The development and future of Factory Asia1
Richard Baldwin and Rikard Forslid
First draft: 28 June 2013

1.

INTRODUCTION

Like a gigantic, impossibly complex but wonderfully efficient factory, East Asia churns out avast array of manufactured goods with world-beating price-quality ratios. But this is not aseries of national efforts. Manufacturing processes that used to be performed in single
factories (mostly in Japan and Korea) have been fractionalised and dispersed across the region– creating what Baldwin (2006) called ‘Factory Asia’.
This paper looks at the underlying interconnected processes that have led to the developmentof Factory Asia – namely the fractionalization of the manufacturing process into stages andthe dispersion of these stages around Asia. It does so by developing the TOSP (tasks,
occupations, stages, products) framework that was informally introduced in Baldwin (2012a).
The TOSP framework views the production of goods as the performance of a range of tasks
that are organised into occupations (collection of tasks) and stages (collections ofoccupations). Typically offshoring occurs at the level of stages rather than tasks or
occupation.
This framework is then used to examine the likely effects of improving ICT on the future of
Factory Asia. Two dimensions are distinguished: fractionalisation of the production process(slicing up the value chain), and their spatial dispersion (offshoring stages).
A key premise of this paper is that it is a trap to think of Factory Asia from the perspective of
traditional trade theory. It is tempting the think of the fractionalisation as simply a further stepin the century’s long march from autarky to free trade. After all, the fast-lane of Factory Asia
involves the offshoring of low-skill intensive stage to nations that are abundant in low-skill
labour while knowledge-intensive stages remain in nations that are well-endowed withknowledge workers. A natural, but incorrect, way to think of this is as nations’ shifting
resources to their comparative advantage sectors.
As this misthinking is pervasive, the rest of the introduction is devoted to contrast the old
globalisation paradigm – which views the process as driven by the steady lowering of trade
costs – with an alternative narrative that views globalisation as two processes rather than one.
1.1. Globalisation as two unbundlings
Globalisation is often viewed as linear – a progressive integration of national economies
driven by lower technical and manmade trade costs. In trade economists’ jargon, globalisation
is basically the move from autarky to free trade done slowly. The sharp trends in Figure 1
suggest that this is a mistake.
Globalisation was associated with an agglomeration of economic activity in what used to becalled the industrialised nations. From 1820 to 1988, the G7’s share of global output rosefrom 22% to 67%. Since then, the share has plummeted and is now back to the level it firstattained in 1900. About the same time, the world saw a massive shift in manufacturing
activity from G7 nations to a handful of developing nations that have come to be called
1

This paper was written for the ADB’s project “The Future of Factory Asia”. Our contribution draws
heavily on the authors’ earlier works to inform policy and help direct future research.

Emerging Economies. In the two decades from 1970 to 1990, the G7’s manufacturing share
dropped from 71% to 65%. The subsequent two decades saw it plummet to 50%. The share
shifts, however, where not generalised. Only seven nations saw their share of manufacturingrise by more than ½ of one percentage point (China, Korea, India, Indonesia, Thailand,
Turkey and Poland). Plainly we are in a new phase of globalisation – a phase that is distinct
from earlier phases. There are many names for this new globalisation paradigm – the global
value chain (GVC) revolution, fragmentation, trade in tasks, etc.

2010,
50%

40%
30%

1820,
22%

G7, 47%

50%
40%
RoW

30%
20%

China,
19%

10%

10%

5%

1990

1985

1980

1820
1839
1858
1877
1896
1915
1934
1953
1972
1991
2010
G7 nations’ share of global GDP, 1820 –
2010.

1975

0%

0%

6 Risers,
9%

3%

1970

20%

60%

2010

50%

70%

2005

60%

1990,
65%

2000

70%

80%

1995

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.

Figure 1: Globalisation: One process or two?
Source: Authors’ elaboration of data from unstats.un.org and Maddison’s database.

On the back of this prima facie evidence, it would seem we need two processes to explain
globalisation’s main outlines, not one. Falling trade cost won’t cover it. In a 2006 paper for
the Finnish Prime Minister’s office, Baldwin (2006a) characterises this as globalisation’s two
great unbundlings. The first unbundling – up to the mid or late 1980s – is the traditional,
linear process driven by lower trade costs. The second unbundling was driven by better
Information and Communication Technology (ICT). The 1st unbundling allowed consumption
and production to be separated by great distances but production stages remained bundled infactories and industrial districts. The ICT revolution sparked the 2nd unbundling by
unbundling the factories. Improved ICT made it economical for stages of production formerly
performed in rich-nation factory to be unbundled and dispersed to low-wage nations.
The development of Factory Asia was one of the first manifestations of the 2nd unbundling,although a similar development occurred in North America and Europe.

2.

FACTS: THE DEVELOPMENT OF FACTORY ASIA

One of the most striking features of Factory Asia is the very rapid growth of exports fromemerging East Asian economies – measured in either gross or value added terms. By ‘grossexports’ we mean the standard, customs-based numbers. By value-added exports, we mean
the domestic value added contained in a nation’s gross exports. The difference is that thevalue added figures net out the foreign value added embedded in gross exports. For example,
if Mexico exports a car that contains a US engine, the gross export is the value of the wholecar; the value-added export is just the value that was added in Mexico.
As Figure 2 shows, the growth has been quite uneven. In general, emerging markets have seen
higher growth with China, Cambodia, and Vietnam showing spectacular growth (althoughfrom very low bases in the latter two cases).
For comparison, we include the export growth figures for the G7 and a handful of emerging
markets that are known to be active in outsourcing and GVCs. A point we will explore more
below is that there seems to be a connection between the magnitude of the growth and size ofthe gap between gross and value-added exports (a rough measure of supply-chain trade).
Figure 2: Total export growth, 1995-2009, various nations, gross and value-added.

Total export growth, 1995-2009
East Asians
JPN
TWN
HKG
PHL
KOR
MYS
IDN
THA
SGP
BRN
KHM
CHN
VNM

Gross export
growthVA export
growthTotal export growth, 1995-2009
Other EMs in GVCs
PRT
SVN
MEX
TUR
CZE
HUN
POL
SVK
ROU

Gross export
growthVA export
growthPrimary exporters
ZAF
AUS
CHL
NOR
BRN
RUS
BRA
SAU

G7 nations
FRA
ITA
CAN
USA
GBR
DEU

0%

500%

1000%

0%

500%

1000%

Source: TiVa database with authors’ calculations.

The commonality of emerging markets’ rapid export growth hides an important distinction.
Some of these nations – like Brazil and Russia – are achieve high export growth on the backof the booming demand for commodities. Others are doing it via manufactured goods.
To provide hints as to the sources of the growth, 3 shows the growth decomposition by broad
sector – focusing on primary exports, light manufactured exports, heavy manufactured
exports, and service exports. The left panel shows a wide diversity among East Asian (EA)
nations . Some nations – such as Brunei, Vietnam, and Cambodia have seen their natural
resource based exports account for substantial fractions of their total export growth. For most,however, the key driver was manufactured exports. Only in Hong Kong, Singapore and Japan
have service exports played a large role in VA export growth.
The role of commodity exports has been smaller in the emerging markets involved in Factory
North America (Mexico), and Factory Europe (Poland, Turkey, etc.). The commonality is that
manufacturing exports account for the lion’s share of the growth – often 2/3rd or more.
Figure 3: Decomposition of VA export growth by broad sector, 1995-2009.

VA export growth composition,
EA
BRN
VNM
KHM
MYS
IDN
THA
CHN
JPN
SGP
KOR
HKG
TWN
PHL

VA export growth composition,
Other EMs in GVCs
MEX
ROU
TUR
PRT
SVK
HUN
SVN
POL
CZE

-40%-20% 0% 20% 40% 60% 80%100%
Primary

Lite Manuf

Heavy Manuf Services

0%20%
Primary

40%

60%

80% 100%

Lite Manuf

Heavy Manuf Services

Source: TiVa database with authors’ calculations.

2.1. Connection to supply-chain trade
A property of fast growers that rely on manufacturers is that they seem to be linking-up to
global supply chains to a much larger extent. This section present prima facie evidence that
this link is indeed important for many of the East Asian nations.
The measure of supply-chain trade involvement we use is the share of re-exported
intermediates (REI), that is, imported intermediates that are re-exported either as parts andcomponents, or embedded in final goods. Our aim is to explore the connection between
increases in this measure of SCT and increases in the domestic value-added contained inexports. In both cases, we look at the percentage increase from 1995 to 2009 (which is the fullspan of the TiVa database). The first look at the raw data is not encouraging. 4 shows thescatter plot of the change in 57 nation’s SCT measure in 18 different sectors against the samenations’ growth of domestic value added in exports in the same sectors. The overallcorrelation is unclear.
Figure 4: Growth in supply-chain participation and domestic value added in exports(VA export growth on y-axis, REI growth on x-axis).

REI vs Growth in Domestic VA in exports
2500%
2000%
1500%
1000%
500%
0%
-200%

-100%

0%

100%

200%

300%

-500%
Source: TiVa database with authors’ calculations.

The 2nd unbundling logic, however, suggests that the correlation should differ greatly for‘headquarter economies’ and ‘factories economies’ – as well as across sectors (since
production unbundling has not happen equally in all sectors). Once we separate the East Asia
nations from the others, a positive relationship looks much more plausible. Indeed, Figure 5
shows that a positive link seems to hold for other nations involved in supply-chain trade(those in Factory North America and Factory Europe). The link is completely missing for G5
nations – the Headquarter economies, and driven by outliers in the large economies outside of
Asia for other emerging markets (other EMs).
When we look at sectors – pooling across of nations – the positive association is clear in some
sectors but not in others (Figure ). It is particularly clear in the machinery sectors, Electricaland optical equipment, transportation equipment, and Machinery and equipment notelsewhere classified.
The last cut of the data highlights the growth correlation between supply-chain participation
and VA export growth by country group and by sector (Figure ). Here a couple points standout. First, China and Vietnam are frequently outliers – with big positive growth in bothmeasures. Second, East Asian nations seem to systematically have more positive links
between the two measures than the other nations. This, however, is less true in the classic
outsourcing sectors such as textiles and machinery of various sorts. Indeed in transportation,
the correlation of the East Asian nation and other factory economies (Other SCTers) is not at
all clear.
Figure 5: Supply chain participation and value-added exports, by nation groups (VA
export growth on y-axis, REI growth on x-axis).

2500%

2500%

EA nations

2000%

2000%

1500%

1500%

1000%

1000%

500%

500%

0%
-100%
-500%

100%

2500%

G5

300%

0%
-100%
-500%

100%

2500%

Oth SCTers

2000%

2000%

1500%

1500%

1000%

1000%

500%

500%

0%
-100%
-500%

100%

300%

Oth EMs

0%
-100%
-500%

100%

300%

300%

Source: TiVa database with authors’ calculations.
Note: EA nations are: VNM, IDN, THA, CHN, MYS, PHL, TWN, KOR; G5 nations are: JPN, FRA, USA,
DEU, GBR’ Other SCRers are: SVK, POL, TUR, MEX, HUN; and Other EMs are: BRA, CAN, ZAF, IND,
RUS.

Figure 6: Supply chain participation and value-added exports, all nations by sector (VA
export growth on y-axis, REI growth on x-axis).

01T05: Agriculture, hunting,forestry and fishing

10T14: Mining and quarrying

2300%

2300%

1800%

1800%

IND

1300%

2300%
1800%

1300%

IND

TUR
PHL RUSBRAZAFSVK
CHN
POL
HUN
MEX
CAN
USA
DEU
FRA
KOR
GBR
THA
MYS JPN TWN

300%
-200%
-100%

0%

100%

800%
IDN

200%

300%

-200%
-100%

17T19: Textiles, textile products,
leather and footwear

0%

2300%
1800%

1300%

1300%

PHL

800%

TUR
SVK
POL
HUN
CHNUSA
IDN
IND
BRA
ZAF
MEX
KOR RUS
DEU
FRA
GBR
THA
CAN
MYSTWN JPN

VNM

300%
-200%
-100%

0%

PHL
TUR

100%

300%

-200%
-100%

23T26: Chemicals and non-metallic
mineral products
2300%

1800%

1300%

1300%

300%
-200%
-100%

0%

200%

300%

0%

300%

PHL

-200%
-100%

0%

300%

VNM
SVK
POL

PHL

100%

300%
200%

200%

300%

29: Machinery and equipment, nec
2300%

VNM
PHL

1800%

TUR

SVK

1300%
800%

HUN
IDN
BRA
MEX
ZAF
KOR
THA
MYS
TWN
RUS
USA
DEU
CAN FRA
GBR
JPN

100%

TUR

2300%
1800%

0%

200%

SVK
POL
CHNHUN
IDN
MEX
KOR
BRA
USA
ZAF
DEU
RUS
FRA THA
CAN
GBR
MYS
JPN
TWN

CHN
HUN POL

CHN

-200%
-100%

100%

34T35: Transport equipment
IND

TUR

1800%

800%

300%

IND

300%

30T33: Electrical and opticalequipment

1300%

200%

VNM

800%

PHL

100%

2300%

100%

2300%

VNM

TUR
SVK
POL
IND
CHN MEXBRA
HUN IDN
ZAF
KOR
USA
RUS
CAN
THA
MYS
DEU
FRAJPN
GBR
TWN

0%27T28: Basic metals and fabricatedmetal products

1800%

800%

-200%
-100%

VNM

300%
200%

300%

CHN

20T22: Wood, paper, paper
products, printing and publishing

1800%

SVK
CHN
IDN
POL
INDMEX
HUN
BRA
KOR
DEU
RUS
FRA
GBRZAF
THA
JPN
M
TWN
YSUSA
CAN

200%

SVK

POL
PHL
IDN ZAF INDBRA
HUN KOR
RUS
CAN
USA
DEU
THA
MEX
JPN
GBR
MYS
TWN

300%

100%

2300%

800%

TUR

800%

PHL
BRA
TUR
ZAF
CHN
SVK
RUS
POL
IDN KOR
CAN
HUN
USA
DEU
MEX
THA
MYS
TWN JPN FRAGBR

300%

VNM

1300%

VNM

VNM

800%

15T16: Food products, beverages
and tobacco

IND

800%

IDN
KOR
BRA
THA
MEX ZAFMYS
USAFRADEU
TWN
JPN
RUS GBR
CAN

-200%
300%
-100%

0%

100%

300%
200%

TUR
VNM
SVK

1300%

-200%
300%
-100%

CHN
POL
HUN
MEX
KORIDN
THA
BRA
MYS
USA
ZAF
RUS
DEU
FRA
GBR TWNJPN
CAN

0%

100%

PHL
IND

200%

Source: TiVa database with authors’ calculations.

Figure 7: Supply chain participation and VA export growth, by sector and countrygroup (VA export growth on y-axis, REI growth on x-axis).

300%

2300%

15T16: Food products, beverages andtobacco1800%

20T22: Wood, paper,
printing&publishing

2300%
1800%

VNM

1300%

VNM

EA
G5

800%

Oth SCT
300%

CHN
THA
MYS
KOR
TWN
PHL

IDN

Oth EM-200%-100% 0% 100% 200% 300% 400% 500%
800%

Series1
Series2

800%

Series3

300%

17T19: Textiles, leather & footwear
VNM

2300%

600%

KOR

IDNMYSTHA
TWNPHL

Series4

0%

100%

200%

300%

23T26: Chemicals & non-metallic
mineral prod
VNM

1800%

500%

CHN

400%

EA

300%

G5
IDN

200%
0%

CHN

-200%
-100%

700%

100%

1300%

Oth SCT

THA

50%

100%

-200%
-100%

150%

27T28: Basic metals and fabricatedmetal products
2300%

1300%

EA
G5

CHN

800%
IDN

MYS
TWN
PHL

300%
-200%
-100%

0%

100%

Oth EM

200%

300%

100%

Oth SCT

300%

MYS
TWN

-200%
-100%

0%

Oth SCTOth EM300%

PHL THAKOR
MYS
TWN

-200%
-100%

0%

100%

200%

300%

Source: TiVa database with authors’ calculations.

Oth EM

100%

VNM

1300%
800%

200%

300%

30T33: Electrical and opticalequipment

CHN

EA

G5

IDN

G5

CHN

IDN
KOR
THA

1800%

800%

EA

800%

1800%

CHN

300%

VNM
PHL

1300%

2300%

EA

200%

2300%

29: Machinery and equipment, nec

VNM

Oth EM

34T35: Transport equipment

2300%

1300%

0%

Oth SCT
THA
KOR

Oth SCTTHAMYSTWN KOR
PHL

1800%

VNM

1800%

G5
CHN
IDN

300%

0%

EA

800%

Oth EMMYS
KOR TWN
PHL

-100%
-50%

1300%

PHL

G5

IDN

Oth SCT

300%

TWN
MYS
THA
KOR

Oth EM

-200%
-100%

0%

100%

200%

300%

2.2. Evolution of Factory Asia
Data for the early days of Factory Asia are difficult to come by. One rough indicator that does
reach back to the 1960s is a simple intra-industry trade index (IIT) (Bruhart 2009). The idea
here is that two-way trade in similar products that is either North-South, or South-South islikely to be largely supply-chain trade, i.e. two-way trade in parts and components. Plainly
this is a crude measure, but it is transparent, widely understood and available for most nations
going back to the early 1960s.
Figure 8 shows the measures for Japan (left panel) and China (right panel). The first salient
point is that Japan’s exchange with the Tiger3 nations (Korea, Hong Kong and Singapore) has
been growing steadily since the 1960s. In the early days, much of this was in microelectronics(Grunwald and Flamm 1985). The big turning point came in the mid1980s. The IIT numberswith China, the ASEAN nations, and the South3 (India, Nepal and Sri Lanka) all took offaround 1986 – more or less all at the same time. The IIT numbers climbed steeply till theearly 1990s with all partners but then levelled off for the South3. IIT with China and ASEAN,
however, continued to soar.

50%

Japan's bilateral IIT

40%
35%

China's bilateral IIT

50%
45%

45%

China
Tiger340%

Tiger335%

South325%

25%

Asean720%

Asean7

20%
15%

10%

10%

5%

5%

0%

0%

1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012

15%

1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012

30%

South330%

Figure 8: Japan’s and China’s bilateral IIT, 1962 – 2012.
Source: Authors calculations on Comtrade data.

The right panel shows the same calculations for China. Here we see that the IIT break-throughwas postponed until the late 1970s, but was again first with the Tiger3. China’s IIT with
South3 and ASEAN took off at approximately the same point as did Japan’s.
Turning to the South two-way trade, Figure 9 shows the paths of Japan’s and Korea’s IIT withall 7 ASEANs. Japan’s IIT with three of the big ASEANs – Malaysia, Thailand and thePhilippines – took off at about the same time, i.e. mid- to late-1980s. The sharp rise in IITwith Indonesia and Vietnam came a decade later. For Indonesia (which is still a big
commodity exporter), the rise was much less marked than for the others. Laos and Cambodia
have not really joined the Japanese supply chains according to this indicator. For Korea, thetiming is similar but the engagement is muted compared to Japan.

50%Japan's bilateral IIT

45%
40%
35%
30%
25%
20%
15%
10%

IDN
KHM
LAO
MYS
PHL
THA
VNM

50%

Korea's bilateral IIT

45%

IDN

40%

KHM

35%

LAO

30%

MYS

25%

PHL

20%

THA

15%

VNM

10%
5%

0%

0%

1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012

1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012

5%

Figure 9: Japan’s and Korea’s bilateral IIT with ASEANs, 1962 – 2012.
Source: Authors calculations on Comtrade data.

China's bilateral IIT 50%

45%
40%
35%
30%
25%

45%

IDN
KHM
LAO
MYS

20%

PHL

15%

THA

10%

VNM

IDN

40%

KHM

35%

LAO

30%

MYS

25%

PHL

20%

THA

15%

VNM

10%

5%

5%

0%

0%

1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012

Asean bilateral IIT with
Asean

1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012

50%

Figure 10: China’s and each ASEAN’s bilateral IIT with ASEANs, 1962 – 2012.
Source: Authors calculations on Comtrade data.

To round out the picture, Figure 10 shows the IIT proxy for Factory Asia participation for
China with each ASEAN and for each ASEAN with the others. What we see is that China’s
bilateral IIT with Thailand and Malaysia jumped in the mid-1980s, but it did not start until theearly 1990s with Indonesia and the Philippines. The IIT measure slopes up from the later
1990s for Vietnam but does not really take off until the early 2000s. Cambodia’s IIT with
China has taken off only in the 2010s. As for the ASEANs among themselves, we see asimilar timing. The ‘big four’ ASEANs, Thailand, Malaysia, Indonesia and the Philippines,
see their IIT scores jump in the mid-1980s. Vietnam’s jump is delayed till the late 1990s witha significant acceleration in the 2000s.
More direct evidence comes from the IDE-JETRO international input-output table. Thisshows the country of origin of imported manufactured goods purchased by the manufacturingsector of each East Asian economy. Error! Reference source not found. has three panels
corresponding to the shape of Factory Asia in 1985, 1990 and 2000.
Table 1: Widening and deepening of the Asian Manufacturing Matrix, 1985, 1990, 2000.

(Share of manufactured inputs bought by column nation’s manufacturing sector from the row
nation; numbers less than 2% are zeroed out; own-nation purchases are also zeroed out)
1985
Indonesia
Malaysia
Philippines
Thailand
China
Taiwan
Korea
Singapore
Japan
RoW
1990
Indonesia
Malaysia
Philippines
Thailand
China
Taiwan
Korea
Singapore
Japan
RoW

China

2000
Indonesia
Malaysia
Philippines
Thailand
China
Taiwan
Korea
Singapore
Japan
RoW

Indonesia

Malaysia

Philippines

ThailandSingapore
8%
16%
0%
0%
14%
3%

Taiwan

Korea

Japan

9%
14%
Thailand

12%
11%
Singapore

7%
10%
Taiwan

8%
16%
Korea

8%
Japan

18%
44%

10%
17%

8%
13%

5%

Singapore
2%
12%

Taiwan

Korea

Japan

7%
11%

4%

2%

3%
China

3%
12%
15%
Indonesia

7%
14%
19%
Malaysia

4%
19%
Philippines

5%

3%

8%

8%
23%

7%
10%
20%

4%
2%
2%
8%
21%

China

Indonesia

Malaysia

Philippines

Thailand

3%

4%

2%
2%

3%

2%
4%

7%
16%

4%
3%
5%
4%
13%
15%
20%

3%
3%
14%
22%

3%
5%
8%
6%
20%
20%

4%
3%
3%
4%
16%
17%

3%
3%
2%

3%
5%
3%
4%
19%
38%

2%

2%
4%
14%
15%

Notes: The columns would sum to 100% if we had included each nation’s supply of inputs to its own manufactured sector (a number that isoften greater than 50%) and if we had not zeroed out the low numbers (less than 2%). RoW equals Rest of World.
Source: IDE-JETRO, Asian input-output matrix (7 sectors) for 1985, 1990 and 2000; see, for example, www.ide.go.jp/English/Publish/Books/Sds/082.html.

The top panel shows the situation in 1985 when Factory Asia was very simple. With the
exception of Singapore, East Asian nations sourced their imported manufactured inputs fromJapan and the rest of the world – all the rows are dominated by zeros except those of the Japanand the rest of the world (mainly the US and Europe). By 1990 (second panel), it was more
complex: ‘Triangle trade’ still dominated the picture with the low-wage nations (first 5
columns) buying inputs from Japan and the rest of the world but providing no inputs in return.
Now, however, Japan is not the only headquarter economy. Taiwan, Korea, and Hong Kong
experienced their own hollowing-out phases and new triangle trade appears. This new triangle
trade involves the shipment of parts from the new HQ economies to the ‘factory economies’(China and the advanced ASEANs, Indonesia, Malaysia, the Philippines and Thailand). Thiscan be seen from the emergence of new non-zero entries in the rows for Taiwan, Korea andSingapore.
By 2000, Factory Asia was really complex. Firms based in the ‘factory economies’ began tosource parts from other factory economies rather than from the HQ economy alone. In
particular, Thailand, Malaysia and China became important suppliers of parts to other ‘factory
economies’ including each other. In short, the input-output matrix went from simple triangle
trade to a much more complex situation where the ‘factory economies’ were both makers andbuyers of parts and components.

This rise of China’s position in the Matrix between 1990 and 2000 is especially noteworthy.
At the beginning of the decade, it neither bought nor sold much manufactured inputs in East
Asia. By the end of the decade, we see many entries for the Chinese column (which shows itspurchase pattern) and the Chinese row (which shows which nations depend a lot on inputs
from China). The flourishing of intra-ASEAN trade is also clear from the comparison of 1990
and 2000.
The message of Error! Reference source not found. is clear. By 2000, the competitivenessof manufacturing firms in East Asia depended in a serious way on the smooth functioning ofregional trade. A disruption of trade between, say, Malaysia and China, could cause serious
problems for Japanese and Korean firms trying to sell in the US.

3.

ECONOMICS OF SUPPLY-CHAIN UNBUNDLING

Globalisation’s 2nd unbundling shifted the locus of globalisation from sectors to stages of
production. This requires an analytic focus on supply chains. The economics of this change isbest looked at by decomposing it into two phenomena: fractionalisation and dispersion.
Fractionalisation concerns the unbundling supply chains into finer stages of
production.
Dispersion concerns the geographic unbundling of stages.

The two are linked in so far as the organisation of stages may be crafted with dispersion, i.e.
offshoring, in mind. This section considers them in turn and their interlinks.
3.1. Supply chain unbundling: The functional dimension
A prime assumption behind our conceptualisation of Factory Asia is that the ICT revolution
triggered the 2nd unbundling and this resulted in Factory Asia. The main avenue ofinvestigation is thus to understand how ICT improvements fostered production unbundling.
Here we start with fractionalisation, putting off issues of dispersion (offshoring) temporarily.
The TOSP framework

Tasks:
Occupations:

A

B

C

D

Occupation

Stages:
Product:

E

F

Occupation

G

H

Occupation

Stage

I

J

Occupation

K

L

Occupation

Stage

Product

Figure 11: Tasks, occupations, stages and product – the TOSP framework
Source: Baldwin (2012a).
Notes: The circles represent individual tasks, the rectangles represent individual occupations and the ovals
represent individual stages of production.

To this end, it is useful to view a firm’s supply chain at four levels of aggregation (Figure 11):


Tasks.

This is the list of everything that must get done to produce value for the corporation; the list
includes all pre-fabrication and post-fabrication services.


Occupations.

One natural intermediate aggregation of tasks is an ‘occupation’ – the group of tasks
performed by an individual worker.
Stages.

Stages are defined as a collection of occupations that are performed in close proximity due tothe need for face-to-face interaction, fragility of the partially processed goods, etc. This is acritical level of aggregation since supply chain internationalisation typically involves theoffshoring of stages rather than individual occupations or individual tasks.
Products.

The product is the supply chain’s output broadly viewed.
3.1.1. Optimal task per occupation and occupations per stage
With this aggregation scheme in mind, consider the economics of the optimal:
1. Tasks per occupation; and2. Occupations per stage.
Adam Smith illustrated the first issue an example – an 18th century pin factory where making
a pin twelve distinct ‘tasks’, or what Smith called ‘operations’. The full list oftasks/operations was: drawing out the wire, straightening the wire, cutting the wire,
sharpening the pointy end, grinding the top end, making the pinhead (which itself involves
three distinct tasks/operations), attaching the pinhead, whitening completed pin, and putting
the pins in into the packaging.
Smith reported that pin factory managers had workers specialise in particular tasks. Workers
did not make pins; they performed particular sets of tasks. This allowed each worker to getreally good has his or her assigned task. The downside of splitting up tasks is the difficulty of
coordinating the whole process. This is the fundamental trade-off we focus on – the benefit of
specialisation versus the cost of coordination. Our key trade-off differs fundamentally from
Costinot (2009) and Bloom et al (2009); see Box 1.
Fractionalisation and improved communication technology versus information
technology
Since we are addressing the history and future Factory Asia, we focus on how ICT
developments alter fractionalisation. Here the important point is that ICT affects the optimal
division of labour via two channels, as Bloom et al (2009) have stressed.
First, communication and organisational technologies – call them coordination
technologies (CT) for short – lower the marginal cost of coordination.

Intuitively, better CT will make it easier to slice up production processes into more stages,
and it will make it easier to disperse stages internationally. Thus CT will tend to foster thecurrent trends in Asia towards more vertical specialisation, more offshoring, more FDI andmore intra-industry trade.
Second, information technology (IT) lowers the marginal benefit to specialisation;
think of how robots make it easier for individual workers to master more tasks without
loss of efficiency.

3D printing is the extreme where IT allows a single worker to perform all tasks simply by
operating one machine. For example Japanese industry is a leading user of industrial robots.
Without these machines, far more stages of production would have been offshored to lowskill abundant neighbours.



Plainly, CT and IT cut in opposite directions. Better CT favours greater
fractionalisation by making it cheaper; better IT discourages it by making it lessnecessary.2

To explain and explore these effects in greater detail and with greater precision of thought, we
present a one-line sketch model of optimal fractionalisation.
Box 1: Related theoretical frameworks in the literature
The Costinot (2009) model looks at optimal ‘team size’ (akin to our stages) thatturns on a very different trade-off, namely specialisation versus risk. Big teams
allow workers to specialisation in particular tasks and this lowers average timecost. Big teams, however, are assumed to be riskier.
Specifically, the model assumes an exogenous probability that each worker will
fail to complete the assigned task. To avoid risk-pooling – which would obviate
all the analysis and results – the paper adopts a series of strong assumptions. Firstno worker can help any other; a failure in any task is a failure in all tasks, so moreworkers mean more failure. Second, there can be no inventory that smooths-overtask-level failures. Third, the firm cannot reduce the probability of failure withmanagers a la Garciano (2000).
While specialisation-versus-risk this is the fundamental trade-off in the model, thepaper discusses it’s the key trade-off as if it were a specialisation-versustransaction-costs trade-off.3 That is, the discussion simply asserts that the failurerisk is due to contracting problems, and then it simply asserts that contracting
problems are transaction costs.
Bloom et al (2009) also model the team size choice based on a trade-off that isquite different to ours. Drawing on the seminal work by Garicano (2000), Bloom
et al (2009) focus on firm hierarchy, where production requires problems to besolved and hierarchy is a way of economising on workers’ problem-solvingtraining. Their core trade-off is between spending more to train workers to solve
problems by themselves, and spending more on managers who are not directly
productive but who have the knowledge to solve all problems.
3.1.2. Functional unbundling: A basic model
To crystalise thinking about our specialisation-versus-coordination trade-off, this subsection
provides a simple model of that allows us to be more precise about the basic trade-off
between specialisation gains versus coordination costs as well as the very different effects that
better CT and IT have on supply chain fractionalisation. We work in partial-equilibrium
setting since when firms make these choices, they are likely to ignore the impact of theirdecisions on labour and product markets. Likewise, we initially work in a closed economy to2This insight – which is due to Bloom et al (2009) – has recently received some empirical support
from Lanz et al (2011). They find that offshoring of business services complements manufacturing
activities, in the sense that increased import penetration in business services is associated with a shift
in local task content from information and communication related tasks towards tasks related tohandling machinery and equipment. Offshoring of other services complements local informationintensive tasks in that it shifts local task composition towards ICT-related tasks.

3

The iid nature of the risk is adopted for technical reasons, namely to convex-ify outcomes in a waythat allows the authors to work in a general setting.

separate the organisation issues from the offshoring issues. Even though they are ultimately
linked, intuition is severed by first dealing with them separately.
One factor, one‐tier organisation
Consider a firm with a constant-returns-to-scale process whereby producing a good requires
performance of a given list of tasks. To be concrete, view this as a continuum of tasks that wearbitrarily list them along the range zero to unity. The tasks are performed using homogenous
labour and there is, in the simplest model, only one organisational choice – the number ofoccupations into which the tasks are organised. Stages are left out of the analysis for the time
being.
The problem is to assign tasks to occupations optimally. In principle this assignment would
involve a matching of task-types to worker-types. Even with homogenous workers theassignment problem could be complex if the degree of task-level efficiency depended upon
the group of tasks assigned per worker. For example, it would be reasonable to assume that aworker would gain more efficiency by specialising in related tasks – say, painting tasks or
welding tasks. In such a case, it would be natural to define the occupation by the nature oftasks assigned to it (e.g. painters, welders, etc.). While this degree of resolution is desirable,
making progress would require seemingly arbitrary specificity concerning the efficiency
effects of task specialisation.
To keep the analysis streamlined and transparent, we assume that the tasks as well as theworkers are homogenous. With this simplification, all occupations are symmetric and the only
choice is the range of tasks assigned to a typical worker. That is, each worker’s ‘occupation’is defined by his/her range of assigned tasks and all occupations will be identical.
The gains from specialisation are modelled as a link between the amount of labour per tasks
and the range of task per occupation. Specifically, the labour input coefficient increase withthe range tasks performed by a single worker. There are many ways of micro-founding suchan outcome but to keep things focused on essentials we simple assume the hours per tasksrises with the range of tasks per worker. What we have in mind is some sort of learning curve.
From the production-cost perspective, the least efficient arrangement is to have each worker
doing every task. The most efficient is to have each worker specialised in only one task. The
least cost organisation with a given number of occupations will be to assign an equal range oftask to each occupation; occupations are symmetric in equilibrium. Specifically, if there are no
occupations, a range of 1/no task is assigned to each occupation.
Greater specialisation, however, engenders greater coordination costs among occupations. A
worker specialising in a range of tasks that is one-noth of all tasks must coordinate with (no-1)
other occupations. For simplicity, the between-occupation coordination costs are all identicaland given by the parameter as o (chi is a mnemonic for coordination). Ignoring withinoccupation coordination, the number of coordination-pairs is no(no-1)/2.
More formally, the organisational cost-minimisation problem is:n 1min{no } wl a[no ; ]  wl o no ( o ) ; a '[]  0
2(1)Here w is the wage, the function a[;] captures the efficiency-inducing effect ofspecialisation. That is, the per-task labour input coefficient ‘a’ falls as the number ofoccupations goes up (the range of task per work is 1/no with no symmetric occupations, so ahigher no is associated with greater specialisation of workers). Here ‘’ – a mnemonic forautomation – parameterizes the impact of IT on the efficiency-specialisation effect; a[no;]indicates the first derivative with respect to no as usual.
The first-order condition is:
1(2)
2
The second order condition holds if a[;] is decreasing (i.e. the second derivative is positive)
which is true if there are diminishing returns to specialisation – an assumption we maintainthroughout.
0;

The solution is illustrated in Figure 12. The marginal benefit of increased specialisation (i.e.
more occupations) is shown a[n;] while the marginal coordination costs is rising with thenumber of occupations as shown. The optimal specialisation is n1 as shown. This illustrates
the specialisation-coordination trade-off that is central to our thinking on functional
fragmentation.
With this framework, it is straightforward to illustrate the distinct impact of IT and CT.
Improved IT makes it easier for one worker to master many tasks without loss of efficiency –this is why a[n;] is downward sloped. Improved IT, which is parameterised as a rise in in 
(automation) to ”, shifts down the marginal benefit of specialisation. In other words, themarginal benefit of additional specialisation is lower, when  is higher. With the higher ”,
the optimal number of occupations is n2 rather than n1.

(n-1/2)

euros

”(n-1/2)a[n;]a[n;”]
1

n2

n1 n3

Number ofstages/occupations

Figure 12: Functional unbundling: Stages and occupations in symmetric case.
Improved CT (communications), by contrasts, makes it easier to coordinate occupations; thisshows up as a reduction  to ”. The result is a lower marginal cost of increasing the numberof occupations; graphically this shows up as a shift down in (n-1/2). The resulting optimal
number of occupations is therefore n3 rather than n1.
One factor, two‐tier organisation
It is straightforward to introduce additional levels of organisational hierarchy. We do this byassuming that occupations can be organised into ‘stages’ as a means of economising on
coordination costs. The occupation-level coordination costs remain as before, but we assume
that workers only face this for occupation inside their own stage. The new element is the costof coordinating among stages. For simplicity, we assume that all stages are symmetric from

this perspective. Specifically, if all occupations are broken up into ns stages, each stage will
have to coordinate with ns(ns-1)/2 other stages.
The modified cost minimization problem then becomes:n 1
n 1 
min {no , ns } wl a[ no ns ;  ]  wl   o no ( o )   s ns s
2
2 


(3)

Note that the range of tasks per symmetric occupation is now 1/nons, so the argument of a[;]is nons, and the coordination cost parameter for stage-coordination is denoted as s. The firstorder conditions are:0  a '[ no ns ;  ]ns   o ( no  1/ 2) ,0  a '[ no ns ;  ]no   s ( ns  1/ 2)(4)

The simplest solution occurs when the ’s are equal so the solution involves an equal numberof stages and occupations. In this case, Figure 12 continues to characterise the basic trade-off
between coordination and specialisation – as well as the role of CT and IT. More complex
combinations of parameters would yield a different number of occupations and stages.
With a basic framework in place for the fractionalisation of production processes, we turn tothe issue of spatial dispersion.
3.2.

Geographical unbundling: Balancing dispersion & agglomeration forces

If it were not for offshoring, fractionalisation would be purely a matter of industrialorganisation. To put in an international dimension we now turn to location decisions. The
touchstone principle is that firms seek to put each stage in the lowest cost location.
In reality, places differ along many dimensions that matter for the location. The World
Economic Forum’s competitiveness index, for example, has 110 different measures. Our goal
here, however, is to illustrate the first order trade-off that has influenced the development ofFactory Asia. What we focus on is factor costs – for example, low versus high wage – as thegain from offshoring. The cost of offshoring is the downside.
The cost calculation involves a trade-off between direct factor costs and ‘separation’ costs.
The direct costs include wages, capital costs and implicit or explicit subsidies.
The separation costs should be broadly interpreted to include both transmission andtransportation costs, increased risk and increased face-to-face managerial time.

The location decision may also be influenced by local spillovers of various types. In some
sectors and stages, say fashion clothing, proximity between designers and consumers may be
critical. In others, product development stages may be made cheaper, faster and more
effective by co-location with certain fabrication stages. Yet other stages and sectors aremarked by strong technological spillovers that make clustering of producers the natural
outcome.
3.2.1. Production efficiency versus coordination costs
The first aspect of spatial unbundling turns on a trade-off that is closely aligned with thefunctional unbundling discussed above. Offshoring a particular stage can save on production
costs but raise coordination costs.
To crystalize the fundamental economic logic, we adopt a simple setting with two nations –high-tech North and low-wage South – and stages that vary continuously in their technology
intensity. We work with a ‘spider-like’ production process (Baldwin and Venables 2013)
whereby the engineering of the production process implies no particular sequencing of stages.

Each stage produces a ‘part’ and the parts get assembled into the good in the final stage called‘assembly’. This permits us to order the stages in analytically convenient ways.
Specifically, the stages – exogenously defined in this section – are arranged in order ofincreasing tech-intensity, i.e. in order of increasing North comparative advantage. This meansthat the high-tech North tends to have a comparative advantage in ‘high’ stages (those with anindices near unity) while low-wage South has a comparative advantage in ‘low’ stages.
More specifically, the per-stage production cost is wnani in North and is wsasi in South, whereani and asi are the North and South unit labour input coefficient for stage i and the w’s are thenational wages. Since there are only two locations, relative cost is all that matters, so wenormalise North’s wnani to unity for all stages.4 Recalling that we have arranged stages suchthat North’s comparative advantage is greatest in stages with high indices, South cost perstage starts below North’s (i.e. below unity) and rises steadily to a number above unity.
A particularly simple case is illustrated in Figure 13 where wsasi rises linearly with thesophistication of the stage;  is the slope of this relationship, i.e. wsasi =’i where ‘i’ indexes
the stages and i ranges from 0 to 1. Plainly South is the low cost producer in stages from zero
to 1/’ and North is the low cost producer in the rest. It is convenient to think of  as thestrength of North’s comparative advantage in stages since as  rises, more stages are mostcheaply produced in the North.
To study the offshoring solution, the first anchor point is the cost minimising outcome whencoordination costs are zero. As mentioned, the answer is that the lower tech stages are placedin South – namely, stages from 0 to 1/ -- with the rest in North. Separating stages, however,has implications for coordination costs. The nature of these costs matters greatly.
'i

euros

wsasi = 2ieurosns(1- ns)

”iwnan=1

1

0

1/'

1/2

1/”

1

Stages0

1/2

1StagesFigure 13: Comparative advantage, coordinate cost and optimal unbundling.
Note: Here North is offshoring stages to South.
Products with complex coordination demands
We begin with a case where coordination demands are complex, in the sense that every stage
needs to coordinate with every other stage. An illustration of this is given in Table 2, which isdrawn for the six stage case where stages 1 to 2 are undertaken in South and the rest in North.
The table allows for different coordination costs for coordination of stages within North, n,
within South, s, and ‘international’, I.
Table 2: Coordination-cost matrix: Complex good case4

As wages are exogenous here, we could do this by choosing North labour as numeraire and choosing
units for all stages since that ani=1 for all i.

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6
Stage 1  sStage 2  s

sStage 3  I

InStage 4  I

I

n

n

Stage 5  I

I

n

n

n

Stage 6  I

I

n

n

n

n

Note: This table discretises the continuum of parts for the sake of illustration.
To keep the expressions simple and to sharpen the intuition, we start with the simple example
where within-nation coordination costs are zero, i.e. n = S = 0 but I >0, so total
coordination costs are:wn  I ns (1  ns )where wn, ns, and nn are the North wage and number of stages in South and North
respectively. We assume that international coordination costs involve North labour, and haveassumed that the mass of stages equals unity. Here coordination cost varies with the range ofstages offshored to South according to a parabola.
It is important to note that this convexity means that coordination cost act as an agglomeration
forces. That is to say, the coordinate-cost-minimising solution is to keep all stages bundled
together. Coordination costs are maximised when stages are split evenly between North andSouth. Production cost considerations, by contrast, act as a dispersion force. The optimal
unbundling and offshoring of stages from North to South involves the usual balancing ofdispersion and agglomeration forces.
More formally, taking coordination and production costs together (assuming the linear
example, i.e. wsasi=i where 0<<1), total costs as a function of the range of stages in South,[0, n s), are: n / 2  1  n    n (1  n )
2
s

s

I

s

s

This is a quadratic function whose second derivative is negative if and only if coordination
costs are not too high relative to the strength of North’s comparative advantage, specifically</2. We assume this regularity condition so that the first order conditions indicate cost
minimising rather than cost maximising solutions.
The first order condition with respect to the range of stages placed in South, n s, is: ns 1   I (1  2ns )  0Solving the first order condition with respect to the range of stages placed in South, we have:
ns 

1 I
  2 I

This spatial unbundling setting presents some unusual features.

First, it is subject to threshold behaviour, with the threshold at I >/2. The reason for this issimple. The solution is the cost-maximising solution for I >/2 but the cost-minimizing
solution for I </2. Thus:
If coordination costs are high relative to North’s comparative advantage (as measured by ),then we have a corner solution, i.e. all stages are clustered in one nation.5
Which nation it is depends upon North’s comparative advantage.6 Production is cheaper in the
North if and only if  > 2; otherwise the cluster is in South.
For coordination costs lower than the clustering threshold (i.e. I </2), stages will bedispersed internationally according to 0.
Thus a continuous reduction in coordination costs starting from a very high level would havea discontinuous effect on offshoring at the threshold.
A second unusual feature is that cheaper coordination may lead to more or less production inSouth. The sign of the impact of I on ns depends switches at  = 2. If South has a strong
comparative advantage – in the sense that more than half the stages would be produced there
based solely on production cost considerations (e.g. ” as in Figure 13), then considerations ofcommunication costs will lead to ‘too much’ production in the South. The point is that eventhough Southern production costs are higher, the fact that more than half the stages are there
already means that sending more stages lowers coordination costs. In this case, lower
communication costs will lead to less offshoring to South. By contrast, if , is high, say ’ in
Figure 13, then lower I will increase the offshoring to South.
One particularly intuitive case is when North has a strong comparative advantage in parts, e.g.
’ in the diagram, so more than half the parts are produced in North. In this case, coordination
cost considerations tend to reduce the amount of offshoring from the North to the South. As
coordination costs fall, the range of stages placed in the South rises.
To summarise, even in the simplest framework that allows a trade-off between efficiency andcoordination costs, the relationships between easier communication and offshoring is far frommonotonic. The convex nature of coordination costs tends to create tipping points andcomparative static results that can flip signs according to parameters that may be hard for the
econometrician to observe.

 measures North’s comparative advantage in the sense that the range of stages where North is the
low cost producer ranges from 1/ to unity, so this expands as  rises.

5

6

Total cost with all stages in the South and North, respectively, will be /2 and 1.

4.

CONCLUDING REMARKSThis paper looks at the underlying interconnected processes that drove Factory Asia’s
development from the mid-1980s. We focused on two complementary trends:
fractionalization of the manufacturing process into stages, and the dispersion of these stages
around Asia. The paper organised the thinking by providing a formal model for the TOSP
(tasks, occupations, stages, products) framework that was informally introduced in Baldwin(2012a). The TOSP framework views the production of goods as the performance of a rangeof tasks that are organised into occupations (collection of tasks) and stages (collections ofoccupations). Typically offshoring occurs at the level of stages rather than tasks or
occupation.
4.1. Policy issues
While no formal evidence-based policy recommendations can be made based on our paper’s
contribution, a number of important themes emerge.
First, geography is an important determinant of the ease of participating in Factory Asia. Just
as it is easier to set up a supply plant in or near an industrial district, joining Factory Asia ismuch easier for nations that are proximate to the headquarter economies in East Asia – Japan,
Korea, Taipei,China, Singapore, and Hong Kong. As Factory Asia is not so developed,
proximity to other factory economies is also important – especially proximity to China which
is a massive and highly-competitive producer of industry inputs (parts and components).7 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.
The intuition is similarly straightforward. In the main production unbundling sectors –electrical and mechanical machinery – fractionalised production processes involve timesensitive and shipping-cost sensitive elements. Being nearby other supply-chain traders – both
headquarter and factory economies – makes it easier to join Factory Asia. Another way to put
this is that ‘regional comparative advantage’ matters as well as ‘national comparativeadvantage’ when it comes to joining an international production network.
Second, size matters. Nations that have over a billion consumers (India and China) 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.
Third, providing assurances to tangible and intangible property rights is likely to be an
important element in attracting supply chain production. As such production is necessarily
networked; some firms or networks of firms must be coordinating the process. Such firms arenaturally reluctant to expose their managerial, technical and marketing knowhow to tacit or
explicit expropriation, which would facilitate the emergence of new competitors.
4.2. Future research
The charts in the paper suggest many correlations and relationships that can and should betested more formally using the newly developed TiVa database. Such empirical work will be
critical in developing evidence-based policy recommendations. How important are intellectual
property rights protection versus quick port clearance? How damaging is distance toparticipation in international supply chains (controlling for other factors)? How important areformal free trade agreements overall, and particular provisions specifically (e.g. investment
provisions, versus capital mobility provisions).
7

It is a quirk of language, but is remarkable that one can get from ‘supply chain’ to ‘supply China’ by moving
just one letter!

A wide range of institutional measures exist in databases such as the World Bank’s Doing
Business, and the WEF’s Global Competitiveness Report, and detailed work arise from thetrade facilitations literature. These are essentially right-hand side variables that have beenused to explain macro growth trends. It would be important to sort out how these various‘institutional or policy’ measures interact with more fundamental determinants such as
distance from headquarter economies, distance from final goods markets, wages, etc. The key
contribution of the new value-added databases is that we now have left-hand side measures ofsupply-chain participation. Moreover, the network nature of the new data should allow us togo beyond simple nation-by-nation approaches where a nation’s own right-hand side features
are all that is allowed to affect outcomes. This would allow us to look at regional as well as
national comparative advantage and perhaps better identify which sectors are more likely tobe successful in which nations.
Factory Asia has been deepening and widening at a historically unprecedented rate since the1980s. Despite the Global Crisis, the Great Trade Collapse and the rise of anti-globalisation
elements in rich nations, Factory Asia does not seem to be levelling off. New nations like
Vietnam seem to be joining with success. In short, the future of Factory Asia seems bright.
The key question is: How can developing nations join, and how can they make sure thatjoining leads to an ever denser participation in value networks. Those are questions that
economists would be well advised to tackle.

REFERENCESBALDWIN, R. (2006): "Globalisation: The Great Unbundling (S)," Economic Council of
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