijt − ∆V Aijt = α0 + α1 ∆F V Ashijt + α2 ∆DV AF Xshijt + λij + θit + ηjt + ijt

(5)where ∆Eijt is the growth rate of gross exports/GDP ratio of industry j in country i in yeart. ∆V Aijt is the growth rate of value added/GDP ratio. ∆F V Ashijt is the growth rateof the GVC backward linkage, and ∆DV AF Xshijt is the growth rate of the GVC forward

9

linkage. λij is country-industry pair fixed effect, which captures the time-invariant effects(such as distance, culture relationship, and political relationship). θit is country-time fixed
effect, which captures the country-level shock (such as the trade costs). ηjt is industry-time
fixed effect, which captures the industry-level shocks. The sign of α1 is supposed to bepositive: The negative relationship between value added and gross exports is stronger forcountry-industry pairs with the higher GVC backward linkage. The sign of α2 is supposed
to be negative: The negative relationship between value added and gross exports is weakerfor country-industry pairs with the higher GVC forward linkage.
Second, we test our hypothesis (2), that is, we investigate whether the GVC position—
upstreamness—can further explain the heterogeneity in the puzzle across country-industry
pairs. The regression is:∆Eijt − ∆V Aijt = β0 + β1 ∆GV CP Oijt + β2 ∆GV CP Aijt + λij + θit + ηjt + it(6)where GV CP Oijt measures the relative position of GVC participation—upstreamness—and
we control for the total GVC participation denoted by GV CP Aijt . Since the GVC backward
linkage and forward linkage have opposite effects, the expected sign of β2 is ambiguous. The
coefficient that we are interested in is β1 , which is supposed to be negative: The negative
relationship between value added and gross exports is weaker for a country-industry pair
specialized in the more upstream stage.

3.3

Data

We use the 2021 release of OECD Trade in Value-Added (TiVA) database, which covers theyears 1995 to 2018. The indicators in database are provided for 66 countries (including all
OECD, European Union, ASEAN and G20 countries) and 45 industries. To be consistent
with the “value added-exports puzzle” in Bergoeing et al. (2004), we focus on 22 OECD
countries and 17 manufacturing industries. The main indicators in TiVA database include
gross output, value added, gross exports, gross imports, domestic and foreign value added
content of gross exports/imports, and so on. These indicators could be in 1-4 dimensions.
The indicators used in this paper are presented in Table 1.
First, we use EXGR, VALU and GDP to construct Eij and V Aij . Second, we use EXGR

10

Table 1: Main Indicators from TiVA
Variables

Indicator Dimensions

Definitions

EXGR

exp cou | exp ind | imp cou

gross exports

VALU

prod cou | prod ind

value added

GDP

prod cou

gross domestic production

EXGR DDC

exp cou | exp inddirect domestic value added content of gross exports

EXGR BSCI

VA sou cou | VA sou ind | exp cou | exp ind

origin of value added in gross exports

Note: exp cou: export country; exp ind: export industry; prod cou: production country; prod ind: production industry; VA sou cou: value
added source country; VA sou ind: value added source industry.
Source: OECD Trade in Value Added (TiVA) indicatorsand EXGR DDC to obtain F V Ashij . Finally, we use EXGR and EXGR BSCI to obtain
DV AF Xij . Notice that EXGR BSCI shows origin country-industry pair of value added ingross exports, and thus we can use this information to know whether origin country-industry
pair of value added is further re-exported to third countries.

3.4

Results

Using TiVA data, the results are presented in Table 2. Panels A-C show the results forthe country, the industry and the country-industry pair level, respectively. The patterns arerobust across different aggregate levels. Here, we focus on the country-industry pair level. In
columns 1 and 2, we control for the GVC backward linkage and forward linkage separately,while in column 3 we control for both of them. The sign of α1 is always significantly positive,
and the sign of α2 is always significantly negative. This confirms our hypothesis (1): The
negative relationship between value added and gross exports is stronger with the higher
GVC backward linkage while it is weaker with the higher GVC forward linkage. The resultsof column 4 also show that the negative relationship between value added and gross exports
is weaker for a country-industry pair specialized in the more upstream stage. This confirms
our hypothesis (2). We note that the effect of the total GVC participation—backward plus
forward—is ambiguous, which is positive at the country level, insignificant at the industry
level, and negative at the country-industry level.

11

Table 2: Gross Exports, Value Added and GVC
(1)

FVAsh
DVAFXshGVCPO
GVCPAConstant
Observations
R-squared
Country FE
Year FEFVAsh
DVAFXshGVCPO
GVCPAConstant
Observations
R-squared
Industry FE
Year FEFVAsh
DVAFXshGVCPO
GVCPAConstant
Observations
R-squared
Country-industry FE
Country-year FE
Industry-year FE

(2)
(3)
(4)
Gross Exports - Value Added
Panel A: country level1.548***
0.953***(0.160)(0.163)-0.662*** -0.379***(0.0814)(0.0689)-0.440***(0.0473)
0.523***(0.191)0.869
2.159*
1.284
1.431(1.396)(1.271)(1.212)(1.211)
440
440
440
440
0.549
0.542
0.587
0.592
X
X
X
X
X
X
X
X
Panel B: industry level
1.648***
0.685***(0.236)(0.197)-0.724*** -0.576***(0.0671)(0.0769)-0.574***(0.0550)0.200(0.234)5.458***
4.997***
5.186***
5.214***(0.965)(0.899)(0.929)(0.935)
340
340
340
340
0.576
0.671
0.692
0.692
X
X
X
X
X
X
X
X
Panel C: country-industry level1.283***
0.188**(0.151)(0.0810)-0.846*** -0.812***(0.0339)(0.0416)-0.693***(0.0268)-0.575***(0.116)
3.799
7.889***
7.620***
7.494***(2.691)(2.313)(2.277)(2.376)
7,480
7,480
7,480
7,480
0.374
0.635
0.637
0.636
X
X
X
X
X
X
X
X
X
X
X
X

Note: Standard errors are in parentheses.
***significant at 1%.

*Significant at 10%; **significant at 5%;

Source: OECD Trade in Value Added (TiVA) indicators

12

3.5

RobustnessFlückiger and Ludwig (2015) observed a reduction in the export volumes of European countries due to increased Chinese export competition. In order to control for the effect ofChina’s export competition, we add the growth rate of export competition (∆ECijt ) toequation (5). The export competition for industry j in country i in year t is defined as:
P
Excjt
∈
[0,
1].
Ex
is
export
from
China
in
industry
j
in
year
t.
ECijt = P Ex
cjt
k6=i Exkjt is
kjt
k6=i

the world exports except exports from country i. The larger the EC is, the more intensivecompetition from China the industry j in country i faces.
The results are presented in Table 3. At first sight, the positive effect of Chinese export
competition seems odd as the stronger competition from China for an industry in a country
can have a negative effect on its exports. A possible reason for this result is that China more
focuses on input production and OECD countries more focus on output production, which
can generate increases in exports by both China and OECD countries.
Table 3: Gross Exports, Value Added and GVC
(1)

(2)

(3)

(4)

Gross Exports - Value Added
Panel A: country-industry level
FVAsh1.268***(0.149)

DVAFXsh0.218***(0.0819)-0.821***(0.0334)-0.782***(0.0407)

GVCPO-0.671***

GVCPA(0.0263)-0.479***EC

7.607***

4.813***

4.917***(0.109)4.962***

Constant(0.959)-168.3***(0.817)-101.1***(0.822)-103.8***(0.801)-105.0***Observations(21.84)7,480(18.62)7,480(18.73)7,480(18.25)
7,480

R-squared

0.415

0.652

0.654

0.653

Country-industry FE

X

X

X

X

Country-year FE
Industry-year FE

X
X

X
X

X
X

X
X

Note: Standard errors are in parentheses.
***significant at 1%.

*Significant at 10%; **significant at 5%;

Source: OECD Trade in Value Added (TiVA) indicators

13

4

Conclusion

What can explain that the value added-exports puzzle holds in some countries/industries
but does not hold in other countries/industries from 1996-2018? To answer this question,using the country-industry level data for 22 OECD countries and 17 manufacturing industries
over 1995-2018, we have investigated whether the GVC participation and position are factors
significantly affecting the puzzle. Our main findings are twofold. First, the puzzle is stronger
for countries and industries with the greater GVC backward linkage, while it is weaker forcountries and industries with the greater GVC forward linkage. Second, the puzzle is weakerfor countries and industries that focus more on the upstream stage.
Our results thus indicate that Bergoeing et al. (2004) were right in that vertical specialization, or GVC participation, is a factor that can explain the puzzle. We, however, must
be careful that the two measures of vertical specialization, the backward and forward GVC
linkages, have the opposite effects.

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