“The development and future of
Factory Asia”
Richard Baldwin and Rikard Forslid
RIETI seminar: Ideas for a research agenda
4 December 2013, Tokyo
Overarching question
• How to make global value chains (GVC) work
for developing nations?
• Study Factory Asia = best example.
Some background
• Globalisation changed
• Today’s process should not be studied using
only 20th century tools.
• KEY change:
– “De-nationalisation of comparative advantage”
Globalisation changed
20%
0%
0%
G7 nations’ share of global GDP, 1820 – 2010.
5%
6 Risers,
9%
3%
1970
10%
1820
1839
1858
1877
1896
1915
1934
1953
1972
1991
2010
10%
China,
19%
2010
1820,
22%
RoW
30%
2005
20%
40%
2000
30%
G7, 47%
50%
1995
2010,
50%
40%
60%
1990
50%
70%
1985
60%
1990,
65%
1980
70%
80%
1975
1988,
67%
World manufacturing share
80%
Source: unstats.un.org; 6 risers = Korea, India,
Indonesia, Thailand, Turkey, Poland
G7 nations’ share of global manufacturing, 1970
– 2010.
‘Smile curve’: Distribution of value
Share of value
added
Post-1990 value
distribution
1970s & 1980s
value
distribution
Stage
Pre-fab Fabrication Post-fab
services
services
Global GDP shares, 1960-2012
80%
67%
70%
60%
G7,
48%
50%
40%
30%
RoW
10
gainers
27%
20%
10%
Post-1990:
• G7 share loss goes to 10
developing nations.
• RoW see little change.
11%
0%
1990
China, Brazil, Mexico, Poland, India, Turkey, Russia, Korea, Indonesia, Venezuela
People in poverty (under $2/day)
Millions under $2/day by
national income class
Lomiddle
1993
HiMiddle
Low
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2010
1,600
1,400
1,200
1,000
800
600
400
200
-
1990
Post 1993
• Hi-middle poverty plummets.
- 650 million fewer poor!
• Others’ poverty keeps rising.
Globalisation: 3 cascading constraints
High
High
High
=
Preglobalised
world
=
1st
unbundling
=
2nd
unbundling
Steam revolution
High
High
Low
Stage A
Stage B
Stage C
ICT revolution
High
Low
Low
Stage
A
Stage
B
Stage C
20th century comparative advantage
Stage
A
Stage
B
Goods crossing
borders
Stage
A
Stage
C
• Goods = ‘bundle’ on national knowhow,
labour, capital, institutions, etc.
• National economies only connected via
competition in goods markets.
Stage
B
Stage
C
21st century comparative advantage
Stage A
Stage C
1) Supply-chain linkages: Cross-border flows
of goods, know-how, ideas, capital &
people.
2) Doing business abroad: Application of
tangible & intangible assets in developing
nations.
Stage B
• Goods = mixture of national knowhow, labour,
capital, institutions, etc. (e.g. hi-tech + low
wages).
• National economies connected via much
richer flows: knowhow, goods, services,
people, capital, etc.
Why it matters
• OLD: Study national performance looking at
national factors.
– ‘Team Japan’ versus ‘Team Germany’
Regress growth/exports/etc on national right-hand
side variables.
• NEW: Study national performance looking at
regional and national factors.
– ‘Factory Asia’ versus ‘Factory North America’
Regress growth/exports/etc on national & regional
right-hand side variables and/or allow interactions
depending upon supply-chain exposure.
First steps in study GVC and
development
• Shifting resources to trade sectors is prodevelopment.
• Growth in value added exports is one measure
of this.
• First axis of investigation:
– Is rapid value-added export growth related to
supply-chain participation?
Value added v. Gross exports
Total export growth, 1995-2009
East Asians
JPN
TWN
HKG
PHL
KOR
MYS
IDN
THA
SGP
BRN
KHM
CHN
VNM
Gross export
growth
VA export
growth
Total export growth, 1995-2009
Other EMs in GVCs
PRT
SVN
MEX
TUR
CZE
HUN
POL
SVK
ROU
Gross export
growth
VA export
growth
Primary exporters
ZAF
AUS
CHL
NOR
BRN
RUS
BRA
SAU
G7 nations
FRA
ITA
CAN
USA
GBR
DEU
0%
500%
1000%
0%
500%
1000%
Special interest of VA exports
• Indirectly measures growth in domestic
resources in trade sector (worldclass).
• Close to many development mechanisms:
– Technology adoption;
– Skill upgrading;
– Formation of domestic industrial capacities:
• Human, institutional, infrastructure, etc.
How measure supply chain
participation?
• TiVa has several; many more construct-able.
– FVA (Foreign Value Added share)
– REI (Reexported intermediates)
• REI seems to work better.
First look at relationship
Hope
• Faster domestic valueadded export growth
correlated with faster REI
growth.
• Plot vertical axis = Growth
in domestic value added in
exports
• Plot horizontal axis =
Growth in REI trade (supplychain participation)
Data
• Plot all nations, all 18 goods
sectors.
• Growth from 1995 to 2009.
Little correlation
REI vs Growth in Domestic VA in exports
2500%
2000%
1500%
?
1000%
500%
0%
-200%
-100%
0%
-500%
100%
200%
?
300%
But theory to rescue
• The correlation should depend upon:
– Nations:
• Headquarter v factory economies
• Primary-resource exporters v manufactures exporters
– Sectors:
• GVC sectors (mech & elec machinery, chemicals, etc)
• nonGVC sectors
Thinking about nation groups
VA export growth composition,
1995 to 2009
VA export growth composition,
1995 to 2009
PHL
BRN
CHN
SAU
TWN
CHL
KOR
NOR
THA
IDN
VNM
MYS
AUS
SGP
CAN
JPN
RUS
VNM
ZAF
KHM
KHM
BRN
BRA
HKG
-25%
0%
Manufactures
25%
50%
Services
75% 100%
Primary
-20% 0% 20% 40% 60% 80% 100%
Manufactures
Services
Primary
Thinking about nation groups
VA export growth composition,
1995 to 2009
PRT
VA export growth composition,
1995 to 2009
GBR
TUR
FRA
ROU
USA
MEX
DEU
POL
CZE
ITA
SVN
CAN
HUN
JPN
SVK
0%
25%
50%
Manufactures
75%
Services
100%
0%
20%
Manufactures
40%
60%
Services
80%
100%
Primary
Aside: BRICS asunder
VA export growth composition,
1995 to 2009
IND
CHN
ZAF
BRA
RUS
0%
25%
Manufactures
50%
Services
75%
100%
Primary
Relationship by nation groups?
Relationship by sector: Primary
01T05: Agriculture, hunting,
forestry and fishing
10T14: Mining and quarrying
2300%
2300%
1800%
1800%
IND
1300%
1300%
IND
VNM
VNM
800%
300%
-200%
-100%
TUR
PHL RUS
BRA
ZAFSVK
CHN
POL
HUN
MEX
CAN
USA
DEU
FRA
KOR
GBR
THA
MYS JPN TWN
0%
100%
800%
IDN
PHL
BRA
TUR
ZAF
CHN
SVK
RUS
POL
IDN KOR
CAN
HUN
USA
DEU
MEX
THA
MYS
TWN JPN FRAGBR
300%
200%
-200%
300%
-100%
0%
100%
15T16: Food products, beverages
and tobacco
2300%
1800%
VNM
1300%
800%
300%
-200%
-100%
TUR
SVK
POL
PHL
IND
IDN
BRA
HUN KOR
ZAF
RUS
CAN
USA DEU
THA
MEX
GBR
JPN
MYS
TWN
0%
100%
CHN
200%
300%
200%
300%
Relationship by sector: Light manuf
17T19: Textiles, textile products,
leather and footwear
20T22: Wood, paper, paper
products, printing and publishing
2300%
2300%
1800%
1800%
1300%
1300%
VNM
800%
300%
-200%
-100%
SVK
CHN
IDN
POL
INDMEX
HUN
BRA
KOR
DEU
RUS
FRA
GBRZAF
THA
JPN
M
YSUSA
TWN
CAN
0%
800%
PHL
TUR
100%
300%
200%
-200%
300%
-100%
VNM
PHL
TUR
SVK
POL
HUN
CHNUSA
IDN
IND
BRA
ZAF
MEX
KOR
RUS
DEU
FRA
THAGBR MYSTWN JPN
CAN
0%
100%
200%
300%
Relationship by sector: heavy manuf
23T26: Chemicals and non-metallic
mineral products
2300%
27T28: Basic metals and fabricated
metal products
2300%
VNM
1800%
1800%
1300%
1300%
800%
300%
-200%
-100%
TUR
SVK
POL
IND
CHN MEX
BRA
IDN
HUN
ZAF
KOR
USA
RUS
CAN
THA
MYS
DEU
FRAJPN
GBR
TWN
0%
800%
PHL
300%
100%
200%
-200%
300%
-100%
VNM
IND
PHL
SVK
POL
CHNHUN
IDN
MEX
KOR
BRA
USA
ZAF
DEU
RUS
FRA THA
CAN
MYS
GBR
JPN
TWN
0%
100%
TUR
200%
300%
Relationship by sector: GVC manuf
30T33: Electrical and optical
equipment
2300%
34T35: Transport equipment
IND
TUR
1800%
2300%
VNM
PHL
1800%
VNM
SVK
POL
1300%
PHL
1300%
CHN
HUN POL
CHN
800%
300%
-200%
-100%
800%
HUN
IDN
BRA
MEX
ZAF
KOR
THA
MYS
T
WN
RUS
USA
DEU
CAN FRA
GBR
JPN
0%
100%
-200%
300%
-100%
0%
100%
29: Machinery and equipment, nec
2300%
1800%
TUR
VNM
SVK
1300%
800%
300%
-200%
-100%
CHN
POL
HUN
MEX
KORIDN
THA
BRA
MYS
USA
ZAF
RUS
DEU
FRA
TWNJPN
GBR
CAN
0%
100%
IND
IDN
KOR
BRA
THA
MEX ZAF
MYS
USA
DEU
FRA
GBR
TWN
JPN
RUS
CAN
300%
200%
TUR
SVK
PHL
IND
200%
300%
200%
300%
Relationship by nation & sector
2300%
15T16: Food products, beverages and
tobacco
20T22: Wood, paper,
printing&publishing
2300%
1800%
1800%
VNM
1300%
VNM
1300%
800%
300%
CHN
800%
CHN
THA
MYS
KOR
TWN
PHL
IDN
300%
2300%
-200%
-100%
1800%
-200%
-100% 0% 100% 200% 300% 400% 500%
800%
IDNMYS
THA
34T35:
Transport
equipment
KOR TWN
PHL
0%VNM
100%
PHL
17T19: Textiles, leather
1300% & footwear
VNM
700%
200%
300%
EA EMs
CHN
800%
600%
G5
IDN
KOR
THA
500%
300%
CHN
Oth EM
SCTers
MYS
TWN
400%
-200%
-100%
300%
IDN
200%
100%
0%
0%
THA
MYS
KOR TWN
PHL
-100%
-50%
0%
50%
100%
150%
100%
200%
300%
Relationship by nation & sector
23T26: Chemicals & non-metallic
mineral prod
2300%
27T28: Basic metals and fabricated
metal products
2300%
1800%
1800%
1300%
1300%
VNM
800%
800%
CHN
-200%
-100%
THA
MYS
TWN KOR
PHL
0%
100%
34T35: TransportCHNequipment
2300%
300%
IDN
300%
VNM
200%
300%
1800%
-200%
1300% -100%
IDN
MYS
TWN
VNM
PHL
PHL
0%
THA
KOR
100%
200%
CHN
300%
EA EMs
800%
G5
IDN
KOR
THA
300%
-200%
-100%
Oth EM
SCTers
MYS
TWN
0%
100%
200%
300%
Relationship by nation & sector
34T35: Transport equipment
29: Machinery and equipment, nec
2300%
2300%
VNM
PHL
1800%
1800%
1300%
VNM
1300%
EA EMs
CHN
CHN
800%
IDN
KOR
THA
300%
-200%
-100%
MYS
TWN
0%
100%
200%
2300%
G5
800%
IDN
Oth EM
SCTers
300%
PHL THAKOR
MYS
TWN
2300%
-200%
-100%
1800%
300%
200%
300%
EA EMs
CHN
G5
IDN
KOR
THA
300%
1300%
-200%
-100%
PHL
800%
VNM
300%
0%VNM 100%
30T33: Electrical and optical
1300%
equipment
1800%
800%
34T35: Transport equipment
PHL
IDN
CHN
-200%
-100%
0%
TWN
MYS
THA
KOR
0%
100%
200%
Oth EM
SCTers
MYS
TWN
300%
100%
200%
300%
Facts to theory
• How does unbundling happen?
– Fractionalisation of production process;
– Geographical dispersion of stages.
Production unbundling: Some theory
Trade-off: Specialisation vs
coordination costs
euros
(n-1/2)
Marginal costs (coordination)
Marginal benefits (specialisation)
a[n;]
1
n1
Number of
stages/occupations
Trade-off: Specialisation vs
coordination costs
euros
(n-1/2)
Better IT lowers benefit of
fragmentation (automation)
a[n;]
1
n2
n1
Number of
stages/occupations
Trade-off: Specialisation vs
coordination costs
euros
(n-1/2)
Better CT lowers cost of
fragmentation (coordination
easier)
a[n;]
1
n1 n3
Number of
stages/occupations
Geographical dispersion
• Odd economics:
– Clustering/agglomeration
– Convex coordination costs
euros
Total cost of coordinating
given number of stages in
two locations
ns(1- ns)
0
1/2
N
1 Stages
Research agenda?
• Link between domestic value-added exports and
development (industrial production, GDPPC, etc).
– Finer look at domestic value added exports and
domestic value added, by sector, nation groups, etc.
• ‘Dense-ifying’ participation in value network
– Not really a ‘chain’; IO matrix, not a IO column.
• Does the partner matter?
– Does the REI-growth link vary by source of
intermediates?
• What institutional & policy variables determine
supply-chain participation (as measured by REI)
Three policy issues
• Geography matters
– Geography is an important determinant of the ease of
participating in Factory Asia.
– This is nothing more than an assertion that forward
and backward linkages matter at the regional level as
well as at the national or industrial district level.
– ERGO: Policy to foster participation in Factory Asia
should have a geographical dimension as well as the
usual income level dimension.
– In particular, proximity may be less important for
certain sectors and distant nations may be well
advised to focus on these.
Three policy issues
• Size matters.
– Nations that have over a billion consumers (the PRC
and India) can pursue policies that smaller nations
cannot.
– In essence the two giants can leverage their local
market as a powerful attraction force for supply chain
segments.
– ERGO: Policy recommendations should not blinding
point to China’s success as the right way forward.
Costa Rica’s success in supply-chains maybe be more
relevant to some small Asian nations.
Three policy issues
• Regulatory network effects matter.
– Factory Asia requires firms’ tangible and intangible
assets to be protected inside the participating
nations.
– Disciplines for these are emerging from megaregionals.
– Asian policy should focus on what this means for
Factory Asia; one-size may not fit all, but one-size
disciplines may foster the development and
spread of Factory Asia.
END
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