Unveiling the evolving sources of value
added in exports
Richard Baldwin, Rikard Forslid and Tadashi Ito
Draft: February 2015

1. INTRODUCTION
Export growth has long been considered an essential aspect of nations’ prosperity.
Traditionally, exports were broken down into primary goods, manufactured goods andservices – with a great emphasis on manufactured exports given the critical role of industry intechnological progress and GDP growth. Changes in recent years – known as ‘servicification’in Europe and the ‘smile curve’ in Asia (Baldwin, Ito and Sato 2014) – have blurred thesecategories.
Servicification and smile-curve analysis refer to changes in the sources of value in
manufacturing – specifically the increased role of services in manufactured goods. The
importance of this change has long been noted in policy and business (Kimura 2003). The
basic point was popularised with the well-known example of Apple’s iPod where the lion’sshare of this manufactured goods was actually created in the service sector; most of the valueadded in an iPod comes from services produced in the US, not fabrication in China (Linden,
Kraemer and Dedrick (2009).
Such anecdotes have recently been supplemented by more systemic empirical evidence due tothe breakthrough came with the development of ‘value added trade’ concepts (Koopman,
Wang and Wei 2014, Timmer et al 2014). These enabled and encourage the implementationin large multinational input-output tables such as World Input-Output Database (WIOD) andthe Trade in Value Added (TIVA) database, which added many North American andEuropean nations to the nation-coverage that existed hereto in the Asian Input-Output (AIO)
table (maintained by IDE-JETRO since the 1970s). These new ways of thinking about exports
and new data sets have allowed economists to trace out the true origin of value added inexports by nation and by sector. The results are striking.
1.1. SERVICIFICATION OF MANUFACTURING
The value added in a given dollar of manufactured exports can be decomposed into shares ofvalue that are added in the primary sector (e.g. the cost of raw materials), in the
manufacturing sector (e.g. fabrication), and in the service sector. The latter consist of services
that are necessary to make and export the manufactured goods such as R&D, transportation,business services, and retail and wholesale services. OECD (2014) has shown that properly
accounting for the service-sector origin of manufactured value added indicates that services
accounts for about half the EU’s and US’s exports. Pilat (2005), Pilat et al. (2008) have shownthat the service sector share has risen steeply in OECD nations. Lodefalk (2013) shows thatthe share of services in total Swedish inputs doubled between 1975 and 2005. He also showsthat services embed in Swedish manufactured goods account for a major share of Sweden’sCentre for Economic Policy Research, Graduate Institute, Geneva, and University of Oxford
Centre for Economic Policy Research and University of Stockholm
Institute of Developing Economics, Japan External Trade Organisation


1

service exports. Similar studies show the same trend in other individual European nations(Boddin and Henze 2014, Crozet and Milet 2014, Kelle 2013, Kelle and Kleinert 2010).
Baldwin, Ito and Sato (2014) show that the share of value added in manufactured products has
shifted decisively away from manufacturing and towards services since the 1990s in Asia
using the AIO tables.
The left panel of Figure 1 illustrates the magnitude of the effect using a 22-sector breakdownof exports. The chart plots one line for each of the 22 sectors (which range from livestock totrade and transport services).1 Each line displays three shares: the change, between 1985 and2005, in the share of export value added that ultimately originates in the primary,manufacturing or service sectors. For example for machinery exports, the three-way allocationof value added input shares was 5.3%, 83.4% and 11.3% (for primary, manufacturing andservices) in 1985 (Table 1). This shifted to 4.7%, 62.2% and 33.1% by 2005. Figure 1 plots
the changes in the shares, namely -0.6, -21.2 and +21.8 percentage points.
Table 1: Example of economy wide smile curve: Machinery exports
VA source‐sector:
Primary
Manufacturing
Service

VA share 1985
5.3%
83.4%
11.3%

VA share 2005
4.7%
62.2%
33.1%

Change‐0.6%‐21.2%
21.8%

Source: “The smile curve: Evolving sources of value added in manufacturing” (Baldwin, Ito and Sato (2014).

The key point that comes out of Figure 1 (left panel) is that the service value-added content of
all exports has risen substantially, while the manufacturing share has decreased substantially.
The right panel of Figure 1 shows the pattern is also pervasive when we slice the data byexporting nations rather than exporting sectors. Again each line show three changes in value
added source-sector shares. The red lines show the changes for all the Asian nations in our
sample over the 1995 to 2005 period. Plainly the servicification of manufacturing is clear in
the sense that services are more important in export value added for all the nations andmanufacturing is a less important source of value added.

The remarkable point here is that the pattern does not differ substantially between developed
and developing nations.2 For almost all of the 9 Asian nations in the AIO database, theincrease in services as the value added source is on the order of twenty percentage points.
Another key point is that – somewhat surprisingly – the pattern of servicification is quite
similar for high-technology nations like Japan and Korea, and low-wage nations like thePhilippines.
The second remarkable fact emerging from the right panel of Figure 1 is that the smile curve
seems to be a relatively recent phenomenon. Looking at the 1985 to 1995 period, which are
shown by blue lines, we see the mirror image pattern with manufacturing providing an

1

Specifically, the sectors are Paddy, Other agricultural products, Livestock, Forestry, Fishery,

Crude petroleum and natural gas, Other mining, Food, beverage and tobacco, Textile, leather, andthe products thereof, Timber and wooden products, Pulp, paper and printing, Chemical products,
Petroleum and petrol products, Rubber products, Non‐metallic mineral products, Metal products,
Machinery, Transport equipment, Other manufacturing products, Electricity, gas, and water supply,Construction, and Trade and transport.
2

The lines are not labelled by nation (to avoid cluttering the chart).
2

increased contribution while services provides a steady or slightly reduced source of value
added.
Figure 1: Smile curve by nation and industry
Smile curve for exports, all sectors,
9-nation aggregate, 1985-2005

Change in VA source sector, 19851995 v 1995-2005
30%

15%

20%

VA share change

25%

5%
-5%
-15%

10%
0%
-10%
-20%

-25%
VA from
primary

VA frommanufacturing-30%

VA from
service

VA from
primary

VA frommanufacturing

VA from
service

Source: “The smile curve: Evolving sources of value added in manufacturing” (Baldwin, Ito and Sato (2014))

A detailed look at the data reveals that much of this increased use of services is coming fromforeign sources. Table 2 presents the example of Thai machinery exports. Between 1985 and2005, the share of service value added (in Thai machinery exports) coming from domestic
sources fell from 94% to 40%. This drop was offset by increased sourcing of service value
added from Japan, the US and China, inter alia. This is a common pattern where the overall
increased importance in service value added in manufactured goods is accompanied by a big
shift toward foreign sourcing for the extra service value added.
Table 2: Value shift to services-sectors: Machinery exports from Thailand
ExporterThailandThailandThailandThailandThailandThailandThailandThailandThailandThailandIndustry
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: “The smile curve: Evolving sources of value added in manufacturing” (Baldwin, Ito and Sato 2014).

1.2. THE POSSIBLE CAUSES
Servicification could arise from many sources. One is quite mechanical.


Reclassification

One of the big trends in industrial organisation over the past decades has been the rise ofoutsourcing by firms. In the 1970s, companies like Nestle would have most services
performed by company employees. As Nestle was classified as a manufacturing firm, thevalue added from its workers providing service inputs was classified as manufacturing value3added – regardless of the nature of the task performed.3 Employees in the marketing
department would obviously be providing a service value-added input, but many of themwould, in the value added calculations, be counted as manufacturing workers. When Nestle
started outsourcing much of its marketing work to marketing firms (who are classified as
being in the service sector), the proportion of service value added in Nestle products rose
simply due to the reclassification of the service tasks from the manufacturing to the service
sector.
The increase in measured servicification that arises from reclassification can happen even if
there is no change in the nature of products, the nature of the production process, or change inthe relative price of inputs from the service sector and from the manufacturing sector. The
next two possible explanations involve either a change in the types of tasks required to make aproduct, or in the relative price of those tasks. As it turns out, there are two very evident
sources of task-composition shifts.


Task-composition-shift: Connecting services

The first task-composition-shift idea stems in a rather mechanical way from increased
outsourcing of tasks both within and between nations. Specifically, the connections among
spatially separate production units typically involve service links. For example, moving
marketing tasks from inside the Nestle building to outside the Nestle building would – withno other changes – require more telecommunications, transportation of people, mailing ofproduct samples, etc. Since all those things cost something, they contribute to the value addedof the final good. Thus, the outsourcing per se tends to increase the share of services in the
final good’s value added. In the international context, offshoring certain parts of production
would lower the cost of the parts (and thus the parts’ share of the final good value added)
while simultaneously increasing the inputs of, e.g. transportation services to move the parts
back and forth.
Task-composition-shift: Changing final goods

The second task-composition-shift explanation arises from changes in the nature of the final
manufactured goods. Automobiles provide a good example. Cars in the 1970s were basicallymachines that allow drivers to get somewhere. Today’s cars have a great deal of software inthem – everything from automated engine diagnostics to sophisticated GPS maps with various
features. As much of the cost of software comes from the service sector, the share of service
value added in cars rose. Ultimately, the argument is that consumers are demanding
manufactured goods that have features which are more service-input intensive thanmachinery. Sophisticated design is another example beyond software. In essence, the sourceof servicification arises since firms are embedding more services into their machines.
Task-relative-price-shift

The task-composition explanations work by shifting the share of tasks towards tasks
performed by the service sector. Another class of explanations rests on changes in the relative
price of service tasks even without changing the task composition. For a variety of reasonsinvolving the need for coordination and face-to-face interaction, offshoring of tasks tends tobe easier when it comes to intermediate goods rather than intermediate services. Since
offshoring of a task is typically driven by cost-reduction motives, there is a very naturaltendency for offshoring to reduce the relative price of the offshored tasks. If most of the3

The latest national account practices attempt to correct for this, but the basic
misclassification issues persist. See Berlingieri (2013) for details.
4offshored tasks are things performed by the manufacturing sector, the offshoring would – invery mechanical way raise service value added in final manufactured goods.
The point can be illustrated with a numerical example. Suppose that a car is made with three
things: oil, assembly workers and designers. Initially all three are combined inside a factoryin, say, the US and – to be concrete – the oil costs $20 per car, the assembly worker costs $20
per car, and the design services cost $20. The total value added of the car is $60 with a third
come from the primary sector (oil), a third from the manufacturing sector (assembly) and athird from the service sector (design services). If the assembly is offshored to Mexico and its
cost falls to $10, the car will appear to have been servicified – even if there is noreclassification, no shift in task-composition. Now, because of the shift in task relative prices,
the service value added share is 20/50, which is higher than the pre-offshoring share of 20/60.

2. A SIMPLE MODEL OF OUTSOURCING AND SERVICIFICATIONThis section presents a very simple model that illustrates how outsourcing could produce theservicification of manufacturing.
In the model, the production of one unit of a manufacturing good is associated with a fixed setof tasks that are either performed inside the firm by a (composite) factor of production, L, or
outsourced.4 The size of tasks is normalized so that one unit of L is required for one task, andthe range of tasks is normalized to the interval [0,1]. Tasks can be undertaken within the firmor outsourced; we denote n as the numbers of tasks outsourced so 1-n are done within thefirm. The tasks are not ordered, so this is a ‘spider’ production process rather than a sequential‘snake’ process in the terms of Baldwin and Venables (2014).
Outsourcing involves costs in terms of transportation services, services of intermediaries etc.
The cost of outsourcing varies among tasks, and we order tasks in increasing cost ofoutsourcing, so tasks with a higher index involve higher outsourcing costs. The extra units ofservice required when outsourcing tasks from i = 0 to i = n is given by g(n), with g’(n)>0,
g’’(n)>0. We denote the (constant) unit cost of service as c so the total extra service cost is
cg(n).
The cost of producing one task within the firm is w. Its cost when outsourced is w*, wherew*<w (this provides incentives for outsourcing). We do not, in this model, distinguishbetween domestic and foreign outsourcing, since we are trying to illustrate the logic ofservicification overall. In this set up, the simple cost minimization outsourcing problem facing
a typical firm is:minn w  (1  n)  w * n  c  g (n)The first order condition, (w-w*) = cg’(n), which determines the firm’s choice between
outsourcing, n, and in-house production is displayed in Figure 1. The gains from outsourcing
a task is illustrated by the horizontal line w-w*. The curve cg’(n) depicts the marginal cost ofoutsourcing, which increases in n. A firm will outsource until the cost of outsourcing equals
the cost of producing the same task in-house. That is, until the point where the curvesintersect. The intersection determines the optimal n.
Using the functional form g(n) = n, where >1 , and minimizing costs with respect to n gives
the optimal number (mass) of outsourced tasks:545Outsourced tasks are perfect substitutes to domestically produced tasks.
Note that the second order condition holds since g’’(n) < 0.
5

1/( 1) w  w*n'   c 

(1)

Figure 1: Optimal outsourcing$

c.g’(n)

w-w*

n

n'
Source: Authors’ elaboration.

We are now in the position to analyze the effects of the ITC revolution, which implied lower
costs for transportation and lower coordination costs. This corresponds to a fall in c in themodel. The effect is increased outsourcing (a higher n) as seen from the equation above.
Figure 2, illustrates the effect of a fall in c and the attendant rise of n from n’ to n”.
Outsourcing increases and the production of the domestic tasks decreases.
Figure 2: The effect of lower transportation or communication costs$c.g’(n)

w-w*

n

n' n”
Source: Authors’ elaboration.

We can now use the model to illustrate several of the possible sources of the servicification of
manufacturing. Note first that the per unit value added of this product – ignoring any profits

6since we are working with perfect competition – is just the costs, namely
VA  w(1  n)  w* n  cg(n) . The service value added share isServiceVA share 

w * n1  cg(n)w  (w  w*)(n1  n2 )  cg(n)(2)

where n1 and n2 are the outsourced tasks that are reclassified as service tasks and those thatare not (respectively). In our simple set up, the service value-added share was zero so (2)gives the change in the service value added share for this product.
The two terms in the numerator show two sources of servicification. First, outsourced tasks(n1) which are service tasks that were previously misclassified as manufacturing tasks raise
the service value added share. Here outsourcing involves a reclassification where tasks that
previously were performed by a manufacturing firm, and therefore where classified as
manufacturing value added, now become reclassified as service value added. Second, while
the outsourcing saving money for the producer (it is cost-minimizing), extra connection
services are required. This is a task-composition-shift related to connecting services, where
the service share of export increases.
The denominator shows the task-relative-price-shift effect. As all the outsourced tasks involve
lower costs, i.e. w* instead of w, the outsourcing (i.e. higher n) tends to boost the service
value added share. Since the cost of the connecting services is unaffected by the choice of n,
the impact is seen for both reclassified (n1) and un-reclassified tasks (n2), but it is strongest for
the un-reclassified ones.

3. EVIDENCE SUGGESTING AN EMPIRICAL STRATEGY
When thinking about which causes of pervasive servicification are likely to be important, two
additional questions arise immediately:Which service sectors are most important in supplying the extra service value added toexports?
Which nations provided the heightened service value added to exports?

An advantage of AIO table is its detailed industry classification, which makes it a suitabledata set to analyse the source of service value added by sector and by supplying country.

Figure 2: Which service sectors are most important in providing export value added?
7

2.5

All 9 countries

2.0
1.5
1.0

-

0.4
0.3
0.2
0.1
-

1.0

Retail &Wholesale

0.6

FinInsurUnclass
Telecom
Building
Govt

0.5

0.5

Other
Services
Transport

China

Other
Services
Transport
Retail &Wholesale
FinInsurUnclass
Telecom
Building
Govt

Japan

0.8

Transport
Other
Services
Retail &Wholesale

0.4

FinInsurUnclass
Telecom
Building
Govt

0.2
-

0.1

Thailand

0.1
0.1
0.0

Other
Services
Transport
Retail &Wholesale
FinInsur

0.0

Unclass
Telecom
Building
Govt-

Source: Authors’ calculations on IDE-JETRO AIO database.

Figure 2 shows the contribution of the various service industries to export value-added from1990. The top left panel shows the aggregate for all nine Asian nations in our sample, namely
the three big nations (Japan, Korea and China), five ASEAN nations (Malaysia, Indonesia,
Thailand, Philippines and Singapore), and Taiwan. Plainly the rise in service value added
contributions to export value is concentrated in just three service industries, namely
Transportation services, Retail and Wholesale services, and Other services. Consider these inturn.
The rise in the cost share of transportation services – recall that a supplying industry’s value
added share in exports is also its cost share – is perfectly in line with the ‘Task-compositionshift: Connecting services’ explanation discussed above. As manufacturing firms spatiallydisperse production, goods that used to be shipped only once from one factory to consumers
are now shipped among factories before being shipped to consumers.
The augmented role of Retail and Wholesale services is suggestive of the same cause.
International production networks are not all organised inside the same firm (Gereffi 2014). In
this case, more ‘middle men’ could be expected to be involved in the ‘connecting services’.
Along the same lines, it is also possible that some of the change is down to reclassification.
To the extent that good ICT lowers transaction costs and thus lowers the benefits oforganising things inside a single firm, we could expect that one of the things that might be8

outsourced from the original manufacturing firms is service task such as shepherding theshipping of parts and components along the supply chain.
There are also indications that some manufacturing value added is being reclassified aswholesale activity due to extreme production unbundling. Bernard and Fort (2013) use US
establishment activities to show that some service sector value added really should beconsidered as manufacturing value added. In particular, some firms in the wholesale sector are
actually heavily involved in activities related to the production of manufactured goods. Theseso-called ‘factoryless goods producers’ – such as the vacuum firm Dyson – design the goods
and coordinate the production activities without actually owning factories. This is not a small
scale anomaly. Reclassifying ‘factoryless goods producers’ back to the manufacturing sector(where they arguable belong) would shift between 0.6 and 1.3 million US workers wholesale
to manufacturing, in 2002, and between 0.4 and 1.9 million in 2007. To the extent that thisissue plagues the statistical practices of Asian nations, the rapid rise in Retail and Wholesale
service inputs may partly reflect a misclassification of value added from manufacturing toservices.
The prominent role for ‘other services’ probably indicates other factors are at play. Other
services includes: Real estate, Medical and health service, Restaurants, Hotel, etc. This list
suggests that reclassification may be important. For example, if a factory runs a guest houseand canteen in 1990 but outsources these tasks to an independent service provider in the
2000s, we would see an increase in the sourcing of value added from manufacturing toservices.
The second natural question when tracing down the origin of servicification is:Where is the extra service value-added being sourced from?

Figure 3 indicates that the US, China and Japan dominate the increased servicification – atleast when we aggregate across all export sectors.
Figure 3: Which Asian service suppliers are most important in servicification?
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
-

US
China
Singapore
Malaysia

Japan

Thailand
Korea
TaiwanPhilippines
Indonesia

Source: Authors’ calculations on IDE-JETRO AIO database.

It would seem that the importance of each of the three suppliers is down to different factors.
The US’s rapid rise could be a combination of the reclassification and change in products.
9

The outsource of services has advanced rapidly in the US, following the advice of Harvard
professor Michael Porter that firms should stick to what they do best and outsource the rest –i.e. competitive advantage. To the extent that US firms pursued this, the service content of
most intermediate goods sourced from the US would contain a rising share of service value
added – even with no change in the products or production methods. The trend towards
embodying services in manufactured goods (software, design, etc) would also explain the rise.
For China the story is likely to be different. Since China’s role as a supplier of value added
has risen so rapidly across the board, it is not surprising that it is also an important source ofservice value added. To the extent that Chinese intermediate goods include services (e.g. withthe reclassification of jobs discussed above), the rapid rise in sourcing intermediate goods
from China would also imply a rapid rise in Chinese service value added in nation’s exports.
The result for Singapore is not surprising given its role as a trade entrepôt in Southeast Asia.
A great deal of Southeast Asian trade is transhipped through Singapore and Singapore’s firms
often provide additional connect services such as pre-shipment inspection, customs handling,etc.
While all the sectors experienced servicification when averaged across nations – there is morevariability across sectors for particular nations. Moreover, the degree of GVC participation
varies quite a lot across nations and sectors, as is shown in the next section.

4. DATA AND EMPIRICAL STRATEGY
4.1. DATAAn Asian Input-Output (AIO) table has been constructed by the Institute of Developing
Economies, Japan External Trade Organization (IDE-JETRO). The data sets are available
from 1985 every 5 years to 2005. From 1990 onwards, 76 to 78 industries are covered, whichis larger than 35 industries of World Input Output Database.6 This feature of higher
disaggregation allows us to better analyse the service value-added. The data set 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 trading 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).
4.1.1. Computation of export value-added
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 eachof these inputs themselves use purchased inputs. Thus we have to iteratively trace all thevalue added to its origin. The recursive use of information in an international IO table enables
us to determine the source of value 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 the6

AIO table has slightly different codes between 1990, 1995 data and 2000, 2005 data. To
enable across industry analyses over the whole period, we have made a concordance between
the two. To keep the complete consistency of the industry codes over the whole period, someindustries are aggregated and the number of industries after the concordance is 64, as isshown in the list of industries in the appendix.
10

intermediate goods used as inputs, so a recursive application can generate a full map of where
all a product’s value was added.
4.1.2. Methodology
The iteration process described above yields the export value-added. However, a handy tool
using the matrix algebra to make the above computation is available. Koopman et al. (2011)
proposes a methodology based on Leontief inverse matrix. Johnson and Noguera (2012),
Timmer et al. (2014), or De Backer and Miroudot (2014) use the same methodology. We
1follow suit. Essentially, the value-added in exports are computed as VAE  v  I  A  E ,where I is an identity matrix, A a global intermediate input coefficients matrix,  I  A 

1

the Leontief inverse matrix, v is a matrix (or a stacked vector, depending on the level ofdisaggregation one aims to obtain) of the ‘direct’ value-added share of the total production
value for each exporter/industry, E is a matrix (or a stacked vector, depending on the level ofdisaggregation one aims to obtain) of gross exports. Intuitively put, pre-multiplying the grossexports E by the Leontief inverse and the ‘direct’ value-added share, we incorporate both‘direct’ and ‘indirect’ contribution.
4.1.3. Measures of GVC participation
The profession has not yet settled on the best measure of GVC participation so we rely on avariety of measures that have been used in the literature. This section describes the measuresand considers some of the differences among them.
By way of introduction, it is worth noting that these concepts strike many – including many
international economists – as rather complex, perhaps over complicated. The source of thisimpression is the centuries-long tradition of ignoring imported intermediates. In the trademodels that virtually every economist in the world has studied (Ricardian model, HeckscherOhlin model, etc.) a nation’s exports are 100% made at home and all of its exports are final
goods so there is no issue of the exports moving on to third nations. This basic idea is also
cooked into standard international macroeconomics in the sense that the models assume thatthe home nation’s imports depend only on its GDP and relative prices. They do not accountfor the fact that the Thailand nation may be importing, e.g. engines from Japan, to produce
cars that are sold to the Philippines. In the latter case, the Philippines’s income would affectits demand for Thai cars which would then affect Japan’s exports of engines.
Given the massive rise in the internationalisation of production, the upshot is that tradeconcepts in the 21st century are inevitably more complex than those studied by mosteconomists when they were in university. The complexity is unavoidable. It can, however, bemade more straightforward than is often the case.
As Baldwin and Lopez-Gonzales (2014) point out: ”While the terms for supply-chain trade
are numerous, the essential concepts are universal. There are three basic supply-chain tradeconcepts: (i) importing to produce, (ii) importing to export, and (iii) value-added trade.” For
all three, it is critical to distinguish between participation on the ‘sourcing’ side, i.e. buying-in
foreign parts and services, and the ‘selling’ side, i.e. selling parts and services to foreigners.
Typically, developing nations are engaged in both as they import intermediate goods andprocess them further. The resulting good may be sold domestically or exported (sometimes
called re-exported). Developed nations can also participate on both the sourcing and sellingsides as in the case of US-Mexico Maquiladora trade where US companies send parts to
Mexico for labour-intensive processing then bring them back for final processing and either
local sale or export sale.
11I2P, I2E, E2P, E2P and E2R: Gross trade measures
One of the broadest measures of GVC participation is called Importing-to-Produce (I2P)
which is simply the total usage of foreign intermediate goods measured in ‘gross’ (i.e.
traditional) terms. This looks at how important imported intermediates are to the importing
nation’s industrial production. It is a broad measure of the internationalisation of production
since some goods produced in, say, Germany are relying on the production of other nations.
As the name suggests, I2P is most naturally viewed as a ‘sourcing’ side measure (e.g. Japan’s
imports of rubber that are used in making Japanese goods). However, every import is anexport for some other nation. Thus we can easily turn I2P into a selling-side measure. For
example, summing up – across all the importing countries in the sample – the I2P that comesfrom Japan will yield Japan’s exports of intermediate goods and services. This concept might
logically be called exporting-to-produce or E2P. In Figure 4 it is the total of exports in box #2.
All these measures based on gross trade flows can be normalised by total imports (for I2P) or
exports (for E2P) when comparing across nations, or by the world total of I2P or E2P.
Figure 4: GVC measures based on traditional (gross) trade flows: Sales side.

Source: Authors’ elaboration.

The I2P and E2P concepts fail to distinguish between imported intermediates that are put into
goods that are then sold locally and those that are put into goods that are then sold abroad.
The next refinement of GVC participation measurement focuses on intermediates used inexport, rather than in all production as is the case for I2P and E2P. These measures focuses
only on imported intermediates that are embedded into goods and services that aresubsequently exported. This is closer to popular conceptions of ‘global value chains’. Here theimporting nation can be thought of as a node in an international production network. The key
feature is that foreign intermediates are used to produce goods and services that aresubsequently exported.
Following the namely logic, these GVC trade flows could be called importing-to-export, or
I2E. The OECD’s Trade in Value Added (TiVA) database publishes a statistic related to thiscalled REI, which is the share of a nation’s imported intermediate inputs that are exported.7
As with I2P and E2P, there is a sourcing and sales side of the I2E concept. That is, some of anation’s intermediate exports are embodied in foreign goods that are then exported. Our
7

Although it is not clear whether REI is based on value-added import flows or gross importflows. I2E is constructed with gross trade data.
12

naming logic would suggest that this would be called E2R – exporting to re-export. In Figure
4, this would be the sum of trade in boxes #4 and #5.
One additional refinement on the GVC trade measure that is commonly used concerns
intermediate exports that are processed abroad and then re-imported – as in Maquiladoraplants that straddle the US-Mexico border. This can be called re-importing for the sending
nation (e.g. US in the Maquiladora case) and re-exporting for the receiving nation (e.g.
Mexico in the Maquiladora case). This is box #5 in Figure 4.
These measures can very naturally be normalised by total imports (for E2R) or exports (forE2P) when comparing across nations, or by the world total of I2P or E2P.
These gross-trade-based measures have the great advantage of being based on trade flows as
measured by customs data, i.e. they are in traditional or ‘gross’ terms. This means that they
are available for all nations for long periods and at quite detailed product level (all nations
have detail down at least to the HS6 level, i.e. about 5000 products). Another advantage isthat it does not require the elaborate value-added calculations described above that can
introduce errors when the IO tables are not exact.
The drawback is that there can be some double counting and one cannot know the ultimate
source of the intermediate good. A couple of examples service to illustrate the drawbacks. For
an example of the double counting, consider US imports of Canadian intermediates that
include some value that was added in the US. The point is that US re-imports many of theintermediates it exports to Canada after some processing has been performed in Canada. As
an example of confused nation of origin, we can continue with the US-Canada example.
When Canada imports parts from the US to export to, say, Britain, the I2E concept will
allocate all the parts to the US. However, some of the value in the US intermediate export to
Canada may have been added in a Mexican Maquiladora plant. Thus the true source of theintermediate is mis-classified as being solely from the US when in fact a part of it was madein Mexico.
Value added measures of GVC participation
The AIO table is well suited to address the double-counting and mis-allocation issues that
may arise from the I2P, E2P, I2R and E2R notions. A great deal of work has been recently
undertaken to set up OECD-based versions of the AIO table. The main ones are known as
TiVA and WIOD.
Figure 5: GVC measures based on value-added trade flows.

13

Source: Authors’ elaboration.

As part of this recent effort, a number of GVC measures have been developed that can bethought of as the value-added version of the gross trade measures of GVC participation. The
basic concepts can be seen in Figure 5. Here Home exports (‘gross exports’) are decomposed
into value that is added inside Home, domestic value added, and value that is added in othernations, foreign value added. As explained above, gross exports exactly equal the sum ofdomestic and foreign value added. The easiest concept is the domestic value added in final
good exports. In this case, Home’s value added goes directly to foreign end users and stays
there. Some of Home’s domestic value added, however, will be embodied in intermediate
goods so the domestic value added may end up somewhere other than the initial importer.
If the domestic value added is used in foreign goods that are sold in the importing nation, thisis the value-added equivalent of E2P; domestic value added is used to produce locallyabsorbed goods. This occurs in the box #2. If the imported value added is used in theimporting nation’s exports, then we have a concept akin to E2R, as shown in box #3. If thevalue added ends up on a good that is sent back to Home, it is the value added equivalent ofreimports.
In this analysis, we only break down domestic export value added since the exercise isundertaken for each nation and the ‘foreign value added’ will be traced out as part of theexercise for the foreign nation.
GVC participation of 9 Asian countries is shown in Table 3,
Table 4 and Table 5, in terms of Import-to-produce, backward participation and forward
participation, respectively. These graphs show a general trend for deeper GVC participationfrom 1990 to 2005. As is expected, Import-to-produce has a similar picture with backward
participation because both are similar concepts. The difference is whether it is computed
based on gross export values or on value-added exports. In levels, China, Indonesia and Japan
have relatively low GVC participation, whereas Malaysia and Singapore have relatively high
GVC participation. Forward participation shows a notably different picture than the former
two. Except China, all the other countries have similar level of GVC participation. Japan and
Korea, the two “head-quarter” economies have much higher level than the case in backward
participation. Japan and Korea produce key components and send them to the developing
countries and then these components are assembled and exported to third countries.
Table 3: Measures of GVC participation for 9 Asian countries – Import to produce (I2P)

14

0.5
0.45
0.4
0.35
0.3

I2P19900.25
0.2

I2P1995

0.15

I2P2000

0.1

I2P2005

0.05
0Source: Authors’ calculations on IDE-JETRO AIO database.

Table 4: Measures of GVC participation for 9 Asian countries – Backward participation
(I2E)
0.6
0.5
0.4
Backward1990

0.3

Backward1995
0.2

Backward2000

0.1

Backward2005

0

Source: Authors’ calculations on IDE-JETRO AIO database.
Table 5: Measures of GVC participation for 9 Asian countries – Forward participation
(E2R)

15

0.3
0.25
0.2
Forward1990

0.15

Forward1995
0.1

Forward2000

0.05

Forward2005

0

Source: Authors’ calculations on IDE-JETRO AIO database.
It is also worth looking at differences of these GVC measures by industries.

Table 6 shows the simple mean values of the import-to-produce index by industries for the
manufacturing sector. Industries such as, 034: Chemical fertilizers and pesticides, 036:
Refined petroleum and its products, 045D: Heavy electric machinery, 046A: Electronics andelectronic products have relatively higher import-to-produce index. Backward participationindex and Forward participation index by industries are in appendix.

Table 6: Import-to-produce, averages across countries by industries

16

0.5
0.45
0.4
0.35
0.3

I2P_sum1990

0.25

I2P_sum1995

0.2

I2P_sum2000

0.15

I2P_sum2005

0.1
0.05
0

0.5
0.45
0.4
0.35
0.3

I2P 1990

0.25

I2P 1995

0.2

I2P 2000

0.15

I2P 2005

0.1
0.05
0

0.5
0.45
0.4
0.35
0.3

I2P 1990

0.25

I2P 1995

0.2

I2P 2000

0.15

I2P 2005

0.1
0.05
0

Source: Authors’ calculations on IDE-JETRO AIO database.

17

4.2. EMPIRICAL STRATEGY
When looking at servicification, we are especially interested in the contribution of offshoringand GVCs – if for no other reason than the fact that governments have policy levers that canpotentially encourage, discourage or reshape offshoring. Moreover, the real economic
consequences of servicification are much less interesting to the extent it is driven by thebroader, more mechanical determinants such as reclassification. Our primary goal is thus tolook at the role of offshoring on servicification.
The key challenge is to separate offshoring’s impact from that of the other possible
explanations. This is intrinsically difficult since servicification is widely observed acrossindustries and nations. As Table 7 shows for a 24-sector disaggregation, every single sector
saw a substantial increase in the contribution of services to value added in exports.
Table 7: Shift in source-sector value added shares in exports averaged across 9 nations,
1985-2005.
Change in value added share (by source sector)

Primary Sectors
Paddy
Other agric. Products
Livestock
Forestry
Fishery
Crude petro. & natural gas
Other mining

Primary

Manufacturing

Service

5%
1%
‐9%
‐6%
1%
0%
2%

‐14%
‐9%
‐6%
‐1%
‐10%
‐8%
‐17%

9%
8%
15%
7%
9%
8%
15%

‐11%

‐4%

16%

‐6%

‐13%

19%

‐10%
‐8%
1%
‐13%
2%
‐2%
0%
‐1%

‐7%
‐12%
‐18%
‐2%
‐21%
‐15%
‐20%
‐21%

17%
20%
18%
15%
18%
18%
20%
22%

‐1%
‐1%
‐1%
‐2%

‐16%
‐16%
‐9%
‐10%

17%
17%
10%
12%

Manufacturing sectors
Food, beverage & tobacco
Textile, leather, & products
thereof
Timber & wooden products
Pulp, paper & printing
Chemical products
Petro & petrol products
Rubber products
Non‐metallic mineral products
Metal products
Machinery

Service sectors
Electricity, gas, & water supply
Construction
Trade & transport
Services

Source: Authors’ calculations on IDE-JETRO AIO database.

4.3. REGRESSION DESIGN
Perhaps the most straightforward way to design the regressions would be to gather data on
non-GVC drivers of servicification and put those into the regression. If we had information,for example, on the extent of corporate restructuring (in particular outsourcing) by sector, byyear for the years in our main sample (1990, 1995, 2000, and 2005), we could control for the18

impact of reclassification by including this proxy. Unfortunately, such measures are notavailable for the nations in our sample – especially for the developing nations such as theASEAN nations. Our solution is to instead use fixed effects to control for unobservable that
do not vary by sector, by year, and by nation.
The first regressions to run involve ‘total servicification’, i.e. the increase between 1990 and2005 of service sector inputs into exports in all 64 sectors. The regressor of interest is various
measures of changes in GVC participation. The test is to see if GVC participation helps
explain total servicification once we have controlled for common industry and country factors
using fixed effects. Note that since we are working with changes in the key variables, thefixed effects taking out common trends by industry, exporting nation and time period (i.e.
1990 to 1995, 1995 to 2000, etc).
4.3.1. Total servicification
Specifically, we estimate the following equation by Ordinary Least Squares: ServiceVA sharei jt   0   0 GVC Partici jt
 fixed effects   i jt
where ‘Service VA share’ is the five year change in the share of export value added thatstems from the service sector (regardless of origin nation), and ‘GVC Partic’ is the change in
one of several proxies for GVC participation by the exporting sector. The observations are bynation (nine nations), by sector (64 sectors) and three time periods (1990-95, 1995-00, 200005).
4.3.2. Foreign-sourced servicification
We also run a more pointed set of regression that changes the focus from total servicification
to foreign servicification. That is to say, we seek to explain the increase in foreign-service
value-added in nations’ exports – again by export sector, by exporting nation and by time
period. The underlying thinking is that GVC participation may be particularly conducive tosource service-value-added from abroad.
This is possible since the granularity of our data allows us to distinguish increases in service
value added by country of origin. Given our interest in the role of GVC participation in theservicification, one very natural regression focuses on increases in service value added
embodied in exports that stem from foreign sources. The idea here is that such service value
added is most likely to be associated with GVC participation.
This regression design has the additional merit of allowing us to use the change in domestic
service value added as a control. That is, we suppose that the by export sector, by exporting
nation, by period change in domestic service value added is picking up non-GVC relatedchanges such as reclassification, changes in the nature of products and production processes,
etc.
Thus we estimate the following equation by Ordinary Least Squares.

 ForeignService VA sharei jt  0  0GVC Partici jt  DomesticService VA sharei jt  fixed effects  i jt
where ‘ForeignService VA share’ is the change in foreign service value-added shares ofexporter country i industry j at time period t .Thus, we have three periods. ∆is19

our variable of interest, which represents the change in Global Value Chain index for exporter
country i industry j at time period t .
As discussed above, a part of the increase of foreign service value-added shares could be
attributed to general trends (such as reclassification). To control this general trend,‘DomesticService VA share’ is added as a control variable, which represents the change indomestic service value-added shares of exporter country i industry j at time period t .

5. RESULTS
5.1. TOTAL SERVICIFICATION RESULTS
Table 8 shows the estimation results for the total service value-added as the dependent
variable with the import-to-produce as GVC measure. Neither I2P nor the cross terms
between I2P and the developing countries dummy shows statistically significant coefficientestimates.
Table 8: Estimation results using change in Import-to-Produce as GVC measure - Total
Service Value-added

Change in Import-toProduce(1)Change in Total
Service Valueadded share(2)
Change in Total
Service Valueadded share(3)
Change in Total
Service Valueadded share-0.00656(-0.11)-0.00468(-0.03)0.0111(0.17)

Change in Import-toProduce* developing
countries-0.00205(-0.01)I2P*Machinery
I2P*Motor vehicles
I2P*-Electronics
I2P*Plastic products
I2P*Metal products
I2P*Wearing apparel
Observations
R2

1124
0.080

1124
0.080

-0.255(-0.79)-0.755(-1.94)0.0313(0.12)
0.00350(0.01)
0.0908(0.26)
0.0675(0.19)
1124
0.084

t statistics in parentheses*
p < 0.05, ** p < 0.01, *** p < 0.001

The lack of significance of GVC participation suggests that the other factors are the dominant
force. That is, things like reclassification, changes in the nature of products and production
must be driving the total servicification. We also see that there is enormous variation since the20

R2 is so low. The I2P measure is insignificant with or without allowing interactions for thelevel of development (column 2), or industries interactions (column 3).
The results for the more pointed proxies for GVC participation – namely importing to export(the sourcing side participation) and exporting to re-export (the sales side participation) – are
not as universally insignificant. When we put in only the I2E and I2R measures, we find bothare negative, but only the former is significant.
Table 9: Estimation results using changes in GVC I2E and E2R participation as GVC
measure - Total Service Value-added

Change in GVC I2E participation

Column 1-0.0228***(-3.59)I2EChange*developing

Column 2-0.0629(-1.62)0.0412-1.05

I2EChange*Machinery
I2EChange*Motor vehicles
I2EChange*Electronics
I2EChange*Plastic products
I2EChange*Metal products
I2EChange*Wearing apparel
Change in GVC E2R participation-0.0822(-1.93)

E2RChange*developing

0.0126-0.11-0.113(-0.92)

E2RChange*Machinery
E2RChange*Motor vehicles
E2RChange*Electronics
E2RChange*Plastic products
E2RChange*Metal products
E2RChange*Wearing apparel
Observations
R2

1124
0.094
21

1124
0.096

Column 3-0.104***(-7.69)0.0596-0.63
0.138***-4.49
0.108*-2.25-0.144(-1.27)0.0427-0.36
0.106***-6.79-0.139**(-3.22)

0.225
-0.59
-1.913***(-3.32)0.195-0.61
0.907**
-3.29
0.499
-0.94
-0.56
(-0.34)1124
0.156

t statistics in parentheses*
p < 0.05, ** p < 0.01, *** p < 0.001

As our theory section illustrated, outsource/offshoring almost mechanically raises the total
service value added via the reclassification, composition, and/or relative prices effects. The
negative coefficients are thus not in line with the theory.
This suggests that the theory would have to be stretched to understand the negative
coefficients. One possibility is the selection effect – i.e. a change in the types of goods aparticular nation exports within the broad categories we are using.
For example, inside the Motor Vehicle sector, some goods are intrinsically more
service-task intensive and some are intrinsically more manufacturing-task intensive.
What may be happening is that GVC participation – especially on the sourcing side –is leading nations to concentrate their exports more on those that are moremanufacturing-task intensive.

This would explain how the overall servicification would be less marked for nations
participating more vigorously in GVC activities.
The Column 2 results provide weak support for this interpretation. Here we see that theservicification is negative overall (the point estimate is -0.06) but less negative for developing
nations (the interaction term is +0.04). Although none of the estimates are statisticallysignificant, they are in line with the idea that outsourcing is particularly important in sectorsthat are manufacturing-task intensive – especially for developed nations. In other words, thisinterpretation views the negative sign not as an indicator that GVC participation is leading toless servicification, but rather that GVC activity is prevalent in manufacturing-task intensive
products.
Looking at Column 3, we see the story is even more complex when we allow GVC
participation (on both the sales and source side) to affect total servicification differently by
sector. For instance, I2E in Motor vehicles, has a positive impact on servicification, whilemore E2R has a strong negative impact. This lines up with the selection interpretation. The
products and countries where there is a great deal of I2E are the ones where the GVC
participation involves a lot of imported parts and limited local processing in developing
nations. As these are likely to be fabrication intensive, we see a positive coefficient. When
thinking about nations where there is a lot of E2R, it may well be products that are design orservice-task intensive.
5.2. FOREIGN SERVICIFICATION RESULTS
We now turn to the second specification that concentrates on foreign-service value-added inexports. The estimation results with the import-to-produce index are in Table 10.
The first column shows the benchmark case where we include only the change in the I2P
measure of GVC participation and fixed effects. The result is encouraging in the sense that the
impact of heightened GVC participation shows statistically significant positive coefficientestimate. To control for general trends, the second column includes Change in Domestic
Service Value-added share. The point estimate is positive, however, it is statisticallyinsignificant. Importantly, the estimated impact of greater I2P is unaffected by its inclusion.
As it turns out, there is little correlation between the foreign-service value-added sharechange and the domestic one, as22

Figure 6 shows. The changes in domestic and foreign service-value-added shares by sector
and by exporter are not correlated.
Figure 6: Lack of correlation between foreign and domestic servicification.

Source: Authors’ elaboration based on AIO data.

To check whether the effect is heterogeneous across types of nations – especially developed
versus developing nations – we include an interaction term in the third column, namely theinteraction term between the import-to-produce and the developing countries dummy variable.
Here China, Indonesia, Malaysia, Philippines, Singapore and Thailand are considereddeveloping countries.
The results show that there is in fact a bigger impact on developing nations. Indeed, the point
estimate on the general variable turns negative while the impact for developing nations is
positive and significant. The negative estimate, however, is very poorly estimated. We cannot
reject the hypothesis that it is zero or slightly positive. This is an interesting finding. It
suggests that when developing nations import more intermediates, they shift to import more
services directly and indirectly. The same is not true for developed nations.
THIS IS THE INTERNATIONAL SMILE CURVE IT THINK

A natural interpretation of this would be to posit that developed nations have acomparative advantage in manufacturing-related services and that the GVC
participation is allowing them to buy labour intensive goods (that are not particularly
service intensive) while selling more good that are service intensive.

Given highly varied nature of international production share across industries, we turn next toaddressing the question of which industries affected most by the GVC-linked foreign
servicification documented in the first three columns. Specifically, the fourth column
separates out the effect of the import-to-produce index by industry to see if there is adifference across industries. For example, I2P_Machinery is the interaction term between theimport-to-produce and the dummy for the machinery industry. There are substantial
differences across industries. Interestingly, the biggest effects are not in the classic offshoring
industries such as machinery, and electrical and electronic goods.

23

Table 10: Estimation results using change in Import-to-Produce as GVC measure
Change in Foreign service value-added share

Column 1

Column 2

Column 3

Column 4**-0.02780.0105(-1.19)(1.16)

0.0206

Change in Import-to-Produce(2.45)

0.0206(2.44)

0.0528Change in Import-to-Produce*developing countries-0.000822(-0.19)Change in Domestic Service Value-added share*

2.23-0.000723(-0.16)

I2P*Machinery
I2P*Motor vehicles
I2P*Electronics-0.000384(-0.09)0.0182(0.4)
0.0116(0.21)-0.00493(-0.14)0.123***I2P*Plastic products(3.42)
0.208***I2P*Metal products

1124

1124

1124

(4.17)-0.00761(-0.15)
1124

0.046

0.046

0.05

0.071

I2P*Wearing apparel
Observations
2R

t statistics in parentheses*
p < 0.05, ** p < 0.01, *** p < 0.001

As discussed above, there are many measures of GVC participation. The next set ofregressions considers where our main results are robust to the use of different measures. In
particular, estimation results using backward/forward participation as the GVC measure are in24

Table 11.
Whereas GVC forward participation shows a highly statistically significant coefficientestimate, GVC backward participation is insignificant. It means that the more involved acountry/industry is as a supplier of third countries’ exports, the more dependent thecountry/industry is on foreign-service value-added. For example, parts and components
produced in Thailand which are exported to Indonesia for further processing receives muchservice inputs from Japan. The cross-term with the developing country dummy is introducedin the second column. The result indicates no difference by country groups. In the thirdcolumn, the cross-terms between Backward change and the industry dummies and also
Forward change and the industry dummies are introduced. Similar to the case of the importto-produce, there is substantial industry difference in the effect of the global value chains.

25

Table 11: Estimation results using changes in GVC I2E and E2R participation as GVC
measureChange in GVC I2E

Column 1-0.00041(-0.50)I2EChange*developing countries

Column 20.00116-0.23-0.00162(-0.32)I2EChange*Machinery
I2EChange*Motor vehicles
I2EChange*Electronics
I2EChange*Plastic products
I2EChange*Metal products
I2EChange*Wearing apparel
Change in GVC E2Rard participation

0.0917***-16.71

E2RChange*developing countries

0.0705***-4.81
0.0247-1.57

E2RChange*Machinery
E2RChange*Motor vehicles
E2RChange*Electronics
E2RChange*Plastic products
E2RChange*Metal products
E2RChange*Wearing apparel
Change in Domestic Service Value-added share
Observations
R2
t statistics in parentheses*
p < 0.05, ** p < 0.01, *** p < 0.001

0.00672
-1.69
1124
0.241

26

0.00702
-1.76
1124
0.242

Column 3-0.000843(-0.46)0.00109-0.09
0.000801-0.2-0.00015(-0.02)-0.0183(-1.22)0.000358-0.02
0.000656-0.31
0.0873***-15.3-0.0214(-0.43)-0.0523(-0.69)-0.027(-0.64)0.165***
-4.55
0.108
-1.57
0.00719-0.03
0.00413
-1.01
1124
0.261

Change in GVC I2E

Column 1-0.00041(-0.50)I2EChange*developing countries
Change in GVC E2R

0.0917***-16.71

E2RChange*developing countries
Change in Domestic Service Value-added share
Observations
R2

0.00672
-1.69
1124
0.241

Column 20.00116-0.23-0.00162(-0.32)0.0705***-4.81
0.0247-1.57
0.00702
-1.76
1124
0.242

6. CONCLUDING REMARKS AND POSSIBLE POLICY IMPLICATIONS
A great deal of very interesting work has been conducted on GVC using the World Input
Output Database (WIOD) and the OECD’s Trade in Value Added (TiVA) data. This hashelped unveil the true sources of value added in the world, but much less so in Asia. The
problem is that both WIOD and TiVA are based on OECD data that is missing many
important Asian nations – especially the large ASEAN nations that are so important in
Factory Asia. This lacuna is especially irksome since Asia is considered to have built the mostintense and extensive production network. It also started quite early, say in the mid-1980s,
while many other regions started later.
This paper uses the IDE-JETRO AIO table to fill some of the missing gaps in the research.
The main focus here is on the ‘servicification’ of exports – the increase in the share of value
added that stems from service sectors. We document:
1. Which service industries have increased their shares in foreign countries' export valueadded?
The answer is somewhat surprising. Most of the boosted service share is coming from justthree service sectors: Transportation, Retail and Wholesale, and Other Services, whichcontains many non-trade services such and medicine, hotels, restaurants, etc.
2. What are the underlying causes of the shift of export value - added from the
manufacturing sector to the service sector?
The causes of servicification that we focus on fall into three bins. The first is a simple
reclassification of value added from firms that are counted as being in the manufacturingsector (so their value added is counted as coming from this sector) to the service sector. In the
1980s, manufacturing firms often performed many axillary services inside the company thatare nowadays outsourced (e.g. cleaning, accounting, transportation, catering, marketing, etc.).
The second involves changes in the nature of production techniques. Automation of
production tends to involve more IT and computing services ranging from design tomaintenance. Offshoring and outsourcing tend to involve more connection and coordination
services such as transportation and telecom. Presumably the cost savings on the offshoring27

more than compensate for the cost of the extra services, but both sides of that balance service
to raise the value added share of services.
The third type of explanation considers changes in the nature of goods. Firms use services,such as embedded software and advanced design and marketing, to enhance value andincrease product differentiation.
Our regression result are not able to carefully enumerate the importance of the many possible
sources driving this trend, but we are able to show that increases in GVC participation is an
important contributing factor in increasing the foreign service value added in a nation’sexports.
6.1. THE IMPLICATIONS AND POLICY ISSUESThis servicification of manufacturing is transformative for policy – or at least should be.
Governments around the world – but especially in Asia – are heavily invested in promoting
development via industrialisation. The linchpin of such policy is the encouragement,
promotion and support for exports of manufactured goods. Servicification matters for suchpolicies since it blurs the distinction between manufacturing and service sectors. For example
the competitiveness of a nation’s manufactured exports is far more dependent on the local
availability of service that have low, quality-adjusted prices. Trying to promote manufactured
exports without liberalising the import of services may be self-defeating and this increasingly
so as services share in export value added rises.
Second, it means that job creation is subject to more intersectoral linkage than traditionally
thought. For example, a trade agreement that expands export opportunities for a nation’s
manufacturers will produce job creation well beyond the manufactured sector. Thus looking
for evidence of pro-job effects of trade agreements requires looking at the service sector aswell as the manufacturing sectors.
Third, it should help refocus industrial policies on a broader range of ‘good jobs’. As
manufacturing task are increasingly commoditised by competition from low wage nations on
one hand and robots on the other, good manufacturing jobs are increasing those that involve
manufacturing-linked service tasks. The extreme of this trend can be seen in the British
company Dyson which produces high-end vacuum cleaners. However even though thevacuum cleaners carry the Dyson brand, Dyson owns no factories at all. None of its workers
are involved in fabrication. They are engaged in the full range of services necessary to
produce the goods, but they don’t actually make the goods.
Moreover it changes – or should change – the way we think about trade policy, development
and job creation. On trade policy, it strengthens the need to address trade in service barriers at
the same time as trade in goods barriers. There will be a positive feedback effect between
trade policies that facilitate the export of goods and the imports of services. For many nations,this means that opening up their markets to direct and embodied foreign services is key tomaking their manufactured goods competitive. When it comes to development policy, it
argues that the classic focus on factories can be misleading. New goods exports will create
jobs in the service sectors supplying value added to the goods export sectors – as well as
creating jobs directly. Finally on job creation, the pervasive servicification means that
attempts to measure the job creating impact of particular policies must look beyond their
manufacturing sectors and realise that extra manufactured exports directly creates service
sector jobs. Eventually, the topline message is that governments should shift their thinking on
industrial policy. On one hand, it is no longer just industry that need to be addressed, and on
the other hand, the impact of a successful industrial policy be felt far beyond industry.
28

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30

Appendix
Appendix Table 1
List of industries

Service sector

31

SectorManufacturing sector

Industry description
Paddy
Other grain
Cassava, Sugar cane and beet, Oil palm and coconuts, Other food crops
Natural rubber, Fiber crops, Other commercial crops
Livestock and poultry
Forestry
Fishery
Crude petroleum and natural gas
Iron ore
Copper ore, Tin ore, Other metallic ore
Non-metallic ore and quarrying
Milled rice, Other milled grain and flour
Fish products
Slaughtering, meat and dairy products
Oil and fats, Sugar, Other food products
Beverage
TobaccoSpinning
Weaving and dyeing
Knitting
Wearing apparel
Other made-up textile products
Leather and leather products
TimberFurniture
Other wooden products
Pulp and paper
Printing and publishing
Synthetic resins and fiber
Other basic industrial chemicals
Chemical fertilizers and pesticides
Drugs and medicine
Other chemical products
Refined petroleum and its products
Tires and tubes
Other rubber products
Cement and cement products
Glass and glass products
Other non-metallic mineral products
Iron and steel
Non-ferrous metal
Metal products
Heavy electric machinery
Engines and turbines
Ordinary industrial machinery, Specialized industrial machinery, Agricultural machinery
Electronics and electronic products
Other electric machinery and applianceMotor vehicles
Shipbuilding
Motor cycles and bicycles (Motor cycles), Motor cycles and bicycles (Bicycles), Aircrafts, Other transport equipment
Precision machines
Plastic products
Other manufacturing productsElectricity, gas and water supply
Building construction
Other construction
Wholesale and retail trade
Transportation
Telephone and telecommunication
Finance and insurance
Education and research
Other service including Real estate, Medical and health service, Restaurants, Hotel
Unclassified
Public administration

Primary sector

Industry code
001
007A
002X
003X
009
010
011
012
015A
010X
016
018X
021A
021B
017X
022A
022B
023
024
025
026
027
028
029
030A
030B
031
032
033A
033B
034
035A
035B
036
037
038
039
040
041
042
043
044
045D
045E
045X
046A
046B
047A
048B
048X
049
050A
050B
051
052A
052B
053A
053B
054A
054B
054C
054D
056
055

Appendix Table 2: Backward participation by industries
0.35
0.3
0.25
Backward1990

0.2

Backward1995

0.15

Backward2000

0.1

Backward2005

0.05
0

0.35
0.3
0.25
Backward1990

0.2

Backward1995

0.15

Backward2000

0.1

Backward2005

0.05
0

0.35
0.3
0.25
Backward1990

0.2

Backward1995

0.15

Backward2000
0.1

Backward2005

0.05
0Source: Authors’ elaboration based on AIO data.
32

Appendix Table 3: Forward participation by industries
0.6
0.5
0.4
Forward1990
0.3

Forward1995
Forward2000

0.2

Forward2005
0.1
0

0.6
0.5
0.4
Forward1990
0.3

Forward1995
Forward2000

0.2

Forward2005
0.1
0

0.6
0.5
0.4
Forward1990
0.3

Forward1995
Forward2000

0.2

Forward2005
0.1
0Source: Authors’ elaboration based on AIO data.
33