Portrait of Factory Asia: Production
network in Asia and its implication forgrowth - the ‘smile curve’
Richard Baldwin, Tadashi Ito and Hitoshi Sato
February 2014

1. INTRODUCTION
Globalisation’s 2nd unbundling – the geographical unbundling of production processes – istransforming the global economy. This is especially true in the manufacturing sector. A keyelement of this shift is the displacement of value added from high-technology-high-wage
nations to low-technology-low-wage nations. This shift, however, is accompanied by aseemingly paradoxical pair of concerns.
••

Rich nations worry about the loss of manufacturing jobs to a handful of lowtechnology-low-wage nations.
The receiving nations, however, are increasing worried that they are getting the wrong
sorts of jobs.

80%

1990, G7
65%

70%
60%
50%

47%

40%

RoW

20%

China,
18%

6 risers,
5%

10%

9%

2005

2000

1985

1980

1975

1970

0%

1995

3%

2010

30%

1990

World manufacturing share

The first concern has been well documented (UNIDO 2013). As Figure 1 shows, the shift ofmanufacturing from developed to developing nations has been remarkable since 1990,
especially to China.

Source: unstats.un.org;
Note: 6 risers = Korea, India, Indonesia, Thailand, Turkey, Poland

Figure 1: Global shift in manufacturing value added, 1970 – 2010.
On the first concern, advanced technology nations with high wages – such as Japan, UK, andthe US – worry about a hallowing out of their economies as manufacturing jobs are offshored1

to low-technology-low-wage nations. Advanced economies around the world are rethinking
their ‘competitiveness strategies’ and revisiting industrial policy debates, which have notgiven credence since the late 1980s.
On the second concern, the 2nd unbundling has meant that developing nations can now
industrialise by joining supply chains rather than building them, but only certain types of jobs
are being offshored to developing nations. There is a fear that the ‘good jobs’ are staying in
advanced economy cities while the ‘bad jobs’ are being shuffled off to developing economy
factories.
The second concern is less well founded empirically but no less influential. It is often
organised around a keynote intellectual construction – the so-called ‘smile curve’. Introducedby Acer founder and CEO Stan Shih in the early 1990s, the smile-curve logic asserts that theshare of value added in manufactured products is shifting from the fabrication stages to preand post-fabrication services. The general assertion – widely held among developing nation
policy makers – is that this distribution is moving against fabrication stages, i.e. the smile isdeepening as shown schematically in the left panel of Figure 2. As a result, the emerging
markets that are industrialising at historically unprecedented rates are now worrying that they
are getting the ‘bad’ jobs, i.e. jobs associated with low value added per worker, while the‘good’ jobs stay in the North.
The economics literature on the smile curve is surprisingly underdeveloped (although see
Mudambi 2008) even though the concept is widely discussed in the International Business
literature (e.g. Yan and Islam 2011).

Stage’s shareproduct value
addedFirm-level smile curve

Input-sector’sshare of export
value added

Economy-wide smile curve

21st century
value chain

1970s
value
chain

Pre-fabservices

Fabrication

Post-fab Stage
servicesPrimary

OriginManufacturing Services sector

Figure 2: The smile curve: Firm-level versus economy-wide conceptualisation.
The goal of this paper is to shed light on how important the smile-curve notion is at an
aggregate level. In particular, we focus on how value added has shifted along the value chain
when it comes to Asian exports. To this end, we use the newly released 2005 version of
JETRO-IDE’s Asian Input-Output (AIO) table. This data is compiled and the AIO constructedby the Institute of Developing Economies, Japan External Trade Organization (IDE-JETRO)and has been constructing every 5 years since 1985. The table covers 76 sectors and is
focused on Asian nations (Indonesia, Malaysia, Philippines, Singapore, Thailand, China,
Taiwan, Korea, Japan and the US). It includes the US since the US is the major trading2

partner for almost all Asian countries. Partner countries other than the Asian countries and the
US are aggregated as the Rest of the World (ROW).

2. ECONOMY-WIDE SMILE CURVE AND PRIMA FACIE EVIDENCE
The smile curve is based mostly on causal empiricism (although see REITI 2004). There may
be many reasons for this lack of evidence, but one clear problem is that the concept is definedat the firm-level and product level where the notion of a value chain makes sense. Most
systemic data sets, however, are at the economy-wide level where the value chain concept ismore obscure. The point is that firms’ value chains intersect and overlap. Thus at theeconomy-wide level, concepts such as upstream and downstream lose much of their relevance.
What is an upstream service input for one firm will be the final output for another firm, so it ishard to know whether value is being shifted upstream or downstream when the service input
garners greater value added.
To overcome the firm-level to economy-level gap, we re-jig of the smile-curve concept byfocusing on the sectoral origin of value-added in nation’s exports. As a first pass, we workwith a very high level of origin-sector aggregation, namely primary, secondary and tertiary
sectors. In this reformulated version of the smile. The economy-wide versions of the smile
curve is shown in the right panel of Figure 2.
The economy-wide concept surely misses some of the subtle aspects of the firm-level concept.
But for policy purposes, the economy-wide concept is perhaps more pertinent. The basic fearthat offshoring involves sending low-value-added jobs to developing nations and keeping
high-value-added jobs in rich nations is, after all, an economy-wide concern. Moreover, the‘good jobs’ are loosely associated with high-wage service jobs such as research anddevelopment, marketing, product design, etc while the ‘bad jobs’ are loosely associated with
low-wage jobs in fabrication stages. The well-known iPhone example is a good illustration.
The good jobs – and most of the profits – are in California while the low-wage assembly jobs
are in China.
2.1. TRACING BACK THE SOURCE OF VALUE ADDED IN EXPORTS
It is important to note that one cannot simply read off the numbers we are after. From the AIO
table, we can decompose a nation’s exports of, say, transport equipment into direct value
added in the nation’s transport equipment sector and the sector’s intermediate-input purchases.
The purchased inputs may be from primary, secondary or tertiary sectors, but each of these
inputs themselves use purchased inputs. Thus we have to iteratively trace all the value added
to its origin. To start with, we ignore the nationality of the value added and focus on theorigin-sector.
By recursive use of information in an international IO table, we can determine the source ofvalue added in every dollar of exports. The key is that simple accounting identity that states
the sale-value of a product equals the cost of intermediate inputs and the direct value-added of
the industry producing the good. Here value-added refers the cost of primary inputs such as
capital, labour, etc. The same identity applies to the intermediate goods used as inputs, so arecursive application can generate a full map of where all a product’s value was added.
For example, Thai auto exports of worth $10,000 can be decomposed into the value-added ofcountries involved in the car production supply chain, which sources its inputs from chemicalindustry, or metal industry, which themselves sources their inputs from other industries. In
each production stage, the value-added (essentially, labour and capital contribution) is put on.
By tracking down the whole process until the production values reaches to the sum of value3

added, we can decompose the production values into the value-added by industry/country. By
doing this computation, we can find which country/industry contributes to Thailand’s car
exports. In this first paper – where we hope to stick to a level of analysis that is amenable tographical presentation – we often aggregate across all of a nation’s exports. See Figure 3 for aschematic illustration of how we get the three-way decomposition for a nation’s aggregate
exports.
To get a feel for the result of such calculations, consider the case of the exports of textile andleather products. Here our decomposition of the origin-sector of the value added shows that in1985, 21% of the value added came from primary sectors, 70% came from manufacturingsectors (including the textile and leather sector itself) and only 9% came from service sectors.
By 2005, the corresponding figures were 15%, 57% and 28% for primary, secondary andtertiary sectors.

Figure 3: Value-added origin sector.
2.2. PRIMA FACIE EVIDENCE: ECONOMY-WIDE SMILE CURVE
We illustrate concepts with the example of Japan before turning to other Asian nations in ourdataset. Table 1 shows the results of our decomposition of value-added source sectors forJapan’s aggregate exports. In 1985, 6.8% of the value of Japanese exports stemmed fromvalue that was added in the primary sector (in Japan or elsewhere). By 2005, this share had
fallen to 2.1%. The big variation, however, is seen in the manufacturing sector’s contribution.
In 1985 the manufacturing sector was the source of 80.1% of all the value added in Japan’s
exports.
Table 1: Distribution of value-added by broad input sector, 1985 and 2005: Japan
Source sector
Primary
Manufacturing

1985
6.8%
80.1%

4

2005
2.1%
69.3%

Change-4.7%-10.8%

Service

13.1%

28.6%

15.6%

Source: Authors calculations on AIO database
The fact that this number is high is not a surprise. Japan’s exports are heavily skewed towards
manufacturing and most of value added of manufactured goods is added in the manufacturingsector itself (the rest being added in the service and primary sectors that provide inputs into
manufacturing). The share in 2005 fell to 69.3%, which, while still high, is a big drop. As theshares must add to 100%, the service sector saw a corresponding rise in its share of value
added in Japanese exports.
The figures in the table – and corresponding numbers for Korea and Taiwan can begraphically displayed by plotting the changes as in Figure 4. For all three nations, the result isa smile curve of sorts (although perhaps it would be better dubbed the ‘smirk curve’).
The primary sector falls in importance as a source of value added by 5 to 10 percentage points
in all three nations. The service contribution also rises by double digits in all three. When it
comes to the fall in manufacturing’s contribution, Korea stands out in that its share fell by lessthan primary’s contribution, while it fell by more in the other two.

Japan

Korea

Taiwan

25%

25%

25%

15%

15%

15%

5%

5%

5%

-5%

-5%

-5%

-15%

-15%

-15%

-25%

-25%

-25%

Figure 4: Aggregate smile curve, Japan, Korea and Taiwan
Interpreting these aggregate changes is not straightforward. The changes could be driven by
many different factors. Before turning to interpretation, we present the aggregate smile curves
for the other Asian nations in our sample in Figure 5. This charts show that most of thecountries follow the classic smile, or ‘smirk’ pattern. The Philippines and Indonesia arenotable exceptions. These nations see the usual drop in primary and rise in services, but thedrop in manufactured value input is much less marked.

5Thailand

China

25%

25%

15%

-5%
-5%

-5%

-5%
-7%

-15%

-12%

-25%

25%

15%

15%
10%

-5%
-5%

-2%
-10%

-25%

22%

5%
-5%

-5%

12%

Malaysia

25%

5%

15%
5%

Indonesia

-25%

19%

5%

-15%

-10%

-25%

-15%

25%

15%
16%

5%

-15%

Philippines

-1%

-15%
-22%

-25%

Figure 5: Aggregate smile curve, Thailand, China, Philippines, Indonesia, and Malaysia

3. ECONOMICS OF THE SMILE CURVEThe forces behind the smile curve have not been thoroughly investigated theoretically or
empirically. The goal of this section is to present an analytic framework that can help explain
why the smile deepened and how policy makers should think about value-chain issues. 1
Simple economics suggest two explanations for the economy-wide shifts: compositionalchanges across industries, and changes within industries/firms. First, the smile may be down
to the change composition of nation’s exports. That is, even if there were no change in the
production process at the product level, the shift that is evident in Figure 4 and Figure 5could
be due to shift towards exporting goods that happen to be intensive in their use of inputs fromservice sectors.
While the composition of these nation’s exports clearly shifted from 1985 to 2005, a look at
the industry aggregates suggest that this cannot be the whole story. The value-added shifts byindustry are shown in Figure 6 for all of our sectors that are consistently represented in the
AIO back to 1985. Here we see that the smile phenomenon is observed across all industries.

1

This section draws heavily on Baldwin and Evenett (2012) for the analysis of theredistribution of value along firm-level value chains.
6

25%

Primary sectors

25%

Industrial sectors

25%

15%

15%

15%

5%

5%

5%

-5%

-5%

-5%

-15%

-15%

-15%

-25%

-25%

-25%

Service sectors

Figure 6: Smile curves by industry, 1985 to 2005.
The underlying data for these charts are shown in Table 2 (aggregating across all Asian
nations in the AIO). One of the striking points from the table is the consistency acrossindustries of the shift to sourcing value added in exports from service sectors. In all industrial
sectors, the shift to services has been from 15 to 23 percentage points. In service sectors, thefigures varies from 10 to 17. Only in primary sectors do we see a less important shift – withthe range being approximately half that of manufacturers, namely from 8 to 15 percentage
points. See the Appendix for industry by industry charts.
Table 2: Change in sector-origin by industry, Data for smile curves.
Primary sectors
Primary
Paddy
Other agricultural products
Livestock
Forestry
Fishery
Crude petroleum and natural gas
Other mining
Industrial sectors

5%
1%
-9%
-6%
1%
0%
2%

Manufacturing Service
-14%
9%
-9%
8%
-6%
15%
-1%
7%
-10%
9%
-8%
8%
-17%
15%

Primary
Manufacturing Service
-11%
-4%
16%
-6%
-13%
19%
-10%
-7%
17%
-8%
-12%
20%
1%
-18%
18%
-13%
-2%
15%
2%
-21%
18%
-2%
-15%
18%
0%
-20%
20%
-1%
-21%
22%
-1%
-22%
23%

Food, beverage and tobacco
Textile, leather, and the products thereof
Timber and wooden products
Pulp, paper and printing
Chemical products
Petroleum and petrol products
Rubber products
Non-metallic mineral products
Metal products
MachineryTransport equipment
7

Other manufacturing products
Service sectors-2%
Primary

Electricity, gas, and water supply
Construction
Trade and transport

-21%

22%

Manufacturing Service
-1%
-16%
17%
-1%
-16%
17%
-1%
-9%
10%

This conclusion continues to hold when we look at a particular sector for each nation. The
smile curve by nation by industry for the case of Machinery is shown in Figure 7. Even if welook at the same industry across countries, the ‘smirk curve’ remains although it is deeper forsome nations. Figures for the other industries are in the appendix.

Machinery, 1985 to 2005

30%
20%

China

10%

Indonesia

0%

Japan
Korea

-10%

Malaysia-20%

Philippines-30%

TaiwanThailandFigure 7: Machinery smile, by nation, 1985-2005
The second explanation for the value-added shift concerns industry, or firm level changes.
The redistribution of value added by origin-sector could be forced by changes in technology,
competition, relative prices, or corporate restructuring. For example, technological changes
could rise or lower the amount of primary inputs necessary to produce a given product.
Primary share could also fall if the price of primary goods rose by less than the price of thegoods produced by the sectors using them.
3.1. ORGANISING FRAMEWORK: FIRM-LEVEL SMILE CURVE
We start with the definition. Value added of a stage is the difference between the value of thestage’s output and the cost of its intermediate inputs, namely•

Value Added = Price×Output – (Per-unit cost of intermediates)×Output

To connect it to things that might be subject to policies, the first step is to relate the price tothe costs of capital, labour and other primary factors, intermediate costs, and the mark-up,
namely:•Price = Per-unit factor payments + Per-unit cost of intermediate inputs + mark-up
8

where factor payments represents wages, return to capital, technology, etc, and the mark-up is
the premium of price over average cost. Using the price relationship, we get:•

Value Added = (Per-unit factor payments + mark-up)×Output

Observe that the cost of intermediates is netted out. To compare value-added across links inthe value chain, we normalise to get value-added per unit of output, namely:•

Value Added/Output = Per-unit factor payments + mark-up

This is a workable starting point. It tells us that value-added at each stage in a firm-level value
chain consists of factor payments and profits. From this it follows immediately that the only
way to change the distribution of value-added per stage is to change relative factor paymentsor relative profit margins.
One clear source of such changes is offshoring. Imagine that firms in high-tech-high-wage
nations offshore labour intensive stages to low-tech-low-wage nations. The implied costsavings will mechanically reduce the offshored stage’s share of total value assuming that the
profit margin doesn’t change much. The reason is that a stage’s value added is based on its
costs.
This basic cost-accounting effect can be amplified by two additional effects. The first is
relative market power. Offshored tasks tend to be things that can be done in many emerging
nations – most of them eager to attract such stages. The non-offshored stages, by contrast,
tend to involve things where firms naturally have market power due to product differentiation,
branding, etc. In short, offshored tasks become commoditized; the onshore tasks do not. The
second is the combination of high-technology from the lead firm with low-wage labour in theoffshoring recipient. Even if such firms pay local workers more than average (as is often thecase), the combination of high labour productivity and low wages can massively reduce theunit cost of the offshored stage.
This provides one simple story for the smile curve. Some production stages are more readily
offshored than others and North-South offshoring is typically driven by cost-lowering motives.
Thus the value added in the offshored stages could be expected to fall. If the most offshoreable stages involve fabrication, the firm-level smile curve would be a natural outcome.
At the economy-wide level, the story is slightly different. Here the key point is that if it iseasier to offshore manufacturing activities than service activities, offshoring should be
expected to produce an economy-wide smile curve.
3.2. ‘SERVICIFICATION’ OF MANUFACTURING
A very different sort of explanation could come from the ‘servicification’ of manufacturing.
That is, even if there were no change in technology, costs or prices, shifting jobs and tasks
from manufacturing firms to service firms would make it look like less of a product’s total
value added was coming from fabrication. The basic idea here is that manufacturing firms
have outsourced many tasks such as marketing, accounting and cleaning. In most national
accounting systems, such outsourcing would shift value added from a manufacturing firm toservice sector firm and thus this would appear to be an increase in value that is added by the9nation’s service sectors. This would be true even if there were no changes at all in the
production process.
The servicification of manufacturing has long been commented upon by economist tracking
employment data. More recent work documents the trend more carefully (Falk and Peng 2011,
Lodefalk 2010). An important recent paper by Bernard and Fort (2013) document what theycall ‘factoryless manufactures’ as having reached the polar extreme of service separation fromfabrication.

4. DECONSTRUCTING THE VALUE SHIFT TO SERVICES
This section takes a closer look at the prima facie evidence presented in Figure 4, Figure 5,
and Figure 6. We start with the time dimension.
4.1. SMILE CURVES 1985-1995 AND 1995-2005
Unlike other multiregional IO tables that are available (notably WIOD and TiVa), the Asian
IO table exists in electronic form back to 1985. This allows us to investigate whether thesmile curve is a recent phenomenon or whether it also was present in the 1980s.To start with,
we take the first 10 years (1985-1995) and contrast it with the second 10 years (1995-2005) –each time focusing on the changes rather than the levels. Specifically, we calculate the value
added in each nation’s exports fully tracing back service, manufacturing and primary inputs.
The smile curve comes from plotting these changes. Figure 8 shows the facts for Japan and
the Philippines. The figure plots on the vertical axis the change in value that was added in thethree sectors (primary, manufacturing and services) to the nation’s exports. The figure showsthat between 1985 and 1995, the value added in these nation’s exports stemming fromprimary sectors fell for both Japan and the Philippines. The value added originating in
manufactured sectors rose in both nations over the same period, while value added to thesenations exports in service sectors rose for Japan (slightly) but was unchanged for thePhilippines. In summary, there was no smile-curve phenomenon in either of these nations in
the decade running up to 1995.

30%

30%

Japan

20%

10%
0%
-10%

19851995-20%

19952005-30%

VA share change

VA share change

20%

Philippines

10%
0%
-10%

19851995-20%

19952005-30%

10

Figure 8: Smile curves for Japan and Philippines, 1985-95, 1995-05.
The smile appears clearly, however, during the decade following 1995. The value added inprimary sectors fell for both nations as did the value added in manufacturing sectors. The
value added in service sectors rose sharply. As the ‘corner of the mouth’ rises only for the left
side, this might be called a smirk curve rather than a smile curve. The pattern for thePhilippines is broadly similar when it comes to the magnified importance of service sector
inputs, but the drop in manufacturing is less marked while the drop in primary inputs in moremarked.
Having fixed ideas by looking at these two nations, we turn to the remaining Asian nations in
the AIO. The results are striking (Figure 9). There was:••

Little or no shift of the value-added in the 1985-1995 period;
A massive shift between 1995 and 2005.

This pattern holds for all the countries as Figure 9 shows.
We can do this same temporal decomposition aggregating across the Asian nations in the AIO
database but separately by industry. Figure 10 displays the result for exports in selectedindustries. 2 Again we see quite a bit of commonality in the patterns.
While this set of facts is striking, it does not immediately help us identify the drivers of theshift to services. Factory Asia started to develop rapidly from about 1985 but it accelerated
substantially between 1990 and 2000 as China opened up, joined the WTO and Asian nations
unilaterally reformed post-Asian Crisis in ways that were attractive to offshored factories.
Other things equal, this might point to the second unbundling as a key driver. Unfortunately,
the servicification of manufacturing was also booming post 1995 (Lodefalk2010). The main
point here is that the ICT revolution fostered outsourcing and offshoring by making it easierand safer to organise complex interactions at distance.
At the very least, the change over time alerts us to the fact that something has changed inrecent decades to produce this evolution in the service-inputs into Asian exports. It is not auniversal trend. This is helpful in that it rules out many of the standard ‘structural change’
explanations in the growth literature. Studies such as Ngai and Pissarides (2007), Acemoglu
and Guerrieri (2008), and Foellmi.and Zweimuller (2008) stress the secular rise in services
share of GDP and employment. What we see with the contrast between the 1980s and 2000s
is that this cannot be the whole story.

2

The cases of the other industries are in the appendix.
11

-10%

19952005-20%

VA share change

30%

Malaysia

20%

10%

-30%

19952005

19851995
19952005

19851995

-10%
-20%

19952005

-30%

30%

Taiwan20%

Thailand

10%

10%

0%

-20%

-20%
-30%

-30%

-10%

0%

19851995-10%

VA share change

VA share change

19851995

Indonesia

10%

0%

0%

20%

20%

10%

10%

30%

VA share change

20%

20%

30%

Korea

VA share change

China

VA share change

30%

30%

0%
19851995

-10%
-20%

19952005

0%-10%19851995-20%

19952005

-30%

-30%

Figure 9: Aggregate smile curve by nation, 1985-1995 versus 1995-2005

Figure 10: Aggregate smile curve by industry, 1985-1995 versus 1995-2005
The next slice at this aggregate level is to consider the change in the input value-added
distribution by sector and nation. Figure 11 displays the facts for four key export industries:
transport equipment, machinery, metal products, and chemical products for the 1985 to 2005.
The most salient point is the fact that while there is some variation by country by sector, mostshow a strong smile curve pattern.

12

40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%

Transport equipment 40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%

China
Indonesia
Japan
Korea
Malaysia
Philippines
TaiwanThailand

40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%

Indonesia
Japan
Korea
Malaysia
Philippines
TaiwanChina
Indonesia
Japan
Korea
Malaysia
Philippines
TaiwanThailand

Metal products
China

Machinery

40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%

ThailandChemical products
China
Indonesia
Japan
Korea
Malaysia
Philippines
TaiwanThailandFigure 11: Smile curve by nation and industry: Selected export sectors, 1985 – 2005.
4.2. THE GEOGRAPHY OF THE SHIFT TO SERVICES VALUE ADDED
The large shift of the value-added source from the manufacturing sector to the service sector
ignored the issue of national sources. Hereto we have ignored the geographical dimension, i.e.
which countries supplied the extra sector-sector value-added. This misses a key part of theconcern raised by the smile curve – that offshoring is sending the ‘bad jobs’ to emerging
markets while the ‘good jobs’ stay in the lead firms’ home nations. The smile-curve fear isthat high-productivity service sector jobs are stay in, or even moving to advanced nations
while low-productivity fabrication jobs – especially assemble – are moving to developing
nations.
Given the aggregation in our dataset, we cannot really address this good-versus-bad job
dichotomy. We can, however, track the nations that provide the higher service-sector inputs.
Namely, we look at the increase in service VA, by source nation for each exporting
nation/industry. Loosely speaking, we associate the service-sector inputs into nation’s exports
as ‘good jobs’.
To fix ideas and introduce concepts, we start with a single export sector (machinery) and lookat the nation-of-origin of service-sector inputs for Thailand and Japan. For example, the top
panel of Table 3 shows the case of the machinery exports of Thailand. We see that Thailand’s
own-share of service-sector inputs decreased from 94% in 1985 to 40% in 2005. The
corresponding rise in foreign service sector inputs came mainly from Japan, the US and China– although it increased for every nation in our database. The increase in the share of value
added in Thailand’s machinery that was added by Japanese service sectors is quite remarkable.
From just 3% in 1985, it jumped to 40% in 2005. The US’s and China’s increases were also
large, but not where near as dominate as Japan’s.
13This result is consistent with the firm-level smile curve drivers discussed above, although it isfar from conclusive. That is, if Japanese lead firms in the machinery sector are offshoring
fabrication jobs to lower costs, but keeping pre- and post-fabrication service-sector jobs at
home, we should expect the pattern in the top panel of Table 3. The large increase of Thai
export sourcing from US service sectors (the US share of service inputs rises from 2% to12%) is also in line with the canonical smile curve thinking at the firm-level.
An interesting twist, however, is that Thai exports are seeing more of the value added by theservice sectors in other Asian developing nations such as China, Taiwan, Korea andSingapore. Of course, it is possible that the service-sector inputs from, say, the Philippines
involves low-productivity service inputs into intermediate goods, while the service-sector
inputs from, say, the US involves high-productivity market, design and innovation services.
ExporterThailandThailandThailandThailandThailandThailandThailandThailandThailandThailandExporter
Japan
Japan
Japan
Japan
Japan
Japan
Japan
Japan
Japan
Japan

Industry
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Industry
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery
Machinery

Source Country
Thailand
Japan
USA
China
Taiwan
Malaysia
Korea
Singapore
Indonesia
Philippines

SectorService
Service
Service
Service
Service
Service
Service
Service
Service
ServiceCountry share 1985
94.14%
3.04%
2.12%
0.00%
0.19%
0.06%
0.12%
0.05%
0.02%
0.26%

Country share 2005
40.26%
21.54%
11.59%
10.24%
4.29%
3.71%
3.56%
1.99%
1.58%
1.24%

Source Country
Japan
USA
China
Taiwan
Korea
Malaysia
ThailandPhilippines
Singapore
Indonesia

SectorService
Service
Service
Service
Service
Service
Service
Service
Service
ServiceCountry share 1985
99.04%
0.74%
0.04%
0.05%
0.05%
0.04%
0.01%
0.01%
0.02%
0.02%

Country share 2005
89.20%
4.05%
2.47%
1.13%
1.12%
0.52%
0.40%
0.39%
0.38%
0.35%

Table 3: Value shift to services-sectors: Machinery exports from Thailand and JapanThe bottom panel of Table 3 shows the same breakdown for Japanese machinery exports. The
decrease in domestic service-sector sourcing is far less marked than it was for Thailand, withJapan’s share fall only 10 percentage points. This fall was accompanied by modest rises inservice-sector value added in the US (an extra 3 percent points) and China (an extra 2.5
percent points).
4.3. DATA FOR 5 EXPORT SECTORS AND 8 NATIONSWith the basic idea in hand, we turn to a more systematic exploration of the geographicaldimension of the shift to services. To keep the figures manageable we focus on 5 key export
sectors and 8 of the Asian exports in the AIO database. For each of these 40 cases, we look at
the change in service source shares for 10 supplies, namely Japan, USA, China, Singapore,
Korea, Taiwan, Malaysia, Indonesia, Thailand, and the Philippines.
The basic data is displayed in a rather concentrated manner in Figure 12. For each of the 40
cases (5 export sectors for 8 Asian nations) we plot the change in the sourcing share for the14main actors – Japan, the US and China. For comparison, the Japanese figure for Thai
machinery exports (Table 3) is 18.5 percentage points (21.54% minus 3.04%). For Japan itself
in machinery, the number plotted is -9.8% (89.20% minus 99.04%).
Note that the negative numbers in the chart are primarily reflecting a decrease in the ownnation share of service-sector inputs. Table 4 shows the numbers for the five sectors we focus
on. There is a clear pattern whereby large and more developed nations see a lower drop in
own-sourcing.

Textile & leather
Chemical products
MachineryTransport equip
Metal products

Japan
China
Korea
TaiwanIndonesia Thailand Philippines
-8%
-16%
-14%
-22%
-19%
-23%
-33%
-8%
-22%
-24%
-34%
-24%
-30%
-44%
-10%
-22%
-25%
-34%
-19%
-54%
-47%
-6%
-16%
-23%
-27%
-36%
-45%
-82%
-6%
-14%
-28%
-24%
-24%
-40%
-45%

Table 4: Reduction in own-nation service-sector sourcing shares (percentage points,
1985 to 2005).
What the chart shows is that a great deal of the value-added shift towards services has
involved international outsourcing – not just domestic outsourcing. Given the well-known
tight production network links between Japanese companies and the listed Asian nations, thisis a clear indication that the 2nd unbundling is closely linked to the shift in services. For
instance in transportation equipment, where regional production networks are tightly
controlled by lead firms, the service value added shift has been massive away from local
service sectors and towards Japan’s. In the case of Thailand, the share shift away fromdomestic sources and towards Japanese sources was a massive 28 percentage points. The idea
here is that the fact that Thai production facilities have access to worldclass business services
via their associated Japanese lead firms may well be a key to the boom in Thai transport
equipment exports.
The US and Chinese service sectors have also become much more important adders of value
to Asian nation’s exports, with the gain in share typically lying in the 5 to 10 percentage point
range. Notice that we cannot tell what is causing what from the data. In principle, this could
be rich nation firms offshoring manufacturing tasks to developing nations and keeping theservice tasks at home, or it could be emerging market firms buying in worldclass services inorder to make their products more competitive.

15

30%

20%

10%

0%
0

10

20

30

40

-10%
Japan
USA
-20%

China
Sum others (not own)

-30%

Figure 12: Service-input share changes: By national origin 1985 – 2005.
Up to this point, the ‘good/bad jobs’ notion discussed in the introduction seems to pass thefirst look at the date. The exports of Asian developing nations are deriving much more value
added from service-sector inputs, but a great deal of this is coming from advanced technology
nations. But the numbers tell a more nuanced story. All the other nations in our sample saw anincrease in their shares of each other’s service-sector sourcing, but the percentage point
increases were typically small. Taken together, however, the shifts are very important –typically more important than that of the US or China.

5. CONCLUDING REMARKS AND TESTABLE HYPOTHESESThis is, to our best knowledge, the first attempt to show what economy-wide smile curves
look like for Asian nations. Our use of the AIO allows us to show that the smile curve is morethan just an international implication of the shift to services that has been stressed in the
structural change literature. We find that in fact inputs from manufacturing sectors provided
rising shares of nations’ export value added from 1985 to 1995, but sharply falling sharesfrom 1995 to 2005. This pattern of little change in value added via inputs from primarysectors teamed with large shifts in value added from inputs stemming from manufactured
sectors to inputs coming from service sectors. We find this outcome for almost all sectors andalmost all nations.
One informal explanation for this shift is that fabrication has become commoditised. That is,the vast range of nations eager to welcome low-productivity manufacturing jobs has reduced
the markups and labour costs in such stages. In this view, lead firms from high-technologyhigh-wage nations offshore labour-intensive manufacturing task while keeping high-skill
service tasks at home. We find some very indirect evidence in support of this since the overall
16

rise in service-sectors importance has been accompanied by an important shift in sourcing
from domestic service-sector providers to service-providers located in Japan and the US.
More study is necessary to track down the outcome more precisely. For instance, the simple
notion of fabrication jobs moving to developing nations while service jobs move to developed
nations is contradicted by the fact that developing nation exporters are also sourcing more
service inputs from each other.
We view our paper as the first step in a long research effort. As a first step we shall seek to
identify the role of offshoring, or GVC participation in the observed shift to services. To thisend, we can use proxies for GVC-intensity as interaction terms in a Rajan-Zingales-like
regression on the service sector shifts by sector, by country and over time.
We shall also delve more deeply into the types of service inputs that account for the shift. We
suspect that we’ll find the types of service value added from advanced nations differs
substantially from that developing nations such as China.
As an ultimate goal of this work, we would like to better understand the role that servicesector imports – especially from advanced technology nations – plays in the rapid industrial
development observed in Asia since around 1990.

17

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18

Appendix
A sketch of the concept of the value-added computation

Motor Vehicle exports in Thailand, are made from input values from Motor Vehicle industry of Thailand and of Japan, and Value added within
Thailand. Japan Motor Vehicles, in turn, are made from Japan's Iron and Steel industry and others combined with Value added within Japan. And
so on.

19

The computed value-added are aggregated into the three sectors (Primary sector,
Manufacturing sector, and Service sector) as is defined in the industry list above.

20

List of industries
Industry code Industry name
Sector classification
001
Paddy
002
Other agricultural products
003
LivestockPrimary004
Forestry005
Fishery
006
Crude petroleum and natural gas
007
Other mining
008
Food, beverage and tobacco009
Textile, leather, and the products thereof
010
Timber and wooden products
011
Pulp, paper and printing
012
Chemical products
013
Petroleum and petrol products
Manufacturing
014
Rubber products
015
Non-metallic mineral products
016
Metal products
017
Machinery
018
Transport equipment
019
Other manufacturing products
020
Electricity, gas, and water supply
021
Construction
Service022
Trade and transport
023
Services
024
Public administration

21

Appendix Figures for 4.1.: Aggregate smile curve by industry (the other industries),
1985-1995 and 1995-200522

23