developing countries and gvcs: ongoing and future … for artnet... · agriculture and the higher...

DEVELOPING COUNTRIES

PARTICIPATION IN GVCS: ONGOING AND

FUTURE WORK

Javier Lopez Gonzalez, Development Division, OECD Trade and Agriculture

Directorate

Bangkok 13 June 2014

• The international fragmentation of production is re-shaping the world economy.

• 3 key systems: ‘Factory Europe’, ‘Factory Asia’ and ‘Factory North America’(Baldwin and Lopez-Gonzalez, 2013.

• Heightened ‘interconnectedness’; implies that trade is increasingly complementary rather than competing.

• From a policy stand-point this means that impediments may not just affect foreign firms but also the competitiveness of domestic ones (Barriers to imports are barriers to exports)

• This presents new opportunities for policy coordination geared to meet common goals (FTAs, BITS, MFN reduction).

• Regulatory frameworks appear to be increasingly important in view of promoting further specialisation and international competitiveness.

2

Background

• Unravelling GVC activity:

i. Mapping participation;

ii. Identifying drivers – policy and non-policy related;

iii. Understanding consequences (jobs, distribution of gains etc);

• OECD TAD work falls along these lines

• Before, a brief note on how we measure it.

3

Aim

• We have traditionally relied on tariff headings labelled ‘parts and components’, but: – products are not exclusive to one end-use (i.e. think milk

or tyres)

– trade statistics give us no indication of; i) how products are combined (linkages between buying and selling sectors); or ii) about the final destination of the resulting output.

– Are measured gross and not net which can mislead analysts into wrongfully attributing location of value added (iPhone – Kraemer et al. (2011))

• This does not mean that trade statistics are useless! We still need to track product movement. That is where trade policy happens (tariffs).

4

Measuring: Trade flows

To produce a $10k car a factory uses

Direct domestic value added (capital and labour)

Intermediates (domestic steel + imported gear boxes)

5

Measurement: What the factories are

doing...

6

What the workers are doing (value

added)...

• Inter-Country Input-Output table measures:

– Backward linkage (sourcing): Foreign value added content of gross exports.

– Forward linkages (selling): Domestic value added sold to other countries for these to produce gross exports.

– Value added in final demand (Los et al. 2014)

– Other: length or distance to consumer.

• Trade data: – By end use Intermediate good imports and exports

(primary and processed) using BTDxE

• Firm level: – Using targeted surveys or case studies (ultimately it is

firms and not countries which engage in GVCs).

7

Mapping

8

Global Matrix of Value Added Trade

9

Backward Participation

10

Forward Participation

11

... A bigger pie?

4% 6%

78% 67%

2%

3%

6%

5%

2%

3%

3%

5%

6% 12%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1995 2009

Value Added Content of one unit of Chinese Electrical and Optical

Equipment Exports

EU CHN TWN JPN KOR USA RoW

1,238 41,640 26,510

439,944

692

20,852

1,993

31,359

603

18,366

1,045

30,425

1,951

78,747

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

1995 2009

Value of Chinese Exports of Electrical and Optical Equipment by origin

EU CHN TWN JPN KOR USA RoW

12

South East Asia increasingly looking

inwards for sources of intermediates…

13

… Mix of services, primary and

electrical equipment domestic value

added in exports…

0.2

.4.6

.81

TH A

1 995 200 5 20 09

0.2

.4.6

.81

KHM

1 995 200 5 20 09

0.2

.4

.6

.8

1

SG P

1 995 200 5 200 9

0.2

.4

.6

.8

1

I DN

19 95 200 5 200 90

.2.4

.6.8

1

M YS

19 95 2 005 200 9

0.2

.4.6

.81

VNM

199 5 2 005 200 9

0.2

.4.6

.81

PHL

199 5 2 005 2 009

0.2

.4.6

.81

BRN

199 5 2 005 2 009

Chemicals&Fuel Electrical_Equipment

Transport_Equipment light_manufacturing

manufacturing_machinery primary

services

14

… with foreign value added mainly in

services…

0.2

.4.6

.81

TH A

1 995 200 5 20 09

0.2

.4.6

.81

KHM

1 995 200 5 20 09

0.2

.4

.6

.8

1

SG P

1 995 200 5 200 9

0.2

.4

.6

.8

1

I DN

19 95 200 5 200 90

.2.4

.6.8

1

M YS

19 95 2 005 200 9

0.2

.4.6

.81

VNM

199 5 2 005 200 9

0.2

.4.6

.81

PHL

199 5 2 005 2 009

0.2

.4.6

.81

BRN

199 5 2 005 2 009

Chemicals&Fuel Electrical_Equipment

Transport_Equipment ligth_manufacturing

manufacturing_machinery primary

services

15

… and interesting ‘complementarities’

between domestic and foreign value

added…

Chemicals&Fuel

Electrical_Equipment

Transport_Equipment

ligth_manufacturing

manufacturing_machinery

primary

services

-.1

0.1

.2.3

Cha

ng

e in

dom

est

ic v

alu

e a

dde

d s

hare

-.1 0 .1 .2Change in imported value added share

Fitted values Change Dom

Philippines

Chemicals&Fuel

Electrical_Equipment

Transport_Equipment

ligth_manufacturing


primary

services-.04

-.02

0

.02

.04

Cha

nge

in d

omes

tic v

alue

add

ed s

hare

-.15 -.1 -.05 0 .05 .1Change in imported value added share


Thailand

Chemicals&FuelElectrical_Equipment

Transport_Equipment

ligth_manufacturing


primary

services

-.1-.0

5

0

.05

Cha

nge

in d

omes

tic v

alue

add

ed s

hare

-.1 -.05 0 .05 .1Change in imported value added share


Indonesia

Chemicals&FuelElectrical_EquipmentTransport_Equipment

ligth_manufacturing


primary

services

-.3

-.2

-.1

0.1

.2

Cha

nge

in d

ome

stic

va

lue

adde

d s

har

e

-.2 -.1 0 .1Change in imported value added share


Cambodia

16

Moving forward but at different speeds?

… but what determines participation?

17

• A simple econometric approach (Policy versus Non-policy or structural):

• Clustering standard errors to correct for country and year-specific omitted factors

• Reiterating the exercise for four broad types of activities

• Quintile regressions

𝐵𝐴𝐶𝐾𝑊𝐴𝑅𝐷𝑖𝑡 = 𝑓(𝑁𝑃𝑂𝐿𝑖𝑡1 ,… ,𝑁𝑃𝑂𝐿𝑖𝑡

𝑁 ,𝑃𝑂𝐿𝑖𝑡1 ,… ,𝑃𝑂𝐿𝑖𝑡

𝑀 , 𝜀𝑖𝑡 )

𝐹𝑂𝑅𝑊𝐴𝑅𝐷𝑖𝑡 = 𝑓(𝑁𝑃𝑂𝐿𝑖𝑡1 ,… ,𝑁𝑃𝑂𝐿𝑖𝑡

𝑁 ,𝑃𝑂𝐿𝑖𝑡1 ,… ,𝑃𝑂𝐿𝑖𝑡

𝑀 , 𝜀𝑖𝑡 )

where: (𝑁𝑃𝑂𝐿𝑖

1 ,… ,𝑁𝑃𝑂𝐿𝑖𝑁 and 𝑁𝑃𝑂𝐿𝑗

1 ,… ,𝑁𝑃𝑂𝐿𝑗𝑁) are country-specific indicators of non-policy

characteristics of country i in year t and (𝑃𝑂𝐿𝑖1,… ,𝑃𝑂𝐿𝑖

𝑀 and 𝑃𝑂𝐿𝑗1 ,… ,𝑃𝑂𝐿𝑗

𝑀 ); are the country-specific

indicators of policy determinants of GVC trade; and (𝜀𝑖𝑗𝑘 ) is the error term.

18

Mostly structural but policy can play a

role

-0.4

-0.2

0

0.2

0.4

0.6

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

SA

UB

RN

RU

SU

SA

AR

GA

US

BR

AJP

NN

OR

ZA

FC

HL

IDN

IND

NZ

LG

BR

TU

RG

RC

FR

AC

AN

DE

UP

RT

ITA

ES

PH

KG

LV

AC

HE

PO

LM

EX

BG

RD

NK

AU

TS

WE

FIN

NLD

VN

MC

HN

KO

RK

HM

ISR

TH

ALT

UIS

LC

ZE

MLT

SV

NM

YS

BE

LT

WN

PH

LIR

LE

ST

SV

KH

UN

SG

PLU

X

Backward participation (ratio)

Non-policy & constant Trade policy Investment opennness Residual Actual ratio

• Market size plays less of a role in backward and forward integration in agriculture and mining

• Level of development is a differentiating factor of integration across sectors:

• E.g. the higher the GDP per capita the lower the backward engagement in agriculture and the higher the forward engagement in manufacturing

• FDI openness has a more pronounced impact in mining and services as compared to manufacturing or agriculture

• Tariffs and RTAs seem to impede GVC integration more in manufacturing than in agriculture or mining and extractive industries

Drivers vary significantly by sector

• Hard to assess due to data availability. – But a lot can be done using trade data intelligently

(intensive, extensive margins, duration, netowork analysis, Haussman-Hidalgo)

– Need to evaluate other source of IO tables such as EORA.

– Look into combining IO data with trade data to add granularity.

• Think about what upgrading means, how we can capture it and what its determinants are.

• But also how GVC participation and inequality are linked.

20

What about developing and least-

developed country participation?

• Aim: To shed light on how the proliferation of GVC activity has affected the distribution of wage-income within the working population.

• Data: WIOD for calculation of both GVC indicators and wages

• Caveats: Wage-income does not capture the Bill Gates or the unemployed… No capital returns (Piketty, 2014). But 75% of household income is derived from wages (OECD, 2013).

21

GVCs and wage-income inequality?

22

Global inequality falling but adjustment

at the top end of distribution…

.4.5

.6.7

.8

Gin

i calc

ula

ted

with

'V

alu

e A

dd

ed

'

1995 2000 2005 2010year

with WIOD_GINI1

World inequality measured

.4.5

.6.7

.8

Gin

i calc

ula

ted

with

'w

ag

es'

1995 2000 2005 2010year

with WIOD_GINI0


100

120

140

160

180

Ra

tio

of

top 1

0 o

n b

ott

om

10

1995 2000 2005 2010year

with r90t10


20

25

30

35

40

Ra

tio

of

top 1

0 o

n b

ott

on 5

0

1995 2000 2005 2010year

with r90t50


3.5

44

.55

5.5

Ra

tio

of

top 5

0 o

n b

ott

om

10

1995 2000 2005 2010year

with r50t10


23

Mainly driven by within changes across

skill levels between countries… 0

.51

1.5

Th

eil

ind

ex

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

200

7

200

8

200

9

for countries

Theil decomposition of World inequality

Between variation Within variation

0.5

11

.5

Th

eil

ind

ex

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

200

7

200

8

200

9

for skills



0.5

11

.5

Th

eil

ind

ex

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

200

7

200

8

200

9

for sectors



0.5

11

.5

Th

eil

ind

ex

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

200

7

200

8

200

9

for development



24

Strong Development dimension

IND

IDN

ROMBGR

BRA

RUS

LTU

TUR

POL

HUN

CZE

KOR

SVN

PRT

ESP

FINSWE

GBR

AUS

BEL

DNK

AUT

CANNLD

USA LUX

.1.2

.3.4

.5.6

WIO

DG

INI

7 8 9 10 11Per Capita GDP (natural logarithm)

95% Confidence Interval Linear Prediction

obs

25

Preliminary evidence of negative

correlation wrt backward participation

JPN

RUS

USA

BRA

AUS

IND

CHN

GBR

IDN

TUR

ITADEU

FRA

LVA

POL

ROM

CANESP

FIN

AUT

KOR

SWEDNK

PRT

MEX

GRCCYPLTU

NLD

TWN

BGR

SVN

CZEBELSVK

EST

IRL

HUNMLT

LUX

.1.2

.3.4

.5.6

WIO

DG

INI

0 .2 .4 .6Backward Participation

95% Confidence Interval Linear Prediction

obs

26

The long-run: Countries with higher

backward participation have lower

wage-income inequality…

• Fall along 5 broad categories

• Predictions of literature on impact are not unambiguous (HOS, trade in tasks predict different effects) therefore it is an empirical issue.

27

Determinants

28

But in the short-run, +ve changes in

participation lead to higher inequality

• To tease out mechanisms and in particular justify long and short term differences

• To look at how the composition of the backward linkage (whether low, medium or high-skill) matters.

• To further differentiate the origin of these backward linkages

• To think about how the forward linkage and inequality could be linked.

29

More research is needed

Thanks!

[email protected]

This is joint work with: Przemislaw Kowalski, Alexandros Ragoussis and Cristian Ugarte and

Pascal Archard

30

Thanks

mailto:[email protected]

• Going back to the basics… What intermediates are countries trading and with whom?

• Caveats: – what is an intermediate good?

Hard to define products by end-use and added complication of exclusivity of current methods (milk example)

– What is it being used for?

Hard to establish how production is connected. Who is the selling and the using sector and therefore interlinkages

– What value is being added to this good?

Trade stats are gross and therefore hard to establish nature of activity

– Very little data on services flows and no decomposition by end use

• But still very useful: remember that trade policy mainly based on products not value added.

Trade flows

31

• UN-BEC nomenclature identifies i) intermediate; ii) capital; and iii) consumption goods.

• But complex GVC participation requires further ‘digging’.

• OECD-BTDIxE provides this granularity and can easily be extended to decompose trade along 11 different end-use categories.

Decomposing trade by end use

32

Global trade and contribution to export

growth

33

• Changes mainly in Int-Prim, fuel, medicaments and phones but contribution to export growth still mainly intermediates

Focus SEA: Evidence of moving away

from simple assembly?

34

OTH ESA MEN WCA SAS SEA

INT-PRIM 3.9 16.1 2.7 22.3 5.8 2.1

INT 47.6 43.8 18.0 12.1 37.4 40.5

FUEL 4.6 14.3 58.8 54.8 0.5 3.5

CONS 15.9 18.9 15.5 7.4 51.6 23.4

CAP 16.5 4.9 4.0 3.0 3.1 17.2

XMEDIC 1.7 0.2 0.3 0.0 1.0 0.2

XPC 3.0 0.3 0.3 0.0 0.2 7.1

XCARS 6.0 1.4 0.3 0.2 0.3 5.6

XPHONE 0.8 0.1 0.2 0.0 0.0 0.5

XPRCS 0.0 0.0 0.0 0.0 0.0 0.0

XMISC 0.1 0.0 0.0 0.1 0.0 0.0

Total 100.0 100.0 100.0 100.0 100.0 100.0


INT-PRIM 4.3 9.8 1.7 12.1 8.5 1.9

INT 41.3 37.3 16.7 8.4 35.7 37.7

FUEL 9.2 25.9 64.6 70.0 5.5 5.7

CONS 14.9 12.8 10.1 4.8 40.0 20.8

CAP 16.8 5.3 4.0 3.5 3.8 18.7

XMEDIC 2.2 0.1 0.2 0.0 1.5 0.2

XPC 2.3 0.1 0.2 0.0 0.1 8.0

XCARS 6.8 2.6 1.0 0.1 0.9 4.8

XPHONE 1.6 0.3 0.5 0.0 0.0 2.1

XPRCS 0.6 5.8 1.1 0.4 3.9 0.2

XMISC 0.1 0.0 0.0 0.6 0.0 0.0

Total 100.0 100.0 100.0 100.0 100.0 100.0


INT-PRIM 6.3 13.9 1.9 18.0 8.7 2.5

INT 42.2 34.0 18.3 18.9 34.1 43.1

FUEL 12.7 31.8 68.1 51.8 14.8 6.3

CONS 13.1 8.7 7.1 6.4 30.5 18.2

CAP 15.1 4.6 2.6 4.5 4.5 18.2

XMEDIC 3.1 0.1 0.6 0.1 3.0 0.3

XPC 1.1 0.1 0.1 0.0 0.2 6.2

XCARS 5.1 2.2 0.7 0.2 1.6 3.0

XPHONE 0.9 0.1 0.1 0.0 1.2 2.1

XPRCS 0.4 4.3 0.4 0.2 1.4 0.1

XMISC 0.1 0.1 0.1 0.0 0.0 0.0

Total 100.0 100.0 100.0 100.0 100.0 100.0

Export shares in 1998/99




INT-PRIM 3.9 16.1 2.7 22.3 5.8 2.1

INT 47.6 43.8 18.0 12.1 37.4 40.5

FUEL 4.6 14.3 58.8 54.8 0.5 3.5

CONS 15.9 18.9 15.5 7.4 51.6 23.4

CAP 16.5 4.9 4.0 3.0 3.1 17.2

XMEDIC 1.7 0.2 0.3 0.0 1.0 0.2

XPC 3.0 0.3 0.3 0.0 0.2 7.1

XCARS 6.0 1.4 0.3 0.2 0.3 5.6

XPHONE 0.8 0.1 0.2 0.0 0.0 0.5

XPRCS 0.0 0.0 0.0 0.0 0.0 0.0

XMISC 0.1 0.0 0.0 0.1 0.0 0.0

Total 100.0 100.0 100.0 100.0 100.0 100.0


INT-PRIM 4.3 9.8 1.7 12.1 8.5 1.9

INT 41.3 37.3 16.7 8.4 35.7 37.7

FUEL 9.2 25.9 64.6 70.0 5.5 5.7

CONS 14.9 12.8 10.1 4.8 40.0 20.8

CAP 16.8 5.3 4.0 3.5 3.8 18.7

XMEDIC 2.2 0.1 0.2 0.0 1.5 0.2

XPC 2.3 0.1 0.2 0.0 0.1 8.0

XCARS 6.8 2.6 1.0 0.1 0.9 4.8

XPHONE 1.6 0.3 0.5 0.0 0.0 2.1

XPRCS 0.6 5.8 1.1 0.4 3.9 0.2

XMISC 0.1 0.0 0.0 0.6 0.0 0.0

Total 100.0 100.0 100.0 100.0 100.0 100.0


INT-PRIM 6.3 13.9 1.9 18.0 8.7 2.5

INT 42.2 34.0 18.3 18.9 34.1 43.1

FUEL 12.7 31.8 68.1 51.8 14.8 6.3

CONS 13.1 8.7 7.1 6.4 30.5 18.2

CAP 15.1 4.6 2.6 4.5 4.5 18.2

XMEDIC 3.1 0.1 0.6 0.1 3.0 0.3

XPC 1.1 0.1 0.1 0.0 0.2 6.2

XCARS 5.1 2.2 0.7 0.2 1.6 3.0

XPHONE 0.9 0.1 0.1 0.0 1.2 2.1

XPRCS 0.4 4.3 0.4 0.2 1.4 0.1

XMISC 0.1 0.1 0.1 0.0 0.0 0.0

Total 100.0 100.0 100.0 100.0 100.0 100.0




• Focus on SEA • In 98/99 - relative to

world, mainly Consumption and Capital goods.

• by 2010/11 Intermediates rise but Cons declines

• Evidence of moving away from assembly?

• But perhaps not for Xphone?

35

Factory Asia?

ESA MEN WCA SAS SEA ESA MEN WCA SAS SEA

INT-PRIM 0.8 0.4 0.5 0.9 1.4 INT-PRIM 7.7 3.6 4.0 14.6 3.8

INT 4.5 5.2 3.1 2.9 18.4 INT 43.4 44.4 27.1 44.8 50.2

FUEL 1.8 2.7 2.7 0.8 4.1 FUEL 17.4 23.5 23.7 12.2 11.1

CONS 1.8 2.3 2.5 1.2 4.5 CONS 17.7 19.6 21.6 19.1 12.2

CAP 1.2 0.7 2.5 0.4 5.6 CAP 11.3 5.9 21.5 6.5 15.3

XMEDIC 0.1 0.1 0.0 0.0 0.1 XMEDIC 0.6 0.8 0.3 0.6 0.2

XPC 0.1 0.1 0.0 0.0 1.6 XPC 0.5 0.6 0.1 0.0 4.4

XCARS 0.1 0.2 0.2 0.1 0.4 XCARS 1.2 1.3 1.6 2.0 1.1

XPHONE 0.0 0.0 0.0 0.0 0.6 XPHONE 0.1 0.2 0.0 0.2 1.7

XPRCS 0.0 0.0 0.0 0.0 0.0 XPRCS 0.1 0.2 0.1 0.0 0.0

XMISC 0.0 0.0 0.0 0.0 0.0 XMISC 0.0 0.0 0.0 0.0 0.0

Total 10.3 11.7 11.5 6.5 36.6 Total 100.0 100.0 100.0 100.0 100.0

ESA MEN WCA SAS SEA ESA MEN WCA SAS SEA

INT-PRIM 13.1 1.5 17.5 7.8 1.1 INT-PRIM 14.6 1.7 19.8 8.3 1.8

INT 29.6 13.2 15.7 31.3 24.7 INT 33.0 14.9 17.8 33.4 39.0

FUEL 30.0 65.3 49.1 14.0 2.3 FUEL 33.4 73.9 55.5 15.0 3.6

CONS 6.9 4.8 3.9 29.3 13.7 CONS 7.7 5.5 4.4 31.3 21.6

CAP 3.5 1.9 2.0 4.1 12.6 CAP 3.8 2.2 2.2 4.4 19.9

XMEDIC 0.1 0.6 0.0 2.9 0.2 XMEDIC 0.1 0.6 0.0 3.1 0.3

XPC 0.1 0.1 0.0 0.1 4.6 XPC 0.1 0.1 0.0 0.1 7.3

XCARS 2.1 0.5 0.0 1.4 2.6 XCARS 2.4 0.6 0.0 1.5 4.1

XPHONE 0.1 0.1 0.0 1.2 1.5 XPHONE 0.1 0.1 0.0 1.3 2.3

XPRCS 4.3 0.4 0.2 1.4 0.1 XPRCS 4.8 0.4 0.2 1.5 0.1

XMISC 0.1 0.1 0.0 0.0 0.0 XMISC 0.1 0.1 0.0 0.0 0.0

Total 89.7 88.3 88.5 93.5 63.4 Total 100.0 100.0 100.0 100.0 100.0

Exports within the region

Exports out of the region

Exports within the region

Exports out of the region

• SEA intra regional exports represent 36% of total exports

• Composition of these overwhelmingly intermediates

• Extra-regional exports (64%) also important intermediates but more geared towards final products (consumption and Capital

• Further dig into the data: – Combining trade and IO data to obtain

measures of vertical specialisation

– Exploit firm level datasets

– Explore Eora

• Add dimensionality: – Network analysis

– Further refine analysis of products traded (Hausman-Hidalgo)

36

Way forward

developing countries and gvcs: ongoing and future … for artnet... · agriculture and the higher...

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