Heterogeneity of impact on national income from participating in the
global value chain---- An empirical study based on worldwide value-added tradeLiu, Jiaruiа

Abstract
This research focuses on analysing how participation in global value chain(GVC) affects acountry’s national income per capita. I start with an introduction of the methods to decompose
trade in value added from Inter-country input-output Tables, and the definition of indicators tomeasure GVC integration. Then I use these indicators calculated from EORA Global MARIO
database to fit in fixed-effect models and analyse the effect of GVC on national income per capita.
The results show that GVC-related contents embedded in gross trade have positive effects while
traditional trade contents have no significant effects. Also the result indicates that higher GVC
participation and GVC position have heterogeneous influences on the national income per capita
of countries across different income-levels. Lastly I will compare specific countries’ performancein GVC, using the sector-level decomposed data and explain the reasons.

Key words: Global value chain; Input-output Table; International trade

Graduate School of Economics, Kobe University. Address: 657-8501, 2-1 Rokkodai, Nada,Kobe,Hyogo.
Email:f846044251@gmail.com
а1

1. Introduction

The modern division of labor in international industries continues to evolve from the division oflabor among products to the division of labor within products, so that GVC trade has become
more and more popular. It is said in Zheng(2020) that along with the growth of trade in final
products, trade in intermediate products has grown rapidly, making both the expansion andintensive margins of global value chains continue to increase. Theoretically, developed countries
can benefit from on a wealth of diversified technologies and intermediate products to continuously
reconfigure production among countries, taking advantage of the comparative advantages of eachcountry to obtain the dividend of technological diversification. The economic development ofdeveloping countries relies on abundant cheap labor resources, undeveloped land, environmentand other natural endowments to attract the transfer of intermediate products and technologies
from developed countries. So questions naturally arise: whether a country's national income will
get increased by participating in GVC and does different countries get the same influence?
However, there are few empirical researches about solving these questions. In section 2, I give aliterature review of former researches about GVC, decomposition and the benefit of participation
in GVC.
An important influence of the development of GVC trade is that it makes the calculation of export
value based on traditional perspective unreliable. Traditional trade mainly focuses on theinternational exchange of final products, or the products which are directly absorbed in thatimported country. If the circulation process of intermediate products becomes more lengthy andthe direction becomes more complicated (back and forth), the measurement of export value based2

on traditional trade will be much larger than the actual added value, so-called double counting. In
order to calculate the “real” exports accurately, researchers have developed and improved a set ofdecomposition methods based on the international input-output table. Using these decomposition
methods, export value in value-added of each country and industry can be calculated and used forsolving the questions above.
In section 3, I summarize a brief introduction to the decomposition methods and the calculation of
GVC-related indicators.
In the rest parts of this paper, firstly the data source are provided, and then I describe theregression equations for analyzing the relationship between national income per capita and
GVC-related indicators. Finally, the results and conclusions are given.

2. Literature Review
Researches about GVC by using input-output tables dates back back to Hummels et al.(2001), in
which the definition of vertical specialization was firstly given (see Figure1). The production
process of country2 can be described as: country2 imports intermediate products from country1 (A)and uses other domestic intermediate products (B), labor and capital (C) to create gross output,
which is divided into two parts: domestically absorbed (D) and exports (E). The verticalspecialization measure(VS) is defined as A/(D+E)*E, A/(D+E) represents the intermediate
contents imported which are input for output per unit, so the whole product equals to the whole
imported contents embedded in gross exports (i.e foreign value added share). Also, this paper
provided a the VS1 concept which means “domestic value added contents embedded in the3re-export of the imported country ”. Hummels’s decomposition method (HIY) is considered as an
early version of the later Koopman(2014) and Wang et al.(2013), while the shortage of HIY is thatit didn’t provide the calculation of VS1 (i.e indirect domestic value added share). Koopman andWang(2010) improved HIY by using the value added share matrix which allows to calculate both
the domestic value added, VS and VS1.

Figure 1: Vertical Specialization(Source:Hummels(2001))

The researches of Koopman et al.(2010, 2012, 2014) provide a more detailed decomposition ofexports into 9 terms (KPWW method), the method calculates value added components by sourcing
countries/sectors, absorbed destinations, and product categories, for example, value added
embedded in the exports of final products or intermediate products which are used to produce final
products). This method also considers the double counting contents, when back and forth trade
happens and intermediate goods travel across countries over twice.
Wang et al.(2013) update KPWW to a 16-term decomposition which can fully decompose grossexports into 1.Domestic value added (DVA) absorbed as final products, intermediate products
4used to produce domestic final demand. 2.DVA in intermediate products which is produced by theimporter into re-exported final products to the third countries (including re-exported back to theorigin country (RDV)). 3.Foreign value added absorbed as final products, intermediate products
by different FVA sourcing countries: sourcing from the direct importer or sourcing from othercountries. 4.Pure double counting contents.
Besides these, there are also introduction files in some value added trade database whichinterpreting decomposition methods.
There are many papers focusing on comparing changes in countries’ GVC participation or GVC
positions, few of them analyze the relationship between GVC participation and national income.
Kummritz(2015) uses WIOD and OECD ICIO database of year 1995, 2000, 2005, and 2008, andgets a conclusion that higher GVC participation benefits one country’s domestic value added(GDP), he also finds that i2e and e2r indicators have robust and significant effects on mid andhigh income countries, but not significant on low income countries. Another working paper isAnna Ignatenko et al.(2019) , showing a similar relationship between national income per capita
using Eora Multi Regional Input-Output (MARIO) database. They also conclude that these
positive effects are only related to GVC trade but not to conventional trade.
One issue in Kummritz’s research is that he treats countries’ income level as a time-invariant
character, but as countries’ GNI per capita changes, income level is non-fixed (See appendix ofCristina Constantinescu et al(2017). Hence, there is a need to reconsider the relationship between
GVC participation and income per capita across countries at different income levels . In addition,
whether different GVC positions have an effect on GDP per capita also needs to be studied.

53. Methodology and data
3-1. KPWW
For simplicity, consider a 3-country 2-sector IO Table ( See Table 2 ). Each cell of intermediatedemand block represents a 2 by 2 matrix, each cell of final demand and gross output blocks
represents a 2 by 1 matrix, and each cell of value added and gross input blocks represents a 1 by 2
matrix. Let A be the intermediate input coefficient matrix (6 by 6 ), Aijmn = Iijmn / Xjn ,it gives the
direct input across sectors needed to produce each unit of gross output. Let v be the value added
coefficient matrix (6 by 6) ,where diagonal element gives value added share of gross output,

vim=Vim / Xim. i,j∈(1,2), mn∈(R,S,T).

Table 1 : 3-country 2-sector non-competitive International IO Table
Intermediate demand

Final demand

GrossCountry
R

Country
S

Country
T

CountryCountrCountr

R

yS

yT

IRR

IRS

IRT

FRR

FRS

FRT

XR

ISR

ISS

IST

FSR

FSS

FST

XS

ITR

ITS

ITT

FTR

FTS

FTT

XT

Value added

VR

VS

VT

Gross input

XR

XS

XT

Country
R
Intermediate

Country

input

S
CountryToutput

It is easily to get: ( I-A )-1 F = X by using the relationship: AX+F=X, where F is a 6 by 1 matrix,
each element is the sum of the elements in final demand block by row, it represents all countries’
final demand for each country’s each sector, for instance, final demand for country m’s sector i is6

Fi m   Fi mn . Multiplying v

and ( I-A )-1 - which is known as Leontief inverse- gives thenvalue added multiplier. contents of the gross output induced by each country’s final demand. For
example, as for country S’s final demand column FimS, we can compute:
 v1R

0


 ...


0


...
v

R
2
S
1

v

v2S
v1T
...

0  B11RR
 RR
 B21
 SR
 B11...  B21SR
 TR
 B11
T  TRv2  B21

B12RR

B11RS

B12RS

B11RT

RR
22
SR
12
SR
12
TR
12
TR
22

RS
21
SS
11
SS
21
TS
11
TS
21

RS
22
SS
12
SS
22
TS
12
TS
22

RT
12
ST
11
ST
21
TT
11
TT
21

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B

B12RT  F1RS

B22RT  0

B12ST B22ST  ...

B12TT 
TT 
B22
 0

...
RS
2

F

F1SS
F2SS
F1TS
...

0 



 ①
... 


F2TS 

and get a 6x6 matrix:

 v1R B11RR F1RS
 R RR RS
 v2 B21 F1
 v S B SR F RS
 1 11 1
 v2S B21SR F1RS
 T TR RS
 v1 B11 F1
 vT BTR F RS
 2 21 1

v1R B12RR F2RS
v2R B22RR F2RS
v1S B12SR F2RS
v2S B12SR F2RS
v1T B12TR F2RSTR RS
v2T B22
F2

Each element of ② ,v1R B11RS F1SS
v2R B21RS F1SS
v1S B11SS F1SS
v2S B21SS F1SS
v1T B11TS F1SS
TS
v2T B21
F1SS

v1R B12RS F2SS
v2R B22RS F2SS
v1S B12SS F2SS
v2S B22SS F2SS
v1T B12TS F2SSTS
v2T B22F2SS

v1R B11RT F1TS
v2R B12RT F1TS
v1S B11ST F1TS
v2S B21ST F1TS
v1T B11TT F1TS
TT TS
v2T B21F1

v1R B12RT F2TS v2R B22RT F2TS 
v1S B12ST F2TS 
②v2S B22ST F2TS 
v1T B12TT F2TS 
TT TS v2T B22F2 

vim Bijmn F jnS represents that, the value added contents produced by country

m sector i which is induced by the final demand of country S for country n sector j,1 or simply
country m sector i’s exports in value added to S through country n sector j’s final product exports.
If we focus the 1st and 2nd rows, they means country R sector 1 and 2’s exports in value added to S
respectively.
Applying the same way and substitute gross export E for F, we can obtain a Value added exports
matrix:1

In order to produce the intermediate products used by country n’s sector j to produce the final demand of country

S for country n sector j, country m sector i needs to produce

Bijmn F jnS gross output, multiplying it with v gets thevalue added parts generated by country m sector i.

7 v1R B11RR E1R
 R RR R
 v2 B21 E1
 S SR Rv B E
VBE   1S 11SR 1R
 v2 B21 E1
 T TR R
 v1 B11 E1
 v T B TR E R
 2 21 1

v1R B12RR E2Rv1R B11RS E1Sv1R B12RS E2Sv1R B11RT E1T

v2R B22RR E2R

v2R B21RS E1Sv2R B22RS E2Sv2R B12RT E1T

v1S B12SR E2R

v1S B11SS E1Sv1S B12SS E2Sv1S B11ST E1T

v2S B12SR E2R

v2S B21SS E1Sv2S B22SS E2Sv2S B21ST E1T

v1T B12TR E2R

v1T B11TS E1Sv1T B12TS E2Sv1T B11TT E1T

TR R
v2T B22E2TS S
v2T B21E1TS S
v2T B22E2TT T
v2T B21E1

v1R B12RT E2T 
v2R B22RT E2T 
v1S B12ST E2T 
③
v2S B22ST E2T 
v1T B12TT E2T 
TT T 
v2T B22
E2 

Note that E is computed as sum of intermediate and final use of other countries for the targetm

sector, i.e. Ei 

I
j nm

mn
ij

  Fi mn . Therefore, vim Bijmn E nj gives the value added contentsnm

produced by country m sector i embedded in the exports of country n sector j. This matrix actuallyprovides DVA, FVA and DVX in each sector’s exports, as:

DVA in exports ofcountry 1 sector 1

FVA in exports ofcountry 1 sector 1

 v1R B11RR E1R
 R RR R
 v2 B21 E1
 S SR R
 v1 B11 E1
 v2S B21SR E1R
 T TR R
 v1 B11 E1
 v T B TR E R
 2 21 1

v1R B12RR E2R
R
2
S
1
S
2
T
1
T
2

RR
22
SR
12
SR
12
TR
12
TR
22

R
2
R
2
R
2
R
2
R
2

v1R B11RS E1S
R
2
S
1
S
2
T
1
T
2

RS
21
SS
11
SS
21
TS
11
TS
21

S
1
S
1
S
1
S
1
S
1

v1R B12RS E2S
R
2
S
1
S
2
T
1
T
2

RS
22
SS
12
SS
22
TS
12
TS
22

S
2
S
2
S
2
S
2
S
2

v1R B11RT E1T
R
2
S
1
S
2
T
1
T
2

RT
12
ST
11
ST
21
TT
11
TT
21

T
1
T
1
T
1
T
1
T
1

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E

v B E
v B E
v B E

v B E
v B E
v B E

v1R B12RT E2T 
v2R B22RT E2T 
v1S B12ST E2T 
v2S B22ST E2T 
v1T B12TT E2T 
TT T 
v2T B22E2 

DVX in exports ofcountry 1 sector 1

If separating gross export into intermediate and final product exports, and by destination, we canobtain Koopman et al.(2014)’s 9-term decomposition (sectors aggregated):(  B RR F RS   B RS F SS    B RS F ST )   S RS RS  R T  R,S


R
R 
RS
SR
RS SR
RR 1
RR  
E  v  (  B F   B A ( I  A ) F   S R   S R
RS SR
RR 1 R
B A (I  A ) E
 S

R
   v T B TR F RS  B TR A RS ( I  A SS ) 1 F SS  B TR A RS ( I  A SS ) 1 E ST R S R







④

8

These 9 items are, respectively: ①sum of DVA of R’s final products exports. ②sum of DVA of
R’s intermediate products exports, produced into domestically absorbed final products. ③sum of
DVA of R’s intermediate products exports, produced into final products re-exported to a third
country. ④sum of DVA of R’s intermediate products exports, produced into final products
re-exported back to origin country R (re-imported value-added as final).⑤sum of DVA
re-imported as intermediate products to produce domestic final use.⑥Pure-double countedintermediate products exports due to domestic source. ⑦sum of FVA of R’s final products
exports. ⑧sum of FVA of R’s intermediate products exports, produced into domesticallyabsorbed final products.⑨Pure-double counted intermediate products exports due to foreignsource. A simple relationship between gross exports, DVA, DVX and FVA can be found in
Koopman et al.(2014) and Aqib et al.(2017): 1. DVA + FVA = gross exports, 2. DVA = Directly
absorbed contents + DVX, 3. DVX = IV( DVA in intermediates re-exported to 3rd country in final
products ) + Contents finally returns + Double Counting. Define GVC-related trade as DVX+FVA
and unrelated trade as the contents absorbed by the direct importer.
Table2 gives an understanding of different kinds of flows of goods calculated by KPWW’s 9 items
respectively. Notice that the 9 terms from KPWW don’t give DVA in exports that is re-exported tothe 3rd country from in intermediate goods (i.e.vRBRSASTXT) as Koopman et al.(2014) suggest,
sum of ③~⑥ only provides a part of Hummels’s VS1. Therefore, another more detailed method
WWZ which is explained in Wang et al.(2013), is used to compute DVX.

9

Table 2 Implication of KPWW’s 9-term decomposition2

#

Terms

Trade flows①

v R  B RR F RS

fin
R 
S

②

v R  B RS F SS

int
fin
R 
S  
S③

v R   B RS F ST

int
fin
R 
S  
T④

v R  B RS F SR

int
fin
R 
S  
R⑤

v R  B RS A SR ( I  A RR ) 1 F RR

int
int
fin
R
S
R 
R

⑥

v R  B RS A SR ( I  A RR ) 1 E R

int
int
or intRSR  fin Export⑦ v B F

int
fin
T
R 
S

⑧
⑨

3-2

S R

S R

S  R T  R,S

S R

S R

S R

T

TR

RS

T R S R  v B A (I  A ) FSS  v B A (I  A ) E

S

T

TR

RS

SS 1

S R T R

T

TR

RS

SS 1

T R S R

int
int
fin
T
R 
S  
S
int
int
fin or int
TR 
S  

 Export

GVC indicators

Two well-known GVC indicators: GVC participation and GVC position, are defined as:
GVC participation = (DVX+FVA) / Gross Exports
GVC position = ln ( 1+ DVX / Gross Exports ) - ln ( 1+ FVA / Gross Exports )In the equations, DVX and FVA share in gross exports are defined as forward and backward
participation, respectively. They indicate the formation of countries participating in GVC: as anexporter of intermediate products for producing other countries’ exports, and as an importer who
uses foreign intermediate products to produce exports.
In the other hand, GVC position is an index which shows if countries contribute more value added2

 represents a process of producing finished products from intermediate products.
10

in other countries’ exports, i.e. more upstream or reversely.

3-3

Data

It is known there are at least 6 kinds of globally ranged international Input-Ouput Table databases( see Inomata (2019) ), covering different regions and sectors/products. Due to different versions,some databases built across years are not consistent (for example OECD-ICIO).
The database I use to compute DVA, FVA, DVX and other related indicators is EORA26 which
covers 190 countries and 26 sectors during 2001-2016. In addition, data of other national accounts
such as GDP, gross capital formation are taken from World Bank Database, the classification ofincome level is based on World Bank’s threshold of countries’ GNI per capita released in 2012:
low(<$1005), lower middle(<$3975), upper middle(<$12275) and high income. The summary ofmain variables is as follows, note that countries relying on oil export are likely to have higher
position index but less relativity to the idea of GVC upstreamness, such as R&D, design..so they
are excluded.
Table 3: Summary statistics

Variable

Obs

Mean

Std. Dev.

log( GDP per capita )log( DVA )log( DVX )log( FVA )log( Total )log( Gross capitalformation per capita )GVC participation
GVC position
Forward participation
Backward participation
Trade openness

2718

8.56

1.62

WithinStd.Dev
0.41

2718
2718
2718
2718

15.46
14.41
14.15
15.75

2.52
2.63
2.75
2.56

2187

0.15

2718
2718
2718
2718
2543

0.52
0.03
0.28
0.24
90.01

Min

Max

2.97

12.15

0.47
0.49
0.52
0.48

9.71
8.74
8.32
10.30

21.35
20.34
20.59
21.56

1.65

0.48

-5.33

3.41

0.13
0.15
0.10
0.13
56.23

0.03
0.04
0.03
0.03
14.21

0 .24
-0.41
0.07
0.00
11.86

0.94
0.49
0.82
0.68
442.6211

Figure2 shows forward, backward participation and GVC position indexes of some representative
countries at different income level. Specifically, middle income: China, Philippines and Thailand,high income: USA, Japan and Germany. ( GVC position indexes are scaled on the right axis). The
results show that high income countries’ GVC position indexes are decreasing and mid income
countries’ position indexes are rising. This reflects the characteristics of today's GVC trade, that is,
as developing countries improve their skills in production, they shift more value added contents of
mid and high tech products from imports to domestic production, as their DVX share increases
and FVA share decreases, the GVC position indexes rise up.

Figure 2: Forward / Backward participation and GVC position indexes of different countries12

To check the changes of DVX and FVA at sector level, for each country above, I calculate theshare of different sectors’ DVX in the total GVC-related trade (namely, DtT ratio). Figure3&4
show the evolution during 1995-2015.
After joining in WTO, China has firstly shown an decrease in DtT ratio of mid/high tech
manufacturing: Electrical Machinery & Petroleum, Chemical and Non-Metallic Products andafter 2010, China’s DVX of these sectors turned to increasing. Additionally, after the financial
crisis, China’s DVX of financial product exports has grown rapidly from 5.4% to 8% in the total
GVC-related trade. In Philippines and Thailand, rapid growing of DtT ratios can be seen inElectrical Machinery and Petroleum, Chemical and Non-Metallic Products respectively. And forthese countries, DtT ratios in low-tech manufacturing such as Agriculture, Mining or retail tradeare all getting smaller. This phenomenon most likely means that as production ability increases
and income rises up, the production of low-tech manufacturing in GVC will shift from where used
to be low income countries to the countries at lower income level.

Figure 3: DVX / GVC-related trade of different sectors in China

13

Figure4: DVX / GVC-related trade of main exporting sectors in Philippines

Figure 5: DVX / GVC-related trade of main exporting sectors in Thailand

14

On the contrary, DtT ratios of mid/high-tech manufacturing in high income countries have showna downward trend in recent years (taking DEU and JPN for example: Figure6), which confirms thefact that middle income countries have improved their competitiveness in the production of
mid/high-tech products.

Figure 6: DVX /GVC-related trade of mid/high-tech manufacturing in high income economies153-4 Regression model
Referring to Kummritz (2015) and Anna Ignatenko et al.(2019), to estimate the impact of GVC
participation on income per capita, the following equation is used:ln (Y)i,t  Xi,t-1  Control  ui  vt  it  ⑤
N

Y/N denotes dependent variables divided by population, such as GDP (according to OECD(2005),
this paper uses GDP per capita to represent national income per capita), and the factors of
production. X denotes GVC-related variable. Because it is difficult to find a valid instrument
variable which can fully represent the economical meanings of GVC participation or position,
lagged variables are taken to reduce the bias caused by endogeneity and reverse causality alongwith the two-way fix effects. In addition, Kummritz (2015) suggests that another reason to takelagged variables is that the effect of GVC participation on domestic value added is always on thenext period.
When comparing countries at different income levels, I add dummy variables multiplied byexplanatory variables to form interaction terms for regression.

4. Results
Table4 shows the result of estimating GVC participation’s effects on GDP per capita and its
contribution factors in a Cobb-Douglas production function. Average years of schooling is used toapproximate a country’s human capital (see OECD: human capital), and growth of productivity is
calculated as residual of the equation: log(GDP per capita) = log(GCF per capita) + Year of16

Schooling +u. Lag in GDP is controlled when independent variables includes share of trade inGDP, in order to rule out the possibility that the share increases by a decline in GDP.
Table 4: Estimates of GVC-related trade’s impacts on GDP per capita and its contribution factors3log(Capital per

Year ofcapita )Schooling

（3）

（4）

(5)

0.195

-0.9571.275

0.971***（0.090）（0.379）(1.024)( 0.150)

0.000

0.375

-2.462-0.315(0.770)( 1.337)(0.305)log(GDP per capita)（1）
GVC-related trade in GDP
GVC-unrelated trade in
GDP

（2）

**

**

（0.160）0.472

log(GVC-related trade)

Factor productivity***( 0.048)0.247***

log(GVC-unrelated trade)(0.038)Adj. R squared

0.92

0.75

0.76

0.68

0.68

Obs

2405

2405

2050

2004

1993

Country,Year

Fixed effectControls

Lag in GDP,

Trade

Trade Openness

Openness

Lag in GDP, Trade Openness

Column(1) shows that the higher the proportion of GVC-related trade in GDP is, the higher theincome per capita in the next period will be. And when controlling share of GVC-related trade inGDP, increasing share of GVC-unrelated trade in GDP does not give a statistically significant
effect on income per capita. This result is consistent with Anna et al.(2019). Column(2) confirms
this relationship by estimating elasticity of income per capita with respect to GVC-related andunrelated trade: when increasing by 10%, GVC-related trade has a larger effect on income thanunrelated trade by about 2.7%.

Note: *,**,*** represents statistically significant at 10%,5% and 1% level and heteroskedasticity -corrected
standard errors are reported in parentheses.The same applies to all the following tables.
317Column(3)-(5) shows the effect of GVC-related trade on production factors: unlike the previous
study, I get a significant negative relationship between GVC-related trade and gross capitalformation, which implies GVC-related contents in exports should be the cause of elimination ofless competitive firms when facing international trade - leading to a decrease in capital investment- instead of GVC-unrelated contents. And for productivity, there is a significant positive
relationship which indicates the effect of GVC on technology acquirement (technology spillover
effect) and productivity improvement. However, in column(4) I didn’t find a significant
relationship between human capital and either GVC-related or unrelated trade, this suggest theneed to use a better variable rather than years of schooling.
In the next section, I estimate the effect of GVC participation on income, including forward andbackward participation across different income levels. Results are shown in Table 5. Overall, GVC
participation has a positive impact on income per capita. In colomn(2), after classifying countries
into four economic levels, the results show that the higher the economic level, the greater theeffect of GVC participation on national income. Among them, low-income countries do not show
significant results. In order to further analyze this result, I divide GVC participation into forward
and backward participation, and interact them with income-level dummy variables respectively.
The result is shown in column(4). It should be noted that in column(3), the coefficients of forward
and backward participation are very close, so a F-test is used to test whether they are significantly
different, however, the result doesn’t allow to reject the null hypothesis ( p-value = 0.1808), that is
to say, on the overall level, forward and backward participation may have a similar effect on
income.

18

Table 5: Estimates of GVC participation’s impacts on GDP per capita across income levels
log(GDP per capita)(1)
GVC participation

(2)

(3)

(4)

2.051

***

(0.565)GVC participation:low income

0.747(0.846)

GVC participation:lower middle

1.362*

income(0.708)GVC participation:upper middle

1.753***

income(0.602)
GVC participation:high income

1.809***(0.573)Forward participation

2.229***(0.587)

Backward participation

1.964***(0.629)Forward participation:low income-0.004(1.004)

Forward participation:lower middle

0.980

income(0.817)
Forward participation:upper middle

1.937***

income(0.584)
Forward participation:high income

1.818***( 0.552)

Backward participation:low income

1.651**(0.737)

Backward participation:lower middle

1.510**

income(0.594)
Backward participation:upper middle

1.119

income(0.744)
Backward participation:high income

1.350*(0.690)Adj. R squared

0.76

0.78

0.75

0.79

Obs

2405

2389

2405

2389

Fixed effect

Country,Year

Controls

Lag in Unrelated Trade,Trade Openness
19The result in column(4) suggests that backward participation has a significant income-increasing
effect for low and lower middle income countries, while for upper middle and high income
countries, the effect becomes weaker and less significant. However, as for forward participation,
the result is just the opposite: for upper middle and high income countries, the effects of forward
participation are higher than backward participation but not significant for low and lower middle
income countries. That is to say, there exists heterogeneity in the impacts of forward and backward
participation on income across countries at different income levels.
When estimating the relationship between GVC position and national income per capita, I findthat the heterogeneity of the impact of GVC participation on countries at different income levels is
also reflected in GVC position(See Table 6). In the equations of column(2) and (4), trade openness
is used to control the size of a country’s trade when changing GVC position.
Table 6: Estimates of GVC position’s impacts on GDP per capita across income levels
log(GDP per capita)
GVC position

（1）

（2）

1.140**

0.327（0.449）（0.400）GVC position:low income
GVC position:lower middle income
GVC position:upper middle income
GVC position:high income

(3)

（4）

-1.062**-1.278**(0.478)(0.497)0.309-0.103(0.312)(0.295)1.445***

1.051**(0.467)(0.513)

1.267***

0.662*(0.337)(0.395)

Adj. R squared

0.74

0.76

0.78

0.79

Obs

2567

2405

2519

2389

Fixed effect

Country,Year
Lag inControls

Unrelated
Trade,
Gross Exports

Lag inUnrelated

Lag in Unrelated

Trade,

Trade,

Gross Exports,

Gross Exports

Trade Openness

Lag in Unrelated
Trade,
Gross Exports,
Trade Openness

20The results indicates a higher GVC position has a negative effect on income for lower income
level countries, and a positive effect for higher income level countries. It is not surprising after
estimating the effects of forward and backward participation in the former section but the questionis why the heterogeneity occurs.
The result can be understood by analyzing the differences in the production sectors of DVX andFVA of countries at different income levels. For example, lower income level and lesstechnologically developed countries do not have the ability to produce diverse products
domestically, so increasing forward participation for them is nothing more than increasing the
proportion of simple, low-tech products in the export of intermediate goods, while expanding
low-tech production is very limited in raising national income, on the other hand, increasing
backward participation means increasing the import of diverse products from foreign countries, sothat they can improve their own productivity or production diversity through learning by doing
and technology spillover effects, thereby increasing national income. A characteristic of thislearning by doing behavior is that exporting sectors of producing DVX and using FVA in
low-income countries are different, they need to use FVA from sectors where they are not good at
producing DVX (See Figure 7).We can see that Cambodia uses a lot of FVA of Textiles andWearing Apparel sector which is not a major proportion in DVX. Similarly, in India’s
GVC-related trade, DVX mainly comes from low/mid-tech manufacturing while high-tech
manufacturing sector such as Electrical and Machinery has a large FVA share.
For upper middle income countries, the result turns to be different. Because of a higher level oftechnology, DVX embedded in their exports mainly comes from mid/high-tech sectors, so the21effects of GVC participation on income also mainly comes from forward participation. See Chinaand Philippines, both countries produce large DVX of Electrical and Machinery while also usinglarge FVA of the same sector, suggesting an effect of technology upgrading - especially
Philippines’s DtT ratio of Electrical and Machinery increased by about 71.3% and FtT ratio
decreased by about 38.2% from 2000 to 20154. However, as upper middle income countries areapproaching developed countries in terms of productivity and capacity, the motivation oftechnology upgrading provided by the backward participation is becoming smaller. For highincome countries, the effects of backward participation mainly come from benefits of verticalspecialization and lower cost through outsourcing, therefore both forward and backward
participation have positive effects on income.

4

Author’s calculation
22

Figure 7: Composition of sectors producing DVX/ using FVA in different countries (2015)23

5. Concluding remarks
In this paper, I firstly decompose the EORA Global MARIO tables (Eora26) by using KPWW andWWZ methods to obtain GVC indicators. Secondly, through regressions, I come to the following
conclusions: 1. there is heterogeneity across countries at different economic levels in the impacts
of GVC participation on pulling income per capita. Low and lower middle income countries
benefits more through backward participation, upper middle and high income countries benefits
more through forward participation. 2. As a extension of 1, GVC position index also has different
effects on countries at different economic levels, for low-income countries, lower GVC position
has a positive effect on national income, while it is the opposite for high income countries. Finally,
I make an explanation by comparing sectors which produce or use DVX / FVA across countries:
lower income countries use FVA of diversified intermediates from other countries to increase thevariety of exports, and get technology upgrading through learning by doing. And for higher
income countries, playing a role as suppliers of DVX of mid/high-tech intermediate products is adriver of economic growth.
To summarize, the biggest contribution of this paper is that, compared to previous research, I use
data covering a wider range of time and regions to analyze not only GVC participation, but also
the impacts of forward and backward participation as well as GVC position index on national per
capita income, and the heterogeneity of the impacts across countries at different income levels.
However, there still remains some problems that need to be further studied in this research, forexample: when discussing the technology spillover effect, the method in this paper is to use theresidual of the C-D production function, as an approximation of this effect,while this method is
criticized by many researchers. It is necessary to find a better approach to calculate TFP and the24technology spillover effect. Moreover, although Eora26 covers the most countries, theclassification of sectors of it is too general, in order to analyze different countries’ performances in
GVC by looking into more detailed sectors’ DVX/FVA , other I-O tables are required.

25

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